Research in Economic History, Volume 24

LIST OF CONTRIBUTORS Metin M. Cos- gel Department of Economics, University of Connecticut, Storrs, CT, USA Harald Edqu...

Author: Alexander J Field | Gregory Clark | William A Sundstrom

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LIST OF CONTRIBUTORS Metin M. Cos- gel

Department of Economics, University of Connecticut, Storrs, CT, USA

Harald Edquist

Institute for Research in Economic History, Stockholm School of Economics, Sweden

Magnus Henrekson

Institute for Research in Economic History, Stockholm School of Economics and Research Institute of Industrial Economics, Sweden

Jun Seong Ho

Academy of Korean Studies, Republic of Korea

Pedro Lains

Institute of Social Sciences, University of Lisbon, Portugal

James B. Lewis

Oriental Institute, University of Oxford, Wolfson College, UK

Scott A. Redenius

Department of Economics, Bryn Mawr College, PA, USA

Johan So¨derberg

Department of Economic History, Stockholm University, Sweden

Ebru Guven Solakoglu

Department of Economics, Fatih University, Istanbul, Turkey

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EDITOR’S INTRODUCTION Research in Economic History volume 24 includes seven papers, four of which were submitted and evaluated through our regular channels. An additional three were solicited from among those presented at the conference Toward a Global History of Prices and Wages, held in Utrecht in August 2004. Owing to the emphasis in these papers on data and the relevance of their findings for our understanding of long-run economic growth and development in different parts of the world, we encouraged a number of authors from this conference to submit their work. Three of these contributions have already been published in REH 23, and an additional three are included here. Associate editor Greg Clark took responsibility for soliciting, refereeing, selecting, and editing the submissions. Our leadoff article is Harald Edquist and Magnus Henrekson’s study of productivity growth after three major technological revolutions: steam, electricity, and ICT. The authors distinguish between producing and using sectors and find a substantial lag between initial innovations and observable increases in productivity growth. They also provide evidence of rapid price decreases for steam engines, electric motors, and ICT products (computers and telecommunication equipment), but find no persuasive evidence that steam engine producing or electrical machinery industries experienced high productivity growth. In the more recent episode, in contrast, substantially higher productivity growth rates are found in the ICT-producing as opposed to ICT-using industries. One explanation may be that although hedonic price methods have been used to adjust output growth upward in ICTproducing industries, to try and account for quality improvement, such methods have not been applied retrospectively to the steam engine or electrical machinery industries. Our second contribution is Scott Redenius’s detailed study of regional differences in the cost of bank loans in the United States between 1887 and 1975. A referee praised the judiciousness and attention to detail with which these estimates have been constructed, and concluded that this will eventually be recognized as ‘‘the definitive paper on bank interest rates in the U.S.’’ Redenius finds that regional rates in the United States differed widely following the Civil War and that some of these differences persisted until ix

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EDITOR’S INTRODUCTION

after the Second World War. He also finds that the establishment of the Federal Reserve System led to a shrinking of regional differentials, and that borrowing rates in the South were lower than has been previously suggested in the literature. Our third paper, by Ebru Guven Solakoglu, argues that even though average heights declined between 1867 and 1887 in the United States, the extension of railroads had a positive influence on this indirect measure of the standard of living. Reduced transportation costs made food more readily available, and this effect partially counterbalanced the deleterious impact of urbanization and the concomitant rise in the prevalence and ease of transmission of infectious disease. The effect of the spread of railroad transportation on height is estimated by focusing on the influence of railroads on the marketable surplus of protein. Subsequent to 1887, Solakoglu finds no positive effect of the extension of the railroad net. We follow with Pedro Lains’ study of Portuguese economic growth between 1850 and 1950. Lains finds that from the mid-nineteenth century through the First World War, Portuguese growth languished relative to European trends. This pattern, however, was reversed during the interwar period, when Portugal grew more rapidly, achieving partial convergence with European averages, for example with respect to labor productivity. Lains note that this growth took place within an institutional environment of increased protection and state intervention, but also benefited from a compositional shift toward sectors with higher levels of factor productivity. Growth was facilitated as well by renewed inflows of capital from outside the country, which occurred in spite of a protectionist foreign trade stance. The last three papers are from the 2004 Prices and Wages conference and, as noted, benefited from Greg Clark’s editorial oversight. They take us into regions less frequently traversed by quantitatively oriented economic historians. The first of these is Metin Cos- gel’s study of agricultural productivity in the early Ottoman Empire. Using data from tax registers to estimate labor productivity in grain farming, he concludes that Ottoman regions had, in the fifteenth and sixteenth centuries, achieved levels of labor productivity in agriculture comparable to those evident in Western Europe circa 1850. Cosgel’s paper is followed by a contribution from Johan So¨derberg that examines the level and volatility of grain prices in Cairo and Europe during the middle ages, and the degree of integration of grain markets within and between the two regions. He finds that prices were higher and more volatile in Cairo, and that prices trended downward over time in Europe but not in Cairo. Correlating deviations from linear trends in the log of grain prices, he

Editor’s Introduction

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finds integrated grain markets in north-western Europe no later than the fourteenth century, but no evidence that prices in Cairo covaried with those from markets in north-western Europe. And whereas variations in flood levels in the Nile valley had measurable influences on grain prices in Cairo, So¨derberg finds little detectable influence of climactic fluctuations on European grain prices. The final paper, by Jun Seong Ho and James B. Lewis, studies the evolution of wages, rents, and interest rates in Southern Korea in the eighteenth and nineteenth centuries. They find a pattern of expansion up through the mid-nineteenth century, followed by contraction prior to the influence of Japanese imperialism in the last quarter of the century. Research in Economic History welcomes innovative contributions to economic history in any area. We continue to have more flexibility than other outlets in publishing data rich papers running to somewhat greater length. Potential authors may submit their work in hard copy or (this is preferred) as attachments in an email addressed to the editor (afi[email protected]). We are flexible on matters of style and formatting for the first round of submission. If it looks as if the paper has a reasonable likelihood of acceptance, we will ask that you prepare it according to JAI/Elsevier guidelines, and are happy at any stage to send you a copy of these guidelines as well as a recently prepared manuscript for use as a template. Alexander J. Field Series Editor

TECHNOLOGICAL BREAKTHROUGHS AND PRODUCTIVITY GROWTH Harald Edquist and Magnus Henrekson ABSTRACT This study consists of an examination of productivity growth following three major technological breakthroughs: the steam power revolution, electrification and the ICT revolution. The distinction between sectors producing and sectors using the new technology is emphasized. A major finding for all breakthroughs is that there is a long lag from the time of the original invention until a substantial increase in the rate of productivity growth can be observed. There is also strong evidence of rapid price decreases for steam engines, electricity, electric motors and ICT products. However, there is no persuasive direct evidence that the steam engine producing industry and electric machinery had particularly high productivity growth rates. For the ICT revolution the highest productivity growth rates are found in the ICT-producing industries. We suggest that one explanation could be that hedonic price indexes are not used for the steam engine and the electric motor. Still, it is likely that the rate of technological development has been much more rapid during the ICT revolution compared to any of the previous breakthroughs.

Research in Economic History, Volume 24, 1–53 Copyright r 2007 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0363-3268/doi:10.1016/S0363-3268(06)24001-1

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HARALD EDQUIST AND MAGNUS HENREKSON

1. INTRODUCTION According to many observers (e.g., Castells, 1996, 1997; Greenwood, 1997; Litan & Rivlin, 2001) we have just experienced a technological revolution based on a breakthrough in information and communication technology (ICT). This revolution has already profoundly impacted the way we lead our lives and produce goods and services. Moreover, significantly higher rates of productivity growth were observed in the latter half of the 1990s compared to the 1970s and 1980s, in particular in the United States. This tendency was discernible in several other countries too, but on closer inspection it appears that it may only be true for the sectors producing the new technology. Still, toward the very end of the last millennium the ICT revolution carried high hopes for a new era, ‘‘a new economy’’, entailing a permanent upward shift in long-term productivity growth rates. Or as one extremely influential policymaker at the time put it: ‘‘the recent acceleration in labor productivity is not just a cyclical phenomenon or a statistical aberration, but reflects – at least in part – a more deep-seated, still developing, shift in our economic landscape’’ (Greenspan, 1999, p. 3). Throughout human history there have been a number of important technological breakthroughs. Schumpeter (1939) argued that new products and technologies, giving rise to ‘‘gales of creative destruction’’, would have a large impact on the economy for several decades. But how can we distinguish truly revolutionary changes from other changes? Bresnahan and Trajtenberg’s (1995) concept of a general purpose technology (GPT) is useful in this context. They argue that whole eras of technical progress are driven by a few GPTs, characterized by pervasiveness, inherent potential for technical improvements and innovational complementarities giving rise to increasing returns to scale. GPTs are believed to play a decisive role for long-term productivity development as the new technology is diffused throughout different sectors of the economy (Helpman & Trajtenberg, 1998). More specifically, Lipsey, Bekar, and Carlaw (1998) maintain that a GPT has the following four characteristics: (1) wide scope for improvement and elaboration; (2) applicability across a broad range of uses; (3) potential usefulness in a wide range of products and processes; and (4) strong complementarities with existing or potential new technologies. However, as noted by David and Wright (2003), based on these criteria Lipsey et al. (1998) come up with a list of GPTs that is so lengthy that the term revolutionary becomes grossly devalued. Hence, the GPT framework has limitations when it comes to distinguishing revolutionary technologies

Technological Breakthroughs and Productivity Growth

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from new technologies of lesser importance. Moreover, it can be argued that the GPT framework also suffers from ex-post bias. A clear set of criteria to distinguish among all possible technologies and not simply an ex-post definition of the technologies that matter would be highly useful, but such an analysis is beyond the scope of this paper. The GPT framework can be compared with the broader concept of technoeconomic paradigm (TEP) (Perez, 1983; Freeman & Soete, 1987). According to Freeman (1987) a TEP is a systematic relationship among products, processes, organizations and institutions that coordinate economic activity. Changes of TEPs are pervasive changes in technology affecting many branches of the economy and giving rise to entirely new sectors. A characteristic of this type of technical change is that it affects the input cost structure and the conditions of production and distribution for almost every branch of the economy. The definition of a GPT is more precise than the definition of a TEP. However, the TEP literature clearly distinguishes between ‘‘deeper conceptual breakthroughs’’ and subcategories that presuppose the deeper change (e.g., the steam engine vs. railways and steamships, the internal combustion engine vs. motor vehicles and the integrated circuit vs. personal computers and the Internet). By using the criteria suggested by the GPT and TEP perspectives on technological breakthroughs and focusing on the period since the eve of the industrial revolution in the UK, we reach the conclusion that the number of innovations that can rival the ICT revolution in importance is exceptionally small. Arguably, there are only three innovations that qualify: the steam engine, the internal combustion engine and electrification. But what impact does a major technological breakthrough have on the economy, notably on the level and rate of growth of productivity? How long does it take before the new technology has spread throughout the economy, fundamentally altering modes and patterns of production and consumption? The purpose of this paper is to explore these questions. In particular, we intend to explore whether each breakthrough is unique in its effects or whether one can detect a general pattern. We will compare the effects of three technological breakthroughs, namely the steam power revolution, electrification and the ICT revolution.1 Our paper is purely empirical and we have no pretension to make any conceptual or theoretical contribution to the GPT or the TEP literature. The questions above are very broad and more specifically we address the following questions: (i) Have these technological breakthroughs been important for productivity growth?

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(ii) What similarities and dissimilarities are there between technological breakthroughs? (iii) What similarities can be found in the pattern of productivity growth after the breakthroughs and do they differ across countries? (iv) Is productivity growth different in sectors producing the new technology compared to sectors using it? Most studies that compare technological breakthroughs use either a macroeconomic perspective based on quantitative data or a microeconomic perspective based on qualitative data. We believe that the questions above must be analyzed with a combination of different perspectives, including the micro, macro and industry levels. By combining these three perspectives and analyzing three major technological breakthroughs, it will be possible to gain new knowledge on the impact of different technological breakthroughs on productivity growth. The paper is organized as follows. Section 2 consists of a methodological discussion related to our investigation. In Sections 3–5, we examine each technological breakthrough in detail and investigate its impact on productivity growth. Finally, we analyze our results from all three breakthroughs in order to find answers to the above questions. Our results show that it takes a long time from the moment of the original invention until a substantial increase in the rate of productivity growth can be observed. For the steam engine this was about 140 years (90 if the Watt steam engine is treated as the original innovation), while it was 40–50 years for electrification and the ICT revolution. We also find evidence of rapid price decreases for steam engines, electricity, electric motors and ICT products. This indicates rapid productivity growth in the industries producing the new technology. However, we cannot find direct evidence that the steam engine producing industry in the UK and the electric machinery industry in the US had particularly high productivity growth rates. For the ICT revolution the highest productivity growth rates were found for the ICT-producing industries throughout the six countries that we investigate.

2. METHODOLOGICAL DISCUSSION For the steam engine and electrification, we will use secondary sources covering primarily Germany, Sweden, the UK and the US. For the ICT revolution, we will present evidence from six different countries: Finland, France, Germany, Sweden, the UK and the US. Our investigation rests on


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empirical data mostly drawn from quantitative research. This raises issues concerning sources, concepts and productivity measurement. Before we delve into these matters it should be stressed that it is extremely hard, perhaps infeasible, to come up with ‘‘sharp’’ tests of causal effects from new GPTs to significant productivity growth, in particular concerning GPTs introduced long ago. There are long lags involved, the real world is exceedingly complex and general patterns are unlikely to repeat themselves from one GPT to the next in closely similar fashions. Instead we have to content ourselves with exploratory analyses with the aim of documenting whether identified patterns are consistent with fairly loosely formulated hypotheses. 2.1. Sources For the ICT revolution we use primary data taken from the OECD structural analysis industrial database (STAN) (OECD, 2003b) to analyze sectoral productivity in manufacturing for six countries.2 The inclusion of Germany, France and Finland gives us a more complete picture of productivity development for Western European countries. Given that we include the three largest countries in Europe and two countries from northern Europe with a high degree of specialization in ICT-producing industries, we judge that our results can be seen as reasonably representative for the most recent breakthrough. For the steam engine we primarily focus on the UK productivity development, while most of the evidence about electrification comes from the US. This is almost wholly due to data constraints. We are aware that this limitation may exclude important parts of the complex process of technological development and its implications for productivity growth. It is evident that technological processes may have evolved differently across countries, not least as a result of sizeable institutional differences. Another limitation of our study is that we primarily, but not exclusively, focus on the impact of the new GPT on productivity in manufacturing, despite the fact that a large part of overall technological change takes place outside manufacturing. Difficulties in measuring productivity in the service sector and the ensuing lack of data force us to accept this limitation. 2.2. Data Quality and Measurement Issues We will present estimates of both labor productivity growth and total factor productivity (TFP) growth.3 The data presented for earlier technological

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breakthroughs should be used with caution. Nevertheless, the available data make it possible to gain a better understanding of the general patterns of productivity development. Mokyr (1993) argues that if it is true that in modern industrial societies, the construction of national income statistics gives rise to theoretical and data problems, for 18th-century Britain the problems are much greater and national income estimates can only be ‘‘controlled conjectures’’. Nonetheless, growth cannot be analyzed without them. Hence, when we write that ‘‘the growth rate was x percent’’ during a certain period this always mean estimated to be x percent by the cited author(s) given all the limitations of the study in question. As long as numerous independent sources are used and the data are viewed with these caveats in mind, we deem that the quality and accuracy of productivity data for earlier technological breakthroughs is sufficiently high to warrant conclusions about productivity development and the diffusion of the technology in the economy. Another issue concerns the use of hedonic price indices.4 The hedonic approach to price measurement is used to take quality changes into account. It redefines goods in terms of their characteristics so that modified or new models do not open up a new product category, but simply represent a new combination of characteristics (Scarpetta, Bassanini, Pilat, & Schreyer, 2000). There are no consistent hedonic price indices available for steam power and electrification.5 For the ICT revolution, hedonic price indices are used to adjust quality change in output for some countries. France, Sweden and the US use hedonic adjustments for some ICT products, while Finland, Germany and the UK do not (Scarpetta et al., 2000). Cross-country comparability of output and productivity could thus be impaired in sectors with rapidly falling prices such as the computer industry.

3. THE STEAM ENGINE 3.1. The Early Development of the Steam Engine The first widely used steam engine was invented by Thomas Newcomen in 1712. The Newcomen engine was mostly used in mining and consumed relatively large amounts of coal. It took several decades for the steam engine to become modified for productivity enhancing use and to diffuse among countries and industries. In 1765 James Watt developed the separate condenser (patented in 1769). Watt realized that if the main cylinder could be kept hot all the time, and condensation occurred in a separate cold vessel,


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fuel savings could be fourfold (Mokyr, 1994). The fuel-saving innovation made it possible to use the steam engine at locations where coal was scarce (Nuvolari & Verbong, 2001). Thanks to Watt’s innovation the steam engine could become an important power source in factories (Robertson, 1955). However, the Watt steam engine had serious limitations and it was not until reliable high-pressure boilers were developed and put to effective use in the 1840s (the Lancashire boiler; Crafts, 2004) that steam power could be deployed on a large scale in factories and transportation (railways and sea vessels). A further important improvement was the introduction of the Corliss engine in the early 1860s (Rosenberg & Trajtenberg, 2004).6 In particular, the switch to steam ships hinged crucially on the introduction of high-pressure steam and in fact steam ships did not replace sailing ships to any great extent until the late 1800s.

3.2. The Diffusion of the Steam Engine Table 1 presents crude estimates of total steam power capacity in 15 different countries in 1840–1896.7 According to Table 1 the US and the UK had the highest steam power capacity in 1840 totaling 760,000 and 620,000 horsepower, respectively.8 Thus, these two countries accounted for more than 80 percent of the total world steam power capacity in 1840. At that point other large European countries, such as France and Germany, had a modest steam power capacity in relation to the UK and the US. However, the growth rate of steam power capacity was higher between 1840 and 1896 for all other countries included in Table 1. Germany had the highest annual growth rate in steam power capacity (9.9 percent p.a.). Hence, most countries caught up with the UK and the US during the second half of the 19th century. In 1896 the US and UK share of world steam power capacity had decreased to 48 percent. Nevertheless, the UK had the highest capacity per inhabitant in 1896/1897. Table 1 shows that the steam power capacity per 100 inhabitants in 1896/1897 was 34 horsepower for the UK compared to 25 for the US. The corresponding figures for France and Germany were 15 horsepower. Portugal, Russia and Italy had the lowest capacity per 100 inhabitants (see Table 1). Hence the catch up was far from complete. In manufacturing, the steam engine was first adopted in the UK, but the initial adoption was slow. According to Nuvolari and Castaldi (2003) the total number of steam engines installed in British mining and manufacturing in 1800 was only 2,191. The price difference between steam power

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Table 1.

Crude Estimates of Steam Power Capacity in Different Countries 1840–1896 (Thousands of Horsepower).

Country 1840

1850

1860

1870

1880

1888

1896

20 40 NA 90 40 10 NA NA NA 20 10 NA NA 620 760 1,650

100 70 NA 370 260 40 10 NA NA 70 20 NA NA 1,290 1,680 3,990

333 160 10 1,120 850 50 30 10 10 200 100 20 90 2,450 3,470 9,380

800 350 30 1,850 2,480 330 130 40 30 920 210 100 140 4,040 5,590 18,460

1,560 610 90 3,070 5,120 500 250 90 60 1,740 470 220 230 7,600 9,110 34,150

2,150 810 150 4,520 6,200 830 340 180 80 2,240 740 300 290 9,200 14,400 50,150

2,520 1,180 260 5,920 8,080 1,520 600 410 170 3,100 1,180 510 580 13,700 18,060 66,100

Annual Growth Rate 1840–1896

Per 100 Inhabitants in 1896/1897

9.0 6.2 NA 7.8 9.9 9.4 NA NA NA 9.4 8.9 NA NA 5.7 5.8 6.8

6 18 11 15 15 5 12 20 3 3 7 10 19 34 25 NA

Note: The steam power capacity figures include capacity of fixed, railway and shipping steam power. The figures of steam power per 100 inhabitants are based on steam power capacity in 1896 and population figures in 1897. NA ¼ Not available. Source: Mulhall (1899) and authors’ calculations.


Austria Belgium Denmark France Germany Italy Netherlands Norway Portugal Russia Spain Sweden Switzerland UK US The world

Thousands of Horsepower


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and waterpower remained high. However, the cost disadvantage was gradually overcome by the mobility advantage and the increased efficiency of new generations of steam engines (Atack, Bateman, & Weiss, 1980). The initial adoption of steam power in US manufacturing was even slower. According to Atack et al. (1980) there was only one manufacturing plant using steam power in the US before 1776 compared to 130 in Britain. In 1838 the total steam power capacity in US manufacturing was 36,100 horsepower (Atack et al., 1980). A crude estimate of the corresponding figure for the UK was 350,000 horsepower (Tann, 1988). These figures, although crude, suggest that by the 1840s the UK was far ahead of the US in steam power capacity in manufacturing. But once adoption gained momentum in the US it became rapid. By 1869 the total power capacity in US manufacturing had increased to 1,216,000 horsepower. Around 1820, waterwheels probably outnumbered steam engines by 100 to 1 in the US, but by 1870 this difference had narrowed to about 5–4 (Atack et al., 1980). These figures indicate that the breakthrough in the diffusion of the steam engine in the US manufacturing took place in the middle of the century. Fenichel (1966) shows that by 1899 steam accounted for four-fifths of total primary power capacity in US manufacturing.9

3.3. The Steam Engine and Productivity Growth 3.3.1. Steam Power Development in the UK One way to investigate the impact of the steam engine on productivity growth is to analyze how TFP developed during the period when the steam engine was introduced in manufacturing. Most evidence indicates that the growth in output and TFP in the UK did not increase until the beginning of the 19th century (see Table 2). This was more than 35 years after the invention of Watt’s steam engine and 90 years after Newcomen’s engine. Crafts (2004) argues that productivity growth did not increase substantially until the 1850s (see Table 2). But can the increase in TFP and labor productivity growth in the UK be ascribed to the introduction of the steam engine? Several studies suggest that the impact of steam power on TFP growth was quite small (see Von Tunzelmann, 1978; Crafts, 2004). Moreover, there are few indications that the steam engine had a substantial productivity-enhancing effect initially. One way of measuring the contribution of new technology is to use the concept of social savings. Social savings are usually measured as the gain in consumer surplus from the fall in costs due to new technology.10 Estimates

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Table 2. Crafts


Growth during the British Industrial Revolution, 1760–1913 (Percent p.a.). Output

Capital Stock

Labor Force

Labor Productivity

TFP

1760–1780 1780–1831 1831–1873 1873–1899 1899–1913

0.6 1.7 2.4 2.1 1.4

0.25 0.6 0.9 0.8 0.8

0.35 0.8 0.75 0.55 0.55

0.25 0.9 1.65 1.65 0.85

0.0 0.3 0.75 0.75 0.05

Feinstein 1760–1780 1780–1831 1831–1860

1.1 2.7 2.5

0.5 0.7 1.0

0.4 0.7 0.7

0.7 2.0 1.8

0.2 1.3 0.8

Note: Weights: Crafts: capital 0.4, labor 0.6; Feinstein: capital 0.5, labor 0.5. Source: Crafts (2002, 2004).

of social savings are invariably small for the steam engine. Von Tunzelmann (1978) estimates that the savings from using Watt’s engines over Newcomen’s in 1800 were approximately 0.11 percent of national income. Crafts (2004) analyses the impact of steam engines on British labor productivity growth by using the growth accounting framework that Oliner and Sichel (2000) developed to assess the impact of ICT on US labor productivity growth.11 Table 3 reports Crafts’ estimates of the contribution of stationary steam engines, railways and steamships to British labor productivity growth in 1760–1910. Table 3 shows that the impact of steam technology on labor productivity growth, measured as the increase in steam power capital in all sectors as a share of total income and TFP growth in the steam power industry as a share of total output, was 0.01–0.02 percentage points per year throughout the period 1760–1830.12 During 1830–1850, the contribution of steam technology increased to 0.2 percentage points of the annual labor productivity growth of 1.65 percent (see Table 3).13 In 1850– 1910 the contribution of steam increased to 0.31–0.41 percentage points.14 The increase in the contribution of steam technology to labor productivity growth during the second half of the 19th century was to a great extent due to the large investments in railways during the period. For example, in 1830–1850 railways contributed 0.16 percentage points to labor productivity growth, while the contribution from stationary steam engines was estimated to be a mere 0.04 percentage points. It is important to point out that the railway industry was not producing steam engines, but rather using steam power technology. Moreover, it was not until after 1850 that the


Table 3.

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The Contribution of Steam Technology to British Labor Productivity Growth, 1760–1910 (Percent p.a.). 1760–1800

1800–1830

1830–1850

1850–1870

1870–1910

0.004 0.005 0.01

0.02 0.001 0.02

0.02 0.02 0.04

0.06 0.06 0.12

0.09 0.05 0.14

Railways Capital deepening TFP Total

— — —

— — —

0.14 0.02 0.16

0.12 0.14 0.26

0.01 0.06 0.07

Steamships Capital deepening TFP Total

— — —

— — —

— — —

0.02 0.01 0.03

0.05 0.05 0.1

0.01

0.02

0.20

0.41

0.31

Stationary steam engines Capital deepening TFP Total

Total steam technology

Note: The total steam technology contribution is based on the combined impact of capital deepening and TFP growth from stationary steam engines, railways and steamships. Source: Crafts (2004).

contribution of steam technology to labor productivity growth increased. However, the contribution from the stationary steam engine producing industry never exceeded 17 percent of total labor productivity growth. Crafts’ (2004) estimates show that the steam engine had little influence on labor productivity growth in the period 1760–1850. This suggests that 140 years after Newcomen’s steam engine and 85 years after Watt’s steam engine, no substantial TFP growth had taken place within the steam power producing industry. However, after 1850 steam technology started to contribute more to labor productivity growth. This time period coincides with the introduction of the high-pressure steam engine. Steam engine capacity also increased rapidly during this period. In 1830 total steam power capacity in the UK was 160,000 horsepower compared to 2.06 million in 1870 and 9.65 million in 1907 (Kanefsky, 1979; Crafts, 2004). Hence, capacity in terms of horsepower grew by 5.5 percent p.a. in the 1830–1907 period. Crafts’ growth accounting approach also has shortcomings. Field (2006a) argues that the key message of the social savings approach is that in the absence of new technology the saving flows would have been invested elsewhere. This would have resulted in economic growth, although not quite as large. According to Fogel (1964) this meant canals and river dredging in

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the hypothetical absence of the railroad.15 Fogel estimated that in 1890 GNP was 4 percent higher as a result of the railroad. The approach used by Crafts includes the portion of the effect of capital deepening on labor productivity that is the consequence of the accumulation of particular steam engine capital goods. As pointed out by Field (2006a) the message of the Fogel approach is that in the absence of the steam engine, capital would have been accumulated in a slightly inferior range of capital goods. As a result, the growth accounting approach used by, inter alia, Crafts (2004) and Oliner and Sichel (2000) may overestimate the impact of the new technology. However, it is also possible to argue that the growth accounting approach underestimates the impact of the steam engine, since it does not take account of spillover effects from the steam power producing industry to steam power using industries.16 It is possible that increased flexibility and reliability due to the introduction of the steam engine in the production process could have generated substantial productivity growth in manufacturing industries using the new steam power technology. Nuvolari and Castaldi (2003) maintain that if the steam technology stimulated the generation of further technical or organizational innovations in sectors applying the new GPT, its economic impact cannot be appropriately assessed by means of growth accounting and social savings. Table 3 indicated that the stationary steam engine producing industry did not have a large impact on aggregate productivity growth in 1760–1910 (see Table 3). However, the steam engine may have had an impact on productivity in other sectors of the economy. Even though aggregate TFP growth was low in the UK it seems that some sectors experienced very high growth rates, thanks to the introduction of the steam engine in their production processes. Table 4 shows that steam power was used intensively in a few industries only. Throughout the period 1800–1907 mining, textiles and metal manufactures accounted for more than 50 percent of the steam industrial power (Nuvolari & Castaldi, 2003). However, important sectors including agriculture and the service sector excepting transport were in fact very slow at adopting the steam engine (Crafts, 2004). This might be the reason why productivity growth stemming from the steam engine did not result in high aggregate productivity growth in the UK during this period. So which industries experienced the highest productivity growth? McCloskey (1981) estimates TFP growth for a number of individual industries. McCloskey’s figures have been widely criticized. Harley (1993) claims that McCloskey exaggerated productivity growth in several industries (cotton, wool and shipping). In Table 5 the average annual TFP growth for different


Table 4.

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Steam Power by Industry in the UK, 1800–1907.

Industry

1800

1870

Number of Engines

%

Mining Textiles Metal manufactures Food and drink trades Paper manufactures Building trades Chemicals Public utility (water-works, canals, etc.) Others

1,064 469 263 112

48.6 21.4 12.0 5.1

13 12 18 80

160

Total

2,191

%

Steam HP (Power in Use)

%

360,000 513,335 329,683 22,956

26.2 37.4 24.0 1.7

2,415,841 1,873,169 2,165,243 266,299

26.5 20.5 23.7 2.9

0.6 0.6 0.8 3.7

27,971 17,220 21,400 36,000

2.0 1.3 1.6 2.6

179,762 347,647 182,456 1,379,376

2.0 3.8 2.0 15.1

7.3

44,375

3.2

309,025

3.4

100

Steam HP (Power in Use)

1907

1,372,940

100

9,118,818

100

Source: Nuvolari and Castaldi (2003).

Table 5.

Crude Estimates of Annual Total Factor Productivity Growth for Different UK Industries, 1780–1860.

Cotton Worsteds Woolens Iron Canals and railways Shipping Agriculture All others

McCloskey

Harley

2.6 1.8 0.9 0.9 1.3 0.5 0.4 0.02

1.9 1.3 0.6 0.9 1.3 0.5 0.7 0.02

Source: McCloskey (1981) and Harley (1993).

industries in 1780–1860 are presented based on both McCloskey (1981) and Harley (1993). The figures indicate that productivity estimates for different industries must be analyzed with caution. Nevertheless, it is possible to draw some conclusions. It is, for example, evident that the textile industry had a high rate of productivity growth during this period. The textile industry was also an intensive user of steam power (see Table 4).

14


An important issue is how productivity increased in the sector producing steam engines.17 By the size of the output shares for production of steam it is clear that the steam power producing industry was small compared to the rest of the economy. Crafts (2004) argues that in the period 1800–1840 there were few innovations in the steam power producing industry and the costs of steam engines did not fall. The subsequent period of rapid innovation resulted in large cost reductions. One of these innovations was the automatic variable cut-off mechanism of the Corliss steam engine that resulted in substantial improvement in fuel efficiency in the mid-19th century (Rosenberg & Trajtenberg, 2004). Hence, the price of steam power had approximately halved by the mid-1850s and in 1910 the annual cost of steam horsepower had fallen by approximately 80 percent compared to the beginning of the 19th century (Crafts, 2004). These observations suggest rapid productivity growth in the steam engine producing industry after 1850.

3.3.2. Steam Power Development in other Countries As already noted considerable time elapsed before the steam engine diffused in the US. According to Robertson (1955) the British sought to prevent export of the steam engine abroad. By 1838 only 5 percent of the total power used in US manufacturing was generated by steam engines. Rosenberg and Trajtenberg (2004) argue that it was not until around 1850 when the Corliss engine was introduced in the manufacturing process, that steam started having a substantial impact on productivity growth in the US. As in the UK, textiles, primary metals and machinery industries were the key industries in the process of industrialization. The fraction of power generated from steam increased in the textile and primary metals industries from 1/4 in 1870 to 1/3 in 1910. However, by then another key technology had started to change the production process in manufacturing. For Finland, France, Germany and Sweden we have been unable to uncover sufficient data to accurately investigate the impact of steam power on productivity growth. From a macroeconomic perspective the productivity and growth increases took place later in these countries compared to the UK (Fisher, 1992). For the period 1820–1870 GDP per capita growth in Germany was 0.7 percent p.a. The corresponding figures for Sweden and France were 0.7 and 0.8 percent p.a., respectively (Maddison, 1991). However, it has not been possible to investigate the importance of steam power for this development.


15

3.4. Concluding Remarks on the Development of Steam Power Aggregate productivity growth did not accelerate until after 1850 in the UK, i.e. 140 and 85 years after Newcomen’s and Watt’s steam engines were invented. Hence, one cannot detect an effect on TFP until quite long after the invention had been made. Crafts and Mills (2004) note that ‘‘the contribution of steam power to industrial output and labor productivity was at its strongest after 1870’’. One interpretation of this is that the real potential of steam technology did not materialize until the high pressure steam engine had been invented. From this invention until sizable productivity effects could be detected no more than 20–40 years elapsed. Furthermore, most of the productivity increases for the period appeared in sectors that were using the steam engine intensively, i.e. textiles and railways. The cost of steam power fell rapidly after 1840 as a result of a series of technical improvements of the original design. Notably, this opened the way for intensive use of the steam engine in the transportation sector. This may indicate a high productivity growth in the steam engine producing industry after all. However, the output of the steam engine producing industry remained less than 1 percent of total output in the UK throughout the 1760– 1860 period. This could be one reason why the detectable effects of the steam engine producing industry remained small until the mid-19th century.

4. ELECTRIFICATION We now switch the main focus from the role of the steam engine in British manufacturing to the US electrification process. The focus on US manufacturing is governed by data availability. To the greatest extent possible we also present complementary evidence from other countries, notably Sweden and the UK. The invention of the dynamo was crucial for the 19th-century electric industry. The principle behind the dynamo – the theory of electromagnetic induction – was discovered by Michael Faraday in 1831 (Byatt, 1979). However, it took over 40 years until the dynamo could be used commercially. The basic technological innovations raising energy efficiency in electricity generation to levels permitting commercial application occurred during 1856–1880 (David, 1991). In 1867 a number of inventors came up with the idea of using an electromagnetic field energized by the dynamo itself. The Gramm dynamo was based on this principle and was able to

16


generate electricity inexpensively enough for the commercial use of electric lighting. Other inventions such as the Swann–Edison lamp in 1879 and the Edison central generating station in New York and London in 1881 were also important for the diffusion of electricity. Moreover, innovations such as transformers and alternators made it possible to use alternating current instead of direct current, which substantially lowered costs for transmitting electricity.

4.1. Diffusion of Electricity Electric energy in the 19th century was produced by prime movers driven primarily by falling water (hydroelectric power) or by steam (thermal power).18 Electricity is not a prime mover, but rather a form of energy that is easy to transport from the power source to the end user, which gives rise to efficiency and flexibility gains. The process of electrification began in the 1880s both in Europe and the US (Goldfarb, 2005; Hughes, 1983; Byatt, 1979; Landes, 1969). In the beginning, application was largely limited to lighting. Later, electrification spread to tramways and railways. Innovations such as the electric motor eventually came to revolutionize manufacturing. The large-scale use of motors in manufacturing started around 1900 in the UK. By 1907, electric motors in factories consumed about half of the total amount of electricity produced, and by 1912 factories used three times as much electricity as did traction (Byatt, 1979). The industries of other large European countries, such as Germany and France, were also rapidly electrified in the late 19th and early 20th century (Milward & Saul, 1977). According to Landes (1969) the most striking achievements occurred in Germany. In 1907 the capacity of electric generators in Germany and the UK was roughly the same.19 However, in 1925 the total capacity of German electric generators was 13,288,800 horsepower compared to 8,510,000 for the UK. Moreover, German companies such as Siemens & Halske and Allgemeine Elektrizita¨ts-Gesellschaft (AEG) became the world’s leading manufacturers of electric equipment (Hughes, 1983). In 1913 the German electric machinery industry was twice as big as that of Britain and only slightly smaller than of the US (Landes, 1969). In the US, electricity was first used as a commercial power source in 1882. The use of electricity in manufacturing increased slowly. In 1899, 4 percent of the total primary horsepower capacity in manufacturing used energy from purchased or firm-generated electric power. This had risen to 21 percent in 1909, 50 percent in 1919 and 75 percent in 1929


17

Table 6. Available Horsepower Capacity in US Manufacturing, 1869–1954 (Thousand Horsepower). Year

Total Primary Capacity

Non-Electric Capacity

Primary Electric Motors

Secondary Electric Motors

1869 1879 1889 1899 1904 1909 1914 1919 1923 1925 1927 1929 1939 1954

2,346 3,411 5,845 9,811 13,033 18,062 21,565 28,397 32,667 34,359 38,236 41,122 49,893 110,181

2,346 3,411 5,845 9,633 12,605 16,393 17,858 19,432 19,426 19,243 19,336 19,328 21,077 35,579

178 428 1,669 3,707 8,965 13,241 15,116 18,900 21,794 28,816 74,602

16 297 1,089 2,913 4,684 6,647 8,796 9,976 11,201 12,050 16,011 19,514

Note: Primary electric motors are those driven by purchased electricity. Secondary motors are driven by self-generated electricity and are excluded from total primary power available. Source: Du Boff (1979).

(Woolf, 1984). Table 6 presents figures from Du Boff (1979) for total primary capacity in manufacturing divided into non-electric capacity and electric motor capacity. The figures indicate that the rapid expansion of purchased and firm-generated electricity was somewhat more modest compared to what Woolf argues. Still, the expansion of primary and secondary electric motors was rapid.20 Moreover, the adoption of electricity was very uniformly distributed across manufacturing industries (Jovanovic & Rousseau, 2005). Table 7 compares electrification in five manufacturing industries in the UK and the US. Even though the figures are not fully comparable some conclusions may be drawn. The figures point to large differences in the electrification process across industries in both countries. Industries such as engineering, shipbuilding and vehicles and chemicals were electrified much more rapidly compared to cotton textiles and coal mining. In iron and steel, coal mining and cotton textiles, Britain lagged behind the US. Byatt (1979) documents that these industries were slow in adopting electric motors in their production processes compared to both the US and Germany. Moreover, according to estimates by Broadberry (1997), the US/UK relative

18

Table 7.


Degree of Electrification in Six Industry Groups: Britain and the US, 1904–1924 (Percent).

Industry Cotton textile Iron and steel Engineering, shipbuilding and vehicles Chemicals and allied Coal mining

1904 UK US UK US UK US UK US UK US

1907

1909

a

5 7 8

46 46

74 65

19 16

18

22

43

92 72

31 42

4b

66 59

20 20

1924

53

25

32

1919

6 19

12

NA

1912

43 53

Source: Byatt (1979). a All textiles. b All mining.

labor productivity level in the cotton industry increased from 151 in July 1909 to 174 in December 1914. Why did Britain lag behind both Germany and the US in adopting electric motors in manufacturing? It appears that mining and textile industries that were early in adopting the steam engine in their production processes in Britain were much slower in adopting electricity compared, for example to chemicals and engineering, or shipbuilding and vehicles (see Table 7). One possible explanation for this is that the large investments in steam engines made those industries reluctant to invest in new electric technology. This suggests that technological choices are often path dependent and are not always socially optimal. Similar evidence from other areas supports this view (see David, 1985). It is interesting to note that the US textile industry quickly switched from steam to electricity. In fact, Jovanovic and Rousseau (2005) find that the industries that quickly switched to electricity had been heavy users of steam. One parallel that comes to mind is the Gerschenkron (1952) thesis that ‘‘relative backwardness’’ may facilitate economic growth, since it is easier to imitate the technologically leading countries. Similarly, a new GPT may be more readily adopted in a country where the previous GPT has not yet become so deeply entrenched. In Sweden, electricity started to be used in lighting in 1876. In 1885 there existed 111 dynamos with a capacity of 1036 horsepower (Hjulstro¨m, 1940). The diffusion of electricity was rapid in Swedish industries. Sweden


19

was also successful in innovation that permitted electricity to be transmitted over long distances without substantial power losses (Scho¨n, 1990). Initially, the primary source of electricity was steam power; in 1885, 82 percent of the electricity produced came from steam power and the remainder from hydropower. In 1900 the relationship was largely reversed and 60 percent of the electricity was produced by hydropower (Hjulstro¨m, 1940). Swedish manufacturing rapidly adopted electricity in the production process. Fig. 1 shows the development of Swedish electric motor capacity in the manufacturing and handicraft industry. It is evident that Swedish manufacturing was electrified very rapidly at the beginning of the 20th century. From 1906 to 1937 the power capacity of electric motors increased more than 20-fold. Which industries were then electrified most rapidly? Table 8 shows that electric motor capacity increased in all industries throughout the period investigated. For the period 1913–1931 the most rapid expansion took place in wood and cork with a capacity increase of 452 percent. Food manufacturing, leather, furs and rubber products, and non-metallic mining and quarrying also experienced increases exceeding 300 percent in their electric motor capacity in 1913–1931. The growth was slowest in textiles, wearing apparel and made-up textile goods – which is in accordance with the findings for the UK presented earlier.

Electric motor capacity

2500000

2000000

1500000

1000000

500000

0 1906

Fig. 1.

1910

1914

1918

1922

1926

1930

1934

Electric Motor Capacity in the Swedish Manufacturing and Handicraft Industries, 1906–1937 (Horsepower). Source: Hjulstro¨m (1940).

20


Table 8. Electric Motor Capacity in Different Swedish Industries (Horsepower) and Percentage Change, 1913–1931. Industry

Ore-mining and metal industries Non-metallic mining and quarrying Wood and cork Paper and paper products, printing and allied industries Food manufacturing industries Textiles, wearing apparel and madeup textile goods Leather, furs and rubber products Chemicals and chemical products Power, lighting and waterworks Total

Electric Motor Capacity 1913

1920

158,984

384,699

22,470

1931

Percentage Change 1913–1920

1920–1931

1913–1931

582,253

142

51

266

56,252

92,535

150

65

312

27,632 134,355

79,292 225,460

152,428 580,674

287 68

92 158

452 332

28,152

64,505

132,365

129

105

370

34,708

63,988

98,019

84

53

182

6,165

15,663

26,342

154

68

327

13,134

31,691

45,033

141

42

243

6,095

17,461

22,916

186

31

276

431,695

939,011

1,732,565

118

85

301

Note: The percentage change refers to the whole period, not percent p.a. Source: Hjulstro¨m (1940).

4.2. Electricity and Productivity Growth 4.2.1. Productivity Development in the US Table 9 presents estimates of the compound annual growth rate of labor and TFP growth in the US non-farm business sector 1889–1948. Productivity growth is measured from peak to peak over the business cycle. According to Field (2003) choosing business cycle peaks for beginning and end points largely controls for the variations in capacity utilization that occur over the business cycle. The results show that labor and TFP growth was high during the period 1889–1901. In 1901–1919 productivity growth slowed down and it did not start to increase until the 1920s. It is unlikely that electrification had a sizable effect on productivity growth in 1889–1901. For example, in 1899 only 4 percent of the total primary horsepower capacity in manufacturing used energy from purchased or firm-generated electric power (see Section 4.1).


21

Table 9. Compound Annual Growth Rates of Labor and Total Factor Productivity in the US Private Non-Farm Economy, 1889–1948. Period

LP

TFP

1889–1901 1901–1919 1919–1929 1929–1941 1941–1948

2.9 1.7 2.3 2.4 1.7

2.2 1.1 2.0 2.3 1.3

Note: Labor productivity is defined as output per man-hour. LP ¼ labor productivity; TFP ¼ total factor productivity. Source: Kendrick (1961) and authors’ calculations.

Table 10.

Compound Annual Growth Rates of Labor and Total Factor Productivity in US Manufacturing, 1889–1948.

Period

Field LP

1889–1919 1919–1929 1929–1941 1941–1948

1.3 5.5 2.6 0.2

Period TFP

0.7 5.1 2.6 0.5

1889–1899 1899–1909 1909–1919 1919–1929 1929–1937 1937–1948

Kendrick LP

TFP

1.4 1.3 1.1 5.5 2.0 1.5

1.1 0.7 0.3 5.1 1.5 1.7

Note: Labor productivity is defined as output per man-hour. LP ¼ labor productivity; TFP ¼ total factor productivity. Source: Field (2006b), Kendrick (1961) and authors’ calculations.

Table 10 presents estimates of compound annual labor and TFP growth in US manufacturing for different periods. The estimates are based on Kendrick (1961) and Field (2006b).21 These show large increases in both labor productivity and TFP growth in manufacturing for the period 1919– 1929. The estimated growth rate in labor productivity and TFP was 5.5 and 5.1 percent, respectively. As in the case of the steam engine several decades elapsed from the installation of the first power station producing electricity until there is evidence that electrification had a substantial impact on productivity growth within manufacturing. Why did productivity growth in manufacturing increase some 40–50 years after the introduction of the first commercial electric power stations? And which manufacturing industries experienced the highest productivity growth during this period?

22


David (1991) argues that it took considerable time for the manufacturing sector to adopt the new technology and use it efficiently. According to David, electrification paved the way for a thorough rationalization of factory construction designs and internal layouts of production. One such rationalization was the shift from shafts to wires in the production system (Devine, 1983). Before electricity was introduced, the production process was built around a large power source, such as a waterwheel or a steam engine. The power source turned iron and steel ‘‘line shafts’’ via pulleys and leather belts. Often all machines in an entire factory were linked to a single power source through these line shafts. The entire network of line shafts rotated continuously no matter how many machines were actually in use. If one line shaft broke, production stopped in the entire factory. It is evident that production systems built around a single power source were very energy consuming and lacked flexibility. The first electric motors used in production just replaced steam engines and continued to turn long line shafts. But, it was soon discovered that large energy savings could be realized if a group of machines were driven from a short line shaft turned by its own electric motor. A further step was to connect a single electric motor to each machine. This unit drive innovation used less energy than the line shaft drive. Yet, the most important economic impact of the unit drive system was the increased production process flexibility that it entailed. Machines could be run only when needed. Moreover, machines could be organized in a natural sequence for manufacturing. In this way the unit drive offered an opportunity to obtain greater output per unit of inputs (Devine, 1983). The reorganization of production processes around a new technology turned out to be time consuming. David (1991) maintains that it was not until half of the factory mechanical drive capacity had been electrified that productivity growth in manufacturing began to increase. In addition, David and Wright (2003) point out in some detail that in order for electric power to gain full momentum a number of political and institutional changes were also necessary. Table 11 shows the ratio of primary electric motor capacity to total primary capacity in US manufacturing. The data support David’s hypothesis that it was not until the end of the 1920s that half of the mechanical drives had been electrified. To support his hypothesis David shows that there is a correlation between the change in the rate of productivity growth from 1909–1919 to 1919–1929 and the ratio of secondary electric motor capacity in 1929 to that capacity in 1919. A simple linear (OLS) regression of


Table 11.

23

Electric Motor Capacity/Total Primary Capacity in US Manufacturing, 1899–1954 (Percent).

Period

Electric Motor Capacity/Total Primary Capacity

1899 1904 1909 1914 1919 1923 1925 1927 1929 1939 1954

1.9 3.3 9.2 17.2 31.6 40.5 44.0 49.4 53.0 57.8 67.7

Source: Du Boff (1979).

15 industries confirms that the increase in secondary motor capacity accounts for approximately 25 percent of the variation in productivity growth from 1909–1919 to 1919–1929.22 In subsequent work David and Wright (1999) provide more compelling evidence in support of the view that the productivity surge in the 1920s can be attributed to the diffusion of a new GPT rather than to multiple, largely unrelated sources. An interesting observation can also be made for the production of electricity and for electric machinery. Woolf (1984) finds that there was a substantial increase in the rate of productivity growth in the sector producing electricity. In 1902, 7.3 lbs of coal was needed to generate 1 kilowatt hour of electricity. In 1917 the figure had fallen to 3.4 lbs and by 1932 only 1.5 lbs were needed. Table 12 presents figures from Kendrick (1961) on compound annual labor and TFP growth in different manufacturing industries in the US. According to these estimates the substantial productivity increase did not appear in the industry producing electric machinery. For the period 1919–1929 annual TFP growth in US manufacturing was 5.1 percent, while TFP growth in electric machinery was only 3.5 percent per year.23 The change in TFP growth from 1909–1919 to 1919–1929 for manufacturing and electric machinery is 4.9 and 3.2 percentage points, respectively. Hence, productivity growth increased substantially in the sector producing electricity, but not in the sector producing electric machinery. The productivity effects were materialized in sectors using electric machinery rather than in sectors producing it.24

24

Table 12.


Compound Annual Growth Rates of Labor and Total Factor Productivity in US Manufacturing, 1899–1937.

Industry

Food Beverages Tobacco Textiles Apparel Lumber products Furniture Paper Printing, publishing Chemicals Petroleum, coal products Rubber products Leather products Stone, clay, glass Primary metals Fabricated metals Machinery, nonelectric Electric machinery Transportation equipment Miscellaneous Total manufacturing

1899–1909

1909–1919

1919–1929

1929–1937

LP

TFP

LP

TFP

LP

TFP

LP

TFP

0.6 1.3 1.7 1.4 0.9 0.2 0.7 3.0 3.9 1.3 3.0 2.5 0.5 2.7 3.7 2.8 1.8 1.3 1.3 1.1

0.3 0.9 1.2 1.1 0.7 0.4 0.8 2.4 3.8 0.6 0.7 2.2 0.1 2.2 2.6 2.3 1.0 0.6 1.1 0.8

0.0 6.6 5.9 1.7 3.3 1.0 0.4 0.5 3.2 0.3 1.8 7.6 0.9 1.0 0.4 2.0 0.7 0.0 7.4 0.6

0.4 5.8 4.8 0.9 2.7 1.2 0.5 0.3 3.0 0.7 1.0 7.1 0.5 0.7 0.5 1.8 0.7 0.3 6.8 0.6

5.2 0.5 7.0 2.4 3.9 2.9 4.2 4.9 3.7 7.9 8.6 8.1 2.5 6.1 5.6 5.0 2.9 3.9 8.7 5.3

5.2 0.2 4.3 2.9 3.9 2.5 4.1 4.5 3.7 7.2 8.2 7.4 2.9 5.6 5.4 4.5 2.8 3.5 8.1 4.5

0.9 13.5 7.3 4.3 2.1 0.2 0.3 4.4 2.6 3.0 5.5 3.4 3.2 1.7 0.9 0.5 1.9 2.8 0.2 2.2

1.5 14.1 6.1 4.5 2.5 0.4 0.5 4.2 2.6 3.0 2.7 3.9 3.5 2.2 1.3 1.0 2.2 3.1 0.4 2.8

1.3

0.7

1.1

0.3

5.4

5.1

2.0

1.9

Note: Labor productivity is defined as output per man-hour. LP ¼ labor productivity; TFP ¼ total factor productivity. Source: Kendrick (1961) and authors’ calculations.

4.2.2. Evidence for other Countries Table 7 indicates that the UK lagged behind the US in the electrification of many important industries. According to Byatt (1979) the UK industry was very slow in investing in electric motors. The UK also lagged behind the US in terms of productivity growth. Floud (1994) estimates that annual TFP growth in the British economy decreased from 1.4 percent in 1856–1873 to 0.5 percent in 1873–1913. Labor productivity growth was slower in the UK relative to most other industrialized countries for the period 1913–1950 (Maddison, 1991). Why then was the UK slower in adopting electricity? Byatt (1979) argues that investments in electric motors had an impact on the UK economy, but not to the same extent as in the US. One reason for


25

the late adoption of electricity in the UK could have been that other energy sources were cheaper than electricity. The UK had the most developed applications of steam as a power source and it was probably therefore more costly to invest in electricity. The evidence indicating that sectors with welldeveloped steam capabilities were slow in investing in electricity supports this explanation. Table 13 shows annual labor productivity growth for 12 German manufacturing and handicraft industries in 1925–1938.25 The estimated total annual labor productivity growth in German manufacturing and handicraft was 2.5 percent in 1925–1938.26 Labor productivity was particularly high in metal producing, metal processing and chemical industries in the late 1920s. However during the 1930s, the rate of labor productivity growth decreased considerably in the metal producing and metal processing industries, while it remained relatively high in the chemical industry. However, throughout the period 1925–1938, the chemical and metal processing industry had the highest annual labor productivity growth at 4.9 and 3.4 percent, respectively.

Table 13.

Compound Annual Growth Rate of Labor Productivity in Different German Industries, 1925–1938.

Industry Stone and soil production Metal producing industrya Metal processing industrya Chemical industry Textiles Leather production Clothing industry Wood products Paper products Food production Gas, water and electricity Constructionb Total

1925–1929

1929–1938

1925–1938

6.2 6.6 8.1 6.7 0.8 0.4 2.4 0.8 4.5 1.2 4.5 1.6

1.9 0.3 1.3 4.1 3.5 1.3 4.9 0.9 0.7 1.4 2.2 2.5

0.6 1.8 3.4 4.9 2.2 1.0 2.7 0.8 1.9 1.3 2.9 1.3

2.8

2.4

2.5

Note: Labor productivity is defined as output per worker. Source: Hoffmann (1965) and authors’ calculations. a Employment was only available for the total metal producing and processing industry. It is therefore assumed that the change in employment was the same in these industries in 1925–1938. b Labor productivity estimates for construction are for the 1926–1938 period.

26


When it comes to productivity development in Sweden it appears that Sweden followed the US pattern. Scho¨n (2000) shows that labor productivity growth in Swedish manufacturing increased from 1.5 percent p.a. in 1896–1910 to 2.9 percent in 1910–1935.27 Table 14 shows labor productivity growth for different industries in the Swedish manufacturing and handicraft industries in 1913–1939. As in the US, labor productivity growth accelerated in 1919–1929. Chemicals and chemical products and power, lighting and waterworks experienced the highest rates of productivity growth in 1919– 1929. However, as indicated in Table 8, electric motor capacity did not increase the most in these industries. Thus, one cannot establish a clear correlation between labor productivity growth and the increased use of electric motors for different industries within Swedish manufacturing during the years 1919–1929. Finland’s productivity growth was similar to the US and Sweden during electrification. According to Jalava and Pohjola (2005) annual labor productivity growth in the Finnish non-residential business sector increased from 1.9 percent in 1900–1913 to 3.1 percent in 1920–1938. Moreover, they estimate that the use of electrical capital goods contributed 1.2 percentage points of the 4.5 percent growth in value added in 1920–1938 compared to 0.4 percentage points of the 3.0 percent growth in value added in 1900–1913. Hence, the contribution of electrical capital goods increased from 13 percent of total value added growth in 1900–1913 to 27 percent in 1920–1938.

Table 14.

Compound Annual Growth Rate of Labor Productivity in Different Swedish Industries, 1913–1939.

Industry

1913–1919 1919–1929 1929–1939

Ore-mining and metal industries Non-metallic mining and quarrying Wood and cork Paper and paper products, printing and allied industries Food manufacturing industries Textiles, wearing apparel and made-up textile goods Leather, furs and rubber products Chemicals and chemical products Power, lighting and waterworks

2.8 3.7 0.0 2.2 0.1 1.0 2.8 6.3 0.4

4.3 4.7 0.3 4.4 3.0 1.7 0.1 11.2 7.7

2.5 4.6 1.0 2.6 1.8 0.8 0.8 3.8 4.9

Total

1.7

3.8

2.0

Note: Labor productivity is defined as value added per worker. Source: Scho¨n (1988), Kommerskollegium (1913–1939) and own calculations.


27

4.3. Price Development of Electric Motors We noted above that compound annual TFP growth in US manufacturing was 5.1 percent, while TFP growth in electric machinery was only 3.5 percent in 1919–1929. This suggests that the industry actually producing the electrical equipment was not able to take advantage of its own technology to the same extent as other industries. Table 15 shows the price development for a number of different electric motors (in terms of SEK/horsepower) produced by the Swedish company Luth & Rosen during the 1920s. According to Table 15 the price of 3–15 horsepower electric motors fell rapidly in Sweden during the 1920s. On average prices fell by approximately 70 percent from 1919 to 1929. CPI calculations by Myrdal (1933) and Johansson (1967) indicate that total price deflation during this period was 37 percent. Hence, the real price of electric motors decreased substantially,28 which is a clear indication of productivity gains in the electric motor producing industry in Sweden.29 These results call the productivity findings for US electric machinery into question. It is reasonable to presume that the industry producing the electric motor also should be the industry that most

Table 15.

Price Series of Electric Motors Produced by Luth & Roseń, 1919–1929 (SEK/Horsepower).

Number of hp Model

3 hp C20

5 hp C21

7.5 hp C50

10 hp C51

15 hp C80

Indexa

CPI

Real Index

1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929

335 319 195 157 110 122 103 103 125 112 111

250 237 143 114 78 86 71 71 86 78 77

232 215 128 100 71 74 54 54 67 61 60

189 175 106 84 58 63 49 49 60 53 54

160 148 89 53 47 48 39 39 48 44 45

100 93.5 56.4 42.6 30.9 33.3 26.6 26.6 32.5 29.3 29.3

100 100.4 86.2 69.8 64.9 64.9 66.0 63.8 63.1 63.4 62.7

100 93.2 65.4 61.1 47.6 51.3 44.8 41.7 51.5 46.2 46.7

Note: All motors had the following characteristics: Alternating current, 1,500 revolutions per minute, 190–500 V. hp: horsepower. Source: Ljungberg (1990), Myrdal (1933), Johansson (1967) and authors’ calculations. a The index is an equally weighted price index of the five engines presented in the table above.

28


rapidly understood how the electric motor could be used efficiently in the production process. Moreover, the increase in demand for the electric motor should have resulted in increased production and thereby allowed the industry to benefit from economies of scale. Table 15 indicates that the price per horsepower fell more rapidly for electric motors with more than 5 horsepower. Moreover, a 15 horsepower electric motor in Sweden was much cheaper in 1929 compared to a 7.5 horsepower motor in 1919 both in nominal and real terms. To the extent that companies were buying electric motors with higher capacity during the 1920s, the real price of motor capacity installed fell even more than what is indicated by the price change of each motor category. Finally, it is likely that the quality of an electric motor increased during the 1920s in terms of reliability, duration, etc. Ordinary price indexes do not take such quality improvements into account. Edquist (2005a) constructs hedonic and matched model price indexes for electric motors in Sweden for the period 1900–1935.30 He finds that during the 1920s, current prices for electric motors decreased by 13.2 and 12.2 percent per year depending on whether hedonic or matched model price indexes were used. Table 16 shows the estimated labor productivity growth for electric machinery based on the hedonic and matched model price indexes estimated by Edquist (2005a).31 According to Table 16, annual labor productivity growth in the Swedish electric machinery industry between 1920 and 1929 was 12.1 and 10.8 percent when hedonic and matched model deflators were used. Therefore, there is strong evidence that productivity growth in the Swedish electric motor producing industry was very high during the 1920s. However, it is still a puzzle why productivity did not increase more in US electric machinery during the 1920s. Table 16.

Labor Productivity Growth in Electric Machinery in Sweden, 1913–1935 (Percent p.a).

Year

1913–1919 1920–1929 1930–1935 1913–1935

Growth Rate Hedonic Deflation

Matched Model Deflation

7.2 12.1 2.5 3.0

4.2 10.8 2.0 3.8

Note: Labor productivity is defined as production value per person employed. Source: Edquist (2005a).


29

4.4. Concluding Remarks on Electrification Evidence from the electrification process shows that productivity growth did not increase in US manufacturing until the 1920s, i.e. 40 years after the first electric power stations were established. Similar patterns can be observed in Sweden and Germany. Electrification took place in all the investigated countries in the 1880s. However, British manufacturing was slow in adopting the new technology, especially in industries that had a well-developed production system based on steam power technology. In the US there was high productivity growth in the sectors producing electricity, but not in the industry producing electric machinery. Thus, it appears that the productivity effects were largely materialized in sectors using electric machinery rather than in sectors producing it. One possible explanation to these findings is that quality improvements were insufficiently considered when productivity was measured in the producing industries. As we will see below, this stands in contrast to contemporary estimates of productivity in ICT-producing sectors where a large part of productivity increases may be attributed to assessed improvements in quality.

5. THE ICT REVOLUTION 5.1. Background In 1947 Bardeen, Brattain and Shockley invented the transistor. The transistor became the basis for numerous electronic innovations. Many of these innovations formed what is called the information and communications technology (ICT) sector. During recent decades the ICT sector has undergone a technological revolution. The development of numerous innovative technologies has given rise to a plethora of new products providing the basis for development within the ICT sector. Communication satellites in the 1960s, fiber optic cables in the 1970s and cellular telephones first introduced during the 1980s are significant examples of such product innovations. The Internet is yet another innovation that is believed by many to be a crucial driver of future economic growth (e.g., Litan & Rivlin, 2001; Lipsey et al., 1998). In this section we investigate the economic impact of the ICT sector on productivity in the US and five European countries (Finland, France, Germany, Sweden and the UK). The selection of countries was governed by data availability, but there is strong reason to believe that the conclusions for these countries are readily applicable to other European countries on

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a similar income level. The following questions will be discussed in depth: (1) What effect has ICT investments had on aggregate productivity growth? (2) In which industries can we find increased productivity growth? (3) Have there been any spillover effects from ICT-producing to the ICT-using industries? 5.2. What is ICT? Before analyzing the economic impact of the ICT sector it is important to define ICT. We adhere to OECD’s definition. For a manufacturing industry to be defined as an ICT industry, the manufactured products (OECD, 2002b): (1) must be intended to fulfill the function of information processing and communication including transmission and display and (2) must use electronic processing to detect, measure, and/or record physical phenomena or to control a physical process. For a service industry, products must be intended to enable the function of information processing and communication by electronic means. Productivity measurements within the service sector give rise to several measurement problems.32 Therefore we have chosen to focus on manufacturing (see Table 17). For certain industries, the OECD definition of ICTproducing industries has been at a very disaggregated level. Therefore it is not possible to calculate value added and labor productivity at the disaggregated level used in Table 17. The following industries are defined as ICT-producing: office, accounting and computing machinery (ISIC 30), electric machinery Table 17. ISIC 3rd Revision 30 3130 3210 3220 3230 3312 3313

ICT-Producing Industries in Manufacturing. Economic Activity Office, accounting and computing machinery Insulated wire and cable Electronic valves and tubes and other electronic components Television and radio transmitters and apparatus for line telephony and line telegraphy Television and radio receivers, sound or video recording or reproducing apparatus and associated goods Instruments and appliances for measuring, checking, testing, navigating and other purposes, except industrial process equipment Industrial process equipment

Source: OECD (2002b). ISIC: International Standard for Industry Classification.


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and apparatus (ISIC 31), radio, television and communication equipment (ISIC 32) and medical, precision and optical instruments (ISIC 33).

5.3. The ICT Revolution and Productivity Growth Despite heavy investments by firms in computers and other ICT technology in the 1970s and 1980s, productivity growth slowed down in most countries. The first oil crisis has been pointed out as one of the explanations for the productivity slowdown (Hulten, 2001).33 Nevertheless, the slowdown has remained a puzzle for economists, especially since it occurred when firms started to invest in computers that were believed to have a major positive effect on productivity. During the 1990s, ICT investments were extremely large. During 1990– 1996, US investments in computers rose by 28.3 percent per year (Jorgenson & Stiroh, 1999). Sichel (1999) reports that the annual increase for computer investments during 1996–1998 was 41.8 percent. Calculations also show that ICT accounted for about half of the increase in real capital in the US during the period 1990–1996 (Andersson, 2001). The available data for OECD countries show that ICT investments rose from less than 15 percent of total non-residential investment in the business sector in the early 1980s, to between 15 and 35 percent in 2001 (OECD, 2004). Did these investments have any substantial impact on productivity growth in different countries? In Table 18, we present data for the average annual growth rate of labor productivity for the six economies we study, as well as average labor productivity growth for the EU countries.34 It is evident that the US is the only country that experienced a significant increase in the growth rate of labor productivity in the late 1990s and early 2000s. None of the other economies shows a similar increase in productivity growth during the late 1990s. We can also see that average labor productivity growth in EU countries decreased substantially during the second half of the 1990s. In this respect, development in the EU on the whole has been the opposite of what has occurred in the US. During 2001, the US and most EU countries experienced an economic slowdown. Falling growth rates called the narrow ‘‘new economy’’ concept into doubt. Many observers have associated the concept with bankruptcies among dotcoms and other firms. However, productivity growth in the US remained high, despite the general downturn in the economy (Council of Economic Advisers, 2002). Table 19 shows that growth in labor productivity as well as TFP growth increased considerably during the period 1995–2001

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Table 18. Average Annual Growth of GDP per Person Employed in Selected Countries, 1980–2001 (Labor Productivity). Country

1980–1989

1990–1995

1996–2001

Finland France Germany Sweden UK US EU-15

2.5 2.1 NA 1.6 2.2 1.5 1.8

2.5 1.3 2.2 2.5 2.0 1.2 2.0

1.9 1.4 1.6 1.9 1.6 2.1 1.3

Note: Calculations for Germany use growth figures beginning in 1992 due to the reunification with East Germany in 1990 and 1991. Figures for the period 1980–1995 have been taken from Scarpetta et al. and figures for the period 1996–2001 are based on OECD (1998), OECD (2000), OECD (2002a) and OECD (2003a). NA ¼ not available. Source: Scarpetta et al. (2000), OECD (1998), OECD (2000), OECD (2002a) and OECD (2003a).

