Asian Economic Papers. Volume 7, Issue 1, Winter 2008

Export Performances in China and India A Comparative Analysis of China’s and India’s Recent Export Performances* Kalia...

Author: Jeffrey D. Sachs | Bokyeong Park | Wing Thye Woo | Naoyuki Yoshino (editors)

54 downloads 541 Views 3MB Size Report

This content was uploaded by our users and we assume good faith they have the permission to share this book. If you own the copyright to this book and it is wrongfully on our website, we offer a simple DMCA procedure to remove your content from our site. Start by pressing the button below!
Report copyright / DMCA form

DOWNLOAD PDF

Export Performances in China and India

A Comparative Analysis of China’s and India’s Recent Export Performances*

Kaliappa Kalirajan Foundation for Advanced Studies on International Development and National Graduate Institute for Policy Studies 7-22-1 Roppongi Minato-ku Tokyo 106-8677 Japan [email protected] Kanhaiya Singh National Council of Applied Economic Research Parisila Bhawan 11, Indraprastha Estate New Delhi 110 002 India

Abstract

Drawing on the convergence theory, one would expect that the export performance of India (a latecomer to integrating with the global economy) would be at least on par with that of China because China’s performance has happened as predicted by the theory. This study, using performance measures based on the endogenous growth theory that internalizes the ability to export the maximum possible exports under the determinants of exports including the existing behind the border and beyond the border constraints, shows that India’s export performance is still far behind that of China. The implication of this study is that India’s reform measures need to be bolstered effectively to catch up and to overtake China.

1. Introduction In the ranking of the largest economies of the world measured by their gross domestic products in terms of 1995 constant US$, China and India stood at the 19th and 20th positions in 1980, but in 2005 the ranking places them at the 7th and 12th positions, respectively. Such a quantum jump of these two economies, particularly China, over two and a half decades is remarkable.1 What is interesting is, measured in terms of per capita income in current interna-

Asian Economic Papers 7:1

*

This paper was presented at the Asian Economic Panel Meeting at the Brookings Institution, Washington, D.C., on 10 April 2007. Comments and suggestions on an earlier version by discussants Lael Brainard, Brookings Institution, and Zhang Xiaojing, Chinese Academy of Social Sciences, and participants are gratefully acknowledged. A special thanks to Wing Thye Woo for commissioning this study for this Panel Meeting.

1

Woo (1998), Sachs and Woo (2000), and Lardy (2002) have provided a comprehensive exposition about the factors behind China’s successful economic performance.

© 2008 The Earth Institute at Columbia University and the Massachusetts Institute of Technology


tional dollars with purchasing power parity, China was lagging behind India by US$ 223 in 1980, but overtook India with a difference of US$ 1,450 in 2000. Based on the IMF data, the per capita income in current international dollars with purchasing power parity in 2005 worked out to be US$ 3,320 and US$ 7,150 for India and China, respectively. The dynamic growth performance of China and the respectable growth performance of India raise several interesting questions.2 For example, is China’s growth miracle different from what we observed in other Asian countries? Although China has demonstrated its potential to grow faster consistently for several years, why doesn’t India exhibit the same kind of dynamism? As a latecomer, what can India learn from China’s growth process? These interesting and important questions have occupied the minds of development economists. There is now a rich literature on the economic developments of these two countries including their reform processes and their impacts on macroeconomic policies and overall economic growth. Though some of the conclusions in these studies are controversial, there is consensus that opening up the economies for export-led growth through trade liberalization is a crucial factor among others, which signiªcantly inºuenced the growth performance.3 Is China’s growth performance anything special? When China’s growth experience is examined against the growth patterns of other Asian countries, particularly Japan, it is noticeable that Japan’s growth rate fell 15 years after its catching-up process started in 1955, whereas China has continued its growth for more than 25 years.4 However, when China’s share of global GDP is compared with that of Japan’s, it is evident that the latter’s share of global GDP grew faster than that of the former during Japan’s catching-up process. Thus, there do not seem to be any signiªcant miracles in the growth performances of China when compared with that of Japan.5 Nev-

2

In the eyes of many observers, by the end of the 1990s India had moved to being a “six percent growth” economy: not a “miracle” perhaps, but certainly respectable.

3

For example, some authors found differences in the political system as the key instrument creating variations in the performance of the two countries. Sachs and Woo (2000) labeled the competing interpretations of China’s post 1978 economic growth process as institutional innovations versus institutional convergence, which are in other words, the Experimentalist School and the Convergence School, respectively. Important econometric studies of the linkage between trade reform and the rate of economic growth include Sachs and Warner (1995) and Frankel and Romer (1999).

4

The starting period of the catch-up process for a country is based on the IMF’s notion of having an annual rise in exports of more than 10 percent for 3 years continuously (IMF 2004, chapter II).

5

In this context, it is worth noting the publication Growth without Miracles by Garnaut and Huang (2001).

2

Asian Economic Papers


ertheless, China’s growth performance looks more impressive if its integration into the global economy in terms of international trade in goods is considered. For example, China’s total merchandise trade increased from US$ 1,155 billion in 2004 to US$ 1,422 billion during 2005. The surge in China’s exports has drastically changed the structure of East Asia’s trade surplus with the United States and the European Union in favor of China from Japan. Drawing on the convergence theory, if, as a latecomer, China has been able to improve its export performance faster, why not India, which opened up its economy much later than China? It is in this context that this paper examines the merchandise export performances of China and India with the following three empirical questions: (1) If China’s exporting environment is emulated by India, what would be the latter’s export performance?; (2) If India’s exporting environment is duplicated by China, what would be the latter’s export performance?; and (3) How far are China and India from reaching their exports potential with their trading partners given the existing “behind the border” constraints and “beyond the border” constraints to exports?6 The following section brieºy describes important trade policy reforms in China and India. Section 3 discusses the concept and measurements of potential exports and data, which is followed by empirical estimations of different measures of potential exports from China and India with their trading partners. This section also provides the simulation results of export performances in China and India with the assumption of China emulating the exporting environment of India and India duplicating the exporting environment of China respectively. A ªnal section discusses what India can learn from the export performance of China to improve its trade policy reforms.

2. Trade policy reforms of China and India 2.1 China Trade policy in China underwent a major change between 1979 and 1980, when the central government decided to establish four Special Economic Zones (SEZs) in two coastal provinces, Guangdong and Fujian, to attract foreign direct investment and 6

Behind the border constraints to export, within the home country, which mainly include regulatory policies that impede competition, restrictions on foreign trade and investment, tolerance of business cartels, monopoly privileges given to public enterprises, and the cost and performance of infrastructure services that are important to the functioning of businesses, services such as ports, customs and transport, generally affect the domestic costs of production. Beyond the border constraints mainly refer to non-tariff barriers and other institutional rigidities of partner countries, which generally inºuence the shifting of the export frontier.

3



new technologies (National Statistical Bureau, various years; henceforth, NBS(a)). This was the beginning of China’s Open-Door Policy. Initial success encouraged Chinese policymakers to adopt similar policies in 14 east coastal cities in 1984, which were further extended to a far wider area of China’s east coast region in 1985 and in the following years. It is worth noting that the 12 East Coast provinces, out of the total 30,7 contributed two-thirds of China’s total exports in 1990 (China Custom Statistical Bureau 2002). The openness of the Chinese economy was accelerated in the 1990s, after Deng Xiaoping’s push for faster economic reforms and openness in 1992. Twenty inland cities became “open cities” that could enjoy a series of preferential policies in 1993. Border areas in North and West China, namely, Xinjiang, Inner Mongolia, Heilongjiang, Yuannan, and Guangxi, were also opened to border trade (Wang 2004). FDI, which was only US$ 1.7 billion in 1985, increased dramatically in the 1990s. In 1995, FDI increased to US$ 37.5 billion, and then to US$ 40.7 billion in 2000, and to US$ 72.4 billion in 2005. Domestic and foreign trade sectors were opened to FDI in the late 1990s. Foreign enterprises, which include enterprises with investment from Chinese Hong Kong, Macao, and Taiwan, played more and more important roles in the manufacturing sector of China (Jiang 2002). Trade policy was not shifted immediately from import-substitution to exportorientation. For a long period during the reform era, it was a mix of both importsubstitution and export-orientation, but gradually shifted toward the East Asian growth model of export-oriented growth. High import tariffs remained in China, although the real tariff rate was far lower, due to various preferential policies and smuggling. In 1995, for example, the average nominal tariff rate on electronic products was 40 percent, but the actual rate (that is, tariffs actually collected as a share of the value of imports) was only 11.8 percent (National Statistical Bureau, various years; henceforth NBS(b)). In the 1980s and 1990s, there were also trade-related investment measures (TRIMs) such as the requirement of domestic components in production, and foreign exchange balance requirements. Despite these measures, the foreign-invested industries were not foreign-exchange earners in the 1980s and the early-to-mid 1990s because their exports could not exceed their imports before 1998, though they did contribute to economic growth, employment generation, and an increase in foreign trade (Wang 2004). 7

This includes four Minority Autonomous Regions and three Central-Administrated Municipalities. The total number became 31 later (NBS(a)).

4



There were more changes in the 1990s. In 1996, joint ventures with foreign investment were allowed to deal with foreign trade. In 1998, private enterprises were also allowed to engage in foreign trade. The state monopoly in foreign trade was gradually replaced by market competition. Deduction, or removal, of tariff and non-tariff barriers was also an important part of trade policy reform. From 1982–92, the nominal tariff rate, as an average, reduced from 56 percent to 43 percent. From 1992–03, it further reduced from 43 percent to 11 percent (Wu 2003).8 The average tariff in 2005 was 9.9 percent. Non-tariff barriers, for example, import licensing and other requirements for special import approvals, were reduced in the 1990s and eliminated in the early 2000s as the government’s commitment toward joining the WTO grew. There were major changes after the WTO accession in 2001, too. Concerning TRIMs, mainly requirements on domestic components, export performance, and foreign exchange balance of foreign enterprises, were removed. Upon China’s WTO accession in 2001, the banking/insurance and telecommunication sectors, which were not opened to FDI before, were opened.9 Not only were the trade policies relating to FDI changed, trade liberalization also occurred in the domestic sectors. More and more manufacturers that produced export goods were also permitted to directly purchase inputs and sell products overseas. Thus, it is apparent that trade policy reforms signiªcantly contributed to economic growth in China, which was more or less on average at the two-digit level over more than 2 decades.10 Nevertheless, there is room for further improvement in China’s trade policies.11 Some analysts have suggested that the imbalance of policy treatment between FDI and domestic investment, which favors FDI, has resulted in rent-seeking behavior and inefªciencies. In addition, there are needs for further policy reform toward

8

As mentioned earlier, the actual tariff rate in the 1990s should be far below the ofªcially announced rate because of various tariff exemptions and deductions, and smuggling. This should not be the case in the early 1980s, because the coverage of policy preferences on tariff deduction was only limited at the time, and smuggling was less serious.

9

Sachs and Woo (2003) argued that the Chinese leadership’s opinion has been that in the short-run, there could be signiªcant displacement of Chinese state banks by foreign banks, but in the long run, Chinese banks (most likely private ones) would rise in importance.

10

Literature indicates that countries that liberalized their trade (raising their trade-to-GDP ratio by an average of 5 percentage points) between 1950 and 1998 enjoyed on average 1.5 percentage points higher GDP growth compared with their pre-reform growth rates (Greenaway, Morgan, and Wright 2002; Baldwin 2003).

11

Drysdale, Huang, and Kalirajan (2000) argued for the need for more trade policy reforms to enhance China’s trade efªciency. Gang Fan and Xiaojing Zhang (2003) discussed how the further reform agenda can be designed to achieve another period of 2 decades of high growth.