Table 19.

Average Annual Growth Rates of Labor Productivity and TFP in the US, 1973–2001. 1973–1995

Labor productivity growth rate (percent) Business cycle effect ¼ Structural labor productivity Capital services Labor quality ¼ Structural TFP

1995–2001

Change (Percentage Points)

1.39

2.60

1.21

0.02 1.37 0.72 0.27 0.37

0.46 3.07 1.29 0.31 1.44

0.48 1.70 0.57 0.04 1.07

Note: Labor productivity is the average of income- and product-side measures of output per hour worked. TFP is labor productivity less the contributions of capital services per hour (capital deepening) and labor quality. Productivity for 2001 is inferred from data for the first three quarters. Source: Council of Economic Advisers (2002).

compared to 1973–1995. Figures in Table 19 are based on a model that takes the effects of the business cycle on productivity into consideration. According to these calculations, structural labor productivity growth increased by 1.7 percentage points between the periods 1973–1995 and 1995–2001.35 The corresponding figure for structural TFP growth is 1.07 percentage points, i.e. a tripling of the pace in the 1973–1995 period.


33

The productivity performance of the US economy has intensified the debate about the effect of ICT on productivity throughout the whole economy. Research results have shown that investments in ICT play an increasingly important role for productivity growth. In recent years, however, several researchers have pointed out that a dramatic increase in productivity has only been experienced in a few industries (Jorgenson & Stiroh, 2000; Gordon, 2000).

5.4. Productivity Growth: Industry Evidence Has ICT influenced productivity growth in the whole economy, or has productivity growth accelerated in just a few industries? To make an in-depth analysis, we present results from productivity calculations at the sectoral level for manufacturing. We begin by presenting detailed information for the US (Tables 20 and 21).36 For the other countries, we present information for those three industries with the highest rate of productivity growth in 1996–2000/2001. Table 20 indicates that the compound annual labor productivity growth rate increased considerably in US manufacturing in 1996–2000. Compound annual labor productivity growth increased from 3.7 percent in 1990–1995 to 4.5 percent in 1996–2000. Labor productivity growth increased in 13 out of 20 of the industries between 1990–1995 and 1996–2000. This could indicate a spillover effect from ICT-producing to ICT-using industries.37 However, a closer inspection reveals two industries in the US with much higher growth rates in labor productivity in the 1990s: office, accounting and computing machinery (OAC) (ISIC 30) and radio, television and communication equipment (RTC) (ISIC 32). The compound annual productivity growth rate for these two industries in 1996–2000 was 31.1 and 20.8 percent, respectively. In Table 20 labor productivity is defined as production value per person engaged. Intermediate inputs are not deducted from the production value, which implies double counting of intermediate inputs. Production value may therefore be a poor measure of output when industry trends are analyzed (Bailey, 1986). Table 21 presents estimates of compound annual productivity growth for different US manufacturing industries in 1990–2003 defined as value added per person employed.38 Table 21 confirms the result that the highest productivity growth took place in the industry producing computers and communication equipment in 1990–2003. The compound annual productivity growth for computer and electronic products (NAICS 334) was 26.6 percent in 1996–2003.39

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Table 20.


Compound Annual Growth Rates of Labor Productivity in Different US Manufacturing Industries, 1980–2000.

Industry

ISIC

1980–1989

Food products and beverages Tobacco products Textiles, textile products, leather and footwear Wood and products of wood and cork Paper and paper products Publishing and printing Coke, refined petroleum products and nuclear fuel Chemicals and chemical products Rubber and plastic products Other non-metallic mineral products Basic metals Fabricated metal products excl. machinery and equipment Machinery and equipment Machinery and equipment, n.e.c. Office accounting and computing machinery Electric machinery and computing Radio, television and communication equipment Medical, precision and optical instruments Motor vehicles, trailers and semitrailers Other transport equipment Manufacturing and recycling n.e.c.

15 16 17–19

2.7 1.5 2.6

1.7 4.3 4.0

1.9 0.3 5.5

20

2.0

0.2

0.7

21 22 23

2.5 1.1 2.7

1.9 0.3 2.7

1.7 1.5 3.4

24 25 26

3.2 4.0 2.4

2.0 2.9 1.7

2.5 3.3 1.8

27 28

2.9 1.9

3.5 2.4

1.3 1.9

29–33 29 30

5.2 NA NA

9.2 3.5 24.3

10.6 1.7 31.1

31 32

NA NA

5.7 18.3

3.0 20.8

33

NA

4.2

3.7

34

4.4

2.7

3.9

35 36–37

0.9 1.7

0.8 2.0

3.5 2.9

15–37

2.9

3.7

4.5

Total manufacturing

1990–1995

1996–2000

Note: Labor productivity is defined as production value per person engaged. Source: OECD (2003b) and authors’ calculations.

Results for the five European countries show much less evidence of spillovers to the rest of the economy. First, as already shown, there is no detectable increase in aggregate productivity growth compared to the mid1990s. Second, compared to the US, there is little evidence of spillovers within manufacturing. Table 22 reports the three industries with the highest


Table 21.

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Compound Annual Labor Productivity Growth in Different US Manufacturing Industries, 1990–2003.

Industry Food, beverage and tobacco products Textile mills and textile product mills Apparel, leather and allied products Wood products Paper products Printing and related support activities Petroleum and coal products Chemical products Plastics and rubber products Non-metallic mineral products Primary metals Fabricated metal products Machinery Computer and electronic products Electric equipment, appliances and components Motor vehicles, bodies and trailers and parts Other transport equipment Furniture and related products Miscellaneous manufacturing Total manufacturing

NAICS

1990–1995

1996–2003

311–312 313–314 315–316 321 322 323 324 325 326 327 331 332 333 334 335 3,361–3,363 3,364–3,366, 3,369 337 339

4.0 4.0 3.5 3.6 0.8 0.2 6.6 2.3 3.7 3.8 3.8 3.1 0.8 19.1 3.0 4.3 2.2 2.0 1.3

0.3 3.0 10.2 1.2 1.1 1.0 3.2 3.8 4.0 2.5 3.5 1.5 2.7 26.6 4.9 5.1 1.8 1.6 3.6

4.1

5.8

31–33

Note: Labor productivity is defined as value added per person employed. Source: Bureau of Economic Analysis (2004) and Bureau of Labor Statistics (2005).

rates of labor productivity growth during 1996–2000/2001.40 In France, Sweden and Finland there were two ICT-producing industries that had the highest annual productivity growth. However, for Finland and Sweden OAC (ISIC 30) is not among the three sectors with the highest productivity growth. Instead, electric machinery and apparatus (ISIC 31) had the second highest labor productivity growth in Finland and medical, precision and instruments (ISIC 33) in Sweden. In Germany, OAC (ISIC 30) had the highest productivity growth for the period 1996–2000. However, RTC (ISIC 32) ranks only third, with an annual productivity growth of 14.0 percent. For the UK OAC (ISIC 30) holds first place, but there are no data available for electric machinery and apparatus (ISIC 31) and RTC (ISIC 32). In all European countries investigated, an ICT-producing industry had the highest productivity growth. For Finland, France and Sweden the industry with the second highest growth was also ICT-producing. In Germany

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Table 22. The Three Manufacturing Industries with the Highest Compound Annual Growth Rate of Labor Productivity Growth in Five European Countries 1996–2000/2001. Country

ISIC

Growth

Finland Radio, television and communication equipment Electric machinery and apparatus Basic metals

32 31 27

19.9 6.0 4.2

France Office accounting and computing machinery Radio, television and communication equipment Motor vehicles, trailers and semi-trailers

30 32 34

21.2 19.9 10.5

Germany Office accounting and computing machinery Coke, refined petroleum products and nuclear fuel Radio, television and communication equipment

30 23 32

18.0 16.9 14.0

Sweden Radio, television and communication equipment Medical, precision and optical instruments Motor vehicles, trailers and semi-trailers

32 33 34

25.0 12.1 6.3

UK Office accounting and computing machinery Motor vehicles, trailers and semi-trailers Other transport equipment

30 34 35

7.5 3.8 2.7

Note: Labor productivity is defined as value added per person engaged. Figures for Sweden and Finland cover the 1996–2001 period. Figures for Germany and France cover 1996–2000 period and figures for the UK are for the 1996–1999 period. For the UK labor productivity is defined as value added per person employed. Source: OECD (2003b) and authors’ calculations.

RTC had the third highest labor productivity growth. The comparison for the UK is incomplete, because of lack of data for some industries.

5.5. Spillovers to the Rest of the Economy Aggregate data show that the US had very high aggregate productivity growth during the second half of the 1990s relative to the preceding 20-year period. However, more disaggregated data for manufacturing shows that


37

high productivity growth rates were experienced in just a few industries, notably in the ICT-producing industries, while productivity growth in ICTusing industries remained at levels similar to previous periods. Gordon (2000) argues that the productivity revival in the US occurred primarily within durable goods production and particularly in the ICT-producing industries. Should we then expect spillover effects to ICT-using industries in manufacturing and non-manufacturing? Evidence from the two earlier breakthroughs suggests that the large productivity gains were not realized until long after the introduction of the new GPT. David (2001) points out that the increase in TFP growth in the US in the 1920s was very evenly distributed across industries. In contrast, it appears that most of the productivity growth during the 1990s was very unevenly distributed across industries, most of it taking place in ICT-producing industries. Harberger (1998) makes a distinction between a ‘‘yeast-like’’ process of growth characterized by evenly distributed growth throughout most of the economy and a ‘‘mushroom-like’’ process with productivity growth in just a few sectors. David (2001) argues that the patterns of TFP growth were starting to move from a ‘‘mushroom-like’’ process to a ‘‘yeast-like’’ process in the late 1990s. Recent studies of productivity performance and ICT suggest that ICT has had substantial impact on productivity in a wide range of different industries and not only in the ICT-producing industries. Stiroh (2002) and Van Ark, Inklaar, and McGuckin (2002) distinguish between ICTproducing industries, intensive ICT-using industries and less intensive ICTusing industries.41 Stiroh (2002) finds that in the US, the ICT-producing and the intensive ICT-using industries accounted for all of the productivity revival (after 1995) that can be attributable to the direct contributions from specific industries. Oliner and Sichel (2000) also attribute a crucial role to the manufacture of computers, but they do not find that it accounts for all of the productivity increase. They estimate that use of ICT equipment together with improved production technology for computers account for approximately two-thirds of the increase in productivity growth in the US.42 Europe lags the US in terms of productivity growth. Still, it appears as though productivity growth has increased in the ICT-producing industries also in Europe even though the pattern is somewhat different. Within manufacturing RTC (ISIC 32) has had astounding productivity growth in several countries, while computer manufacturing played a larger role in the US. The phenomenal growth in RTC is particularly pronounced in Sweden and Finland.

38


Which sectors have accounted for increased productivity growth noticeable at the macro level in the US and why has not aggregate productivity growth increased in Europe? It appears that parts of the economy outside of manufacturing in the US have had a higher increase in productivity than corresponding sectors in Europe. McKinsey Global Institute (2001) maintains that the greater part of the increase in productivity in the US economy is concentrated in three sectors in addition to the ICT sector (semiconductors included): retail trade, wholesale trade and financial services. Table 23 shows that both the US and most EU countries experienced rapid increases in labor productivity in ICT-producing industries. According to Van Ark et al. (2002) the contribution of these industries to aggregate productivity growth was slightly lower in the EU compared to the US. Moreover, the largest difference appears to have taken place in ICT-using services. According to Van Ark et al. the differential between the US and

Table 23. Annual Labor Productivity Growth of ICT-Producing, ICT-using and Non-ICT Industries in the EU and the US, 1990–1995 vs. 1995–2000 (Percent). Productivity Growth United States

GDP Share

EU

2000

1990–1995 1995–2000 1990–1995 1995–2000 Total economy

EU 100

US

1.1

2.5

1.9

1.4

100

ICT-producing industries ICT-producing manufacturing ICT-producing services

8.1 15.1 3.1

10.1 23.7 1.8

6.7 11.1 4.4

8.7 13.8 6.5

5.9 1.6 4.3

7.3 2.6 4.7

ICT-using industriesa ICT-using manufacturing ICT-using services

1.5 0.3 1.9

4.7 1.2 5.4

1.7 3.1 1.1

1.6 2.1 1.4

27.0 5.9 21.1

30.6 4.3 26.3

Non-ICT industries Non-ICT manufacturing Non-ICT services Non-ICT other

0.2 3.0 0.4 0.7

0.5 1.4 0.4 0.6

1.6 3.8 0.6 2.7

0.7 1.5 0.2 1.9

67.7 11.9 44.7 10.5

62.1 9.3 43.0 9.8

Note: Labor productivity is defined as value added per person employed. EU includes Austria, Denmark, Finland, France, Germany, Ireland, Italy, Netherlands, Spain, Sweden and the United Kingdom, which represents over 90 percent of the EU GDP. Source: Van Ark et al. (2002). a Excluding ICT-producing industries.


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Europe is heavily caused by different productivity development in retailing, wholesale trade and financial services. Estimates show that 0.90 percentage points out of a total productivity growth differential of 1.1 percentage points between the US and Europe in the late 1990s emanated from these industries. Table 23 also shows that ICT-producing manufacturing and the ICTusing service sector were larger in the US compared to the EU measured as a share of GDP. The ICT-producing manufacturing sector had a share of 2.6 percent of total GDP in the US compared to 1.6 percent in the EU. The ICT-using service sector share of total GDP was 26.3 percent in the US compared to 21.1 percent in the EU. Thus, the sectors where productivity growth increased most during 1995–2000 were relatively larger in the US.

5.6. Concluding Remarks on the ICT Revolution The transistor was invented in the late 1940s, but computers and cellular phones did not become consumer products until the 1980s. Aggregate labor productivity growth increased in the US in the latter part of the 1990s. However, aggregate labor productivity growth did not increase to the same extent in the EU countries. Nonetheless, labor productivity growth was much faster in the ICT-producing industries compared to ICT-using industries for the US and for the five European countries that we have investigated for the period 1996–2001. Moreover, the aggregate productivity gap between the US and the EU was mainly due to productivity differences in retailing, wholesale trade and financial services. One possible reason for this could be that the US has been faster than Europe in implementing institutional and political changes that facilitate the exploitation of the economic potential of the new GPT (Litan & Rivlin, 2001).

6. SUMMARY AND CONCLUSIONS We have examined three technological breakthroughs and the development of subsequent productivity growth. We will now summarize and draw some conclusions from our investigation. Moreover, we discuss some measurement issues concerned with comparing productivity growth in different countries and for different time periods.

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6.1. Patterns of Productivity Growth after Major Technological Breakthroughs When comparing technological breakthroughs it is important to keep in mind that every new technology has unique characteristics. Few technologies fulfill the requirements for being classified as a GPT or TEP. The three technological breakthroughs investigated here are different from each other. The interdependence between different technologies can also be highly complex. For example, electricity replaced the steam engine in the industrial production process, but the steam engine was also important initially as a primary source for producing electricity. Furthermore, the ICT revolution presupposed the existence of an extensive electricity network. These examples imply that technological breakthroughs cannot be analyzed solely as individual cases. We have shown that some major technological breakthroughs have impacted importantly on productivity growth in manufacturing, but also on aggregate productivity. It also appears that the impact of different key technologies has differed substantially across countries and industries. However, one major similarity for all three technological breakthroughs is that the productivity effects took place a considerable time after the initial innovation. David (1990, 1991) argues that when considering technological paradigm shifts, with the potential to create the core of a new technological regime, a time scale of 40–50 years may be necessary for the full impact of productivity growth to become evident in the conventional indicators. Our findings for the steam engine and the ICT revolution support David’s view. However, it took much longer from the time when the steam engine was invented until it had an impact on productivity growth (some 140 years) than for the electricity breakthrough and the ICT revolution, where 40–50 years elapsed before increases in manufacturing productivity growth rates could be observed. These findings suggest that it is not the technological invention that directly affects growth, but rather the additional innovations made to improve the new technology that is important for productivity growth (this will be further discussed below). Why does the process of additional innovation take so long? And what is the character of these innovations? Even though there are similarities between the productivity pattern following major technological breakthroughs, there are also important differences among them. The steam engine did not have a substantial impact on aggregate productivity growth in the UK until the 1850s, i.e. 140 years after Newcomen’s original invention. The steam engine was not adopted by all sectors in the economy, but it was intensely used in a few key industries such


41

as textile, coal mining and transportation. These industries also appear to have had a higher than average productivity growth (McCloskey, 1981; Harley, 1993). There is no clear evidence that the productivity growth associated with the steam engine in the UK took place in the industries producing the new technology. However, even though there are no direct statistical observations of high productivity growth in the steam engine producing industry, the price of steam power decreased substantially around 1850. This is a strong indirect indication of high productivity growth in the steam engine producing industry. Both quantitative and qualitative studies have provided evidence that electrification had a substantial but delayed influence on productivity growth in US manufacturing. There was an increase in productivity in the electricity-producing sector. However, increased productivity growth was not discernible in the sector producing electric machinery. Kendrick (1961) suggests that compound annual TFP growth was 3.5 percent per year in the industry producing electric machinery in the 1919–1929 period, while manufacturing as a whole had a TFP growth of 5.1 percent p.a. during that period. This suggests that the productivity effects took place in sectors using electric machinery rather than in sectors producing it. David (1991) pointed out the relationship between increases in the rate of productivity growth and investments in electric motors. The same results cannot be found for Sweden. Moreover, even if there were substantial investments made in machinery with electric motors there was no substantial productivity increase in this particular industry. This is a major difference compared to the ICT revolution, where productivity increased by far the most in ICT-producing industries. In Section 4.3 it was shown that there was a substantial fall in real prices of electric motors during the period 1919–1929. Unlike the ICT revolution there are no consistent hedonic price indices for the periods covering earlier technological breakthroughs, which suggests that the productivity effects from earlier technological breakthroughs may be underestimated. For the ICT revolution we have seen large increases in the productivity growth for the sector producing ICT technology during the 1990s. However, it has not been possible to find evidence of spillover effects to other manufacturing industries. One of the reasons for the high labor productivity growth for the ICT-producing industries could be that hedonic price indices are used when deflating the value added for these industries. Still, this cannot be the whole explanation, since there are some countries with high productivity growth in the ICT-producing industries that do not use hedonic price indices, i.e. Finland, Germany and the UK.43 Despite the productivity

42


increase in the ICT-producing industries it is only in the US that aggregate productivity growth has been at a significantly higher level compared to earlier periods. The increase in the productivity growth differential between the US and the EU in 1995–2000 can mostly be explained by differential productivity growth in retailing, wholesale trade and financial services. Another interesting point is the difference between the intensity of ICT use among ICT-using sectors. The major difference in productivity growth between the EU and the US has arisen in service industries with a high ICT intensity. Evidence from the steam engine revolution suggests that the industries using steam power technology intensely were those that had the highest productivity growth increases. The same pattern was observed for the US economy during electrification; productivity growth increases took place disproportionately in sectors that increased their use of electric motors in the production process.

6.2. Measurement Errors For the six countries studied we have shown that a large share of aggregate productivity growth in manufacturing during the latter half of the 1990s occurred in ICT-producing industries. A crucial assumption behind this result is that there are no systematic measurement errors. However, there are a number of problems with measuring production and productivity and these problems are likely to have increased in recent decades.44 First, most countries in our investigation use double deflation to arrive at value added in fixed prices. Double deflation implies that the value of gross output and intermediate inputs are deflated separately with an output price index and an intermediate input price index, respectively. However, Finland has not introduced double deflation in their national accounts, which implies that inputs are not deflated separately. If double deflation were introduced in the Finnish national accounts, productivity for different industries would change. This especially holds for industries that are using inputs with rapidly shifting prices, like ICT products. Output of the ICTproducing industry is largely an input for other industries. This implies that the deflation of production value and value added in the ICT-producing industry greatly affects the distribution of productivity growth between ICT-producing and ICT-using industries. Second, it is almost impossible to construct completely true deflators for the ICT sector (Brynjolfsson & Hitt, 2000), where technology changes rapidly. Nordhaus (1997) argues that capturing the impact of new technologies


43

on living standards is beyond the practical ability of official statistical agencies. The quality of the goods that we consume today is much higher compared to the quality of ‘‘the same’’ goods a decade ago. Countries use different methods to account for the rapid quality changes that take place. Sweden, the US and France – but not Finland, Germany and the UK – use hedonic price indices for some of the ICT products. This has so far resulted in larger estimated quality improvements and thus volume increases (Pilat & Lee, 2001). Different methods for capturing quality improvements can have a large effect on productivity. Edquist (2005b) shows that productivity levels in the ICT-producing industry in Germany, Sweden and the US change substantially depending on which country’s value added price deflator that are used. Since there are no consistent hedonic price indices for the industries producing steam engines and electric machinery, it is likely that if quality adjustments had been made for their output, recorded productivity growth would have been higher for those industries as well. Edquist (2005a) constructs hedonic and matched model price indices for electric motors in Sweden for the period 1900–1935. He finds that during the 1920s, PPI-deflated hedonic and matched model price indexes decreased by 4.8 and 3.7 percent per year, respectively. This is a strong indication of high productivity growth in the industry producing electric motors in 1919–1929. Moreover, the difference between the hedonic and matched model price indices is only 1.1 percentage points. One reason for this is that the same quality characteristics are used for the hedonic and matched model price indices, i.e. speed, power and maximum voltage. It is likely, that productivity growth in electric machinery would have been considerably lower if these quality aspects were not taken into account. Third, the recent technological shift has given rise to enormous intangible investments in new business and production systems, personnel training, etc. Brynjolfsson and Yang (1997) estimate that each dollar invested in computer hardware is associated with intangible investments of 10 dollars. These intangible investments are usually treated as current costs and not as investments, which reduces value added and the growth rate (in the medium term). Fourth, an increasing share of production consists of services where deflation is often more difficult than for goods, since a larger share of the value depends on intangible characteristics (degree of accessibility, customer adaptation, delivery time, etc.). Van Ark and Smits (2002) argue that new product applications based on electricity were mainly concentrated to manufacturing, while the real challenge for ICT is to change the production

44


processes in services. Research on the finance and health care sectors in the US has shown that measurement problems have led to underestimates of productivity growth (e.g., Cutler, 2004).45 Thus, there are measurement problems that can cause both an underestimate of aggregate productivity growth, and an overestimate of increases in ICT production.

6.3. Concluding Remarks Our empirical investigation of three different technological breakthroughs suggests that it takes a long interval from the time of the original invention until a substantial increase in the rate of productivity growth can be observed. For the steam engine this period was about 140 years (85 years if the Watt steam engine is treated as the original innovation), while it was around 40–50 years for electrification and the ICT revolution. On the other hand, if we consider the high-pressure steam engine as the innovation that paved the way for the real steam engine revolution, then the time lag from innovation to greater rates of productivity growth is no longer for the steam engine than for the two subsequent technologies that we examine. From the theoretical literature on GPTs as well as from our investigation it seems as though both innovational complementarities and technological dynamism are crucial for productivity growth. This implies that an invention by itself would have little effect on an economy if there is no scope for the users of the new technology to improve their own technologies, and if continuous innovational efforts would not increase the efficiency by which the generic function is performed. For example, innovations that made the steam engine more efficient had to occur before it was introduced in the industrial production process. Moreover, before electricity could be used in manufacturing several types of electric machines had to be invented. A further explanation for the delayed productivity effects is that it takes time to develop organizational innovations, i.e. systems that permit organizations to use new technology efficiently. At first the new technology may often just be performing the same function as the old technology, and in this process a great deal of existing productive capital will be ‘‘creatively destroyed’’ which further tends to delay the point where positive productivity effects at the more aggregate level can be observed (Greenwood, 1997). Political decisions may also be called for before the full potential of the new GPT can be reaped. There may be strong vested interests tied to the old GPT, which manage to block reforms that would facilitate the deployment of the new GPT.


45

Concerning the pattern of productivity growth after major technological breakthroughs we find evidence of rapid price decreases for steam engines, electricity, electric motors and ICT products. This indicates rapid productivity growth in the industries producing the new technology. However, we cannot find strong direct evidence that the steam engine producing industry and electric machinery had particularly high productivity growth rates. For the ICT revolution the highest productivity growth rates have been found for the ICT-producing industry. There is thus no clear evidence of any particular productivity growth pattern after major technological breakthroughs. We argue that one explanation for the high productivity growth rates in the ICT-producing industries could be that no hedonic price indexes were used for the steam engine and the electric motor. Further research is called for to investigate the impact on productivity growth if hedonic price indexes are used for steam engines or electric motors. Another explanation could be that the technological development of semiconductors and integrated circuits could not be matched by the steam engine or the electric motor. There is simply no equivalent to ‘‘Moore’s law’’ for other technological breakthroughs than the ICT revolution.

NOTES 1. We do not examine the impact of the internal combustion engine. The reason for this omission is purely pragmatic. The body of literature is meager and the introduction of the internal combustion engine largely coincides with electrification. 2. For the US we also use data from the Bureau of Economic Analysis (2004) and the Bureau of Labor Statistics (2005). 3. Labor productivity is usually based on data of value added and labor input. TFP estimates are based on data for value added, employment, hours worked, capital stock and factor shares. TFP accounts for the effect of capital input on productivity, but the measure is derived on the assumption that the marginal products of labor and capital are equal to their respective market prices and that production is characterized by constant returns to scale. 4. A hedonic price index is any price index that makes use of a hedonic function. A hedonic function is a relation between the prices of different varieties of a product, such as the various models of personal computers, and the quantities of characteristics in them (Triplett, 2004). Hedonic price indices are further discussed in Section 6.2. 5. Section 4.3 includes estimates of hedonic price indices for electric motors based on Edquist (2005a). 6. The Corliss engine had more advanced valves that allowed a much greater fuel efficiency and a uniform and uninterrupted flow of power. 7. The steam power capacity estimates in Table 1 include capacity of fixed, railway and shipping steam power.

46


8. The standard unit for measuring power capacity is horsepower, where one unit is equivalent to a rate of 550 foot-pounds per second. 9. Primary power means the work done by ‘‘prime movers’’ which convert energy of nature directly into the energy of motion. 10. This approach was applied to railroads in Fogel’s (1964) famous study. 11. Oliner and Sichel (2000) identify the contribution from ICT to labor productivity growth as three types of ICT capital deepening (computer hardware, software and communication equipment) weighted by the shares of these types of capital in income and through TFP growth in the ICT-producing industry weighted by its share in gross output. 12. Crafts (2004) does not calculate the rate of technical change in steam power as a TFP residual, instead he estimates the TFP as the aggregate social savings determined by the reductions in steam power costs. 13. The average annual labor productivity growth of 1.65 percent refers to the years 1931–1973. 14. Annual labor productivity growth in Britain averaged 1.65 percent in 1873– 1899 and 0.85 percent in 1899–1913 (see Table 2). 15. The approach assumes that aggregate saving flows would have been largely unaffected by the absence of the particular innovation under study. 16. We define spillovers as increases in labor productivity in the using sectors beyond what one would expect from the capital deepening effect alone. In other words, spillover effects are the contribution to TFP growth in the using sectors resulting from the introduction of the new technology. 17. The estimates of TFP growth in the steam engine producing industry is based on an incomplete data set and should therefore be analyzed with caution. 18. According to Du Boff (1979) a prime mover is an engine that utilizes the potential energy of nature and directly converts it into energy of motion. Modern mechanical prime movers are the steam engine, the steam turbine, the hydro turbine, the internal combustion engine and the jet turbine. 19. Landes (1969) estimates are based on the industrial censuses in Germany and the UK in 1907. According to these estimates, the capacity of electric generators in the UK and Germany was 2,341,900 and 1,830,000 horsepower, respectively. However, the British figures are based on capacity of engines and motors, while the German the power produced in regular operation. 20. Primary electric motors are those driven by electricity purchased from utilities outside the manufacturing plant. Secondary motors are driven by electricity from generators and prime movers within the plant itself. They represent no addition to power available for use, since some of the plant’s own power generating capacity must be employed to generate their electric energy (Du Boff, 1979). 21. Kendrick (1961) provides estimates of TFP growth rates within manufacturing for the benchmark years 1929, 1937 and 1948. According to Field (2006b), 1937 is not a peak of the business cycle. Field has therefore calculated TFP growth rates within manufacturing for the subperiods 1929–1941 and 1941–1948. His calculations are based on Kendrick’s estimates for output and labor input combined with capital input data from the Bureau of Economic Analysis. 22. David’s regression results are based on TFP estimates adjusted for energy inputs based on Woolf (1984). However, David’s OLS regression is still significant


47

when we run the regression with productivity estimates based on Kendrick’s (1961) two input approach (available upon request). 23. The compound annual labor productivity growth was 5.4 percent in US manufacturing, but only 3.9 percent in electric machinery (see Table 12). 24. Kendrick (1961) provides estimates at the industry level from 1899. Therefore, it is possible that productivity increased in electric machinery before 1899. 25. The figures in Table 13 are based on estimates by Hoffmann (1965). The reliability of Hoffmann’s estimates for the period 1850–1913 has been questioned (see Fremdling, 1995; Burhop & Wolff, 2005). Therefore, we only report estimates for the period 1925–1938. 26. Hoffmann (1965) does not present any comparable figures for the period 1914–1924. 27. Scho¨n (2000) defines labor productivity as real value added per hour worked. 28. Our own investigation of the price of a 20 horsepower electric motor produced by ASEA for the Swedish market shows that the nominal price for this motor did not increase at all for the period 1914–1935 (ASEA, 1914, 1935). Total price inflation during this period was about 35 percent, which provides further evidence of a substantial fall in real prices of electric motors. 29. It is important to point out that a price decline does not necessarily mean that productivity gains have been made. A price decline could also be due to increased competition in a specific market. 30. The hedonic and matched model price indexes are based on prices and characteristics collected for slip-ring electric motors with 1–100 horsepower. Thereby, it is assumed that other electric motors would have a similar price development. 31. Labor productivity has been defined as production value per person employed. Unfortunately it has not been possible to calculate labor productivity based on value added which implies that only single deflation is used to calculate productivity. 32. When measuring productivity in the service sector, it is difficult to determine whether quality improvements for produced services have occurred. This problem is further discussed in Section 6. Moreover, many statistical agencies do not use any consistent method to measure prices in the service sector. 33. The difficulty for the oil hypothesis has been explaining why low productivity growth rates persisted in the 1980s after oil prices collapsed. 34. The selection of 1996 as the initial year for the last period follows other productivity studies of the ‘‘new economy’’ such as Oliner and Sichel (2000) and Nordhaus (2001). The choice of final year is governed by data availability. 35. The structural labor productivity growth is defined as labor productivity growth minus the growth that is due to business cycle effects. 36. In Table 20 labor productivity is calculated for the period 1980–2000 and is defined as production value per person engaged. The reason is that value added deflators were not available for all industries in the STAN database. In Table 21 labor productivity is calculated for the period 1990–2003 and labor productivity is defined as value added per person employed. Table 21 is based on figures from Bureau of Economic Analysis (2004) and Bureau of Labor Statistics (2005). 37. We define spillovers as increases in labor productivity in the using sectors beyond what one would expect from the capital deepening effect alone. In other

48


words, spillover effects are the contribution to TFP growth in the using sectors resulting from the introduction of the new technology. 38. The productivity estimates are based on Bureau of Economic Analysis (2004) and Bureau of Labor Statistics (2005). The BEA and BLS use the North American Industry Classification Standard (NAICS) instead of the International Standard for Industry classification (ISIC) used by OECD (2003b). This implies that the estimates in Tables 20 and 21 cannot be directly compared. 39. Computer and electronic products (NAICS 334) include computers and communication equipment. 40. Information for Finland and Sweden is from 1996–2001 and for the UK from 1996–1999. 41. Stiroh (2002) defines an intensive ICT-using industry as an industry with above median ICT share of capital services in 1995. Van Ark et al. (2002) largely base their distinction of ICT intensive and less ICT intensive industries on the definition provided by Stiroh (2002). 42. As pointed out in Section 3 the growth accounting framework used by Oliner and Sichel includes the portion of the effect of capital deepening on labor productivity that is the consequence of the accumulation of particular ICT capital goods. Field (2006a) argues that this is problematic since capital would have been accumulated in slightly inferior capital goods in the absence of ICT. Moreover, the growth accounting approach does not take spillover effects into account. Nevertheless, according to Oliner and Sichel (2000) TFP growth in computer production and computer related semiconductor production alone accounts for one-fourth of the increase in labor productivity growth 1996–1999 compared to 1991–1995. 43. Hedonic price indices are thoroughly discussed below. 44. This problem was noticed by the so-called Boskin Commission (see Boskin, Dulberger, Gordon, Griliches, & Jorgenson, 1997), which calculated that the annual inflation rate in the US during the preceding quarter-century was overestimated by slightly more than one percentage point. 45. Intuitively it is easy to understand that it can be particularly difficult to discriminate between price increases and volume in health care. When a new, more expensive drug replaces an existing drug, how should the cost increase be divided between increased effectiveness and increased prices? Perhaps the improved effectiveness is so great that there is actually a decrease in price, or the improvement is so marginal that almost all of the cost increase should be treated as a price increase.

ACKNOWLEDGMENTS We are grateful for useful comments and suggestions from two anonymous referees, Alexander Field, Ha˚kan Lindgren, Peter Meyer, Karl Gunnar Persson, Daniel Waldenstro¨m and participants at seminars at the Stockholm School of Economics, Groningen Growth and Development Centre and Lund University. Moreover, we are grateful for valuable input by participants at the annual conference of the Economic History Society in Leicester,


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April 2005. Financial support from the Jan Wallander and Tom Hedelius Foundation is also gratefully acknowledged.

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NEW NATIONAL BANK LOAN RATE ESTIMATES, 1887–1975 Scott A. Redenius ABSTRACT Regional rates of return in the United States differed widely following the Civil War and some differences persisted until well after World War II. Our understanding of the evolution of short-term interest rates is based primarily on portfolio rates of return estimated from bank accounting data. This paper uses new national bank loan rate series for 1887–1975 to present a revised view of the evolution of regional short-term interest rates. Two findings are of particular interest. The organization of the Federal Reserve System was accompanied by significant convergence in regional bank loan rates. Rates in the postbellum South were lower than previously thought.

1. INTRODUCTION Surveys of bank loan rates consistently find differences in rates between U.S. bank markets. While rate differences between major metropolitan areas are small, there are significant regional differences for rural markets. In 2005:Q4, rates on commercial bank farm loans varied from 6.6 percent in Appalachia to 8.2 percent in the Mountain states (Board of Governors of


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SCOTT A. REDENIUS

the Federal Reserve System, 2005). While these differences may appear large, they are modest by historical standards. To give a sense of the size of earlier rate differentials, Table 1 reports average rates on prime commercial paper – comparable to today’s prime lending rate – reported in Bradstreet’s from 1881 to 1883. Rates were between 5.5 and 6.0 percent in most Eastern cities but rose to 12.0 percent or more in some Southern and Western cities. Regional differences between rural areas were even greater. To contemporaries and to later economic historians, the existence of such large differences in regional short-term interest rates and their persistence over many decades raised questions about the effectiveness of the U.S. financial system in allocating resources between regions and therefore about its role in the nation’s economic development. However, the problem researchers faced in studying the differentials was the lack of consistent data on regional short-term interest rates. Rates published in the contemporary financial press are not entirely satisfactory because the nature of the loan instruments may have differed across cities, reporting was sporadic for many smaller cities, and no rates were reported for rural areas. A number of government surveys provide more comprehensive geographical coverage but do not form a consistent time series. To overcome this problem, Davis (1965) computed regional portfolio rates of return – gross and net rates of return on earning assets – from the income statements and balance sheets of national banks published in the U.S. Comptroller of the Currency, Annual Report (1863–1976).1 Subsequent efforts have focused on constructing measures that more closely correspond to loan rates.2 Smiley computed rates of return on private earning assets (loans and non-U.S. government securities), and Smiley, James, and Bodenhorn estimated loan rates. A summary description of each series is given in Table 2. This paper presents new loan rate series that were constructed to address limitations of the previous series. One limitation is that the previous series cannot be used to evaluate the effect the organization of the Federal Reserve System had on regional loan rates. There is a gap in Smiley’s series between 1913 and 1916, and there are significant differences in the construction of his postbellum and interwar series. In addition, the previous series do not include adjustments for changes in the structure of the source data in 1917. Although much of the literature on the loan rate differentials has focused on the role of legal and institutional change in promoting rate convergence, the potential impact of the Federal Reserve Act has been overlooked.3 Yet, a stated goal of the Fed’s intellectual backers, in particular Laughlin and Willis, was to reduce regional rate differentials. Laughlin (1912) felt the Fed would accomplish this by setting

New National Bank Loan Rate Estimates, 1887–1975

Table 1. City

Prime Commercial Discount Rates, 1881–1883. Rate

New England Boston, MA Bridgeport, CT New Haven, CT Portland, ME Providence, RI

5.80 5.50 6.00 6.23 5.63

Middle Atlantic Baltimore, MD Buffalo, NY New York, NY Philadelphia, PA Pittsburgh, PA Troy, NY Utica, NY

5.71 5.93 5.82 5.75 6.41 6.00 5.50

Upper South Louisville, KY Memphis, TN Nashville, TN Norfolk, VA Raleigh, NC Richmond, VA Wilmington, NC

7.32 7.30 8.00 7.50 6.00 7.83 7.15

Eastern Lower South Atlanta, GA Augusta, GA Charleston, SC Macon, GA Montgomery, AL Savannah, GA Selma, AL

9.19 8.01 7.97 9.79 12.00 8.93 10.00

Western Lower South Dallas, TX Galveston, TX Houston, TX Little Rock, AR Marshall, TX New Orleans, LA San Antonio, TX Waco, TX

12.00 8.64 11.08 10.00 10.45 7.47 9.36 12.61

Region East New England

57

City

Rate

Old Northwest Chicago, IL Cincinnati, OH Cleveland, OH Columbus, OH Dayton, OH Detroit, MI Evansville, IN Grand Rapids, MI Indianapolis, IN Milwaukee, WI Peoria, IL Toledo, OH

6.68 6.64 7.29 7.78 6.54 7.15 7.35 8.00 6.91 7.79 7.61 7.09

Eastern Plains Burlington, IA Des Moines, IA Davenport, IA Kansas City, MO Minneapolis, MN Sioux City, IA St. Joseph, MO St. Louis, MO St. Paul, MN

8.19 9.00 8.00 9.57 8.49 9.00 9.00 7.83 8.80

Western Plains Leavenworth, KS Lincoln, NE Omaha, NE

8.00 10.00 10.00

Mountain Denver, CO Salt Lake City, UT

12.00 13.50

Pacific San Francisco, CA

6.20

Canada Halifax, NS Montreal, QC Toronto, ON Winnipeg, MB

5.85 6.50 6.40 7.80

States

Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, and Connecticut

58

SCOTT A. REDENIUS

Table 1. (Continued ) Region Middle Atlantic South Upper South Eastern Lower South Western Lower South Midwest Old Northwest Eastern Plains Western Plains West Mountain Pacific

States New York, New Jersey, Pennsylvania, Delaware, Maryland, and the District of Columbia Virginia, West Virginia, North Carolina, Kentucky, and Tennessee South Carolina, Georgia, Florida, Alabama Mississippi, Louisiana, Texas, Arkansas, Oklahoma, and Oklahoma and Indian Territories Ohio, Indiana, Illinois, Michigan, and Wisconsin Minnesota, Iowa, and Missouri Dakota Territory, North and South Dakota, Nebraska, and Kansas Montana, Wyoming, Colorado, New Mexico, Idaho, Utah, Arizona, and Nevada Washington, Oregon, and California

Notes: The figures are simple averages of the rates reported for all or part of the period 1 October 1881–28 April 1883. Where high and low figures are given in the source, the average is used in the computations. The regions are defined above. Source: Bradstreet’s (1881–1983).

uniform discount rates, improving the marketability of commercial paper, and providing an elastic currency. Willis (1914, p. 19) predicted that once the system began operations ‘‘there will be no such wide (geographical) fluctuations of interest rates y as now exist.’’ Another limitation is that the previous series are not a reliable guide to the behavior of Southern loan rates. The previous series are biased upward because an important category of Southern loans was omitted from the computations and because non-portfolio sources of revenue that were of greater importance in the South were included. The bias affects both the size of the rate differentials between the South and other regions and the timing of rate convergence. This is a significant issue because Southern credit markets have generated so much interest in the economic history literature and postbellum Southern bank loan rates have received special attention in the literature on the rate differentials (Davis, 1965, pp. 388–393; James, 1981). The new series address both these issues. They also incorporate a number of methodological innovations that make them more accurate and less

Rate Series. Availability

Series

Estimated Rates

Davis (1965)

Earning assets

James (1974a)

b

Period Covereda

Frequency

1888–1914

Annual

Loans

1888–1911

Semiannual

Smiley (1976a, 1976b)

Private earning assetsc

1888–1913

Annual

Smiley (1981a, 1981c)

Loans

1916–1937

Annual (1916–1925) Semiannual (1926–1937)

1916–1940 Bodenhorn

Loans

1880–1960

Annual (1916–1925) Semiannual (1926–1940) Annual

Redenius

Loans

1887–1975

See Table 4

Reported Areas

Rate Series

Weights

Reserve-city national banks by region Country national banks by region Reserve-city national banks by city Country national banks by state Reserve-city national banks by city Country national banks by state Reserve-city national banks by city Country national banks by state National banks by state

Davis (1965)

No

From author

No

From author

Yes

From author

No

National banks by state

Bodenhorn (1995)d From author

Yes

See Table 4

59

Notes: The series listed in the Table are computed from actual or estimated revenue. Excluded are rate proxies computed from net income such as the net rate of return on earning assets series constructed by Davis (1965) and Smiley (1973). a The periods are as stated by the authors. b In Davis’ series, earning assets include loans and discounts, overdrafts, U.S. bonds held, premiums on bonds, other securities, and real estate (Smiley, 1973, pp. 8–14). c Private earning assets include loans and non-U.S. government securities (Smiley, 1973, 1976b). d Weighted and unweighted regional series are given in Bodenhorn (1995, Table 12–A1). The state-level series are no longer available.


Table 2.

60

SCOTT A. REDENIUS

volatile. Advances in information technology allowed more extensive corrections to the income statement and balance sheet data and the use of additional data from the Annual Reports and other sources in the estimations. Other innovations take advantage of new research on contemporary bank operations. In addition, the new series are longer than previous series, covering 1887–1975. This spans the entire period for which national bank accounting data from the Comptroller’s Annual Report can be used to reliably estimate loan rates.4 The paper is organized as follows. An overview of the source data and estimation methodology is given in Section 2. Section 3 considers the interpretation of loan rates derived from national bank data. The following two sections provide an evaluation of the new series. Section 4 discusses caveats regarding the estimation procedure, and Section 5 compares the behavior of the new series to that of other interest rate series. A revised history of regional loan rates follows in Section 6. Included are a detailed regional breakdown of the rate differentials, a discussion of the behavior of Southern loan rates, and an examination of the changes in regional loan rate differentials that followed the organization of the Federal Reserve System. Section 7 concludes with some thoughts on potential research uses for the new series. In addition, Appendix A contains a description of corrections made to the source data, and the annual loan rates for the United States and for the regions defined in Table 1 are included in Appendix B.

2. SOURCE DATA AND ESTIMATION METHODOLOGY This section outlines the procedure used to construct the new loan rate estimates from the source data.5 As for previous series, the data are from the income statements and balance sheets of national banks published in the U.S. Comptroller of the Currency, Annual Report (1946–1961), supplemented by balance sheet data from the Abstracts. The published income statements were compiled from forms national banks submitted semiannually to the Comptroller’s Office. The semiannual income periods are described in Table 3 along with the notation used to identify them throughout the paper. Balance sheet data were collected through call reports that banks were required to submit several times each year. The frequency and amount of geographical detail in the rate series are constrained by the way the individual bank data were tabulated for publication


Table 3. Dates 1 Sept. 1887–31 Aug. 1906 1 Sept. 1906–31 Dec. 1906 1 Jan. 1907–

61

Income Periods.

Notation

Semiannual Income Periods

1887:II–1906:I 1906:II 1907:I–

I: Mar.–Aug. and II: Sept.–Feb. II: Sept.–Dec. I: Jan.–June and II: July–Dec.

Notes: The notation is based on a calendar year. However, prior to 1940, the Annual Reports were not on a calendar basis. Earlier Reports contain income statements for the year ending in August or June.

Table 4.

Characteristics of the New National Bank Loan Rate Estimates.

Income Periods

Reported Areas

1887:II– 1887:II–1927:I

National banks by state Reserve-city national banks by city Country national banks by state Reserve-city national banks by city (reserve cities with at least three national banks) Country national banks by state and other reserve-city national banks

1927:II–1938:I

Income Periods

Income Statements Covering

Annual series 1887:II–1906:I 1906:II–1907:I 1907:II–1939:I 1939:I–

Sept.–Aug. Sept.–June July–June Jan.–Dec.

Semiannual series 1887:II–1906:I 1906:II 1907:I–1911:I 1925:II–1941:II

Sept.–Feb. and Mar.–Aug. Sept.–Dec. July–Dec. and Jan.–June July–Dec. and Jan.–June

Notes: For 1939:I–1941:II, semiannual income statements were published only once per year. The semiannual and annual statements are used to construct semiannual statements through 1941:II. The semiannual income statements for 1939:I are included in two observations in the annual series: 1938:II–1939:I and 1939:I–1939:II. Semiannual income statements for 1917:I were also published, but they are not used in the estimations.

in the Reports.6 The characteristics of the new series are summarized in Table 4. The following equation is used to estimate annual loan rates for each reported area from the source data, and the denominator is used for the

62

SCOTT A. REDENIUS

associated weights. rt0:5 ¼

Loan interest revenuet Loanst0:5 Loan lossest

(1)

where t is the income period t (annual index), Loan int. rev.t, the loan interest revenue in semiannual periods ending t and t+0.5, Loanst0.5, the average loans in semiannual periods ending t0.5 and t, Loan lossest, the loan losses in semiannual periods ending t and t+0.5, and rt0.5, the average loan rate in semiannual periods ending t0.5 and t. In Eq. (1), loans are lagged under the assumption that average bank loan maturities at origination were about 6 months. Thus, loans made in period t0.5 (the ‘‘loan period’’) would be collected in period t (the ‘‘income period’’). As James (1978, p. 251) noted, if current loans are used rather than lagged loans, loan rates for areas of rapid real loan growth, such as the postbellum South and West, will be biased downward. Using current loans will also introduce a downward bias during periods of price inflation. In Eq. (1), current loan losses are subtracted from lagged loans because interest was not collected on loans that were written off. Therefore, the loan rates will more closely approximate contract (ex ante) rather than realized (ex post) rates of return on bank loan portfolios (James, 1978, p. 250).7 Though rate estimation with Eq. (1) looks straightforward, it is complicated by limitations of the source data. The Comptroller did not directly report the values of each of the terms in Eq. (1) in all periods. The additional sources used and assumptions made in order to perform the estimation are described in greater detail in the subsections below along with the method used to construct balance sheet variables for each income period. 2.1. Loan Interest Revenue Loan interest revenue is reported directly in the income statements only from 1926:II.8 For earlier periods, loan interest revenue must be estimated. For 1917:II–1926:I, the income statements report total interest and dividend revenue, and loan interest revenue is computed as follows. Loan interest revenuet ¼ Interest and dividend revenuet X rUSti United States debtti i

rFRBSt Federal Reserve Bank stockt rOst Other securitiest rBBt Bankers’ balancest

ð2Þ


63

Revenue from non-loan sources in Eq. (2) represents current revenue: capital gains and losses were reported separately from interest and dividend revenue. For 1887:II–1917:I, only total revenue is reported in the income statements and revenue from other sources (‘‘non-interest revenue’’) and recoveries must also be estimated and removed to arrive at loan interest revenue. For 1869:I–1887:I, only net income is given and loan rates cannot be reliably estimated. The sources and methods used to compute the rates of return (r’s) used in Eq. (2) are given in Table 5, and the computation of selected terms in Eq. (2) is described in greater detail below.

2.1.1. U.S. Government Securities Current income on U.S. government securities is computed by multiplying the par value of securities of each class by the average coupon or interest rate for that class. Through the 4 March 1915 call date, the par value of U.S. government securities was reported on the balance sheets with premiums reported in a separate account. Thereafter, the reported book values are treated as par values.

2.1.2. Other Securities Other securities include non-U.S. government debt securities and stocks. Revenue from other securities must be computed differently than for U.S. government securities. The book values on the balance sheets include premiums or discounts, and rates of return are available in the form of yields, not coupon or dividend rates. Because there are few yield series for individual security classes that cover the whole period and there are no usable systematic data on the composition of bank portfolios available prior to 1 September 1909, no differentiation is made between categories of other securities. For rates of return, previous estimations have used yield indexes for high-grade bonds. James used the Macaulay (1938) railroad bond index, and Bodenhorn used the Homer and Sylla (1996) corporate bond index. However, for 1926:II–1927:I, the estimated rate of return on other securities is about 1 percent higher than the Homer–Sylla and Macaulay indexes. Two factors are likely responsible for this difference: stocks are included and national bank portfolios of non-U.S. government debt securities were not uniformly high grade. The yield index described in Table 5 is roughly in line with the Board of Governors of the Federal Reserve System (1943) index for industrial, railroad, and public utility bonds that Smiley used in constructing his interwar series.

64

SCOTT A. REDENIUS

Table 5.

Earning Asset Categories and Rates of Return.

Category U.S. government securities (US)a Pre-World War I bonds Held to secure circulationb Held to secure federal depositsb Otherb

Income Periods

Rate of Return

1887:II–1915:I

Average coupon rate on bonds held with the Treasurer to secure circulation Average coupon rate on bonds held with the Treasurer to secure federal deposits Average coupon rate on outstanding bonds less U.S. bonds held as above Average coupon rate on bonds held with the Treasurer as above Average coupon rate on outstanding Liberty loan bonds Average coupon rate on outstanding Victory notes Average interest rate on outstanding certificates of indebtedness Average interest rate on outstanding Treasury notes Average interest rate on certificates of indebtedness and Treasury notesd Average coupon rate on outstanding Treasury bonds 6 percent statutory dividend rate

1887:II–1915:I 1887:II–1915:I

All pre-World War I bondsc Liberty loan bondsc Victory notesc Certificates of indebtednessc Treasury notesc War savings certificatesc

1915:II–1926:I

Treasury bondsc Federal Reserve Bank stock (FRBS)e Other securities (OS)

1922:II–1926:I 1914:II–1926:I

1916:II–1926:I 1919:II–1926:I 1917:II–1926:I 1922:II–1926:I 1917:II–1926:I

1899:II–1926:I

1887:II–1899:I

Bankers’ balances (BB)

a

1887:II–1926:I

Homer and Sylla (1996) corporate bond yield index (adjusted to equal the rate of return on other securities for 1926:II–1927:I)f Macaulay (1938) adjusted railroad bond yield index (adjusted to equal the above index for 1899:II– 1900:I) 0.85 the rate paid on bankers’ balances in New York: 1 September 1887–31 December 1917, 2.00 percent; 1 January 1918–31 March 1918, 2.25 percent; and 1 April 1918–30 June 1926, the New York Clearinghouse’s ceiling rate on bankers’ balances

The coupon or interest rates for U.S. securities are from U.S. Secretary of the Treasury, Finance Report (1865–1927). The rates for each date are weighted averages with the par values of outstanding, unmatured debt serving as weights. Average rates of return for each income period are computed using the same weighting method used for the balance sheet data. b After the 4 March 1915 call date, all U.S. bonds and bond premiums are reported in a single category. The 4 March 1915 proportions of U.S. bonds for circulation, U.S. bonds for deposits, and other U.S. bonds held are used to extrapolate values for the remaining call dates in 1915:I. c Breakdowns of U.S. government security holdings are given only for selected call dates and interpolated for other call dates. d Average rates for certificates of indebtedness and treasury notes are used because there is insufficient information with which to compute average rates for war savings certificates. e Federal Reserve Bank stock is 3 percent of the book value of common capital stock and surplus. f The rate of return on other securities is estimated as (Interest and dividend revenue from securities – Interest revenue from U.S. securities – Dividends from Federal Reserve Bank stock)/other securities held.


65

2.1.3. Bankers’ Balances Bankers’ balances were reported on the aggregated balance sheets in several formats over the period, with balances held with reserve agents, other national banks, and non-national banks often reported separately. For purposes of the estimation, all categories of bankers’ balances are considered interest bearing. The rates used are based on those prevailing in the New York market, the only market for which rate information is consistently available. New York rates are also of particular importance because more bankers’ balances were held in New York than in any other city, and New York was the price leader in the market for bankers’ balances (Redenius, 2005a). The rate paid is assumed to be 85 percent of the rate prevailing in New York, roughly the average rate national banks received on bankers’ balances as a percentage of the New York Clearinghouse’s average ceiling rate on bankers’ balances for 1926:II–1930:I.9 2.1.4. Non-Interest Revenue and Recoveries There are no systematic data on non-interest revenue and recoveries prior to 1917. In previous estimations, non-interest revenue and recoveries have been assumed negligible. In practice, this results in a discontinuity in the series in 1917. Non-interest revenue and recoveries as percentages of loans and of total revenue and recoveries are given by region in Table 6 for 1917: II–1919:I. As a percentage of loans, exchange and collection revenue was lowest in the East and highest in the Southern and Pacific regions, and other revenue was highest in the East. The total column reports the biases in the loan rates for 1917:II–1919:I that would result from including non-interest revenue and recoveries in total revenue. On average, loan rates would be overstated by 0.78 percent. However, there is also a cross-sectional impact because the biases are larger for the Lower South and West and smaller for the Midwestern regions. If the figures in Table 6 are representative of the earlier period, then loan rates for the Lower South and West have been overstated in previous estimations. In order to adjust for non-interest revenue and recoveries, total revenue and recoveries for income periods through 1917:I are multiplied by the 1917:II–1919:I ratios of interest and dividend revenue to total revenue and recoveries. This method should provide a reasonable estimate of interest and dividend revenue in the preceding years. In 1917:II–1919:I, revenue from exchange and collection charges had yet to experience the full impact of the Fed’s payment activities. Revenue from the fiduciary and other activities authorized by early amendments to the Federal Reserve Act remained small, and recoveries were fairly constant through the early 1920s.

66

Table 6.

Non-Interest Revenue and Recoveries, 1917:II–1919:I.

As a Percentage of Lagged Loans Minus Current Loan Losses

As a Percentage of Total Revenue and Recoveries

Other Revenue

Recoveries

Total

Exchange and Collection

Other Revenue

Recoveries

Total

New England Middle Atlantic Upper South Eastern Lower South Western Lower South Old Northwest Eastern Plains Western Plains Mountain Pacific

0.11 0.11 0.27 0.62

0.57 0.52 0.36 0.35

0.09 0.19 0.13 0.21

0.77 0.82 0.77 1.18

1.30 1.18 3.05 5.83

6.96 5.70 4.03 3.33

1.13 2.09 1.46 1.98

9.39 8.97 8.54 11.14

0.46

0.34

0.23

1.03

3.71

3.00

2.19

8.90

0.16 0.18 0.17 0.20 0.34

0.32 0.24 0.27 0.37 0.38

0.13 0.14 0.14 0.34 0.19

0.61 0.56 0.57 0.91 0.91

1.86 2.12 1.84 1.79 3.51

3.71 2.87 2.88 3.25 3.97

1.57 1.73 1.45 3.04 1.97

7.15 6.72 6.17 8.08 9.46

United States

0.19

0.42

0.17

0.78

2.05

4.49

1.89

8.43

Notes: The percentages of total revenue and recoveries are computed as follows. The ratio of each item to total revenue and recoveries is computed for 1917:II–1918:I and 1918:II–1919:I and averaged. Prior to averaging, ratios that are out of line with the trend for that reported area are replaced with values for the other year or a similar reported area. The ratios for 1917:II–1919:I are then used to construct the percentages of lagged loans minus current loan losses. Source: U.S. Comptroller of the Currency, Annual Report (1917–1919).

SCOTT A. REDENIUS

Exchange and Collection


67

2.2. Loans Loans must include those asset categories that generated loan interest revenue as reported in the income statements. In defining loans, I have included those asset categories that were at some point included under ‘‘loans and discounts’’ in the aggregated balance sheets. These include loans and discounts, overdrafts, customers’ liability under letters of credit, open market commercial paper, bank acceptances purchased or rediscounted, and federal funds sold. Also included are loans rediscounted with other banks or the Federal Reserve System that were reported separately on the balance sheets for call dates 12 September 1916–6 September 1921. Between 1947 and 1968, loans reported in the aggregated balance sheets are gross loans minus valuation reserves. For this period, gross loans constructed from other tables in the Annual Report and Abstracts are used. Previous researchers have differed in their treatment of overdrafts. Davis and Bodenhorn treated overdrafts as loans while James and Smiley did not.10 Failing to classify overdrafts as loans creates three problems. (1) Overdrafts were reported with loans and discounts in the 1867–1893 Reports and from 1947 onwards. Excluding overdrafts will therefore create discontinuities in the loan rate series at those dates. (2) Overdrafts were a significant portion of total loans in the South and West during the late postbellum period, especially in cotton-growing areas. At their peak in the 1900 annual loan period, overdrafts were 13.4 percent of loans and discounts in the Western Lower South and 17.9 percent for the semiannual loan period 1900:II. Percentages for individual reported areas were as high as 67.6 percent. Therefore, excluding overdrafts will bias loan rates upward significantly for those areas. (3) As a result of efforts by the Comptroller’s Office, overdrafts declined to negligible levels in the years preceding the organization of the Federal Reserve System. Therefore, if overdrafts are not classified as loans, the relative decline of Southern loan rates during this period will be exaggerated. 2.3. Loan Losses Loan losses were not reported on a consistent basis over the period. For 1887:II–1917:I, loan losses must be estimated from total losses: loan losses, premiums on U.S. government securities written off, losses and premiums written off on other securities, and other losses. Of the latter three categories, premiums on U.S. government securities written off were the largest and most predictable. However, the only available information on

68

SCOTT A. REDENIUS

the relative importance of premiums written off is for 1884:II–1887:I, when they were separately reported in the income statements. Through 1915:I, premiums written off are estimated as 19 percent of premiums reported on bank balance sheets, the average of the three 1884:II–1887:I annual percentages, and subtracted from total losses to provide a rough estimate of loan losses. While I believe this improves on previous methods of estimating loan losses, any bias arising from the method used to estimate loan losses or the failure to remove non-loan losses from total losses will not be significant for most reported areas.11 From 1917 to 1947, loan losses were reported separately from other losses and reported loan losses can be used directly in Eq. (1). Starting in 1948, loan losses consist of loan losses and transfers to loan valuation reserves, and from 1969, they consist of loan losses, provision for loan losses, and total transfers to valuation reserves.12

2.4. Computing Balance Sheet Averages One difficulty in the estimation is the different nature of the income statement and balance sheet data. The income statements report flows over each income period, whereas the balance sheets report stocks for each call date. In order to match the data from the two sources, averages of the balance sheet variables for each period are computed using the method developed by James (1974b, pp. 400–403). An additional problem is that the banks that filed call reports during a loan period were not necessarily the same banks that filed income statements at the end of the corresponding income period. Some banks that submitted call reports subsequently liquidated and did not file income statements. Other banks that reopened after being in receivership, conservatorship, or voluntary liquidation filed income statements without having filed call reports during the loan period. Changes in operational status are identified by cause in the Annual Report, and compensating adjustments are made to the balance sheet variables at each call date as follows.13 The balance sheet variables for each reported area are multiplied by the ratio of the capital of income reporting banks to reported capital.14 As the information becomes available in the source, adjustments are also made for state banks that converted to national charters, state banks that merged or consolidated with national banks, and state banks that were purchased by national banks. Excluded from the corrections are voluntary liquidations arising from reorganizations and mergers with other national banks.15


69

3. INTERPRETATION The rationale for estimating loan rates from bank records is that such estimates will provide information about regional money market conditions that cannot be obtained from contemporary interest rate sources. Nevertheless, like other sources, the nature of the data used in the estimations imposes some limitations on the interpretation of the loan rates and their usefulness for addressing specific questions. National bank loan rates. The new series are estimates of average rates on national bank loans. They should also provide reasonable proxies for rates charged by other banks and rates paid by businessmen and farmers who relied on bank credit.16 However, national bank loan rates may not provide a reliable guide to rates charged by non-bank lenders or the rates paid by those who borrowed from them. For example, the loan rates provide a poor measure of rates Southern sharecroppers paid for merchant credit. Average loan rates. There are two implications of the fact that the estimates constructed with Eq. (1) are average loan rates. First, average loan rates are unconditional portfolio rates. By contrast, rates on individual bank loans vary with loan, bank, and borrower characteristics, e.g., default risk. Eq. (3) expresses rt as a weighted average of the rates on loans in different default risk categories. X rt ¼ l tj rtj (3) j

where rt is the average loan rate in period t, ltj, the proportion of loans in risk category j in period t, and rtj, the rate on loans in risk category j (rtj ¼ (rt|j)) in period t. Eq. (3) shows that geographical variation in average loan rates can arise from differences in loan composition (the ltj’s) in addition to differences in the rates charged on specific types of loans (the rtj’s). Second, banks with larger loan portfolios are weighted more heavily in the rate averages. For example, on the 1 September 1910 call date, only 0.54 percent of all national banks were located in New York City, but banks there held 15.91 percent of national bank loans. This issue also arises with the state-level rates. For example, while only 5.31 percent of Georgia national banks on 1 September 1910 were located in Atlanta, they held 40.82 percent of Georgia national bank loans.17 Contract or effective loan rates? Ideally, Eq. (1) would include all revenue received on bank loans so that the rate estimates would represent the effective rates that banks charged and borrowers paid for credit. However, the

70

SCOTT A. REDENIUS

rates estimated here are neither contract nor effective loan rates. Effective rates can be higher than contract rates because (1) fees or commissions (‘‘loan fees’’) are charged in addition to interest; (2) borrowers are required to hold compensating balances; (3) the loan contract’s terms, e.g., collecting interest in advance or charging interest for a period longer than the desired loan period, increase the rate paid relative to the stated rate.18 The rates computed with Eq. (1) incorporate the revenue banks received from (3) but not (1) or (2). Of these, loan fees – 1.75 percent of loan interest revenue for 1942–1968 – were not sufficient to generate much difference between estimated and effective loan rates. Local loan rates. The loan rates estimated from national bank records have been assumed to represent loans to borrowers in the same reported area. Given that information and transactions costs tend to increase with distance, this is a reasonable assumption for most customer loans. However, loans reported on the balance sheets included not only customer loans but loans to other banks and open market paper. The bulk of these represented loans to borrowers in other areas (‘‘inter-area lending’’) at rates that were largely determined in regional or national markets. There is not enough information available to fully assess the extent of inter-area lending, but it appears to have been modest for most of the period considered here. Loans to other banks averaged 1.07 percent of loans for 1887:II–1916:I, were 2.97 percent on 4 March 1919, and averaged 1.71 percent for 1929–1938.19 Acceptances averaged 1.13 percent of loans for 1917–1928 and 1.84 percent for 1929–1938 while open market commercial paper averaged 2.00 percent for 1929–1938. In addition, some banks used their correspondents to place funds directly in urban call loan markets. Such lending was about 2.07 percent of loans on 31 January 1920.20 However, after declining during the Great Depression, inter-area lending grew in significance near the end of the period. Federal funds alone were 7.31 percent of loans on 31 December 1975. Short-term loan rates. Bank loan rates have been assumed to be a reasonable proxy for short-term interest rates. This appears to have been the case for the postbellum period. Orthodox banking practice in the form of the real bills doctrine favored short maturities, and national bank loans were at least nominally short term (James, 1978, pp. 54–71; White, 1998). For example, on 30 June 1916, 60.64 percent of outstanding national bank loans were scheduled to mature within 90 days. One factor in the short duration of postbellum loans is that national banks engaged in little mortgage lending. On 28 April 1909, the first date for which data are available, only 1.14 percent of loans were secured by real estate.21 However, national banks subsequently expanded into mortgage and consumer lending and loan

Percentage of Loans


71

30

30

25

25

20

20

15

15

10

10

5

5

0 1910

1920

1930

1940 1950 Year

1960

1970

0

Fig. 1. National Bank Loans Secured by Real Estate, 1909–1975. Notes: For most of the period, only one value is available per year, that for the call date nearest to June 30. Loans secured by real estate were not separately reported in 1910 and 1914. With the exception of 1909 and 1913, the percentage is for national banks in the continental United States. Source: U.S. Comptroller of the Currency, Annual Report (1911–1975).

maturities increased.22 As shown in Fig. 1, the percentage of national bank loans secured by real estate grew rapidly in the 1920s and 1930s before stabilizing at around 25 percent of loans after World War II. In addition, the importance of longer-term commercial lending grew in the late 1930s. Table 7 provides some information on postwar bank loan maturities. In 1946 and 1955, more than 57.0 percent of business loans were for 6 months or less and the 1947 percentages for farm production loans were even higher.23 Thus, rates on a large portion of bank loan portfolios would still have been sensitive to changes in credit market conditions. What is different from the postbellum period is the higher percentage of intermediate- and long-term loans and the resulting increase in average maturities. Therefore, bank loan rates become a less reliable measure of short-term rates in later periods.

4. CAVEATS There are several potential sources of error in estimating loan rates from national bank data. One issue is simply the nature of the source data.

72

Table 7.

Original Loan Maturities. Farm Loans

Business Loans

20 June 1947

30 June 1956

5 Oct. 1955

16 Oct. 1957

Mortgage

Production

Purchase of Real Estate

Other Purposes

% of national bank loans

47.09

47.05

50.96

1.36

3.36

0.55

3.71

Short-term Demand 6 months or less 6–12 months

15.7 42.0 7.7

14.6 42.6 8.9

62.6

24.0 17.0

9.8 53.2 31.0

8.2 11.1 12.7

7.8 43.6 28.2

7.3 6.6

7.8 8.0

10.3 8.4

11.0 22.0

4.9 0.9

7.8 18.1

12.6 3.2

17.5 3.1

15.3 2.8

14.9 3.8

19.0 6.0

0.3

30.1 12.0

3.8 0.8

Intermediate 1–3 years 3–5 years Long-term 5–10 years Over 10 years

Notes: The figures for business loans are from surveys of Fed-member banks and those for farm loans are from surveys of insured commercial banks. The percentages of national bank business and farm loans are based on the composition of loans reported in the Abstracts for the nearest dates; the percentage for 20 November 1946 is interpolated from the 29 June and 31 December 1946 call dates. On 30 June 1956, 56 percent of mortgage loans were for the purchase of real estate (Herr, 1957, p. 134). Source: Allen (1947, Table 5, 1956, Table 10), Arlt (1959, Tables 1 and 4), Cagle (1959, Table 3), Smith (1947, Table 21), Smith and Allen (1947, Table 5), and U.S. Comptroller of the Currency, Abstracts (1946–1961).

SCOTT A. REDENIUS

20 Nov. 1946

Maturity


73

Though the Comptroller’s Office standardized data collection, the accounting practices that generated the data – especially in the early part of the period – were less formal than today. Call dates were specified ex post so that banks could not manipulate their books in advance, but this did not prevent banks from misreporting their condition in their call reports. It is not clear, however, that this would significantly bias the estimations.24 Another limitation of the source data is the lack of complete data with which to implement the corrections for changes in bank operational status. I believe that a significant portion of the remaining volatility of the estimated loan rates could be eliminated if complete data were available. A second potential source of error are the assumptions made in order to compute loan interest revenue through 1926:I with Eq. (2). In the estimations, it is assumed that the average rates of return given in Table 5 are correct and that there was no meaningful variation in actual rates of return between reported areas. Since loan interest revenue is computed as a residual in Eq. (2), any violations of these assumptions can bias the loan rates. However, unlike errors arising from accounting practices, some quantitative evaluation of the biases arising from this source is possible. In what follows, I assume that the quantities are measured correctly and that any errors are in the assigned rates of return. This leaves three potential sources of bias: errors in the average rates of return, variation in actual rates of return between reported areas, and the interaction between rate and quantity variation. Table 8 examines the biases that would arise from using Eq. (2) to compute loan interest revenue for 1926:II–1927:I. What are the important sources of bias? Variation in the rates of return on Federal Reserve Bank stock and U.S. government securities is not a concern. In addition, because the average rates of return on other securities and bankers’ balances are calibrated using the income statement data, there is no significant aggregate bias from these sources in 1926:II–1927:I. However, the ‘‘Banks by state’’ columns of Table 8 reveal that there was variation in the composition of earning asset portfolios and in the rates of return on other securities and bankers’ balances.25 The standard deviations of the estimated loan rate biases from these sources – 0.180 for other securities and 0.045 for bankers’ balances – suggests that violations of the estimation assumptions introduce only small biases. They also suggest that the discontinuities in the series associated with the switch from using Eq. (2) to compute loan interest revenue in 1925:II–1926:I to using reported loan interest revenue in 1926:II–1927:I are small.26 How large are the potential biases in earlier periods? Though these cannot be estimated, there are reasons to think such biases are also small. One is the

74

Table 8.

Variable Means and Standard Deviations. Banks by State

Reserve-City Banks by City

Country Banks by State

SD

Mean

SD

Mean

SD

Earning assets, 1926:II–1927:1 Lagged loans U.S. securities Federal Reserve Bank stock Other securities Bankers’ balances

64.02 11.80 0.38 16.66 7.15

6.94 3.15 0.07 7.48 3.91

67.96 13.05 0.38 11.94 6.68

9.67 6.06 0.08 6.11 4.82

59.99 10.56 0.38 21.44 7.63

8.40 4.12 0.08 9.07 4.46

Rates of return, 1926:II–1927:I Other securities Bankers’ balancesa

5.29 1.52

0.86 0.27

5.51 1.29

1.61 0.49

5.09 1.71

1.38 0.25

0.000

0.180

0.039

0.232

0.074

0.257

0.002

0.045

0.030

0.085

0.025

0.046

0.78

0.32

0.82

0.42

0.72

0.33

0.19 0.42 0.19

0.22 0.14 0.09

0.17 0.47 0.17

0.25 0.20 0.18

0.20 0.34 0.17

0.21 0.14 0.12

Biases in loan rates, 1926:II–1927:Ib From rates of return on other securities From rates of return on bankers’ balances Other revenue and recoveries, 1917:II–1919:I Collection and exchange Other revenue Recoveries

Notes: The means are weighted by the denominators used to construct each variable. The standard deviations are unweighted. Source: U.S. Comptroller of the Currency, Annual Report (1917–1919, 1926–1927). a The values for New York and Boston banks for this period are inflated and are excluded from the computations. b Net figures are not computed because of problems with the New York and Boston data noted previously.

SCOTT A. REDENIUS

Mean


75

prominence of loans in bank earning asset portfolios. Assume, for example, that the portfolios contained 60 percent loans, 15 percent other securities, and 7.5 percent banker’s balances. A 0.1 percent overstatement of the rate of return on other securities would reduce the estimated loan rates by only 0.025 percent, on average. A similar error in the rate received on bankers’ balances would reduce the estimated loan rates by only 0.0125, on average. In addition, rate variation between reported areas may have been less of a problem in earlier periods because bankers’ balances were relatively more important. For example, bankers’ balances were 13.54 percent of earning assets in 1889:II–1890:I while other securities were only 4.75 percent. If rate variation for other securities and bankers’ balances in earlier periods was similar to what is reported in Table 8, this would tend to reduce biases from rate variation. However, as reported in the remaining columns of Table 8, biases from rate variation have systematically different effects on estimated reserve-city and country bank loan rates. For 1926:II–1927:I, rates on other securities were 0.42 percent higher and rates on bankers’ balances 0.42 percent lower for reserve-city banks than country banks. Because the biases for the two work in opposite directions, the net loan rate biases will tend to be smaller. However, in earlier periods, the greater importance of bankers’ balances suggests that country loan rates will be biased upward and reserve-city loan rates biased downward. In addition to errors in the rates of return used in Eq. (2), biases can arise from the procedure used to estimate and remove non-interest revenue and recoveries. The adjustments discussed in Section 2 are appropriate for 1917:II–1919:I, but there is no information available with which to assess the relative importance of non-interest revenue and recoveries in earlier periods. Statistics for these variables are also reported in Table 8.

5. EMPIRICAL EVALUATION As stated in the introduction, short-term rates estimated from national bank records have been favored in research by economic historians because of the limitations of the other available sources. This also means there are no perfect benchmarks with which to evaluate the new series. Nonetheless, comparisons with other sources can both validate the new series and identify differences between the new series and other interest rate series. In each case, I also discuss the limitations of the comparison made. The time-series behavior of national bank loan rates is compared to capital market, money market, and other bank rate series. Fig. 2 compares the

SCOTT A. REDENIUS

Annual Percentage Rate

76 11

11

10

10

9

9

8

8

7

7

6

6

5

5

4

4

3

3

2

2

1

1 0

0 1890 1900 1910 1920 1930 1940 1950 1960 1970 Year National bank loans Other securities

Long-term U.S. bonds

Fig. 2. National Bank Loan and Capital Market Rates, 1887–1975. Notes: National bank loan rates are for the continental United States. The rates are constructed as weighted averages of the annual state-level series. Long-term U.S. bond yields are computed on a different basis in the first two sources. To eliminate the discontinuity, the earlier series is adjusted to the level of the later series using the average 1941 values for the two series. Tick marks are placed at the start of each year, and the points in the figure correspond to the middle of the loan periods. Source: National bank loan rates: see text. Long-term U.S. bonds yields: Board of Governors of the Federal Reserve System (1943, 1976), Federal Reserve Bulletin (1971–1976). Yields on other securities: see Table 5.

loan rates with two capital market series: the yield index used to compute interest and dividend revenue from other securities in Eq. (2) and a yield index for long-term U.S. government securities. The loan rates were higher in most periods than the yield index for other securities and well above that for U.S. government securities. The difference between the loan rates and the yield index for other securities was largest at the start of the period. In the 1960s, loan rates declined relative to both yield indexes. This was due, at least in part, to the growing importance of lending in the federal funds market, where rates were typically well below those on customer loans. Nevertheless, the comparison shows that the estimated loan rates tended to move with capital market rates.



77

11

11

10

10

9

9

8

8

7

7

6

6

5

5

4

4

3

3

2

2

1

1 0

0 1890 1900 1910 1920 1930 1940 1950 1960 1970 Year National bank loans Treasury bills (90-day, new issues)

Short-term business loans Certificates of indebtedness

Fig. 3. National Bank Loan and Money Market Rates, 1887–1975. Notes: National bank loan rates are for the continental United States. The rates are constructed as weighted averages of the annual state-level series. Short-term business loan rates are not computed on a consistent basis over the period. To eliminate discontinuities, the earlier series is adjusted using the averages for 1928:II–1929:I and 1939 to the level of the later series. The 1971–1975 series is then adjusted to the level of the earlier series using the correction given in the Federal Reserve Bulletin (1971, p. 470). Tick marks are placed at the start of each year, and the points in the figure correspond to the middle of the loan periods. Source: National bank loan rates: see text. Rates on certificates of indebtedness: see Table 5. Treasury bill yields and short-term business loan rates: Board of Governors of the Federal Reserve System (1943, 1976) and the Federal Reserve Bulletin (1971–1976).

Fig. 3 compares the loan rates to money market and other bank rate series. Here, the rates on certificates of indebtedness and Treasury bills provide measures of the short-term riskless rate of return. In principle, the loan rates should match the money market rates more closely than the capital market series, but this is not the case in practice. The movements of the series are similar in direction but differ in magnitude. Rates on business loans and Treasury bills declined relative to loan rates during the Great Depression and World War II before rising again in later years. In this case, the difference in the behavior of the two loan series is the result of

78

SCOTT A. REDENIUS

differences in the geographical areas represented: the estimated loan rates are for all national banks and the average rates on short-term business loans are for selected Fed-member banks in leading cities.27 Loan rates in urban areas fell relative to rural areas during this period, and urban and rural rates only returned to their pre-Depression relationship in the 1950s. The new series can also be evaluated using less geographically aggregated rate data published by Bradstreet’s and the Federal Reserve System. The Fed compiled rates on business loans for New York City, ‘‘Northern and Eastern cities,’’ and ‘‘Southern and Western cities.’’ For purposes of comparison, I constructed series to correspond as closely as possible to the Fed series from the reserve-city loan rates. Like Smiley’s 1916–1937 series, the movements of the loan rates and the Fed series are quite similar (Smiley, 1981b, Figs. 1–3). Rates on customers’ prime commercial paper for Federal Reserve Bank and branch cities were also reported in the Federal Reserve Bulletin for July 1918–December 1933. In all cases, the reserve-city loan rates and Bulletin rates are similar in level and movement. The crosssectional correlations between reserve-city loan rates and the Bulletin rates vary from 0.36 to 0.81, and the majority are statistically significant at the 0.0001 level. Between 1887 and 1916, the correlations between the reserve-city loan rates and Bradstreet’s time loan rates vary between 0.31 and 0.85. The correlations with Bradstreet’s commercial discount rates for 1902– 1916 range from 0.37 to 0.79. There are, however, some differences between the level and movements of the loan rates and the Bradstreet’s rates.28 For the postbellum portion of the series, the tabulations of interest rate surveys conducted by the Comptroller’s Office in 1899, 1902, and 1910 provide a more useful reference than the rates from Bradstreet’s. The 1899 survey requested average rates on outstanding loans for 12 July 1889, 18 July 1894, and 30 June 1899, and the 1910 survey for 30 June 1910. The 1902 survey requested average rates over the year ending 30 April 1902. National bank survey rates are given in Table 9 alongside estimated rates for loan periods that correspond as closely as possible to the survey dates. As evidenced by the correlation coefficients, the survey and estimates rates exhibit similar cross-sectional behavior. All of the correlations are at least 0.80 and are statistically significant at the a ¼ 0.0001 level. The survey and estimated rates are also reasonably similar in level. The U.S. survey and estimated rates for 1889 are nearly identical, but the survey rates are higher in subsequent years. The estimated rates are lower by 0.78 percent in 1894, 0.21 percent in 1899, 0.28 percent in 1902, and 0.59 percent in 1910. There are also some regional differences between the survey and estimated rates, but these appear to be idiosyncratic.

Comparison of Survey and Estimated Loan Rates: National Banks by Region. 1889

1894

1899

1902

1910

Survey

Estimated

Survey

Estimated

Survey

Estimated

Survey

Estimated

Survey

Estimated

5.42 5.32 7.30 8.64 11.14 6.84 7.88 10.32 11.42 8.90

5.17 5.30 7.23 9.02 12.79 6.97 7.94 11.26 11.57 9.82

5.08 4.88 6.87 8.22 9.83 6.47 7.29 9.68 10.91 8.69

3.95 4.29 6.26 6.91 9.00 5.79 6.40 8.91 8.98 8.22

4.70 4.65 6.23 7.54 9.35 5.47 6.05 8.33 9.61 7.19

4.66 4.31 6.26 8.00 10.01 5.10 5.25 9.70 9.27 7.99

4.90 4.93 6.15 7.44 8.79 5.50 5.89 7.69 8.42 6.96

4.06 5.06 5.82 7.27 9.08 4.67 5.31 7.65 8.12 6.88

5.35 5.69 6.26 8.29 9.60 6.20 7.14 8.31 9.21 7.48

4.95 4.90 6.15 7.09 8.01 6.41 5.97 7.81 8.48 7.00

United States

6.33

6.38

5.97

5.19

5.32

5.11

5.53

5.25

6.49

5.90

SD (weighted) SD (unweighted)

1.64 2.57

1.98 3.22

1.65 2.38

1.67 3.09

1.28 2.22

1.75 3.42

1.10 1.84

1.31 2.54

1.26 1.72

1.37 1.91

New England Middle Atlantic Upper South Eastern Lower South Western Lower South Old Northwest Eastern Plains Western Plains Mountain Pacific

Correlation

0.89

0.80

0.88

0.92


Table 9.

0.87

79

Notes: Where possible, the estimated loan weights are used to compute the average survey and estimated rates. However, the 1899 and 1902 survey responses were tabulated using the reserve-city designations in effect at the time of the surveys. They therefore include some reserve cities for which estimated rates and weights are not available. Survey rates for these cities are averaged with rates for country banks in the state using respondent bank loans as weights. In addition, for 1889, North and South Dakota are consolidated into Dakota Territory. Only those states for which survey and estimated rates are available are included in the analysis. The 1889, 1894, and 1899 estimated rates are for the 1889:I, 1894:I, and 1899:I semiannual loan periods. The 1902 estimated rates are for the 1901 annual loan period. The 1910 estimated rates are for the semiannual loan period 1901:I. Source: National bank loan rates: see text. Survey rates: U.S. Comptroller of the Currency, Annual Report (1899, Table 67, 1902, Table 84, 1910, Table 99).

80

SCOTT A. REDENIUS

Table 10.

Comparison of Survey and Estimated Loan Rates: Reserve-City National Banks by Region. 1889

1894

1899

1902

Survey Estimated Survey Estimated Survey Estimated Survey Estimated New England Middle Atlantic Upper South Eastern Lower South Western Lower South Old Northwest Eastern Plains Western Plains Mountain Pacific

5.10 4.98 6.80

3.79 4.81 6.75

4.40 4.44 6.50

2.78 3.73 5.27

6.04 7.44 8.40

6.52 7.84 9.14

5.55 6.66 8.00

5.70

7.02

United States

5.33


0.77 1.18

Correlation

0.75

4.71 5.88 7.14

4.20 4.34 5.60 6.00 7.80 4.61 5.40 6.90

3.19 3.71 4.71 7.33 9.64 3.95 4.21 6.84

6.00

8.01

5.54

5.10

4.86

3.98

1.19 1.68

0.91 1.06

1.10 1.25 0.70

6.38

4.59 4.69 5.30 6.56 6.24 5.12 5.3 6.45 6.34 6.04

2.76 4.89 4.72 5.32 6.13 3.89 4.66 6.45 5.11 5.98

4.50

3.80

4.91

4.52

0.56 1.01

0.92 1.55

0.48 0.74

0.91 1.24

0.76

0.76

Notes: Only those reserve cities for which survey and estimated rates are available are included in the analysis. The regional averages are computed as described in Table 9. Source: See Table 9.

As for previous loan rate series, there are more significant differences between the survey and estimated rates for reserve-city national banks. Table 10 compares the estimated reserve-city rates with rates from the 1899 and 1902 surveys. Though the correlations remain statistically significant at the 0.0001 level, they are considerable lower than those in Table 9. In addition, assuming the survey rates are accurate, the estimated rates underestimate rates charged by reserve-city banks by 0.23 percent in 1889, 0.88 percent in 1894, 0.70 percent in 1899, and 0.39 percent in 1902. While most of the regional differences appear to be idiosyncratic, estimated rates for New England (Boston) are quite low at each date. The size of this discrepancy is difficult to explain. Only if no non-loan sources of revenue are removed from total revenue in Eq. (2) do estimated rates for Boston approximate the survey rates. Boston rates are also generally well below the average Bradstreet’s rates on call loans, time loans, and commercial discounts prior to 1909, though Boston is not unique in this regard. Table 11 compares the survey and estimated rates for country national banks. The correlations are in this case only slightly lower than those for the state-level series. Overall, the estimated rates are higher than the survey rates


Table 11.

81

Comparison Survey and Estimated Loan Rates: Country National Banks by Region. 1889

1894

1899

1902

Survey Estimated Survey Estimated Survey Estimated Survey Estimated New England 5.62 Middle Atlantic 5.97 Upper South 7.39 Eastern Lower South 8.64 Western Lower South 11.14 Old Northwest 7.43 Eastern Plains 8.06 Western Plains 10.74 Mountain 11.42 Pacific 9.46

6.10 6.34 7.31 9.18 12.89 7.32 8.01 11.75 11.54 10.43

5.53 5.75 6.92 8.22 10.27 7.28 8.08 10.10 10.91 9.30

4.75 5.49 6.38 7.01 9.80 6.77 7.03 9.51 8.96 8.33

5.07 5.52 6.30 7.63 9.73 6.34 7.06 8.73 9.61 8.49

5.84 6.04 6.42 8.15 10.90 6.30 6.85 10.48 9.25 9.28

5.13 5.54 6.27 7.48 9.38 5.91 6.68 8.17 9.23 7.79

5.00 5.47 5.97 7.47 9.76 5.49 6.25 8.16 9.29 7.81

United States

7.19

7.53

6.99

6.36

6.35

6.79

6.29

6.14


1.81 2.49

2.04 3.14

1.71 2.24

1.66 2.98

1.44 2.08

1.76 3.20

1.33 1.79

1.58 2.38

Correlation

0.88

0.78

0.87

0.90

Notes: The survey and estimated rates are for equivalent areas. Country bank survey rates are computed from the reserve-city and country bank rates as described in Table 9. Only those states for which survey and estimated rates are available are included in the analysis. The regional averages are computed as described in Table 9. Source: See Table 9.

by 0.34 percent in 1889 and 0.44 percent in 1899 but lower by 0.63 percent in 1894 and 0.15 percent in 1902. Turning to the regional cells in the table, there do not appear to be any large systematic differences between the survey and estimated rates that are not accounted for by the differences between the U.S. survey and estimated rates. The similarity in level and cross-sectional variation between the survey and estimated rates provides some assurance regarding the accuracy of the new series. Similar tables constructed with James’ and Smiley’s postbellum rate series reveal several differences between their series and the new series. Consistent with the discussion of overdrafts in Section 2, Smiley’s series for the Lower South are higher than the new series and the survey rates. For the middle survey years, James’ series are much lower than the new series and the survey rates, especially for some of the larger reserve cities. In addition, the correlations between the new reserve-city rates and the survey rates are higher than those of the other series. However, Smiley’s Boston series is

82

SCOTT A. REDENIUS

closer to the level of the survey rates than are James’ series or the new series, though it is not clear why this should be the case.29 It is not surprising that there are some discrepancies between the survey rates and the new series given the different nature of the survey and estimated loan rates. First, the estimated rates are somewhat volatile, and this will tend to reduce correlations between the survey and estimated rates. Since the regional reserve-city differentials were smaller, such volatility will have a greater effect on correlations between the reserve-city rates. Second, the survey rates may not provide an accurate record of actual loan rates. The instructions that accompanied the survey forms did not specify how banks were to compute their average loan rates (Annual Report, 1899, p. xxiv, 1902, p. 42).30 Not all banks completed the 1899 survey, and this also appears to have been true for the 1910 survey. Response bias is of particular concern with the 1899 survey. Overall response rates were 55.6 percent for 1889, 59.3 for 1894, and 67.4 for 1899, but response rates for the South and for the Mountain states were lower. Finally, there are timing issues. The 1899 and 1910 survey rates are for specific dates whereas the estimated rates are computed over loan periods. Since rates in many reported areas were highly seasonal, the rates on the loans held on the survey dates may have differed from those that generated the revenue used in computing the estimated rates.31 Cyclical developments also come into play with the 1894 data.

6. A REVISED VIEW OF REGIONAL SHORT-TERM INTEREST RATES The central features of the rate differentials can be most readily identified by examining changes in rate dispersion from 1887 to 1975. In Fig. 4, rate dispersion is represented by weighted and unweighted standard deviations of the annual state-level loan rates.32 As shown in the figure, rates have tended to converge over time. The unweighted and weighted standard deviations declined from 3.45 and 1.81, respectively, at the start of the period to just over 0.25 in the 1960s. However, it appears that macroeconomic shocks at times halted or reversed rate convergence. The Great Depression, World War II, and the turbulence of the 1970s are all evident. In contrast with Bodenhorn (1995, Fig. 12–3), the 1890s Depression does not have a dramatic impact on rate dispersion. Though it is difficult to match the annual rate series with business cycle events precisely, the postwar recessions are also evident. In addition, there was little decline in rate dispersion during the 1920s and from the late 1950s through the 1960s.

Standard Deviation

New National Bank Loan Rate Estimates, 1887–1975 3.75 3.50 3.25 3.00 2.75 2.50 2.25 2.00 1.75 1.50 1.25 1.00 0.75 0.50 0.25

83 3.75 3.50 3.25 3.00 2.75 2.50 2.25 2.00 1.75 1.50 1.25 1.00 0.75 0.50 0.25

1890 1900 1910 1920 1930 1940 1950 1960 1970 Year Weighted

Unweighted

Fig. 4. Standard Deviations of National Bank Loan Rates, 1887–1975. Notes: Only states and territories in the continental United States are included in the analysis. Because of changes in reported areas, the standard deviations in the figure are constructed from two series. In the first, Dakota Territory takes the place of North and South Dakota and there are no observations for Oklahoma. In the second, Oklahoma and Indian Territories are consolidated. The first series is used through the 1890 loan period and the second thereafter. To eliminate discontinuities, the earlier series is adjusted to the level of the later series using the standard deviations for the 1891 loan period. Tick marks are placed at the start of each year, and the points in the figure correspond to the middle of the loan periods. Source: See text.

Fig. 5 gives unweighted standard deviations for reserve-city and country banks.33 Because of changes over time in the set of reserve cities for which rates can be estimated, standard deviations for two sets of reserve cities are included. As shown in Fig. 5, rate dispersion was much greater for country than reserve-city banks. The standard deviation of 1887–1927 reserve-city rates was roughly half that for country banks until about 1920. The dispersion for the 1916–1937 set of reserve cities was greater than for the earlier set of reserve cities but was still generally less than for country banks. Figs. 4 and 5 can also be used to provide a rough evaluation of the role of legal and institutional change in reducing rate dispersion. Though the unweighted standard deviations decline rapidly for a few years after 1887, postbellum rate convergence appears to have proceeded gradually thereafter.

SCOTT A. REDENIUS

Standard Deviation

84 3.75 3.50 3.25 3.00 2.75 2.50 2.25 2.00 1.75 1.50 1.25 1.00 0.75 0.50 0.25 1890

1900

1910 Year

Country National Banks by State

1920

1930

3.75 3.50 3.25 3.00 2.75 2.50 2.25 2.00 1.75 1.50 1.25 1.00 0.75 0.50 0.25 1940

1887 - 1926 Reserve Cities

1916 - 1937 Reserve Cities

Fig. 5. Standard Deviation of National of National Bank Loan Rates, 1887–1937. Notes: Only states and territories in the continental United States are included in the analysis. District of Columbia country national banks are included with the reserve city of Washington. Standard deviations for country banks are constructed as described in Fig. 5. The 1887–1926 reserve cities include those cities designated in the National Banking Act. The group of 1916–1937 reserve cities include those cities for which rates are available for the 1916–1937 loan periods. The standard deviations are unweighted. Tick marks are placed at the start of each year, and the points in the figure correspond to the middle of the loan periods. Source: See text.

Sylla (1969) argued that the 1900 Gold Standard Act reduced rate dispersion by lowering barriers to the organization of country national banks in high rate regions. Yet, the new series do not show a marked acceleration in country bank rate convergence after 1900. In addition, James’ (1976b, 1978, Chapters 1 and 6) claim that country bank rate differentials actually increased after 1900 is not borne out by the behavior of the new series. One of the largest relative declines in the standard deviations occurs between 1914 and 1921. The unweighted standard deviation for country banks declined from 1.61 to 0.91 between 1914 and 1921 and from 0.67 to 0.43 for the set of 1887–1926 reserve cities.34 This is the period covering the organization and early operations of the Federal Reserve System. To better identify


Table 12.

85

The Decline in Loan Rate Differentials, 1914–1921. Regional Rate Minus Eastern Rate

Decline

1914

1921

1914–1921

Upper South Eastern Lower South Western Lower South Old Northwest Eastern Plains Western Plains Mountain Pacific

0.66 1.83 3.37 0.37 1.10 3.21 3.27 1.15

0.59 1.47 2.10 0.30 0.93 1.86 1.99 0.76

0.07 0.36 1.27 0.07 0.17 1.35 1.28 0.39

United States

0.72

0.53

0.19

Notes: The estimated rates in the table are for the 1914 and 1921 loan periods. The regional rates are weighted averages of the annual state-level series. Source: See text.

the geography of rate convergence that accompanied the organization of the Federal Reserve System, Table 12 examines the change in regional rates between 1914 and 1921. The decline in the rate differentials with the East was modest for most regions but large for the three highest rate regions in 1914: the Western Lower South, the Western Plains, and the Mountain states. That rates converged between 1914 and 1921 is confirmed by a number of contemporaries. Willis (1917) stated that ‘‘Interest rates have undoubtedly been y rendered more uniform than in the past’’ (p. 274) and specifically cited the impact on Southern interest rates (p. 272). Burgess (1927, pp. 287–289) also noted the decline in rate dispersion in data for eight Federal Reserve Bank cities. The unweighted standard deviation of the average rates he reported declined from 1.15 in 1911–1913 to 0.41 in 1922–1924. The new series can also be used to examine the regional rate developments behind the other changes in rate dispersion in Fig. 4. Fig. 6 provides an overview of the evolution of U.S. regional interest rates from 1887 to 1975.35 The first thing to note is that all of the regional rates tended to move together over time and with the capital market rates in Fig. 2. The second is the large regional differences in rates apparent at the start of the period. Eastern rates were lowest and rates rose to the south and west. Also evident is the subsequent convergence of rates observed in Fig. 4. However, the format of Fig. 6 makes it somewhat difficult to separate changes in the size of the rate differentials between regions from changes in the general level of interest rates. In subsequent figures, the East is used as a reference region,

SCOTT A. REDENIUS


86

10.5 10.0 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5

10.5 10.0 9.5 9.0 8.5 8.0 7.5 7.0 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 1890 1900 1910 1920 1930 1940 1950 1960 1970 Year Region:

East South

Midwest West

Fig. 6. National Bank Loan Rates, by Region, 1887–1975. Notes: The regional rates are weighted averages of the annual state-level series. Tick marks are placed at the start of each year, and the points in the figure correspond to the middle of the loan periods. Source: See text.

and Eastern rates are subtracted from the other regional rates. While this has the advantage of more clearly illustrating the rate differentials, it should be kept in mind that the series in these figures incorporate estimation errors for both the region in question and the Eastern reference region. Fig. 7 presents the information in Fig. 6 in the revised format. In 1887, Midwestern rates were 2.27 percent above Eastern rates but declined relative to Eastern levels to the point where rates in the two regions were indistinguishable by the mid-1950s. Western rates were highest at the outset – 4.88 percent above Eastern rates – and except for the period 1900–1940, the West was typically the highest rate region. Western rate differentials with the East declined to about 1920, widened in the 1930s, and narrowed slowly until the late 1960s before widening again. In 1887, Southern rates were 2.86 percent higher than Eastern rates. Though adjustments for overdraft lending and non-interest revenue and recoveries lower postbellum Southern rates relative to Eastern rates by as much as 1.07 percent, they do not change the qualitative behavior of Southern loan rates noted in previous work: postbellum Southern rates converged more slowly than those of other



87 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5 -1.0

6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5 -1.0 1890 1900 1910 1920 1930 1940 1950 1960 1970 Year Region:

Fig. 7.

Midwest West

South

National Bank Loan Rate Differentials by Region, 1887–1975. Notes: See Fig. 6. Source: See Fig. 6.

regions. Otherwise, the behavior of Southern rates is more similar to Western than Midwestern rates. In addition to the regional differences in Fig. 7, there were substantial differences within each region. Rates in the three Midwestern subregions are shown in Fig. 8. Within the Midwest, rates also increased from East to West at the start of the period. Rates were 1.51 percent above Eastern rates in the Old Northwest, 2.32 percent in the Eastern Plains, and 6.44 percent in the Western Plains. Rate differentials for all three subregions declined over time. However, as noted in Table 12, rates in the Western Plains declined more rapidly in the late 1910s. They also appear to have been affected by the Great Depression and World War II to a greater extent than rates in the other two subregions. The rates for the Old Northwest and Eastern Plains became indistinguishable from Eastern rates in the mid-1950s. Western Plains rates remained higher, and the difference between Western Plains and Eastern rates began to widen again after the mid-1960s. Rates for the two Western subregions are given in Fig. 9. In 1887, rates in the Pacific states were 4.54 percent above Eastern rates and 5.21 percent in

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88 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5 -1.0

6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5 -1.0 1890 1900 1910 1920 1930 1940 1950 1960 1970 Year Region:

Eastern Plains

National Bank Loan Rate Differentials, Midwest, 1887–1975. Notes: See Fig. 6. Source: See Fig. 6.


Fig. 8.

Old Northwest Western Plains

6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5 -1.0

6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5 -1.0 1890 1900 1910 1920 1930 1940 1950 1960 1970 Year Region:

Fig. 9.

Mountain

Pacific

National Bank Loan Rate Differentials, West, 1887–1975. Notes: See Fig. 6. Source: See Fig. 6.


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the Mountain states. The difference between Pacific and Eastern rates had declined significantly by around 1910, and Mountain rates declined rapidly in the late 1910s. To an even greater extent than the Western Plains, rate differentials for the Mountain states rose during the Great Depression and for both subregions during World War II. Pacific rate differentials rose more gradually through this period. Thereafter, rates fell toward Eastern levels, but rates in both subregions were higher throughout. Rates in the Mountain states rose relative to Eastern rates after the mid-1960s. Rates for the Southern subregions are given in Fig. 10. At the start of the period, the subregional rate differentials also increased to the South and West. Rates in the Upper South were 1.46 percent above Eastern levels, 3.00 percent for the Eastern Lower South, and 5.38 percent for the Western Lower South.36 As for the other regions, rates for each subregion converged toward Eastern levels during the postbellum period, albeit more slowly. Rates for the Western Lower South declined substantially just before 1920. Lower South rates converged further toward Eastern levels during the 1920s before rate differentials for all three subregions increased in the 1930s. Unlike the other regions, there were changes in the relative positions of the 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5 -1.0

6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5 -1.0 1890 1900 1910 1920 1930 1940 1950 1960 1970 Year Region:

Fig. 10.

Upper South Western Lower South

Eastern Lower South

National Bank Loan Rate Differentials, South, 1887–1975. Notes: See Fig. 6. Source: See Fig. 6.

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Southern subregions. Eastern Lower South rates fell below those for the Upper South in the 1930s and 1940s. In the 1950s, the Western Lower South emerged as the low rate subregion and rates for that subregion declined to near Eastern levels. The differentials between the Southern subregions and the East widened again in the late 1960s. There were also east–west and north–south differentials within some subregions. In the Eastern Lower South, the estimated rates were generally higher in Florida and Alabama than in South Carolina and Georgia, though this pattern disappeared by World War I. In the Western Lower South, rates were also generally higher in Texas and Oklahoma than in Arkansas, Mississippi, and Louisiana, but these intraregional differentials had declined by the end of World War I. After World War II, rates were lowest in Texas and Louisiana. In New England, rates in Maine, New Hampshire, and Vermont were higher than in Massachusetts, Rhode Island, and Connecticut throughout. In the Western Plains states, rates in the Dakotas were higher than in Kansas and Nebraska into the 1960s. In the Pacific region, rates were lower in California than in Oregon and Washington through the 1930s. In addition, there was wide rate variation among the Mountain states but no clear geographical pattern.

7. CONCLUSION The new national bank loan rate series presented here have a number of potential research uses. They provide a reference against which rates on individual loans reported in contemporary sources can be compared. They can also be used to evaluate the impact of macroeconomic shocks or policy innovations, including changes in monetary policy, on bank loan rates or rate differentials. However, it is my hope that they will be used in research on the factors that generated the historical bank loan rate differentials. Despite more than a century of research, there is as yet no final consensus on the causes of the rate differentials and rate convergence during the postbellum period. The new series should aid future research on the postbellum period and facilitate the extension of research on the rate differentials to later periods. One additional topic suggested by the behavior of the new series is the relationship between the Fed’s organization and rate convergence. At the time, the Fed’s failure to bring about a greater equalization of interest rates was viewed with disappointment. Some of the Fed’s early advocates and some within the Fed attributed this to the failure of features of the System


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that were designed to reduce the rate differentials. Willis (1922) noted that the Fed had not brought about the anticipated expansion of the commercial paper market. And both Willis (1922) and the Federal Reserve Board, Annual Report (1922, pp. 15–16) observed that the availability of cheaper credit through the discount window did not have a large impact on bank loan rates. Yet the empirical behavior of loan rates in the years immediately following the Fed’s founding suggests that the Fed may, in fact, have reduced the effect of one or more of the factors responsible for the postbellum rate differentials. Perhaps the impact of the discount window or the Fed’s effect on the commercial paper market was greater than was appreciated at the time. Rate convergence may also have resulted from the decline in the cost of transferring funds between regions, changes in bank reserve behavior, or the Fed’s seasonal adjustment of the monetary base. In any event, identifying links between Fed activities and rate convergence may shed new light on the causes of the postbellum rate differentials.

NOTES 1. For earlier examples of this technique, see Riefler (1930, Table 22), Henderson (1944), and Federal Reserve Bulletin (1927, pp. 461–463). 2. See Smiley (1975, pp. 593–594) and James (1978, p. 245) for critiques of Davis’ portfolio measures. 3. Davis (1965) and Sushka and Barrett (1984) examined the role of legal and institutional innovations they believed facilitated interregional lending and reduced postbellum rate differentials. For Davis, the relevant innovation was the development of the commercial paper market and for Sushka and Barrett, the growth of the stock market. Sylla (1969) and James (1976a, 1976b, 1978) focused on legal innovations that they believed reduced barriers to entry in high rate regions and reduced rates through increased competition. Sylla argued that the relevant development was the passage of the Gold Standard Act in 1900, which reduced minimum capital requirements for national banks and increased the profitability of note issue. James assigned this role to the state passage of general incorporation laws and the subsequent lowering of minimum capital requirements for state banks. 4. In principle, national bank accounting data could be used to estimate statelevel rates until passage of the Riegle-Neal Interstate Banking and Branching Efficiency Act of 1994, which allowed individual banks, rather than just bank holding companies, to operate across state lines. Prior to that, it is reasonable to assume a bank’s income was generated by operations in the state where it was chartered. However, rate estimation is not possible using the Comptroller data after 1975. To 1975, income statements and balances sheets are for ‘‘national banks.’’ Starting in 1976, the published income statements report income ‘‘of foreign and domestic offices and subsidiaries of national banks’’ while the balance sheets are for ‘‘domestic offices and subsidiaries.’’ Given the large foreign operations of money

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center banks, rates estimated from the later data are not a reliable guide to domestic loan rates. 5. An extended discussion of the data and estimation procedure is given in Redenius (2005b). Also included is a detailed summary of the methods used to compute previous series. 6. Aggregated balance sheets were published for each call date through the 1962 Report. Thereafter, aggregated balance sheets were published only for the call dates closest to June 30 and December 31 each year. 7. The interpretation of the estimated loan rates is discussed in greater detail in the next section. Ex-post loan rate series are also available from the author. 8. For 1926:II–1927:I, there is also a revenue category ‘‘interest not classified.’’ This is allocated proportionately to interest revenue from loans, securities, and bankers’ balances. Starting in 1969, ‘‘Income on Federal funds sold and securities purchased under agreements to resell’’ were reported separately from loan interest revenue. In the estimations, they are included in loan interest revenue. 9. James and Smiley assumed all bankers’ balances were interest bearing and earned what they took to be the prevailing rate on bankers’ balances. Bodenhorn (1995, p. 448) assumed that only balances held with reserve agents were interest bearing because other balances ‘‘may have represented little more than transitory clearings and would not have earned interest.’’ The formulation used here is equivalent to assuming that 85 percent of bankers’ balances earned the maximum New York rate and 15 percent did not earn interest. Bodenhorn’s formulation results in lower estimates of interest revenue from bankers’ balances. On 20 June 1917, the last call date for which deposits with reserve agents were reported separately from those held with other national banks, only 50.6 percent of bankers’ balances were held with reserve agents. In 1926:II–1927:I, 72.4 percent of bankers’ balances were held with national banks. 10. Smiley (1973, p. 13) justified this omission as follows: ‘‘Overdrafts are not an intentional loan at some particular rate of interest by the bank. They are not included in the bank’s decision as to how to allocate its funds.’’ Smiley’s assessment is consistent with the description found in many contemporary bankers’ manuals. However, the large overdrafts allowed by Southern and Western banks typically represented loans for which the amount and term of credit was left to the borrower, but for which credit limits, interest rates, and sometimes security were specified in advance (Redenius, 2004a). 11. Previously, ratios of losses to losses and premiums written off (‘‘loss ratios’’) for 1884:II–1887:I have been used to estimate losses in later periods. Smiley (1976b) used the U.S. loss ratio to estimate loan losses for all reported areas. James (1974b, pp. 415–418) estimated loan losses using the loss ratio for each reported area. Bodenhorn (1995, p. 449) followed James but after 1900 used a ‘‘similar average’’ computed from the 1918–1922 Reports. There are three problems associated with estimating loan losses using the 1884:II– 1887:I loss ratios. (1) Since the 1884:II–1887:I period coincided with the refunding of a U.S. government bond issue widely held by national banks and a large reduction in national bank holdings of U.S. government securities, loss ratios for the period may not be representative. (2) Premiums in the bond market and on the balance sheets varied over time in response to changes in interest rates and government debt


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management. This further suggests the 1884:II–1887:I ratios may not be appropriate in all income periods. (3) Premium writeoffs should be acyclical and loan losses countercyclical. Using the loss ratios will tend to understate loan losses in economic downturns. Estimating premiums written off using the premium account, as described in the main text, addresses issues (2) and (3). In principle, it should be possible to estimate premiums written off using bond prices and the individual bank balance sheets published in the Annual Report. However, I was not able to reproduce the premium writeoffs reported for 1884:II–1887:I with reasonable accuracy. I thank Joseph R. Mason for providing some of the data for this exercise. 12. Since transfers to valuation reserves reduced federal income tax liabilities, banks typically kept reserves at the authorized maximum. Therefore, transfers served largely to replenish reserves as loan losses were realized. However, in the years immediately following official recognition of valuation reserves, the initial accumulation of reserves results in an overestimate of loan losses. 13. From 1962, data on national bank receiverships are taken from Federal Deposit Insurance Corporation, Annual Report (1962–1975). 14. This method was developed by Smiley (1981b, pp. 891–892) to correct for the effect of bank failures during 1930–1933. His corrections were based on the capital and surplus of failing banks. Capital is used here because it is the only variable consistently available in the Annual Report tables that report changes in operational status. Prior to 1933, capital is the par value of capital stock. Thereafter, it includes preferred stock. Starting in 1963, the tables report total capital accounts. In all cases, it is assumed that the composition of the affected bank’s balance sheet is representative of banks in its reported area. 15. Prior to the 1909 Report, the voluntary liquidation tables do not provide information about the purpose of the liquidation. For this period, I assume voluntary liquidations by reserve-city national banks were associated with reorganizations or mergers with other national banks. 16. Though not identical in level, rates charged by national banks, state banks, private banks, savings banks, and loan and trust companies were highly correlated across reported areas (Redenius, 2002, Appendix B). 17. This feature of the estimated loan rates is relevant for empirical testing of the Market Power Hypothesis advanced by Sylla (1969) and James (1976a, 1976b, 1978) as an explanation for the early postbellum rate differentials. Since larger banks tended to operate in more competitive markets, such markets are also disproportionately represented in the estimated loan rates. For empirical evidence on the market structure distribution of national bank loans in 1890, see Redenius (2003). 18. For extended discussions, see Thompson (1916) and James (1978, pp. 79–88). 19. In principle, postbellum interbank loans were reported as ‘‘bills payable’’ or ‘‘notes rediscounted’’ on the borrowing bank’s balance sheet. However, because external borrowing might be interpreted as a sign of weakness, banks often attempted to conceal such borrowing by reporting it elsewhere on their balance sheets (James, 1978, pp. 149–164). Therefore, the balance sheet figures upon which this computation is based are likely understated. After the organization of the Federal Reserve System, the bulk of bills payable and notes rediscounted represented

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discount window borrowing. The 4 March 1919 figures are from a survey of national bank loans and the later figures are from the loan schedules. 20. The amount of outstanding loans made for correspondents in the numerator is for city banks. The denominator is estimated loans of national banks on 31 January 1920. For earlier information on New York banks’ call loan placements, see Watkins (1929, Chapter 8). Banks also lent directly or through syndicates to firms in other areas (Holthausen, 1947; Arlt, 1959). However, the Annual Report does not report the amount of such loans extended by national banks. Evidence presented by James (1978, pp. 164–170) suggests that inter-area lending was more prevalent than suggested by the figures given in the main text. Based on a 31 January 1920 Comptroller survey of the regional ‘‘location of borrowers,’’ 17.07 percent of city bank loans were in other U.S. regions and 1.42 percent in U.S. possessions and foreign countries. Given the fact that the survey data were tabulated by region, these figures would appear to represent lower bound estimates of inter-area lending. However, 17.07 percent may overstate the size of interregional credit flows initiated by banks. Local bank transactions could result in reported interregional loans as follows. A New York importer sells goods to an Atlanta wholesaler on 90-day credit. The New York firm signs the wholesaler’s note and takes the note to its local bank for discount. The New York bank now holds the debt of a firm in another region even though the transaction was conducted with a local customer. It appears that reserve-city banks were more active inter-area lenders. Lamoreaux (1998) found that 36 percent of 1901 Suffolk National Bank loans by value consisted of open market commercial paper. By contrast, White’s (1998) example of the A. Levy bank suggests that country bank portfolios were dominated by local loans. This appears to have been the case in later periods. For 1929–1938, the relative importance of acceptances, open market paper, and interbank loans was greater for reserve-city national banks: 9.03 percent for Boston, 7.96 percent for New York, and 8.60 percent for Philadelphia. In addition, a 1946 survey of Fed-member bank business loans suggests that New York and Chicago banks were particularly active lenders outside of their Federal Reserve districts (Holthausen, 1947, pp. 499–500). 21. Loans secured by real estate include commercial, industrial, and agricultural loans in addition to loans used to purchase real estate. Not all of these loans were long term. On 30 June 1916, 29.47 percent of loans secured by real estate were scheduled to mature within 90 days. While this was lower than for other loan categories, it is well above what would be expected if all loans secured by real estate had been mortgages with maturities typical of those used to finance real estate purchases. Average mortgage lengths on 1 January 1890 were 4.660 years (U.S. Census Office, 1895, p. 107). 22. See Keehn and Smiley (1977) for a discussion of the limits placed on mortgage lending by postbellum national banks. The subsequent expansion of mortgage lending was facilitated by the easing of these restrictions under Section 24 of the Federal Reserve Act. The percentage of bank loan portfolios that could be secured by real estate and the maximum legal length of mortgage loans were further increased by subsequent legislation. 23. See also Federal Reserve Bulletin (1939, pp. 560–562) and Jacoby and Saulnier (1942).


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24. For an example, see Brayer (1945). 25. Some of the variation in the rate of return on other securities appears to be the result of differences in portfolio composition. The lowest rates of return on other securities were for reported areas where municipal bonds were a large component of other security portfolios. 26. An alternative estimation strategy would be to adjust each series to eliminate the discontinuity between 1925:II–1926:I and 1926:II–1927:I as was done for noninterest revenue and recoveries through 1917:I. Such adjustments are not made here because the discontinuities are comparatively small and it is less clear that such adjustments would be appropriate for the entire period to 1926:I. 27. An alternative bank rate is the prime lending rate. The prime rate reported in Board of Governors of the Federal Reserve (1976) and the Federal Reserve Bulletin closely tracks the rate on short-term business loans in Fig. 3. 28. Panic years are excluded from the reported correlations. Bradstreet’s reported New York rates separately for most of the period, and New York is not included in the analysis. However, New York loan rates are similar to the rates on commercial discounts reported in Macaulay (1938). See also James (1978, Appendix A) and Smiley (1975, Fig. 1). The material generated by these comparisons is too extensive to include here, but the city graphs and correlation tables are available from the author. 29. As part of this project, I also computed the rate of return on private earning assets for the reported areas and income periods described in Table 4. The series are also available from the author and are described in greater detail in Redenius (2005c). However, the new Boston series is also well below the survey rates. 30. There is also some question as to whether the Comptroller’s Office computed the published survey rates as simple or weighted averages of the survey returns, though weighted averages appear most likely. For more on the surveys, see Redenius (2004b). 31. The estimated semiannual loan rates are also highly seasonal. Seasonality in the estimated rates can arise for two reasons: the seasonality of actual loan rates and the seasonality of loan repayments. Seasonality generated by loan repayments can also result in discrepancies between the survey and estimated rates. 32. This method follows Smiley (1981b, Fig. 6). The weighted and unweighted standard deviations given in Fig. 4 provide two perspectives on rate dispersion. The reason the unweighted standard deviations are higher than the weighted standard deviations at the start of the period is that states in high rate regions had relatively few national banks. The unweighted standard deviations fall as rates in those regions decline. Movements in the weighted standard deviations reflect the growth of national banking in high rate regions in addition to changes in the rate differentials over time. 33. Fig. 5 closely follows Smiley (1981b, Fig. 6). 34. The 1914–1921 comparison sidesteps a number of temporary factors that may have affected rate dispersion, including Comptroller John Skelton Williams’ crusade against usury and U.S. participation in World War I. For the former, see U.S. Comptroller of the Currency, Annual Report (1915–1920). The Farm Credit System was also organized during this period. However, because the initial legislation focused on the mortgage market and the system was slow to organize, it likely had little effect on average bank loan rates during this period.

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35. Figs. 6–10 differ from those in previous work in two ways: the regional definitions are different and the loan rate series are not smoothed. 36. The differences within the Lower South have been obscured in the regional aggregations used previously. Smiley (1975) noted the significant differences between rates in the Upper and Lower South. 37. U.S. Secretary of the Treasury, Finance Report (1900, pp. LXXVIII–LXXXI and 10–11, 1901, pp. 149–150). Smiley (1973, Appendix B, 1976b) first identified this issue.

ACKNOWLEDGMENTS Collection of the national bank data used in the loan rate estimations was supported by a grant from Bryn Mawr College and by the Yale University Economics Department. Collection of city interest rate data from Bradstreet’s and the Federal Reserve Bulletin was supported by Knox College. I wish to thank Howard Bodenhorn, Lance E. Davis, John A. James, and Gene Smiley for sharing their knowledge of earlier rate estimations. I also thank Timothy W. Guinnane, Michael T. Rock, David F. Weiman, the editor, and an anonymous referee for their comments on earlier drafts of the paper. Courtney Adams, Selva Baziki, Christine Bohanak, Shinn Chu Cheng, Kateryna Chernova, Ashley Elser, Sarah Friedman, Mishayl Naek, Aditi Nayar, Akshay Shende, and Mridula Shrestha helped compile the data. Kari Siegle provided editorial assistance. I am grateful to all of them for their contributions to this project.

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and markets in the twentieth century. Burr Ridge, IL: Irwin Professional Publishing (Chapter 12). Bradstreet’s (1881–1883, 1886–1916). Wall Street Journal. Brayer, H. O. (1945). Boom Town Banker – Central City Colorado, 1880. Bulletin of the Business Historical Society, 19, 67–95. Burgess, W. R. (1927). The reserve banks and the money market. New York: Harper & Brothers Publishers. Cagle, C. H. (1959). Security pledged on business loans at member banks. Federal Reserve Bulletin, 45, 1114–1129. Davis, L. E. (1965). The investment market, 1870–1914: The evolution of a national market. Journal of Economic History, 25, 355–399. Federal Deposit Insurance Corporation. (1962–1975). Annual report. Washington, DC: GPO. Federal Reserve Board. (1922). Annual report. Washington, DC: GPO. Henderson, J. S. (1944). Regional differentials in interest rates. Southern Economic Journal, 11, 113–132. Herr, W. McD. (1957). Loans to buy farm real estate. Federal Reserve Bulletin, 43, 133–141. Holthausen, D. McC. (1947). Term lending to business by commercial banks in 1946. Federal Reserve Bulletin, 33, 498–517. Homer, S., & Sylla, R. (1996). A history of interest rates (3rd ed., revised). New Brunswick, NJ: Rutgers University Press. Jacoby, N. H., & Saulnier, R. J. (1942). Term lending to business. New York: National Bureau of Economic Research. James, J. A. (1974a). The evolution of the national money market, 1888–1911. Electronic data file (STATA). James, J. A. (1974b). The evolution of the national money market, 1888–1911. Ph.D. dissertation, Massachusetts Institute of Technology. James, J. A. (1976a). Banking market structure, risk, and the pattern of local interest rates in the United States, 1893–1911. Review of Economics and Statistics, 58, 453–462. James, J. A. (1976b). The development of the national money market, 1893–1911. Journal of Economic History, 36, 878–897. James, J. A. (1978). Money and capital markets in postbellum America. Princeton: Princeton University Press. James, J. A. (1981). Financial underdevelopment in the postbellum South. Journal of Interdisciplinary History, 11, 443–454. Keehn, R. H., & Smiley, G. (1977). Mortgage lending by national banks. Business History Review, 51, 474–491. Lamoreaux, N. R. (1998). Were bank special intermediaries in late nineteenth century America? Commentary. Federal reserve bank of St. Louis, Review, 80, 33–36. Laughlin, J. L. (Ed.) (1912). Banking reform. Chicago: The National Citizens’ League for the Promotion of a Sound Banking System. Reprinted New York: Arno Press, 1980. Macaulay, F. R. (1938). Some theoretical problems suggested by the movements of interest rates, bond yields and stock prices in the United States since 1856. New York: National Bureau of Economic Research. Redenius, S. A. (2002). Between reforms: The U.S. banking system in the postbellum period. Ph.D. dissertation, Yale University. Redenius, S. A. (2003). Bank market power and regional differences in postbellum U.S. loan rates. Working Paper, Bryn Mawr College.

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Redenius, S. A. (2004a). Overdraft lending by postbellum banks. Working Paper, Bryn Mawr College. Redenius, S. A. (2004b). U.S. Comptroller of the Currency, Annual report: Bank Rate Surveys. Working Paper, Bryn Mawr College. Redenius, S. A. (2005a). Clearinghouse regulation, federal policy, and interest rates on bankers’ balances. Working Paper, Bryn Mawr College. Redenius, S. A. (2005b). National bank loan rates, 1887–1975. Working Paper, Bryn Mawr College. Redenius, S. A. (2005c). The rate of return on private earning assets, 1887–1975. Working Paper, Bryn Mawr College. Riefler, W. W. (1930). Money rates and money markets in the United States. New York: Harper & Brothers Publishers. Smiley, G. (1973). The evolution and structure of the national banking system, 1870–1913. Ph.D. dissertation, University of Iowa. Smiley, G. (1975). Interest rate movement in the United States, 1888–1913. Journal of Economic History, 35, 591–620. Smiley, G. (1976a). U.S. interest rate estimates, 1888 to 1913. Electronic data file (Excel). Smiley, G. (1976b). Revised estimates of short-term interest rates of national banks for states and reserve cities, 1888–1913. Working Paper, Marquette University. Smiley, G. (1981a). U.S. interest rate estimates, 1916 to 1940. Electronic data file (Excel). Smiley, G. (1981b). Regional variation in bank loan rates in the interwar years. Journal of Economic History, 41, 889–901. Smiley, G. (1981c). State and city loan rate estimates, 1916–1940. Working Paper, Marquette University. Smith, T. (1947). Security pledged on member bank loans to business. Federal Reserve Bulletin, 33, 664–680. Smith, T., & Allen, P. T. (1947). Commercial bank loans to farmers. Federal Reserve Bulletin, 33, 1216–1227. Sushka, M. E., & Barrett, W. B. (1984). Banking structure and the national capital market, 1869–1914. Journal of Economic History, 44, 463–477. Sylla, R. (1969). Federal policy, banking market structure, and capital mobilization in the United States, 1863–1913. Journal of Economic History, 29, 657–686. Thompson, C. W. (1916). Factors affecting interest rates and other charges on short-term farm loans. U.S. Department of Agriculture, 26 August, Bulletin, 409. U.S. Census Office. (1895). Report on real estate mortgages in the United States at the eleventh census: 1890. Washington, DC: GPO. U.S. Comptroller of the Currency. (1863–1976). Annual report. Washington, DC: GPO. U.S. Comptroller of the Currency. (1946–1961). Abstracts. Washington, DC: GPO. U.S. Secretary of the Treasury. (1865–1927). Finance report. Washington, DC: GPO. Watkins, L. L. (1929). Bankers’ balances. New York: McGraw-Hill Book Company. Reprinted New York: Arno Press, 1980. White, E. N. (1998). Were banks special intermediaries in late nineteenth century America? Federal Reserve Bank of St. Louis, Review, 80, 13–32. Willis, H. P. (1914). The Federal Reserve Act. American Economic Review, 4, 1–24. Willis, H. P. (1917). What the Federal Reserve System has done. American Economic Review, 7, 269–288. Willis, H. P. (1922). The federal reserve system – a retrospective of eight years. Political Science Quarterly, 37, 553–584.


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APPENDIX A The source data contain several types of errors or other anomalies that would decrease the accuracy and increase the volatility of the rate estimates if not addressed. The frequency of such problems declined over time, and with the exception of typographical errors, there were few problems following the adoption of the more detailed income statement format in 1917. Each type of problem is described below with the corrections made. Data entry and typographical errors. Row totals, column totals, and other checks were used to identify and correct data entry errors and typographical errors in the source wherever possible. Where the row and column checks pointed to the same correction, the entry was adjusted accordingly. In a small number of cases, errors were built into either the row or column checks. Corrections were made if the value for the period was unusual and an appropriate value could be determined. Income statement anomalies. For some observations, the number of banks, capital, or surplus given in the income statements is over- or understated relative to the what is reported on the balance sheets. In such cases, the values of the income statement variables typically also differ from their trends for those reported areas. In each case, the income statement values are replaced with interpolated values computed as follows. 1 I t1 I tþ1 It ¼ þ (A.1) EAt 2 EAt1 EAtþ1 where t is the income period t (annual index), It, the relevant income statement value in t, and EAt, the average earning assets in t with loans lagged one semiannual period. In order to preserve as much of the source information as possible, the corrections are made to the semiannual income statements where possible. For the semiannual interpolations, observations from a year earlier and a year later are used rather than adjacent observations in order to preserve the seasonal component of the series. However, the interpolation method described by Eq. (A.1) results in the loss of some of the cyclical component of the series. In a small number of cases where there are sequential anomalies, interpolations are performed analogously for 2 or 3 successive periods. Extraordinary income statement values. Some observations contain exceptionally large values for total revenue, often accompanied by high values for ‘‘expenses and taxes’’ or ‘‘losses and premiums.’’ These anomalies are

100

SCOTT A. REDENIUS

more common for reserve cities, particularly in the West, and appear to have been associated with mergers and other types of bank restructuring. In each case, values for the affected variables are replaced with interpolated values computed with Eq. (A.1). The Gold Standard Act. The Act of 14 March 1900 authorized the exchange of bonds of the Loans of 1904, 1907, and 1908–1918 into Consols of 1930. The older bonds had higher coupon rates and traded at substantial premiums. To facilitate the conversions, Congress allowed the Treasury to pay holders of the old bonds a cash premium when exchanged for the new bonds. National banks exchanged most of the eligible bonds held to secure circulation and deposits between passage of the Act and 31 December 1900, when the Treasury halted further exchanges. The bulk of the exchanges were completed by 30 June 1900.37 Since the impact on total revenue and losses cannot be reliably estimated, the income statements for 1900:I are replaced with interpolated values computed with Eq. (A.1). No adjustments are made to the 1900:II income statements. Interpolations, 1963–1964. The figures in the 1963 and 1964 income statements were reported in millions rather than thousands of dollars, resulting in some loss of relevant information. Loan rates for Delaware are interpolated from the 1962 and 1965 values. In addition, loan losses and transfers to valuation reserves are interpolated for all reported areas. Additional interpolations. Loan rates are also interpolated for a handful of cases where there is insufficient information to implement corrections for changes in the operational status of large banks or where large reported loan losses or changes in the operational status of banks that were unrepresentative for their reported area created spikes in the loan rates computed with Eq. (1).

The loan rates in the table below are weighted averages of the annual state-level series. Through 1938, the loan periods cover roughly the given calendar year. Loan periods lag the income periods (Table 4) by six months. From 1939, the loan periods cover the second half of the previous year and the first half of the listed year. For example, the 1939 rates are for the semiannual loan periods 1938:II and 1939:I.