5



Figure 1. Pattern of economic growth of India

transparency and better business environment (Sachs and Woo 2003; Huang and Khanna 2003). 2.2 India Figure 1 presents a simpliªed record of India’s aggregate growth (growth in real GDP at factor cost, 1993–94 prices) performance over the 52 years from 1951–52 to 2002–03. It also plots trend growth (TG) rates for each decade starting 1951–61 and some of the key events responsible for slowdown episodes and includes a summary table indicating the coefªcient of variation across decades and average growths after ignoring the drought and crisis periods. Sweeping policy changes were made in the trade sector during the 1990s in India, though at a pace slower than in China. Customs tariffs are now lower and quantitative restrictions on imports have been eliminated. Export restrictions have been reduced along with the implementation of various export promotion measures. However, the pace of tariff reforms slowed after 1996–97. Whereas the peak rate of duty has been reduced gradually, the average tariff rate remained broadly unchanged at about 30 percent during 1997–02, though the average tariff was about 18 percent in 2005, which is almost double that of China. This tariff rate is also high by the current world standards. Figure 2 shows plots of four indicators of tariff-related trade barriers, all-products simple mean, standard deviation of tariff lines, simple mean of tariff lines for manu6



Figure 2. Trade reforms in terms of tariff policy across selected countries

Source: World Development Indicator, 2002.

factured goods, and share of tariff lines with international peaks. When compared with countries, such as China, Brazil, South Korea, Sri Lanka, Malaysia, Japan, and the United States, India turns out to be an outlier in terms of all-products simple mean tariffs. What is most disturbing is the number of lines with world peak. It appears that the Indian authorities simply look at the highest rates prevailing anywhere in the world and adopt the same tariff without much analysis. There are also concerns about the institutional role in determining tariffs. At least four institutions are assigned the role of ªxing tariffs in one way or the other. Among them, the Tariff Commission is the most relevant. The commission has resources to determine tariffs with more techno-economic analysis, but it has never been involved in tariff determination or regulation since its inception in September 1997. Then, there is the Tariff Research Unit (TRU) (presumably the most effective in determining tariffs) in the revenue department of the Ministry of Finance, which obviously would be more concerned about short-term effects of changes in tariffs, particularly on revenue, than long-term effects on trade and growth. The Ministry of Agriculture reportedly determines agricultural tariffs. Besides, there is an antidumping directorate in the Ministry of Commerce to look into complaints of dumping of agricultural products such as skimmed milk powder from the European Union. Thus, lack of institutional coordination may not be overlooked. Though the medium-term exports strategy (MTES 2002–07), which was announced in January 2002, aimed to increase India’s share in world trade from about 0.7 per7



cent to 1 percent by 2006–07, the current target is to reach 1.5 percent of world trade by 2009.12 Latest trade ªgures in the World Trade Report 2006 reveal that in calendar year 2005, India’s merchandise exports were worth US$ 90 billion that is approximately 0.89 percent of total global exports worth US$ 10,121 billion. China’s share, on the other hand, increased from 6.67 percent in 2004 to 7.52 percent in 2005 with the country exporting goods worth US$ 762 billion during the year. Although India’s share in world total merchandise exports surged from 0.4 percent in 1992 to 0.8 percent in 2002, it took 3 long years for India to move another step farther. At this rate, the target of reaching 1.5 percent of world trade by 2009 would not be that easy to achieve. To keep pace with the growth in world trade and grab a larger share of the world exports market, India has to aim higher. The 5-year Export and Import (EXIM) Policy (2002–07) announced on 31 March 2002 intended to remove all quantitative restrictions on exports except for a few sensitive items reserved for exports through the state trading enterprises. It also outlined a farm-to-port approach for exports of agricultural products with a special focus on the cottage sector, handicrafts, and assistance to states for infrastructure development for exports (ASIDE). New private sector-run SEZs were created to provide investors an export-friendly environment. The incentives offered under the SEZ scheme included duty-free importation/domestic procurement of goods for the development of the SEZ and setting up of units, 100 percent FDI in the manufacturing sector under the automatic route, 100 percent income tax exemption for the ªrst 5 years, and 50 percent tax exemption for 2 years thereafter. Other incentives included sub-contracting part of production abroad, reimbursement/exemption for central sales tax on domestic purchases by the SEZ units and retention of 100 percent foreign exchange earnings in the Exchange Earners Foreign Currency (EEFC) Account. In terms of ªnancing SEZs, overseas banking units (OBUs) that were exempt from cash reserve and statutory liquidity requirements (CRR and SLR, respectively), were permitted to set up in SEZs. These OBUs have given access to SEZ units and SEZ developers to international ªnance at international rates. SEZ units were exempt from external commercial borrowing (ECB) restrictions and were allowed to make overseas investments and carry out commodity hedging. SEZs were exempt from central sales tax in respect of supplies from domestic tariff area (DTA) and transactions from DTA to SEZs were treated as exports under the Indian Income Tax and Customs Acts.

12

The MTES is a comprehensive exercise, which includes product and market identiªcation for exports and indicates sector-wise strategies for identiªed potential sectors.

8



The number of goods reserved for the small-scale sector is set to reduce further. The strategic sectors identiªed for providing special focus include electronics, electrical goods, and engineering goods referred to as “3Es” (Chadha 2003). Policy on entry of direct foreign investment has been eased greatly, but investors continue to face a daunting regulatory framework beyond the foreign investment regime itself. Although policy initiatives are yielding favorable results to some extent, the foregoing discussion indicates that there are several concerns and issues, which mainly involve behind the border constraints issues, that need to be addressed if exports are to grow faster. How effective have these trade policy reforms been in improving the export performances of China and India? Export performance can be measured in several ways. A simple conventional method is to work out the growth rate of absolute values of exports between two times and compare it with another time within the country, or compare it with the growth rate of another country during the same period. Though this kind of measure is useful in a way; what is more interesting is to measure the country’s potential exports, given the determinants of exports and compare it with its own actual exports. Such a measure provides a better understanding of the link between trade policies and export performance, which is explained in the following.

3. Measuring export performances of China and India 3.1 Methodology I A common feature of all performance measures is that performance is deªned with respect to a benchmark. Though there are several methods to arrive at a benchmark, the method of comparing one’s own potential to his or her own actual achievement is more appealing because any performance improvements come from “within.” The endogenous growth theory popularized by Romer (1986) and Lucas (1988) facilitates the assumption of internalization of the “within” aspect through policy measures that increase the incentive to innovate and to have an impact on the long-run growth rate of an economy (Roberts and Setterªeld 2007). In line with these arguments, potential exports can be measured by following either a general equilibrium approach or a disequilibrium framework. In the former approach, a home country’s exports to all its trading partners, which may be exhaustive and represent a general equilibrium framework, would be estimated and added up to arrive at total values of exports. Alternatively, drawing on Kalirajan (1999), in a disequilibrium framework where a home country’s actual exports are assumed to differ from its potential exports with respect to each trading partner and the partner-speciªc export gap is

9



explicitly included in the model explaining export ºows and the speciªc estimation method yield potential exports. Whereas there are several studies following the former approach, studies using the latter approach are sparse in the literature.13 The gravity model has been established in both approaches as a popular methodology to measure potential trade between countries. The gravity model, which is deªned following Newton’s Law of Gravitation, explains trade ºows between two countries as directly proportional to the product of each country’s “economic mass” that can be measured by GDP and inversely proportional to the distance between the countries (Bergstrand 1985). It is one of the most frequently estimated empirical relationships in economics. Earlier studies have estimated the difference between observed values and the predicted values that are calculated from OLS estimates of the gravity model as potential exports (Baldwin 1994; Nilsson 2000). A simple baseline gravity model can be written as equation (1). Xij C Yib Y jg Dij- d ,

(1)

where C, ␤, ␦, and ␥ are positive coefªcients to be determined empirically. Xij refers to exports of country i to country j. Yi and Yj are the national gross domestic products of countries i and j respectively; Dij is the distance between country i and country j relative to the average distance between country i and all its trading partners. For simplicity of exposition, the time subscript is avoided. Taking the logarithm, the base line of equation (1) can be conveniently represented in log-linear form as equation (2). lnXij ⫽ ␣ ⫹ ␤lnYi ⫹ ␥lnYj ⫺ ␦lnDij .

(2)

The real-world situation is too complex to be represented by a simple equation like (2). The geographical size, population, trade policies, and openness to trade of the importing country are also important factors affecting exports from any country. It is a bilateral relationship and representing such factors by a vector of variables Zij, and an error term (⑀ij) representing other left out variables and the deviation of the selected functional form from the actual relationship whose impact on export is considered to be on average negligible. Thus, the gravity equation (2) can be written in a more general form as equation (3). Thus, equation (3) in general can be estimated taking panel of data across time and across countries. 13

Drysdale, Huang, and Kalirajan (2000) used the disequilibrium framework to evaluate the efªciency of China’s bilateral trade with its 57 trading partners for the period 1991–95, and Kalirajan (2000) used it to examine Australia’s export efªciency with its trading partners in IOR-ARC.

10



lnXij ⫽ ␣ ⫹ ␤lnYi ⫹ ␥lnYj ⫺ ␦lnDij ⫹ ␭Zj ⫹ ⑀ij .

(3)

Researchers have used a number of dummy variables in the set of Zij to augment the model. An important assumption in this model is that the exporting environment in the home country does not impose any restrictions on the home country’s exports. In other words, this model although admitting that there are behind the border constraints in the home country and also that the home country faces beyond the border constraints in partner countries, these constraints are not important and are randomly distributed across observations. In other words, the assumption is equivalent to saying that there are no signiªcant behind and beyond the border constraints for exports of home country. However, effects of regional trading arrangements, connectivity by road/sea, language afªnities, historical relationships, and product preferences shown through brand names have been included in the gravity of equation (3). OLS methods or variants of OLS have been used to estimate models such as equation (3). 3.2 Methodology II In Methodology I, it was assumed that behind and beyond the border constraints to export are not signiªcantly affecting export ºows from the home country (China and India). This means that the impact of behind and beyond the border constraints to export on export ºows from China and India are merged with the statistical error term ⑀ with “normal” characteristics in equation (3). However, such an assumption may be restrictive and may not be in line with reality. We would like to elaborate on this by concentrating on important means to promote trade ºows between countries. One such means is trade liberalization. Trade liberalization, from a theoretical viewpoint, promotes efªciency by re-allocating resources to productive uses, stimulates competition, increases factor productivity, increases trade ºows, and thereby promotes economic growth (Wacziarg 1997). However, empirical facts on trade ºows across countries do not always support this theoretical viewpoint. Export ºows are constrained by three factors: (a) natural constraints, which are geographical distance and transport cost; (b) behind the border constraints, which are institutional and infrastructure rigidities that exist in exporting countries; and (c) beyond the border constraints, which are institutional and infrastructure rigidities that exist in importing countries. The impact of the latter constraints can be divided into two groups, namely, “explicit beyond the border” constraints and “implicit beyond the border” constraints. Beyond the border constraints, which are explicit, are mainly tariffs and exchange rate. The impacts of these constraints on the home country’s exports may be measured from the coefªcients of variables such as average tariffs and real exchange rate, which can be included directly into the gravity model. On the other hand, identifying and measuring implicit beyond the border constraints that

11



emanate from institutional and policy rigidities of importing countries are very difªcult, and are considered as “given” for the present study. Nevertheless, these implicit beyond the border constraints can be reduced or eliminated through multilateral and bilateral negotiations to a considerable extent. Behind the border constraints in the home country could arise due to socio-economic, institutional, and political factors in the home country. For example, large government size (Rodrik 1998), weak and inefªcient institutions in the home country in terms of, for example, custom and regulatory environments, port inefªciency, and inadequate e-business (Bhagwati 1993; Rodrik 2000; Levchenko 2004), and political inºuences through powerful lobbying by organized interest groups (Gawande and Krishna 2001) have been found to affect export ºows, among other things. The combined effects of behind the border constraints to export, however, which may be interpreted as an “economic distance” factor referred to by Anderson (1979) and Roemer (1977) can be measured on export ºows. This requires that the error term of the standard gravity model (3) needs to be decomposed into u, indicating the impact of behind the border constraints, and v, indicating “normal” statistical errors and implicit beyond the border constraints. lnXij ⫽ ␣ ⫹ ␤lnYi ⫹ ␥lnYj ⫺ ␦lnDij ⫹ ␥Zj ⫺ uij ⫹ vij .

(4)

Thus, apart from the geographical distance constraint, the behind the border constraints and explicit beyond the border constraints need to be included explicitly into the standard gravity model. Unfortunately, most of the empirical trade models do not consider this argument, as they do not incorporate these constraints into their trade model.14 However, OLS estimation of the gravity equation (4) leads to biased results. Drawing on Kalirajan (2007), the procedures developed for estimating stochastic frontier production functions (Aigner, Lovell, and Schmidt 1977; Meeusen and van den Broeck 1977), which do not require the researchers to have information on the exact components of u, can be used to estimate the modiªed gravity equation that includes the impact of behind the border constraints and explicit beyond the border constraints to export for a given level of implicit beyond the border constraints.

14

12

Recently, Anderson and van Wincoop (2003) suggested an approach to tackle this problem, which they name as “multilateral resistance.” However, their suggested method suffers from a number of limitations. For example, they assumed symmetric trade costs to solve their model, which is an unrealistic assumption. Also, their modeling of multilateral resistance, as a function of distance and tariffs only, ignores the presence and impact of variation in behind the border trade resisting factors in home country, and the implicit beyond the border constraints in respective importing countries.



The estimation procedure requires the assumption, which may be veriªed statistically, that u is a truncated (at zero) normal with mean ␮ and variance s2u and takes values either 0 or greater than 0. When u takes the value 0, this means that the impact of behind the border constraints are not important and the actual exports and potential exports are the same, assuming that the inºuence of v is not signiªcant (i.e., v ⫽ 0). When u takes the value other than zero, this means that the effects of behind the border constraints are important and they reduce potential exports depending on the value of u. Thus, the term u represents the difference between potential and actual exports in logarithmic values, which is a function of the inefªciencies that are within the exporting countries’ control. It is also assumed that error term v captures the inºuence on trade ºows of other variables, including measurement errors and implicit beyond the border constraints that are not under the control of the exporting country and are randomly distributed across observations in the sample. Maximum likelihood methods can be used to estimate the above modiªed gravity model and the magnitude of u. Computer programs such as STATA and FRONTIER 4.1 can be used to estimate the modiªed gravity model.15 3.3 Data The trade data are taken from the Direction of Trade Statistics of the IMF. Data on real GDP, which is a proxy for the size of the economy; population (POP), area (AREA), and tariff barriers are taken from the World Development Indicators (WDI) 2004 and WDI CD-ROM 2004. The most recent information on weighted average tariff rate for the primary products (TBPR), manufactured products (TBMFG) and all products (TBALL) have been used. Openness to trade is measured by trade in goods taken as a fraction of the gross domestic product (TRDGZ). Perception about prevailing restrictions on imports published in World Competitiveness Report 2004 of World Economic Forum (WEF) (Sala-i-Martin 2004) has been used to proxy non-tariff barriers. The non-tariff barrier is calculated as an index (NTBI) on a scale of 1–7 where lower values of index indicate higher non-tariff barrier. Thus, the expected sign of NTBI is positive. Factors such as the macroeconomic environment, the quality of public institutions, and technology are also important determinants, and are likely to affect the intensity of import across countries. WEF publishes a growth competitiveness index (GCI) on a scale of 1–7 where a higher value indicates a higher level of competitiveness. The GCI is founded on the previous three factors and, interestingly, GCI and NTBI are highly correlated (Sala-i-Martin 2004). Therefore, these variables are used selectively. All variables are taken in logarithms or fractions. 15

13

Details of the estimation procedure of FRONTIER 4.1 are given in Coelli (1996). Asian Economic Papers


4. Empirical results and discussion 4.1 Absence of behind the border constraints Both models estimated in this study for China and India separately were as follows: lnXij ⫽ ␣ ⫹ ␤lnGDPj ⫹ ␥lnDISij ⫹ ␦lnPOPj ⫹ ⑀ij ,

(5)

lnXij ⫽ ␣ ⫹ ␤lnGDPj ⫹ ␥lnDISij ⫹ ␦1lnPOPj ⫹ ␦2TRDGZ ⫹ ␦3LAREA ⫹ ␦4TBPR ⫹ ␦5NTBI ⫹ ⑀ij .