East United States

East

1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904

6.44 6.47 6.30 5.91 5.65 5.38 5.12 5.33 5.23 5.17 4.92 5.01 5.00 5.25 5.17 5.14 4.64

5.47 5.46 5.42 5.34 4.93 4.73 4.68 4.16 4.56 4.56 4.31 4.15 4.37 4.28 4.81 4.61 4.74 3.85

Midwest

West

New Middle Upper Eastern Western Old Eastern Western England Atlantic South South Lower South Lower South Midwest Northwest Plains Plains West Mountain Pacific 5.43 5.58 5.35 5.19 4.65 4.55 4.59 3.92 4.22 4.41 4.20 4.11 4.41 4.45 4.06 4.28 4.29 3.96

5.48 5.40 5.45 5.41 5.07 4.82 4.73 4.27 4.73 4.63 4.35 4.17 4.36 4.23 5.06 4.71 4.87 3.83

8.33 8.67 8.78 8.30 7.69 7.90 7.24 7.67 7.78 7.43 7.70 7.85 7.86 7.86 7.47 7.41 7.23 7.10

6.93 7.20 7.21 6.82 6.55 6.48 5.76 6.36 6.33 5.93 6.06 6.39 6.27 6.25 5.82 5.81 5.36 5.33

8.47 8.94 9.23 8.63 7.59 7.96 7.27 7.58 8.61 8.22 8.22 8.43 8.65 8.09 7.27 7.75 7.73 7.44

10.85 11.00 10.94 10.00 9.10 9.66 8.99 9.25 9.15 8.95 9.55 9.33 9.33 9.51 9.08 8.78 8.86 8.61

7.73 7.61 7.63 7.14 6.81 6.34 5.96 6.03 5.85 5.71 6.06 5.43 5.28 5.29 5.10 5.11 4.92 4.86

6.97 6.76 6.89 6.87 6.28 5.89 5.53 5.68 5.52 5.39 5.59 4.94 4.99 4.99 4.67 4.80 4.59 4.45

7.79 8.10 8.10 6.72 7.02 6.21 6.11 6.13 5.92 5.68 6.20 5.63 4.92 5.12 5.31 5.01 4.89 4.93

11.90 11.38 10.74 9.45 9.35 9.26 8.13 8.21 8.24 8.30 9.27 8.52 8.44 8.02 7.65 7.52 7.18 7.21

10.35 10.01 10.04 9.51 8.81 8.66 7.27 8.19 8.29 8.08 8.78 8.39 7.71 7.35 7.47 7.89 6.61 6.75

10.68 10.46 10.81 10.09 9.22 9.27 7.03 8.83 9.40 8.97 9.44 9.07 8.02 7.81 8.12 8.93 7.35 7.54

10.01 9.58 9.29 8.89 8.36 7.96 7.55 7.45 6.98 7.06 8.10 7.78 7.42 6.92 6.88 7.06 6.12 6.24

101

Year

South


APPENDIX B

102

APPENDIX B (Continued ) East

South

Midwest

West

United New Middle Upper Eastern Western Old Eastern Western Year States East England Atlantic South South Lower South Lower South Midwest Northwest Plains Plains West Mountain Pacific 5.00 5.22 5.11 5.29 5.79 5.77 5.74 6.13 6.12 6.00 6.29 6.05 6.03 5.98 6.93 6.71 6.21 6.29 6.16 5.87 6.02 5.86 5.81 6.09 6.06 5.19 4.94 4.72

4.33 4.71 4.69 4.53 5.03 4.93 4.96 5.34 5.43 5.28 5.65 5.25 5.44 5.39 6.18 6.28 5.68 5.70 5.66 5.31 5.58 5.26 5.26 5.69 5.63 4.69 4.34 4.38

4.42 4.61 4.89 4.48 5.19 4.92 4.71 5.40 5.43 5.30 5.75 5.22 5.41 5.00 5.82 6.33 5.80 5.66 5.48 5.26 5.38 5.09 5.27 5.87 6.16 4.59 4.35 4.41

4.31 4.73 4.65 4.54 4.99 4.94 5.02 5.32 5.42 5.27 5.63 5.25 5.44 5.47 6.25 6.27 5.66 5.71 5.70 5.33 5.62 5.30 5.25 5.65 5.51 4.71 4.33 4.37

7.24 7.05 6.34 7.21 7.63 7.27 7.31 7.63 7.43 7.25 7.72 7.83 7.43 7.11 8.28 7.42 7.07 7.37 7.08 6.98 7.01 6.71 6.72 6.88 6.62 6.02 5.73 5.64

5.55 5.33 5.22 5.58 6.17 6.14 5.86 6.20 6.01 5.94 6.22 6.56 6.34 6.14 6.96 6.30 6.27 6.58 6.40 6.28 6.28 6.23 6.24 6.31 6.14 5.71 5.56 5.39

7.92 7.17 6.56 7.32 7.80 7.43 7.52 7.47 7.25 7.11 7.78 7.57 7.43 7.20 8.53 8.25 7.15 7.39 7.33 7.21 7.14 6.45 6.61 6.76 6.21 5.58 5.29 5.28

8.46 8.48 7.21 8.75 8.98 8.29 8.65 9.12 8.87 8.64 9.17 9.09 8.33 7.88 9.31 8.00 7.78 8.09 7.62 7.56 7.64 7.29 7.21 7.44 7.23 6.47 6.06 6.01

5.03 5.05 5.05 5.36 5.84 6.09 5.90 6.33 6.20 6.19 6.38 6.24 6.09 6.02 7.13 6.81 6.35 6.33 6.19 5.85 5.86 5.98 5.86 6.19 6.24 5.45 5.39 4.44

4.61 4.71 4.68 4.89 5.50 5.89 5.39 5.80 5.75 5.65 5.81 5.70 5.75 5.72 6.82 6.55 5.98 6.04 5.99 5.63 5.72 5.83 5.70 6.01 6.08 5.25 5.30 4.13

5.08 4.90 5.21 5.61 5.79 5.97 6.17 6.65 6.43 6.38 6.63 6.56 6.17 6.07 7.35 6.93 6.61 6.37 6.23 5.92 5.74 5.95 5.86 6.32 6.28 5.52 5.25 4.92

7.36 7.27 6.62 7.25 7.60 7.31 7.96 8.31 8.02 8.49 8.73 8.21 7.51 7.26 7.95 7.77 7.54 7.62 7.14 6.93 6.99 6.96 7.00 7.14 7.21 6.71 6.30 6.08

6.82 6.51 6.00 7.27 7.62 6.80 6.89 7.28 7.15 7.07 7.37 7.18 6.77 6.97 7.82 7.22 6.83 7.05 6.74 6.67 6.84 6.95 6.61 6.38 6.67 5.72 5.54 5.50

7.86 8.17 7.10 8.28 8.87 7.93 8.79 8.73 8.95 8.55 9.24 8.91 8.27 8.10 8.86 8.03 7.67 7.55 7.18 7.03 7.34 7.12 7.06 7.40 7.42 6.55 6.41 6.41

6.21 5.62 5.37 6.67 6.92 6.25 6.06 6.66 6.39 6.43 6.53 6.40 6.04 6.42 7.33 6.83 6.44 6.82 6.56 6.54 6.67 6.90 6.51 6.17 6.53 5.59 5.42 5.39

SCOTT A. REDENIUS

1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932

4.41 4.08 4.09 3.94 3.66 3.82 3.98 3.98 3.94 3.28 3.11 2.97 2.70 2.92 3.45 3.69 3.60 4.04 4.24 4.26 4.43 4.34 4.99 5.18 5.24 5.10 5.59 5.80 5.65 5.88 5.90 5.96 6.00 6.12 6.20 6.65

4.55 3.98 4.05 3.92 3.64 3.73 3.97 3.95 3.90 3.17 3.07 3.23 3.08 3.38 3.65 3.88 3.88 4.39 4.45 4.52 4.79 4.72 5.00 5.19 5.30 5.32 5.78 6.02 5.88 5.99 5.98 6.07 6.17 6.47 6.61 7.23

4.38 4.11 4.10 3.94 3.67 3.84 3.99 3.99 3.96 3.31 3.12 2.91 2.61 2.81 3.40 3.65 3.54 3.96 4.20 4.20 4.36 4.26 4.98 5.18 5.22 5.04 5.55 5.74 5.59 5.85 5.89 5.93 5.96 6.06 6.13 6.55

5.88 5.80 5.83 5.99 5.58 5.43 5.44 5.46 5.37 4.36 4.16 3.98 3.79 4.29 4.67 4.88 4.75 5.16 4.97 5.27 5.29 5.17 5.48 5.53 5.74 5.79 6.15 6.23 6.02 6.24 6.25 6.36 6.33 6.47 6.70 7.16

5.47 5.46 5.46 5.54 5.30 5.22 5.24 5.13 5.14 4.23 4.08 4.07 3.95 4.57 4.83 4.86 4.76 5.20 5.01 5.34 5.43 5.31 5.68 5.77 5.91 5.83 6.26 6.32 6.06 6.34 6.13 6.40 6.34 6.54 6.69 7.00

5.52 5.11 4.80 5.41 4.82 4.82 4.94 5.34 5.07 4.05 3.84 3.44 3.29 3.87 4.30 4.55 4.55 5.13 4.90 5.29 5.45 5.39 5.81 5.77 6.06 6.05 6.48 6.42 6.21 6.46 6.54 6.61 6.68 6.75 6.94 7.51

6.36 6.38 6.60 6.58 6.15 5.87 5.81 5.77 5.69 4.61 4.36 4.19 3.92 4.33 4.74 5.04 4.82 5.15 4.98 5.23 5.15 5.02 5.26 5.32 5.53 5.66 5.95 6.08 5.92 6.09 6.17 6.21 6.15 6.29 6.57 7.06

4.66 4.44 4.70 4.55 4.17 4.28 4.45 4.37 4.28 3.48 3.45 3.35 3.09 3.41 3.81 3.90 3.89 4.33 4.47 4.50 4.63 4.55 4.92 5.14 5.23 5.11 5.72 5.84 5.56 5.85 5.82 5.73 5.93 6.19 6.34 6.81

4.42 4.32 4.55 4.45 3.98 4.09 4.28 4.24 4.18 3.40 3.40 3.22 2.92 3.26 3.67 3.77 3.78 4.24 4.39 4.40 4.56 4.45 4.85 5.10 5.19 4.98 5.66 5.79 5.48 5.81 5.77 5.74 5.89 6.16 6.27 6.76

4.89 4.37 4.59 4.39 4.20 4.25 4.37 4.18 4.12 3.39 3.37 3.40 3.26 3.48 3.85 3.87 3.93 4.38 4.45 4.57 4.69 4.63 4.91 5.11 5.23 5.23 5.74 5.85 5.65 5.84 5.79 5.45 5.92 6.22 6.42 6.85

6.13 5.62 6.12 5.68 5.68 5.87 5.96 5.74 5.43 4.32 4.01 4.38 4.38 4.85 5.12 5.20 4.67 4.86 5.20 5.17 5.06 5.19 5.48 5.51 5.71 6.01 6.17 6.26 6.00 6.21 6.28 6.21 6.33 6.49 6.78 7.15

5.44 5.15 5.43 5.40 5.33 5.38 5.42 5.49 5.55 4.75 4.40 4.50 4.47 5.20 5.35 5.02 4.85 5.29 5.31 5.38 5.57 5.19 5.56 5.64 5.60 5.72 6.34 6.33 6.12 6.44 6.40 6.45 6.40 6.62 6.76 7.66

6.76 6.80 7.13 6.60 6.35 6.26 6.29 6.22 6.15 4.99 4.80 5.00 4.99 5.60 5.69 5.54 5.24 5.63 5.57 5.78 5.79 5.69 6.17 5.99 6.00 6.14 6.47 6.57 6.36 6.61 6.57 6.53 6.50 6.73 6.98 7.49

5.31 5.00 5.26 5.27 5.21 5.28 5.32 5.40 5.46 4.71 4.34 4.43 4.39 5.14 5.30 4.95 4.79 5.23 5.26 5.32 5.53 5.12 5.45 5.58 5.53 5.65 6.32 6.28 6.07 6.40 6.36 6.43 6.38 6.60 6.71 7.69

103

4.84 4.59 4.73 4.65 4.39 4.50 4.63 4.63 4.59 3.80 3.63 3.53 3.31 3.70 4.18 4.27 4.18 4.62 4.68 4.76 4.90 4.74 5.20 5.35 5.42 5.39 5.92 6.03 5.81 6.08 6.07 6.09 6.14 6.31 6.45 7.00


1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968

104

APPENDIX B (Continued ) East

South

Midwest

West

United New Middle Upper Eastern Western Old Eastern Western Year States East England Atlantic South South Lower South Lower South Midwest Northwest Plains Plains West Mountain Pacific 1969 1970 1971 1972 1973 1974 1975

7.84 7.98 7.48 7.46 8.97 10.23 8.35

7.75 7.76 7.08 7.01 8.75 10.13 7.98

8.21 8.39 7.44 7.44 9.09 10.40 8.44

7.66 7.65 7.02 6.93 8.69 10.09 7.90

8.08 8.40 7.95 8.02 9.35 10.32 8.64

7.89 8.21 7.90 7.95 9.19 10.07 8.47

8.58 8.80 8.33 8.58 9.91 10.54 8.50

7.91 8.28 7.75 7.73 9.11 10.35 8.84

7.54 7.76 7.33 7.33 8.91 10.18 8.29

7.51 7.67 7.26 7.29 9.00 10.30 8.27

7.52 7.90 7.32 7.23 8.57 9.94 8.17

7.92 8.27 8.12 7.95 8.81 9.50 8.78

8.12 8.20 7.81 7.67 8.94 10.33 8.60

8.20 8.68 8.47 8.39 9.25 10.03 9.02

8.10 8.10 7.66 7.51 8.86 10.40 8.50

SCOTT A. REDENIUS

THE NET EFFECT OF RAILROADS ON STATURE IN THE POSTBELLUM PERIOD Ebru Guven Solakoglu ABSTRACT This study utilizes a different approach to examine the effect of transportation on the average height of people in the postbellum period, simply by modeling the change in the marketable surplus of protein. The results show that railroad development had a positive effect on average height from 1867 to 1887, and after this period, its effect was negligible. This study suggests that the factors with negative effects – such as urbanization, industrialization, and infectious diseases – dominated the positive effects of railroad development leading actual average height to decrease from 1867 to 1887.

1. INTRODUCTION Biological measures of the standard of living, such as calorie or protein intake, reflect the influence of variation in consumption. Such variation affects outcomes such as height, weight, or mortality (Haines, 1998; Steckel, 1995, 1999; Haines, Craig, & Weiss, 2003; Craig & Weiss, 1998). Height is determined by genetics and environment, where environment can be counted as the main factor that determines differences in average height Research in Economic History, Volume 24, 105–117 Copyright r 2007 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0363-3268/doi:10.1016/S0363-3268(06)24003-5

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EBRU GUVEN SOLAKOGLU

between populations. In other words, two children of the same community who would have the same height given the same economic conditions may produce different heights in different economic environments (Eveleth & Tanner, 1990). Similarly, transition from high to low levels of mortality in developed nations is a result of survivorship that is determined by health, which is mostly related to income and nutritional intake.1 There are many factors that determine stature, including diet, wealth, disease, and work intensity during the growing years. Poverty results in retarded growth and income growth results in an increase in heights due to better and/or changed diet, along with better housing and medical care (Steckel, 1999). Nonetheless, there is a puzzling trend in the United States during the second quarter of the nineteenth century: stature fell during a rapid economic expansion accompanied by rising levels of income. This trend, however, reversed direction after the 1870s (Komlos, 1987; Steckel, 1995; Costa & Steckel, 1997).2 There are many arguments to explain the paradox of decreasing average height with increasing levels of income. The decrease in average height may have resulted from other factors, such as urbanization, specialization, migration, mortality, the more rapid spread of disease because of transportation improvements, or deterioration in diet. Komlos (1987), for example, argued that urbanization and industrialization increased the demand for food relative to food production. As the labor force in agriculture grew more slowly, growth in calorie and protein intake decreased. Therefore, deterioration in diets decreased average height during this period. Ohio National Guard data support this argument by showing a 0.9 inch decrease in average height of the cohort born between 1880 and 1896 among residents in cities with population of 50,000 or more (Steckel & Haurin, 1994). Substantial migration to eastern and northeastern states affected nutritional intake, which in turn affected average height. In examining the railroad transportation effect on stature from 1867 to 1906, this study takes a different approach from that used in earlier studies. Our hypothesis is that railroads make basic nutrients more accessible and hence may have a positive impact on stature. Access to transportation lowers the cost of obtaining nutrition. But it also increases the incentives and opportunities for farmers to export, thus decreasing the domestic availability of nutrients from the surplus regions.3 Moreover, there are other ways in which railroad factors might negatively impact stature. In particular, railroads also increase migration that may make it easier for diseases to spread among regions.4 In our analysis, we assume these factors are constant or negligible5 and simply analyze the effect of railroads on

The Net Effect of Railroads on Stature in the Postbellum Period

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nutritional intake per person, and indirectly on stature. Furthermore, we try to account for farmers’ growing incentive to export as railroad mileage increased, to better understand changes in stature. Based on our findings, we hope to shed some light on the declining height paradox observed in the nineteenth century. The remainder of the study is organized as follows: the next section describes the data and explains the methodology and estimation methods. The following section explains the results and discusses changes in heights during the examined period for the United States; first, not accounting for international trade; second, accounting for international trade and the effects of increased exports in this period. Concluding remarks are presented in the last section.

2. DATA, METHODOLOGY, AND ESTIMATION To calculate marketable surplus and convert it into protein per person, we bring together statistics on total production of wheat, corn, cattle, and hogs for 48 states, imports and exports from 1866 to 1906, railroad mileage for each state, and population.6 We used the following information to calculate the amount of protein in wheat, corn, beef, and pork to find protein intake: 100 g of wheat bread produces 8.7 g of protein, whereas 100 g of corn produces about 4.9 g of protein. A 100 g of beef, on average, produces 29.7 g of protein, whereas 100 g of pork produces about 26.1 g of protein.7 We use the equation below to estimate the marketable surplus after subtracting on-farm consumption: M ¼ O CP age1g1 þ age2g2 þ age3 þ age4 þ age5 þ age6 where M is the marketable surplus,8 O the total production in bushels of corn or wheat, or in pounds for beef or pork, C the amount of consumption of the product per person per year, P the proportion of rural population to total population, g the adult equivalence ratios for the age groups.9 The steps to convert the total production of these products to a measure of marketable surplus are as follows. First, we convert the total production to a numeraire to standardize the measure of physical production. Second, we subtract the corn consumption of livestock and the consumption of farm household members.10 Consumption estimates for a typical man in one year are 8.0 bushels of corn, 1.6 bushels of wheat, 50 pounds of beef, and 150 pounds of pork. Consumption by age is also taken into account by specifying adult equivalence ratios. For example, for the first age group the

108


equivalence ratio is 0.15, for the second age group it is 0.75, and for the rest it is 1.00. After calculating the marketable surplus for each product, and further adjusting the marketable surplus for corn by subtracting the consumption for livestock production, we convert the surplus (in bushels) into a surplus in 100 g of protein for each state by multiplying the converted surplus by protein per unit of output.11 This procedure defines the total protein intake from consuming this output in each state of the United States. To obtain the average protein intake per person in a state, total protein intake in a state is divided by the state population within that period. In the next step, we regress protein intake per person in grams on railroad mileage pooling the states of the United States for the period in question with the following equation:12 Prit ¼ a þ bRit þ it

(1)

where Pri is the total amount of 100 g of daily protein intake per person obtained from the output in a state, Ri the total railroad mileage in a state, and t a time index. The error term, eit, is assumed to have zero mean and constant variance. The coefficient we obtained from this regression defines the effect of railroad development on (100 g) daily protein intake per person. Multiplying this coefficient by the effect of daily protein intake on average height gives an estimate for the effect of railroad development on average height. Eq. (2) shows this effect. @H @H @Pr @R @Pr @R

(2)

The first part of the right-hand side of the equation represents the change in average height in inches (denoted as H) as a result of a change in daily protein intake, which is equal to 0.1829 according to Craig and Weiss (1998). They found that an increase of 100 g of protein intake per day increased average height by 0.1829 inches for white recruits of the Union Army who enlisted between 1861 and 1865.13 The second part of the righthand side of the equation represents the coefficient estimate that we get from the regression of daily protein intake per person on railroad mileage. The multiplication of these two coefficients gives us an estimate for the effect of railroad development on average height for the examined period. We have two samples to examine the effect of railroad development on the behavior of average height in the United States. Our investigation starts with the sample that consists of 48 states of the United States. We


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then examine the same sample after making adjustments for imports and exports.14 We allow parameters to vary across states, which was supported by the observed heterogeneity. In addition, F-tests and LM tests favor this specification over constant coefficient specification (OLS). In other words, we reject homogenous consumption behavior (daily protein intake per person) across states. Hence, we need to modify the model to allow heterogeneous behavior across states.15 The effect of railroad development on average height is examined for three model specifications: OLS, fixed, and random effects; and three periods: 1867–1906, 1867–1886, and 1887–1906. There are three reasons for investigating the change in average height as a function of railroad development for two subperiods as well as the whole period. First, we observe that annual average track construction after 1886 was greater than in earlier years. In addition, for western states, the development of railroads only began to have an impact on markets after the 1880s. Second, as a result of monopolistic power exercised by companies in rail transportation in many regions, a law was passed in 1887 to prevent unjust discrimination by the railroads. The Interstate Commerce Act of 1887 prohibited the pooling of traffic and profits, required freight rates to be publicized, and made it illegal for a railroad to charge more for a short haul than for a longer one. The last reason accounted for the significant fluctuations in average height before 1887. Consequently, we run two separate regressions, allowing the parameters to differ between the two time periods. This gives us a chance to compare average height between the two periods. For both subperiods, the above equation is estimated by ordinary least squares, and with fixed-and randomeffect specifications.

3. NET EFFECT FOR THE UNITED STATES The estimates for daily protein intake per person from the marketable surplus of all products obtained from the regressions are multiplied by the estimated value of the change in average height as a result of the change in (100 grams) daily protein intake per person. Thus, we obtain the effect of railroad development on average height. Table 1 reports the results for all periods next to US rows. After adding up the effects of railroad development on average height for four agricultural commodities, we find that average height increased about 0.47 inches

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Table 1.

The Effect of Railroad Development on Average Height. Effect of Railroad Development on Average Heights (qH/qR) from Wheat

1867–1906 US 0.068898R US adjusted 0.076708R 1867–1886 US US adjusted

Corn

Beef

Porka

Total

0.476784F, 0.447812F,

0.025697R 0.023923R

0.035300F, 0.035537F,

0.468882 0.430565

0.368708R, 1.028904R, 0.211524R, 0.978442R,

0.053480R 0.052218R

0.142863R, 0.142589R,

1.593955 1.384772

1887–1906 US 0.264748R US adjusted 0.168451R

0.762565R, 0.420908R, 0.142077F, 0.065167 0.731289R, 0.424090R, 0.141565F, 0.002817

Note: qH/qR represents the estimate for the effect of railroad development on average height; F, R denote Hausman w2 statistics favoring either fixed or random effects, respectively. Significance at 1% level. a Hog imports are not reported in the bulletins, probably because of very few hog imports into the United States.

on this account from 1867 to 1906. Large amounts of corn accessibility seem to be the reason for increases in average height in this period. From 1867 to 1886, estimates for daily protein intake per person from marketable surplus of all products are positive, suggesting a positive marginal effect of railroad development on protein accessibility from these four agricultural products. However, the effect of beef is not statistically significant. These results suggest that railroad development is estimated to increase daily protein accessibility per person by 202 g from wheat, 563 g from corn, 29 g from beef, and 78 g from pork. High levels of corn accessibility explain the biggest portion of the increase in average height, followed by wheat accessibility. The total effect of railroad development on average height is calculated as 1.6 inches. This finding is somewhat surprising given that the average height of whites actually decreased by 0.75 inches in this period. From 1887 to 1906 the results were reversed in sign for all products but corn. The estimated changes in average height from railroad development for this period are calculated as 0.26, 0.76, 0.42, and 0.14 inches for wheat, corn, beef, and pork, respectively, whereas beef is once again the only product that does not provide a statistically significant effect on daily protein intake per person. These results suggest that average height decreased because of the negative effect of railroad development on daily protein


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accessibility per person from all commodities but corn. Wheat and meat accessibility decreased dramatically in this period. Yet the accessibility of corn was still the most important factor that helps to keep up with basic needs in terms of the influence of daily protein intake on stature. Overall, railroad development caused average height to decrease by 0.07 inches in the United States from 1887 to 1906.16 This finding is also interesting since many studies show that average height of whites increased about 1 inch in this period. Accounting for the positive impacts of railroads on markets, as well as the government attempts to prevent unjust monopolistic discrimination by the railroads after the mid 1880s, it is possible to argue that the marginal productivity of railroads on health was increasing in the second period. Empirical works support this argument. For example, Guven Solakoglu and Goodwin (2005) studied the effects of reduced transaction costs on the price behavior of wheat and corn during the same period. They found that the relative prices of wheat and corn converged faster in the second period due to increased efficiency. Depending on their results we can suggest other factors that improved public health and personal hygiene over the period of the study, which had stronger effects in the second period, possibly along with other unknown factors may have led average heights to increase. In summary, our results suggest that railroad development may not be a reason for the increase in average height, taking into account that these results only capture the marginal impact of the railroad. Notice that our findings do not take into account the effect of international trade. However, in all periods of our analysis, farmers had a great incentive to export in order to increase their income. The United States became a net exporter of all four products, especially wheat, in all periods in question. If we do not account for the trade effect, we overestimate the marketable surplus, and therefore protein accessibility in the nation. This affects the estimates of the marginal effect of railroad development on average height. Therefore, in the following section, our goal is to correct the estimates by including the amounts of imports and exports of wheat, corn, beef, and pork in our analysis.

4. NET EFFECT AFTER ACCOUNTING FOR INTERNATIONAL TRADE In order to account for international trade, we subtract the amount of net exports of four products from their production levels and then calculate the

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marketable surplus for each state.17 Including the net exports in our analysis, and hence correcting the amount of accessible marketable surplus for each state, suggests that our estimate of the marketable surplus of wheat, and consequently our econometric estimates for this commodity, are the most heavily influenced.18 This is because the country is an important exporter of wheat in all periods in question. The rows indicated as US adjusted in Table 1 report the results for all periods. After subtracting net exports from US wheat production from 1867 to 1886, the effect of wheat accessibility on average height as a result of railroad development is an increase of 0.21 inches. After 1886, however, this effect suggests a decrease in average height by 0.16 inches, although the estimate is not statistically significant. The United States was also a net exporter of corn and beef in all periods. Daily protein intake per person from net exports of corn and beef as a result of railroad development is estimated to have affected average height negatively only by 0.03 and 0.002 inches, respectively, from 1867 to 1906. The effect of trade on pork is also minor for all periods.19 Finally, after correcting for the influence of international trade, the estimated change in average height from railroad development is 0.43 inches from 1867 to 1906, 1.38 inches from 1867 to 1886 and 0.0028 inches from 1886 to 1906.20

5. CONCLUDING REMARKS In this study, we examine the impact of railroad development on US average height changes from 1867 to 1906. Our results show that railroad development led to an increase in average height of 0.43 inches from 1867 to 1906, and 1.38 inches from 1867 to 1886. However, the effect of railroad development on average height is negligible after 1887. These findings seem to be inconsistent with the actual trends in US average height, especially from 1867 to 1887. Earlier studies indicated that average height of whites decreased 0.8 inches, from 67.4 inches in 1865 to 66.6 inches in 1887. However, from 1887 to 1906, average height increased about 1 inch, from 66.6 to 67.6 inches (Costa & Steckel, 1997; Haines, 1998). Railroad development implies reduced transportation costs, which makes it possible to obtain nutrition at lower cost. But it also allows individuals to travel more easily, hence increasing the ease of spreading disease. Using railroad trackage miles as a proxy for railroad development, however, does not capture the movement of people and diseases in the model. Thus, finding significant effect of railroad development on average height does not tell us


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whether we are capturing the quality or the quantity of its effect on nutritional intake.21 Therefore, our findings suggest that we should put greater weight on the negative effects of urbanization, industrialization, and infectious diseases on nutritional intake in explaining the paradox. This conclusion is consistent with that of many other studies.22 Thus, our results may suggest that negative externalities coming from railroad development, such as more rapid spread of disease, remain important factors in explaining the paradox. Infectious diseases created by interregional trade, migration, and public schooling may decrease nutritional intake and increase mortality. This may also help to explain the relatively low average height in the northeastern states. If the negative externalities of railroad development dominate its effect on increased daily protein intake per person, then the total effect of railroad development on average height would be negative. After all, our results complement Komlos’ and others arguments, and thus they suggest a greater emphasis on their arguments while explaining the paradox.

NOTES 1. See Steckel (1986) for the impact of dietary deficiencies on slave children; Fogel (1994) for the ‘‘disappearing labor force’’ in France in the 1790s due to low levels of consumption; Costa and Steckel (1997) for a discussion on relationships between economic growth and health as measured by stature. 2. There is a substantial literature explaining the so-called ‘‘Antebellum Puzzle.’’ One of the explanations was an increase in inequality (Steckel, 1995); another explanation was rapid economic growth and a sectoral shift in production during industrialization (Komlos, 1987); and another one is access to transportation (Craig & Weiss, 1998). 3. See Craig and Weiss (1998) for their findings that the negative effects of disease and sale of surpluses dominate the positive benefits of decreased cost of access to nutrition. 4. Craig and Weiss (1998) investigated the correlation between adult height and the local availability of nutrition considering transportation access in the form of navigable waterway. Their results suggested that if a county was on a navigable waterway, then heights were decreased in that county. They argued that transported diseases or the incentive to export might be the reason for finding a negative relationship between height and transportation. 5. It is difficult to incorporate the influence of migration into our analysis because of lack of data. 6. Statistics are provided from Poor (1873, 1881, 1890), Bureau of the Census (1989), and Department of Agriculture (1907a, 1907b, 1908, 1910a, 1910b). Population data include total and urban population, and population for six age groups of each state for every 10 years. Therefore, growth rates between decennial censuses were converted into annual rates.

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7. We used cracked wheat bread to calculate protein intake from wheat. We averaged two food groups to calculate protein intake from corn: cornbread or johnnycake and cornmeal; 100 g of each produce 8.7 and 1.1 g of protein, respectively. We averaged three items to calculate protein intake from beef: dried-chippeduncooked, flank steak-braised, and ground-beef-cooked; 100 g of each produce 34.3, 30.5, and 24.2 g of protein, respectively. We averaged two items to calculate protein intake from pork: Canadian bacon-cooked and loin roast or chops, where 100 g of each produce 27.6 and 24.5 g of protein, respectively (Food for Health, 1986). 8. We use marketable surplus, output quantity disposed of in the marketplace after on-farm consumption is accounted for, as a measure of average height since height is a function of access to resources (Steckel, 1995). Since height is a measure of consumption of basic necessities, a measure of marketable surplus is widely used to calculate protein intake per person (Craig 1993; Craig & Weiss, 1998). See Atack and Bateman (1987) for justification of the technique. 9. ‘‘Age 1’’ stands for ages under 5; ‘‘age 2’’ for ages between 5 and 14; ‘‘age 3’’ for ages between 15 and 24; ‘‘age 4’’ for ages between 25 and 44; ‘‘age 5’’ for ages between 45 and 64; and ‘‘age 6’’ for ages over 64. 10. The corn equivalent conversion rates for wheat and corn are 1.00. The corn equivalent bushels needed for cattle and hog production are 1.50 and 5.00 per pound, respectively. See Craig’s (1993) study for more information and conversion rates. 11. For example, 100 bushels of corn is equal to 254 100 g (25,400 g), whereas 254 100 g of corn includes 1244.6 g of protein. 12. Although railroads were the most important form of transportation, taking into account that the development was almost completed during the examined period, they were not the only form of transportation that substantially affected protein intake. Canals and steamboats were also significant in making nutrition available. Craig and Weiss (1998) investigated the correlation between adult height and the local availability of nutrition in infancy of white men who were born between 1838 and 1842 using the indicators of nutrition, measured by protein and calories, wealth per capita, and transportation access in the form of navigable waterway. They found that both wealth and nutrition variables have positive and statistically significant effects on adult height. However, they also found a negative relationship between height and transportation for that period. Their results suggested that if a county was on a navigable waterway, then heights were decreased in that county. 13. Although we used the figure of 0.1829 from Craig and Weiss (1998) as an estimate of the effect of an increase of 100 g of protein intake per day on heights, we undertook a set of simulations with this figure to find plausible upper and lower bounds by constructing a simulation one standard deviation from the point estimate using the standard error given in the study. These bounds are given in the results section. 14. We also pooled the data into five regions and repeated the exercise for each region in order to capture the differences between these regions. Our analyses for different regions help us to understand the marginal effect of railroad development on production decisions, and hence the marketable surplus and protein accessibility in these regions. For example, the estimates show that farmers in the northeastern states moved away from wheat and corn production toward pork production. If we assume that the entire marketable surplus was consumed, then our results suggest


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that average height in this region decreased 0.15 inches as a result of railroad development from 1867 to 1907. Average height was only slightly affected by railroad development in this region from 1867 to 1886; however this effect was very strong from 1887 to 1906 and the average height was estimated to have decreased almost by 0.5 inches. This was the highest decrease in average height as a result of the negative marginal effect of railroad development on protein accessibility when compared with other regions. This finding is not surprising when we consider the data for the Ohio National Guard following the Civil War. We did not report the results because pooling the data for five regions implicitly assumed that every region included intraregional trade, but not interregional trade. Obviously, this assumption was not reasonable when we argued that railroad development integrated scattered railways and brought regional markets together into a national market. By assuming no interregional trade in our analysis, we underestimated the change in average height for the net importer regions. Equally, we overestimated the change in average height for the net exporter regions. The results are available upon request. 15. Although random-effects formulation treats coefficients as random with common means, and therefore it will not cause any aggregation problem, fixed-effects model treats these coefficients as fixed and different, and will imply a complicated aggregation problem for consumption behavior. Since our data includes all the states of the United States, there should not be aggregation problem when using fixedeffects model for studying consumption behavior in the entire United States. Indeed, we specify the model through Hausman w2 statistical tests. 16. The lower and upper bounds as a result of our sensitivity analyses are as follows: For all periods an increase of 0.37 and 0.57, for 1867–1886 an increase of 1.25 and 1.93, for 1887–1906 a decrease of 0.05 and 0.08. 17. The data for exports and imports of farm products from 1867 to 1906 consists of aggregate US exports and imports. Unfortunately, exports and imports data for states do not exist. Therefore, to continue our analysis by using the advantages of panel data, we used population proportions for each state as an approximation to obtain state-level exports and imports. 18. We tested for heteroskedasticity as before, and the test results suggest using GLM over OLS to get more efficient estimates for all periods and for all commodities. The Hausman statistics are also not affected from opening to trade, and still favor the fixed-effects model only for corn and pork from 1867 to 1886 and for pork after 1886. For the rest, Hausman statistics favor the random-effects model. 19. As hog imports were not reported in the bulletins, they are not included in calculations of marketable surplus. The data might not be reported because there may not exist any hog imports in that period. If they exist, then marketable surplus and protein accessibility from pork are underestimated. 20. The lower and upper bounds as a result of our sensitivity analyses when international trade is accounted for are as follows: for all periods an increase of 0.34 and 0.52, for 1867–1886 an increase of 1.09 and 1.68, for 1887–1906 a decrease of 0.0022 and 0.0034. 21. Craig and Weiss (1998), for example, did not find any influence of quantity of nutritional intake on height regardless of the combination of nutrition taken. 22. Haines et al. (2003), for example, found that countries with transportation access had significantly higher death rates than those without such access.

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ACKNOWLEDGMENTS I would like to offer special thanks to Professor Lee A. Craig for providing his valuable advice, guidance, and encouragement. I also thank Alex Field and an anonymous referee on helpful comments and suggestions about the analysis.

REFERENCES Atack, J., & Bateman, F. (1987). To their own soil: Agriculture in the antebellum north. Ames: Iowa State University Press. Costa, D. L., & Steckel, R. H. (1997). Long-term trends in health, welfare, and economic growth in the United States. In: R. H. Steckel & R. Floud (Eds), Health and welfare during industrializationm, NBER Project Report (pp. 47–90). Chicago: University of Chicago Press. Craig, L. A. (1993). To sow one acre more. Baltimore, London: The John Hopkins University Press. Craig, L. A., & Weiss, T. (1998). Nutritional status and agricultural surpluses in the antebellum United States. In: J. Komlos & J. Baten (Eds), The biological standard of living in comparative perspective (pp. 190–207). Stuttgart: Franz Steiner Verlag. Eveleth, P. B., & Tanner, J. M. (1990). Worldwide variation in human growth. Cambridge: Cambridge University Press. Fogel, R. W. (1994). Economic growth, population theory, and physiology: The bearing of long-term processes on the making of economic policy. The American Economic Review, 84(3), 369–395. Food for Health (1986). A nutrition encyclopedia. California: Audrey Ensminger, Pegus Pr. Guven Solakoglu, E., & Goodwin, B. K. (2005). The effects of railroad development on price convergence among the states of the USA from 1866 to 1906. Applied Economics, 37(15), 1747–1761. Haines, M. R. (1998). Health, weight, nutrition, and mortality: Evidence on the ‘‘antebellum puzzle’’ from union army recruits in the middle of the nineteenth century. NBER Working Paper series on historical factors in long run growth, 107. Haines, M. R., Craig, L. A., & Weiss, T. (2003). The short and the dead: Nutrition, mortality, and the ‘‘antebellum puzzle’’ in the United States. The Journal of Economic History, 63(2), 382–413. Komlos, J. (1987). The height and weight of west point cadets: Dietary change in antebellum America. Journal of Economic History, 47(4), 897–927. Poor, H. V. (1873). Manual of the railroads of the United States for 1872–1873. New York: H.V. & H.W Poor. Poor, H. V. (1881). Manual of the railroads of the United States for 1882. New York: Sampson Low and Co. Poor, H. V. (1890). Introduction to Poor’s manual of the railroads of the United States for 1890. New York: H.V. & H.W Poor. Steckel, R. H. (1986). A peculiar population: The nutrition, health, and mortality of American slaves from childhood to maturity. Journal of Economic History, 46(3), 712–741.


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Steckel, R. H. (1995). Stature and the standard of living. Journal of Economic Literature, 33, 1903–1940. Steckel, R. H. (1999). Nutritional status in the colonial American economy. William and Mary Quarterly, 3rd series, 56(1), 31–52. Steckel, R. H., & Haurin, D. R. (1994). Health and nutrition in the American midwest: Evidence from the height of Ohio national guardsmen, 1850–1910. In: J. Komlos (Ed.), Stature, living standards, and economic development: Essays in anthropometric history (pp. 117–128). Chicago: University of Chicago Press. U.S. Bureau of the Census (1989). Historical statistics of the United States: Colonial times to 1970. Washington (Government printing office): Kraus International Publications. U.S. Department of Agriculture (1907a). Corn crops of the United States: 1866–1906, Bulletin 56, Bureau of Statistics, Washington (Government printing office). U.S. Department of Agriculture (1907b). Number and farm value of farm animals in the United States: 1867–1906. Bulletin 64, Bureau of Statistics, Washington (Government printing office). U.S. Department of Agriculture (1908). Wheat crops of the United States: 1866–1906. Bulletin 56, Bureau of Statistics, Washington (Government printing office). U.S. Department of Agriculture (1910a). Imports of farm products into the United States, 1851–1908. Bulletin 74, Bureau of Statistics, Washington (Government printing office). U.S. Department of Agriculture (1910b). Exports of farm products from the United States, 1851–1908. Bulletin 75, Bureau of Statistics, Washington (Government printing office).

GROWTH IN A PROTECTED ENVIRONMENT: PORTUGAL, 1850–1950 Pedro Lains ABSTRACT From 1850 to 1913, the Portuguese economy expanded slowly and diverged from the European core. In contrast, during the interwar period, Portugal achieved higher growth and partially caught up to the levels of labor productivity of Western Europe. Higher growth in Portugal after World War I occurred in a framework of protection and increasing levels of state intervention. Growth was due to structural changes that favored sectors with higher levels of factor productivity. Such changes were associated with growth in domestic demand and higher levels of investment, and were helped by sustained export levels, the continuation of essential imports, and the restoration of capital inflows.

1. INTRODUCTION A better understanding of the causes of European economic growth has much to gain from knowledge of the experiences of falling behind and catching up on the continent’s poor periphery. Theories of the causes of rapid industrialization have to be validated by investigating why peripheral Research in Economic History, Volume 24, 119–160 Copyright r 2007 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0363-3268/doi:10.1016/S0363-3268(06)24004-7

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countries remained behind during most of the nineteenth century and why that changed during the twentieth century. From the mid-nineteenth century to 1913, Portugal failed to get closer to the levels of income per capita and labor productivity of the front-runners, despite the fact that it was increasingly involved in the international economy. Between 1913 and 1950, Portugal’s growth experience changed in a remarkable way. Its rate of economic growth increased and, for the first time since industrialization began, the gap between it and the European core was partially bridged (though part of the recovery was due to the slowing down of economic growth in the European core after 1929). Thus, the Portuguese economy expanded slowly when more favorable external conditions developed, and expanded more rapidly when international economic conditions were less favorable. Portugal’s growth experience in the century up to 1950 was not unique. In fact, in a few number of peripheral countries, rapid growth and catching up was also first achieved in the interwar period, as a prelude to the period of higher growth during the golden age, although many such experiences were more limited in time because of political instability or civil wars.1 The change in the pattern of economic growth in the poor European periphery in the period between the wars has attracted little attention in the literature and has not been given sufficient recognition. But understanding it is most relevant for the discussion of explanatory models of European economic growth, as it contributes to the study of the effects of different levels of openness and state intervention on the growth of the less industrialized economies, as well as the effects in changes in capital flows. Historical evidence on the connection between growth fluctuations and changes in tariff policies in Europe is varied. Bairoch (1972, 1976) has argued that free trade hampered growth in France, Germany, and Italy after 1860 and that the return to protectionism in the 1880s was beneficial to growth. For him, tariff policy was particularly important for demand in the agrarian sectors, which still had a large share in the output of those countries; contrarily, free trade fostered growth only in Britain, because of the earlier start of industrialization and the larger share of industry in total output. O’Rourke (2000) confirms these findings for a larger sample of seven European countries, but he argues that tariffs impacted positively on growth by lowering the price of capital goods, relative to final goods, and thus fostering investment.2 Hadass and Williamson (2003) argue that the increase in the terms of trade for the world periphery between 1870 and 1914 – which they show in the paper – had a negative impact on growth in the periphery because it

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reinforced the comparative advantage of agriculture and thus penalized industry, which was the sector that carried growth in those years. In contrast, the observed increase in the terms of trade of the core countries reinforced their comparative advantage in industry and thus overall growth. These authors point out however that these effects were relatively small – accounting for about one-fifth of growth – and that growth was above all conditioned on the fundamentals inside each country. After World War I, terms of trade collapsed in all the regions they analyze, and they conclude that decline favored industrialization. Portugal, however, does not fit these patterns in at least two main respects. First, growth during the nineteenth century was led by the industrial sector and not by agriculture, despite the predominance of agrarian exports and the increase in her terms of trade. Second, Portugal’s terms of trade improved after World War I and growth in the following period occurred in both agriculture and industry.3 The reasons for higher growth during the interwar period in Portugal lie elsewhere, namely in structural changes that favored sectors with higher levels of factor productivity.4 This paper is structured as follows. The next section describes the main fundamentals of the Portuguese economy in the period under analysis, discusses the economic and financial impact of World War I, and provides an explanation for the stabilization of the monetary and financial indicators from mid-1920s onwards. We shall see that the government’s stabilization policies were associated with the improvement of the balances in the external accounts. Section 3 discusses the main trends in economic growth and structural change in the period from 1850 to 1950. We show there that the share of the industrial sector expanded fastest during the period of slower economic growth to World War I. After the war, the shares of industry and agriculture remained virtually constant and the economy expanded more rapidly. These shifts were associated with changes in tariff policy and state intervention. Section 4 analyses the growth of factor productivity and shows that the contribution of agriculture to overall growth after 1914 was due to higher growth of investment in the sector. The paper ends with a concluding section.

2. ECONOMIC OVERVIEW, WORLD WAR I, AND STABILIZATION By the mid-nineteenth century, Portugal had the most backward economy in Western Europe, with a level of income per capita below that of Spain, Italy,

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and the Scandinavian countries.5 A high level of backwardness was evident in many aspects. First, there were vast areas of the territory which were not put into productive use and large parts of land which were left fallow, despite the fact that the labor force was to a large proportion still employed in the agricultural sector. On top of that, the share of animal output in total agrarian production was relatively small which implied a deficient use of natural manure and animal power, that were not substituted by chemical fertilizers until late nineteenth century and machinery well into the twentieth century (Lains, 2003b). In the industrial sector, there was the predominance of traditional activities and limited use of mechanization and coal or other sources of non-animal energy. The transport sector was also poorly developed, with bad roads, few ports, and no relevant canals. Lisbon and Oporto were connected by railways only in 1877 and before that the best link between the country’s two largest cities was by sea. The society at large also had many signs of serious underdevelopment. The state was relatively inefficient, constrained by political instability and scarcity of financial resources which implied low levels of investment in infrastructure and education. The military gained control over the southern province of Algarve only in the 1860s; until then the region was ruled by militias. Portugal’s extreme backwardness was partially overcome in the years from 1850 to 1913. First, the industrial sector expanded faster than agriculture, as we shall see in more detail in the next section. There were also productivity gains in the agricultural sector. Other facets of change in the Portuguese economy included changes in the structure within both the agricultural and the industrial sector. In agriculture, a major transformation concerned the increase in the area under cultivation at the expense of the decline in the uncultivated area or the area left fallow. This was a major source of the increase in labor productivity as more land was put into use. In contrast, land productivity levels did not change in significant ways, as the introduction of new processes and techniques in agriculture was relatively slow. In the industrial sector, there were also some relevant productivity gains and changes in structure, which were associated with higher levels of protection. Yet, due to highly protective tariffs, both in the domestic and the colonial markets, at the outbreak of the War, Portugal had a relatively large share of its industrial labor force occupied in the textile sector, which was relatively inefficient.6 Notwithstanding tariff protection, the degree of internationalization of the Portuguese economy increased from at least 1870 to 1914, as foreign trade, capital imports and emigration expanded faster than the rest of the economy. Trends in capital imports were not sustained, though. Following a


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balance of payments crisis, Portugal left the gold standard in 1891 and the state partially defaulted on its debt the following year. As a consequence, there was a sharp decline of foreign lending to the state, which had been up to then a major channel for capital imports. Exports also expanded at slower pace in the last two decades before the war, as Portugal did not manage to keep her share in the markets for agricultural products, such as wine and live animals, due to competition from Mediterranean and South American exporters.7 The slow pace of economic growth in the decades up to 1914 went together with the slow development of institutions and infrastructures. But there were some positive signs in institutional development. First, the control of the State over the territory increased significantly and was universally achieved by the eve of the War. Second, literacy rates rose in significant ways and, at the same time, mortality fell and urbanization increased.8 Third, the financial system became more developed and widespread.9 Finally, there was an important effort in building railways, roads, and other infrastructures, mainly up to the 1890s. Such developments were made possible by increasing government deficits that were financed either domestically or in the international capital markets. Such positive economic and institutional developments were nevertheless insufficient and Portugal failed to catch up to the levels of income per capita of the front-runners.10 But half a century of slow but sustained growth led the Portuguese economy to a higher degree of maturity, in terms of its structure and overall productivity levels. Those changes proved to be fundamental for the response to the distresses provoked by World War I, as we shall see. The war was greatly disruptive for the Portuguese economy, in spite of the fact that the country had only a minor participation and that its territory was not directly affected by warfare. In 1916, Portugal entered the war on the Allied side and the first immediate consequence was an increase in public expenditure and in government deficit, which led to an increase in the money supply and inflation.11 The war also affected negatively the growth of exports from Portugal (by volume) and, more importantly, reexports from the colonies. Portugal was unable to take advantage of some export opportunities and the export of colonial produce declined following the trend in tropical products.12 In contrast, imports maintained its upward trend. Emigrant remittances declined too and the financing of the country’s balance of payments was severely affected. The participation in the war was compensated by a loan from the British government which temporarily eased the external and the government financial disequilibria (Vale´rio, 1994). But that was not sufficient and, as state revenues did not keep up with

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expenses, the Banco de Portugal kept printing money and prices continued to rise sharply.13 After the war, several measures were taken in order for the government to gain more leverage over the economy, as was happening contemporarily in the rest of the European continent. That change occurred first as a response to the effects of the War on the supplies of energy, raw materials, and main food staples, in order to alleviate minor food shortages and to keep operating the industries which depended on imports of energy and other industrial inputs. The interwar period was also marked by a high level of political instability which lasted to the very end of the Republican regime (1910–1926). Yet in the midst of political instability, the government managed to introduce fiscal and tariff reforms in 1922 and 1923, respectively. Both reforms aimed to restore import tariff levels and income taxes which had been eroded by high inflation, as most tariffs were specific and not ad valorem. In 1924, a foreign reserve fund was created which retained 50 percent of the earnings in gold and foreign currencies from exporters and there was an important sale of silver coin reserves by Banco de Portugal. A new bank law of 1925 imposed stricter measures for the sector, in terms of capital requirements, and redefined the role of the central bank, which ceased its activity as a commercial bank, and the government gained higher control of its administration. The war debt to Great Britain was virtually cancelled in 1926. Finally, emigrant remittances, traditionally a major source of financing the current account balance, recovered with monetary stabilization. In 1926, a dictatorship was imposed by a military coup and a new surge of instability followed, which again had a negative impact on the main financial and monetary variables. In 1928, Salazar was designated Minister of Finance and from there he started gaining control of the government which he led as a dictator from 1932 to 1968. Social unrest and strikes marked Portugal’s political life until well into the 1930s and it only stopped as the result of the repression and the limitation of political rights imposed by the dictatorship.14 The first years of consolidation of the new regime were not however accompanied by economic recovery. On the contrary, there was a small decline in the trend rate of growth of the economy after the 1934 peak (see below). The new finance minister enhanced the measures that had been taken before he came to power and the financial situation of the country kept improving steadily and the government could refuse a loan from the League of Nations, which had been negotiated by the finance minister before Salazar. One further major help in reaching the new equilibrium was


125

the return of domestic capital that had fled the country during the War, which was estimated at 60–70 million pound sterling, accumulated in 1929, and should be compared to an estimate of British investment in Portugal of 21–25 million pounds and exports which totalled 10 million pounds, also in 1929 (Telo, 1994, p. 797; Vale´rio, 1994, p. 463). Also in 1929, final steps were made to redress the state finances by increasing further tariff levels. Finally, in 1930 a new colonial statute led to severe cuts in the central government’s expenditures with the colonies, which led to the elimination of the deficit on that account. Table 1 and Figs. 1–6 provide the data on the evolution of Portugal’s main fundamentals. Table 1 depicts the high rates of price inflation and exchange devaluation which started off during the War and were aggravated in the post-war period. At an annual rate of 58.8 percent, between 1914 and 1918, Portugal had the highest war inflation in Western Europe, surpassed only by Italy, Finland and the hyperinflation in Germany and Austria. During the following period from 1918 to 1924 Portugal remained at the top of the inflation league in Europe. During this inflationary period, relative prices moved in favor of the industrial sector, in contrast to what happened during the second half of the nineteenth century (see Fig. 2). The depreciation of the exchange rate followed closely the inflation pattern, because the escudo was left to float. Table 1 also shows the growth of the money supply, total public debt, and the ratio of the budget deficit to GDP. In all cases there was a considerable expansion, as compared to the Table 1.

1854–1891 1891–1914 1914–1918 1918–1924 1924–1929 1929–1939 1939–1945

Monetary and Fiscal Indicators for Portugal, 1854–1945. GDP Deflator (Annual Change %)

Exchange Rate (Annual Change %)

Money Supply (Annual Change %)

Total Public Debt (Annual Change %)

Budget Deficit ()/ Surplus (+) % of GDP

0.39 0.92 58.81 30.84 3.33 0.10 15.22

0.00a 0.69 8.68 60.28 4.17 1.85 1.58

3.23 0.68 21.37 37.68 5.20 6.21 27.77

5.12 0.46 11.29 41.70 3.48 2.84 5.54

1.5 0.3 6.8 8.7 3.3 +0.9 0.9

Source: GDP deflator and terms of trade: Appendix Table A2; Money supply (M1), total public debt, and exchange rate: Vale´rio (2001, pp. 568–571, 707–710, 737), Budget deficit: Mata and Vale´rio (1994, 1996, p. 205). a Portugal was on the gold standard from 1854 to 1891.

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Agriculture Industry Total

80 60 40 20

Fig. 1.

1955

1950

1945

1940

1935

1930

1925

1920

1915

1910

1905

1900

1895

1890

1885

1880

1875

1870

1865

1860

1855

1850

0

Price Indices, 1850–1958 (1953 ¼ 100). Source: Appendix Table A1.

200 180 160 140 Pind/Pagr 120 100 80 60 40

Fig. 2.

1955

1950

1945

1940

1935

1930

1925

1920

1915

1910

1905

1900

1895

1890

1885

1880

1875

1870

1865

1860

1855

1850

20

Relative Prices, 1850–1958 (1953 ¼ 100). Source: Appendix Table A2.

pre-war levels and in all cases there was a sharp reduction in the years between 1924 and the outbreak of World War II. Fig. 3 depicts the evolution of the average tariff, measured as import duties divided by import values. This is a rough indicator of tariff protection due to changes in the composition of imports and also due to a defective


127

50 45 40 35 30 25 20 15 10 5

Fig. 3.

1957

1951

1945

1939

1933

1927

1921

1915

1909

1903

1897

1891

1885

1879

1873

1867

1861

1855

0

Tariffs, 1855–1958 (% of Import Values). Source: Appendix Table A3.

100.000 ExportVal ExportVol

10.000

ImportVal ImportVol

1.000 100 10

Fig. 4.

1955

1950

1945

1940

1935

1930

1925

1920

1915

1910

1905

1900

1895

1890

1885

1880

1875

1870

1865

1860

1855

1

Trade by Value and Volume, 1855–1958 (Semi-Log Scale). Source: Appendix Table A3.

evaluation of imports by Portuguese official trade statistics. Yet, the trends shown for the nineteenth century can be confirmed by a weighted index (Lains, 1987). For the twentieth century, the decline in average duties after the 1914 peak and the increase after the 1920 trough are probably exaggerated. In any case, the steep U curve shown by the data was the outcome of the fact that tariffs were predominantly set at fixed rates and thus they did

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40 35 M/GDP X/GDP (M+X)/GDP

30 25 20 15 10 5

Fig. 5.

1955

1950

1945

1940

1935

1930

1925

1920

1915

1910

1905

1900

1895

1890

1885

1880

1875

1870

1865

1860

1855

0

Trade Ratios, 1855–1958 (% of GDP, by Volume). Source: Appendix Table A3.

1.000 PX PM ToT

100

10

Fig. 6.

1955

1950

1945

1940

1935

1930

1925

1920

1915

1910

1905

1900

1895

1890

1885

1880

1875

1870

1865

1860

1855

1

Export and Import Prices, and Terms of Trade, 1855–1958 (Semi-Log Scale, 1953 ¼ 100). Source: Appendix Table A2.

not accompany the increase in import prices. What the data show is that the measures taken after 1922 to increase tariff protection were effective and restored the levels of protection that were in place before the advent of the inflationary period.


Table 2.

1910–1913 1918–1928 1929–1937 1938–1950

129

Structure of Imports (1958 Prices, Percent).

Foods, Beverage and Tobacco

Other Consumption Goods

Transport Equipment

Other Machines and Equipment

Intermediate Goods

Energy

15.8 14.1 10.8 6.1

10.0 7.6 11.3 8.4

1.9 2.5 4.6 5.2

4.3 4.2 6.9 6.6

60.6 62.8 57.1 61.2

7.4 8.8 9.4 12.5

Source: Computed from Batista et al. (1997, p. 106).

Fig. 4 shows the evolution of foreign trade and Fig. 5 the evolution of the ratio of trade to GDP, both measured by value and volume. The increase in protection after 1920 did not stop imports from growing by volume. Trade ratios, however, stabilized in that decade because of the slight downturn in the evolution of exports by volume. Yet the terms of trade, shown on Fig. 6, moved in favor of Portugal in that same period and again that trend contradicted what happened in the second half of the nineteenth century. After the war and particularly after 1929, the structure of Portuguese imports changed, as shown in Table 2. The main change was the decline in the share of imports of foodstuffs, beverages and tobacco, from 14.1 percent in 1918–1928 to 10.8 percent in 1929–1937. Contrarily, imports of other consumption goods, as well as transport equipment, machinery, and energy increased slightly. Intermediate goods for industry also declined slightly. These changes reflect protection for the agricultural sector, particularly cereals. To sum up, during the interwar period, there was a sharp increase in prices and monetary instability, accompanied by large budget deficits. Yet, tariffs were restored, imports kept their increasing trends, and exports declined only slightly. The trade deficit – which the official data strongly exaggerates – was financed by the return of capital imports and emigrant remittances. Moreover, the Portuguese economy benefited from termsof-trade improvements and the industrial sector benefited from an increase in relative prices.

3. ECONOMIC GROWTH AND STRUCTURAL CHANGE, 1850–1950 The main phases of the growth of Portuguese income per capita during the century to about 1958 are given in Table 3 in the form of growth rates in

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Table 3. Growth of Real Income Per Capita in Portugal, 1855–1958 (Peak-to-Peak Annual Growth Rates; Percent). 1855–1870 1870–1882 1882–1902 1902–1922 1922–1934 1934–1947 1947–1958 1855–1958

0.83 0.15 1.03 0.60 1.54 1.11 2.16 0.78

Note: Peak years (1855 and 1958 excepted) are derived from a log-linear time trend for 1851–1958. Source: Appendix Table A1.

140 120 Agriculture Services

100

Industry GDP

80 60 40 20

Fig. 7.

1958

1953

1948

1943

1938

1933

1928

1923

1918

1913

1908

1903

1898

1893

1888

1883

1878

1873

1868

1863

1858

1853

1848

0

Growth of Output, 1848–1958 (1953 ¼ 100). Source: Computed from Appendix Table A1.

periods defined between peak years (see also Fig. 7). The data show that in the first phase from 1855 to 1870 there was a substantial decline in income per capita at the rate of 0.83 percent per year. The fall in income per capita was led by the sharp decline in agricultural output which was due to the fall of wine production as a consequence of the spread of disease in the vines. Portugal was a highly agricultural economy, in which wine accounted for about one-third of the total agrarian output, and thus economic growth was


131

much sensitive to changes in climate or other natural conditions.15 Growth of income per capita resumed after 1870 but in a first phase income expanded only moderately. From the 1881 peak to the end of the nineteenth century, the trend rate of economic growth increased to 1.03 percent per year. According to the data in Table 3, most of the last quarter of the nineteenth century was spent in recovering the economic decline from the 1850s and the 1860s. During the first decade of the twentieth century, agriculture had again a negative phase, which partially offset the positive performance of the industrial sector. In sharp contrast with what happened in agriculture, industrial output expanded at relatively stable rates of growth throughout the decades from 1870 onwards and showed some tendency to rise at the end of the century (see Table 6 below). The peak year of 1923 marked an important turning point in Portugal’s growth experience, as income per capita expanded since then at rates which had not been seen before. In the years after 1923, growth was common to the three sectors of the economy and in some periods there was a small increase in the share of agriculture in national output. There was a slight slowing down after 1934, but growth resumed at a faster pace after 1947. Table 4 compares the growth of Portugal’s GDP per capita with growth in three European peripheral and an average for nine European core countries, during what can be termed Maddison’s phases of economic growth. The table shows that the comparative performance of the Portuguese economy was relatively poor during the first two phases down to 1913. During the period from 1913 to 1929, Portugal’s income per capita growth rate increased, although it performed worse than Spain and Greece. Yet Portugal kept on growing after 1929, in contrast to Spain, which was affected by the Civil War (1936–1939). After 1938, Portugal’s growth performance was also better than that of Greece and that can be explained by the fact that Greece was directly affected by World War II and the civil war that followed. As a result, Portugal performed better than the rest of the peripheral countries shown in the table when the whole 1913–1950 period is considered. Table 5, based on estimates for GDP per capita, tells the same story of catching up to the European core. As is shown, Portugal diverged in 1870– 1890, at 0.41 percent per year, and even more in 1890–1913, at 0.92 percent per year. It is interesting to note that divergence after 1890 was more important in Portugal and that it also occurred in Spain and Ireland, in contrast to what happened in those countries in the period before 1890. In the case of Portugal, there was a slight divergence during 1913–1929, but after the New York Stock Exchange crash, Portugal started catching up for the first time, again in marked contrast to what was happening in the rest of

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Table 4. Growth of Real Income Per Capita in the European Periphery and Core, 1870–1950 (Maddison’s Phases of Development; Annual Growth Rates between 3-Year Averages; Percent).

1870–1890 1890–1913 1913–1929 1929–1938 1938–1950 1870–1913 1913–1950 1870–1950

Portugal

Spain

Greece

Ireland

European Core

0.66 0.40 1.35 1.28 1.56 0.52 1.40 0.93

1.48 0.76 1.65 3.53 1.48 1.09 0.31 0.73

– – 2.45 1.50 –2.72 0.54 0.51 0.53

1.21 0.84 0.33 0.87 0.94 1.01 0.66 0.85

1.07 1.32 1.39 1.16 1.00 1.21 1.21 1.21

Note: European core is based on an unweighted average index for the following countries: Belgium, Denmark, France, Germany, Italy, the Netherlands, Norway, Sweden, and UK. Three-year averages, except for 1870/1871, for Spain (1870 and 1890) and Ireland (1870, 1890, and 1913). Source: Maddison (1995, 2001) and Appendix Table A1 for Portugal.

Table 5. Catching Up of Real Incomes Per Capita in the European Periphery, 1870–1950 (Maddison’s Phases of Development; Annual Growth Rates between 3-Year Averages; Percent).

1870–1890 1890–1913 1913–1929 1929–1938 1938–1950 1870–1913 1913–1950 1870–1950

Portugal

Spain

Greece

Ireland

0.41 0.92 0.04 0.12 0.55 0.68 0.19 0.28

0.41 0.56 0.26 4.64 0.47 0.11 0.89 0.47

– – 1.04 0.33 3.69 0.66 0.69 0.64

0.14 0.48 1.04 0.29 0.06 0.19 0.54 0.35

Note: Convergence defined according to: f ¼ ½ðyi =y9ÞðtþnÞ =ðyi =y9ÞðtÞ ½1=n where yi is income per capita for the four countries in the table and y9 the average for the United Kingdom, France, Germany, Belgium, the Netherlands Italy, Sweden, Denmark, and Norway. Source: See Table 4.


133

50

45

40

35

1955

1950

1945

1940

1935

1930

1925

1920

1915

1910

1905

1900

1895

1890

1885

1880

1875

1870

30

Fig. 8. Portuguese GDP Per Capita as Per Cent of the European Core, 1870–1958. Source: Computed from Appendix Table A1 and Maddison (1995).

the countries of the European periphery shown in the table. Catching up was further helped by the reduction of growth in the European core after 1929. Fig. 8 shows that the ratio of Portuguese GDP per capita to the average of the nine European core countries declined quite sharply, between 1870 and 1875, from 50 to 45 percent, stabilized in the years up to 1890, declined only slightly to 1900, and then sharply again to 1915. From then on, the ratio evolved in an irregular way but with a positive trend to at least 1947. However, in this year, Portugal’s relative position was still below the one that existed in 1870. The observed pattern of growth of income per capita can be related to changes in the structure of the economy before we turn to the analysis of the evolution of factor productivity. Data on the evolution of the structure of the output of the Portuguese economy during the century from 1850 to 1950 are presented in Table 6. The data are based on direct evaluation of output at 1958 prices for the period from 1910 to 1950. For the previous period, we have indices for physical output growth (agriculture and industry) and a proxy index for the growth of services to compute backward extrapolations of the structure in 1910. As shown there, Portugal had in the beginning of the period a small level of industrialization, as the industrial sector accounted for only 11.7 percent of total output, whereas the agricultural sector accounted for 47.8 percent. The share of the agricultural sector in GDP declined thereafter to 40.4 percent, in 1880, to increase in the following two decades. Such evolution of the weight of the agricultural sector is closely

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Table 6.

Portugal: Composition of GDP, 1850–1950 (Percent). Agriculture

Industry

Services

47.8 38.6 40.8 40.4 40.9 40.1 37.1 30.4 31.5 30.6 32.1

11.7 17.4 17.2 17.1 21.5 24.7 27.1 25.8 28.0 28.7 30.3

[40.5] [44.0] [42.1] [42.6] [37.6] [35.3] 35.8 43.9 40.5 40.6 37.6

a

1850 1860b 1870 1880 1890 1900 1910 1920 1930 1940 1950 Source: Appendix Table A1. a 1851. b 1861.

related to trends in the growth of output. In fact, the decline of the agricultural output share down to 1860 happened in a decade of severe contraction of output, whereas the recovery of the share between 1880 and 1910 was associated with the expansion of output.16 Portuguese economic growth had to be somehow related to the performance of its largest sector, agriculture, but the fact that economic growth could be driven by an increasing participation of the agricultural sector has to be explained. By 1910, the share of agricultural output in total output was virtually the same as in 1860, and from 1920 to 1950 the agricultural share increased only very slightly. The largest increase in the share of the industrial sector occurred between 1850 and 1860, from 11.7 to 17.4 percent, and then it took five decades to reach a level of 27.1 percent. After 1910, the share of the industrial sector remained relatively stable, increasing only in the decade from 1940 to 1950. The evolution of the labor force and of its structure can only be assessed indirectly due to lack of data. Table 7 sets down the available data on total population, population in the 15–64 years bracket, and active population. The data on active labor force provided by the three censuses from 1890 to 1911 imply that the male active population is larger than the number of males in the 15–64 years bracket. After 1911, male active population is marginally smaller than males in the same age bracket. In other words, population censuses up to 1911 include child labor and labor after 65 years of age, in contrast with the censuses thereafter. Consequently, the census

Total Population (0 0 0)

1864 1878 1890 1900 1911 1920 1930 1940 1950

Population and Labor Participation Rates. 15–64 Years as % of Total Population

Active as % of Total Population

Active as % of 15–64 Years

Total

Males

Total

Males

Total

Males

Total

Males

4,188 4,551 5,050 5,423 5,960 6,033 6,826 7,722 8,441

2,006 2,176 2,430 2,592 2,829 2,856 3,256 3,712 4,060

61.3 61.4 60.5 60.3 59.6 61.0 61.7 61.3 63.5

59.3 59.7 58.9 58.7 57.6 59.2 60.4 60.4 63.1

– – 50.1 45.3 42.7 40.2 36.9 35.9 37.9

– – 66.2 66.6 65.3 – 56.0 57.7 60.9

– – 82.8 75.1 71.7 65.9 59.8 58.7 59.6

– – 112.4 113.4 113.4 – 92.7 95.6 96.5


Table 7.