(6)

The variables are as deªned earlier. Over a small span of time the relative size of the trading partners and the exporting environment in the home country are not expected to change signiªcantly. Therefore, for the purpose of analyzing trading characteristics of the countries concerned during the recent period, the average values of exports during 2000–03 and average size of economies for 2000–02 are considered appropriate.16 Data on trade restrictions and openness to trade are also taken for the period 2000–02. Thus, there is an inbuilt lag in the value of explanatory variables. In the place of NTBI, the variable GCI was also used in the estimation for India. The selected sample sizes of the partner countries, which are the same 77 countries for both China and India, represent about 90 percent and 80 percent of exports from China and India, respectively, and therefore the estimated models can be considered to be representative models for these economies in a general equilibrium framework.17 All the equations were estimated by OLS and a complete diagnostic result is provided in the respective tables. A series of estimations have been completed to delineate the strengths and weaknesses of both countries. At the outset, the basic model (5) with GDP, distance, and population with respect to partner countries was estimated for China and India and the results are reported in Table 1. The base model was further expanded to include the proxies of openness and explicit beyond the border constraints and the results are presented in Table 2. Almost all the estimated equations are statistically consistent, and the R2 values are reasonably high. However, the magnitudes of the coefªcients are markedly different between China and India. Whether the size and signiªcance of these variables are

16

Because 2001 is characterized by a number of political and terrorist disturbances, including data from 2000 is expected to provide a better average, while considering the most recent available consistent data for countries of interest. Further, there are statistical advantages in taking average values as it reduces the problems of heteroskedasticity and functional forms leading to more reliable interpretation of the relationships.

17

For the purpose of the present study of comparing the performances of China and India emulating the exporting environment of each other, it is necessary to consider the same countries with which both China and India traded during the sample periods.

14



Table 1. Base gravity model with distance, aggregate GDP in terms of US$ at 1995 prices and population for China and India, 2000–03 Code: Model Number: Sample size:

China CH-9 77

India IN-9 77

CONSTANT LDIST LGDP LPOP

⫺7.071** (2.951) ⫺0.773* (0.180) ⫺0.882* (0.068) ⫺0.076 (0.097)

⫺4.398*** (2.437) ⫺1.021* (0.175) ⫺0.633* (0.059) ⫺0.149⫹ (0.095)

R2 S.E.

0.836 0.870

0.793 0.856

Diagnostic test Serial correlation

0.386 [0.53]

[0.08 [0.7] [0.791 [0.37] [0.252 [0.88] [0.109 [0.74]

Function form Normality Heteroskedasticity

1.072 [0.30] 28.126 [0.0] 3.28 [0.07]

Note: When there is problem of heteroskedasticity, White heteroskedasticity adjusted standard errors are presented. Values in parentheses () are standard errors and values in square brackets [] are p values. *Signiªcant at the 1 percent level; **Signiªcant at the 5 percent level; ***Signiªcant at the 10 percent level; Signiªcant at the 15 percent level.

robust or not in the presence of other variables is an important issue and is discussed later. The relative distance variables in both models of China have smaller coefªcients than those of India.18 It appears that the production process in China, which is characterized by large manufacturing volumes, is able to absorb the distance effects much more efªciently than India. The production cost in China is comparatively lower than that in India and the advantage derived from this is reºected in the size of the relative distance variable. It may be noted that the average distance of China from its trading partners is greater than that of India from its trading partners (Table 3). Therefore, India has to be more efªcient in cost management in order to compete with China in the same product group or else it has to design alternative strategies related to product and market. For example, empirical studies examining the costs of doing business in India often have cited that private ªrms have to have their own power generators in order to avoid the problem of a power shortage, which tends to increase production costs (Rajan 2006). Further, China is more concerned with other barriers to trade rather than distance. For example, in Model CHN-14 (Table 2), the relative distance variable becomes insigniªcant when a tariff barrier to primary sector products is introduced. In addition, as new variables are added, the coefªcient of the relative distance variable in China’s models continues to reduce. Therefore, it can be safely argued that China’s cost advantages are great instruments to boost their exports compared to India. 18

15

The results could have been better, had we disaggregated exports of China and India by commodity categories such as labor-intensive, agriculture-intensive, and resource-intensive. We thank the discussant, Lael Brainard, for pointing out this issue. Asian Economic Papers


Table 2. Augmented gravity model with area, openness to trade, and other trade barriers for China and India, 2000–03 Code: Model Number: Sample size:

China CH-14 77

India IN-13 77

India IN-14 77

CONSTANT LDIST LGDP LPOP TRDGZ LAREA TBPR NTBI GCI

⫺13.858* (3.395) ⫺0.269 (0.202) ⫺0.641* (0.132) ⫺0.432*** (0.229) ⫺0.007* (0.0025) ⫺0.141 (0.093) ⫺0.032*** (0.018)

⫺11.680* (2.616) ⫺0.567* (0.182) ⫺0.409* (0.086) ⫺0.666* (0.150) ⫺0.0060** (0.0027) ⫺0.145*** (0.078)

⫺10.94* (2.540) ⫺0.542* (0.184) ⫺0.300** (0.123) ⫺0.742* (0.169) ⫺0.0056** (0.0027 ⫺0.160** (0.079)

R2 S.E.

0.870 0.790

0.846 0.750

0.846 0.75

Diagnostic test Serial correlation Function form Normality Heteroskedasticity

0.006 [0.94] 0.910 [0.34] 56.90 [0.00] 5.280 [0.02]

0.319 [0.57] 0.093 [0.70] 3.011 [0.22] 0.355 [0.55]

0.71 [0.40] 0.195 [0.66] 0.591 [0.74] 0.529 [0.47]

⫺0.355** (0.146)

⫺0.313 (0.240)

⫺0.560** (0.228)

Note: When there is the problem of heteroskedasticity, White heteroskedasticity adjusted standard errors are presented. Values in parentheses () are standard errors and values in square brackets [] are p values. *Signiªcant at the 1 percent level; **Signiªcant at the 5 percent level; and ***Signiªcant at the 10 percent level.

Table 3. Summary matrix of distances (kilometer) being negotiated by China and India across the sample structures of trade partners Sample size:

China 77

India 77

Mean Minimum Maximum

9,931.5 956.2 19,286.0

8,490.4 678.6 16,937.4

The coefªcient of size of the economy measured by GDP is consistently signiªcant in all formulations. The size of this coefªcient is larger for China than that for India in both models. However, when variables such as openness to trade and growth competitiveness are added in the model, the size of coefªcient of GDP reduces for China and India (see Table 2 in comparison with Table 1). Nevertheless, the coefªcient of GDP is larger for China than for India. This means that clearly India has to progress signiªcantly to manufacture and export premium products consumed in richer countries as compared to the manufacturing activities in China. Population is indirectly covered in the size of the economy, and it can be argued to have independent demand side effects also. For example, subsistent economies also need basic livelihood amenities such as cheap clothing and food. Countries such as China and India, which have a high degree of mechanized production systems with

16



cheap labor, could be a potential source of imports provided the importing country has a conducive trade regime. This fact is revealed when the coefªcients of population variable across models are compared. The openness to trade variable (TRDGZ) is introduced in Models CH14, IN13, and IN14 along with the area variable (Table 2). Clearly, exports ºow more from both countries to those countries, which trade a higher proportion of their GDP. The coefªcient of TRDGZ is almost equal for both China and India. In the case of China, GCI is not a signiªcant variable; instead, tariff barriers to primary sector products are more important in reducing its exports. Even non-tariff barriers are insigniªcant in affecting China’s exports. On the other hand, in the case of India, non-tariff barriers and the growth competitiveness index act alike in affecting its exports growth. Recall that the expected sign of coefªcient of NTBI is positive because a higher value of NTBI means fewer problems in importing, whereas lower values mean the opposite. To calculate potential exports, it is important to estimate the equation in a general equilibrium framework so that as many trading partners as possible, indicating as much distance as possible, are covered. Nevertheless, such a general equilibrium framework may not take into account all country-speciªc characteristics of the home country that inºuence its exports. Therefore, in this exercise we put each country in the exporting environment of the other to simulate each other country’s potential exports. The key difference in export performance is expected to arise due to the change in the values of the relative distance variable, as all other variables remain more or less the same across trading countries. Models CHN14 and IND14 given in Table 2 were used for simulating the exports from China and India with the assumption that they switched their exporting environments between them. Simulations were carried out by applying the coefªcient of India, which proxies the exporting environment faced by India, on trade data concerning China and vice versa. The simulated gain/loss in exports is presented in Appendixes 1 and 2. As a summary, when the coefªcients of China are applied to calculate India’s simulated potential exports, it results in very high values for India (672.9 percent), which implies that if India enjoys China’s exporting environment, it would increase its exports drastically. On the other hand, when India’s coefªcients are applied to China, it leads to lowering of exports from China by 91.7 percent, clearly indicating that China has been operating at much higher efªciency levels than India. Thus, there is much for India to learn from China to improve its export performance. This result also implies that there are signiªcant behind the border constraints to export more in India than in China, which is examined in the next section.

17



Table 4. Modiªed augmented gravity model with area, openness to trade, other trade barriers, and behind the border constraints to export for China and India, 2000–03 Code: Sample size: CONSTANT LDIST LGDP LPOP TRDGZ LAREA TBPR GCI T Sigma square Gamma Eta Mu Loglikelihood

China 77 ⫺12.675* (3.262) ⫺0.258 (0.208) ⫺0.644* (0.138) ⫺0.429** (0.217) ⫺0.006* (0.0023) ⫺0.139 (0.096) ⫺0.036** (0.016) ⫺0.322 (0.254) ⫺0.228 (0.321) ⫺0.543*(0.115) ⫺0.834*(0.226) ⫺0.138**(0.068) ⫺0.43**(0.22) ⫺157.68

India 77 ⫺8.56* (2.228) ⫺0.549* (0.178) ⫺0.314** (0.118) ⫺0.728* (0.175) ⫺0.006** (0.003) ⫺0.147** (0.072) ⫺0.566** (0.232) ⫺0.186 (0.202) ⫺0.642*(0.221) ⫺0.875*(0.232) ⫺0.067(0.121) ⫺0.56**(0.272) ⫺120.67

Note: Values in parentheses () are standard errors. *Signiªcant at the 1 percent level; **Signiªcant at the 5 percent level.

4.2 Presence of behind the border constraints Drawing on Kalirajan (2007), the following modiªed augmented gravity model was estimated using panel data from 2000–03 and the results are presented in Table 4: lnXijt ⫽ ␣1 ⫹ ␤lnGDPjt ⫹ ␥lnDISijt ⫹ ␦1lnPOPjt ⫹ ␦2TRDGZt ⫹ ␦3LAREAt ⫹ ␦4TBPRt ⫹ ␦5NTBIt ⫹ ␦6T ⫹ vijt ⫺ uijt .

(7)

The variables are as deªned earlier and T refers to time, which takes values 1, 2, 3, and 4 respectively, for data from 2000, 2001, 2002, and 2003. The variable uij is assumed to be non-negative truncations of the normal distribution with mean, ␮, and variance, ␴2. Further, the assumption that uijt ⫽ ␩ituij ⫽ { exp[⫺␩(t ⫺ T )] }uij means that behind the border constraints to export have been varying over time. This assumption implies that if the estimate of ␩, which is provided by the computer program FRONTIER 4.1 simultaneously along with the parameters of equation (7), is positive then the behind the border constraints decline exponentially to its minimum value, uij, at the last period T of the panel. In this case, the gap between potential and actual exports has been declining. The coefªcient estimates for constant, which is larger than the estimates of equation (6) as expected due to the speciªcation of equation (7), and most variables are signiªcant at least at the 5-percent level. Further, these coefªcient estimates have the signs that concur with the theory. The coefªcient ␥ presents a measure of the total variation that is due to country speciªc behind the border constraints to export. The ␥ coefªcient is an average over the period. That is, ␥ ⫽ [(⌺t ␴2ut) / (⌺t ␴2ut ⫹ ␴2vt)] / T, where is ␴2ut is the variance of the one-sided error term at period t, ␴2vt is the variance of the random error term at period t, and T is the total number of periods. The estimate of ␥ is large and signiªcant

18



at the 1 percent level. This means that the decomposition of the error term into u and v in equation (7) is valid for the present data set and the deviation of actual exports from potential exports is due to behind the border constraints and not by just random chances. It may be interesting to see how the ␥ coefªcients vary over time. This is equivalent to examining whether the inºuence of behind the border constraints to export within the home country have been decreasing from one period to another or not. To put it differently, it investigates whether policy reforms toward promoting exports in China and India have been effective during the sample period. Information on the temporal behavior of ␥ can be obtained by examining the ␩ coefªcient. The ␩ coefªcient considers whether the impact of country speciªc behind the border constraints on reaching potential exports have been decreasing from one period to another or not. If the ␩ coefªcient were positive, then the impact of country speciªc behind the border constraints to export would be decreasing over time. If, however ␩ were zero or not signiªcant, then the impact of country speciªc behind the border constraints to export could be considered constant over time. In this model, the ␩ coefªcient is positive and signiªcant for China, whereas it is positive but not signiªcant for India. This implies that policy reforms in India do not appear to be effective in reducing behind the border constraints to export during the sample period, though policy reforms seem to be effective in China. Overall, from these results the following can be inferred. Behind the border constraints (measured by u) contribute a large and signiªcant proportion to the variation in the gaps between potential and actual exports in equation (7) for both China and India. This point is further emphasized by the signiªcance of ␥. In other words, country-speciªc factors including trade policy are important determinants of potential and actual exports. The results given in Table 4 indicate that the impact of behind the border constraints to export has reduced over time during the sample period for China but not for India. With the existing trade resistance between China and its trading partners, and India and its trading partners, China has been able to reduce the gap between its potential and actual exports with a majority of the member countries more than India could do over time. The analysis shows that an average of approximately 86 percent of potential exports have been realized by China, whereas only about 68 percent of potential exports have been realized by India (Appendix 3). This clearly indicates that there is an urgent need to design and intensify trade policy reforms to enhance its effectiveness toward reducing constraints to export in India and in this respect, India certainly can learn from China’s experience, which requires a detailed study. However, India needs to study carefully the recently debated regional income inequality problems created by China’s surging export reve-

19



nues in order to avoid the occurrence of such social problems while increasing India’s exports.19

5. Conclusion Thus, China’s export performance contrasted with that of India over the years indicates that an important determinant of the beneªts that developing countries can reap from globalization is whether behind the border constraints to export can be decreased consistently through appropriate policy measures. However, although this study did not explore what kind of behind the border constraints need to be eliminated in India to facilitate the realization of its export potential, conjectures can be made from China’s experience. Drawing on Hayami (1997) who argued that poor countries could structure their institutions to bring about rapid development through the borrowing of technologies, the adoption of technology from abroad is important for India, and appears to be constrained mainly by a lack of infrastructure and proper institutions. “Catching up with China” is a worthwhile slogan for India’s new millennium, along with a national commitment to grow at 10 percent a year. Both goals may be feasible and attainable, and within India’s grasp, provided infrastructure and institutional reforms are intensiªed effectively. China has not only managed a high rate of investment, but has kept the prime lending rate (PLR) at a relatively low 8 percent; the interest rate spread between lending and deposit rates was conªned to 2.6 percent. In India, the PLR is 12 percent, and the interest rate spread is at 3.4 percent. Clearly, China’s conªgurations are more conducive to high domestic investment. Even though the Indian stock markets were established much earlier than China’s, in terms of market capitalization, China is ahead at US$ 231.3 billion, which is 2.20 times that of India’s. Chinese banks extend credit, measured as a ratio of GDP, at a rate of two-and-a-half times India’s. Even in ªscal decentralization, the Chinese Central government transfers 51.4 percent of the tax revenue to the provinces, whereas in India the ªgure is about 36.1 percent. This discussion has revealed important ªndings, which can be helpful in making strategies with respect to trade policy in India. The cost competitiveness of China appears to help its exports in negotiating large distances. India needs to learn from China. It has to develop cost advantage and product process so that high-value markets can be captured. Duties and taxes are still on the higher side as compared to world standards, and they need to be reduced further, as higher duties and taxes 19

20

We are thankful to Zhang Xiaojing for pointing out this important issue to us.



lead to higher domestic prices and reduced market size by reducing domestic consumption, and hence deprive the scale-of-economy effect and make Indian ªrms less competitive. A larger consumption base will lead to an increase in labor productivity through competition and provide backstop to domestic producers against external shocks. Duties merit reduction on several other grounds also. The proven technological potential of the country can best be exploited and made robust by exposing the economy to external competition by strategically reducing tariffs. Lowlevel tariffs have strong signaling effects, besides reducing inefªciencies in resource allocation and operations. A relatively restrictive foreign investment regime in India needs review. FDI ºows should be viewed as a vehicle of technology transfer, spillover effects in production processes, and of increasing exports.20 Continuation of small-scale industry reservation in the case of many sectors of production deprives the beneªts of scale economy and a strategic decision of de-reservation should be taken for all the products where export potential exists. The poor quality of public infrastructure including power and transport remains a key problem for business enterprises (see Appendix 4). The sooner it is rectiªed the better and, therefore, it is argued that the government should continue its efforts in building infrastructure instead of managing production units. Relatively sluggish clearing at ports and customs houses and rampant corruption are increasing costs to domestic manufactures and they must be addressed through technological measures and a greater participation of the private sector. The state-owned port trust is extremely inefªcient, and the government has rightly assigned some responsibilities to international operators recently. It is not that India has not proved its successful performance in the trade sector. As argued by Rajan (2006), India has proven that it could compete in the services trade sector despite the poor infrastructure in high-value-added, high-skill industries where the output is relatively lightweight and relatively less dependent on ports and electricity. For example, during the 1990s, India’s service sector grew at an average annual rate of 9 percent, contributing to nearly 60 percent of the overall growth rate of the economy. Further, India’s exports of services grew annually on average at 17 percent per year in the 1990s, which is about two and a half times faster than the domestically focused part of the services sector (Hoekman 2004). Thus, it is argued that India should nurture this comparative advantage effectively by relaxing behind the border constraints rather than introducing new constraints

20

21

Unlike other studies, which are cross-country based, this study is country-speciªc (India vs. its trading partners and China vs. its trading partners) and therefore FDI could not be used as an explanatory variable in the gravity model estimation.



such as over-regulation of the higher education system. Yet, in order to provide sustained employment to several million people, India cannot underestimate the beneªts of following the East Asian growth model of labor-intensive manufacturing, which is also causally linked with the services sector.

Appendix 1. Simulated annual potential exports of China using coefªcients from the India model

Algeria Argentina Australia Austria Bangladesh Bolivia Brazil Cameroon Canada Chad Chile Colombia Costa Rica Denmark Dominican Republic Ecuador Egypt El Salvador Ethiopia Finland France Germany Ghana Greece Guatemala Honduras Hungary Indonesia Italy Jamaica Japan Jordan Kenya Korea RP (S) Madagascar Malawi Malaysia Mali Mauritius Mexico Morocco Mozambique Netherlands New Zealand Nicaragua Nigeria Norway Pakistan Panama Paraguay Peru Philippines Poland Portugal Romania

22

Simulated potential exports (US$ million)

Percentage difference of simulated potential exports over actual average exports

CHN as IND

CHN as IND

63.6 63.4 193.7 213.3 342.4 9.1 346.7 20.3 321.0 4.7 80.2 92.0 22.0 207.0 36.6 23.2 170.3 32.2 52.1 186.4 898.8 1,825.9 40.9 119.2 23.4 10.6 155.9 599.2 639.3 11.1 5,197.4 41.9 41.9 2,947.7 15.3 16.1 651.3 9.3 15.9 363.7 88.4 15.5 663.2 54.1 7.7 149.2 105.3 378.0 10.3 6.8 53.9 548.8 245.7 144.7 79.2

⫺82.31 ⫺90.11 ⫺97.21 ⫺76.11 ⫺77.45 ⫺16.65 ⫺84.04 ⫺66.43 ⫺95.49 313.41 ⫺94.08 ⫺73.70 ⫺80.32 ⫺87.59 ⫺71.85 ⫺88.02 ⫺83.61 ⫺82.25 ⫺46.23 ⫺88.15 ⫺88.36 ⫺90.36 ⫺80.69 ⫺87.97 ⫺91.83 ⫺90.21 ⫺91.47 ⫺86.29 ⫺90.91 ⫺88.00 ⫺91.33 ⫺89.43 ⫺80.25 ⫺84.29 ⫺89.25 53.63 ⫺89.27 ⫺79.37 ⫺91.89 ⫺89.75 ⫺81.06 ⫺55.61 ⫺94.68 ⫺93.84 ⫺89.03 ⫺87.94 ⫺88.28 ⫺68.84 ⫺99.51 ⫺96.83 ⫺80.98 ⫺86.85 ⫺82.36 ⫺66.64 ⫺77.56



Appendix 1. (continued)

Russia Senegal Singapore South Africa Spain Sri Lanka Sweden Switzerland Tanzania Thailand Trinidad And Tobago Tunisia Turkey Uganda United Kingdom United States Uruguay Venezuela Zambia Zimbabwe



CHN as IND

CHN as IND

370.0 17.6 1,212.1 190.4 501.2 89.1 272.5 276.8 39.3 921.5 12.6 72.5 267.8 37.2 1,101.0 3,611.7 11.7 46.5 12.4 15.4

⫺90.37 ⫺78.49 ⫺89.12 ⫺89.68 ⫺88.20 ⫺88.81 ⫺84.38 ⫺84.13 ⫺71.01 ⫺81.27 ⫺75.40 ⫺50.66 ⫺82.85 13.15 ⫺92.84 ⫺96.74 ⫺94.77 ⫺89.64 ⫺66.47 ⫺61.37

Source: Author’s estimation from the results of Table 3.

Appendix 2. Simulated annual exports of India using coefªcients from the China model

Algeria Argentina Australia Austria Bangladesh Bolivia Brazil Cameroon Canada Chad Chile Colombia Costa Rica Denmark Dominican Republic Ecuador Egypt El Salvador Ethiopia Finland France Germany Ghana Greece Guatemala Honduras Hong Kong Hungary Indonesia Italy Jamaica Japan Jordan Kenya

23



IND as CHN

IND as CHN

401.8 917.0 2,417.0 3,158.2 968.9 58.9 4,155.2 89.4 4,682.1 13.4 711.6 596.5 171.3 2,602.5 254.9 166.4 1,030.4 180.4 193.1 1,979.9 14,365.2 27,411.3 104.0 1,462.5 162.3 54.9 15,387.4 1,259.7 3,828.3 10,234.4 70.2 40,031.6 189.8 151.0

574.4 834.8 412.9 3,286.5 ⫺14.2 1,635.3 911.0 462.0 530.2 350.4 790.5 754.8 1,529.2 1,257.1 1,640.3 1,617.1 239.2 3,586.1 166.7 2,850.7 1,209.8 1,181.0 15.7 904.4 674.2 249.9 511.9 1,874.0 522.9 632.0 816.2 2,055.9 113.9 ⫺9.2



Appendix 2. (continued)

Korea RP (S) Madagascar Malawi Malaysia Mali Mauritius Mexico Morocco Mozambique Netherlands New Zealand Nicaragua Nigeria Norway Pakistan Panama Paraguay Peru Philippines Poland Portugal Romania Russia Senegal Singapore South Africa Spain Sri Lanka Sweden Switzerland Tanzania Thailand Trinidad And Tobago Tunisia Turkey Uganda United Kingdom United States Uruguay Venezuela Zambia Zimbabwe



IND as CHN

IND as CHN

9,952.2 82.3 48.5 5,286.0 40.5 113.3 3,057.8 295.0 60.2 9,688.7 611.2 43.0 469.9 1,401.1 1,962.5 82.1 61.5 338.1 2,308.9 2,004.6 1,655.4 477.3 3,162.9 92.0 15,646.4 1,996.9 6,681.6 425.4 3,210.8 3,121.1 117.9 5,243.0 114.8 245.6 2,353.7 160.7 14,061.0 50,080.9 139.0 377.5 54.9 93.2

1,095.2 611.1 97.0 680.6 67.9 ⫺32.8 833.6 331.5 47.4 925.4 721.1 1,725.0 28.2 1,759.7 885.1 134.3 704.7 861.5 695.0 1,434.3 965.9 1,343.1 392.0 227.4 1,193.6 424.8 785.7 ⫺45.6 1,626.6 753.4 8.7 698.9 843.6 382.2 471.8 156.8 453.9 388.1 479.7 944.5 95.9 510.7

Source: Author’s estimation from the results of Table 3.

Appendix 3. Realization of potential exports (%) of China and India with partner countries Number of partner countries Realization of potential exports (%)

China

India

40–49 50–59 60–69 70–79 80–89 90–100

6 9 12 16 30 4

10 13 31 15 6 2

China

India

86

68

Mean level of realization of potential exports, % Source: Author’s estimation from the results of Table 4.

24


25

69 54 16

Rank

4.8 3.9 5.3

47 68 31

Rank

Score

3.2 3.7 5.6

Score 4.7 3.7 6.7

Score

Note: Total number of countries considered

102. Score of 1

underdeveloped and 7

3.2 3.7 5.6

Score 69 54 16

Rank

Port infrastructure quality

as extensive and efªcient as world’s best.

20 37 2

Rank

Railroad infrastructure quality

Source: Global Competitiveness Report 2003–2004, World Economic Forum.