Source: Vale´rio (2001, pp. 51, 55–56, 164, 178) and Mitchell (1996; Table A2).

135

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data imply a substantial contraction in total labor force participation rates, from 50.1 percent in 1890 to 37.9 percent in 1950, and a once and for all decline of 10 percent points between 1910 and 1930. Thus, we opted to use as a proxy for the evolution of the labor force the evolution of males in the 15–64 years bracket, which implies that we consistently do not include child labor and labor older than 65 years of age throughout the years. This option affects to a relevant extent only the evolution of labor force during 1910– 1930 and that should be taken into account in the interpretation of the results. Our option depicts a growth of active population in line with estimates based on a model for interpolation of active population between census years.17 According to that data, there was a gradual decline in the expansion of the total labor force all the way to 1920, although in this latter decade the reduction in the rate of growth was more important. After 1920, however, the labor force expanded more rapidly, due to the contraction in emigration after that year. Table 8 shows the shares of the total labor force in the three sectors based on the structure of active population given by two parish censuses for 1841, 1862, and the official population census for 1890, 1900, 1911, 1930, 1940, and 1950. The structure for the intermediate census years is estimated by linear interpolation.18

Table 8.

Evolution and Sectorial Distribution of Male Labor Force, 1841–1950. Total Male Labor Force

1841 1862 1864 1878 1890 1900 1911 1920 1930 1940 1950

Percent

(0 0 0)

Agriculture

Industry

Services

993 1,161 1,189 1,298 1,432 1,522 1,629 1,691 1,967 2,241 2,562

67.5 65.9 [66.0] [66.6] 66.9 66.4 61.0 [60.9] 60.9 57.8 53.8

15.9 15.5 [15.7] [17.0] 18.3 18.8 21.7 [21.2] 20.7 21.0 24.6

16.6 18.5 [18.2] [16.3] 14.8 14.8 17.3 [17.9] 18.4 21.1 21.6

Note and Source: Total males in the 15–64 bracket computed from Table 7, except for 1841 and 1862 which are based on growth rates for labor force to 1890 from Reis (2005). Shares from Reis (2005) (for 1841 and 1862) and Vale´rio (2001, p. 164) (for 1890 to 1950, except for 1864, 1878, and 1920, which are linear interpolations from adjoining years).


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Table 9. Growth of Output, Labor Force and Labor Productivity, 1860–1950 (Annual Growth Rates, Percent). Agriculture

Industry

Services

Total

(a)

Output 1860–1880 1880–1890 1890–1900 1900–1910 1910–1920 1920–1930 1930–1940 1940–1950

1.03 2.77 1.36 0.40 1.64 4.51 1.81 2.82

0.95 3.23 3.00 1.97 0.15 4.35 2.02 4.16

0.70 0.90 0.65 1.00 2.14 2.97 1.73 2.66

0.87 2.10 1.45 0.68 0.31 3.83 1.84 3.15

Labor force 1860–1880 1880–1890 1890–1900 1900–1910 1910–1920 1920–1930 1930–1940 1940–1950

0.77 0.86 0.54 0.15 0.40 1.52 0.78 0.62

1.28 1.44 0.88 1.94 0.16 1.28 1.46 2.96

0.09 0.01 0.61 2.06 0.80 1.80 2.71 1.59

0.70 0.82 0.61 0.62 0.42 1.52 1.31 1.35

Labor productivity 1860–1880 1880–1890 1890–1900 1900–1910 1910–1920 1920–1930 1930–1940 1940–1950

0.26 1.91 0.82 0.25 2.04 2.99 1.03 2.20

0.33 1.79 2.12 0.03 0.01 3.07 0.56 1.20

0.79 0.89 0.04 1.06 1.34 1.17 0.98 1.07

0.17 1.28 0.84 0.06 0.11 2.31 0.53 1.80

Source: Output from Appendix Table A1 and labor force from Table 8, with linear interpolations. a The years for output are the centre of three-year averages.

Table 9 shows aggregate and disaggregate growth rates for output, labor force, and labor productivity. Labor productivity in the whole economy expanded faster during 1880–1900, at 1.39 percent per year, and slowed down in the following two decades. After 1920, labor productivity growth was again more rapid, at 2.13 percent per year, and despite the slowing down in the 1930s, on the whole, labor productivity expanded more rapidly after World War I. The productivity of labor employed in the agricultural

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sector in some occasions expanded at a similar pace or even more rapidly than that of the industrial sector, as was the case in the decade from 1880 to 1890 and in the three decades from 1920 to 1950. Thus, the increasing importance of the agricultural sector was clearly associated with a better overall economic growth performance. This was so particularly during the 1920s and the 1940s. In order to explain the increasing contribution of agriculture to Portuguese economic growth after World War I, we have to go beyond the analysis of labor productivity and evaluate the contribution of capital investment.

4. PRODUCTIVITY GROWTH AND THE CONTRIBUTION OF AGRICULTURE In this section, we carry out an analysis of the factors behind productivity increases in the agricultural sector and the aggregate economy within a growth accounting framework. Table 10 provides a summary of the available data on the growth of factors and factor productivity in the agricultural sector and the total economy, based on proxy estimates for the growth of capital in agriculture for the century ending in 1950 and for the growth of total capital in the interwar period. In the agricultural sector, we see that the growth of labor and capital productivity expanded at quite similar rates during 1865–1902, 0.67 and 0.78 percent per year, respectively. This period of higher growth was followed by one of slower growth of labor productivity Table 10.

Growth Labor, Capital and Total Factor Productivities, 1910–1950 (Annual Growth Rates; Percent).

1

2

3

4 ¼ 3–2

5 ¼ 1–2

6 ¼ 1–3

7

Output

Labor

Capital

K/L Ratio

Labor Productivity

Capital Productivity

TFP

Agriculture 1865–1902 1902–1927 1927–1951

1.41 0.35 2.36

0.74 0.13 0.97

0.63 0.86 1.44

0.11 0.73 0.47

0.67 0.22 1.39

0.78 0.51 0.92

All sectors 1910–1934 1934–1947

2.17 2.09

1.00 1.31

1.25 3.89

0.25 2.97

1.17 0.78

0.92 1.80

Source: Lains (2003a, 2003b).

0.72 0.20 1.59–1.63 0.72 0.02


139

and decline of capital productivity, till 1927. From then on, both productivity growth rates increased again, although the performance of labor was better than that of capital. Table 10 also shows that the ratio of capital to labor in agriculture increased throughout the period and that such capital deepening was associated with overall total factor productivity growth. Agrarian investment was materialized in more cattle, the expansion of tree cultures, such as olive oil and vines, and the increase in the use of fertilizers and the use of mechanization.19 Total factor productivity in agriculture expanded at 1.6 percent per year after 1927 which compares relatively well with factor productivity growth elsewhere in Europe.20 For the aggregate economy, we only have data starting in 1910 because we lack data on total capital investment before that year. The lower part of Table 10 shows that aggregate labor productivity expanded at a faster pace than aggregate capital productivity and considerably so after 1934, when there was a marked decline in the productivity of capital. Such patterns are reflected in the growth of total factor productivity which virtually stagnated in the period from 1934 to 1947. The ratio of capital to the labor force in the aggregate economy expanded rather fast after 1934 and that was mainly due to the increase in investment in the non-agricultural sector. By the account given here, this implies that capital productivity in the non-agricultural sector had a negative performance, particularly after 1934. Capital imports were relatively small in size when World War I broke out and thus Portugal’s rate of domestic investment was not directly affected by the disintegration of the international capital markets in its aftermath. There is evidence of some capital flight during the War and also for its return from the early 1920s onwards. The evidence regarding capital flows and domestic investment is very scanty, though. The pattern that emerges from the observation of factor productivity growth is that the performance of the agricultural sector was better than that of the non-agricultural sector. That outcome helps explain why the Portuguese economy managed to obtain higher productivity gains by shifting to a higher participation of agriculture in economic growth, during the interwar period. In order to investigate the mechanisms that led to such an outcome, we need to look more deeply to the structure of the economy to find out how the new capital invested in the agricultural sector had higher levels of productivity than capital invested in the rest of the economy. The data for this in-depth level of analysis is scant but it is sufficient to reach some further results that confirm our overall analysis. By decomposing the growth of labor productivity in the growth of land per agricultural worker and the growth of output per hectare, we have

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reached elsewhere the conclusion that labor productivity growth in agriculture was a consequence of an increase in land productivity.21 In fact, before 1930, the land–labor ratio increased slightly or remained stable and output per hectare increased only slightly. After 1930, the land–labor ratio declined and yet output per hectare increased at an unprecedented pace. Yet this happened without major changes in yields of the main agricultural staples. Agrarian productivity growth was achieved by structural shifts within the agricultural sector toward production with higher land values and higher labor productivity levels.22 Animal output’s share in total agrarian output increased from 23.6 percent in 1900–1909 to 35.9 percent in 1954–1958, whereas fruits and vegetables increased from 6.5 to 12.7 percent in the same time period.23 Together, these two sectors accounted for almost half of total agrarian output in the 1950s, up from 30 percent in the beginning of the century. Structural transformations in Portuguese agriculture can be explained in terms of the evolution of aggregate domestic demand which was enhanced by agricultural protectionism and state subventions, particularly to wheat and other cereals. Such change was a consequence of higher levels of contraction of imports, due to higher levels of tariff protection and state intervention. But agrarian structural change was also related to the expansion of demand as a consequence of overall output growth and that was particularly important for animal output. The role of demand in fostering agricultural output growth in Portugal is confirmed by showing that there was a positive correlation between long-run output and price trends.24 The observed changes in the agricultural sector compare favorably with what happened in the rest of the economy and in particular in the industrial sector. In fact, the structure of the Portuguese industry remained relatively stable in the period from 1930 to 1950 for which we have information shown in Table 11. There were of course some changes, as the share of chemicals, non-metallic products, and basic metals expanded throughout the same period. Yet the fact that those sectors were relatively small, accounting for only 6.1 percent of total output in 1930 and 9.1 percent in 1950, implied that the overall impact was also small. Table 11 also shows labor productivity levels across the Portuguese economy with the highest possible disaggregate level allowed by the available statistical data. Labor productivity in agriculture was clearly below that of manufacturing but it compared well to productivity in textiles and ‘other’ as well as in construction and services. We do not have disaggregated data on agrarian labor productivity, but the disparities across sectors were certainly large. In fact, data on land productivity by region for 1952–1956 show wide differences in the 270 departments (concelhos) ranging from 0.3 to


Table 11.

141

Structure of Output and Labor Productivity in Portugal, 1930–1950.

Agriculture, forest, fishing Mining and quarrying Manufacturing Food, beverage, tobacco Textiles, clothes, footwear Wood, cork, furniture Paper, publishing, printing Chemicals, rubber, petrol Non-metallic mineral products Basic metals, machinery Other Construction Electricity, gas, water Trade, finance, rents Transport, communications Services GDP at factor cost

Output Shares

Labor Productivity

(Percent)

(Contos Per Head)

1930

1940

1950

1958

1930

1940

1950

1958

33.1 0.4 24.6 5.4 6.8 4.3 0.9 1.4 0.8 3.9 1.2 1.8 0.6 16.3 3.8 19.4 100.0

33.0 0.7 23.8 4.5 6.2 3.3 1.0 1.7 1.1 4.7 1.1 2.6 0.8 17.0 3.9 18.3 100.0

32.0 0.6 25.4 3.9 6.7 3.3 1.0 2.2 1.5 5.4 1.2 3.6 1.1 17.0 4.3 16.0 100.0

26.8 0.6 28.8 4.0 7.0 2.7 1.6 2.9 2.0 7.1 1.4 4.7 2.3 17.5 5.3 14.0 100.0

7.1 9.3 20.0 40.1 11.4 29.2 25.1 131.1 26.5 20.2 13.7 4.0 27.3 30.7 14.6 9.8 10.9

7.4 11.1 18.0 22.4 10.3 18.6 25.8 63.7 23.4 27.2 25.5 6.8 45.1 29.4 15.3 13.2 11.8

9.1 11.2 19.2 22.6 12.5 16.2 24.6 95.4 26.7 29.0 12.3 10.3 51.3 29.8 18.1 14.9 14.0

10.6 12.3 23.8 31.6 15.8 15.5 33.3 52.8 29.5 28.2 36.7 11.8 90.5 32.3 24.9 16.6 17.2

Source: Computed from Batista et al. (1997).

2.4 contos/hectare. The range of labor productivity at the 18 district level (distrito) is 1–2. This implies that labor in the agrarian sector of the top six districts, which accounts for 31 percent of total agrarian output, has productivity levels above the national average for manufacturing.25 If we take into account the fact that labor productivity in agriculture varied widely, as shown by this regional data, we may conclude that in some agrarian sectors productivity was above that of textiles, construction, and services. This conclusion is confirmed by the overall picture given by Caetano (1961), based on an interpretation of the first comprehensive industrial census for 1957–1959, which states that Portugal’s industry was characterized by many units with what he terms an ‘anti-economical dimension’ and a very small coverage of the country.26 The character of industrialization would change markedly in the following decade, as Portugal adhered to EFTA in 1959 and the country opened up.27 According to Aguiar and Martins’ (2005) shift-share analysis, the agricultural sector contributed with 23.9 percent of total labor productivity

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growth during 1910–1950, whereas industry contributed with 35.5 percent and services with 40.6 percent. In no other period during the twentieth century analyzed by the authors did agriculture contribute to productivity growth in such a way. More importantly, 85 percent of overall labor productivity growth in 1910–1950 was a result of intrasectoral productivity growth and just the remaining 15 percent can be attributed to shifts of labor toward sectors with higher productivity or with productivity growing above the average. About one-third of intrasectoral growth is attributed again to changes within the agricultural sector. Labor productivity changes in the industrial sector occurred fundamentally in construction and energy (i.e. electricity), whereas the manufacturing proper sector lagged behind. Moreover, the observed productivity changes occurred mainly in the traditional sectors, namely, textiles and the food and wood industries. Building also increased its domestic output share. In contrast, the capital-intensive sectors had negative labor productivity growth rates. The short-run effects of tariff and other state protection in the interwar period were positive in the sense that the Portuguese economy responded and higher growth was achieved. That response implied an increase in the contribution of agriculture to domestic output. There was clearly a shift in the specialization of agriculture toward products with higher levels of factor productivity. Labor productivity in manufacturing expanded at slower rates than in agriculture, despite the overall increase in the capital/labor ratio (see Table 10). The fact that such low-key forms of structural change led to a positive impact on Portugal’s total factor productivity growth reflects the structure of the domestic economy. On the demand side, the fact was that there was still scope for growth of the consumption of comparatively more sophisticated agrarian products with higher levels of labor and capital productivity. On the supply side, it reflects the potential advantage point of investment in the agrarian sector, as factor productivity in agriculture could be higher than in some industrial branches. The mechanism which led to higher productivity levels in agriculture is peculiar to the Portuguese economy and presumably to the other peripheral European economies.

5. CONCLUSION Portugal, as other poor countries of the European periphery, did not follow closely the growth of the core economies in the last quarter of the nineteenth century. The reasons for this lack of response are a matter of dispute in the literature. Some authors stress the negative impact of protectionist policies


143

followed by Portugal. Yet, the countries in the Balkan region also did not converge despite the fact that tariff protection there was introduced only later on, during the last decade of the century, and even then in a mild form (Lains, 2002). Government may also have been responsible for diverting productive investment to excess expenditure and debt, but that would have been the case only in Portugal, Spain, and Greece, as the Balkan countries kept balanced accounts throughout the period between about 1870 and 1914. Other structural factors, such as low literacy levels, were a common feature in these countries and that may have contributed to the poor performance of their economies. That list of negative factors did not disappear after the War and in many cases they were aggravated. Tariff and other forms of protectionism in the periphery were intensified; the level of state intervention was increased everywhere; and budget deficits became the norm, with the expected consequences in domestic prices and in currencies. The fact that after World War I a country such as Portugal attained higher growth rates of GDP per capita and that, for the first time in many decades, managed to catch up, albeit only partially, to the European core implies that other positive factors offset the negative consequences of protection, and financial and monetary distress provoked by the war and its aftermath. The economic history of Portugal in the interwar period shows that we need to look beyond the above factors in order to explain its performance. This paper shows that interwar growth in Portugal was achieved through productivity gains within agriculture and industry, as well as by moving factors to construction and services. Productivity gains were obtained in sectors that we may classify as traditional. Yet Portugal was such an underdeveloped country in the European context that the relatively simple pattern of structural change that took place could and did have a significant impact on overall economic growth. Structural changes occurred in a context of increased tariff and other forms of protection from external competition and output growth was directed toward the domestic market. This was possible thanks to the existence of favorable conditions in the balance of payments that allowed higher levels of investment, which occurred despite the slowing down of the international economy. Our result shows that peripheral countries can achieve higher growth rates within an unfavorable context in the international economy. The literature has paid a lot of attention to the financial and monetary distress of the interwar period and to the decline in the growth of international transactions in goods, services, capital, and people. Less attention

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has been paid to what happened to domestic output which in many cases expanded fast. This paper does not imply that protection is good for growth in any case. In fact, we did not address the question of knowing what would have happened in the counterfactual situation where external markets would be buoyant and the Portuguese economy would be wide open to influences from abroad – the world of the interwar period was far too removed from such a scenario. Our conclusion refers only to the situation in which the international economy stagnated and Portuguese producers benefited from exploring further the possibilities provided by the growth of domestic demand and by state protection. Such an outcome was made possible by higher levels of domestic savings and investment. There is the possibility that the higher intensity of growth that occurred in Portugal during the interwar period was made possible by the fact that Portugal was backward but not too backward. That would mean that the Portuguese economy had achieved by 1914 some degree of development and industrialization. That would have made a difference and would help explain why the success on the closest periphery of Europe was possible. Growth before 1950 was necessary for the country to achieve the minimum social and economic capabilities in order to take full benefits of the international economic boom during the golden age (1950–1973), when the economy opened up and benefited from the exploration of external markets and capital imports.

NOTES 1. For growth rates in the periphery, see Maddison (1995), Good and Ma (1999), and Lains (2003a). Spain had an acceleration of growth after World War I, which was interrupted by the emergence of civil war in 1936. See Palafox (1991), Carreras (1995), Prados de la Escosura and Sanz (1996), and Prados de la Escosura (2003). The performance of the Italian economy during the interwar period was in the midway between industrialized and peripheral Europe. See Rossi and Toniolo (1992), Mattesini and Quintieri (1997), Perri and Quadrini (2002), and Madsen (2001a). In Greece and Yugoslavia, the period of higher growth following World War I was interrupted by instability in Central Europe, following the 1931 bank crises in Germany and Austria. See Kostelenos (1995) and Christodoulaki (2001). Bulgaria and Turkey also caught-up in the period from the early 1920s to the beginning of World War II, whereas Latin America diverged. See, respectively, Pamuk (2001) and Taylor (1998). See also Milanovic (2003). 2. The literature on the relationship between protection and growth has seen a boost in recent years. See Frankel and Romer (1999), Rodrı´ guez and Rodrik (2000),


145

Madsen (2001b), Irwin (2002), Irwin and Tervio¨ (2002), Vamvakidis (2002), Williamson (2002), Estevadeordal, Frantz, and Taylor (2003), and Clemens and Williamson (2004). 3. According to Blattman, Hwang, & Williamson (forthcoming), the increase in the volatility of the periphery’s terms of trade after World War I was prejudicial to investment and thus to industrialization and growth. 4. A similar perspective is given on comparison of Mexico and Portugal by Lains (2006). 5. For relative income levels in the European periphery, see Maddison (1995) and Lains (2002). Portugal could be classified by mid-nineteenth century as an ‘extremely backward’ country, in the sense introduced by Gerschenkron (1962), referring to a level of development below the threshold of the group of converging countries. See also Abramovitz (1986). 6. For evidence on the Portuguese economy during the period, see, among other works, Justino (1988/1989), Pedreira (1990), Reis (1993), Lains (1995), and Lains and Silva (2005, Vols. 2–3). 7. There are no estimates for balance of payments for Portugal for the whole period covered in this paper. For partial data, see Mata (1993), Esteves (2003), and Lains (2003c, Chap. 2). 8. See Reis (1993). 9. See Reis (1995, 2002). 10. None of the European countries with levels of income per capita close to Portugal’s in around 1870 caught up in the period to 1913. See Maddison (1995), Good and Ma (1999), and Lains (2002). 11. On the interwar period see among others Carvalho (2001). 12. See Lains (2003c, Chap. 7). 13. See Feinstein, Temin, and Toniolo (1997). 14. See Gomes and Tavares (1999) and Pinto (2003). 15. See for agricultural growth Justino (1988/1989) and Lains (2003b). 16. An ongoing research points to an expansion of the service sector output faster than population. Such revision however does not change our main results. In fact, in the extreme case that services expanded as fast as industry, the division of total output between agriculture, industry, and services would be (percent): in 1850: 59.7, 17.3, 23.1; and in 1890: 45.0, 23.5, 31.4. This would imply a faster decline in agricultural share and a slower rate of industrialization of the economy, before 1910. This alternative outcome does not however change our main conclusions. 17. See Vale´rio (2001, Chap. 4). 18. See for the parish censuses, Reis (2005). 19. See Gomes, Barros, and Caldas (1944). 20. See Federico (2004). 21. See Lains (2003b). 22. See Pereira and Estaćio (1968). 23. The increase in the cattle stock was made possible by the growing usage of stables in the western regions north of Lisbon, as opposed to the use of land intensive pasturage in the southern region of Alentejo. 24. See Reis (1993, Chaps. 2, 3). 25. Pereira and Estaćio (1968, pp. 23–24, 51).

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26. See Caetano (1961, p. 931). See also Pintado (2002). 27. After 1960, there was a clear change in the emphasis of economic policy favoring the industrial sector. See for example, Moura (1973).

ACKNOWLEDGMENTS Previous versions of this paper were presented at seminars at Faculdade de Economia do Porto, Universidad de Zaragoza, and V World Congress of Cliometrics (Venice). I would like to thank for the comments from participants and from an anonymous referee.

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STATISTICAL APPENDIX Table A1.

Output and Population, 1848–1958 (1958 Prices). 106 escudos

1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861

Agriculture

Industry

Services

GDP

5,244 4,352 4,314 4,966 4,654 4,544 4,229 5,616 3,864 4,418 4,007 3,756 3,925 3,944

– – – 1,219 – – 1,496 1,525 1,566 – – – – 1,777

– – – [4,213] – – [4,235] [4,219] [4,259] – – – – [4,487]

– – – 10,397 – – 9,960 11,359 9,689 – – – – 10,208

Population (0 0 0)

GDP Per Capita escudos

3,833 3,822 3,863 3,873 3,881 3,865 3,893 3,878 3,915 3,943 3,969 4,012 4,057 4,125

– – – 2,685 – – 2,558 2,929 2,475 – – – – 2,475

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Table A1. (Continued ) 106 escudos Agriculture 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902

4,335 3,548 3,755 4,769 4,433 4,459 4,410 4,352 4,611 4,306 4,428 4,270 4,477 4,557 4,564 4,490 4,269 4,403 4,839 5,034 4,691 4,685 6,168 5,665 5,738 6,363 6,181 6,242 6,054 6,457 6,699 5,429 5,807 6,068 6,296 6,379 7,591 6,953 6,712 7,800 8,002

Industry – – – 1,686 1,826 1,921 1,867 1,938 1,942 1,673 1,946 2,322 2,190 2,181 1,991 2,305 2,243 2,190 2,045 2,210 2,367 2,334 2,388 2,355 2,809 2,933 2,958 2,731 3,177 2,946 2,900 3,107 2,962 3,214 3,152 3,594 3,694 3,817 4,131 3,954 4,082

Services

GDP

– – – [4,605] [4,643] [4,683] [4,703] [4,707] [4,758] [4,796] [4,819] [4,847] [4,861] [4,907] [4,940] [4,959] [4,977] [5,026] [5,102] [5,115] [5,165] [5,230] [5,278] [5,308] [5,372] [5,413] [5,458] [5,510] [5,562] [5,594] [5,645] [5,691] [5,715] [5,747] [5,754] [5,790] [5,840] [5,876] [5,910] [5,997] [6,046]

– – – 11,059 10,902 11,063 10,981 10,997 11,311 10,775 11,193 11,438 11,527 11,646 11,495 11,754 11,489 11,618 11,986 12,359 12,224 12,249 13,834 13,328 13,920 14,709 14,597 14,483 14,792 14,997 15,245 14,228 14,484 15,030 15,203 15,764 17,124 16,646 16,754 17,750 18,129

Population (0 0 0)


4,161 4,194 4,190 4,233 4,268 4,305 4,324 4,327 4,374 4,409 4,430 4,455 4,468 4,511 4,541 4,558 4,575 4,620 4,690 4,702 4,748 4,807 4,852 4,880 4,938 4,976 5,017 5,065 5,113 5,143 5,190 5,232 5,253 5,283 5,290 5,323 5,368 5,401 5,433 5,513 5,558

– – – 2,613 2,554 2,570 2,540 2,541 2,586 2,444 2,527 2,567 2,580 2,582 2,531 2,579 2,511 2,515 2,556 2,628 2,574 2,548 2,851 2,731 2,819 2,956 2,909 2,859 2,893 2,916 2,938 2,719 2,757 2,845 2,874 2,962 3,190 3,082 3,084 3,220 3,262


151

Table A1. (Continued ) 106 escudos Agriculture 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943

7,097 7,066 6,987 6,611 6,208 7,568 7,240 6,678 6,701 6,509 6,385 6,732 6,237 6,283 6,526 6,143 5,749 5,691 6,031 7,340 7,834 7,127 7,717 6,692 9,717 7,140 9,037 8,448 9,677 9,758 10,638 11,354 9,641 7,868 11,041 10,826 11,339 9,559 11,614 10,921 12,395

Industry 4,330 4,507 4,123 4,210 4,594 4,433 4,483 4,871 5,108 5,469 5,455 5,263 5,326 5,242 4,747 4,325 4,380 4,830 5,464 6,257 6,615 6,403 6,424 6,977 7,125 7,261 7,537 7,499 7,438 7,736 8,271 8,462 8,505 8,462 9,175 9,458 9,253 8,953 9,239 9,407 9,585

Services

GDP

[6,113] [6,178] [6,236] [6,265] [6,319] [6,348] [6,407] 6,442 6,797 6,946 7,022 7,008 7,029 7,183 7,158 6,061 7,759 8,221 8,301 8,583 8,682 8,760 9,126 9,449 10,260 10,125 10,577 10,874 11,073 11,243 11,740 12,125 12,130 11,689 12,449 12,602 12,736 12,678 13,203 13,278 13,836

17,540 17,752 17,346 17,086 17,121 18,349 18,129 17,991 18,606 18,924 18,862 19,003 18,592 18,708 18,431 16,529 17,888 18,742 19,796 22,180 23,131 22,290 23,267 23,118 27,102 24,526 27,151 26,821 28,188 28,737 30,649 31,941 30,276 28,019 32,665 32,886 33,328 31,190 34,056 33,606 35,816

Population (0 0 0)


5,620 5,679 5,732 5,759 5,809 5,835 5,890 5,924 6,020 6,006 5,994 6,070 6,117 6,142 6,199 6,078 6,097 6,078 6,167 6,211 6,254 6,339 6,409 6,527 6,600 6,655 6,738 6,815 6,915 7,000 7,078 7,187 7,263 7,387 7,466 7,565 7,683 7,769 7,785 7,856 7,947

3,121 3,126 3,026 2,967 2,948 3,144 3,078 3,037 3,091 3,151 3,147 3,130 3,039 3,046 2,973 2,719 2,934 3,084 3,210 3,571 3,699 3,516 3,630 3,542 4,106 3,685 4,030 3,936 4,077 4,105 4,330 4,445 4,168 3,793 4,375 4,347 4,338 4,015 4,374 4,278 4,507

152

PEDRO LAINS

Table A1. (Continued ) 106 escudos Agriculture 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958

13,924 11,263 11,807 13,265 12,277 13,069 14,166 15,717 13,214 16,250 15,836 15,331 15,443 16,023 15,279

Industry

Services

GDP

10,169 10,794 12,248 13,132 13,759 13,480 13,370 14,405 15,643 15,801 16,270 17,899 19,037 19,955 20,720

14,373 14,335 15,148 16,033 16,269 16,343 16,564 17,287 17,544 18,422 18,853 19,434 19,983 20,489 21,004

38,466 36,392 39,203 42,430 42,305 42,892 44,100 47,409 46,401 50,473 50,959 52,664 54,463 56,467 57,003

Population (0 0 0)


8,009 8,107 8,172 8,265 8,348 8,418 8,502 8,549 8,557 8,592 8,609 8,656 8,686 8,745 8,778

4,803 4,489 4,797 5,134 5,068 5,095 5,187 5,545 5,422 5,874 5,919 6,084 6,270 6,457 6,494

Notes and Source: All values are at constant 1958 prices. For the period after 1910, data are from Batista, Martins, Pinheiro, and Reis (1997, pp. 5–8) which is expressed at 1958 prices and at factor cost (due to adjustments, totals in this table are slightly different from those of the source at the maximum of 1.6 percent). Data for the period from 1848 to 1910 are backward extrapolations using indices for agriculture from Lains and Sousa (1998) (see also Lains, 2003b), and for industry from Lains (1995, pp. 211–212, column IPI). Both indices are based on prices from around 1900. The growth of the service sector for the period before 1910 was proxied by the growth of total population. Population is from Neves (1994, pp. 221–223). For the period after 1910, agriculture includes fisheries; and industry includes mining, and electricity, water, and construction. No data are available for those sectors for the period before 1910. In 1911, the Portuguese currency changed name from milreis to escudo.

1850 1851 1852 1853

Table A2.

Domestic Prices and Terms of Trade, 1850–1958 (1953 ¼ 100).

Agriculture (PA)

Industry (PI)

1.08 1.01 0.97 1.15

0.52 0.52 0.54 0.54

GDP (P)

0.78 0.73 0.71 0.83

Relative Prices (PI/PM)

Exports (PX)

Imports (PM)

Terms of Trade (PX/PM)

48.5 51.8 55.8 47.3

– – – –

– – – –

– – – –


153

Table A2. (Continued )

1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894

Agriculture (PA)

Industry (PI)

1.44 1.51 1.78 1.75 1.49 1.57 1.55 1.57 1.61 1.56 1.57 1.49 1.54 1.62 1.60 1.49 1.45 1.43 1.46 1.45 1.50 1.54 1.57 1.67 1.78 1.71 1.62 1.57 1.63 1.63 1.53 1.45 1.47 1.46 1.43 1.48 1.67 1.68 1.68 1.78 1.84

0.56 0.60 0.63 0.65 0.69 0.65 0.65 0.68 0.81 0.99 1.01 0.95 1.02 0.91 0.85 0.90 0.87 0.79 0.88 0.92 0.93 0.90 0.88 0.78 0.75 0.71 0.77 0.73 0.73 0.68 0.69 0.69 0.67 0.64 0.65 0.68 0.71 0.70 0.65 0.71 0.72

GDP (P)

1.03 1.08 1.26 1.24 1.07 1.12 1.11 1.13 1.16 1.15 1.16 1.10 1.14 1.18 1.15 1.09 1.06 1.04 1.07 1.07 1.11 1.13 1.14 1.19 1.26 1.21 1.15 1.12 1.16 1.15 1.08 1.04 1.04 1.04 1.02 1.06 1.18 1.19 1.18 1.25 1.29

Relative Prices (PI/PM) 38.9 39.4 35.7 37.1 46.4 41.8 41.9 43.4 50.4 63.6 64.2 63.5 66.0 56.1 53.6 60.8 59.8 54.9 60.5 63.6 62.1 58.3 56.3 46.9 42.4 41.3 47.7 46.4 44.5 41.6 44.9 47.6 45.8 44.0 45.3 46.1 42.2 41.5 38.7 39.8 39.0

Exports (PX)

– 1.26 1.44 – – – – 1.49 – – – 1.74 1.74 1.73 1.73 1.62 1.61 1.58 1.59 1.54 1.48 1.48 1.43 1.41 1.31 1.33 1.35 1.32 1.31 1.19 1.18 1.11 1.09 1.07 1.09 1.11 1.14 1.16 1.15 1.23 1.19

Imports (PM)

– 4.65 5.15 – – – – 5.23 – – – 5.58 5.72 6.13 6.37 5.44 5.40 5.85 5.34 5.29 5.04 5.14 4.71 4.49 4.08 4.31 4.72 4.31 4.29 3.45 3.44 3.48 3.35 3.15 3.32 3.80 3.89 3.68 3.39 3.57 3.48

Terms of Trade (PX/PM) – 27.1 27.9 – – – – 28.4 – – – 31.1 30.4 28.2 27.2 29.8 29.8 27.0 29.7 29.2 29.4 28.9 30.4 31.4 32.2 31.0 28.6 30.5 30.6 34.6 34.2 32.0 32.7 33.8 33.0 29.2 29.3 31.6 33.9 34.4 34.2

154

PEDRO LAINS

Table A2. (Continued )

1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935

Agriculture (PA)

Industry (PI)

1.75 1.71 1.84 1.94 1.94 1.91 1.84 1.83 1.99 1.87 1.73 1.69 1.77 1.85 1.81 1.86 1.97 2.04 [2.04] [2.04] 2.20 3.00 4.06 6.26 8.08 13.5 18.3 24.3 37.7 52.7 45.8 44.3 47.9 48.9 46.9 43.6 39.3 40.7 39.8 43.6 37.8

0.70 0.69 0.74 0.79 0.82 0.88 0.84 0.79 0.79 0.78 0.70 0.70 0.73 0.72 0.81 0.76 0.88 0.90 [0.90] [0.90] 1.53 3.9 7.38 11.66 7.14 20.7 22.6 22.9 34.0 40.3 28.7 29.3 27.6 30.1 31.8 31.0 27.9 26.8 26.0 27.6 27.4

GDP (P)

1.23 1.20 1.29 1.37 1.37 1.36 1.32 1.31 1.41 1.33 1.23 1.20 1.26 1.31 1.29 1.32 1.42 1.47 [1.47] [1.47] 1.90 3.57 5.97 9.35 7.78 17.8 21.1 24.2 36.6 46.9 37.7 37.2 38.1 39.6 39.6 37.3 33.9 34.3 33.1 35.8 32.7

Relative Prices (PI/PM) 40.3 40.6 40.4 40.8 42.2 46.0 45.6 42.9 39.7 41.5 40.6 41.4 41.0 38.9 44.6 40.6 45.0 44.0 [44.0] [44.0] 69.7 130.2 181.7 186.2 88.4 152.5 123.5 94.4 90.1 76.3 62.7 66.2 57.7 61.7 67.7 71.0 71.1 65.9 65.4 63.4 72.3

Exports (PX)

Imports (PM)

Terms of Trade (PX/PM)

1.18 1.16 1.21 1.43 1.39 1.56 1.54 1.48 1.45 1.46 1.35 1.35 1.39 1.48 1.46 1.48 1.47 1.46 1.46 1.43 1.52 1.88 2.46 3.70 4.26 9.63 10.6 12.2 22.5 31.1 28.9 27.7 28.4 34.6 37.5 38.8 33.2 31.2 30.6 31.8 31.9

3.59 3.38 3.81 4.74 4.46 5.08 4.78 4.80 4.99 5.01 4.59 4.43 4.70 5.02 5.08 5.30 5.51 5.17 5.13 5.30 7.48 9.17 12.5 19.2 18.3 37.5 50.7 57.9 93.3 135.4 93.3 81.3 80.8 80.7 75.5 64.6 57.6 60.7 52.5 52.2 50.6

32.8 34.2 31.7 30.1 31.2 30.7 32.3 30.9 29.0 29.2 29.5 30.5 29.6 29.5 28.8 27.9 26.7 28.3 28.6 27.0 20.3 20.5 19.7 19.3 23.3 25.7 21.0 21.1 24.1 22.9 30.9 34.1 35.2 42.9 49.7 60.0 57.6 51.4 58.3 60.9 63.1


155

Table A2. (Continued ) Agriculture (PA)

1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958

40.2 44.5 42.6 41.7 46.9 55.1 61.8 69.9 86.7 90.5 99.1 97.2 101.5 107.3 98.0 102.4 106.7 100.0 100.4 99.6 110.0 108.7 106.5

Industry (PI)

27.9 35.7 35.4 36.8 52.8 61.1 73.3 98.7 102.3 93.2 79.6 90.9 86.1 89.6 91.7 98.9 100.7 100.0 95.4 95.6 98.8 100.0 100.6

GDP (P)

34.3 40.4 39.2 39.2 49.9 58.2 67.4 84.1 94.4 91.7 89.8 94.4 93.3 97.8 95.5 100.0 102.2 100.0 97.8 100.0 103.4 103.4 103.9

Relative Prices (PI/PM)

Exports (PX)

Imports (PM)

69.4 80.1 83.0 88.2 112.5 110.9 118.6 141.1 118.0 102.9 80.3 93.5 84.8 83.5 93.6 96.6 94.3 100.0 95.0 96.0 89.8 92.0 94.5

30.9 32.8 33.7 34.1 44.1 69.5 111.1 97.7 97.8 97.7 98.2 102.7 100.9 101.3 94.7 104.8 109.1 100.0 96.5 96.3 98.9 98.0 89.4

49.1 48.2 46.8 43.2 58.9 66.2 80.7 90.6 94.3 94.7 92.5 100.3 105.1 96.7 90.0 108.3 110.9 100.0 96.3 97.9 101.1 105.6 97.1

Terms of Trade (PX/PM) 62.9 68.1 72.1 78.9 74.9 105.0 137.7 107.8 103.7 103.2 106.1 102.4 96.0 104.8 105.2 96.8 98.3 100.0 100.2 98.3 97.8 92.7 92.1

Notes and Source: Price indices for agriculture, industry, and GDP are from Justino (1990, p. 24) for 1850–1912; Vale´rio (1984, p. 103) for 1914–1927 and 1927–1947; and Neves (1994, pp. 180–200) for 1947–1958. Base year for the four series are, respectively, 1863–1886, 1914, 1927, and 1977. For lack of data, constant prices were assumed for 1912–1914. Prices for exports and imports and terms of trade are from Lains (1995, pp. 237–238, columns PXC and PMC) for 1855–1910; and from Batista et al. (1997, pp. 104–105) for 1910–1958.

Trade, Tariffs, and Trade Shares, 1855–1958 (1958 Prices and Percent). 106 escudos

Exports (Current Values)

Imports (Current Values)

Exports (1958 Prices)

Percent Imports (1958 Prices)

Tariffs (Current Values) 3.6 3.9

– – – – 7.3 – – – 7.8 8.3 8.1 7.4 8.8 10.1 11.5 12.8 12.7

– – – – 29.2 – – – 26.6 28.0 30.8 29.0 26.4 24.8 26.6 29.5 32.2

– – – – 548 – – – 664 669 642 643 745 876 840 915 935

– – – – 543 – – – 462 476 488 442 471 446 442 538 591

– – – – 5.1 – – – 7.5 6.6 6.7 6.8 6.4 6.6 6.5 7.6 8.3

Trade Ratios (1958 Prices) M/GDP

X/GDP

(M+X)/ GDP

3.4 4.1 – – – – 5.3 – – – 4.2 4.4 4.4 4.0 4.3 3.9 4.1 4.8 5.2

4.8 6.3 – – – – 5.4 – – – 6.0 6.1 5.8 5.9 6.8 7.7 7.8 8.2 8.2

8.2 10.4 – – – – 10.7 – – – 10.2 10.5 10.2 9.9 11.1 11.7 11.9 13.0 13.3

Tariffs (% of Current Imports)

19.4 18.5 – – – – 17.4 – – – 28.4 23.7 21.9 23.3 24.2 26.6 24.3 25.7 25.9

PEDRO LAINS

1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873

156

Table A3.

28.3 35.3 30.3 31.6 28.2 33.5 34.4 32.9 32.7 27.1 28.3 30.1 34.8 33.2 35.2 43.5 45.2 37.6 27.2 34.6 30.0 35.0 33.6 36.8 45.9 45.8 55.0

1,021 984 953 950 786 796 1,097 975 1,111 1,113 1,085 1,170 1,481 1,181 1,372 1,288 1,112 1,084 1,254 1,125 1,122 1,279 1,397 1,561 1,796 1,619 1,749

544 666 625 683 672 756 708 742 739 763 800 842 1,009 1,024 1,030 1,112 1,128 993 778 941 837 947 966 939 941 996 1,051

8.5 9.7 9.4 10.2 9.9 11.8 9.0 10.2 11.0 10.8 11.7 12.2 13.2 15.1 14.2 13.9 14.8 13.3 10.5 13.9 13.5 16.1 15.3 12.9 11.8 14.8 15.5

4.7 5.7 5.4 5.8 5.8 6.5 5.9 6.0 6.0 6.2 5.8 6.3 7.2 7.0 7.1 7.7 7.6 6.6 5.1 6.6 5.8 6.3 6.4 6.0 5.5 6.0 6.3

8.9 8.4 8.3 8.1 6.8 6.9 9.2 7.9 9.1 9.1 7.8 8.8 10.6 8.0 9.4 8.9 7.5 7.2 8.2 7.9 7.7 8.5 9.2 9.9 10.5 9.7 10.4

13.6 14.2 13.7 13.9 12.7 13.4 15.1 13.9 15.1 15.3 13.6 15.1 17.9 15.0 16.5 16.6 15.1 13.8 13.3 14.5 13.5 14.8 15.5 15.9 16.0 15.7 16.7

30.1 27.5 30.9 32.3 35.0 35.2 26.3 30.8 33.5 39.9 41.2 40.6 37.9 45.5 40.3 32.0 32.7 35.4 38.4 40.1 45.1 45.8 45.6 35.0 25.7 32.4 28.2

157

14.0 14.8 12.5 15.8 11.4 10.4 15.4 13.1 11.7 11.4 10.5 15.9 17.4 15.4 17.0 17.2 15.5 16.1 23.5 21.0 19.4 22.0 24.8 28.4 37.5 29.1 31.3


1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900

158

Table A3 (Continued ). 106 escudos Exports (Current Values) 28.4 26.5 26.7 24.6 19.7 21.9 22.3 23.6 27.2 35.7 34.1 34.2 35.3 27.2 33.6 56.0 55.2 83.4

51.6 52.0 57.5 60.3 58.4 55.8 58.1 64.0 62.8 69.5 68.1 74.6 89.0 69.3 76.4 129.3 137.4 178.4


Imports (1958 Prices)

1,644 1,634 1,794 1,807 1,729 1,830 1,814 1,734 1,879 2,165 2,077 2,099 2,152 1,697 1,978 2,655 2,007 2,020

1,048 1,053 1,118 1,168 1,235 1,222 1,200 1,239 1,201 1,273 1,202 1,403 1,685 1,272 991 1,368 1,071 902

Tariffs (Current Values) 14.7 13.8 15.7 16.7 17.4 17.3 16.1 17.2 15.8 16.0 16.0 17.0 20.0 17.0 12.0 12.0 11.0 10.0


X/GDP

(M+X)/ GDP

5.9 5.8 6.4 6.6 7.1 7.2 7.0 6.8 6.6 7.1 6.5 7.4 8.9 6.7 5.3 7.3 5.8 5.5

9.3 9.0 10.2 10.2 10.0 10.7 10.6 9.5 10.4 12.0 11.2 11.1 11.4 8.9 10.6 14.2 10.9 12.2

15.2 14.8 16.6 16.8 17.1 17.9 17.6 16.2 17.0 19.1 17.6 18.5 20.3 15.6 16.0 21.5 16.7 17.7


28.5 26.4 27.4 27.7 29.7 31.1 27.7 26.9 25.2 23.0 23.5 22.8 22.5 24.5 15.7 9.3 8.0 5.6

PEDRO LAINS

1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918

Imports (Current Values)

Percent

229.4 691.0 932.6 1,252 2,299 2,957 2,484 2,342 2,662 2,679 2,529 2,406 1,674 1,706 1,901 1,966 2,295 1,994 2,352 2,300 2,074 2,416 2,459 2,471 3,318 3,899 4,050

2,247 2,063 1,885 3,248 2,725 2,725 2,648 2,354 2,241 2,657 2,552 2,179 2,186 2,269 2,344 2,557 2,586 2,967 3,276 3,023 3,483 3,270 3,811 3,168 3,695 2,895 2,965

1,218 1,788 1,788 2,100 2,392 2,120 2,584 2,798 3,200 3,223 3,253 3,613 2,821 2,728 3,518 3,656 4,401 3,940 4,740 4,770 4,662 3,984 3,609 2,974 3,556 4,015 4,154

14.0 26.0 40.0 69.0 147 198 213 271 342 455 554 565 552 539 555 595 612 599 578 619 543 483 528 421 437 479 510

6.8 9.5 9.0 9.5 10.3 9.5 11.1 12.1 11.8 13.1 12.0 13.5 10.0 9.5 11.5 11.4 14.5 14.1 14.5 14.5 14.0 12.8 10.6 8.8 9.9 10.4 11.4

12.6 11.0 9.5 14.6 11.8 12.2 11.4 10.2 8.3 10.8 9.4 8.1 7.8 7.9 7.6 8.0 8.5 10.6 10.0 9.2 10.5 10.5 11.2 9.4 10.3 7.5 8.1

19.4 20.5 18.6 24.1 22.1 21.7 22.5 22.3 20.1 24.0 21.4 21.6 17.8 17.4 19.1 19.5 23.1 24.7 24.5 23.7 24.4 23.3 21.8 18.3 20.2 18.0 19.6

6.1 3.8 4.3 5.5 6.4 6.7 8.6 11.6 12.8 17.0 21.9 23.5 33.0 31.6 29.2 30.3 26.7 30.0 24.6 26.9 26.2 20.0 21.5 17.0 13.2 12.3 12.6

159

107.0 222.2 224.5 443.7 684.3 946.1 854.3 730.4 712.7 1,027.0 1,071.0 944.6 810.9 790.8 801.2 909.0 923.6 1,026 1,202 1,140 1,327 1,613 2,960 3,934 4,035 3,166 3,237


1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945

160

Table A3 (Continued ). 106 escudos Imports (Current Values)

4,586 4,305 4,295 4,090 5,332 7,559 6,808 6,271 7,290 8,164 8,602 8,283 8,291

6,855 9,438 10,330 9,041 7,862 9,471 9,952 9,513 10,062 11,407 12,671 14,354 13,787


Imports (1958 Prices)

Tariffs (Current Values)

4,179 3,749 3,808 3,611 5,038 6,448 5,582 5,609 6,756 7,586 7,781 7,563 8,291

7,194 9,131 9,546 9,079 8,484 8,491 8,710 9,238 10,141 11,314 12,165 13,191 13,787

867 1,131 1,073 1,225 1,071 1,348 1,488 1,357 1,484 1,641 1,738 1,845 1,874


X/GDP

(M+X)/ GDP

18.4 21.5 22.6 21.2 19.2 17.9 18.8 18.3 19.9 21.5 22.3 23.4 24.2

10.7 8.8 9.0 8.4 11.4 13.6 12.0 11.1 13.3 14.4 14.3 13.4 14.5

29.0 30.4 31.6 29.6 30.7 31.5 30.8 29.4 33.2 35.9 36.6 36.8 38.7


12.6 12.0 10.4 13.5 13.6 14.2 15.0 14.3 14.7 14.4 13.7 12.9 13.6

Notes and Source: Trade values are based on Batista et al. (1997, pp. 101–102) for the period 1910–1958 and extrapolated backward by indices for exports and imports from Lains (1995, pp. 231–234, columns XC, XFC, MC, and MFC). For trade ratios we used GDP from Table A1. Tariffs are from Lains (1992).

PEDRO LAINS

1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958

Exports (Current Values)

Percent

AGRICULTURAL PRODUCTIVITY IN THE EARLY OTTOMAN EMPIRE Metin M. Cos-gel ABSTRACT This paper provides standardized estimates of labor productivity in arable farming in selected regions of the early Ottoman Empire, including Jerusalem and neighboring districts in eastern Mediterranean; Bursa and Malatya in Anatolia; and Thessaly, Herzegovina, and Budapest in eastern Europe. I use data from the tax registers of the Ottoman Empire to estimate grain output per worker, standardized (in bushels of wheat equivalent) to allow productivity comparisons within these regions and with other times and places. The results suggest that Ottoman agriculture in the fifteenth and sixteenth centuries had achieved levels of labor productivity that compared favorably even with most European countries ca. 1850.

1. INTRODUCTION Economic historians have long tried to determine how agricultural productivity has varied over time and between societies. The magnitude of variations in productivity is often at the core of such important historical debates as whether there was an agricultural revolution, when and where it happened, and how the standard of living has varied among societies. Identifying the variations in productivity is also required to be able to determine Research in Economic History, Volume 24, 161–187 Copyright r 2007 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0363-3268/doi:10.1016/S0363-3268(06)24005-9

161

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the divergence of incomes and reversals of fortune in history and to examine the effects of climate, resources, technology, and institutions on productivity. Although there are reliable estimates of agricultural productivity in various European countries in the nineteenth century, little is known about productivity in other parts of the world and for the pre-industrial period (Clark, 1999). This paper aims to close this gap by estimating agricultural productivity in the Ottoman Empire during the fifteenth and sixteenth centuries. By the mid-sixteenth century, the Ottomans had built a vast empire that controlled the lands between the Crimea in the north to Egypt and the Arabian Peninsula in the south, and between the Persian Gulf in the east to central Europe and North Africa in the west. They carefully recorded and preserved detailed information about all taxpaying subjects and taxable activities under their control, providing the historian a wealth of information for studying the economic history of these lands (Cos-gel, 2004). I use this information to estimate output per worker in various representative regions of the Empire and make temporal and spatial comparisons of productivity. The results suggest that grain output per worker in Ottoman provinces in the fifteenth and sixteenth centuries was higher than even some countries in northwestern Europe in the mid-nineteenth century. Standardized estimates of labor productivity presented in a comparable format should benefit various areas of research in Ottoman and general history alike. Economic historians of the Ottoman Empire who specialize in other regions or time periods can follow, and if necessary revise, the procedure proposed here to estimate productivity in those regions or times. The results also suggest new areas of research, ranging from using these estimates in providing better answers to some of the old questions of Ottoman historiography to asking entirely new questions. By contributing estimates from eastern Europe and western Asia to the archive of known agricultural productivities in the world, the results will make it possible for the general historian to use these estimates for comparative studies of economic performance and living standards.

2. ESTIMATING COMPARATIVE PRODUCTIVITY To estimate labor productivity in agriculture, economic historians have either used indirect measures based on aggregate data, or measured productivity directly from disaggregated data at the farm or village level.1 Direct measurements of productivity have used information about agricultural inputs and outputs recorded in a variety of documents, such as probate

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inventories and manorial rolls, to estimate yields and labor productivity (Overton, 1979; Allen, 1988a; Karakacili, 2004). Yield and productivity are typically reported in standard units of measurement to facilitate comparisons with other times and places. The literature can be categorized according to whether the primary objective is to compare productivity among places, over time, or both. Studies in the first group typically focus on productivity differences between nations or regions at some fixed point in time, seeking to explain what caused these differences (Clark, 1987). Those in the second group chart the growth of productivity in a fixed place, identifying periods of significant growth and explaining their causes and consequences. For example, the problem of identifying the nature, timing, and causes of the agricultural revolution in England has been at the center of one of the well-known controversies in economic history, generating a debate between those who argue that an agricultural revolution accompanied and even contributed to the industrial revolution of the late eighteenth century and those who either view the event as happening much earlier or not happening at all.2 Studies of the third type essentially combine the first two approaches by comparing how the growth of productivity varied over time between nations. Influential studies of productivity by Bairoch (1965) and Wrigley (1985), for example, compare the growth of labor productivity among nations to understand differences in patterns of urbanization and industrialization.3 This study aims to contribute to the literature in all three dimensions. Its primary objective is to provide direct estimates of labor productivity in eastern Europe and western Asia in the pre-industrial period for inclusion in the list of estimates available for comparison. Despite the high demand for comparable estimates of productivity in these regions, the demand has not yet been met satisfactorily by systematic, comprehensive analysis of available sources. Although historians of the Ottoman Empire have published numerous studies to examine agricultural taxes and production in various districts, they have generally refrained from making temporal or spatial comparisons of productivity. Despite McGowan’s (1969) early exception to this trend, regional historians have typically chosen to limit their analysis to the geographic boundaries and local measurement units of the sources, rather than produce estimates of output and productivity in real, standard, thus comparable units. For a comprehensive analysis of agricultural productivity in the Ottoman Empire during the fifteenth and sixteenth centuries, I use data from the tax registers of various regions of the Empire that represent its geographical diversity during this period.4 These regions include Gaza, Jerusalem and

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southern Syria in eastern Mediterranean, Erbil in northern Iraq, Bursa and Malatya in Anatolia, and Thessaly, Herzegovina, and Budapest in Europe.5 For some of these districts, tax registers are available for multiple dates, making it possible to examine both temporal and spatial variations in productivity. In addition to using tax registers as primary sources of information on tax-paying subjects and taxable agricultural activities, I rely on recent studies of Ottoman society and economy as secondary sources to supplement information required by some parameters of the estimation procedure. The estimation procedure is consistent with recent studies of labor productivity in agriculture (Allen, 2000; Clark, 1999; Karakacili, 2004). Focusing on the arable sector, I estimate the grain output (wheat, barley, and other cereal grains and legumes) per arable worker. To facilitate regional and temporal comparisons of productivity, I convert local measurements and currencies into standard units and report final estimates in bushels of wheat equivalent. Because primary or secondary sources did not always provide direct information on all parameters, several simplifying assumptions had to be made to generate the first comprehensive set of systematic and comparable estimates of labor productivity in these regions. The sources, methods, and simplifying assumptions of the estimation procedure are provided in detail in an appendix to allow future researchers to improve on these estimates.

3. LABOR PRODUCTIVITY IN THE OTTOMAN EMPIRE In the absence of direct information on some parameters of the estimation process, it may be more appropriate to consider carefully all possible scenarios that could have determined the quantities of output and labor and first estimate productivity as a range, rather than a single point. The third and fourth columns in Table 1 show the low and high estimates of productivity (based on two alternative sets of scenarios described in detail in the appendix) in representative Ottoman districts. The next two columns summarize this information into simpler indices for easier comparison of productivity across districts. The first index is defined simply as the average of the low and high estimates of productivity for each region and date. Such an index can be misleading, however, if the relative price of wheat varied significantly between regions and over time. An index of labor productivity measured in bushels of wheat equivalent at local contemporary prices would


Table 1. District

Bursa Bursa I˙nego¨l I˙nego¨l Yarhisar Yarhisar Yarhisar Ermeni Pazarı DomanicDomanicYenis-ehir Yenis-ehir So¨g˘u¨d So¨g˘u¨d So¨g˘u¨d Go¨l Go¨l Go¨l Yenice-i Taraklu Yenice-i Taraklu Geyve Geyve Geyve Akyazı Akyazı Akyazı Akhisar Akhisar Goÿnu¨k Goÿnu¨k Goÿnu¨k Beg˘ Bazarı Beg˘ Bazarı Beg˘ Bazarı Estergom Novigrad Budapest Budapest Srem

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Estimates of Grain Output per Worker in the Ottoman Empire. Year

1521 1573 1521 1573 1487 1521 1573 1573 1487 1573 1521 1573 1487 1521 1573 1487 1521 1573 1487 1573 1487 1521 1573 1487 1521 1573 1521 1573 1487 1521 1573 1487 1521 1573 1570 1570 1546 1562 1566

High Estimate (Bu. of Wheat Equivalent)

300 121 276 133 313 250 113 57 218 86 446 199 255 185 116 362 176 122 162 75 325 151 112 145 77 84 363 185 206 197 119 203 181 130 197 379 132 225 213

Low Estimate (Bu. of Wheat Equivalent)

132 53 122 59 138 110 50 25 96 38 196 88 112 81 51 159 78 54 71 33 143 66 49 64 34 37 160 81 90 87 52 89 80 57 87 167 58 99 94

Index of Labor Productivity (Bursa, 1521 ¼ 100) At Contemporary Local Prices

At Bursa, 1521 Prices

100 40 92 44 104 83 38 19 73 29 149 66 85 62 39 121 59 41 54 25 108 50 37 48 26 28 121 62 69 66 40 68 61 43 66 127 44 75 71

100 52 92 57 101 83 48 24 70 37 149 85 82 62 50 116 59 52 52 32 104 50 48 47 26 36 121 79 66 66 51 65 61 56 85 163 56 97 107

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Table 1. (Continued ) District

Gyula Gyula Trikala Herzegovina Evia Island C - emis-gezek C - emis-gezek C - emis-gezek Mardin Berriyecik Hasankeyf Nisibin Akc- akala Singar Habur Ana MarasMalatya Gerger Kahta Behesni Antep Antep Antep Tel-Ba¯ s-er Tel-Ba¯ s-er Nehr u¨l-Ceva¯ z Nehr u¨l-Ceva¯ z Erbil Ajlun Ajlun Gaza Lajjun Lajjun Nablus Hawran Jerusalem Safad Source: See note 4.

Year

1567 1579 1454 1477 1474 1518 1541 1566 1564 1564 1564 1564 1564 1564 1564 1564 1563 1560 1560 1560 1560 1536 1543 1574 1543 1574 1543 1574 1542 1538 1596 1596 1538 1596 1596 1596 1596 1596

High Estimate (Bu. of Wheat Equivalent)

176 242 104 256 160 183 100 76 162 169 152 340 149 105 43 209 164 103 74 85 114 415 486 294 613 417 426 201 326 206 305 268 430 642 202 330 182 181

Low Estimate (Bu. of Wheat Equivalent)

77 106 46 113 70 81 44 34 71 74 67 150 66 46 19 92 72 45 32 37 50 183 214 129 270 183 187 88 143 91 134 118 189 282 89 145 80 80

Index of Labor Productivity (Bursa, 1521 ¼ 100) At Contemporary Local Prices

At Bursa, 1521 Prices

59 81 35 86 53 61 33 26 54 56 51 114 50 35 14 70 55 34 25 28 38 139 162 98 205 139 142 67 109 69 102 90 143 214 67 110 61 61

63 95 36 88 49 52 32 25 52 72 49 122 53 37 11 66 49 37 26 30 41 148 156 94 197 134 137 65 126 82 114 103 167 241 123 118 70 72


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face the risk of underestimating productivity in areas where wheat was expensive relative to other grains and overestimating it in areas of relatively cheap wheat. The second index avoids this problem, because it uses not the local contemporary price weights but fixed weights. By controlling for differences in the price ratio, it provides an index better suited for regional and temporal comparisons of productivity when prices vary significantly. The two indices in Table 1 take as their base the average productivity estimate for the villages of Bursa, the first capital of the Ottoman Empire, in 1521. Whereas the first index is measured in local contemporary prices of each district, the second uses the price weights of the base district and date. These estimates help us to identify some of the systematic regional differences in productivity in the Ottoman Empire. Ottoman provinces varied significantly in climate, natural resources, and institutional history, raising questions about comparative performance in agricultural production. The information in Table 1 shows some of the general differences in productivity between the different regions of the Empire. During the second half of the sixteenth century, labor productivity was generally high in the region known as the Fertile Crescent, corresponding roughly to the lands between the Nile and the Euphrates and Tigris rivers, as can be seen in the estimates for Erbil, Gaza, and Hawran. Labor was generally less productive in the European districts during the same period, as seen in the estimates for Budapest, Srem, and Gyula. Productivity sometimes varied significantly even within a region. Although the climate and institutional history could be uniform among the districts within a region, soil quality, irrigation possibilities, and population density could vary significantly, causing variations in labor productivity. Whereas Lajjun, a district along eastern Mediterranean, recorded one of the highest productivities, other districts in the same region (for example, Safad and Jerusalem) were remarkably lower.6 Similarly, whereas labor productivity was high in Novigrad, it was significantly lower in some of the other European districts. Estimates of labor productivity also help to identify productivity variations over time. There are noticeable patterns of productivity change during this period. The estimates for the fifteenth century are generally lower than those for the early sixteenth century, indicating a growth in labor productivity throughout the Empire. The estimates are mixed, however, for the sixteenth century, generally considered to be the height of the Empire’s long reign of six centuries. Although historians generally agree that the sixteenth century was a period of demographic growth and economic expansion in the Ottoman Empire, it is not clear whether this growth and expansion also

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meant an increase in the economic performance and living standards of Ottoman subjects on average. In a pioneering analysis of the wages of construction workers in Istanbul and other Ottoman cities, O¨zmucur and Pamuk (2002) have shown that real wages actually declined during the sixteenth century, a trend similarly observed in other European cities as well. Our results show that a parallel decline took place in rural incomes and labor productivity in grain farming in some Ottoman districts. Although productivity rose or remained stagnant in some regions during the second half of the sixteenth century (for example, in Ajlun, Lajjun, Budapest, and Gyula), it generally declined significantly during the same period in Anatolia. The sharp decline in labor productivity in Anatolia is consistent with the well-known demographic growth in this region and the recent research that has found a negative relationship between rising population and labor productivity in other parts of the world. It is also consistent with the general fall in agricultural productivity observed in various parts of Europe between 1500 and 1600 (Allen, 2000). Having estimates of labor productivity would also contribute to recent debates surrounding the performance of the Ottoman economy after the sixteenth century. Whereas the previous generation of historians spoke of an Ottoman decline during this period, recent research has rejected the notion of a decline, seeking to revise or reinterpret the periods of Ottoman history. Although very few scholars would nowadays take the notion of an absolute decline seriously, the timing of the relative slip of the Middle East (compared to northwestern Europe) remains an issue. Whereas some historians would identify the end of the ‘‘Golden Age of Islam’’ in the twelfth century as the turning point, others (for example, Marxists) would point to the western imperialism of the eighteenth and nineteenth centuries, and still others would see the problem in other religious, military, technological, or institutional causes rooted in other centuries. Despite being involved in an essentially quantitative debate on economic performance, however, participants have so far been unable to offer any direct quantitative evidence to substantiate their claims about the performance of the economy during this period. At the heart of the debate is the question of how well the Ottoman economy has performed over time, which has been difficult to quantify. Although O¨zmucur and Pamuk’s (2002) recent study of long-term trends in real wages may help settle some of the issues in the debate, other issues will remain because urban wages tell only part of the story for a primarily agrarian state like the Ottoman Empire. Our estimates will help to learn about the rest of the story by establishing a benchmark against which productivity in other periods can be compared.


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4. COMPARATIVE PRODUCTIVITY Measuring productivity in standard units makes it possible to use this information not just for issues of limited local interest and regional analysis but also for broader questions and global comparisons. There are numerous questions of global importance that require reliable estimates of agricultural productivity in the Ottoman Empire for answers. If one of the fundamental tasks of economic history is to understand the nature and causes of the rise of northwestern Europe, the other is to understand why close neighbors and trading partners in eastern Europe and western Asia lagged behind. Having reliable estimates for these regions would make it possible to observe how incomes and productivity differed from northwestern Europe before the industrial revolution and whether and how fast productivity grew over time. By comparing these trends, we can examine whether there was a significant gap in productivity, when and why it started, and whether there was a direct causal relationship between the growth of agricultural productivity and the rise of industry in these regions. To compare labor productivity in the Ottoman Empire with other times and places, we have to proceed with great caution and choose comparable estimates carefully. International and intertemporal comparisons of productivity may be problematic, because estimates could be based on different sources of data and methods of estimation could be incompatible, in addition to the usual complications caused by differences in prices and units of measurement. To minimize these difficulties, we have to restrict comparisons to regions and time periods with well-developed scholarship that have resulted in commonly accepted estimates of agricultural productivity. Economic historians have recently developed several innovative methods for measuring agricultural productivity in studying the nature and causes of gaps in incomes and productivity between nations. They have had limited success, however, in producing commonly accepted estimates for the preindustrial period. This is true even in the highly developed scholarship of English history.7 England’s leadership in economic development has made her the focus of attention for various important questions of economic history, and the availability of sources has allowed scholars to examine these questions in great detail. But historians have so far failed to reach a consensus on productivity levels in pre-industrial England and the magnitude of productivity growth in agriculture since that time. These estimates have generally emerged in the context of the debate on the nature and timing of the agricultural revolution and vary significantly among scholars based on their sources of data, approaches to the problem, and method of estimation.

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For a more reliable comparison, we turn to estimates of labor productivity in the nineteenth century. There is less debate on what the outputs were in the nineteenth century in England than in other countries in Europe. Clark (1999) has provided estimates of output per worker in various countries in Europe ca. 1850, which we can use as benchmark for comparison. Since his estimates are given in bushels of wheat equivalent, they are directly comparable with ours. To simplify the comparison, let us summarize the productivity estimates for the Ottoman Empire by aggregating them according to the distinct geographic regions and identifiable time periods to which they belong. Ottoman districts for which we have information from the tax registers can be categorized into three distinct geographic groups based on differences in climate and religious and institutional history. These groups consist of the districts in the east corresponding roughly to the Fertile Crescent, the European districts in the west (further separable between those in southeastern Europe conquered long before the sixteenth century and those further north in Serbia and Hungary conquered during the sixteenth century), and the districts in the core lands of the Empire in Anatolia. For some of these groups the tax registers provide information for multiple periods of time, making it possible to aggregate the estimates separately for different time periods. Although we do not currently have comprehensive information for all villages in each of these regions and for all times, we can use the available data for preliminary generalizations about comparative productivity. Table 2 shows how output per worker in various parts of the Ottoman Empire in the fifteenth and sixteenth centuries compared with various countries in Europe ca. 1850.8 To be consistent with Clark’s (1999) figures, productivity estimates for the Ottoman Empire are reported based on the same index (England, 1851 ¼ 100). Output per worker in England was clearly far ahead of most other countries in Europe during the nineteenth century. Productivity was also generally higher in northwestern Europe than in eastern Europe during this period, falling gradually as we move from the west of Europe to the east, as Clark (1999, p. 211) also noted. Although our estimate for southeastern Europe in the fifteenth century appears to be consistent with this trend, the estimate for Serbia–Hungary in the sixteenth century is significantly higher than Clark’s estimate for the same region in the nineteenth century. In any case, our estimates show that the eastward decline of productivity was purely a pre-industrial and European phenomenon, inapplicable to Ottoman provinces during the fifteenth and sixteenth centuries. They also show only small gains in labor productivity in agriculture between the pre-industrial period and the nineteenth century, rather


Table 2.

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Comparative Productivity in Ottoman Empire and Europe.

Region

Anatolia Anatolia Anatolia Fertile Crescent Fertile Crescent Southeastern Europe Hungary-Serbia Britain Netherlands Belgium Ireland France Germany Romania Austria Sweden Hungary Russia

Date ca.

Output per Worker (England, 1851 ¼ 100)

1490 1520–1540 1560–1575 1540 1590 1450–1475 1545–1580 1851 1850 1850 1851 1850 1850 1870 1854 1850 1854 1870

58 64 40 76 68 39 53 100 54 37 47 44 42 40 32 37 30 29

Notes and Source: Clark (1999) estimates output per worker in England in 1851 at the equivalent of 272 bushels of wheat. Output per worker estimates for Ottoman regions are based on Table 1. Estimates for European countries are from Clark (1999).

than the dramatic increase in productivity implied by the notion of the agricultural revolution that supposedly took place in northwestern Europe during this period according to some historians. Indeed, output per worker was higher in some districts of the Ottoman Empire in the fifteenth and sixteenth centuries than in most countries in northwestern Europe ca. 1850. One has to be careful, of course, in interpreting these results. They are clearly preliminary estimates that aim to identify areas for further research into important questions of economic history rather than to provide definitive answers to such questions. It is also important to note the limitations of using labor productivity to assess overall economic performance. Because output per worker is a partial measure of productivity, it does not include information about a variety of factors, such as input ratios, that may have also affected productivity. If workers in one region worked with more land or machinery than workers in another region, then their productivity would of course be higher. Even though the technology or input ratios may have been similar between regions, there is still the effect of climate,

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irrigation facilities, land quality, and various other economic, social, and cultural factors. Further research into the peculiarities of regions and times may be necessary to explain what caused the differences in the productivity of arable labor.

5. CONCLUDING REMARKS Using information from the tax registers of the Ottoman Empire recorded during the fifteenth and sixteenth centuries, this paper has developed estimates of labor productivity in grain farming in various parts of eastern Europe and western Asia. By standardizing and comparing productivity estimates across regions and over time, we are able to identify some general tendencies in comparative performance and even reach preliminary conclusions on the question of how output per worker in these regions compared with various countries in Europe. The estimation procedure and simplifying assumptions are made abundantly clear in order to allow other researchers to examine these first estimates critically and to modify them as necessary in answering various longstanding questions in the economic history of these regions, or to ask new ones. The analysis suggests future work in at least three areas. The first is to improve the estimates themselves and expand their geographic and temporal coverage. When no direct information was available on some parameters of the estimation procedure, such as local units of measurement and the size of the household or the proportion of their labor devoted to grain farming, I had to make simplifying assumptions based on other information and secondary sources to generate estimates. The accuracy of these estimates can thus be greatly improved with better information on these parameters. The procedure can also be used to generate estimates of labor productivity in other regions of the Ottoman Empire or for the same regions at other times. The second area of future work is to understand the causes and consequences of differences in labor productivity. Although generating estimates and identifying patterns are essential tasks of quantitative inquiry into productivity, they are only the first steps. For a more satisfactory understanding of productivity, we also need to examine whether and how geographic, institutional, demographic, and other differences affected productivity, and what productivity differences implied for living standards and long-term growth. A related field of analysis made possible by these estimates is comparative history. Understanding labor productivity in grain farming in the Ottoman


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Empire has clear implications for various important questions of historical scholarship. For example, how differently, if at all, did agricultural productivity affect industrial growth in these regions? How commonly was the productivity decline seen in some regions of the Ottoman Empire during the sixteenth century observed elsewhere in the world, and was there a common cause? Numerous other questions emerge about the nature, causes, and consequences of comparative performance.

NOTES 1. Well known in the first category are Bairoch’s (1965) index based on the production of vegetable-based calories and Wrigley’s (1985) index based on the proportion of population engaged in agriculture. 2. See, for example, Allen (1999), Clark (1999, 2004a, 2004b), and Overton (1996). 3. For a more recent comparative study of this type, see Allen (2000). 4. Sources of data are the Ottoman tax registers numbered 5, 23, 44, 64, 111, 113, 161, 186, 345, 365, 373, 388, 410, 449, 453, 507, 549, 580, 970, 1,050 in the Prime Ministry Archives in Istanbul; and 67, 68, 69, 72, 75, 80, 97, 99, 100, 101, 106, 112, 142, 164, 181, 185, 192, 570, 580, 585 in the Cadastral Office in Ankara. Contents have been published by I˙lhan (1994–1995), O¨zdeg˘er (1988), Alicic (1985), Bakhit and Hmuod (1989a, 1989b), Balta (1989), Barkan and Meric- li (1988), Bayerle (1973), Delilbas-ı and Arıkan (2001), Fekete (1943), Goÿu¨nc- and Hu¨tteroth (1997), Hu¨tteroth and Abdulfattah (1977), Kaldy-Nagy (1971, 1982), McGowan (1983), U¨nal (1999), Yinanc- and Elibuÿu¨k (1983, 1988). The data for Maras-, Srem, and Trikala are systematic samples of the population. The data for other districts are full samples. 5. For easier recognition, I use the better-known current English names, rather than those used by the Ottomans, for some of these regions. 6. For an analysis of the relationship between the tax system and agricultural incomes in this region, see Cos-gel (2006). 7. Some of the pioneering studies in the field, such as Overton’s (1979) method of extracting information from probate inventories, Clark’s (1991a, 2004a) method of estimating productivity from payments to workers for different types of tasks, and Karakacili’s (2004) direct measurement of arable workers’ labor productivity before the pre-industrial period, have focused exclusively on English agriculture. Although there have been various attempts at comparing agricultural productivity between nations or regions, the lack of reliable sources has restricted these comparisons either to the period after 1800 or to places in western Europe. For example, Bairoch (1975, 1976) used the production of vegetable-based calories as an index to compare the level of agricultural development in various parts of the World, but only since 1800; and Wrigley (1985) pioneered the method of using the proportion of population engaged in agriculture to estimate comparative productivities going back to 1500s, but only within western Europe. 8. Given the simple and elementary nature of the intended comparison in Table 2, I did not report separately the index adjusted for differences in the relative price of wheat, because making the adjustment did not change the results significantly.

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9. In comparing the entries in Table A1 with current populations of these regions or with other time periods, one has to keep in mind that district boundaries may have changed since the sixteenth century. 10. For the Ottoman system of taxation, see Cos-gel (2005) and Cos-gel and Miceli (2005). 11. As a measure of weight, the standard kile was equivalent to 25.65 kg. 12. Even though the registers also included the names and numbers of male bachelors, the age criteria for inclusion in this category was not always explicitly specified, making regional comparisons based on this inconsistent information questionable. 13. This would be consistent with Allen’s (1991) assumption that each family supplied three workers. See also Clark’s (1991b) criticisms of Allen’s method. 14. Personal taxes and occasional fees are excluded from the calculation of total tax revenue.

ACKNOWLEDGMENTS I wish to thank Greg Clark, two anonymous reviewers, Eona Karakacili, Phil Hoffman, and the participants in the ‘‘Towards a Global Study of Prices and Incomes’’ conference in Utrecht, the Netherlands, in August, 2004, for comments and suggestions. Financial support from the University of Connecticut Research Foundation and the National Science Foundation (under Grant No. SES-0433358) is gratefully acknowledged.

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Goÿu¨nc- , N. (1979). Hane Deyimi Hakkında. I˙.U¨.E.F. Tarih Dergisi, 32, 331–348. Goÿu¨nc- , N., & Hu¨tteroth, W. D. (1997). Land an der Grenze: Osmanische Verwaltung im Heutigen Turkisch-Syrisch-Irakischen Grenzgebiet im 16. Jahrhundert. Istanbul: Eren. Hu¨tteroth, W. D., & Abdulfattah, K. (1977). Historical geography of Palestine, Transjordan and Southern Syria in the late 16th century Erlanger Geographische Arbeiten (Vol. 5). Erlangen: Fra¨nkische Geographische Gesselschaft. I˙lhan, M. (1994–1995). Erbil Vilayeti Mufassal ve Mu¨cmel Tahrir Defteri (H. 949/M. 1542). Belgeler Cilt XVI, Sayı 20. I˙nalcık, H., & Quataert, D. (1994). An economic and social history of the Ottoman Empire, 1300–1914. New York: Cambridge University Press. Kaldy-Nagy, G. (1971). Kanuni Devri Budin Tahrir Defteri (1546–1562). Ankara: Ankara U¨niversitesi Dil Tarih-Cog˘rafya Faku¨ltesi Yayınları. Kaldy-Nagy, G. (1982). A Gyulai szanzsak 1567 es 1579. evi osszeirasa. Bekescsaba: Bekes Megyei Leveltar. Karakacili, E. (2004). English agrarian labor productivity rates before the black death: A case study. Journal of Economic History, 64, 24–60. Lewis, B. (1952). Notes and documents from the Turkish archives. Jerusalem: The Israel Oriental Society. Lewis, B. (1954). Studies in the Ottoman Archives-I. Bulletin of the School of Oriental and African Studies, 16, 469–501. McGowan, B. (1969). Food supply and taxation on the middle Danube (1568–1579). Archivum Ottomanicum, 1, 139–196. McGowan, B. (1983). Sirem Sancag˘ı Mufassal Tahrir Defteri. Ankara: Tu¨rk Tarih Kurumu. Overton, M. (1979). Estimating crop yields from probate inventories: An example from East Anglia, 1585–1735. Journal of Economic History, 39, 363–378. Overton, M. (1996). Agricultural revolution in England: The transformation of the agrarian economy 1500–1850. New York: Cambridge University Press. O¨zdeg˘er, H. (1988). Onaltınci Asırda Ayıntab Livası. Istanbul: I˙stanbul U¨niversitesi Rekto¨rlu¨k. O¨zmucur, S., & Pamuk, S- . (2002). Real wages and standards of living in the Ottoman Empire, 1489–1914. Journal of Economic History, 62, 293–321. U¨nal, M. A. (1999). XVI. Yu¨zyılda C - emis- gezek Sancag˘ı. Ankara: Tu¨rk Tarih Kurumu. Wrigley, E. A. (1985). Urban growth and agricultural change: England and the continent in the early modern period. Journal of Interdisciplinary History, 15, 683–728. Yinanc- , R., & Elibuÿu¨k, M. (1983). Kanuni Devri Malatya Tahrir Defteri (1560). Ankara: Gazi U¨niversitesi Yayınevi. Yinanc- , R., & Elibuÿu¨k, M. (1988). Maras- Tahrir Defteri (1563). Ankara: Ankara U¨niversitesi Yayınevi.

APPENDIX A. MEASURING LABOR PRODUCTIVITY FROM TAX DATA A.1. Sources of Data Studies of Ottoman economy during the fifteenth and sixteenth centuries typically use the tax registers known as tahrir defterleri as source. Conducted


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upon conquering new lands and updated periodically, these registers are the outcome of the government’s attempt to have current information on sources of revenue. They contain detailed information about tax-paying subjects and taxable resources, including the names and legal status of adult males and estimates of tax revenues from productive resources and activities in all villages, towns, tribes, and other taxable units in a district. Although the Ottomans discontinued conducting new registers in most districts after the sixteenth century, they nevertheless preserved existing registers and relied on them for various decisions of government finance. Hundreds of registers have survived from as early as the 1430s, available to researchers in various archives in Turkey and in other countries that were once under Ottoman domination. There now exist registers of regions ranging from Anatolia and the Balkans to Syria and Palestine in the south, Georgia in the northeast, and Hungary and Poland in the northwest, altogether forming an indispensable series of documents for studying the economic and social history of the Ottoman Empire (Cos-gel, 2004). Since estimating the expected tax revenue was the primary purpose of the tax registers, information was not always recorded in ways that allowed direct estimates of agricultural production. For example, enumerators entered the tax amount as a lump sum payment for some villages, making it impossible to estimate individually the outputs of productive activities. They similarly recorded incomplete information about some resources or activities, or recorded potential sources of revenue (such as from ruined mills or uninhabited lands called mezra’as) that could have been idle at the time of the registry. To keep only the relevant and accurate information about agricultural production, I thus omitted those fiscal units that made a single lump-sum payment for taxes, did not provide sufficient information on inhabitants or agricultural taxes, or consisted of ruined or unemployed resources. I also omitted towns, nomadic tribes, and other fiscal units that were not rural settlements engaged in agricultural production. The remaining data thus consist of only inhabited villages for which complete information was available to estimate agricultural production. Table A1 presents summary information about the villages included in the data for the selected districts, some at multiple dates. For each district and date, the table shows the number of villages included in the dataset and the mean and standard deviation of the number of households in these villages. There is a clear upward trend in the average number of households over time, as can be seen in districts for which we have data for multiple dates. In the second half of the sixteenth century, villages in eastern Mediterranean and those in the Singar, C - emis- gezek, and Nehr u¨l-Cevaz

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178

Table A1.

Rural Households in Ottoman Districts.

District

Year

Number of Villages in Sample

Average Number of Households per Village

Standard Deviation

Bursa Bursa I˙nego¨l I˙nego¨l Yarhisar Yarhisar Yarhisar Ermeni Pazarı DomanicDomanicYenis- ehir Yenis- ehir So¨g˘u¨d So¨g˘u¨d So¨g˘u¨d Go¨l Go¨l Go¨l Yenice-i Taraklu Yenice-i Taraklu Geyve Geyve Geyve Akyazı Akyazı Akyazı Akhisar Akhisar Goÿnu¨k Goÿnu¨k Goÿnu¨k Beg˘ Bazarı Beg˘ Bazarı

1521 1573 1521 1573 1487 1521 1573 1573 1487 1573 1521 1573 1487 1521 1573 1487 1521 1573 1487 1573 1487 1521 1573 1487 1521 1573 1521 1573 1487 1521 1573 1487 1521

47 60 27 44 9 10 20 4 38 37 17 46 29 4 27 38 17 75 23 84 26 13 69 24 4 101 34 39 50 49 146 95 108

16.9 32.3 20.0 26.6 6.7 14.6 23.1 40.5 8.2 23.8 18.9 30.1 7.8 16.8 19.9 10.5 18.8 17.1 25.6 14.9 14.6 30.8 19.7 22.0 40.0 16.7 19.7 21.8 18.4 18.7 12.8 16.7 21.6

15.0 22.8 14.7 17.2 3.8 9.6 22.6 34.3 5.6 23.8 12.6 20.2 6.8 10.5 10.3 6.8 12.1 17.9 20.5 13.6 11.3 20.0 17.0 17.8 46.1 28.3 14.3 15.0 13.0 14.8 9.4 13.6 17.7


Table A1.

179

(Continued )

District

Year



Standard Deviation

Beg˘ Bazarı Estergom Novigrad Budapest Budapest Srem Gyula Gyula Trikala Herzegovina Evia Island C - emis- gezek C - emis- gezek C - emis- gezek Mardin Berriyecik Hasankeyf Nisibin Akc- akala Singar Habur Ana MarasMalatya Gerger Kahta Behesni Antep Antep Antep Tel-Ba¯ s-er Tel-Ba¯ s-er Nehr u¨l-Ceva¯ z

1573 1570 1570 1546 1562 1566 1567 1579 1454 1477 1474 1518 1541 1566 1564 1564 1564 1564 1564 1564 1564 1564 1563 1560 1560 1560 1560 1536 1543 1574 1543 1574 1543

178 121 78 295 286 100 199 202 276 231 115 267 330 96 532 227 181 165 64 52 6 342 300 266 145 121 83 101 103 92 98 95 19

21.6 25.4 17.6 24.9 26.2 21.0 26.3 30.7 38.2 18.5 28.8 20.2 27.7 43.3 24.6 18.8 30.8 10.3 13.4 47.8 69.7 10.0 25.2 28.4 28.8 30.1 21.0 15.7 21.6 27.3 22.8 25.0 40.3

22.3 21.2 11.9 22.4 19.1 14.0 21.0 25.4 38.2 27.4 28.1 17.2 31.8 31.2 50.5 37.0 41.6 14.5 13.5 73.0 82.4 24.9 20.5 28.2 24.9 34.1 23.3 15.8 24.2 27.4 25.6 27.5 63.1

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180

Table A1.

(Continued )

District

Year



Standard Deviation

Nehr u¨l-Ceva¯ z Erbil Ajlun Ajlun Gaza Lajjun Lajjun Nablus Hawran Jerusalem Safad

1574 1542 1538 1596 1596 1538 1596 1596 1596 1596 1596

25 52 136 121 199 64 53 215 366 176 283

45.8 29.8 30.6 27.9 46.0 16.1 18.5 29.7 22.4 35.6 45.7

76.1 36.3 32.8 23.5 64.4 18.0 19.4 28.4 23.0 35.9 55.6

Source: See note 4.

regions in eastern Anatolia stand out as the most heavily populated. Villages in the Trikala district in Thessaly were also heavily populated in the fifteenth century.9 A.2. Measuring Output The Ottoman system of taxing agricultural production makes it easy to calculate the gross output of grains.10 Taxes on grains were typically levied as a proportion of output, making the calculation of output a simple matter of multiplying the taxes listed in the registers by the inverse of the tax rate. But the difficulty lies in determining the equivalent of output in a standard unit. The tax registers used a variety of local units for measuring grain, most common being kile, an Ottoman measure of volume. The standard kile was equivalent to 35.27 l or 0.97 Winchester bushels.11 Although for their own accounting purposes the Ottomans tried to standardize units of measurement across regions or at least record taxes in units of standard kile, this was not always possible. When the local unit was different from kile or the local kile varied significantly from the standard kile


181

and enumerators somehow had no choice but to record taxes in local units, they sometimes noted these differences in the tax code of the district to alert the treasury personnel or other users of the register. As long as this practice was followed, it becomes equally easy for us to use the appropriate conversion factor to calculate the standard equivalent of output. The remaining problem is when the enumerators recorded taxes in different (non-kile) units or in non-standard kile without entering any information about how this unit differed from the standard kile. In Jerusalem and surrounding districts, for example, grain taxes were entered in units of ghirara, a commonly used unit in that region but one that could also vary locally (Lewis, 1952). Whenever available I used information from secondary sources to convert these units to the standard kile. But in some cases, no information is available from the registers or other secondary sources on how the local units varied. In the Mardin region, for example, kile clearly varied from one subdistrict to another (as can be inferred from the varying prices of products like wheat and barley), but in unknown ways (Goÿu¨nc- & Hu¨tteroth, 1997). When no direct information was available about the local units used in a district, I determined the rates of conversion based on the price of wheat recorded in the registers and known conversion rates in neighboring districts at that time. For proportionally taxed products like grains, enumerators had to specify a price to convert physical quantities to nominal values in order to calculate the total tax revenue in each village. In cases of unknown conversion rates for a district, I compared the price of wheat specified in the registers of this district with the (standard) prices used in the registers of other districts for the same time period to determine whether the enumerators were likely to have used a standard kile for measurement. If the price appeared too low or too high compared to known standard prices, I relied on comparable prices and conversion rates observed in the region to specify a rate of conversion for this district. Table A2 demonstrates the procedure for standardizing the local units and measurements recorded in tax registers to standard equivalents. Entries in the table show how the local prices and units have varied across Ottoman districts and how these prices have been converted to prices per standard kile and bushel for each district and date. The last column shows the sources used for conversions, whenever such information was available. The absence of a source thus indicates that a rate of conversion had to be constructed based on other information and assumptions. Of course, researchers familiar with sources not stated here are encouraged to supply the information and suggest revisions in the conversion table as necessary.

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182

Table A2.

Units and Prices of Wheat in Ottoman Tax Registers.

Region

Year

Price in Tax Register

Unit

Standard Price per (Winchester) Bushel

Bursa (Hu¨davendiga¯ r)

1487

60

mud

5.2


1521

70

mud

6.0


1573

100

mud

8.6

Estergom Novigrad

1570 1570

12 12

kile kile

12.4 9.3

Budapest Budapest Srem

1546 1562 1566

10 12 14

kile kile kile

10.3 12.4 14.4

Gyula

1567

10

kile

10.3

Gyula

1579

11

kile

11.3

Trikala

1454

8

kile

3.3

Herzegovina Evia Island

1477 1474

24 20

himl

3.1 2.6

C - emis-gezek

1518

8

kile

8.2

C - emis-gezek

1541

9

kile

9.3

C - emis-gezek

1566

12

kile

12.4

Mardin MarasMalatya

1564 1563 1560

3 10 5

kile kile kile

12.4 10.3 10.3

Antep Antep Antep Erbil

1536 1543 1574 1542

5 6 9 90

kile kile kile tagar

5.2 6.2 9.3 9.3

Ajlun

1538

130

ghirara

5.4

Source for Unit Conversion

I˙nalcık and Quataert (1994, p. xl) I˙nalcık and Quataert (1994, p. xl) I˙nalcık and Quataert (1994, p. xl) Bayerle (1973, p. 22n)

McGowan (1969, p. 166) Kaldy-Nagy (1982, p. 400) Kaldy-Nagy (1982, p. 400) Barkan (1943, p. 289) Akgu¨ndu¨z (1990, p. 387) Barkan (1943, p. 189) Barkan (1943, p. 189) Barkan (1943, p. 189)

Barkan (1943, p. 111)

Akgu¨ndu¨z (1990, p. 173) Lewis (1952, p. 17; 1954, p. 491)


Table A2.

183

(Continued )

Region

Year

Price in Tax Register

Unit

Standard Price per (Winchester) Bushel

Ajlun

1596

140

ghirara

5.8

Gaza

1596

250

ghirara

5.2

Lajjun

1538

120

ghirara

5.0

Lajjun

1596

140

ghirara

5.8

Nablus

1596

710

ghirara

6.8

Hawran

1596

150

ghirara

6.2

Jerusalem

1596

500

ghirara

6.9

Safad

1596

130

ghirara

5.4

Source for Unit Conversion

Lewis (1952, p. 1954, p. 491) Lewis (1952, p. 1954, p. 491) Lewis (1952, p. 1954, p. 491) Lewis (1952, p. 1954, p. 491) Lewis (1952, p. 1954, p. 491) Lewis (1952, p. 1954, p. 491) Lewis (1952, p. 1954, p. 491) Lewis (1952, p. 1954, p. 491)

17; 17; 17; 17; 17; 17; 17; 17;

Note: See the Appendix for the details of the conversion procedure. Source: For sources of price data, see note 4.

To determine the total output of grains, I first used the local prices of wheat to convert the nominal values of all grains to their bushels of wheat equivalent. Because the products on the arable were typically subject to proportional taxation, enumerators entered both the quantity and the value of expected taxes from these products for accounting purposes. Although the prices used for this purpose were simply the average prices of these products in the region, rather than the contemporary market prices faced by each village, they provided sufficient information for enumerators to convert physical quantities to values. By reversing the procedure and using the same relative prices for calculation, I was able to convert the information about the nominal values of output into wheat equivalents. A.3. Measuring Labor The other variable we need to estimate in measuring productivity is labor. Although no direct information is available on the quantity of labor on the arable devoted to grain production, this can be estimated from the number

184

METIN M. COS- GEL

of households. The tax registers did not include direct information on labor simply because the Ottomans did not tax labor directly. Rather than tax unobservable labor of households, they based personal taxes on the household as a whole or on the observable characteristics of heads of households like land ownership and marital status. Although the rates and types of personal taxes varied between regions, the records related to them consistently included the names and numbers of heads of households.12 To transform the information about households into an estimate of the labor used in grain production, we need to specify the quantity of labor per household and the proportion of their labor devoted to work on grains, the multiplication of which would provide the desired estimate. Because the tax registers do not provide direct information on either of these quantities, however, it may be too optimistic to aim a reliable single estimate of the labor used in grain production. It may be more reasonable to proceed cautiously and generate low and high estimates based on alternative sets of scenarios and simplifying assumptions. Let us start by generating a high estimate of the range of labor. The quantity of labor in a household would depend on the size of the household and the effective labor input of each member of the family. The problem of determining the size of a household has been highly debated in Ottoman historiography. Given the size of the Ottoman Empire stretching over three continents, the household size could have varied between districts. The size could also have changed over time in each district, for example, during the population expansion of the sixteenth century. Unfortunately, there are no comprehensive estimates of the household size specific to each district and time period that can be incorporated into our estimates. Quantitative studies of the Ottoman Empire that require information on the household size typically proceed by assuming the size to be consistent among districts and over time, and we have no choice but to follow this convention. In his pioneering study of Ottoman population, Barkan (1953) assumed a household size of five, which later studies have generally found as being too high (Goÿu¨nc- , 1979). Leaving the possibility of regional and temporal variations in household size to future researchers, I use Barkan’s estimate of five as the high estimate of household size in all districts and assume on average one member of the family to be ineligible to work (because of age or some other restriction). The total number of workers potentially available to perform all tasks in the household, including farm and domestic work, then becomes four. Suppose on average, workers in a family consisted of a man, a woman, a boy, and a girl, and we denote the quantities of their labor by M, W, B, and G.


185

Effective labor could have differed significantly between age and gender groups. To consider these possibilities, use H to denote the units of standard ‘‘adult male equivalent’’ labor, such that H ¼ M. Studies generally agree that the effective labor, including skill and physical strength, of adults was significantly higher than children, though differences between men and women have been a matter of dispute (Clark, 2003). To estimate such differences in England, Allen (1988b, 1991) uses information about the average annual earnings of these groups as recorded in Young’s data for rural English society, and he finds the average earnings of boys to be about half of men’s. Although Allen also finds a similar difference between the earnings of males and females, one might object to using this difference in estimating the total labor supply of the household available for all activities, because the difference could simply have been caused by such things as unpaid domestic labor and earnings differentials between specialized tasks. That is, it may not be legitimate for us to consider the earnings differences between men and women as an index of their overall marginal contribution to household labor supply, because it included not just farm work but domestic tasks as well. To construct a high estimate of the labor input per household, therefore, let us suppose that there were significant differences between adults and children but no differences between males and females. These assumptions imply M ¼ W ¼ 2B ¼ 2G, with a corresponding estimate for the average labor supply per household equal to 3 H. To construct a low estimate of the household labor, let us consider different arguments about household size and male–female differences in labor input. Criticizing Barkan’s household multiplier as being too high, other studies of Ottoman population have proposed lower estimates for the average size of a household. Although there is no direct evidence to substantiate these arguments for the fifteenth or sixteenth centuries, Goÿu¨nc(1979) was able to construct an estimate based on documents relating to migrants in the nineteenth century. His calculations show that household size was about four, a figure we can use to determine a low estimate of family labor supply. Suppose an average family with four members could supply three workers: a man, a woman, and a child.13 Suppose also that we accept differences between the earnings of men and women as an index of their effective labor inputs and that the differences Allen (1991, p. 487) found in England in the eighteenth century applied equally to the Ottoman population during the fifteenth and sixteenth centuries, such that M ¼ 2W. Supposing age-based differences continued to hold, these assumptions altogether give us the low estimate of household labor supply, equal to 2 H.

186

METIN M. COS- GEL

The remaining issue is to determine the proportion of household labor devoted to grain production. Suppose for simplicity that labor is used for domestic or farming activities and that farming consisted of producing grains or other products. The proportion of labor allocated to grain production must have varied between regions, depending on differences in climate, topography, and other factors affecting regional specialization. Let a denote the proportion of farm labor devoted to grain production. The proportion of household labor allocated to domestic tasks could also have varied by regions, depending on such factors as the size of farms, availability of alternative opportunities, cultural standards on the nature and amount of domestic tasks, the types and sizes of homes, and the division of labor between age and gender groups. Because sources do not provide direct information on domestic labor or on factors that could have affected its proportion in labor allocation, it may similarly be reasonable to proceed by generating low and high estimates for this proportion. A high estimate of the proportion of labor devoted to domestic tasks could be one-half of the total labor supply, suggested by approximately equal populations of men and women and the hypothesis of complete specialization by men and women between farming and domestic tasks jobs. This gives us an estimate for the proportion of total labor for grain production as 0.5 a. A low estimate is suggested by a hypothesis of incomplete specialization, with asymmetric participation between men and women in each other’s activities. More specifically, suppose that women participated more in farming than men participate in domestic activities to such an extent that the proportion of household labor allocated to domestic tasks was only onethird. The corresponding proportion of household labor for grain production would thus be 0.33 a. These assumptions altogether give us the low and high points needed to estimate the total effective supply of household labor devoted to grain production. The low estimate is equal to (2 0.33) a H, and the high estimate is (3 0.5) a H. By determining the values of H and a in a village, therefore, we can calculate the low and high estimates of the labor used in grain production in the village. The value of H is simply the number of households in the village, recorded consistently by the tax registers across regions. The value of a can be estimated from the proportion of production taxes from grains, assuming the ratio of taxes to labor supply to be the same between taxable activities.14 The results of the outlined estimation procedure can be seen in the two tables presented in the main text. Of course, as with all first estimates of historical phenomena based on simplifying assumptions, these figures


187

should be taken with some caution. Given the current state of our knowledge of the Ottoman economy and society during this period, the primary objective of these estimates has been to lay the groundwork for a procedure to calculate labor productivity as accurately as possible. Further research is required to improve the procedure by replacing questionable assumptions with more reliable estimates based on direct evidence.

GRAIN PRICES IN CAIRO AND EUROPE IN THE MIDDLE AGES Johan So¨derberg ABSTRACT This paper compares grain prices between Cairo and Europe during medieval times. Prices were higher and more volatile in Cairo than in Europe. Over time, price levels declined in large parts of Europe but not in Cairo. No price integration can be seen between the European Mediterranean region and Cairo. In north-western Europe, a cluster of urban centers showing similar price movements had emerged in the fourteenth century, at the latest. The Mediterranean area was not integrated into this network. Price integration in north-western Europe may have contributed to the economic advancement of this region in late medieval and early modern times. Climatic fluctuations (in temperature as well as in the water level of the Nile) affected Cairo grain prices. In Europe, on the other hand, shortterm temperature variation did not have an appreciable impact on prices. Western European price integration cannot, it seems, be explained by the existence of a common climatic factor. Early European economic development was facilitated by a robust environment.


189

JOHAN SO¨DERBERG

190

1. INTRODUCTION This paper explores some questions related to the price history of Cairo and Europe during medieval times: How did price levels and trends in Cairo compare to those in Europe? Did price volatility in Cairo differ from European levels? Was volatility reduced over time in any of these regions? Can any price integration be discerned between Cairo and Europe? Were grain prices in Cairo affected by climatic factors to a higher degree than were European prices? These questions can be related to a wider issue: When did the economic decline of the Near East relative to Europe begin? Did Europe perform better during the medieval era? According to one prominent historian, Eliyahu Ashtor, the Mamluk period (1250–1517) comprises two distinct economic phases. The first century, up to the Black Death, was characterized by a stable regime, demographic growth, and economic prosperity with monetary stability. A long period of peace prevailed in Egypt. Few epidemics are recorded in Egypt as well as in Syria, suggesting an improvement of public health. This phase of growth came to a stop with the Black Death beginning in 1347. Population declined, as well as agricultural and industrial production. The Near East suffered from impoverishment during the second half of the fifteenth century, according to Ashtor. Grain prices fell because of declining demand. Compared to the previous century, standards of living were reduced for the great majority. Egypt was still often able to export considerable quantities of wheat and barley, but could no longer store sufficient stocks of grain for times of bad harvests. Salaries for many occupations were declining. The populations of the Near East could no longer afford to eat white bread, and shifted to cheaper grains such as barley and millet. The economic deterioration of the region, Ashtor argues, contrasts with the upswing in most countries of Europe where large segments of the population enjoyed rising living standards.1 Price history thus has a central place in Ashtor’s interpretation that Europe began to surpass the Near East in economic development during the late Middle Ages. His view is not without ambiguity, however. Falling nominal grain prices should have offset the decline in salaries, and it is not a priori clear which trend was dominating the course of real wages. Though Ashtor reported a large amount of price quotations for the Near East, he

Grain Prices in Cairo and Europe in the Middle Ages

191

seldom converted them to Western measurement equivalents, and he rarely expressed them in silver prices that would allow convenient comparison with European prices. For reasons of time and space, the comparative study of real wages between the Near East and Europe is not carried out here. The outline of the paper is as follows. After an introductory presentation of the data used, long-term levels, trends, and volatility in grain prices in Cairo and Europe are compared. The next section deals with aspects of price integration as indicated by correlations in short-term price fluctuations in Cairo and Europe. The third section discusses the impact of climatic factors on Cairo grain prices, e.g., through the effects of the inundation of the Nile. The paper ends with a concluding discussion.

2. DATA This study relies on Cairo prices for the Mamluk period (1250–1517) already published, notably in the monumental work of Ashtor (1969). Focus is on grain prices, not only because these were decisive in determining the standard of living, but also because relatively abundant series are available, in particular, for wheat. Wheat prices are of interest since this was the most important crop in the Near East, playing a major part in the diet of the population (Mortel, 1989). Cairo market prices are usually reported by chroniclers who often give several quotations per year, referring to certain dates or to a period of one or several months. In the wheat price series used here, the average number is four quotations per year, which should reduce some of the random variability of spot prices. Some additional Near East grain prices have been published by Ashtor (1969, pp. 392–395) for Syria and by Mortel (1989) for Mecca. The Cairo series, however, has a far better coverage and appears to behave less erratically.2 Therefore, the discussion to follow will focus on comparisons between Cairo – the largest city in the Near East – and Europe. In order to allow comparison with European data, all prices have been converted to silver values. Grain price quotations in Cairo are originally usually given in silver or copper dirhams. These prices have been converted to silver following two steps. First, gold dinar prices were calculated utilizing the exchange ratio of the silver or copper dirham to the dinar. Second, prices in gold have been converted to silver by means of the bimetallic ratio. The gold content of the dinar is known within quite small margins of error.3 Bimetallic ratios for Cairo have been calculated by previous researchers for a large number of years. The gaps have been interpolated. As the bimetallic

192

JOHAN SO¨DERBERG

ratio was relatively stable, fluctuating around 10:1, this does not seem to be too risky. Yet, conversion to silver prices is not unproblematic. A large part of the available prices refer to the fifteenth century, a century during most of which Egypt lacked a silver currency (most transactions being done in copper coins). The silver prices calculated thus should not be seen as actually having been paid in the market. Still, this procedure appears preferable to any alternative, and has the advantage of facilitating comparison with prices from Europe and other parts of the world.

3. GRAIN PRICE LEVELS AND TRENDS Tables 1–3 give an overview of grain price levels and trends for Cairo and a number of European towns and cities. Table 1 covers the whole period 1260–1512, whereas Table 2 reports on the early period (before 1420) and Table 3 on the late period from 1420 onwards. European silver prices vary between three zones. In this dataset, the first zone includes Germany and Central Europe (Stiftklosterneuberg in Austria and Strasbourg), which show low median prices of substantially less than 20 g of silver per hectoliter. The second zone comprises north-western Europe, where several places exhibit a median price of 20–30 g. The third zone is characterized by a high level of more than 35 g of silver, documented from Valencia and Tuscany. These zones conform to those discerned by Braudel and Spooner (1967, pp. 396–397) for the fifteenth century. To some extent, the high price level in Cairo could be an effect of the fact that famine years are disproportionately represented in the early price records from this city. As a consequence, the median, or other robust measures, are preferable to the mean as a measure of central tendency. The median wheat price in Cairo is substantially higher than at most places in Europe included in Tables 1–3. The use of the median as a robust measure of central tendency should eliminate some of the effects of observations from unusual years of scarcity. There are other alternatives as well. The 5 percent trimmed mean (the mean that would be obtained if the lower and upper 2.5 percent of values were deleted) and Huber’s M-estimator are presented as well.4 The 5 percent trimmed mean usually gives higher values than Huber’s M-estimator, and both these measures tend to fall between the median and the mean. The use of these additional robust measures does not change the general impression of higher wheat prices in Cairo than in Europe.


193

Table 1. Grain Prices in Cairo and Wheat Prices in Selected European Cities and Towns 1260–1512, in Grams of Silver Per Hectoliter. Place

Cairo, wheat Cairo, wheat, estimated Cairo, barley Cairo, broad beans London Chester Exeter Douai Leuven Brugge Brussels Utrecht Strasbourg Stiftklosterneuberg Tuscany Valencia Aragon

Time Trend

N

Mean

Median

5% Trimmed Mean

Huber’s M-Estimator

59.2 48.4

44.6 44.4

54.5 47.2

47.3 45.1

0.136 0.023

131 208

34.6 39.1

25.3 32.6

30.9 35.3

27.0 31.5

0.041 0.049

75 74

26.1 26.9 26.5 29.8 21.9 31.7 23.8 32.7 19.8 13.2 40.5 36.4 26.9

24.4 24.4 24.4 27.9 21.2 28.6 21.4 29.7 16.4 10.2 36.6 36.2 26.7

25.2 26.5 25.7 28.4 21.5 31.1 23.0 31.6 17.5 11.8 39.3 36.2 26.4

24.5 25.7 24.7 27.9 21.0 29.7 22.2 30.2 16.9 10.6 37.7 36.2 25.9

0.615 0.460 0.552 0.146 0.232 0.716 0.289 0.427 0.191 0.129 0.302 0.287 0.368

253 88 190 180 91 164 110 148 113 76 213 93 66

Notes and Source: Cairo prices: Ashtor (1969, pp. 283–292, 295–300); Ashtor (1977, pp. 209–211); Shoshan (1983, pp. 63–67); Allouche (1994, pp. 99–109); Popper (1957, p. 82). Cairo exchange rates dinar/dirham: Ashtor (1969, pp. 277–278); Bacharach (1983, pp. 179–180); Spufford (1986, pp. 300–306); Shoshan (1986, pp. 32–34); Allouche (1994, pp. 95–98). Strasbourg: Hanauer (1878, pp. 57, 91–101). Tuscany and Brugge: International Institute of Social History, Prices and Wages, data files by Malanima, P., and Verhulst, A. E. Utrecht: Data kindly supplied by Jan Luiten Van Zanden. Other series: Allen, Robert C.–Unger, Richard W. Database, supplemented by Verlinden (1959, p. 501) for Brussels. Time trend refers to the rxy correlation between time and the logs of prices. One extreme wheat price quotation from Cairo, from winter 1348–1349 (Ashtor, 1969, p. 285), has not been used in this paper, since it is about 10 times higher than any other quotation in the series. The weight of 1 hl of wheat is assumed to be 72 kg, that of barley 62 kg; Popper (1957, p. 100) (wheat), Hinz (1955, p. 57) (barley). As the hectoliter weight of beans is not known, the price is given per irdabb of 69.6 kg. The exchange ratio between the dinar and the copper dirham for the early sixteenth century has been assumed to be 500, which is slightly higher than the figure of 450–460 for the late fifteenth century as reported by Spufford (1986, p. 302). Tuscany prices refer to Pisa up to 1300, thereafter to Florence. Correlation significant at the 1 percent level (two-sided test). Correlation significant at the 5 percent level.

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Cairo, wheat Cairo, wheat, estimated Cairo, barley Cairo, broad beans London Chester Exeter Douai Brugge Utrecht Strasbourg Tuscany

Mean

Median

5% Trimmed Mean


N

68.5 52.4

55.7 48.2

64.0 51.1

59.4 49.3

72 152

42.6 47.1

35.1 36.9

39.6 43.6

34.7 37.2

41 40

30.1 31.3 30.6 30.6 40.7 36.6 24.3 46.0

28.3 30.6 29.0 29.0 40.5 34.2 18.9 41.5

29.1 31.1 29.7 30.1 40.5 36.0 20.6 44.7

28.7 30.9 29.2 29.4 40.3 35.2 19.4 42.6

160 39 102 87 72 60 48 123

Notes and Source: See Table 1.


Cairo, wheat Cairo, wheat, estimated Cairo, barley Cairo, broad beans London Chester Exeter Douai Leuven Brugge Brussels Utrecht Strasbourg Stiftklosterneuberg Tuscany Valencia Aragon

Mean

Median

5% Trimmed Mean


N

47.8 37.7

32.4 37.7

43.7 37.5

36.2 37.4

59 56

24.9 29.8

23.1 28.9

23.6 27.9

22.6 27.5

34 34

19.4 23.4 21.8 29.1 21.7 24.7 23.5 30.0 16.6 12.2 33.0 36.3 26.2

17.8 22.0 20.7 25.5 20.6 22.6 21.0 25.9 14.9 10.7 30.0 36.2 26.6

18.8 23.1 21.4 26.8 21.3 24.0 22.6 28.6 15.7 11.6 32.2 36.2 26.0

18.2 22.5 20.8 26.1 20.6 23.5 21.7 26.7 15.1 10.6 31.1 36.2 25.7

93 49 88 93 76 92 91 88 65 53 90 90 55

Notes and Source: See Table 1.


195

Finally, all these measures of central tendency are reported for the Cairo wheat prices as estimated from the regression in Table 9 below, where information on the water level of the Nile and northern hemisphere temperature is drawn on. This estimated series is considerably longer than the observed series, since prices are estimated for all years for which water level and temperature data exist. The resulting measures of central tendency in most cases are lower in the estimated wheat prices, which seems plausible. Cairo grain prices tend to decline over time, as seen in Tables 1 through 3. The median wheat price in Cairo even fell below that of Valencia during the period 1420–1512. But too much weight should not be placed on comparisons between central tendency measures that do not take variance into account. It is clear that Cairo and Europe differed with regard to time trends in prices. As shown in the time trend column of Table 1, the price of wheat declined over time in most places in Europe, particularly in the north-west. Cairo does not, however, exhibit any significant decline in grain prices. This latter measurement takes variance into account and should provide a more reliable estimate of price trends. Wheat prices in Cairo thus usually surpassed the highest prices among European cities studied, and clearly exceeded the north-western European price level. The lack of a downward trend in Cairo wheat prices differs from Ashtor’s statement (1976, p. 313; 1977, pp. 212–214) that wheat prices in Egypt were falling in the long term during the Mamluk period. The observation that prices did not decline is, however, well in line with the remark of Ashtor (1976, pp. 315–316) that Arabic authors of the period complained bitterly about the decrease of cultivated area. The lords were, according to Ashtor, primarily interested in tax collection and neglected the upkeep of irrigation dams and canals, and agricultural production was also harmed by the pillaging of troops.

4. PRICE VOLATILITY Table 4 reports two measures of volatility: the coefficient of variation (CV) in the arithmetic and log scales. The CV in the arithmetic scale gives great weight to years of high prices, since these contribute more towards a large standard deviation than do years of unusually low prices. The CV in the log scale treats years of high and low prices symmetrically. Both volatility measures are markedly higher in Cairo than in most places in Europe. In Europe, only Strasbourg and Stiftklosterneuberg (Austria) are

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Table 4. Volatility in Grain Prices in Cairo and Europe: Coefficient of Variation (CV) in the Arithmetic and Log Scales, 1260–1512. Place

Cairo, wheat Cairo, barley Cairo, broad beans London Chester Exeter Douai Leuven Brugge Brussels Utrecht Strasbourg Stiftklosterneuberg Tuscany Valencia Aragon

1260–1512

1260–1419

1420–1512

CV

CV (logs)

CV

CV (logs)

CV

CV (logs)

0.788 0.852 0.828 0.412 0.316 0.370 0.475 0.341 0.431 0.387 0.381 0.853 0.726 0.414 0.161 0.255

0.200 0.241 0.229 0.115 0.095 0.101 0.111 0.111 0.129 0.112 0.101 0.176 0.204 0.108 0.045 0.073

0.740 0.839 0.829 0.361 0.288 0.355 0.299

0.193 0.264 0.247 0.094 0.087 0.097 0.086

0.321

0.093

0.307 0.983 0.950 0.380

0.084 0.200 0.252 0.093

0.806 0.597 0.634 0.330 0.265 0.253 0.606 0.350 0.380 0.411 0.419 0.462 0.499 0.371 0.156 0.215

0.198 0.189 0.193 0.096 0.082 0.076 0.129 0.113 0.116 0.117 0.104 0.142 0.178 0.105 0.044 0.064

Notes and Source: See Table 1. CV is the standard deviation divided by the mean in the arithmetic scale; CV (logs) is the same calculation in the log scale. Cells based on less than 20 observations have been left blank.

similar to Cairo in terms of the CV, in the arithmetic as well as in the log scale. As seen in Table 1, Stiftklosterneuberg has the lowest wheat prices of all European urban centers studied, so we are dealing with fluctuations around a low mean. In the log-scale CV the differentials between various places are smaller, but the general European level still is lower than in Cairo. An inspection of Fig. 1 confirms the impression that volatility was much higher in Cairo than in London. What happened with volatility over time? The observation of declining volatility in Cairo is supported by a calculation of the CV in the log scale. This was reduced from 0.264 to 0.189 for barley between 1264–1419 and 1420–1512, and from 0.247 to 0.193 for beans between the same periods. Volatility does not generally decline in the European series. There are about as many cases of increasing volatility as declining volatility, in the arithmetic as well as in the log scale.5 There is a spatial pattern, though, in the changes in volatility in Europe. Volatility tends to decline in England as well as in the Continental series of Strasbourg and Stiftklosterneuberg. On the other


197

Silver price, grams

1000

100

10

1 1250

1300

1350 Cairo

Fig. 1.

1400

1450

1500

London

Wheat Price Per Hectoliter (Log Scale) in Cairo and London, 1260–1512. Notes and Source: See Table 1.

hand, volatility tends to rise in some of the Low Countries series (Douai, Brugge, and Utrecht). Volatility thus decreased over time in the cheap grains in Cairo. This could be the result of improved efficiency over time in the Cairo barley and beans markets, but it could also be an effect of climatic fluctuations becoming more favorable to the production of these crops. This issue will be discussed below in connection with the effects of temperature and the inundations of the Nile. The Cairo wheat market was probably the largest in the Near East during late medieval times. Why was price volatility so high? Cairo depended upon the flow of grains on the Nile from Upper Egypt (the southern part of the country), where the general price level was lower. In 1416, the Mamluk government purchased wheat at 530 dirhams per irdabb of 69.6 kg and sold it to Cairo millers for 600 dirhams. The latter

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price should have covered the cost of transport and presumably also allowed some profit. The transport cost, then, could hardly have exceeded 10 percent of the price in Cairo. Another sign of efficiency is that boats handling transports on the Nile could be quite large, carrying as much as 350 tons (5,000 irdabbs) of grain.6 Transport costs could not have been the key problem in supplying Cairo with grain. Large amounts of grain were collected by sultans and emirs through the tax system. The Mamluk elite became the major suppliers of grain to Cairo and the other urban markets of Egypt. The sultans controlled large grain reserves, normally amounting to more than 20,000 tons. This may be compared to a cautious fifteenth-century estimate of the total consumption of grain at Cairo at 25,000 tons a year (or 1,000 irdabbs daily). Cairo’s population is estimated to have been in the range of 250,000 to 500,000 by the mid-fourteenth century, declining to between 150,000 and 300,000 by the fifteenth century. The rulers’ granaries would then have been sufficient to feed the population of Cairo for nearly a whole year (Shoshan, 1993, pp. 1, 58, 122, Note 46). Sultans and emirs sometimes disrupted the normal grain trade in order to make profits, for instance by forcing merchants to buy wheat at higher prices than the market rate. For instance, al-Maqrizi reports that the state trade monopoly ‘‘kept foodstuffs out of the reach of people unless they agreed to pay the prices set by the officials’’ in the famine year of 1403–1404 when the Nile failed to reach its plenitude (Allouche, 1994, p. 51). Other sultans, though, took steps to supply Cairo with grain in times of bad harvests, and at times sold grain at prices that were considerably lower than the market price. It also happened that sultans distributed free grain or bread to the poor. Other measures could be taken as well. In 1336, for instance, the sultan ordered grain to be imported from Syria and Palestine.7 Egypt imported grain from Sicily, Cyprus, and other places in times of shortage. Yet, the general impression gained from Ashtor’s (1983, pp. 236, 239, 465–466) major work on Levant trade is that the grain trade was not of large proportions. Trade was limited by Papal bans, and Egyptian merchants did not travel on the Mediterranean trade routes in the later Middle Ages, as they had done before the thirteenth century (Humphries, 1998). Obviously, the measures taken by the Mamluks were not sufficient to bring wheat price volatility down to European levels. Given the limitations of foreign trade and the geography of Egypt, Cairo depended upon the grain supplies from the area surrounding the Nile, which, as will be discussed further below, varied substantially from year to year due to fluctuating water levels. Many regions in Europe, on the other hand, found themselves


199

in a more mixed environment with regard to grain provisioning. This should help in explaining the higher volatility of grain prices in Cairo along with other factors such as the rapacious economic policy of the Mamluks and the restricted foreign trade. As a consequence of the provisioning problems, famines and epidemics became more frequent in Cairo during the fifteenth century. The city was affected by 10 major famines during Mamluk rule, and food shortage was a constant fear among the inhabitants.8 Grain riots expressed popular discontent with high prices and shortages and emerged as a distinct activity in Cairo around 1350. The market inspector often was the target of public anger, but sometimes crowds also protested directly to the sultan. Occasionally, crowds responded by the pillaging of grain in parts of Cairo, or, as in 1513, by demanding that grain shipments to Syria should cease. In Cairo as well as in Europe, the repercussions of price volatility were dampened by the fact that the prices of bread varied less than those of grains. Some inhabitants of Cairo baked their own bread and were thus directly affected by grain price volatility. Others bought their bread from the bakers, while yet others prepared their own dough and carried it to the bakers to be baked. It is not known how many people resorted to these various ways of getting their bread. As a response to high prices in 1415, rationing was imposed on private consumers in Cairo: no person was allowed to buy more than one irdabb of grain. This suggests that the grain market for private consumption was not negligible (Shoshan, 1993, p. 63). Demand probably shifted from ready-made bread to grain in times of famine, as families then tried to hand-mill their grain to avoid paying for this work (Sabra, 2000, p. 167). Price history always is linked to social history and the study of living conditions. There are good reasons to view price volatility as a welfare indicator. Price instability was one of the major problems of pre-industrial economies. High volatility means that consumers suffered from large fluctuations in real income and standards of living. Given the rigidity of nominal wages, the larger the fluctuations, the greater the risk that high prices would lead to poverty, famine, and health damage. Stable prices enhanced welfare (Persson, 1999, pp. 23, 31). In this perspective, Cairo appears to have experienced more of a welfare problem than did Europe. The European situation was advantageous in two respects. First, the price level – as far back as the comparison can be made – was substantially lower in London and other European cities than in Cairo. Second, European consumers should have benefited from the long-term decline in wheat prices.9 No significant downward price trend is seen in

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200

Cairo. The difference in price trends between Cairo and London is evident in Fig. 1. On the other hand, the volatility in Cairo barley and beans prices (but not in wheat prices) was reduced over time. Despite this levelling of prices, volatility in barley and beans prices in Cairo for the period as a whole remained high as compared to wheat prices in Europe. These results can be related to the argument of David Farmer, Gregory Clark, and others that an efficient market for wheat, the largest commodity of the English economy, had emerged at least by the beginning of the thirteenth century. Local prices differed little from the national price level, indicating that local weather conditions did not determine prices, and that wheat could be traded freely and extensively within the limits posed by transport costs (Farmer, 1988, pp. 742–743; Clark, 1999; Galloway, 2000). This observation accords with the fact that the volatility of English wheat markets is far smaller than in Cairo. It should be emphasized, though, that English volatility does not differ much from that of other parts of northwestern Europe. The English wheat market was not unique.

5. PRICE INTEGRATION 5.1. Climate and Prices in Cairo Table 5 reports correlations between wheat price fluctuations in Cairo and temperature. The wheat price series has been detrended by calculating Table 5.

Correlations between Detrended Wheat Prices and Northern Hemisphere Temperature, 1260–1512.

Place Cairo London Brugge Douai Strasbourg Stiftklosterneuberg Tuscany Valencia

Temperature 0.222 0.079 0.051 0.065 0.027 0.043 0.047 0.093

Temperature, lag 1 0.220 0.078 0.026 0.085 0.070 0.070 0.013 0.085

Notes and Source: Detrended prices are residuals from a linear time trend in logs. Prices: See Table 1. Temperature: Jones et al. (1998). Significant at the 5 percent level.


201

residuals from a linear trend in the logs of prices.10 The temperature series used is a northern hemisphere temperature reconstruction by Jones, Briffa, Barnett, and Tett (1998). This series combines evidence from tree rings, ice cores, and historical documents, and is often quoted by climatologists, e.g., Berger (2002, p. 4).11 Temperature variation seems to have had an impact on Cairo prices, unlike the situation in Europe. Climatic factors related to Cairo prices will be discussed further below in connection with the inundations of the Nile. The short-term movements of wheat prices in Cairo do not appear to be linked to European prices. In particular, Mediterranean wheat prices (Tuscany and Valencia) are uncorrelated with Cairo prices.12

5.2. The North-Western European Cluster of Integration Several urban centers in Europe appear to have been economically integrated to a considerable degree. Table 6 summarizes the correlations between the residuals from a time trend of the log of wheat prices. The covariation is far from being restricted to England. London and Exeter prices correlate not only with each other but also with prices in Brugge, Leuven, and the French inland town of Douai; in addition, there is a weaker correlation with Brussels. London also correlates with Strasbourg. Utrecht, Brugge, Leuven, Douai, and Strasbourg are centrally placed in this network: prices there are significantly correlated (at the 1 percent level) with all other towns included in Table 6. There thus existed a cluster of cities and towns in north-western Europe within which short-term price movements were similar. Coastal as well as Table 6. Correlations between Detrended Wheat Prices (Residuals from a Linear Time Trend, Logs) in European Cities and Towns, 1260–1512. Place

Exeter

Utrecht

Brugge

Leuven

Brussels

Douai

Strasbourg

London Exeter Utrecht Brugge Leuven Brussels Douai

0.797

0.363 0.288

0.364 0.291 0.735

0.413 0.445 0.641 0.664

0.284

0.383 0.198 0.532 0.570 0.588 0.371

0.541 0.327 0.422 0.440 0.622 0.570 0.484

Notes and Source: See Table 1. Significant at the 1 percent level.

0.186 0.546 0.666 0.683

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inland towns were part of this network. Among inland towns, Leuven and Brussels show strong internal connections, as do Leuven and Douai. It appears as if contacts were systematic in several directions between England, the Netherlands, and France.13 Strasbourg also belongs to this cluster, since prices there vary with those at several other towns: London, Utrecht, Douai, Brugge, Brussels, Leuven, and Douai. Price integration was weaker outside north-western Europe. Tuscany, for instance, shows significant correlations only with Valencia and Douai, and not very strong ones (rxy ¼ 0.300 and 0.182, respectively). Valencia correlates, apart from Tuscany, only (and rather weakly) with London and Aragon. Aragon shows no other significant correlation than with Valencia. The Mediterranean region thus stands largely separate from the northwestern cluster; it is also internally less integrated than is north-western Europe.14 The north-western cluster was separated from the Mediterranean market. In central Europe, Stiftklosterneuberg (Austria) has an even more peripheral position, price fluctuations there being unrelated to those anywhere else in this dataset. Stephan Epstein (2001) has advanced the general argument that political centralization gave rise to greater market integration during the late medieval era. According to him, jurisdictional fragmentation was the main cause of grain price volatility. Epstein specifically studies the expansion of Florence, which allowed weak rural communities to establish markets and fairs despite urban resistance. The strengthening of government facilitated road improvements, lowered transport costs, and reduced tariffs on domestic trade (Epstein, 2000, pp. 99–108, 116–121). While this stimulated market integration within north Italy, it is apparently not the type of price integration across rather large distances and several political borders that we can see in north-western Europe. The observed correlations between various European towns could in principle be the result of a common climatic factor affecting yields and prices. This appears not to be the case, however, with regard to short-term movements. As reported in Table 5, correlations between northern hemisphere temperature and the residuals from a linear time trend of the log of wheat prices are practically zero. The hypothesis of a common temperature effect on European wheat prices should not be totally discarded, but it is unlikely to have been substantial, and certainly not sufficiently strong as to generate the much higher correlations in Table 6. A striking result from James Masschaele’s study of transport costs in medieval England is that these costs were remarkably low in the fourteenth century. The ratio of the cost of land transport to river transport to sea


203

transport was about 8:4:1. While sea and river transport were clearly cheaper than land transport, the latter cost was not prohibitive. A land trip of 10 miles raised the price of wheat by only 4 percent. Moreover, Masschaele finds no evidence of land transport costs being reduced from the fourteenth to the eighteenth century.15 From the point of view of transport economics, nothing should have prevented a medieval price integration of the type observed above in north-western Europe. A calculation from a real example reported by Masschaele gives the result that the cost of transporting 42 hl of wheat by sea from Hull to Ostende, a distance of 225 miles, added 24 percent to the price at Hull.16 This cost should have been far from prohibitive, and most grain transports certainly were carried out at shorter distances. Why, then, did the north-west distinguish itself from other parts of Europe with respect to price integration? Peter Spufford provides part of the answer. As a consequence of the urban growth of the thirteenth century, the cities of the Netherlands could not be provisioned by means of nearby food supplies only. Substantial amounts of grain were sent to Flanders from northern France. And this was not enough. Grain was also brought from the regions around the Rhine and the Meuse, from southern England, and from the Baltic. Internal waterways provided efficient means of transport for the grain coming in from Antwerp and Amsterdam. The total urban population of Flanders, Brabant, and Holland approached half a million by the mid-fifteenth century (Spufford, 2002). The feeding of this population required a large and regular grain trade across political borders. The Baltic trade, taking place on a regular scale at least from about 1300 onwards and growing strongly in the fifteenth century, was of importance not only in allowing a higher degree of urbanization but it also exerted downward pressure on grain prices in England and other parts of northwestern Europe.17 As shown in Table 1, wheat prices declined in the long term in many urban centers in the north-west. This clearly marks a difference between the price history of north-western Europe and that of Egypt. Land transport in Europe seems to have revived in contrast to sea transport in the second half of the fifteenth century. One reason for this could be the improved safety of land transport. France and several other parts of Europe were increasingly pacified with the creation of standing armies. Sea transport in the Mediterranean, on the other hand, became less safe. The bitter wars between Muslims and Christians brought higher risk. Piracy was a serious additional problem in the western Mediterranean (Spufford, 2002). Sea transport costs in the Mediterranean may thus have risen, not only relative to the cost of land transport but also in relation to the cost of sea

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transport in north-western Europe. This should have given the north-western cluster a cost advantage compared to shipping in the Mediterranean. It is possible, though not demonstrated, that north-western Europe was characterized by unusually low land and sea transport costs. In fourteenthcentury western England, Exeter apparently had a choice of trading grain with London and eastern England on one hand, or Brittany on the other. Campbell (1997) argues that the grain trade in England had declined by 1400 as a result of demographic recession and decreasing market participation on the side of the seignorial economy. A well-integrated coastal wheat market embracing southern England, Flanders, Picardy, Normandy, and other parts of the near continent existed before the Black Death, but declined after the plague. Is there any sign that price integration was weak in the period following the Black Death? In order to explore this issue, the available series have been divided into two periods, 1360–1419 and 1420–1512. Tables 7 and 8 report the correlations between detrended wheat prices (residuals from a linear time trend of the logs of prices). Owing to sparse data, this subdivision into periods cannot be done for all towns included in Table 6. Correlations between three pairs of urban centers – London–Exeter, London–Brugge, and London–Douai – are similar across the two periods, indicating a considerable stability. The most notable change is that Strasbourg is being integrated into the north-western cluster after 1420, as prices in this town begin to vary with those in Brugge, Douai, and London. Also, the connection between Brugge and Douai is somewhat strengthened. The Exeter–Brugge and Exeter–Douai links are however weakened after 1420. The overall impression is one of expansion of the network eastwards to Strasbourg, while the position of Exeter became more peripheral.

Table 7. Correlations between the Residuals from a Linear Time Trend of the Log of Wheat Prices in European Cities and Towns, 1360–1419. Place

Exeter

Brugge

Douai

Strasbourg

London Exeter Brugge Douai

0.819

0.428 0.419

0.373 0.278 0.474

0.341 0.141 0.284 0.351

Significant at the 1 percent level. Significant at the 5 percent level.


205

Table 8. Correlations between the Residuals from a Linear Time Trend of the Log of Wheat Prices in European Cities and Towns, 1420–1512. Place

Exeter

Brugge

Douai

Strasbourg

London Exeter Brugge Douai

0.737

0.374 0.233

0.461

0.497 0.267 0.600 0.566

0.207 0.709

Notes and Source: See Table 1. Significant at the 1 percent level. Significant at the 5 percent level.

The north-western cluster clearly came into existence well before 1420. Several of the links within it were maintained between the post-plague decades and the later medieval era. Price integration in the north-western region is evident before as well as after the mid-fourteenth century. While Galloway and Campbell find indications of English grain markets contracting after the Black Death, results in this paper point rather to the possibility that the disruptions were counterbalanced by the continued existence and even growth of the north-western grain trade network. This is more in line with Epstein’s (2001) argument that markets expanded as a result of rising labor productivity and increased political centralization, reducing the costs of trade in the century after the Black Death. In explaining the successful formation of the north-western cluster we should then study the changing role of political and other barriers to the expansion of trade. Galloway points at several factors as significant in the early formation of an efficient internal grain market in England, including relative security for traders and travelers, the regular access of peasants to market places, a relatively stable currency, standardization of weights and measures, the absence of large tariffs, and the means of enforcing contracts and settling disputes. These transaction costs are all linked to the emergence of a centralized political structure, which seems to have been more firmly established in England than in many parts of Europe (Galloway, 2000). Still, what remains to be explained is the existence of an integrated urban network crossing the political borders of north-western Europe, in which England did not necessarily hold the most central position. The spatial perspective should be widened, and the role of transaction costs for the growth of the north-western cluster of integration deserves to be studied more closely.