India China Japan

Air transport infrastructure quality

Overall infrastructure quality 3.0 4.2 6.8

Score 85 60 8

Rank

Electricity supply quality

Appendix 4. Relative competitiveness index of infrastructure quality across selected countries

6.0 5.4 6.8

Score

45 57 6

Rank

Telephone infrastructure quality

4.3 4.7 6.8

Score

51 47 3

Rank

Postal infrastructure quality




References Aigner, Dennis, Lovell, Knox, and Peter Schmidt. 1977. Formulation and Estimation of Stochastic Frontier Production Function Models. Journal of Econometrics 6:21–37. Anderson, James. 1979. A Theoretical Foundation for the Gravity Equation. American Economic Review 69:106–116. Anderson, James, and Eric van Wincoop. 2003. Gravity with Gravitas: A Solution to the Border Puzzle. American Economic Review 93 (1):170–192. Baldwin, Richard. 1994. Towards an Integrated Europe. London: CEPR. Baldwin, Richard. 2003. Openness and Growth: What’s the Empirical Relationship? NBER Working Paper No. 9578. Cambridge, MA: National Bureau of Economic Research. Bergstrand, Jeffrey H. 1985. The Gravity Equation in International Trade: Some Microeconomic Foundations and Empirical Evidence. The Review of Economics and Statistics 67:478–481. Bhagwati, Jagdish. 1993. India in Transition: Freeing the Economy. Oxford: Oxford University Press. Chadha, Rajesh. 2003. External Sector. In: Mid-Year Review of the Indian Economy 2002–2003, edited by N. N. Vohra, pp. 78–82. Delhi: Shipra Publishing. China Custom Statistical Bureau. 2002. China Custom Statistical Yearbook. Beijing: China Custom Statistical Bureau. Coelli, Tim. 1996. A Guide to FONTIER Version 4.1: A Computer Program for Stochastic Frontier Production and Cost Function Estimation. Center for Efªciency and Productivity Analysis Working Paper No. 96/07. Armidale, Australia: Center for Efªciency and Productivity Analysis, University of New England. Drysdale, Peter, Yiping Huang, and Kaliappa Kalirajan. 2000. China’s Trade Efªciency: Measurement and Determinants. In: APEC and Liberalisation of the Chinese Economy, edited by Peter Drysdale, Zhang Yunling, and Liagang Song, pp. 259–271. Canberra, Australia: Asia Paciªc Press. Fan, Gang, and Xiaojing Zhang. 2003. The Chinese Reform Agenda. In: China’s Role in Asia and the World Economy—Fostering Stability and Growth, edited by J. J. Teunissen, pp. 214–228. The Hague: Fondad. Frankel, Jeffrey A., and David Romer. 1999. Does Trade Cause Growth? American Economic Review 89 (3):379–399. Garnaut, Ross, and Yiping Huang. 2001. Growth Without Miracles: Readings on the Chinese Economy in the Era of Reform. New York: Oxford University Press. Gawande, Kishore, and Pravin Krishna. 2001. The Political Economy of Trade Policy: Empirical Approaches. Working paper, Economics Department. Providence, RI: Brown University. Greenaway, David, Wyn Morgan, and Peter Wright. 2002. Trade Liberalization and Growth in Developing Countries. Journal of Development Economics 67:229–244. Hayami, Yujiro. 1997. Development Economics: From the Poverty to the Wealth of Nations. Oxford: Clarendon Press.

26



Hoekman, Bernard. 2004. The World Bank Trade Research Program: Summary and Synthesis. Development Economics Research Group (DECRG). Washington, D.C.: World Bank. Huang, Yasheng, and Tarun Khanna. 2003. Can India Overtake China? Foreign Policy Magazine July–August. Available at www.foreignpolicy.com/story/ IMF. 2004. World Economic Outlook. Washington, D.C.: International Monetary Fund. Jiang Xiaojuan. 2002. The Foreign Invested Sector in China: Its Contribution to Economic Growth, Structure Upgrade and Power of Competitiveness. Beijing: People’s University Press. Kalirajan, Kaleeswaran. 1999. Stochastic Varying Coefªcients Gravity Model: An Approach in Trade Analysis. Journal of Applied Statistics 26 (2):185–193. Kalirajan, Kaliappa. 2000. Indian Ocean Rim Association for Regional Cooperation (IOR-ARC): Impact on Australia’s Trade. Journal of Economic Integration 15:533–547. Kalirajan, Kaliappa. 2007. Regional Cooperation and Bilateral Trade Flows: An Empirical Measurement of Resistance. The International Trade Journal 21:85–107. Lardy, Nicholas. 2002. Integrating China into the Global Economy. Washington, D.C.: Brookings Institution Press. Levchenko, Andrei A. 2004. Institutional Quality and International Trade. IMF Working Paper No. WP/04/23. Washington, D.C.: IMF. Lucas, Robert. 1988. On the Mechanics of Economic Development. Journal of Monetary Economics 22:3–42. Meeusen, William, and Julian van den Broeck. 1977. Efªciency Estimation from Cobb–Douglas Production Function with Composed Error. International Economic Review 18:435–444. NBS(a) (National Statistical Bureau). various years. China Statistical Yearbook. Beijing: China Statistics Press. NBS(b) (National Statistical Bureau). various years. China Foreign Economic Statistical Yearbook. Beijing: China Statistics Press. Nilsson, L. 2000. Trade Integration and the EU Economic Membership Criteria. European Journal of Political Economy 16 (4):807–827. Rajan, Raghuram. 2006. India: The Past and its Future. Asian Development Review 23 (2):36–52. Roberts, Mark, and Mark Setterªeld. 2007. What Is Endogenous Growth Theory? In: Economic Growth: New Directions in Theory and Policy, edited by P. Arestis, Michelle Baddeley, and J. S. L. McCombie, pp. 14–31. Cheltenham, UK: Edward Elgar. Rodrik, Dani. 1998. Why Do More Open Countries Have Bigger Governments? Journal of Political Economy 106 (5):997–1032. Rodrik, Dani. 2000. Trade Policy as Institutional Reform. Cambridge, MA: Harvard University, Department of Economics. Roemer, John E. 1977. The Effect of Sphere of Inºuence and Economic Distance on the Commodity Composition of Trade in Manufactures. The Review of Economics and Statistics 59:318– 327. Romer, Paul. 1986. Increasing Returns and Long-run Growth. Journal of Political Economy 94:1002–1037. 27



Sachs, Jeffrey, and Andrew Warner. 1995. Economic Reform and the Process of Global Integration. Brookings Papers on Economic Activity 1:1–95. Sachs, Jeffrey, and Wing Thye Woo. 2000. Understanding China’s Economic Performance. Journal of Policy Reform 4 (1):31–48. Sachs, Jeffrey, and Wing Thye Woo. 2003. China’s Economic Growth After WTO Membership. Journal of Chinese Economic and Business Studies 1:1–31. Sala-i-Martin, Xavier. 2004. The Global Competitiveness Report 2003–2004. New York: Oxford University Press. Wacziarg, Robert. 1997. Trade, Competition and Market Size. Cambridge, MA: Harvard University Press. Wang, Xiaolu. 2004. FDI in People’s Republic of China. In: Managing FDI in a Globalizing Economy: Asian Experiences, edited by David H. Brooks and Hal Hill, pp. 79–117. London: Palgrave/ MacMillan. Woo, Wing Thye. 1998. Chinese Economic Growth: Sources and Prospects. In: The Chinese Economy, edited by Michel Fouquin and Françoise Lemoine, pp. 134–152. London: Economica. Wu, Jing-Long. 2003. Economic Reform in Contemporary China. Shanghai: Shanghai Far East Press.

28


General Discussion

Summary of General Discussion on “A Comparative Analysis of China’s and India’s Recent Export Performances” by Kaliappa Kalirajan and Kanhaiya Singh

General discussion According to Fredrik Sjöholm, it is no surprise that India’s poor export performance is caused by internal factors rather than by exogenous ones. He thought the rigorous empirical analysis of export performance was a valuable contribution to the literature. He was less certain of the value of the theoretical framework, which, according to Sjöholm, concerned economic growth rather than international trade. Bhanupong Nidhiprabha urged the authors to increase the sample size, which would improve the maximum likelihood estimations. Hyun-Hoon Lee thought the effect of physical distance on trade might be biased by the sample. For instance, many countries that are relatively close to India, such as African countries, were not in the sample. Moreover, it might be that the results are caused by the fact that some of China’s main trading partners, such as Korea and Japan, happen to be located close to China. Lee also suggested that FDI should be included in the analysis. Siow Yue Chia stressed the importance of Hong Kong in providing logistical support and sharing its extensive experience and knowledge of international trade in explaining Chinese exports. Keun Lee added that the large overseas Chinese population has had a similar positive effect, and it is an interesting question why overseas Indians have not played the same role for India’s exports.



General Discussion

Naoyuki Yoshino was interested in how the industry structure of China and India might affect export performance. He noted that small and medium-sized ªrms are of great importance in Japanese production and export and suggested that a lack of strong and vibrant small ªrms might be an obstacle for exports.

30


Malaysian Capital Controls

The Malaysian Capital Controls: A Success Story?*

Prema-chandra Athukorala Research School of Pacific and Asian Studies Australian National University Research School of Pacific and Asian Studies Australian National University Canberra, ACT 0200 [email protected]

Abstract

This paper contributes to the debate on the use of temporary controls on capital outflows as a crisis resolution measure by examining the outcome of Malaysia’s radical response to the 1997–98 financial crisis. The analysis suggests that carefully designed temporary capital controls were successful in providing Malaysian policymakers a viable setting for aiding the recovery process through the standard Keynesian therapy. Capital controls also assisted banking and corporate restructuring by facilitating the mobilization of domestic resources, and more importantly, by providing a cushion against possible adverse impacts on market sentiment of “national” initiatives. Of course other countries should be cautious in deriving policy lessons from Malaysia because a number of factors specific to Malaysia seem to have significantly conditioned the outcome of the capital-control based recovery package.

1. Introduction Malaysia made headlines in the context of the Asian ªnancial crisis by taking an unorthodox (and risky) policy posture whose key elements were capital controls and expansionary macroeconomic policy. Following this policy choice, which marked a signiªcant departure from the IMF-centered approach adopted by the other crisis-hit countries in the region, the Malaysian economy recovered smoothly, defying widespread pessimism that prevailed in

*


A revised version of a paper presented at the Asian Economic Panel 2007, 9–10 April, Brookings Institution, Washington, D.C. I am grateful to the two discussants, Wei Zhang and Zainal Aznam Yusof, and other participants for comments and suggestions. I have also beneªted greatly from numerous discussions with W. M. Corden.



economic circles at the time. There is, however, an intense debate on whether this episode holds lessons for using capital controls as a tool of crisis resolution. One can distinguish three alternative views. The ªrst view is that this was a case of “locking the stable door after the horse has bolted.” At the time Malaysia made the policy U-turn, capital had already left the country and speculative pressure for capital outºow from the Asian region was coming to an end. The second view holds that capital controls did play a pivotal role in the recovery by insulating the domestic capital market from the world capital market (with respect to short-term ºows) and thus allowing the Malaysian government to engage in ªscal and monetary expansion, and restructuring troubled banks and companies. The third view is that the capital controls, assuming that they did succeed in engineering recovery, would have seriously damaged long-term growth prospects of the economy, in particular by discouraging new investment and impeding capital market access. This paper contributes to this debate by systematically evaluating these views, paying due attention to economic and sociopolitical circumstance behind Malaysia’s radical policy posture and the role played by capital controls in the recovery process. To gain perspective, the Malaysian experience is compared and contrasted with the experiences of Thailand and South Korea (henceforth referred to as Korea) under IMF-supported reform programs, while giving due attention to potentially important inter-country differences relating to the economic structure and the policy context.1 Malaysia’s radical policy choice is the ªrst case in the post-war economic history of an emerging market economy temporarily reversing the cause of capital account opening in a crisis context. It deserves attention given the new-found interest, following the spate of ªnancial crises in emerging market economies in the 1990s, in temporary controls on capital outºows as a crisis resolution measure (Cooper 1999; King 1999; Krugman 1999; Corden 2003). The paper is structured as follows. Section 2 provides an overview of capital account liberalization during the pre-crisis era, and examines patterns of capital ºows in the run-up to the crisis, focusing on the interplay of international capital mobility with the domestic macroeconomic policy and regulatory regime in determining the country’s vulnerability to a crisis. Section 3 examines Malaysia’s initial policy response to the crisis and the economic collapse. Section 4 discusses the radical policy shift, 1

Among the six East Asian countries which were directly affected by the 1997–98 crisis, Indonesia is not an appropriate comparator because political instability and social upheaval had interrupted crisis management in that country during most of the period under study. There is also little to gain from a comparison with the Philippines because the economic disruption caused by the mid 1997 speculative attack was relatively small and the economy had already returned to the pre-crisis growth path by mid 1998.

32



highlighting the political and institutional underpinnings and the nature of the policy package built around capital controls. Section 5 looks at the recovery process under the new policy orientation. Section 6 probes the role of capital controls in the recovery process. The ªnal section draws inferences and policy lessons. To assist the reader in following the unfolding events, a comprehensive chronology of ªnancial, economic, and political events surrounding the crisis is provided in Appendix 1.