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6. CLIMATE, INUNDATION OF THE NILE, AND CAIRO GRAIN PRICES The yearly inundation of the Nile is reported by contemporary observers to have been of great importance not only for actual harvests, but also for the formation of short-run price expectations. Near Cairo, the river reached its lowest point in mid-May and its maximum in late September. The minimum as well as the maximum were measured by means of the Nilometer. The critical level was 16 cubits (around 9.3 meters): if the Nile reached that height, people felt confident that the harvest would be good. This level was normally reached in July or August and was publicly proclaimed. Announcement of the rising of the Nile was prohibited until it had reached 16 cubits. In 1462, the sultan even contemplated destroying the Nilometer as a means of counteracting speculation and unrest (Shoshan, 1993, p. 62; Rabie, 1981, p. 60). Grain dealers could take advantage of the anxiety among the population when there were signs that the annual flood might be insufficient. Merchants were accused of holding back supplies at these occasions, waiting for prices to rise. Harvest predictions based on the minimum level of the Nile also influenced the price of grain by stimulating actors to release surplus from previous years (Popper, 1951, p. 215). Yet, researchers do not agree upon the impact of inundation on grain prices. Boaz Shoshan argues that inundation in the period 1450–1470 cannot explain variations in wheat prices since water levels nearly always exceeded the critical level of 16 cubits. More generally, Shoshan (1983, pp. 57–59) emphasizes the role of monetary factors behind wheat price trends, and strongly plays down all real factors. Surprisingly, no systematic test has to my knowledge been made of the effects of inundation upon grain prices. Sufficient data to do it are available. Table 9 reports the results of a regression analysis of wheat prices (residuals from a linear time trend in logs) upon the water level. Since low minimum and maximum water levels should have lowered yields (or harvest expectations) and pushed up prices, the expected sign of both coefficients is negative. A temperature estimate for the northern hemisphere is also included as an explanatory variable. As higher temperatures should increase the risk of drought and higher prices, the sign of this coefficient should be positive. The results in panel (A) of Table 9 indicate that the water level of the Nile indeed affected grain prices in the short term, just as stated by contemporary observers. The effect of the variation in the minimum level is highly statistically significant with regard to all three types of grain. The maximum


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Table 9. Results of Regression Analysis of Cairo Grain Prices upon the Water Level of the Nile and Temperature, 1264–1512. b

t-value

Prob. (t)

Panel A: Dependent variable: Wheat Constant Minimum level (meters) Maximum level (meters) Temperature (degrees C) R2 ¼ 0.235; adjusted R2 ¼ 0.212; DW ¼ 1.63; N ¼ 108

0.187 0.322 0.291 0.896

1.618 4.048 2.171 2.535

0.109 0.000 0.032 0.013

Panel B: Dependent variable: Barley Constant Minimum level (meters) Maximum level (meters) Temperature (degrees C) R2 ¼ 0.309; adjusted R2 ¼ 0.277; DW ¼ 1.75; N ¼ 69

0.258 0.370 0.268 1.179

1.598 4.220 1.605 2.294

0.115 0.000 0.113 0.025

Panel C: Dependent variable: Beans Constant Minimum level (meters) Maximum level (meters) Temperature, (degrees C) R2 ¼ 0.303; adjusted R2 ¼ 0.271; DW ¼ 1.83; N ¼ 69

0.218 0.359 0.329 0.981

1.441 4.163 2.033 2.022

0.154 0.000 0.046 0.047

Parameter

Notes and Source: Prices: See Table 1. Nile minimum and maximum levels: Popper (1951, pp. 212–214), Toussoun (1922, pp. 143–145). The chronology of the Toussoun series differs from the Popper series and has been adjusted to the latter. Tessmperature: Jones et al. (1998). Grain prices are residuals from a linear time trend, in logs. Minimum and maximum levels of the Nile are residuals from a linear time trend, in the arithmetic scale. The temperature series is given as deviations in degrees C in the northern hemisphere versus the 1961–1970 mean. All variables are measured at lag zero.

level appears to be of somewhat less importance and is significant at the 5 percent level in the case of wheat and beans but not with respect to barley. Temperature variation had an additional effect in the expected direction on prices of all three types of grain. Real factors did influence prices.18 The wheat market should be expected to have been more spatially integrated than the market for lower-priced grains such as barley and beans. Trade should have been taking place over wider distances in the wheat market, since its price could bear higher transport costs. Trade thus would reduce the supply variations for wheat to a larger extent than was the case for low-price grains. Climate effects should then be larger on the prices of barley and beans than on wheat prices. Is this assumption supported by the data?

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Panels (B) and (C) of Table 9 report the results from an identical set of explanatory variables as in Panel (A) but with barley and beans, respectively, as dependent variables. The key result is that the combined explanatory value of the model is better for the cheap grains than for wheat. Climatic variation had stronger repercussions on prices in the barley and beans market than on wheat. This result is in accordance with expectations and supports the hypothesis that the higher relative cost in transporting the cheaper grains limited the extent of their markets. Variation in the Nile minimum level and temperature had a stronger impact on the price of the cheaper grains than on wheat. The above discussion dealt with short-term price changes only. In the longer term as well, changes in the inundation seem to have affected prices. At least this is the impression from Fig. 2, showing the minimum level of the Nile and the price of wheat. The declining price trend from the first decade of the fifteenth century up to the late 1430s corresponds fairly well to the rising trend in the water level of the Nile. To some extent, climatic factors determining the water level of the Nile appear to have had the long-run effect of reducing prices, presumably by 7

250

6 5 150

4 3

100

2

Minimum of the Nile

Silver price of wheat

200

50 1 0 1380

1400

1420

Silver price of wheat per hectolitre

1440

1460

0 1480

Minimum level of the Nile, meters

Fig. 2. The Minimum Level of the Nile and the Price of Wheat in Cairo, 1381–1473. Notes and Source: Prices: See Table 1. Nile minimum levels: Popper (1951, pp. 212–214).


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influencing yields. With time, the plenitude (level of 16 cubits) of the Nile came earlier, and the river reached higher water levels (see also Herring, 1979, pp. 55–56). Late plenitudes could be harmful. The worst possible situation was the one in which the river did not reach plenitude at all. There were only 5 years between 1251 and 1522 in which plenitude was not reached. Before the mid-eleventh century, failure of plenitude was much more common (e.g., in 65 years in the period 641–850 and in 36 years between 851 and 1040) (Popper, 1951, p. 210). The correlation between calendar year and the date of plenitude (with positive values given to early dates of plenitude) is as strong as 0.407, and the correlation between time and the maximum level is even stronger (0.597). The changing water level and timing in the inundation of the Nile thus appear to have been helpful to agricultural production. A conclusion is that the high volatility of Cairo grain prices should not be attributed to deteriorating climatic conditions.

7. CONCLUDING DISCUSSION Five main findings emerge from this study: 1. Grain prices (in silver) were higher in Cairo than in Europe during the Middle Ages. Over time, price levels were declining in large parts of Europe but not in Cairo. In this sense, European consumers, unlike those of Egypt, gained from the long-run price trends. The expansion of the Baltic grain trade could be one factor behind the price decline in northwestern Europe. 2. Grain prices also were more volatile in Cairo than in Europe. As a consequence, the population of Cairo must have suffered from violent swings in living standards. High volatility in prices should be seen as a welfare problem, since fluctuations in living conditions led to famine and deterioration of public health. The increased frequency of famine in fifteenthcentury Cairo is well documented. In this sense, many European cities and towns succeeded better. Over time, volatility was reduced with regard to the cheap grains (barley and beans) at Cairo. This could conceivably have been of importance to large segments of the population, given that demand shifted from wheat to cheaper foodstuffs during the later Middle Ages. The water level of the Nile was rising in the long term, and failure of plenitude became a rare phenomenon. Climatic change leading to a

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higher water level in the Nile should have contributed to declining price volatility of barley and beans. 3. The Cairo wheat market appears to have been more spatially integrated than the market for barley and beans which had a lower price. This is in accordance with theory and can presumably be explained by the greater role of trade in the wheat market. It may be noted that the transport cost of wheat on the Nile seems to have been low, and is unlikely to have caused the high volatility in prices at Cairo. No price integration can be seen between the European Mediterranean region and Cairo. 4. In north-western Europe, a cluster of urban centers showing similar price movements had emerged in the fourteenth century, at the latest. The degree of price integration reached then did not abate in the century following the Black Death. On the contrary, the economic network appears to have been extended eastwards (though parts of western England were possibly peripheralized). The Mediterranean area was not integrated into this network. 5. Climatic fluctuations (in temperature as well as in the water level of the Nile) did affect Cairo grain prices. High temperatures and low water levels led to rising grain prices. Between 23 percent (wheat) and 31 percent (barley) of the variance in prices could be eliminated by reference to climatic and Nile inundation variance. These are not insubstantial figures. In Europe, on the other hand, temperature variation in the short term did not have an appreciable impact on prices. It seems that western European price integration cannot be explained by the existence of a common climatic factor. The more prominent role of climatic factors in Egypt compared to Europe should not be surprising. It lends support to the general view that early European economic development was facilitated by abundant natural resources and a robust environment that was not easily ruined by human activity (Diamond, 1999). In this sense, Europe held a favored position as compared to Egypt and other parts of the Near East. Ashtor based his view that the Near East started to lag behind Europe during the Middle Ages primarily on his assessment of technological and industrial development. Those aspects have not been studied here. It appears, however, that the study of prices carried out here could be well in line with a more general hypothesis that Europe advanced in two respects during the Middle Ages. First, Europe gained in regional economic integration; second, European consumers enjoyed declining wheat prices. Price


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integration in north-western Europe may have been a factor contributing to the economic advancement of this region in late medieval and early modern times. European wheat price integration could be interpreted as an indication of the emergence of a working market economy well before 1400. Cairo does not show any counterpart to this development. However, the overall picture is not entirely gloomy for Egypt. With time, prices of cheap grains at Cairo become more stable. As welfare for broad layers of the population of Cairo was at a low and probably declining level, things would have been even worse without this reduction in volatility. This is a point where Europe was less successful: price stability did not generally improve, despite market integration and improvements in transport. Further research is needed to clarify this issue.

NOTES 1. Ashtor’s (1976, pp. 288–331, 1983, pp. 434–440) gloomy view that the economy of fifteenth-century Egypt was in a state of crisis is shared by Darra¯ g (1961, pp. 57–107), and by Lopez, Miskimin, and Udovitch (1970). The latter authors, however, place greater weight at the devastating long-term effects of the plague than on the policies of the Mamluk rulers. See also Chaudhuri (1990, p. 104), who sees the decline of Egypt and Syria after 1400 as part of a phase of long-term contraction in a wider region. 2. It may be noted, though, that there are no signs of price integration between any of the wheat price series of Cairo, Syria, and Mecca. 3. The gold dinar contained 4.25 g of gold up to 1425 and thereafter 3.41 g, further reduced to 3.37 g from 1468; Bacharach (1973, pp. 82–83, 91). 4. Hampel’s M-estimator was also computed, but since it gave very similar results to Huber’s M-estimator it is not reported in Tables 1–3. 5. Epstein (2001, pp. 159–160) reports a substantial decline in the volatility in European wheat prices during the medieval era. I have been unable to reproduce his results. For instance, Epstein finds a long-term reduction in volatility at Douai, whereas my calculations do not indicate any downward trend. 6. Shoshan (1980, p. 464, Note 9). Another indication of low transport costs is given by al-Maqrizi, who states that the price of one irdabb of wheat at one recent occasion reached 500 dirhams of account, out of which only seven dirhams (1.4 per cent) were made up by transport costs. This price presumably refers to Cairo around 1405. al-Maqrizi does not specify the distance of transportation. Allouche (1994, p. 78). 7. Shoshan (1993, p. 61). 8. Sabra (2000, p. 168). The 10 major food shortages occurred in 1264, 1295–1296, 1336, 1373–1375, 1394–1396, 1402–1404, 1415–1416, 1449–1452, 1469–1470, and 1486–1487; Sabra (2000, pp. 138–164). 9. Falling grain prices should have been beneficial not only to a large part of the urban population, but also to the probably substantial part of the rural population which did not produce a grain surplus. Epstein (2001, pp. 5, 47–48).

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10. Owing to the large number of missing values, it is not meaningful to study market integration at Cairo by calculating correlations between the first differences of grain prices. 11. Alternative series are available. Some of these, such as Crowley (2000), however, use the reconstruction by Mann, Bradley, and Hughes (1998), which has been shown to contain several errors (Mann, Bradley, and Hughes, 2004). 12. There is, however, a significant correlation (at the five per cent level) between Cairo and London wheat prices: rxy ¼ 0.202 at lag zero when both series are measured as residuals from linear time trends. No direct grain trade took place between these cities. Yet we know that very high prices could trigger a grain trade across larger distances than usual, sometimes even linking the Mediterranean area directly to north-western Europe. For instance, grain was taken directly from the Mediterranean to north-western Europe during the famine of 1317; Spufford (2002, pp. 288, 291). Such high-price years also should have stimulated indirect trade, perhaps through France, creating a network of trading partners. Thus, markets can be integrated without trading directly with each other, as underscored by Ejrnæs and Persson (2000) and Ejrnæs, Persson, and Rich (2004). A comparison between the Cairo and London wheat price series reveals that the observed positive correlation partly was produced by simultaneous high prices in the years 1294–1295, 1308–1309, 1346–1347, 1364, 1374–1375, 1400, and 1409 (Cairo prices are not available for the European famine years of 1316–1317). Still, the observed correlation between Cairo and London is rather weak and could be a chance phenomenon. 13. See, e.g., the information given by Murray (2005, pp. 40–41) on the various trade routes supplying Brugge with grain in the thirteenth and fourteenth centuries. 14. Spufford discerns two semi-integrated grain markets in medieval Europe. One centred on Paris, London, and the Low Countries. The other was in the south, stretching from Andalusia to the grain producing areas around the Black Sea and feeding the north Italian cities; Spufford (2002, p. 288). This characterization would appear to overstate the degree of integration of the southern market. As seen above, prices in Spain and northern Italy did not correlate well. The relatively integrated north Italian market comprised not more than a small part of the vast southern region as delineated by Spufford. 15. Masschaele (1993). Harrison (1992) argues that the construction of a large number of high-quality bridges in medieval England contributed towards creating reasonably good land transport conditions, and gives an indication of the increasing sophistication of the medieval economy. See also Spufford (2002, p. 180) on bridge building as the most important part of road improvement in Europe from the late twelfth to the early fourteenth century. 16. Masschaele (1993, p. 273). My recalculation assumes a price of 6 shillings per quarter of wheat, a normal price in the period 1320–1340; Farmer (1988, p. 795). 17. Hybel (2002, pp. 234–241). See also Hammel-Kiesow (2002, pp. 55, 78–90) arguing that the direct route from Western Europe round northern Jutland to the Baltic Sea was well established already by the mid-thirteenth century. 18. In an alternative specification of the model (not reported in Table 9), Tuscany wheat prices were included as an explanatory variable of Cairo wheat prices, over and above the Nile minimum and maximum water levels and temperature. This inclusion did not, however, improve the model fit. This test thus does not provide


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any support for the hypothesis that trade between Egypt and the northern Mediterranean influenced Cairo prices. Tuscany and Cairo wheat prices do not correlate significantly at any lag. The same is true about Cairo and Sicily (Saint-Se´verin) wheat prices, the later series comprising 43 years during the period 1456–1500 as reported by Ashtor (1969, p. 472).

ACKNOWLEDGMENTS An earlier version of this paper was presented at the conference Towards a Global History of Wages and Prices, Utrecht, 19–21 August 2004. Thanks to Greg Clark, Phil Hoffman, Paolo Malanima, Sevket Pamuk, Jan Luiten Van Zanden, and other participants for useful comments. Thanks also to Karl Gunnar Persson, Arne Jarrick, Janken Myrdal, Per Simonsson, and an anonymous referee for helpful suggestions.

REFERENCES Internet Sources Allen, Robert C. & Unger, Richard W. Allen–Unger database: European commodity prices 1260–1914. www.history.ubc.ca/unger/htm_files/new_grain.htm Malanima, P. International Institute of Social History: Prices and Wages. Tuscany: www. iisg.nl/hpw/malanima.xls Verhulst, A. E. Prices of the Sint-Donatiaanskapittel in Brugge, 1348–1800, Brugge. http:// www.iisg.nl/hpw/donat.xls

Published References Allouche, A. (1994). Mamluk economics: A study and translation of al-Maqrizi’s Ighathah. Salt Lake City: University of Utah Press. Ashtor, E. (1969). Histoire des prix et des salaries dans l’Orient medieval. Paris: S.E.V.P.E.N. Ashtor, E. (1976). A social and economic history of the near east in the middle ages. London: Collins. Ashtor, E. (1977). Quelques proble`mes que soule`ve l’histoire des prix dans l’Orient medieval. In: M. Rosen-Ayalon (Ed.), Studies in memory of gaston wiet (pp. 203–234). Jerusalem: Institute of Asian and African Studies, The Hebrew University of Israel. Ashtor, E. (1983). Levant trade in the later middle ages. Princeton: Princeton University Press. Bacharach, J. L. (1973). The Dinar versus the Ducat. International Journal of Middle East Studies, 4, 77–96.

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Bacharach, J. L. (1983). Monetary movements in Medieval Egypt. In: J. F. Richards (Ed.), Precious metals in the Later Medieval and early modern worlds (pp. 159–181). Durham, NC: Carolina Academic Press. Berger, W. H. (2002). Climate history and the great geophysical experiment. In: G. Wefer, W. H. Berger, K.-E. Behre & E. Jansen (Eds), Climate development and the history of the Atlantic Realm (pp. 2–16). Berlin: Springer. Braudel, F., & Spooner, F. (1967). Prices in Europe from 1450 to 1750. In: Cambridge economic history of Europe, vol. IV: The economy of expanding Europe in the sixteenth and seventeenth centuries (pp. 374–486). Cambridge: Cambridge University Press. Campbell, B. M. S. (1997). Matching supply to demand: Crop production and disposal by English demesnes in the century of the Black Death. Journal of Economic History, 57, 827–858. Chaudhuri, K. N. (1990). Asia before Europe: Economy and civilisation of the Indian Ocean from the Rise of Islam to 1750. Cambridge: Cambridge University Press. Clark, G. (1999). Markets and economic growth: The grain market of medieval England. Davis: Department of Economics, University of California. Crowley, T. J. (2000). Causes of climate change over the past 1000 years. Science, 289(14), 270–277. Darra¯ g, A. (1961). L’E´gypte sous le re`gne de Barsbay 825–841/1422–1438. Institut Franc- ais d’E´tudes Arabes de Damas: Damascus. Diamond, J. (1999). Guns, germs and steel: The fates of human societies. New York: W. W. Norton. Ejrnæs, M., & Persson, G. (2000). Market integration and transport costs in France 1825–1903: A threshold error correction approach to the law of one price. Explorations in Economic History, 37, 149–173. Ejrnæs, M., Persson, G., & Rich, S. (2004). Feeding the British: Convergence and market efficiency in 19th century grain trade. Discussion Papers 04–28, Institute of Economics, University of Copenhagen. Epstein, S. R. (2000). Market structures. In: W. J. Connell & A. Zorzi (Eds), Florentine Tuscany: Structures and practices of power. Cambridge: Cambridge University Press. Epstein, S. R. (2001). Freedom and growth: The rise of states and markets in Europe, 1300–1750. London and New York: Routledge. Farmer, D. L. (1988). Prices and wages. In: J. Thirsk (Ed.), The agrarian history of England and Wales II. 1042–1350 (pp. 716–817). Cambridge: Cambridge University Press. Galloway, J. A. (2000). One market or many? London and the grain trade of England. In: J. A. Galloway (Ed.), Trade, urban hinterlands and market integration c. 1300–1600 (pp. 23–42). London: Centre for Metropolitan History, Working Paper Series No. 3. Hammel-Kiesow, R. (2002). Lu¨beck and the Baltic trade in bulk goods for the North Sea region 1150–1400. In: L. Berggren, N. Hybel & A. Landen (Eds), Cogs, cargoes, and commerce: Maritime bulk trade in northern Europe 1150–1400 (pp. 53–91). Toronto: Pontifical Institute of Medieval Studies. Hanauer, A. C. (1878). E´tudes ećonomiques sur l’Alsace ancienne et moderne (Vol. II). Paris: A. Durand et Pe´done-Lauriel. Harrison, D. F. (1992). Bridges and economic development, 1300–1800. Economic History Review, 45(New Series), 240–261. Herring, R. S. (1979). Hydrology and chronology: The Rodah nilometer as an aid in dating interlacustrine history. In: J. B. Webster (Ed.), Chronology, migration and drought in interlacustrine Africa (pp. 39–86). London: Longman.


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Hinz, W. (1955). Lebensmittelpreise in mittelalterlichen Vorderen Orient. Die Welt des Orients, 2, 52–70. Humphries, R. S. (1998). Egypt and the world system in the later middle ages. In: C. F. Petry (Ed.), The Cambridge history of Egypt (Vol. I, pp. 445–461), Islamic Egypt, 640-1517. Cambridge: Cambridge University Press. Hybel, N. (2002). The foreign grain trade in England, 1250–1350. In: L. Berggren, N. Hybel & A. Landen (Eds), Cogs, cargoes, and commerce: Maritime bulk trade in northern Europe 1150–1400 (pp. 212–241). Toronto: Pontifical Institute of Medieval Studies. Jones, P. D., Briffa, K. R., Barnett, T. P., & Tett, S. B. (1998). High-resolution palaeoclimatic records for the last millennium: Interpretation, integration and comparison with general circulation model control-run temperatures. The Holocene 8, 455–471. Data source: Jones, P. D., Briffa, K. R., Barnett, T. P., & Tett, S. F. B. (1998). Millennial temperature reconstructions. IGBP PAGES/World Data Center-A for Paleoclimatology, data contribution series #1998-039, NOAA/NGDC Paleoclimatology Program, Boulder CO, USA. Downloadable data: ftp://ftp.ngdc.noaa.gov/paleo/contributions_by_author/jones1998. Lopez, R., Miskimin, H., & Udovitch, A. (1970). England to Egypt, 1350–1500: Long-term trends and long-distance trade. In: M. A. Cook (Ed.), Title Studies in the economic history of the middle East from the Rise of Islam to the present day (pp. 93–128). London: Oxford University Press. Mann, M. E., Bradley, R. S., & Hughes, M. K. (1998). Global-scale temperature patterns and climate forcing over the past six centuries. Nature, 392(23), 779–787. Mann, M. E., Bradley, R. S., & Hughes, M. K. (2004). Corrigendum. Nature, 430(1), 105. Masschaele, J. (1993). Transport costs in medieval England. Economic History Review, 46(New Series), 266–279. Mortel, R. T. (1989). Prices in Mecca during the Mamluk period. Journal of the Economic and Social History of the Orient, 32, 279–334. Murray, J. M. (2005). Bruges, cradle of capitalism, 1280–1390. Cambridge: Cambridge University Press. Persson, K. G. (1999). Grain markets in Europe, 1500–1900. Cambridge: Cambridge University Press. Popper, W. (1951). The Cairo nilometer: Studies in Ibn Taghrıˆ Birdıˆ’s chronicles of Egypt (Vol. 1). Berkeley and Los Angeles: University of California Press. Popper, W. (1957). Egypt and Syria under the Circassian Sultans 1382–1468 A.D.: Systematic notes to Ibn Taghrıˆ Birdıˆ’s chronicles of Egypt. P. [2]. Berkeley and Los Angeles: University of California Press. Rabie, H. (1981). Some technical aspects of agriculture in Medieval Egypt. In: A. L. Udovitch (Ed.), The Islamic Middle East, 700–1900: Studies in economic and social history (pp. 59–90). Princeton, NJ: Darwin Press. Sabra, A. (2000). Poverty and charity in Medieval Islam: Mamluk Egypt, 1250–1517. Cambridge: Cambridge University Press. Shoshan, B. (1980). Grain riots and the ‘moral economy’: Cairo, 1350–1517. Journal of Interdisciplinary History, 10, 459–478. Shoshan, B. (1983). Money supply and grain prices in fifteenth-century Egypt. Economic History Review, 36(2nd ser.), 47–67. Shoshan, B. (1986). Exchange-rate policies in fifteenth-century Egypt. Journal of the Economic and Social History of the Orient, 29, 28–51. Shoshan, B. (1993). Popular culture in Medieval Cairo. Cambridge: Cambridge University Press.

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WAGES, RENTS, AND INTEREST RATES IN SOUTHERN KOREA, 1700 TO 1900 Jun Seong Ho and James B. Lewis ABSTRACT Since 1997, a quantitative revolution has swept Korean economic history and generated a new paradigm. From 1700 to 1900 the Korean economy expanded and contracted along lines suggested by Adam Smith. Economic expansion was based on productive land and a stable commodity market. The direct result was high real skilled wages. Economic contraction became clear from the mid-nineteenth century when the value of land declined, commodity prices rose, and real skilled wages fell. The contraction was apparent before the appearance of Japanese imperialism and the absorption of Korea into the international commodity market after 1876.

1. INTRODUCTION Korean economic history is in the midst of a quantitative revolution that began in 1997 with a paper by Jun Seong Ho.1,2 Since then, efforts have gone into creating databases for analysis at micro and macro levels (An & Yi, 2001; Yi, 2004).3 With such data, we are finally poised to begin building positivist, data-driven models to explain the pre-industrial, agricultural Research in Economic History, Volume 24, 217–283 Copyright r 2007 by Elsevier Ltd. All rights of reproduction in any form reserved ISSN: 0363-3268/doi:10.1016/S0363-3268(06)24007-2

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Korean economy. This paper reviews earlier theoretical approaches and proposes a model based on Adam Smith: expansion, stagnation, and decline. Declining land prices, declining real wages, shifting land use toward paddy rice (a speculative crop), and rising rice prices show that Korea left a period of expansion in the seventeenth and eighteenth centuries and entered a period of stagnation and decline from the late eighteenth through the nineteenth centuries. In other words, financial, institutional, and ecological limits were reached and the agricultural economy drifted toward monoculture, away from a division of labor, and into price instability.

1.1. From Marx to Smith Until the quantitative turn, Marxist analysis dominated research on the pre1900 economic history of Korea. By Marxist, we mean an orientation toward class analysis, a search for capitalistic relations, and an assumption of teleological, progressive development. Japanese colonial scholars opened the debate by proposing a stagnation thesis. Their stagnation theory argued that Korean socio-economic development had stopped in a pre-feudal or early feudal stage and that Korean society had collapsed and needed the organization and modern development that Japan could supply. The theory denied Korean structural change over the better part of half a millennium or more and obviously supported the colonization of Korea. A Marxist Korean economic historian in the early twentieth century suggested an alternative. Paek Nam-un (1933) proposed that the Korean trajectory paralleled European development in a set of four stages. Paek argued that the Choso˘n period was feudal, and that the state owned all the land, which meant that there were no private owners and no land market. There were sprouts of capitalism at the time the old feudal order was collapsing, but capitalism was imported as the Japanese colonial government introduced a modern land market (Paek, 1933, p. 3). In the post-Liberation (post-1945) period, and into the 1970s, the socalled ‘‘sprouts of capitalism’’ theory grew out of and displaced Paek Namun’s thesis. Historians in both north and south Korea sought to counter the Japanese colonial thesis that Korean socio-economic development had stagnated by re-introducing historical materialist change. The ‘‘sprouts’’ thesis argues that capitalism was indeed developing indigenously, but it was choked off by Japanese imperialism. Proponents find a native feudal period with proto-industrial manufacturing emerging out of agriculture, put emphasis on capitalistic relations in manufacturing, and argue that there was

Wages, Rents, and Interest Rates in Southern Korea, 1700 to 1900

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internal historical change in Korea. The entry of imperialism and colonization stifled a natural development toward capitalism. The theory has tried to combine a model of proto-industry and agriculture derived from Adam Smith with Marxist theories on capitalism. The most influential work has been done by Kim Yo˘ng-so˘p (1971/1994). Kim’s agricultural data come largely from Hwanghae Province in the north, where individual peasants had extensive land holdings and hired stewards to manage their holdings. Kim points to capitalistic land leasing, similar to the English case. In the 1980s, challenges appeared to the ‘‘sprouts’’ thesis that introduced arguments similar to ‘‘involution’’ as proposed by Clifford Geertz (1963) for Indonesia and Mark Elvin (1973) and Philip Huang (1985, 1990) for China. ‘‘Involution’’ refers to a pre-modern agricultural situation that sees the steady intensification of labor inputs but declining per capita productivity, all developing on the back of an expanding population. The thesis argues that peasants put more and more work into the land and raised land productivity to a high level to maintain an expanding population, but rates of return declined and industrialization did not develop. All surplus labor and production were consumed in maintaining a steady supply of food to an expanding population and no surplus capital could accumulate. Production dropped to a subsistence level for most people. For the Korean case, Yi Yo˘ng-hun (1985) argues that the Korean economy between the seventeenth and the nineteenth centuries followed a similar path and saw a maturing of small-scale agricultural cultivators and an expansion of family farming, but the economy did not develop capitalistic land leasing. Therefore, the Korean trajectory differed from Western Europe, and Korea was not developing toward capitalism (Yi, 2004, pp. 371–372). Yi Yo˘ng-hun’s ‘‘involution’’ thesis has been based primarily on data from Kyo˘ngsang Province, in the south, where small-scale peasant landholding was most common. Peasants did not hire stewards to manage their lands, and small-scale, subsistence farming was the norm. Government did not develop legal institutions to protect property rights, and the result was that a competitive land market did not emerge. Following Paek Nam-un, Yi concludes that weak property rights and the lack of a land market meant that capitalism could not emerge under the traditional order. Japanese imperialism succeeded in removing backward government and imposed on Korea modern private property rights, the necessary condition for capitalistic economic growth (Yi, 1985). ‘‘Involution’’ was thus linked to a theory of colonial modernization. Ample land sale documents from before 1900 have thrown serious doubt on assertions that there was no land market.4

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All these theories share nineteenth-century Marxist assumptions of a teleological economic development toward something called as ‘‘capitalism,’’ and all find the Korean case lagging either for internal or external reasons. They all cast the pre-modern economy in a pessimistic light: there were no private property rights to form the foundation for markets and agriculture rarely rose above subsistence. Although most proponents of the ideas put forth above recognize an expansive eighteenth century followed by a contracting nineteenth century, their focus is really on the contrast between the indigenous system prior to the opening of the ports in 1876 and the externally imposed system after 1876. We argue that the trajectory of pre-modern Korean history can best be understood by examining the nineteenth century in relation to what preceded it, not in relation to what followed it. Kenneth Pomeranz (2000) and R. Bin Wong (1997) have proposed Adam Smith’s model of an agricultural economy to explain China, and we find much in this model that can also explain Korea. Particularly attractive is the view that we need not take European historical development of the nineteenth century as the natural trajectory for ‘‘development.’’ Most importantly, we do not need to ask the question: What went wrong in Korea to prevent the development of capitalism? For example, Pomeranz (2000, pp. 91–98) draws on Jan DeVries (1994) and others to argue that European land, labor, and commodity markets before 1800 were closer to the Chinese case than they were to the post-1800 European case; indeed, European markets were probably further from Smithian-style competition (‘‘multiple buyers and sellers with opportunities to choose freely among many trading partners,’’ Pomeranz, 2000, p. 17) than Chinese markets. As R. Bin Wong writes (1997), ‘‘Smith’s world was not Europe of the nineteenth century. In key ways, eighteenthcentury Europe shared more with China of the same period than it did with the Europe of the nineteenth and twentieth centuries’’ (p. 17). Adam Smith’s classical model preceded the industrialization that beset Europe in the nineteenth century. It describes an agricultural economy expanding only at the rate that output expands, reaching limits, and contracting. Goldstone (2002) has recently re-stated this formulation (pp. 333, 376). He attacks the dichotomizing tendency of analyses of growth that pits ‘‘pre-modern’’ (stagnation punctuated by crises) against ‘‘modern’’ (conscious application of science and technology to economic activity) and suggests that all societies have experienced ‘‘efflorescences’’ during which their economies experienced ‘‘a relatively sharp, often unexpected upturn in significant demographic and economic indices, usually accompanied by political expansion and institution building and cultural synthesis and consolidation.’’ England was odd in that after a typical ‘‘efflorescence’’


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(1760–1830), modern growth first appeared with the conscious study and application of engines (steam) to use and manipulate energy for production. It is mistaken to ignore the peculiarities of the English experience and establish industrialization as the natural outcome of all pre-modern growth. In short, there is no universal ‘‘modernity.’’ We have extracted a crude model from Hiram Caton’s (1985, pp. 842–845) reading of Smith that describes the Korean case rather well. Korea in the eighteenth century possessed many promising Smithian characteristics. At least by 1746, there was a legal basis to protect property rights and to enforce contracts and the payment of debts.5 Archival materials gathered and reprinted by the Academy of Korean Studies clearly show extensive and highly competitive markets, unthinkable without protection for buyer and seller (Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n, 1983– present). We also find that the freedom of small peasants in rural markets was widely accepted. Almost anyone had the right to produce and exchange products, labor, capital, and land. Even nobi, a term usually rendered as ‘‘slave,’’ were able to own property and possessed juridical personalities as paid-in members of cooperative societies. Small peasants appeared in rural markets, engaged in specialized activities, actively pursued their selfinterests, and competed in the production and exchange of goods and services. As specializations and division of labor multiplied, productivity improved, capital accumulated, skilled wages were high, and living standards rose. This was macroeconomic growth. Also, like Smith’s ideal social contract, the Confucian society of Choso˘n Korea was imbued with virtue, mutual benevolence, enlightened self-interest, and social commitments to fair and co-operative economic activity while prohibiting unjust and fraudulent business practices. Elsewhere, we refer to the grassroots Confucian socio-economic ethic as a ‘‘moral economy’’ (Jun & Lewis, forthcoming). As the Korean economy entered the nineteenth century, it began to face limits and productivity declined. In Smith’s terms, the system began the transition from expansion to stagnation to decline. The model predicts that productivity will be constrained by natural limits to growth, or by diminishing returns to the division of labor, or some combination of the two. These constraints appeared and they were led by a decline in the productivity of paddy land. Diminishing output to capital input squeezed wages, these fell, and demand declined. The result was that accumulation suffered, investment fell, proto-industrial specializations were abandoned, and land use speculatively shifted toward paddy with an eye to profiteering from rising rice prices. These are the general trends we see from the late eighteenth into the early twentieth century in Korea.

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We prefer a Smithian model for Korea, because it is the model that best fits the data emerging from the new quantitative revolution. The data show us an eighteenth century with a high starting point for land prices and wages and low commodity prices. Below, we begin by providing information on commodity prices and labor costs to reach an estimation of real labor costs or real daily wages. The basic question related to the standard of living is: How did it change over time? An important secondary and comparative question is: Was Korean rice6 more or less expensive than Chinese or Japanese rice or European wheat? To gauge this, we are asking if real wages were higher or lower than Chinese, Japanese, or European wages. We offer a few comparisons with other places, but this is not our main purpose. Our purpose is to clarify the Korean case. Nevertheless, to make comparability easier, we have converted local units to silver grams per metric units.7 In addition to wages, we also look at land prices, land rent, and interest rates. The main question in this connection is: Did land prices, rent rates, and interest rates show a general pattern of rising or declining land evaluation? We will assume that falling land values indicate falling land productivity. Real wages and the land and financial markets all changed at about the same time. The changes suggest a decline in agricultural productivity and this can also be used for international comparison.8 Finally, we should test our Smithian model by examining the expansion or contraction of the division of labor by tracking proto-industries in agriculture. This problem is a major concern of future research, and we are able to comment only briefly at this point. Through documents from the southern village of Yo˘ng’am in Cho˘lla Province, we can get a rough idea of the secular trend in the structure of rice consumption by monitoring the polished rice account from the eighteenth century through the nineteenth century. In the eighteenth century, when the standard of living was fairly high, rice consumption was spread among staple foods and intermediate industrial goods, such as wine, syrup, drinks, cakes, and paste. However, from the late nineteenth and into the early twentieth century, these items disappeared from the accounts, and especially from 1920 onward, polished rice was consumed as a staple food by only the rich. The shift probably indicated a degree of rural de-industrialization.9

1.2. Data Sources and the Limitations of Comparisons Most of our data come from counties in the two southern provinces of Korea, Cho˘lla and Kyo˘ngsang Provinces (Fig. 1: Southern Korean


Fig. 1.

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Southern Korean Provinces.

Provinces).10 These two southern provinces produced nearly half of the national rice production. Price data from these provinces show a high correlation across the southern rice-producing area.11 Recently, local data have become plentiful, and we no longer need to rely on spotty, heterogeneous, central government data. The local data are not local government data, but were produced by cooperative societies, private academies, or clan associations, and can be taken as more accurately reflecting immediate economic conditions. Our methodology compares macro (government sources) with micro data (clan and village association account books) to arrive at a relative understanding of the value of each. Many of the local records are account books, and the accounting is in a form that closely approximates the standards of double-entry accounting (Jun & Lewis, forthcoming). Because the records balance so closely, we can be assured of a high uniformity of measurements, and because the accounts range over centuries, we can construct time-series.

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Agricultural prices are revealed through four markets: commodities, labor, land, and finances. We have extracted and organized our data under these categories. In this paper, we deal only with rice as the proxy for our commodity market. There were generally two commodity markets: protoindustrial goods and agricultural goods. Proto-industrial goods included paper, brush and ink, yeast cakes made from barley for wine production, lamp oil, and sesame oil for cooking, and sesame seeds for fertilizer. Agricultural commodities included poultry (800 g of meat per chicken), red beans (hypothesized at one l at 750 g) (Bank of Korea, 1962-present, and Ministry of Agriculture and Fisheries, 1955-present), and polished, white rice (determined to be one l at 849 g) (Pak, 1980, p. 159). Our rice price data come from six counties around Cho˘lla and Kyo˘ngsang Provinces: Yo˘ng’am, Changhu˘ng, and Haenam in Cho˘lla Province; and Koso˘ng, Kyo˘ngju, and Taegu12 in Kyo˘ngsang Province. The labor market information comes only from Yo˘ng’am. Information on the price of land comes from five counties in the south: Kuye, Yo˘nggwan, and Yo˘ng’am in Cho˘lla Province; and Kyo˘ngju and Sangju in Kyo˘ngsang Province. Information on land rent is taken from Yo˘ng’am and Changhu˘ng counties in Cho˘lla Province. Interest rates are taken from Yo˘ng’am County. The data may have an inherent bias, because a large portion is drawn from Yo˘ng’am County. We believe the biases can be controlled for the following reasons. Yo˘ng’am data correlate highly with macroeconomic data produced by the capital government.13 Yo˘ng’am data appear in highly systemized accounts, virtual double-entry accounts, which not only balance internally, but are also reported homogenously over long time periods.14 In short, given the high quality of the source, we believe that the Yo˘ng’am data can be used as something of a standard with the addition of data from other areas. This geographic weighting of the data from one southern county does not appear significant when discussing the southern rice-growing areas, but is problematic if we attempt to include the northern half of the peninsula.15 The variations are significant, and we do not yet have adequate data on the circumstances in the north. For example, climate, rainfall, crops, and land use varied as one moves from south to north, particularly along a southwest to northeast axis, but less so along a southeast to northwest axis where population was concentrated in optimum circumstances of balanced land use between dry and wet fields (Lewis & Jun, 2001, 2004). Comparability beyond Korea is a concern. Because many parts of China were also rice economies, as were many parts of Japan, Korean data can be directly compared to both. Chinese and Korean societies share countless similarities deriving from the structures of rice production, not to mention


225

ideological and cultural similarities. For example, the Chinese land area called mu is roughly similar to the Korean turak (0.16 acre). While there are enormous similarities, the differences may be hard to spot, but they are significant. For example, there are sharp ecological differences between the Yangzi River Valley and north China, and parts of Korea seem ecologically comparable to both. Any comparison of regions must consider such differences. Differences in labor input are less obvious but also important. We have yet to perform a systematic survey, but we have noticed a difference in labor inputs for rice production. Table 1 illustrates this point. As we can see in Table 1, direct seeding in Korea required more labor input (primarily because of weeding) than transplanting in China, but transplanting times in Korea and China were comparable. Comparisons with Japan are difficult for ecological reasons and complicated further by the large differences between the Japanese economic and political structures. While Korean data might be compared to Chinese data (and perhaps Japanese data), the general institutional and structural conditions of Korea make comparisons with Europe more difficult. The institutional peculiarities of Korea should be kept in mind when considering the role of labor, wages, and the standard of living. For example, Korean taxation included corveé labor, so population assessments cannot be seen apart from government objectives. English parish registers were a roll-call of souls, but Korean government registers were a roll-call of taxable population. The implications are that official Korean statements about labor availability and costs cannot be taken as reflecting the market.16 Privately produced information, such as is used here, is much closer to a picture of the real labor market. Table 1. Differences between Korea and China in Labor Input between Direct Seeding and Transplanting (Workdays/0.16 acre). Korea: No. of Days Direct Seeding

China: No. of Days

Transplanting: Ordinary

Transplanting: Ordinary (Intensive)

Preparing and seeding: 2.5–3 Weeding: 8.5 Harvesting:

Preparing: 1.5 Transplanting: 2 Weeding: 3 Harvesting and threshing: 3

Preparing: 2 (5) Transplanting: 1 (2) Weeding: 3 Harvesting and threshing: 3

Total: 11–11.5 days

9.5 days

9 (13) days

Source: Ilso˘ngnok (16/12/1798); Li Bozhong, 1998 (1984, p. 83); Kim Yo˘ng-so˘p, 1994 (1964, p. 251); Ch’oe Yun-o (1992, p. 77); and Kim, Ko˘n-t’ae (1997, p. 121).

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Another institutional difference between Korea and the European case is in the relative position of government in the overall economy. The Korean case was marked by a highly centralized, highly bureaucratized government that dominated, but usually was unable to control, all aspects of the economy. Of relevance to commodity prices was the institution of grain storage. Government sought to create stable consumption, and this was reflected as a central theme in popular agitation, because stability was an integral part of the ‘‘moral economy of the crowd’’ (Persson, 1999, p. 16). The presence of markets and government storage may have actually given the Korean case greater commodity price stability over circumstances where storage was less important. In Europe and Japan, markets were large and often acted to achieve an acceptable level of price stability, while in Korea, governmentregulated storage was expected to augment stability. In other words, Korea had certain advantages in achieving consumption stability, because the market and grain storage both offered flexibility. The main structural difference between Korea and Europe was the difference between rice and wheat. We have just begun to consider the extent of these differences, and we cannot explore them here. Suffice it to say that the economic ‘‘common sense’’ that emerges from a wheat-based system may not apply at all to a rice-based system and vice-versa. It is increasingly apparent that most agricultural economic theory has been developed for wheat and we now need to create new models to explain rice.17 In the price of flour or meal in a European city, we must add in the miller’s profits and the servants’ wages to the price of wheat. To the price of bread – the end product – we must add in the baker’s profits and his servants’ wages. In the Korean case, threshing was done by the peasant farmer, and husking and milling by a miller. Once husked, the rice could be directly consumed. Other differences are that Korean data are often rural, and European data are often urban. The rural Korean economy was not as highly monetized as the urban European economy. Our study offers data and analysis for the Korean case, and we attempt comparisons below, but we find that we must leave detailed comparisons for later research. At the risk of redundancy, let us pause to summarize our new model before we launch into the analysis of data. Within Korea, a downward trend in land values emerged in the 1820s, indicating a decline in land productivity. This was shortly accompanied by the appearance of a downward trend for real skilled wages in agriculture, the beginning of unstable and rising rice prices, and rises in interest rates. Namely, from the 1830s and 1840s, one epoch of high land values, high real wages for skilled labor, and low, stable commodity prices was showing signs of ending – its demise


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was clear by 1860 – to be followed by a period of declining land values, rising commodity prices, and low real wages. Other contemporary developments suggest a broad conjuncture when the economy was beginning on vast changes: the decline of government storage, shifting land use from dry fields to paddy for speculative purposes, climatic shocks, and other related factors. After the economic stagnation and decline had appeared, the negative aspects were amplified from the 1880s – after the opening of the ports in 1876 – by the appearance of imperialism (primarily Japanese) and the forced, violent absorption of the Korean rice market into an international commodity market. Let us now turn to our data.

2. LABOR COSTS OR REAL WAGES Pak Yi-t’aek and Ch’a Myo˘ng-su (Pak, 2004; Ch’a, 2001) have also addressed wages. Pak drew on homogenous central government data. The problem with government data on wages is that labor was considered a tax and wages were arbitrarily determined by government policy. Government wage data cannot be taken as reflecting a labor market. Ch’a compiled a large variety of information from dissimilar sources, and his study runs into the problem of heterogeneity. Disparate sources present us with serious questions about the comparability of the data. The data employed here comes from the continuous records of cooperative associations in one southern village and carries a great deal of homogeneity. The drawback with our study is the geographical limitation. We may be able to generalize our findings to other southern regions, but we cannot generalize our findings to the north. Real wages are nominal wages divided by a consumer price index. Nominal wages are silver amounts converted from their copper equivalents. The consumer price index is the average of rice prices (Fig. 2), our general proxy for commodity prices. Rice prices may be the best commodity indicator in a rice-eating country (Osamu, 1978). Tables 2 and 3 present summaries of wage and price data in decadal form. Tables 2 and 3 are distilled from Appendices A and C. The village and clan association account books directly report a great deal of labor information, but there are two large problems. One problem is that key details needed to calculate wages may be missing. For example, some records are missing the number of days worked or the number of laborers employed. Depending on the type of work, we can estimate the number of days worked: one day for work connected with rituals (Appendix C, column C: ‘‘ritual food’’) and one

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JUN SEONG HO AND JAMES B. LEWIS 3.5

18 16 14 12

silver grams per kg: dotted lines, scale to right

3.0 2.5

10

2.0

8

1.5

6

1.0

4 2

0.5

0 0.0 1700 1712 1725 1737 1750 1762 1775 1787 1800 1812 1825 1837 1850 1862 1875 1887 1900

Fig. 2. Average Nominal Rice Prices in Copper Coins and Silver Grams (1713– 1900) at Six Southern Locations. Note: The trendline for copper is a solid line, 31-year moving average. The trendline for silver is a dotted line, 31-year moving average. Source: For Yo˘ng’am ( ) (South Cho˘lla): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n (1995). For Haenam ( ) (South Cho˘lla): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n, ed., microfilm, reel no. 35-003205-003216. For Changhu˘ng ( ) (South Cho˘lla): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n, ed., microfilm, reel no. 006904. For Koso˘ng ( ) (South Kyo˘ngsang): Su˘ngch’ong myo˘ngnok ( ). For Kyo˘ngju ( ) (North Kyo˘ngsang): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n, ed., microfilm, reel no. 009574-009579. For Taegu ( ) (North Kyo˘ngsang): Taegu Tanyang U-ssi komunso˘.

day for attaching or repairing locks by a blacksmith (Appendix C, column C: ‘‘repair’’). The ideal data contain: type of laborer, the various wage payments for the job (copper cash, copper cash for wine, copper cash for tobacco, and copper cash for food other than rice), the cost of raw materials, the number of days worked, and the number of people on the job. Where we have no indication of the number of days worked, we have made no estimates for large jobs, such as building work. Another large problem is that some wages were paid in kind (unrefined rice wine and food other than rice) in addition to cash, so their conversion to monetized units is difficult but not impossible. The accounts give us copper cash values for in-kind payments, so we can convert nearly everything to copper cash. From copper cash, we converted wages to rice (Fig. 3), because rice was used as money just as commonly if not more commonly than copper cash. We also converted wages to silver (Fig. 4) for purposes of


Table 2.

Wages, 1710–1899.

Year

Full Wages Nominal unskilled wage (grams of silver per day)

1710–1719 1720–1729 1730–1739 1740–1749 1750–1759 1760–1769 1770–1779 1780–1789 1790–1799 1800–1809 1810–1819 1820–1829 1830–1839 1840–1849 1850–1859 1860–1869 1870–1879 1880–1889 1890–1899

229

1.06 1.19 1.19 0.98 1.33 1.46 1.34 1.15 1.15 0.60 0.95 1.50

Nominal skilled wage (grams of silver per day)

3.15 3.80 5.71 3.01 4.15 3.57 2.60 3.19 3.19 2.25 3.41

Cost of polished rice per liter (849 g) in grams of silver 1.09 0.90 0.99 1.22 0.92 0.66 0.55 0.58 0.64 0.51 0.58 0.51 0.73 0.54 0.50 0.50 0.87 1.00 1.79

Real unskilled wage in kg of rice

Real skilled wage in kg of rice

1.81 1.84 2.30 1.67 2.60 1.99 2.47 2.31 2.31 0.68 0.94 0.83

5.38 5.85 11.0 5.13 8.13 4.86 4.80 6.39 6.39 2.56 3.39

Note: Copper was converted to silver according to the bi-metallic ratios (1650–1880) in Appendix B. Source: Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n (1995).

international comparison, although silver did not circulate and was almost never used as money in this period.18 We took the nominal and real wages in Table 2 and created Figs. 3 and 4. We can see that skilled laborers (e.g., bamboo mat makers, blacksmiths, carpenters, builders, plasterers, masons, horsemen, hypocaust builders, and utensil makers) were rewarded significantly better than unskilled laborers (porters, day laborers, errand boys, and servants). This is not surprising. What is surprising is the change over time. Here, we can see that skilled wages showed a decline from the 1830s, and it seems that the 1830s–1840s, both in nominal and real wages, was the end of one epoch and the beginning

230


Table 3.

Land Prices and Rent, 1710–1899.

Year

Land Price and Rent Land price (grams of silver per 0.16 acre)

1710–1719 1720–1729 1730–1739 1740–1749 1750–1759 1760–1769 1770–1779 1780–1789 1790–1799 1800–1809 1810–1819 1820–1829 1830–1839 1840–1849 1850–1859 1860–1869 1870–1879 1880–1889 1890–1899

271 182 114 287 147 125 100 114 105 128 78.3 94.7 88.2 98.7 75.0 75.0 115 141 175

Land rent (grams of silver per 0.16 acre)

60.6 38.2 27.0 20.0 19.9 13.9 12.5 24.9 21.1 16.2 12.2 12.2 17.0 16.2 25.7

Real land price in kg of rice

248 200 115 234 159 190 182 195 162 247 133 185 120 182 150 150 131 140 97.6

Real land rent in kg of rice

Implied return on land ownership (rent/price as %)

49.4 41.2 40.9

21.1 25.9 21.5

34.3 30.7 26.9 21.3 48.8 28.7 29.9 24.5 24.5 19.4 16.1 14.3

17.5 18.9 10.9 16.0 26.3 23.9 16.4 16.3 16.3 14.8 11.6 14.7

Note: Copper was converted to silver according to the bi-metallic ratios (1650–1880) in Appendix B. Source: Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n (1995).

of another. There was a brief recovery over the 1850s and the 1860s, but the decline was clear from 1870. Unlike the European case where unskilled wages rose to meet skilled wages (van Zanden, 2004), Korean skilled wages fell to meet unskilled wages. When wages are compared with rice prices, we can see that the upward drift of prices began in the 1830s. When prices slumped a bit, wages recovered, but the price trend was climbing. In other words, the long, prosperous eighteenth century ended between the 1830s and the 1860s. Internal disintegration had taken hold, and it was exacerbated later by the shocks of the domestic market becoming enthralled first to the Japanese market and later to the world market.19


231

12

Kgs of rice per day

10 8 6 4 2 0

1780s 1790s 1800s 1810s 1820s 1830s 1840s 1850s 1860s 1870s 1880s 1890s Real Unskilled Wage

Real Skilled Wage

Fig. 3. A Comparison of Skilled and Unskilled Labor Over Time: Real Rice Wage (Full Wages). Note: See Table 2 for data. Source: Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n (1995).

Grams of silver per day

6 5 4 3 2 1 0 1780s 1790s 1800s 1810s 1820s 1830s 1840s 1850s 1860s 1870s 1880s 1890s Nominal Unskilled Wage

Nominal Skilled Wage

Fig. 4. A Comparison of Skilled and Unskilled Labor Over Time: Nominal Silver Wage (Full Wages). Note: See Table 2 for data. Source: Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n (1995).

If we return to the question of comparability across regions, we can extract certain key information from Table 2 and augment that with comparative data from China and Europe (Allen, 2002, 2001a). This we offer in Table 4.

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Table 4.

Unskilled (silver grams) Skilled (silver grams) Unskilled (rice/ bread in kg) Skilled (rice/ bread in kg)


Real Wages for Skilled and Unskilled Labor in Korea and Elsewhere. Korea 1780–1809

Average

Korea 1810–1889

Average

China (Late 1600s)

England (London) 1750–1759

Italy (Milan) 1750–1759

1.06–1.19

1.13

0.6–1.5

1.05

1.55

5.57

2.10

3.15–5.71

4.43

2.25–4.15

3.2

1.81–2.30

2.06

0.68–2.60

1.65

5.38–11.0

8.2

2.56–8.13

5.35

11.1 2.80

5.73

5.42

2.33

8.13

6.30

Note: The price of rice is used as the deflator. Unskilled and skilled labor wages in silver represent nominal wages, but unskilled and skilled in rice/bread represent real wages. Figures for Korea for 1780–1809 and 1810–1889 are full wages. Source: Korean data from Table 2; see text. Other data from Robert C. Allen (2001).

Up to 1809, the highest nominal silver wage in Korea for skilled labor was slightly below Milan for 1750–1759 (5.71 vs. 5.73 silver grams), while unskilled wages were a bit lower (1.19 vs. 2.10). Up to 1809, the highest real wages for skilled labor in Korea (11.0 kg of rice) surpassed London and Milan (8.13 kg of bread for London and 6.3 kg of bread for Milan), but high Korean real wages did not last. Prices rose and real wages fell from the midnineteenth century onward, and there appeared a clear divergence between the Korean and the European cases. Since the origins of the numbers for China and Europe are not completely clear, and such comparisons can only be rough anyway, we also present the average of the Korean range for comparison. Then, the differences between Korea and London or Milan are very different in nominal silver wages but close in real rice wages. The artificiality of putting Korean wages into silver may present problems and such comparisons can only be approximate. Imagine trying to denominate London prices in rice. Indeed, the difficulties of comparisons are challenging and we should persevere, but two points deserve mention. The obvious point is the decreasing spread between skilled and unskilled wages in Korea after 1800. The second point is the implication for international comparison, namely the standard of living in Korea from the eighteenth century into the early nineteenth century was not too dissimilar from Milan, even London, but diverged thereafter. The Korean fall in real wages in the nineteenth century can be starkly conveyed by considering actual consumption. For example, in the


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eighteenth century (1780–1789), the lowest real daily net unskilled wage we could find was 960 g. If an average person required about 210 g per day, then 960 g could have supported about four people with a little left over for housing, clothing, or other necessities. By contrast, in the nineteenth century (1870–1879), the lowest real daily net rice wage for unskilled labor fell to 540 g of white rice or half of what it had been less than a century earlier. While agricultural productivity was high in the eighteenth century, urban areas did not expand their employment opportunities, and any surplus labor that appeared – already small in the case of rice agriculture – was absorbed in agriculture. Although tempting, we cannot use the term ‘‘involution.’’ The system was not destined to result in imbalance for structural reasons having to do with rising population, limited land, and rice agriculture. In fact, the population fell drastically between 1807 and 1822, and this very drop may have contributed to the stagnation that set in from mid-century. As productivity decreased in the nineteenth century and surplus labor expanded, it failed to find a labor market in urban or rural areas. The result was that an agricultural crisis loomed large, a crisis that was marked by significant peasant disturbances from the 1860s and eventually massive migrations to Manchuria.

3. LAND PRICES Franc- ois Simiand in 1932 wrote of periods of expansion and called them ‘‘A-phases’’ and of periods of contraction and called them ‘‘B-phases.’’ Ernest Labrousse, also writing in the 1930s and early 1940s, identified a long cycle of contraction that culminated in the French Revolution. Labrousse considered non-agricultural production to have been at the mercy of the harvest. Bad harvests led to higher food costs and these lowered real wages and brought on declines in manufacturing. Good harvests led to cheaper food, higher real wages, and an expansion of manufacturing. Expansionary periods took living standards substantially above subsistence levels, but periods of contraction brought them down close to subsistence levels and sparked social unrest (Burke, 1990, pp. 53–56; Labrousse, 1944). These views also derive from Smith, and the eighteenth century in Korea was just such an expansionary ‘‘A-phase.’’ At the base of this expansion was the rising productivity of land. Productivity expanded through two sets of incentives slowly introduced by government. The first set of incentives – institutional protection of ownership rights – grew out of the recovery following the Imjin Waeran (Japanese invasion of

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1592–1598). The invasion devastated the population, which did not recover until 1650, and created a labor scarcity. A labor scarcity meant decreased tax revenues. For the post-invasion recovery, the government was in desperate need of funds and even resorted to selling offices. One thing it also had in abundance was land, and government consciously modified institutions to provide incentives for cultivation. Chief among these modifications was to weaken the power of privilege – particularly royal privilege – in obtaining and keeping land and to strengthen the market.20 Adam Smith, as well, advocated the sale of ‘‘crown lands’’ as a way to raise revenues, expand cultivation, and pare government back to minimal interference in citizens’ personal lives and economic activity (Skousen, 2001, p. 436). Nevertheless, forcing royalty to cope with the land market does not mean property rights for all; it only implies the existence of an extensive market. Before the Japanese invasion of the 1590s, the Korean government had instituted clear ownership rights in the Kyo˘ngguk taejo˘n or the first great Choso˘n dynasty legal code of 1485. Herein, land without clear ownership was awarded to those in military service and to small cultivators, and the right was guaranteed.21 The law awarded particular social groups and did not link production to ownership. After the invasion, the 1746 Sok taejo˘n, an extension of the 1485 code, expanded the categories guaranteed ownership rights by adding in the right of the cultivator.22 By recognising the ‘‘cultivator’’ acquiring rights over uncultivated land, we see the perfection of a Smithian model that links property rights to labor. With recognition of labor’s rights to land ownership, the basic connection was made between labor and productivity. In other words, because secure land ownership was thought to enhance production, ownership rights were strengthened. From the seventeenth to the nineteenth centuries, the size of individual land sales was small, averaging 0.85 acre (5.3 turak) in Yo˘ng’am County. Land holdings were not usually contiguous, and most trading was done in the spring, before planting.23 The Korean turak, like the Chinese mu, was also about 0.16 acre,24 but the term refers directly to seeding. One turak required about 6 l (E2.5 kg) of unhusked rice to seed. Fig. 5 shows the trend of paddy-land prices in real rice terms at five southern locations in Cho˘lla and Kyo˘ngsang Provinces.25 The data display the average land price for one turak (0.16 acre) in terms of rice denominated in so˘k (a Korean ‘‘bushel’’: 50 kg weight and 120 l volume).26 These prices are in unhusked rice, not white, polished rice. From this we can see that from about 1740 to 1820, the 11-year moving average ranges between 4 and 7.5 so˘k and thereafter declined.


235

16 14 12 10 8 6 4 2 0 1713 1725 1738 1750 1763 1775 1788 1800 1813 1825 1838 1850 1863 1875 1888 1900

Fig. 5. Average Real Price of Paddy Land at Five Southern Locations, 1713–1900 so˘k (Unhusked Rice 50 kg)/0.16 acre (turak). Note: The trendline is an 11-year moving average. Source: For Yo˘nggwang ( ) (South Cho˘lla): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n, ed., microfilm, reel no. 008064-1, land 009638. For Yo˘ng’am ( ) (South Cho˘lla): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n (1995). For Kuye ( ) (South Cho˘lla) Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n (1995) and Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n, ed., microfilm, reel no. 35-005090-005102 and reel no 35-005179-005188. For Sangju ( ) (North Kyo˘ngsang): So˘nggyungwan University Museum manuscript collection. For Kyo˘ngju ( ) (North Kyo˘ngsang): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n, ed., microfilm, reel no. 009574-009579.

Fig. 6 shows the average nominal price of paddy land in silver grams. The paddy land is again denominated by turak or 0.16 of an acre. The depression in land prices in the nineteenth century is even clearer when cast in silver. The meaning of a falling real land price is multiple, but we interpret the fall as the result of a combination of the expanded supply of paddy land accompanied by a fall in the productivity of this new (and even old) paddy land. From the early decades of the nineteenth century, the absolute amount of paddy land began a gradual expansion at the expense of dry fields (Jun & Lewis, 2002). As more paddy land became available, we see the price of cows inflate rapidly from the mid-nineteenth century, meaning that there developed a scarcity of capital goods. More critically, we also see a decline in capital investments – irrigation works – necessary to make paddy land productive. The second set of incentives that expanded production was direct investment in irrigation. The control of water made paddy land a safer investment

236

JUN SEONG HO AND JAMES B. LEWIS 450 400 350

silver grams

300 250 200 150 100 50 0 1690

1710

1730

1750

1770

1790

1810

1830

1850

1870

1890

1910

Fig. 6. Average Nominal Price of Paddy Land in Silver Grams per turak (0.16 acre) at Five Southern Locations. Note: The trendline is an 11-year moving average (see ) (South Cho˘lla): Han’guk Appendix C for data). Source: For Yo˘nggwang ( Cho˘ngsin Munhwa Yo˘n’guwo˘n, ed., microfilm, reel no. 008064-1, land 009638. For Yo˘ng’am ( ) (South Cho˘lla): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n (1995). For Kuye ( ) (South Cho˘lla) Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n (1995) and Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n, ed., microfilm, reel no. 35-005090005102 and reel no. 35-005179-005188. For Sangju ( ) (North Kyo˘ngsang): So˘nggyungwan University Museum manuscript collection. For Kyo˘ngju ( ) (North Kyo˘ngsang): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n, ed., microfilm, reel no. 009574-009579.

and resulted in paddy being bought and sold at high rates. If we consider investment in irrigation together with land prices, we can see a correlation. In the eighteenth century, paddy land was more expensive because there was less of it, but more importantly, it was expensive, because its productivity was high. This productivity depended on investments in infrastructure. Irrigation works were expensive, were often government projects in part or whole, and represented an enormous capital investment in land. Construction did not ensure their permanence, because floods from devastating rains easily swept away investments. Installations required constant maintenance. For example, devastating rains in 1821–1822 destroyed much investment, and reconstruction afterwards failed to restore the status quo ante. The construction of irrigation works for the three southern provinces of Kyo˘ngsang, Cho˘lla, and Ch’ungch’o˘ng peaked in the late eighteenth century (Fig. 7) and declined thereafter. Fig. 7 is macro data, collected by the central government and records only government activities. Although other


237

1600 1400 1200 1000 800 600 400 200 0 15C

1760

1782

1800

1871

1895

Fig. 7. The Number of Irrigation Facilities in the Three Southern Provinces (1470– 1900). Source: Kyo˘ngsang-do chiriji ( ) (1425); Yo˘ji toso˘ ( ) (1765); Chu˘ngbo munho˘n pigo ( ) (ca. 1907); Man’gi yoram ( ) ˘ pchi ( ˘ pchi ( in two volumes (1808); U ) (1871); U ) (1895).

costs such as fertilizer and unskilled labor did not change, after heavy floods in the nineteenth century, the costs of irrigation repair apparently became too high for recovery. As government investment in infrastructure declined over the nineteenth century, private investment failed to pick up the costs, land productivity dropped, and the price of land dropped.

4. LAND RENT Generally, tenants paid 50 percent of their crop to the owner, but there were regional differences between south and north in rent collection. In the northern provinces, in the spring, the owner was responsible for supplying seed, paying tillage costs, paying tax, and repairing irrigation works. These costs fell to the owner, because the northern tenant’s work took place under the watchful eyes of the owner. The harvest was divided in the field, threshing took place in the field, and the owner received straw and bran from the harvest. Straw was fuel and carried a higher value than straw in the south, because northern winters are much colder. Straw was also used for thatch, matting, rope, bags, and fodder. Bran became livestock feed and fertilizer in both south and north.

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In the southern provinces (Cho˘lla and Kyo˘ngsang), the owner was responsible for much less. The tenant at least had to pay the seeding costs and tax, and might have had to pay the tillage costs and irrigation repair. An early nineteenth-century political economist, Cho˘ng Yag’yong, explains that, in the south, the harvest was taken back to the tenants’ houses, where the grain was threshed in their courtyards, and the tenants kept the straw and bran, hence the tenants carried the costs (Ho˘, 1989, pp. 13–14). Southern tenants had complete control over the production from the fields and presented the owner with the agreed rent at the end of the process. Considering that the tenant may have mis-reported the harvest, the tenant was responsible for considerable costs, including tax. In our data, we see the owners and tenants in Changhu˘ng acting in typical southern fashion, probably because the owner–tenant relations were commercial. The result is that we cannot know the costs of production in Changhu˘ng (seeding, tillage, and irrigation), because all we have are the owner’s accounts and not the tenant’s accounts. In the case of neighbouring Yo˘ng’am, we see more cooperative, ‘‘northern-style’’ relations. In other words, we see that the owner (a village or clan association) generously supplied seed, paid tillage costs and tax, and repaired irrigation works, even though the tenants threshed in their own courtyards and kept the straw and bran. We are fortunate that the custom in Yo˘ng’am was different, because their records reveal itemized costs, and so we are able to calculate the gross and net rents. In Fig. 8, we can see the trend of average real land rents. Gross rent was what the tenant paid to the owner at harvest time in the autumn. Net rent was what was left to the owner after deducting costs. By subtracting costs from income, we can calculate the net rent per 0.16 acre. Fig. 9 combines the Yo˘ng’am net rent data with data from Changhu˘ng and denominates rent in silver grams per land unit. Both real and nominal rents show a steep downward trend from the eighteenth century that bottoms out in the late 1820s and recovers somewhat in the 1830s and 1840s. Falling land rents corroborate falling land prices and also indicate falling land productivity.

5. RATE OF RETURN Rents (Figs. 8 and 9) were divided by prices (Figs. 5 and 6) to produce trends for the rate of return on paddy land (Figs. 10 and 11). Here, we can see a fall-off in the rate of return on land that bottomed out around 1830 and then briefly rallied.


239

25 Gross Rent as light line 20

Net Rent as heavy line

15 10 5 0 1740 1752 1765 1777 1790 1802 1815 1827 1840 1852 1865 1877 1890

Fig. 8. Average Real Land Rent in tu (2.5 kg of Unhulled Rice) per turak (0.16 acre) (1740–1900) in Yo˘ng’am, South Cho˘lla. Note: The trendline for gross rent is a light line for an 11-year moving average. The trendline for net rent is a heavy line for an 11-year moving average. Source: For Yo˘ng’am ( ) (South Cho˘lla): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n (1995).

90 80

silver grams

70 60 50 40 30 20 10 0 1740

1765

1790

1815

1840

1865

1890

Fig. 9. Average Nominal Land Rent in Silver Grams per turak (0.16 acre) (1740– 1900) at Two Southern Locations. Note: The trendline is an 11-year moving average (see Appendix C for data). Source: For Yo˘ng’am ( ) (South Cho˘lla): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n (1995). For Changhu˘ng ( ) (South Cho˘lla): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n, ed., microfilm, reel no. 006904.

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60% Gross Return Rate as light line 50%

Net Return Rate as heavy line

40% 30% 20% 10% 0% 1740 1752

1765

1777 1790

1802 1815

1827

1840 1852

1865

1877 1890

Fig. 10. Average Real Return Rate to Paddy Land (Rent/Price) at Two Southern Locations, 1740–1900. Note: The trendline for gross return rate is a light line for a 31-year moving average. The trendline for net return rate is a heavy line for a 31-year moving average. The starting point for both trendlines is 10 percent (official government fair trade rate). Source: For Yo˘ng’am ( ) (South Cho˘lla): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n (1995). For Changhu˘ng ( ) (South Cho˘lla): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n, ed., microfilm, reel no. 006904.

0.70

rent/price in silver grams

0.60 0.50 0.40 0.30 0.20 0.10 0.00 1740

1765

1790

1815

1840

1865

1890

Fig. 11. Average Nominal Return Rate to Paddy Land (Rent/Price), 1740–1910. Note: The trendline is a 31-year moving average (see Appendix C for data, and Figs. 6 and 9).


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The mid-century recovery in the rate of return mirrors the brief mid-century recovery in rents. We can also detect a slight rise in land prices in the 1830s, but these trends did not last and were overwhelmed by the general declines. We do not know the reason for the brief mid-century recoveries. The seriousness of the overall decline in the rate of return becomes clear when we consider depreciation costs. Government determined the natural depreciation rate on rice to be 10 percent (spoilage, vermin consumption) over a year, and this was the official rate granted at government granaries. Anything above 10 percent as a rate of return on land could be called a profit, and anything below this natural spoilage rate could be called a loss. The average real rate of return dipped below 10 percent around 1813, recovered by 1830, and then dipped below 10 percent in the late 1850s.

6. FINANCIAL MARKET Our final problem is to outline the financial market in Yo˘ng’am County. We believe that a speculative bubble in land appeared from the mid-nineteenth century powered by rising rice prices and declining land productivity. Before examining the data on interest rates, though, we should consider the risks attached to paddy land and the structures that were instituted or evolved to ameliorate those risks. Paddy land is at a much higher risk from drought than are dry fields. Wet rice agriculture is also highly vulnerable to sudden, severe cold in the spring when seedlings are being nurtured and prepared for transplantation. By the late summer or early fall, when the crop is at its height and approaching harvest, the risks of severe flooding and wind damage loom large, because the Korean peninsula is located at the northern end of the Pacific typhoon corridor. Rain and wind can flatten crops and flooding can destroy irrigation works. Government took steps to protect the small, vulnerable tiller. To conform with the dictates of Confucian ideology that preached the advantages of managed stability and to preserve the small owner–tiller tax base, an elaborate grain storage system operated as an agricultural bank for loans as well as to maintain price stability. Government tax policy also introduced elasticity into the system. Tax assessors paid close attention to harvest quality and adjusted taxes through relief measures to track the ups and downs of production (Jun & Lewis, 2002; Lewis, 2003, pp. 112–113). Therefore, paddy land cultivation was actively promoted through the protection of

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property rights, infrastructure investment (irrigation), state granaries, tax policies, and the publication of agricultural manuals. Other steps to decrease risk were social and technological. Local elite groups organized various associations, introduced rational bookkeeping methods, managed seed, paid tillage costs, offered wages, and paid irrigation costs. In addition, they also published agricultural manuals and actively introduced the transplantation technologies developed in the Yangzi River Valley. Transplantation technology in a wet-rice environment continued to expand from its first introduction in the thirteenth and fourteenth centuries, and by the late fifteenth to early sixteenth centuries reached a stable plateau of roughly 30 percent of arable land. Paddy land is more productive than dry fields, and it is more labor intensive on smaller plots of land. The intensification or increased labor input wedded to technological advancement did not push agriculture into decline or stagnation in China (Li, 1998, p. 8) or Korea. Rather, technological advancement significantly reduced the need for certain types of labor. As we saw in Table 1, transplantation in the seventeenth to nineteenth centuries reduced the weeding workday for a turak (0.16 acre) from 8.5 to 3 days. Obviously, there were efficiency gains from technological advances, but short bursts of intensive labor were still required. The Choso˘n social milieu that saw local elites engaged in the development and dissemination of rational methods to improve the agricultural economy was mirrored at the national level. We also see the central government actively engaged in promoting technologies to improve agricultural efficiency for both ideological and fiscal reasons. The pattern was set early in the dynasty by the fourth king. King Sejong (1418–1450) was personally involved in the development of an alphabet to promote literacy, while his government delimited the national boundaries in the north, surveyed land use, counted people, and standardized measures. In the eighteenth century under Kings Yo˘ngjo and Cho˘ngjo (1724–1800), Choso˘n saw a revival with the expansion of the post-Imjin reforms mentioned above (promotion of markets27 and protection of the small cultivator), a strengthened storage system, and the publication of agricultural manuals. The manuals discuss intensification and ways to improve output, and some discuss land reclamation and land clearing to expand the agricultural base. Side by side with these incentives to develop land, to introduce the technologies of paddy cultivation, and to support and regulate the land market and rice production, we also see the expansion of proto-industrial crops such as cotton in the eighteenth century.


243

But the question remains: did risk expand or decline? Can we detect a trend in risk appraisal by looking at interest rates? The maximum official annual rate of interest was 10 percent (Pibyo˘nsa tu˘ngnok, 1727/11/11),28 but the return rate on land fluctuated between 5 and 20 percent. The interest rate (ija) in the financial market within a year might fluctuate between 20 and 100 percent. Averages were between 10 and 50 percent. The return rate on repurchase agreements on land and houses (hwant’oe) varied annually between 15 and 20 percent (Jun, 1998, pp. 189–191) and can be used as a proxy for interest rates. Fig. 12 shows the pattern of the meagre data we have collected thus far on interest rates. Although the data are sparse, we can see that the interest rate declined over the eighteenth century and then began a climb in the nineteenth century. The downward drift of interest rates in the eighteenth century indicates declining risk and probably rising land values. We know that the mix of land was in a kind of balance in the eighteenth century between dry and paddy fields and that dry fields supplied protoindustrial goods and more variety for the diet. But going into the nineteenth century, land returns fell, the balance between dry and paddy fields shifted toward paddy, and interest rates drifted upward. Land ownership began to concentrate in fewer hands, returns from lending rose, and a land bubble emerged.

60% 50% 40% 30% 20% 10% 0% 1741

1766

1791

1816

1841

1866

1891

Fig. 12. Annual Interest Rates in Yo˘ng’am County, 1741–1900. Note: The trendline is a 31-year moving average. Source: Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n (1995).

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7. CONCLUSION We have shown historical changes in prices and wages in southern rural markets. The changes appeared within a macroeconomy that had institutionalized property rights for cultivators. After the Hideyosi invasion, or after 1600, the vast majority of economic decisions we see in documents fit well with a Smithian model that presumes individuals acting more or less in their rational self-interest and competing in the market. Without rational self-interest at work, we would not be able to explain the extensive land sale documents now appearing. But what were these historical changes in prices and wages and what do they signify? From the late seventeenth century into the early nineteenth century, the Korean economy saw a period of expansion, stability, and rising productivity. Real skilled wages were high, prices were stable, returns on land were high, and interest rates were low. Nevertheless, a number of indicators show a conjunction sometime in the mid-nineteenth century that marked the end of this stability. From the data on real wages, we can see that a period of high real wages wavered in the 1830s and 1840s and finally ended in the 1860s. The decline of wages correlates with the beginning of unstable, climbing rice prices. At the same time, the price of paddy land declined and rents declined, indicating a slide in land productivity. As land values fell, interest rates began to rise, pointing to a perception of rising risk. From the mid-nineteenth century, internal and external factors came together to initiate a period of contraction. Government corruption neutered the storage system, investment in irrigation facilities declined, and extreme flooding destroyed crops and irrigation infrastructure (1809–1810, 1814–1815, 1833–1834). As the granaries ceased to operate, farmers began to shift dry fields to paddy in a speculative move to supply more rice, but without investment in irrigation, the value of the land fell. The fragile nature of the rice economy was unable to cope. As land productivity declined, the eighteenth-century expansion of proto-industries declined, and Korea began slipping toward a monoculture of rice. From the latter quarter of the nineteenth century, internal decline was exacerbated by the shock of imperialism. Imperialism’s chief result was to seize the internal agricultural market, accelerate its drift toward paddy land monoculture, and make the economy a part, first of the Japanese market and later of the world market. Korean society and economy entered a long period of low productivity, low real wages, and rural social decrepitude that took perhaps half a century to overcome.


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NOTES 1. All East Asian names are rendered in their proper order with surname first. 2. Yi Yo˘ng-hun (2004, p. 368) dates the beginning of the quantitative turn to 1997 and a paper by Jun Seong Ho (1997). Earlier versions of Jun’s rice price data, as first presented in a doctoral dissertation, have been published by other researchers in Korean (Yi Yo˘ng-hun & Pak Yi-t’aek, 2001), but the data presented here has been revised and are more reliable. Rice price data from the capital were first published by Yi Ho˘n-ch’ang (1996). 3. An and Yi (2001) and Yi (2004) were produced under the auspices of the Naksungdae Institute of Economic Research ( ) where there is also an on-going project to create online historical, economic, statistical databases spanning from 1700 to 1900 (see http://www.naksung.re.kr/index.htm). 4. Some 40,000 documents have been gathered at the Academy of Korean Studies from South Korea alone, of which about 15,000 have been examined so far. 5. The Sok taejo˘n legal code of 1746 ( ) enforced contracts for land sales. 6. ‘‘Rice’’ refers to the variety commonly known as japonica. Rice was the most important commodity in the pre-modern, domestic Korean market. Rice on the stalk represented food, fuel, and even a building material, because the stalk and husk could be burned or used as thatch for roofs and raingear, even stuffing for pillows, but rice did not appear in the market on the stalk. Unhusked rice was presented to the government as tax and to landowners as rental payments, but rice for the market was threshed to remove the stalk and husk. ‘‘Rice prices’’ recorded in rural markets were usually prices for unhusked rice (for seeding), polished rice (for meals), and glutinous rice (ch’apssal, for cakes and candies). These various forms represent differing degrees of polishing or closer and closer steps to an end product. With varying degrees of chaff or bran left on the rice grain, rice had largely three functions. One was as a staple food. Polished rice can be made ready for the rice bowl with only water, cooking fuel, and a few minutes of cooking time. Rice can be directly consumed, and we need make no estimations between an intermediate product (flour) and an end product (bread): threshed and unhusked rice was an end product (Wickizer & Bennett, 1941, p. 63; Allen, 2001b, p. 419). Another function of rice was as an intermediate good to produce: wine, syrup, rice drinks, cakes, paste, and starch. Wine, syrup, rice drinks, and cakes were expensive and figure prominently in ancestor rituals. Rice paste was used to hang paper on sliding doors, windows, walls, and ceiling of a room. Starch was used in clothing, but these products require good harvests and an affluent society. The final function of rice was as money. 7. Peter Lindert (2002) defined the basic terms of comparison. 8. Gregory Clark’s (2001) findings throw into doubt the notion of an ‘‘agricultural revolution’’ in England that preceded and aided the industrial revolution. England posted a modest rate of agricultural productivity growth and that occurred only in the early nineteenth century. 9. Another trend that we have identified was a change in the rate of commercialization. The rate of commercialization seems to have declined from the eighteenth into the nineteenth century, while the reverse was happening in Europe. We can see

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this by a shift in the number of transactions over time from a concentration in the spring (eighteenth century) to a concentration in the autumn (nineteenth century). The change suggests that the high time of exchange shifted from the planting season to the harvest season or from proto-industry to agricultural production. Other trends will appear as data availability and analysis proceed. 10. Our work is possible only because, for the last 25 years, the Academy of Korean Studies (Chung’ang Han’gukhak Yo˘n’guwo˘n, formerly known as the Cho˘ngsin Munhwa Yo˘n’guwo˘n) has been collecting, microfilming, and printing pre1900 documents scattered in private collections across the southern half of the Korean peninsula. Among these, the account books of the clan and village associations of Yo˘ng’am Village in South Cho˘lla are central to the data presented in this paper. The following gives an outline of the general holdings available for these accounts.

Title of Account Book

Period

Size (cm)

Number of Sheets

Head clan association account book Collateral clan association account book Village association account book

1741–present

25 22

726 (to 1928

1819–1884

25 22

689

1761–1945

23.6 20.5 (20.8 18.6)

AD)

1,620

11. For publications in Korean that find strong correlations between the Yo˘ng’am data and many other sources across the southern region, see Jun (1997), Yi and Jun (2001), and Yi (2004, p. 368). 12. Taegu is the only urban data source. 13. Correlations between the Yo˘ng’am village data and the T’akchi cho˘nbugo, a central government record of taxation, have allowed the dating of the T’akchi cho˘nbugo to be revised (1744–1900). The correlations appeared between the tax exemptions recorded by the center and the price fluctuations in the Yo˘ng’am rice price data (Jun, 1998, p. 122). For a brief discussion and application in English, see James Lewis (2003, p. 112). 14. The account books used for much of the data usually disaggregate accounts into three groups: unhusked rice, polished rice, and copper coins. The entries are not recorded in chronological order, because these are not daybooks, and they serve the purpose of comprehensively reporting the financial statements at the end of an accounting period. Rent revenue is the main income, but many items were included, depending on the harvest situation (Jun & Lewis, 2006). 15. Yi Yo˘ng-hun (2004, p. 388) offers arguments against a national market that encompassed both the southern and northern halves of the peninsula. 16. For example, in 1799, the daily wage recorded by the government was polished rice 3 su˘ng (1.09 kg ¼ silver grams 0.41–0.77 per kg) +copper 5 pun (silver 0.5 g).


247

Thus, the average daily wage was about 1.09 g of silver (Pibyo˘nsa tu˘ngnok, 28/03/ 1799), significantly below our findings in Table 2 and odd for the capital city. Occasionally such information appears in central government documents, but the provenance is never clear and may well refer to a policy decision set by government, not by the market. 17. Francesca Bray (1986) has offered a good beginning comparison of social structures, but much more work needs to be done on economic structures. 18. In Appendix C, we report wages paid in copper (column E) and in-kind wages converted to copper (F, G, and H). The cash wages are only the cash element given to the laborer or what the laborer carried away. The full wages are the cash element plus the consumables that were supplied while on the job (wine, tobacco, straw sandals, and food other than rice). These consumables were bought and then given to the laborer while he worked. Although payment was by the job, because we know the number of days worked and the number of people employed, we can calculate the daily cash wages in copper cash (cashCdays worked number of laborers ¼ net copper wages) and the daily full wages in copper cash (cash+cash for wine+cash for tobacco+cash for side dishesCdays worked number of laborers ¼ gross copper wage). Since we know the price of rice in copper and silver (Fig. 1), we can convert copper wages to rice. Since we know the conversion rates from copper to silver (Appendix B), we can convert copper wages to silver grams. In Table 2 and Figs. 3 and 4, we use the full wages converted from copper to silver and to rice (see Appendix C, columns N, O, P, and Q). 19. The new epoch that clearly began from the 1860s to 1870s marked the final end of what had been a proto-industrial economy and initiated a period of unlimited labor supply with low real wages. This period did not end until the mid-to-late 1970s and the inauguration of an activist labor movement. Union organization did not become free until the late 1980s. This situation is reminiscent of Arthur Lewis’s (1955) thesis of the unlimited labor supply that defines a Third World economy. By contrast, Europe experienced labor shortages with high skilled and unskilled wages that provided incentives to develop labor-saving machinery. 20. Sukjong sillok, 3/12/1688: . This policy forced the royal family to cope with the land market instead of relying solely on marriage and inheritance to acquire land. 21. Kyo˘ngguk taejo˘n ( ). ). 22. Sok taejo˘n ( 23. Data from Yo˘nggwang County, Yo˘n’am Village Kim clan documents; Kuye County, Munhwa Village Yu clan documents; Kyo˘ngju County, Yongsan Academy documents, and Sangju County. 24. The seeding area from 46 observations averaged 0.16 acre, with a standard deviation of 0.04 acre (Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n, 1995). 25. Prices came from notary documents. Notary documents were legally required to contain the following information on land prices: (1) date of transaction (year, month, day); (2) buyer’s occupation, family name, and given name; (3) method of obtaining land (by trade, from wife’s family, from father, or land grant from government); (4) reason for sale (urgent financial need, continuous bad harvest, to pay for a daughter’s wedding, funeral costs, or moving); (5) information on the site and

248


registration (taxable amounts, seeding size, land classification into paddy, dry field, forest, possessing a house roofed with thatch or tile, number and type of trees); (6) price (currencies varied by period: copper, unhusked rice, silver in the sixteenth century, cotton in the seventeenth, rice and copper in the eighteenth, copper in the nineteenth, and rice, copper, and even yen in the twentieth century); (7) any related documents attached; and (8) owner’s name and signature, guarantor’s name and signature, and the names and signatures of the scribe and the notary. For example, see the International Institute of Social History website: http://www.iisg.nl/hpw/ korea.pdf, Illustration 3. 26. Transaction costs can also be determined. A land sale required a notary’s fee, which was recorded in the 40,000 or more documents that the Academy of Korean Studies has collected on land and house sales. From an analysis of 15,000 documents thus far, in the eighteenth century, the land transaction cost per transaction was 0.2–0.27 copper yang (1.875–2.531 grams of silver). In the nineteenth century, the cost increased from 1.4 to 1.5 copper yang (13.125–14 grams of silver) per transaction, a growth of over 450 percent. 27. The number of rural markets was between 1,052 and 1,075 from the eighteenth to the nineteenth century. Tongguk munho˘n pigo records 1,064 in 1770; the Imwo˘n kyo˘ngjeji records 1,052 in 1800; and the Chu˘ngbo munho˘n pigo records 1,075 in 1890. 28. The Kyo˘ngguk taejo˘n (ca. 1485) specifies that private loans have an interest ceiling of 10 percent per month, 50 percent per year, and a maximum accrual of 100 percent ( ). The Sok taejo˘n (1746) stipulates a ceiling of 20 percent for both public and private loans ( ). The Sok taejo˘n lowered the earlier ceiling from 50 percent annually to 20 percent, but in 1727, King Yo˘ngjo placed an even lower ceiling on public loans of 10 percent (Pibyo˘nsa tu˘ngnok, 82 kwo˘n, Yo˘ngjo 3/11/11: ).

ACKNOWLEDGMENTS We are grateful to Peter Lindert, Jan Luiten van Zanden, and others who attended the conference on A Global History of Prices and Wages held in Utrecht (August, 2004) at the International Institute of Social History, sponsored by the US National Science Foundation. We also thank two anonymous referees and Gregory Clark, the editor, for comments and suggestions which helped to improve this paper significantly. Our thanks go to the workshop discussants in the Economics Faculty of SungKyunKwan University and to the financial support offered to Jun Seong Ho by the Korea Research Foundation through a Korea Government Basic Research Promotion Fund and to the Academy of Korean Studies for the financial support offered to James B. Lewis.


249

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Glossary


253

254

APPENDIX A. ANNUAL DATA FOR TABLE 2: FOOD Year

Food in Grams of Silver per kg Polished rice

Harvest year (Lunar)

Covered barley

Wheat

Buck wheat

Beans

Red beans

Green peas

Sesame

Herring

Codfish

Alaskan pollock

Chicken

1 l ¼ 849 g 1 l ¼ 765 g 1 l ¼ 650 g 1 l ¼ 750 g 1 l ¼ 665 g 1 l ¼ 750 g 1 l ¼ 750 g 1 l ¼ 833 g 1 l ¼ 600 g One fish: One fish: One fish: 150–200 g 1.2–1.3 kg 450 g

One bird (purely edible meat is about 800 g)

Yeast cake

8th month 5th month 5th month 5th month 5th month 9th month 9th month 9th month 9th month


1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702

Polished barley

0.79 1.04 1.06 1.56 1.15 1.09 0.94 0.83 0.64 0.99 0.97 0.72 0.46 1.54 1.01 1.02

1.98 1.10 0.92

0.73 0.31 0.28

0.57 0.09

0.65 0.84 1.99 2.08 0.90 0.41 0.52

0.04

0.27

0.08

0.38

255

0.88

0.84 0.65


1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738

256

APPENDIX A. (Continued ) Year



Covered barley

Wheat

Buck wheat

Beans

Red beans

Green peas

Sesame

Herring

Codfish

Alaskan pollock

Chicken



Yeast cake


0.68 0.60 1.51 1.46 0.98 1.07 1.30 1.45 1.36 1.42 1.12 0.91 0.90 0.88 0.70 0.73

0.05

1.34

0.75 1.03 1.03 0.37 1.22 2.53 0.47

2.00

1.27

2.82

0.82

1.50

3.76

0.76

0.89 0.48

0.19 0.19

0.54 0.34

0.97 0.41 0.34

1.75 1.76 2.50


1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754

Polished barley

1.49

0.91

0.50

0.85

0.32

0.45 0.39 0.48 0.62

1.00

0.36 0.23

0.47 0.45 0.37 0.45

0.91 0.56

0.86 0.38 0.53 0.40 0.38 0.50 0.38 0.66

0.39 0.38 0.41 0.60

0.29

0.75 0.70 0.61

0.56 0.68

0.24 0.27 0.08

2.74 0.97

5.45 5.87

0.70 0.30

0.62 0.53

1.00 1.13 1.25 0.35 0.53 1.17 1.18

2.08

4.13

4.06 2.97 3.12 3.22 3.91 3.75 3.43 2.35 2.65

3.23 3.26

0.82 0.68

1.53 1.50

1.25

0.91 0.99

0.20 1.41

0.98

0.39 0.80

0.22 0.33

0.27 0.45

0.90 0.71

0.98

4.28

3.02

0.91 1.18 0.98 1.27 0.61 0.82 1.20

257

1.03 2.00 1.08 0.49 0.54 0.65 0.55 0.70 1.39 0.47 0.58 0.53 0.47 0.56 0.70 0.63 0.38 0.50 0.47 0.62 0.51 0.41 0.42 0.80 0.76 0.44 0.44 0.63 0.67 0.77 0.45 0.42 1.03 0.48 0.52 0.55


1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790

258




Covered barley

Wheat

Buck wheat

Beans

Red beans

Green peas

Sesame

Herring

Codfish

Alaskan pollock

Chicken



Yeast cake


0.45 0.66 1.03 0.45 0.79 0.59 0.47 0.60 0.90 0.44 0.50 0.51 0.49 0.53 0.50 0.60

3.86 0.41 0.69 0.32 0.41 0.55 0.43 0.46 0.47

3.32 6.43

1.22 0.90 1.15 2.06 0.81 0.94 1.53 0.86 0.91 0.91 0.59

0.44 0.68 3.41


1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806

Polished barley

0.51 0.60 0.50 1.04 0.49 0.37 0.53 0.65 1.08 0.46 0.27

0.44 0.51

3.67 3.06

0.38

4.28 4.90

0.44 0.54

0.25 0.87

0.62

0.82

0.45

0.47 0.42 0.47 0.47 0.50 0.58 0.58 0.48 0.49 0.69 1.03 0.61 0.69 0.93 0.62 1.21 0.65 0.62

0.48 0.47 0.72 0.63 0.47 0.95 0.58 0.55 0.63 0.56

1.57 1.76 0.84 0.56 0.84 0.70 0.70 0.70 0.84 0.84 1.15 0.76 1.42 0.76 1.78 0.76 1.78 1.27 0.76 1.78

3.06

3.67 2.70 3.33 2.78 3.33

3.33 3.33 0.33

0.89 0.25

0.64 0.64 0.89 1.76 0.78 0.82 0.82 1.52

1.27 1.25 1.21 1.54 1.47 1.84 0.97 0.94 0.98 1.01 0.96 0.96 1.16 1.23 1.64 1.30 1.78 1.75 1.34 3.12 1.97

259

0.39 0.41 0.44 0.66 0.53 0.47 0.42 0.50 0.43 0.54 0.71 0.64 0.46 0.50 0.78 1.10 0.60 0.70 0.99 0.67 0.90 0.88 0.53 0.50

1.22 0.47


1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842

260




Covered barley

Wheat

Buck wheat

Beans

Red beans

Green peas

Sesame

Herring

Codfish

Alaskan pollock

Chicken



Yeast cake


0.60 0.54 0.50 0.51 0.45 0.53 0.37 0.39 0.49 0.56 0.63 0.42 0.37 0.31 0.38 0.61

0.21 0.22

0.34 0.45 1.22 0.56 0.59 0.40 0.43 0.50 0.54

0.17 0.20 0.31

0.44 0.75 0.72 0.81

0.32 0.27 0.19 0.23 0.17 0.20

0.39 0.73

0.43

0.89

3.50 4.45

1.07 1.06 0.89 0.79 0.67 0.41 0.30 0.30 0.41

4.00 1.42 2.43

1.34 1.24 1.24 1.19 1.19 1.15 1.26 0.95 0.79 0.93 0.76 1.09 1.56 0.92 0.85


1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858

Polished barley

0.50

0.60

0.47 0.85 0.75 0.57 0.22

1.15 0.25

1.20

1.03

0.93

1.05 0.34

0.83

1.69 0.57

4.50 1.20

1.11

0.60

0.93 1.13

4.12

1.20

3.75 4.50

1.00 1.01 1.16 1.20 1.20 1.20

3.93 3.12 3.75

4.16 1.37 0.29

1.60 0.90

1.08

0.21 0.90

0.58 1.43

1.04 0.43 0.30

0.75 0.66 0.79 0.72 0.74 0.74 0.76 0.90 1.06 0.90 0.81

0.47 1.08 0.41 0.19 0.19 0.30 0.55 0.41 0.62 0.61 0.48 0.65 0.48 0.63 0.52 0.47 0.57 1.39 1.85 1.48

1.10 0.68 0.45 0.53 0.63 0.61 0.51 0.84 0.72 0.75 0.95 0.93 0.89 0.82 0.88 1.30 1.38 1.86 1.92

1.76 0.81

1.03

1.32 2.01 0.66 0.88 0.73 1.35 2.19 1.81 2.96

0.49 0.90 1.62 0.65 0.37 0.50 0.39 0.85 0.48 1.18 0.79 1.22 0.52 0.43 1.10 0.72 0.97 1.29 1.82 2.76

1.14 1.14

0.45 2.79

1.72 0.76 0.47 0.55 1.22 1.41 1.53

3.18 1.12 1.68 0.86

1.86 0.59

1.58 0.58 0.58

1.95 1.35

1.50 1.50 2.06

1.11 0.61 0.84 0.76 0.49 1.08 0.64 0.66 0.67

1.02 0.50 1.16 1.03 0.30 0.47 0.80 0.54 1.01

2.44

1.44 1.25

4.05 4.05 2.70 4.05 4.05 4.05 3.37 4.05

1.08 1.08 1.08 1.08 1.01 0.95 1.13 1.08

0.81 0.95 0.81 1.08 1.08 1.44 1.35 1.67

261

0.84 0.33 0.49 0.56 0.57 0.69 0.64 0.81 0.65 0.70 0.67 0.88 0.91 0.87 0.77 0.92 0.82 0.60 1.85 0.71 0.43 0.40 0.44 0.95 0.74 1.41 0.94 1.17 1.01 1.14 1.87 1.44 0.98 1.21 1.78 2.59


1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894

262




Covered barley

Wheat

Buck wheat

Beans

Red beans

Green peas

Sesame

Herring

Codfish

Alaskan pollock

Chicken



Yeast cake


2.13 1.57 1.77 2.04 2.42 2.67 1.93 2.76 2.59 2.07 1.95 1.85 1.58 1.50 1.46 2.11

0.67 0.67 1.34 1.08 1.06 1.16 1.10 1.60 3.18 2.11 1.32 1.59 1.14 1.01 0.92 1.10

0.92 1.32 1.72 1.60 1.92 2.10 2.45 2.10 4.95 3.37 3.10 2.37 2.33 2.41 2.15

2.19 3.04 1.35 1.69 1.52

2.36 2.19 2.36

1.86

0.90 0.90 1.56 1.92 1.86 1.57 1.62 2.47 2.80 2.30 2.23 1.86 1.69 1.35 1.37 2.21

2.02 1.01 2.70

3.84 8.25

6.75 1.04

7.50 11.81

4.45 3.24

1.21 1.46 0.74 0.84 1.58

9.37

1.21 1.35

3.00

1.22 1.26 1.35 1.62 1.44 1.35


1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910

Polished barley

Year

Full Wages Un-skilled wage

Skilled wage

Land price

Land rent

Cow

Un-ginned cotton

Cotton cloth

Wood fuel

Lamp oil

Grams of Grams of silver per day silver per day

Grams of silver per 0.16 acre


Grams of silver per head

Grams of silver per kg

Grams of silver per meter

Grams of silver per 9 kg [8 tan ¼ 72 kg (a mule load), 4 tan ¼ 36 kg, 1 tan ¼ 9 kg]

Grams of silver per liter

90.0

Pen/ink set

Paper

Grams of Grams of silver per one silver per 20 sheets (size set 130 35 cm)

115.2 42.8 81.0

263

1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703

Non-Food Goods


APPENDIX A. ANNUAL DATA FOR TABLE 2: WAGES AND NON-FOOD GOODS

264



Skilled wage

Land price

Land rent

Cow

Un-ginned cotton

Cotton cloth

Wood fuel

Lamp oil









71.2 78.9 82.5 37.5 348.2 233.2 316.7 268.7 275.0 224.1 341.7 238.0 194.8 160.8

Pen/ink set

Paper

Grams of Grams of silver per one silver per 20 set sheets (size 130 35 cm)


1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720

Non-Food Goods

208.8 235.6 282.9 139.9 176.8 173.2 110.2 141.5 194.9 142.2

145.2

113.7 100.7 114.8 82.3 133.2 1.36 3.41 56.6 36.5 50.6 53.5 67.3 78.5 72.4 70.4 59.7 41.3 40.1 43.4 32.3 31.2 32.9 64.7

157.5

1.75

5.00

5.44 5.00

3.48 4.17 131.6 7.83

3.49

2.06 2.42 3.30 4.45

265

114.8 297.1 381.5 264.7 290.3 328.1 270.5 284.1 217.5 302.5 237.0 115.1 133.6 152.0 132.6 175.9 195.2 160.1


1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756

266



Skilled wage

Land price

Land rent

Cow

Un-ginned cotton

Cotton cloth

Wood fuel

Lamp oil









73.6 171.2 163.6 87.0 81.7 135.4 132.3 123.5 96.8 114.7 111.0 179.6 192.5 85.9 85.4 90.6 94.0

46.5 25.8 23.5 22.5 21.8 17.0 52.0 24.0 24.7

5.25 2.25 3.68

3.45

3.51 4.76

2.63 1.85

2.30 2.08 1.12 0.85 1.07 1.37 1.41 1.22 1.28 1.40 1.65 1.41 1.06 0.97 0.92

Pen/ink set

Paper

Grams of Grams of silver per one silver per 20 sheets (size set 130 35 cm)

1.94 1.25 1.25 1.88 1.88 1.25 1.25 1.25 0.94 0.94 2.35 0.94

0.69

2.08 2.22

1.94 2.50 1.80 1.80 1.75 1.60


1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773

Non-Food Goods

3.15

1.06

1.38

1.13 1.15 1.13 1.42 1.27

3.80

1.09 1.09

81.0 88.6 133.4 104.0 146.3 140.5 157.1 125.1 134.2 113.6 111.7 97.2 128.0

12.6 22.0 13.6 31.4 17.8 11.4 36.6 17.0 18.0 17.7 13.6 20.5 30.7 8.4 24.2 21.9 14.8 16.2 31.3 14.3 15.8 15.7 13.2 16.5 11.5 15.1 14.9 19.6 2.5

2.91 3.61 3.99 2.77 3.54 2.35

1.02 1.25 1.32 1.09 1.19

3.94

1.43

1.82

0.98 294.0

1.75

1.96 1.74 1.31 1.74

267

5.71 1.09

112.8 87.4 143.1 89.0 101.6 111.2 124.1 91.1 138.7 136.8 118.0 119.2 128.2 100.2 113.0 75.0 86.8 105.9 107.0 114.2 127.6


1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809

268



Skilled wage

Land price

Land rent

Cow

Un-ginned cotton

Cotton cloth

Wood fuel

Lamp oil









73.5 74.3 81.4 76.1 94.2 74.1 68.0 94.4 82.2 64.6 39.2 117.7 65.3 80.5 133.0 95.5 119.4

22.0 15.5 7.2 12.2 4.0 16.8 9.9

2.45 0.98

0.98 1.96 0.98 0.78 1.32 0.98 1.55

3.58 3.72 2.45 3.81 4.97 6.27 3.49

0.49 0.98 0.98 0.98

1.96 1.96 1.96 1.47 1.96 1.96 1.96

Pen/ink set

Paper


0.98

1.37 0.98 0.98 0.98 0.98 0.98 0.98 0.98

1.96 1.96 1.91 2.16 2.48

1.76 1.62 1.32 2.21 1.86 2.01


1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826

Non-Food Goods

1.18 2.25 0.98 0.89 1.78

4.37

1.42

2.76 4.74

1.78 2.20

2.34 2.67 5.34

2.13

90.5 112.0 94.2 79.5 103.5 91.0 97.0 55.7 82.6 101.9 66.8 89.5 100.3 90.6 95.4 89.3 96.6 127.0

3.62 0.89 1.06

1.91 4.05

3.60 2.71

362.2 275.9 329.3 323.9 205.4 165.9 159.5 209.3

143.4 120.7 110.7

2.25 2.92 1.05 1.30 2.19 1.09 0.87 1.01 0.73 0.88 1.88

0.82 0.72 0.89 0.79 0.79 0.72 0.55

1.28 1.13 1.69 1.43

0.71 0.71 0.71 0.71

167.9

205.8

1.19 1.98 1.58 1.58 1.58 1.58 1.58 1.42 1.42

0.30 0.60

1.71 2.11

0.98 0.98 0.98 0.89 0.89 0.89 0.89 0.89 0.89 0.89 0.89 0.79

0.79 0.79 0.79 0.79 0.79 0.71 0.71

0.71 0.57 0.36

0.71 0.36 0.84 0.48

1.03

0.60

1.96 1.76 1.81 1.68 1.87 1.51 1.48 1.74 1.71 1.72 1.78 2.00

2.05 2.21 2.05 1.48 1.62 1.69 1.58 1.62 1.64 1.62 1.28 1.21 1.18 1.24 1.36 1.44 1.39 1.16 1.19 1.40 1.48 1.45

269

0.94 1.37 1.42 2.06 0.36 0.60 0.60 0.54 0.75 1.14

100.6 73.3 22.0 85.2 69.0 68.6 50.4 108.2 70.0 100.5 102.7 80.5 79.2 82.9 100.6 102.4

24.9 16.0 12.3 15.0 22.4 34.6 15.3 19.3 30.2 24.4 14.6 14.3 18.8 17.1 12.6 17.0 12.5 18.2 12.0 12.1 12.8 10.0 13.9 10.2 10.5 8.3 9.5 8.9 25.9 9.2 13.1 17.1 14.4 18.5

1.96 1.96 1.96 1.78 1.78 1.78 1.78 1.78 1.78 1.78 1.34 1.58


1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864

270



Skilled wage

Land price

Land rent

Cow

Un-ginned cotton

Cotton cloth

Wood fuel

Lamp oil









104.5 93.6 79.9 90.0 81.0 106.2 118.3 145.4 141.3 156.4 128.2 54.0 95.6 94.4

17.0 18.5 16.4 14.2 13.5 14.2 20.4 21.2 17.1 21.9 19.2 5.0

0.48 0.48 0.63 0.54 0.75 0.54 0.54 0.54 0.54 0.54 0.54 0.54 1.08

4.04

1.35

3.15

0.54 204.7 198.4

1.11 180.0 210.0 220.0 197.1 334.8 421.2 226.8 180.0 309.1 353.1 423.3 430.2 221.6

2.08 6.66 4.26 1.98 1.80

1.72 1.16 1.82 1.83 1.84 1.58 2.32 1.97

0.94 1.01 0.81

1.69

0.30 0.74 0.77 1.08 1.17 0.44

0.60 0.30 0.75 0.60 0.60 0.54 0.54

2.09 2.15

Paper


0.72 1.32 0.72 0.72

1.62 1.68 1.32 1.31 1.32 1.11 1.20 1.35

1.12

1.87

2.31

Pen/ink set

0.56 0.60 0.45

0.64

0.65 0.65 0.65 0.65 0.65 0.65 0.65 0.81

1.28 1.28 1.08 1.35 1.21 1.21 1.57 1.57


1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881

Non-Food Goods

0.95

1.50

3.68 3.15

196.3 78.7 91.8 109.2 128.0 108.6 153.1 180.0 202.5 42.5 262.5

1.50 187.5 412.5

183.3 211.5 21.8 21.5 13.0 11.4 10.5 19.2 25.7 15.3 17.2 20.8 26.1 27.8 24.8 28.6 34.5 36.4 49.3

391.5 405.0 270.0

0.75

1.85 2.42

2.65 4.04

1.03 0.89 0.49 1.31 1.02

0.63 1.30 0.49

0.49

1.07 0.54

0.87 0.81 0.77 0.77 1.35

896.4

1.36 1.47 1.68 0.94 1.57 1.26 1.50 1.50 1.35 1.75 1.87 1.50 1.87 2.25 1.50

1.14

502.2 950.4

729.0 415.8

271

Note: Copper was converted to silver according to the bi-metallic ratios, 1650–1880 (see Appendix B). Source: For Yo˘ng’am (South Cho˘lla): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n (1995). For Haenam (South Cho˘lla): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n, ed., microfilm, reel no. 35-003205-003216. For Changhu˘ng (South Cho˘lla): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n, ed., microfilm, reel no. 006904. For Koso˘ng (South Kyo˘ngsang): Su˘ngch’ong myo˘ngnok. For Kyo˘ngju (North Kyo˘ngsang): Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n, ed., microfilm, reel no. 009574-009579. For Taegu (North Kyo˘ngsang) (1994).


1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910

272


800 700 600 500 400 300 200 100 0

0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0

Silver grams

Copper coins

APPENDIX B. SILVER VALUES IN KOREA, 1650–1880

1650 1675 1700 1725 1750 1775 1800 1825 1850 1875 Copper coins per one silver unit (37.5 grams) Silver grams per one hundred copper coins

Fig. B1. Silver Values in Korea, 1650−1880. Source: Based on following Table.

Years

1650–1659 1660–1669 1670–1679 1680–1689 1690–1699 1700–1709 1710–1719 1720–1729 1730–1739 1740–1749 1750–1759 1760–1769 1770–1779 1780–1789 1790–1799

One Silver tael (37.5 g) 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Copper Coins per One Silver Unit (37.5 g)

Silver Grams per One Hundred Copper Coins

600.00 500.00 400.00 250.00 260.00 500.00 150.00 189.00 228.00 100.00 200.00 300.00 400.00 383.00 365.00

0.063 0.075 0.094 0.150 0.144 0.075 0.250 0.198 0.164 0.375 0.188 0.125 0.094 0.098 0.103


1800–1809 1810–1819 1820–1829 1830–1839 1840–1849 1850–1859 1860–1869 1870–1879

1 1 1 1 1 1 1 1

273

345.00 383.00 383.00 420.00 475.00 530.00 625.00 700.00

0.109 0.098 0.098 0.089 0.079 0.071 0.060 0.054

Note: The major issue of copper coins began in 1678 at the rate of 400 pieces for one yang (tael) of silver (copper coins 400 pieces ¼ 37.5 g silver ¼ 50 kg white rice in 1678). The varieties of silver were ch’o˘nu˘n ( ) one tael ¼ Chinese copper 1,000 pieces ¼ Korean copper 318 pieces ) one tael ¼ Chinese copper 1,150 pieces ¼ Korean copper 420 pieces (1799); sisang’u˘n ( (1799); cho˘ng’u˘n ( ) one tael Chinese copper 800 pieces ¼ Korean copper 300 pieces (1799). Source: See, Muo Yoˇnhaeng-rok (19/10/1798–2/4/1799) and the following:

1655/12/13: Hyojong sillok (Hyojong 6,

);

); );

1768/07/19: Yijaenango;

274


A Year

B

C

D

Season

No. of Meals

Laborer Type

Fall

1877

Fall

1885

Fall

1824

Spring

20

1836

Spring

5

1782 1798 1808 1821 1823 1826 1832 1833 1840 1830

Spring Spring Spring Spring Spring Fall Fall Spring Spring Fall

16

1

Repair

Bamboo mat maker Bamboo mat maker Bamboo mat maker Irrigation worker Irrigation worker Blacksmith Blacksmith Blacksmith Blacksmith Blacksmith Blacksmith Blacksmith Blacksmith Blacksmith Blacksmith repairs

F

G

H

Wages

I

J

K

L

M

N

O

P

Q

Raw Materials

Work Days

No. of Workers

0.6

3

1

0.42

0.67

2.50

4.04

2.98

4.81

Net Gross Conversions Copper Copper Wage / Wage/Day Net silver Gross Net rice Gross rice Day (E/ (E+F+G wage/day silver wage/day wage/day J K) +H)/ (grams) wage/day (kg) (kg) (J K) (grams)

Cash

Wine

Tobacco

Food other than rice

1.25

0.15

0.29

0.33

1.25

0.50

3

1

0.42

0.58

2.25

3.15

2.23

3.12

1.30

0.45

3

1

0.43

0.58

2.73

3.68

2.26

3.04

2.00

0.35

0.35

0.54

1

4

0.50

0.81

4.90

7.94

7.42

12.03

0.40

0.23

0.50

0.08

1

2

0.20

0.61

1.96

5.93

2.97

8.98

1 1 1 1 1 2 1 1 1 1

1 1 1 1 1 1 1 1 1 1

0.19 0.35 0.50 0.20 0.20 0.37 0.30 0.35 0.27 0.10

0.19 0.35 0.50 0.20 0.20 0.37 0.30 0.35 0.27 0.10

1.86 3.61 5.45 1.96 1.96 3.58 2.67 3.12 2.13 0.89

1.86 3.61 5.45 1.96 1.96 3.58 2.67 3.12 2.13 0.89

2.82 5.81 11.35 5.44 4.90 9.94 4.68 4.15 3.23 2.41

2.82 5.81 11.35 5.44 4.90 9.94 4.68 4.15 3.23 2.41

0.19 0.35 0.50 0.20 0.20 0.73 0.30 0.35 0.27 0.10

0.52 1.25 0.30 0.30 1.00 0.60 0.25

275

1867

E


APPENDIX C. NOMINAL AND CONVERTED WAGES IN YO˘NG’AM COUNTY, SOUTH CHO˘LLA PROVINCE

276

APPENDIX C. (Continued ) A Year

B

C

D

Season

No. of Meals

Laborer Type

E

1

1820

Fall

13

1821

Fall

1

1824

Spring

2

1825

Spring

2

1835

Fall

1782 1798 1823 1824 1826 1833 1836 1857 1857 1863

Spring Spring Fall Spring Fall Spring Spring Spring Spring Spring

15

35 13 116 55 1

Blacksmith repairs Building repairs Building repairs Building repairs Building repairs Building repairs Carpenter Carpenter Carpenter Carpenter Carpenter Carpenter Carpenter Carpenter Carpenter Carpenter

H

Wine

Tobacco

I

J

K

Raw Materials

Work Days

No. of Workers


0.10

L

M

N

O

P

Q


1

1

0.10

0.10

0.89

0.89

1.46

1.46

0.60

0.10

0.06

1

2

0.30

0.38

2.94

3.72

9.19

11.64

0.30

0.17

0.13

1

2

0.15

0.30

1.47

2.94

3.00

6.00

0.20

0.25

0.05

1

2

0.10

0.25

0.98

2.45

2.72

6.81

1

2

0.40

0.40

3.92

3.92

12.25

12.25

1

3

0.17

0.31

1.48

2.76

2.60

4.84

2 4 7 3 23 11 1 1 1 12

1 1 1 1 1 1 1 1 1 1

0.25 0.25 0.50 0.60 0.52 0.36 0.20 0.15 0.15 0.33

0.29 0.39 0.58 0.67 0.77 0.75 0.40 0.32 0.22 0.45

2.45 2.58 4.94 5.88 5.11 3.24 1.78 1.07 1.07 1.95

2.79 3.99 5.66 6.53 7.58 6.68 3.56 2.27 1.56 2.71

3.71 4.15 14.12 16.33 14.20 4.32 2.92 2.54 2.54 2.75

4.23 6.44 16.16 18.15 21.07 8.91 5.84 5.41 3.72 3.82

0.80 0.50 0.50 1.00 3.53 1.80 12.00 4.00 0.20 0.15 0.15 3.90

0.06

0.36 0.20 0.68

0.03 1.52

0.29 0.15 0.95 1.29 0.20 0.10 0.04

0.37 0.07 0.26

4.85 2.29

1.3 0.70 4.00 0.17 1.35

0.07 2.5


Spring

G Wages

Cash

1836

F

1820 1826 1835 1837 1840 1854

Fall Fall Spring Fall Spring Fall

1855 1855

Spring Fall

1855

Fall

1856

Fall

1859 1860 1861 1861 1862 1863 1863 1863 1864 1865 1866 1867 1868 1872 1874 1875 1876 1877

Spring Spring Spring Spring Spring Spring Spring Fall Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring

1885 1892 1896

Spring Fall Fall

67 Ritual food Tax Ritual food Ritual food Ritual food Tax Tax Tax Tax Tax Tax Tax Tax Tax Tax Tax Tax Tax Tax Tax Ritual food Tax

Carpenter Carpenter’s assistant Porter Porter Porter Porter Porter Porter

1.50 0.40

3 3

1 1

0.50 0.13

0.50 0.13

3.15 1.31

3.15 1.31

1.96 3.63

1.96 3.63

0.20 0.08 0.07 0.20 26.00 0.20

1 1 1 1 1 1

1 1 1 1 67 1

0.20 0.08 0.07 0.20 0.39 0.20

0.20 0.08 0.07 0.20 0.39 0.20

1.96 0.78 0.62 1.78 3.07 1.42

1.96 0.78 0.62 1.78 3.07 1.42

6.13 2.18 1.22 3.49 3.30 3.94

6.13 2.18 1.22 3.49 3.30 3.94

0.02

40.00

Porter Porter

0.20 0.20

1 1

2 1

0.10 0.20

0.10 0.20

0.71 1.42

0.71 1.42

1.69 4.44

1.69 4.44

Porter

0.10

1

1

0.10

0.10

0.71

0.71

2.22

2.22

Porter

0.20

1

1

0.20

0.20

1.42

1.42

3.64

3.64

Porter Porter Porter Porter Porter Porter Porter Porter Porter Porter Porter Porter Porter Porter Porter Porter Porter Porter

0.05 0.05 0.05 0.10 0.08 0.08 0.08 0.20 0.08 0.08 0.08 0.11 0.08 0.10 0.10 0.10 0.10 0.20

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0.05 0.10 0.05 0.10 0.08 0.08 0.08 0.20 0.08 0.08 0.08 0.11 0.08 0.10 0.10 0.10 0.10 0.20

0.05 0.10 0.05 0.10 0.08 0.08 0.08 0.20 0.08 0.08 0.08 0.11 0.08 0.10 0.10 0.10 0.10 0.20

0.36 0.60 0.30 0.60 0.48 0.48 0.48 1.20 0.48 0.48 0.48 0.66 0.48 0.54 0.54 0.54 0.54 1.08

0.36 0.60 0.30 0.60 0.48 0.48 0.48 1.20 0.48 0.48 0.48 0.66 0.48 0.54 0.54 0.54 0.54 1.08

0.39 1.43 0.44 0.88 0.67 0.68 0.68 1.50 0.52 0.59 0.45 0.77 0.53 0.43 0.50 0.62 0.63 0.46

0.39 1.43 0.44 0.88 0.67 0.68 0.68 1.50 0.52 0.59 0.45 0.77 0.53 0.43 0.50 0.62 0.63 0.46

Porter Porter Porter

0.15 0.60 0.20

1 1 1

1 3 1

0.15 0.20 0.20

0.15 0.20 0.20

0.95 1.50 1.50

0.95 1.50 1.50

0.65 1.12 0.99

0.65 1.12 0.99

277

Spring Spring


1886 1824

278


B

C

D

Season

No. of Meals

Laborer Type

Spring

1827

Spring

1826

Fall

1839

Spring

1787 1791 1791 1791 1796 1798 1799 1800 1801 1803 1805 1809 1816 1819

Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Spring Fall

5 57

Construction labor Construction labor Construction assistant Construction assistant Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer

F

G

H

Wages Cash

Wine

0.34

0.05

1.60

Tobacco

0.10 0.95

0.20 11.90 0.50 1.40 1.50 4.00 2.00 2.00 1.60 1.60 1.50 1.90 2.30 0.10 0.20

0.18 0.13 0.1

J

K

Raw Materials

Work Days

No. of Workers


0.10

1.00

I

0.8 0.1 0.14 0.15 0.4 0.23 0.2 0.35 0.38

L

M

N

O

P

Q


1

1

0.34

0.49

3.33

4.80

5.05

7.28

1

5

0.32

0.34

3.14

3.33

10.45

11.11

1

11

0.09

0.18

0.89

1.74

2.47

4.83

1

2

0.10

0.19

0.89

1.69

1.07

2.04

1 1 1 1 4 2 1 1 1 1 1 1 1 1

119 5 10 15 10 10 20 15 17 15 19 23 1 2

0.10 0.10 0.14 0.10 0.10 0.10 0.10 0.11 0.09 0.10 0.10 0.10 0.10 0.10

0.11 0.14 0.15 0.11 0.11 0.11 0.11 0.13 0.12 0.10 0.10 0.10 0.10 0.10

0.98 1.03 1.44 1.03 1.03 1.03 1.03 1.16 1.03 1.09 1.09 1.09 0.98 0.98

1.06 1.44 1.59 1.13 1.13 1.15 1.13 1.42 1.27 1.09 1.09 1.09 0.98 0.98

0.96 2.45 3.43 2.45 1.51 1.66 1.34 2.42 2.18 2.22 2.06 2.27 2.00 3.27

1.04 3.43 3.78 2.70 1.67 1.85 1.47 2.95 2.70 2.22 2.06 2.27 2.00 3.27


1782

E

65 Audit

Harvest

Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Day laborer Errand boy Errand boy Errand boy Errand boy Errand boy Horse rent Horse rent Horse rent Horse rent Horse rent Horse rent Horse rent Horse rent Horse rent Horse rent

0.20 1.10 0.10 2.95 0.20 0.20 0.10 0.10 0.10 0.10 0.20 0.50 0.10 0.10 0.10 0.10 0.69 0.10 0.20 0.20 0.25 0.10 1.00 0.10 0.10 0.25 0.08 0.17 0.10 0.05 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40

0.19

0.25

0.03

0.85

0.75

0.10

0.03

0.20 0.25 0.07

0.10

1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

2 8 1 26 2 2 1 1 1 1 2 5 1 1 1 1 7 1 2 2 2 1 10 1 1 5 1 ? 1 1 1 1 1 1 1 1 1 1 1 1

0.10 0.14 0.10 0.11 0.10 0.05 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.13 0.10 0.10 0.10 0.10 0.05 0.08

0.10 0.17 0.10 0.18 0.10 0.05 0.23 0.10 0.10 0.10 0.20 0.21 0.17 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.13 0.10 0.10 0.10 0.10 0.05 0.08

0.98 1.35 0.98 1.11 0.98 0.49 0.98 0.98 0.89 0.89 0.89 0.89 0.79 0.79 0.71 0.71 0.70 0.71 0.71 0.60 0.75 0.54 0.54 0.54 0.54 0.49 0.78

0.98 1.69 0.98 1.72 0.98 0.49 2.25 0.98 0.89 0.89 1.78 1.85 1.34 0.79 0.71 0.71 0.70 0.71 0.71 0.60 0.75 0.54 0.54 0.54 0.54 0.49 0.78

2.00 3.74 2.72 3.09 2.72 1.29 2.23 2.23 2.41 1.07 1.37 1.07 0.85 1.61 1.11 1.03

2.00 4.70 2.72 4.76 2.72 1.29 5.12 2.23 2.41 1.07 2.74 2.23 1.44 1.61 1.11 1.03

1.97 2.09 0.86 0.69 0.44

1.97 2.09 0.86 0.69 0.44

0.47 1.04 1.63 1.91

0.47 1.04 1.63 1.91

0.10 0.05 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40

0.10 0.05 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40

0.98 0.49 3.92 3.92 3.92 3.92 3.92 3.92 3.92 3.92 3.92 3.92

0.98 0.49 3.92 3.92 3.92 3.92 3.92 3.92 3.92 3.92 3.92 3.92

1.58 1.53 13.07 11.88 11.88 11.88 8.00 7.54 9.56 9.80 11.20 12.25

1.58 1.53 13.07 11.88 11.88 11.88 8.00 7.54 9.56 9.80 11.20 12.25

279

Fall Spring Spring Fall Fall Fall Spring Fall Fall Spring Fall Spring Spring Fall Fall Fall Spring Fall Spring Fall Fall Spring Spring Fall Fall Fall Fall Fall Spring Spring Fall Spring Fall Fall Fall Spring Fall Spring Fall Spring


1821 1824 1824 1826 1827 1827 1828 1829 1830 1839 1839 1839 1840 1840 1852 1853 1854 1854 1856 1862 1869 1870 1871 1871 1879 1819 1822 1823 1829 1825 1819 1820 1820 1820 1821 1822 1822 1823 1823 1825

280


B

C

D

Season

No. of Meals

Laborer Type

E

Horse Horse Horse Horse Horse Horse Horse Horse Horse Horse Horse Horse Horse Horse Horse Horse Horse Horse Horse Horse Horse Horse

rent rent rent rent rent rent rent rent rent rent rent rent rent rent rent rent rent rent rent rent rent rent

H

0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40

Wine

Tobacco

I

J

K

Raw Materials

Work Days

No. of Workers


1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

L

M

N

O

P

Q


0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40

0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40

3.92 3.92 3.92 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.16 2.84 2.84

3.92 3.92 3.92 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.56 3.16 2.84 2.84

10.89 10.32 8.52 8.48 8.68 6.25 4.75 4.19 4.19 3.52 7.12 7.12 6.25 5.84 4.62 3.91 6.98 5.74 5.16 3.40 6.60 7.28

10.89 10.32 8.52 8.48 8.68 6.25 4.75 4.19 4.19 3.52 7.12 7.12 6.25 5.84 4.62 3.91 6.98 5.74 5.16 3.40 6.60 7.28


Fall Fall Fall Spring Fall Fall Spring Fall Fall Spring Fall Fall Fall Spring Fall Spring Fall Spring Fall Spring Fall Fall

G Wages

Cash

1826 1827 1828 1831 1831 1832 1833 1833 1833 1834 1834 1834 1835 1836 1836 1837 1837 1838 1838 1840 1853 1856

F

Fall Fall Spring Fall Fall Fall Spring Fall

1835

Fall

1836

Fall

1838

Fall

1853

Fall

1856

Fall

1857

Fall

1808 1826 1827 1833 1870 1826

Spring Fall Spring Spring Spring Fall

1870

Fall

1860

Fall

1861

Fall

1863

Fall

1864

Fall

8 8

5

Ritual food Ritual food Ritual food Ritual food

Horse rent Horse rent Horse rent Horse rent Horse rent Horse rent Horse rent Horse attendant Horse attendant Horse attendant Horse attendant Horse attendant Horse attendant Horse attendant Mason Plasterer Plasterer Plasterer Plasterer Plasterer’s assistant Plasterer’s assistant Servant

0.40 0.40 0.40 0.60 0.60 0.60 0.60 0.20

1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1

0.40 0.40 0.40 0.60 0.60 0.60 0.60 0.20

0.40 0.40 0.40 0.60 0.60 0.60 0.60 0.20

2.84 2.84 2.40 3.60 3.60 3.60 3.60 1.78

2.84 2.84 2.40 3.60 3.60 3.60 3.60 1.78

4.66 8.11 3.33 5.14 4.50 3.79 4.19 3.56

4.66 8.11 3.33 5.14 4.50 3.79 4.19 3.56

0.25

1

1

0.25

0.25

2.23

2.23

3.90

3.90

0.18

1

1

0.18

0.18

1.60

1.60

2.08

2.08

0.15

1

1

0.15

0.15

1.34

1.34

1.93

1.93

0.20

1

1

0.20

0.20

1.42

1.42

3.30

3.30

1

1

0.20

0.28

1.42

1.99

4.18

5.85

1

1

0.20

0.20

1.42

1.42

2.33

2.33

1 1 1 1 1 1

77 8 2 1 1 5

0.53 0.20 0.30 0.30 0.25 0.12

0.55 0.21 0.50 0.70 0.25 0.20

5.80 1.96 2.94 2.67 1.35 1.18

5.96 2.08 4.90 6.23 1.35 1.96

12.09 5.44 9.80 3.56 1.10 3.27

12.42 5.78 16.33 8.31 1.10 5.44

0.10

1

1

0.10

0.10

0.54

0.54

0.40

0.40

0.10

1

1

0.10

0.10

0.60

0.60

1.13

1.13

Servant

0.20

1

2

0.10

0.10

0.60

0.60

0.90

0.90

Servant

0.18

1

2

0.09

0.14

0.54

0.84

0.68

1.05

Servant

0.60

1

2

0.30

0.30

1.80

1.80

2.95

2.95

0.20

0.08

0.20 41.00 1.60 0.60 0.30 0.25 0.60

0.1 0.10

0.13

0.9

0.40 0.40 0.40

0.10


1857 1859 1862 1862 1863 1865 1867 1834

281

282


B

C

D

Season

No. of Meals

Laborer Type

E

1868

Fall

1825

Fall

1826

Fall

1827

Fall

1851

Fall

1854

Spring

1858

Spring

1862

Fall

1869

Fall

1827

Fall

Ritual food Ritual food 8

6

10

9

H

Wine

Tobacco

I

J

K

Raw Materials

Work Days

No. of Workers


L

M

N

O

P

Q


Servant

0.30

1

3

0.10

0.10

0.60

0.60

0.71

0.71

Servant

0.20

1

2

0.10

0.10

0.60

0.60

0.74

0.74

Hypocaust laborer Hypocaust laborer Hypocaust laborer Hypocaust laborer Hypocaust laborer Hypocaust laborer Hypocaust laborer Hypocaust laborer Hypocaust assistant

0.30

1

1

0.30

0.88

2.94

8.62

8.17

23.96

0.50

1

1

0.50

0.50

4.90

4.90

13.61

13.61

1.00

2

1

0.50

0.50

4.90

4.90

12.89

12.89

1

1

0.25

0.51

1.78

3.62

2.91

5.94

0.57

1

1

0.57

0.57

4.05

4.05

6.04

6.04

1.80

3

1

0.60

0.60

3.60

3.60

5.14

5.14

3

1

0.20

0.20

1.96

1.96

5.16

5.16

0.25

0.08

0.1

0.13

0.50

0.03

0.68

1.24 0.60


Fall

G Wages

Cash

1867

F

Fall

1852

Spring

1858

Spring

1862

Fall

1814

Spring

12

1819

Spring

20

1824

Spring

10

1831

Fall

21

10 7

Hypocaust assistant Hypocaust assistant Hypocaust assistant Hypocaust assistant Wooden vessel maker Wooden vessel maker Wooden mortar maker Wooden vessel maker

0.25

1

1

0.25

0.51

1.78

3.62

3.41

6.96

0.15

1

1

0.15

0.15

1.07

1.07

1.61

1.61

0.58

2

1

0.29

0.29

2.06

2.06

3.07

3.07

0.63

1

7

0.09

0.09

0.54

0.54

0.77

0.77

3

1

0.17

0.25

1.63

2.45

2.18

3.27

0.50

0.1

0.15

1.10

0.13

0.03

0.10

0.16

0.20

4

1

0.28

0.37

2.70

3.58

8.98

11.92

2

1

0.20

0.31

1.96

2.99

5.44

8.30

4

1

0.16

0.26

1.45

2.34

3.53

5.70

0.40

0.05

0.05

0.11

0.65

0.05

0.10

0.25

0.28

Note: Copper was converted to silver according to the bi-metallic ratios, 1650–1880 (see Appendix B). Among laborer types ‘‘hypocaust laborer’’ refers to the skilled labor of constructing Korean hypocaust systems in houses and buildings. ‘‘Horse rent’’ refers to the cost of hiring a horse and ‘‘horse attendant’’ refers to the cost of hiring a horseman to tend a rented horse. Under Column C, ‘‘repair’’ refers to a blacksmith hired in, probably for one day or less and one or two meals. ‘‘Ritual food,’’ ‘‘tax,’’ ‘‘harvest,’’ and ‘‘audit’’ also refer to hired labor to transport ritual food or tax grains or assistance in serving ritual food or conducting an audit of the cooperative’s books. This labor probably required only one person and meals for one day or less. It is entered here, because this is the entry found in the account books in reference to meals for labor. Source: Han’guk Cho˘ngsin Munhwa Yo˘n’guwo˘n (1995).


1851

283

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