2. Capital account liberalization, capital inºows, and signs of vulnerability 2.1 Policy trends Malaysia is unique among developing countries for its long-standing commitment to an open foreign trade regime. Malaysia achieved Article VIII status (for current account convertibility) under the IMF Articles of Agreement on 11 November 1968, becoming the fourth Asian economy to do so after Hong Kong, China (15 February 1961); Japan (1 April 1964); and Singapore (9 November 1968).2 A natural companion to its outward-oriented trade policy was a ªrm commitment to the promotion of foreign direct investment (FDI). FDI approval procedures and restrictions on foreign equity ownership were liberal in the 1950s at a time when hostility toward multinationals was the order of the day in the developing world. The emphasis on FDI promotion received added impetus with a notable shift in development policy toward export-oriented industrialization in the early 1970s. At that time, legislation provided for the establishment of special export processing zones, allowing 100 percent foreign ownership and exemption from general labor legislation, including employment quotas for bumiputras (ethnic Malays) for export-oriented investors. The Malaysian policy regime relating to non-FDI capital ºows (that is, international ºows of purely ªnancial capital), too, in general remained liberal throughout the post-war period, compared to most other developing countries (Yusof et al. 1994; Williamson and Mahar 1998). However, liberalization in this sphere was much more cautious and gradual by Malaysia’s own historical record of trade and FDI liberalization. Until about the late 1970s, there were binding restrictions on short-term overseas investment by residents—placing deposits abroad, lending to non-residents, purchasing immobile properties, or investing in foreign equity. Promotion of Kuala Lumpur as a global ªnancial center became a key element of Malaysia’s growth euphoria in the late 1980s. As the ªrst step to give momentum to

2

All other (precrisis) high-performing economies in East Asia achieved Article VIII status much later: Indonesia on 7 May 1988, Republic of Korea on 1 November 1988, Philippines on 8 September 1995, and Thailand on 4 May 1990.

33



the growth of the Kuala Lumpur Stock Exchange (KLSE) as an independent entity, the government announced on 27 October 1998 the delisting (in effect from 2 January 1990) of Malaysian registered companies from the Stock Exchange of Singapore (SES). This split from SES intended to set the stage for developing the KLSE as an independent exchange, to attract international investors in competition with SES. The early 1990s saw a number of initiatives to further liberalization of impediments to portfolio capital inºow to promote the trading on the Kuala Lumpur Stock Exchange with increased participation of institutional investors. In 1992, the Securities Act was passed to enable the establishment of a new Securities Commission (SC) to take over share market monitoring and supervision, previously undertaken by the Capital Investment Committee under the jurisdiction of Bank Negara Malaysia. This initiative gave further impetus to stock market growth under a more ºexible operational framework. In the same year, the ceiling on foreign share holding of local brokerage ªrms was lifted from 30 percent to 49 percent. Tax rates for both foreign and local fund managers were reduced from 30 percent to 10 percent. The Federal Territory of Labuan was inaugurated as an International Offshore Financial Center on 1 October 1990 as part of the government’s long-term plan to enhance the attractiveness of Kuala Lumpur as a regional ªnancial center. It was envisaged that, with the Asia-Paciªc Region emerging as the fastest growing region in the world, Labuan would play a key role in enhancing the attractiveness of Malaysia as a world investment center. Licensed offshore banks, offshore insurance entities, and other offshore companies operating in Labuan were declared as non-residents for exchange control purposes. This initiative enabled these institutions to freely operate foreign currency accounts and move funds into and out of Malaysia without being subject to any exchange control monitoring. Licensed offshore banks were also permitted to accept deposits and grant loans in foreign currency. Investment guidelines were liberalized to allow Malaysian fund management companies to form joint ventures with foreign fund management companies. Management companies of unit trust funds located in Labuan were permitted to invest in Malaysian securities. A generous tax exemption was granted to companies incorporated in Labuan and their expatriate employees. By the end of 1996, 47 offshore banks, 5 offshore insurance and re-insurance companies, 13 trust companies, and 3 fund management companies had been incorporated in Labuan. The ongoing process of capital account opening was temporarily halted in 1994 as the ringgit came under strong buying pressure as the booming economy created expectations about the currency’s increasing strength. From late 1993 speculators brought ringgit in large amounts, increasing short-term deposits and forward transactions. In order to avoid an adverse effect on export competitiveness from a sharp exchange rate appreciation, Bank Negara Malaysia (BNM, the Central Bank of Ma34



laysia) imposed a number of restrictions on capital inºow during January–February 1994 (World Bank 1996, pp. 67–68; BNM 1999b, pp. 288–291). Once speculative pressure subsided and the exchange rate returned to the level of late 1993, BNM gradually removed the controls and freed up capital ºows, completely lifting all restrictions by August 1994. In June 1995, the Finance Minister announced a package of incentives to attract foreign fund managers to Malaysia. Trading in ªnancial derivatives on KLSE began in 1995, with two instruments, namely the KLSE Composite Index Futures and 3-month Kula Lumpur Inter-bank Offer Rate Futures. Notwithstanding signiªcant capital account opening since the mid 1980s, one important long-standing prudential regulatory instrument, namely restriction on foreign currency borrowing by private agents, was left intact. BNM continued to monitor foreign currency borrowings by residents and domestic borrowing by nonresidents under borrowing/lending ceilings stipulated in foreign exchange regulations. At the onset of the ªnancial crisis in mid 1997, the ceilings on foreign currency borrowing by residents and domestic currency borrowing by non-resident controlled companies stood respectively at 1 million and 10 million Malaysian ringgit (RM). 2.2 Capital ºows and signs of vulnerability Foreign capital inºows to Malaysia have historically been dominated by FDI.3 However, there was a signiªcant compositional shift in private capital inºows from about 1993. Capital market liberalization initiatives in Malaysia in the early 1990s coincided with the growing enthusiasm of hedge funds and other institutional investors for emerging-market economies (World Bank 1996). Thus, there was a signiªcant increase in the net inºow of portfolio investment. These capital inºows, driven primarily by the boom in the Malaysian equity market, accounted for 45 percent of total annual capital inºows in 1996, up from 13 percent in the previous year. The volume of “volatile capital,” deªned to cover both short-term borrowings and portfolio capital, had increased to sizable levels by the mid 1990s, resulting in an erosion of the authorities’ ability to defend a speculative attack on the ringgit (Table 1).4 The degree of reserve cover provided for mobile capital declined from over 150 percent in the early 1990s to 57 percent by mid 1997. Increased foreign equity investment fueled a massive stock market boom in Malaysia from the late 1980s. By the mid 1990s, with a market capitalization of around

3

Unless otherwise stated, data reported in this paper come from the Monthly Statistical Bulletin (Bank Negara Malaysia, various years).

4

For a discussion on the rationale behind this “reserve adequacy” measure see Athukorala and Warr (2002).

35



Table 1. Malaysia: End-of-year stock of volatile capital and foreign exchange reserves, 1990–97 Mobile capital,a US$ billion Composition of mobile capital( percent) Short-term debtb Banking sector Non-bank private Portfolio investment Foreign exchange reserves, US$ billion Reserve/Mobile capital ratio (R/MS), percent

1990

1991

1992

1993

1994

1995

1996

1997c

6 100

7 100

12 100

24 100

28 100

32 100

38 100

51 100

26 26 0 74

40 40 0 60

41 41 0 59

28 28 0 72

20 14 6 80

20 14 6 80

26 17 8 74

28 22 6 72

10

11

19

30

26

26

28

28

158

171

149

124

94

80

72

56

Source: Compiled from Bank Negara Malaysia, Monthly Statistical Bulletin, Kuala Lumpur (various issues). The data series on portfolio capital was constructed by accumulating net annual ºows from 1980. Data on stocks of other components of mobile capital are readily available in this source. Note: a. Short-term debt ⫹ portfolio investment. b. Debt with a tenure of one year and below. c. First half of the year.

US$ 200 billion, the KLSE was the third largest stock market in the Asian and Paciªc region after those in Tokyo and Hong Kong, China. At this time, equity market capitalization in Malaysia was over 300 percent of GDP, by far the highest in the world. At the onset of the crisis, foreign investors accounted for only 30 to 40 percent of the activities in the market. However, the actual inºuence of foreign participation on the expansion and operation of the equity market was probably much greater than suggested by this ªgure because local investors always followed foreign investors as market leaders. The stock market boom had direct implications for the operation of the domestic banks; lending for equity market activities turned out to be a major source of bank credit expansion (discussed subsequently). In sum, by the mid 1990s, Malaysia had become a depository for a substantial volume of volatile capital, in particular portfolio investment. The economy was experiencing an equity market bubble in which both foreign investors and domestic banks played crucial roles. In this context, there was a strong possibility of a reversal of capital inºows (triggered by a speculative attack on the currency, as in fact happened in the second half of 1997) to generate economic collapse through wealth contraction and banking sector instability. However, this possibility would not have translated into a ªnancial crisis had it not been for some serious pitfalls on the domestic policy front. Two fundamental sources of vulnerability were particularly important in the Malaysian case: poor corporate governance and weakness in the ªnancial sector (ªnancial fragility).

36



In Malaysia, as in other crisis-affected countries in East Asia, the expansion of the equity market was not accompanied by initiatives to redress the underlying weaknesses of corporate governance (Searle 1999). Most of the listed companies in Malaysia continued to be tightly controlled by a handful of powerful families. These families often retained majority stakes, even in public companies. Moreover, in many cases the interests of company bosses and politicians were closely interwoven. Manipulation of inter-company share transactions to augment proªt in privately owned companies (at the expense of listed companies) was a common occurrence in the Malaysian corporate world. Such malpractice made share trading vulnerable to ªnancial panic because unconnected (minority) shareholders had every reason to worry about how they would be treated during a market downturn. Foreign investors were providing funds to Malaysian ªrms with high debt ratios and long-term alliance relationships, which would not have been acceptable in the West. The extent of subsequent portfolio capital outºows owed much to the realization that a large amount of capital should not have been committed in the ªrst place. When foreign participants started pulling out to avoid currency risk following the onset of the currency crisis in mid 1997, the local players panicked. Based on past experience, the minority shareholders were naturally concerned that they might be hardest hit in troubled times (The Economist 1997, p. 111). The Malaysian banking system has historically been sturdier than its counterparts in most countries in the region. The average capital adequacy ratio (CAR) of Malaysian banks (10 percent) was the highest in Southeast Asia other than Singapore. A large number of Malaysian banks recorded CARs of above 14 percent, as against 8 percent recommended by the Bank for International Settlements (BIS). There was also a requirement that all banks set aside 1 percent of total outstanding loans as a general provision, in addition to speciªc provisions made for problem loans (1.5 percent). Nonperforming loans (NPLs) in the banking system fell from 5.5 percent in 1995 to 3.9 percent in 1996. Foreign currency exposure of the banking system remained low thanks to BNM’s policy of specifying stringent net open positions on foreign borrowing. By mid 1997, the aggregate net open position (bank liabilities denominated in foreign currency net of equivalent assets) of the banking system was less than 5 percent of total bank liabilities (BIS 1998). Despite this apparent soundness, in the lead-up to the crisis there was a heavy accumulation of outstanding domestic credits in the banking system, with a heavy exposure to the property sector (broadly deªned to include share trading and the real estate sector) (Soros 1998). The rate of growth of bank credit to the private sector rose from 12 percent per annum during 1990–94 to over 26 percent during 1994–96. Out-

37



standing credit as a ratio of GDP increased from an average level of 85 percent during 1985–89 to 120 percent in 1994 and then to over 160 percent when the ªnancial crisis broke in mid 1997. This was the highest credit buildup (increase in “private sector leverage”) among the economies of East Asia (Athukorala and Warr 2002). By the end of 1996, total credit to the property sector accounted for around 40 percent of total outstanding bank credit. It is believed that this share could have been much higher (around 55 percent) if unclassiªed loans to conglomerates that are normally used to ªnance property were appropriately taken into account. The increased exposure to the property sector further weakened the ªnancial position of the banks as this lending led to a property glut in the country. By the end of 1997, more than 5.8 million square feet of new ofªce space was under construction in the Kuala Lumpur metropolis, on top of 5.6 million square feet of space available at the time (BNM 1999a). The equity market bubble and the credit boom were underpinned by rapid erosion in the quality of macroeconomic management in the economy. Over many years (except during 1981–86 when Malaysia experienced a major macroeconomic crisis triggered by a public investment boom), the government had maintained a reputation for sound ªscal policies. However, the years following Prime Minister Mahathir’s Vision 2020 Statement of 1990 saw ªscal excesses of increasing intensity. As a result of the “big growth push” to propel Malaysia to developed-country status by 2020, public investment expenditure surged, pushing the ratio of total investment to GDP to 46 percent in 1997, the highest in the region at the time. Much of this expenditure went into huge infrastructure development projects contracted out to private companies in the “patronage network” that provided the political support base for the regime. These companies soon became the dominant players in the equity market. The construction boom also contributed to the credit boom because the supply of “easy” credit from politically connected banks and other “captive” ªnancial institutions was an implicit condition built into the contractual arrangements with construction companies. Rapid growth of government-sponsored bank lending invariably contributed to a weakening of the policy autonomy of BNM. Historically, BNM has maintained a reputation among the central banks in newly independent countries in the British Commonwealth for strict pursuance of the colonial mold of conservative monetary policy and banking regulation (Bruton 1993; Corden 2003). However, in the context of a credit boom that had government backing at the highest political level, BNM had only a limited degree of freedom to take precautionary action against an impending crisis. BNM repeatedly pointed to the risk of rapid credit buildup with a

38



heavy concentration in property and share trading loans in the banking system in 1994, 1995, and 1996. However, it failed to take any action to redress the problem other than to impose some limits on lending to the property sector and for equity market dealings in March 1997.

3. Onset of the crisis, policy muddling through, and economic collapse For over 5 years prior to the onset of the crisis, the exchange rate of the ringgit varied in the narrow range of 2.36 to 2.51 ringgit per U.S. dollar. When the Thai baht came under heavy speculative attack in mid May, the ringgit also experienced heavy selling pressure. BNM held the ringgit ªrmly through continued market intervention for another week and then gave way to market forces on 14 July by ºoating the currency. Between the ªrst week of July 1997 and 7 January 1998 when the currency slide hit bottom (MR 4.88/US$), the ringgit depreciated against the dollar by almost 50 percent. After showing some signs of stability during February and March, the exchange rate continued to deteriorate with wider swings in the following months (until it was ªxed at the rate of MR 3.80/US$). This contrasted with the experience of Thailand and Korea where exchange rates began to stabilize from March 1998. As noted, Malaysia succumbed to the crisis with low foreign debt exposure compared to other crisis-hit countries in East Asia. Thus, unlike these countries, in Malaysia it was portfolio capital that accounted for virtually all the massive capital exodus. Net quarterly ºow of portfolio capital turned negative in the second quarter of 1997 for the ªrst time after 1991 and total net outºow in the next two quarters of the year amounted to over US$ 11 billion. By contrast, interestingly, net short-term bank borrowing increased by about US$ 3 billion during this period. Reºecting the massive reversal of portfolio capital ºows, the share market tumbled in tandem with the exchange rate collapse. Malaysia experienced the biggest stock market plunge among the ªve “crisis” countries in East Asia. By the end of 1997, the ordinary index of the KLSE had fallen by over 50 percent from its pre-crisis level, whipping off almost US$ 225 billion of share values. The price/earning (P/E) ratio of KLSE declined from 22.9 to 11.3 between these time points (Athukorala 1998). Given the low foreign debt exposure, for a while the Malaysian policymakers were able to “muddle through” without an IMF-sponsored rescue package.5 The immediate policy reaction to the currency collapse was to directly intervene in share market operation with a view to punishing speculators. On 27 August 1997, the KLSE banned the short-selling of 100 blue-chip stocks and rules were introduced to dis5

Appendix 1 provides a chronology of Malaysia’s policy response to the crisis.

39



courage the sale of stocks: sellers were required to deliver physical share certiªcates to their brokers before selling and the settlement period was reduced from 5 to 2 days. On 3 September 1997, the Prime Minister unveiled a plan to use funds from the Employees Provident Fund (EPF) to prop up share prices by buying stocks from Malaysian shareholders—but not foreigners—at a premium above prevailing prices. These moves backªred, triggering a massive sell-off of stocks in KLSE and undermining sentiment on other regional bourses. Ironically, the share purchases sponsored by the government were seen by market participants, both local and foreign, as an opportunity to get rid of Malaysian shares, rather than a reason for holding onto them. The ban on short-selling was lifted in early September 1997. After more than 5 months of policy indifference, a major policy package involving signiªcant ªscal and monetary concretionary measures was announced by the then Finance Minister, Anwar Ibrahim, on 5 December 1997. This policy, which was welcomed by the international ªnance community and labeled by the news media as “IMF policy without IMF,” was however quickly abandoned, compounding policy uncertainty. BNM began to resort to expansionary policies to prevent “a recessiondeºation spiral” (BNM 1999a, p. 4). Proposed cuts in government expenditures were restored and a number of large projects that had been put on hold were reactivated. By mid 1998 ªscal policy turned out to be more expansionary to compensate for the slack in private sector demand. A National Economic Recovery Plan (NEAP) was launched on 23 July 1998 (NEAC 1998). It contained a comprehensive reform package encompassing signiªcant ªscal and monetary expansion, in a clear departure from the IMF-centered reform programs being implemented in the other crisis-hit countries in the region at the time. It also proposed setting up an institutional framework for recapitalizing the troubled banks and to resolve mounting corporate distress. Based on these proposals, an asset management company (Pengurusan Danaharta Nasional Berhard, henceforth referred to as Danaharta) was set up to acquire and manage NPLs from banks. This was followed by the establishment in July of a banking and corporate recapitalization company (Danamodal Nasional Berhad, henceforth referred to as Danamodal) as a special agency with the purpose of recapitalizing those ªnancial institutions whose capital adequacy ratio had fallen below 9 percent. Finally, to complement the roles of Danaharta and Danamodal, a Corporate Debt Restructuring Committee (CDRC, a joint public and private sector steering committee) was established in August 1998 to facilitate the restructuring of corporate debts through an out-ofcourt settlement between debtors and creditors.6 The three institutions taken to6

For details on the structure, modalities, and the underlying legal framework of these institutions see BNM (1999b, pp. 220–225) and Mahani (2002, chapter 5).

40



gether provided a systematic institutional framework (apparently designed with the involvement of reputable international consultancy ªrms) for addressing the bad debt problem of the ªnancial system and related corporate distress, which had already begun to emerge as major constraints on the recovery process. The proposed banking and corporate restructuring programs were widely hailed by the ªnancial press as an important step in the right direction. But difªculties in obtaining the required funds precluded concrete policy action by these newly created institutions. A planned attempt to issue sovereign bonds in the United States and Europe to raise US$ 2 billion for implementing the banking-sector restructuring program had to be shelved in late August 1998. Thus, BNM had to continue to cushion the banking sector and debt-ridden companies against the liquidity squeeze caused by the share market crash and capital outºow by keeping a lid on interest rates and injecting liquidity into the system by printing money. By August 1998, the economy was in recession and there were no signs of achieving currency and share price stability. The national account released in the last week of August revealed a contraction of output by 2.8 percent and 6.8 percent, respectively, in the ªrst two quarters. The number of retrenchments in domestic manufacturing jumped from 19,000 in 1997 to over 83,000 in 1998. The unemployment rate increased from 2.6 percent in 1996 to 3.9 percent in 1998. The inºation rate (measured by the consumer price index [CPI]) peaked at 6.2 percent in June, surpassing the previous peak of 5.3 percent recorded in 1991. The combined outcome of economic collapse and the property market crash was a massive increase in non-performing loans in the banking system. This situation was further aggravated by a “ºight to quality” of deposits from smaller banks to large, well managed banks from the fourth quarter of 1997. The competition for funds by the affected institutions resulted in a sharp increase in lending rates (exceeding 20 percent in early 1998) in the banking sector as a whole. These higher lending rates in turn weakened banks’ balance sheets by increasing the level of non-performing loans (BNM 1999b, p. 175). According to BNM data, the proportion of nonperforming loans in total bank assets increased from about 2 percent in July to 3.6 percent in December 1997 and then to 11.8 percent in July 1998. Market analysts believed, however, that the problem was much more severe than the ofªcial ªgures suggested. This was because many companies had begun to roll over debt as part of their survival strategy. Independent estimates of the non-performing loan ratio ranged from 25 percent to 30 percent by mid 1998 (Heibert 1998; Financial Times, 22 August 1998; Soros 1998, p. 144). Banks, because of the deterioration of balance sheets and/or because of over cautiousness in an uncertain ªnancial environment, tended to focus on loan recovery rather than issuing new loans. Consequently, credit 41



contraction began to impact domestic consumption and investment. Falling asset prices created pressure on debtors, forcing them to distress sales of assets, which in turn led to further decline in asset prices. Rapidly deteriorating investor conªdence was reºected in continued liquidation of shares by foreigners and capital ºight. A striking feature of capital ºights from Malaysia from about early 1998 was that they largely took the form of ringgit ºowing (rather than foreign currency) into Singapore. As much as 25 to 35 billion ringgit (US$ 6.3–8.8 billion), amounting to 40 to 60 percent of the total domestic money supply (M1), had ended up in Singapore at the height of the crisis in mid 1998 (IMF 1999b). These ºows were triggered by attractive money market rates of between 20–40 percent in Singapore, which provided a hefty premium over a domestic rate of about 11 percent coupled with a weakening exchange rate for the ringgit. Arbitrage between the two rates by money market dealers in both Singapore and Malaysia began putting pressure on the domestic interest rates in Malaysia. Thus policymakers became increasingly concerned about the “internationalization” of the national currency, which had carried a potential new threat to economic stability and monetary policy autonomy. The strong demand for offshore ringgit and the consequent buildup of offshore ringgit deposits increased the vulnerability of the ringgit, undermining the effectiveness of monetary policy (BNM 1999b, chapter 14; Hood 2001).

4. Capital-control based crisis resolution strategy In this volatile economic climate, the Malaysian government had to choose between two alternatives. The ªrst was to obtain a “good housekeeping seal” on its policies from the IMF. As in Korea and Thailand, this would have stabilized the exchange rate, setting the stage for applying the Keynesian therapy to speed up the recovery. The second option was to resort to capital controls in order to combine a ªxed exchange rate with Keynesian policies, while ignoring vagaries of market sentiments. By this time the IMF had signiªcantly changed its original strategy of “conªdence building through macroeconomic contraction” in favor of expansionary macroeconomic policy (Fisher 2004). The four IMF program countries in the region—Indonesia, Korea, Thailand, and the Philippines—had already reformulated their policies along these lines with the blessing of the IMF. Thus if Malaysia’s reluctance to seek IMF support was purely based on differences of opinion relating to macroeconomic policy, that constraint had become less binding by this time. A widely expressed view in pro-government news commentaries in Malaysia at the time was that it was not eligible for IMF support even if it wanted to seek such sup-

42



port because of its relatively strong balance of payments position and its relatively lower foreign debt (BNM 1999b, p. 5; NEAC 1998, p. 1). However, this view is not consistent with the general IMF practice in assisting member countries in the event of an economic crisis and Malaysia’s own economic conditions at the time. The Philippines, for example, was receiving ªnancing support from the IMF at the time, despite a relatively sound balance of payments position and much lower external debt burden compared to Thailand and Korea. In 1998, following a speculative attack on its currency (real) Brazil was able to obtain a backup credit line from the IMF (primarily as a means of regaining market conªdence), notwithstanding its sizable foreign exchange reserves (over US$ 40 billion) (Krugman 1999, p. 111). Balance of payments need is only one of the eligibility criteria used by the IMF. In any case Malaysia’s foreign exchange reserves were not extraordinarily high at the time (about US$ 15 billion, down from a pre-crisis level of US$ 25 billion). Therefore, if wanted, presumably Malaysia could have entered an IMF program. The real issue was that this option was not politically acceptable to the Malaysian leadership. For over nearly 3 decades New Economic Policy (NEP) (renamed National Development Policy [NDP] in 2000)—perhaps the most comprehensive afªrmative action policy package ever implemented in any country in the world—has been central to the Malaysian economic policy (Snodgrass 1980). Given the intimate links developed between business and government under this program, naturally the positive stabilizing impact of any policy move had to be weighed against its potential negative effect on socio-political stability of the country (Crouch 1998). In his presidential address to the UMNO General Assembly on 19 June 1998, Prime Minister Mahathir summed up his position on this issue as follows: [I]f we have to resort to the International Monetary Fund assistance . . . , the conditions imposed by the IMF will require us to open up our economy to foreigners. There will not be any Bumiputera quota as the New Economic Policy (NEP) is an injustice, and unacceptable to their liberal democracy (Mahathir 1998, p. 60– 61).7 Political scientists will of course continue to debate on the relative importance of pure political motives compared to genuine economic policy considerations behind this policy shift (e.g., Haggard 2000; Jomo 2004). But the fact remains that Malaysia’s social equilibrium is more fragile than that of the socially homogeneous countries like Thailand and Korea. There is little argument among informed Malaysian ob7

It is pertinent to note here that Malaysia managed its mid 1980s crisis on its own, while eschewing IMF support. As in the context of the 1997–98 crisis, political imperatives on which the NEP is based were the prime consideration behind this policy choice (Narayanan 1996).

43



servers that the afªrmative action policy enshrined in NEP had played a crucial role in the country’s impressive economic success as against the generally dismal economic records of other heterogeneous, multiethnic nations in the developing world. In this context, Malaysian policymakers had a strong case to act on their own judgment of which approach to crisis management was in their best interest. Confronted with this policy dilemma, the Malaysian leadership opted for the second alternative, ending the policy uncertainty that had pervaded the policy scene for almost a year. The linchpin of new policy was insulating the domestic ªnancial markets from short-term ªnancial ºows through capital controls. This was expected to set the stage for ªxing the exchange rate and provide breathing space for vigorous pursuance of monetary and ªscal expansion to ªght recession. Although domestic considerations seem to have played the key role in this policy turnaround, the use of capital control to gain breathing space for crisis management had also begun to receive a measure of legitimacy in the international economic policy debate by this time. In particular, Krugman’s (1998) controversial piece in Fortune (appearing two weeks before the announcement of Malaysia’s new policy package), which speciªcally argued for using capital controls as a crisis management tool, received wide publicity in Malaysia.8 There was also growing attention being paid to the newly emerging view that China and Taiwan, the two economies in the East Asian growth league with controls on short-term capital movements, fared much better than the rest of the region during the crisis. The recent experiences of countries like Chile and Slovenia in using capital controls to manage shorter-term capital inºows were cited often also in the media and government reports.9 As a ªrst step, on 31 August 1998 offshore trading of shares of Malaysian companies was banned with immediate effect in a move to freeze over-the-counter share trading in the Central Limit Order Book (CLOB) market in Singapore.10 This was fol8

It is not, however, correct to name (as some authors like Miller 1999 and Hale 1998 have done) Krugman as the intellectual architect of the Malaysia policy turnaround. Apparently the decision to introduce capital control was made by the National Economic Action Council on 6 August (Mahathir 1998), before the Krugman article appeared. Krugman subsequently stated in Singapore that, “It was a shock that while I was speculating idly about that [capital control], Dr. Mahathir was about to do it” (New Straits Times, 26 August 1999). See also Krugman (1999).

9

In a special brieªng to the press following the introduction of capital controls, the Special Function Minister, Zainuddin, stated that before introducing currency controls the Malaysian authorities studied systems operating in Chile, Slovenia and China, and decided to use the Chinese system as a model in designing the Malaysian controls (Star, 5 September 1998).

10

CLOB market was an informal market for shares of Malaysian companies, which operates

44



lowed by the imposition of comprehensive controls over short-term capital ºows (1 September 1998) and ªxed the exchange rate at RM 3.80/US$ (2 September 1998). As BNM clearly stated in its policy announcement, the ªxing of the exchange rate was done while retaining the option of changing it when the underlying economic fundamentals changed. Although the new ªxed rate implied a mild appreciation of the ringgit from the average level for the previous 3 months (around RM 4.18), it represented a 35 percent depreciation against the pre-crisis levels of about RM 2.5. The new capital controls banned trading in ringgit instruments among offshore banks operating in Malaysia and stopped Malaysian ªnancial institutions offering domestic credit facilities to non-resident banks and stockbrokers. With a view to stopping speculative trading in ringgit in overseas markets (predominantly in Singapore), the use of ringgit as an invoicing currency in foreign trade was banned with immediate effect and legal tender on all ringgit deposits held outside the country with effect from 30 September 1998. A 12-month withholding period was imposed on repatriation of proceeds (principal and proªt) from foreign portfolio investment.11 The other measures included restrictions on overseas investment by residents exceeding RM 10,000 and a limit of RM 1,000 on Malaysian overseas travelers and stringent limits on the approval of foreign exchange for overseas travel and investment. A detailed listing of the new exchange control measures is provided in Appendix 2. The controls were strong, but narrowly focused on short-term capital ºows. The aim was to make it harder for short-term portfolio investors, both foreign and local, to sell their shares and repatriate proceeds, and for offshore hedge funds to drive down the currency. With the exception of limits on foreign exchange for foreign travel by Malaysian citizens, there was no retreat from the country’s long-standing commitment to an open trade and investment policy. No new direct controls were imposed on import and export trade. The controls were carefully designed to make it clear that the economy was not hostile to long-term foreign investment. Proªt remittance and repatriation of capital by foreign investors continued to remain free of side by side with the formal share market (Singapore Stock Exchange) in Singapore. At the time, total value of Malaysian shares traded in CLOB amounted to US$ 4.2 billion (Far Eastern Economic Review, 9 March, p. 56). Short-selling of shares continued on this market after such share dealings were made illegal in Malaysia following the onset of the crisis and this was perceived by policymakers in Malaysia as a major factor behind exchange rate and share price instability. CLOB trading was also thought to contribute to ringgit outºow to Singapore. Following the Malaysian move to ban offshore trading of Malaysian company shares, the CLOB market was closed on 15 September. 11

45

This restriction reportedly blocked in US$ 10 billion of foreign investment in domestic securities.



control. Immediately following the imposition of capital controls, BNM did experiment with new regulatory procedures in this area. But these were swiftly removed in response to protest by these ªrms. Moreover, some new measures were introduced to further encourage FDI participation in the economy. These measures included allowing 100 percent foreign ownership of new investment made before 31 December 2000 in domestic manufacturing regardless of the degree of export orientation; increasing the foreign ownership share in the telecommunication project from 30 percent to 69 percent (under the condition that the ownership share is brought down to 49 percent after 5 years), and in stock-brokering companies and insurance sector from a previous uniform level of 30 percent to 49 percent and 51 percent, respectively; and relaxing restrictions on foreign investment in real estate. In early February 1999, the original 12-month holding restriction on portfolio investment was converted into a graduated exit levy.12 Under this system, repatriation levies fell into one of two categories depending on whether the funds entered the country before or after 15 February 1999. For investments made before 15 February a three-tier levy was applied to the principal (the capital value) on how long the funds were retained in the country. For funds entered after 15 February there was a two-tier levy on the repatriation of proªts (but not on the principal): 30 percent on proªt made and repatriated within 1 year, and 10 percent on proªt repatriated after 1 year. In August 1999, the two-tier levy on proªt repatriation was replaced by a uniªed 10 percent levy. An agreement between the KLSE and the Singapore Stock Exchange reached on 26 February 2000 provided for the transfer of the shares trapped in the CLOB market to the Malaysian stock exchange, which allowed trading to resume. The 10-percent exit levy was lifted on 1 May 2001. Most of the newly introduced capital controls were relaxed and subsequently removed at successive stages during the next 2 years. The Malaysian authorities have, however, opted to retain some newly introduced restrictions on investment by offshore banks in ringgit-denominated assets and lending by ªnancial institutions to non-residents, albeit in a much more liberal fashion compared to those in the September 1998 package (Appendix 1). On 21 July 2005, the ringgit peg to the US$ was abolished in favor of a managed ºoating exchange rate system. With the policy autonomy gained through the ªxed exchange rate and capital controls, the government swiftly embarked on a recovery package consisting of two key elements: macroeconomic stimulants, and banking and corporate restructuring. The 1999 Budget Speech presented (in October 1998) predicted an increase in the budget 12

46

According to Stiglitz (2002, p. 124) the World Bank economists worked closely with the Malaysian authorities in designing the new exit levy system.



deªcit from 1.8 percent of GNP in to 3.2 percent in 1999. The 2000 budget saw a further increase in the deªcit to 4.4 percent of GNP. On the expenditure side there were no major new proposals in both budgets beyond some moderate increase in funds earmarked for road and rail projects. On the revenue side there were signiªcant tax cuts and new tax incentives. Among them, the key element was a total waiver of income tax in 1999 and an across-the-board 1-percentage-point reduction in income tax rates proposed for 2000. There were also tax breaks for industries of national and strategic importance and an import duty reduction on machinery and equipment imports. The budget deªcits were ªnanced mostly through issuing Malaysian Government Securities (MGS), which were absorbed largely by provident, pension, and insurance funds. Only about one-third of the ªnancial needs have been raised externally, mainly from bilateral and multilateral sources. To complement expansionary budgetary policy, BNM set on a course of monetary expansion. The statutory reserve requirement (SRR) ratio for banking institutions was cut at successive stages in order to inject liquidity into the debt-ridden banking system. By late 1998 the ratio had come down to 4 percent against a pre-crisis level of 13.5 percent. BNM also revised the formula used in computing the base-lending rate (BLR)13 so that reductions in the intervention rate are better reºected in cost of bank credit. The margin that banks could charge their customers above the BLR was reduced from 4 percent to 2.5 percent. The 3-month inter-bank rate (BNM’s policy rate on which other short-term interest rates are based), which was raised from 10 percent to 11 percent in February 1998 to defend the exchange rate, was reduced in a number of stages to 4 percent by early 1999. The default period for reclassiªcation of bank loans (which was reduced to 3 months from 6 months in January 1998) was changed back to 6 months, with a view to reducing the pressure on the banks to set aside capital against non-performing loans. The other measures introduced to boost credit expansion included an announcement on 9 September 1998 of an indicative annual loan growth target of 8 percent for commercial banks, relaxation of credit limits on lending by commercial banks and ªnancial companies for purchase of property and shares, a scheme for providing soft loans for purchase of cars, a special loan scheme for assisting smaller industries and low-income groups, and relaxing credit limits on credit cards (BNM 1999a). The new policy package placed greater emphasis on the speedy implementation of the banking and corporate restructuring programs initiated in the ªrst half of 1998. The program involved carving out bad debt from the banking system by Danaharta 13

47

The benchmark interest rate prescribed by BNM for lending institution with a view to avoiding unhealthy competition in credit markets.



injection of fresh capital through Danamodal and the CDRC. This well-conceived program remained virtually inactive until then because of difªculties involved in raising required funds. The new policy framework provided a conducive setting for raising required funds from domestic sources. In addition to the bad debt carving out and recapitalization schemes, BNM embarked on an ambitious merger program for domestic ªnance companies and banks, with a view to improving their competitiveness. The merger program for ªnance companies, which aimed at reducing the number of ªnance companies from 39 to less than half of the number through merger and/or amalgamation with banks, has already been completed. The banking merger program aimed to consolidate the nation’s 58 ªnancial institutions into 6 (subsequently increased to 10) banking groups.

5. The recovery The Malaysian economy experienced a 7.5 percent contraction in GDP in 1998, after 11 years of uninterrupted expansion averaging 8 percent per year. This was by far the worst downturn after World War II; GDP contracted by a mere 1 percent during the mid 1980s crisis. The degree of output contraction moderated to 1.3 percent (on an annual basis) in the ªrst quarter of 1999 followed by a positive growth rate of 4.1 percent in the second quarter. Recovery accelerated in the next two quarters, culminating in a growth rate of 5.4 percent for the whole year. The economy had regained the pre-crisis (1996) level of GDP by mid 2000, leaving behind almost 3 “lost” years of economic expansion (Table 2, Figure 1). In line with strong recovery in domestic production, the employment situation improved. The unemployment rate in the economy by the end of 1999 stood at 3.4 percent, only 0.9 percentage points higher than the pre-crisis level (Table 3). The recovery was underpinned by remarkably low inºation, despite the heavy emphasis on ªscal and monetary expansion as part of the recovery strategy. The annual rate of consumer price inºation increased from 2.7 percent to 5.3 percent between 1997 and 1998. The rate of inºation measured in terms of the producer price index increased from 2.7 percent to 10.7 percent between 1997 and 1998 and then declined to 3.2 percent in 1999. Growing business conªdence in the recovery process began to reºect in an impressive rebound in trading on the KLSE from mid 1999. The benchmark Kuala Lumpur Composite Index (KLCI) had almost regained its pre-crisis (end of June 1997) level by the end of February 2000. Market capitalization of the KLSE increased from the historical low of RM 200 billion in August 1998 to over RM 700 billion in February 2000, which was only 5 percentage points short of the pre-crisis (June 1997) level.

48



Table 2. GDP Growth in Malaysia, Korea and Thailand, 1993–06 (percentage change from one year before) Malaysia

Thailand

Korea

9.8 10.0 7.3 ⫺7.4 6.1 8.9 0.3 4.4 5.5 7.2 5.2 5.9

9.2 5.9 ⫺1.4 ⫺10.5 4.4 4.8 2.2 5.3 7.1 6.3 4.5 5.0

9.2 7.0 4.7 ⫺6.9 9.5 8.5 3.8 7.0 3.1 4.7 4.0 5.0

1997Q1 1997Q2 1997Q3 1997Q4

7.6 8.4 7.2 6.1

1.0 ⫺0.6 ⫺1.6 ⫺4.2

4.9 6.0 5.1 2.8

1998Q1 1998Q2 1998Q3 1998Q4

⫺1.5 ⫺5.9 ⫺10.2 ⫺11.2

⫺7.1 ⫺13.9 ⫺13.9 ⫺7.2

⫺5.3 ⫺7.9 ⫺8.1 ⫺6.0

1999Q1 1999Q2 1999Q3 1999Q4

⫺1.0 4.8 9.1 11.7

⫺0.2 3.4 8.4 6.4

5.9 9.7 11.1 10.9

2000Q1 2000Q2 2000Q3 2000Q4

11.7 8.5 8.4 7.1

6.5 6.1 2.4 4.0

13.1 9.4 8.2 4.3

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Source: IMF, International Financial Statistics Database.

Figure 1. GDP in Malaysia, Korea, and Thailand, 1993Q1–2006Q4 (1995

Source: IMF, International Financial Statistics Database.

49


100)

50 4.5 11 2.9 18.2 16.2 8.9

Growth by sectorb(percent): Agriculture, forestry and ªshing (9.8) Industry (41.5) Mining and quarrying (7.7) Manufacturing (29.1) Construction (4.7) Services (48.7)

Asian Economic Papers 23 25.1 0.7 35.3

Fiscal performance (central government) Government expenditure as percent of GDP Gross development expenditure as percent of total expenditure Budget deªcit (central government) as percent of GDP Total public debt as percent of GDP 11.7

3.5 2.3 2.8 0.1

Inºation rate (percent): Consumer price Producer price Domestic goods Imported goods

Foreign debt as percent of total public debt

2.5

Unemployment rate

11.1 8.8 16.2 128.4 58.0 81.2

4.9 6.9 0.7 9.7 13.3 1.1

Growth by ªnal demand categoryb(percent) Consumption (59.3) Private (45.6) Public (13.7) Gross domestic ªxed investment (46.8) Private (34.2) Public (12.6)

Growth of manufacturing productionc(percent) Export-oriented (weight: 0.52) Domestic-oriented (weight: 0.48) MIER consumer sentiments index (1988 ⫽ 100) MIER business conditions index (1988 ⫽ 100) MIER mnfg. capacity utilization rate (percent)

10

Growth of GDP (percent)

1996

Table 3. Malaysia: Selected economic indicators, 1996–06a

14.4

23.3 24 2.4 31.9

2.7 2.7 2.5 2.8

2.6

10.6 13.1 14.6 121.9 59.2 83.2

0.4 10.5 1.9 10.1 10.6 9.9

4.9 4.3 7.6 8.4 8.4 8.6

7.3

1997

14.5

19.9 31.9 ⫺1.8 36.2

5.3 10.7 11.2 9.2

3.2

⫺7.2 ⫺7.7 ⫺13.4 82 42.5 59.5

⫺4.5 ⫺6.5 ⫺0.8 ⫺13.4 ⫺23 ⫺0.7

16.6

19.6 38.5 ⫺3.2 35.9

2.8 ⫺3.5 ⫺3.9 ⫺0.6

3.4

8.9 12.8 13.1 103.7 57.9 80.7

3.9 5.4 1.3 13.5 ⫺5.6 3.1

6.7 3.1 16.3 ⫺5.9 ⫺18.5 11.7

6.1

⫺7.4 ⫺10.3 ⫺10.8 ⫺7.8 ⫺44.9 ⫺57.8 ⫺10

1999

1998

15.0

16.5 49.4 ⫺5.7 36.6

1.2 ⫺3.6 ⫺4.6 1.1

3.1

17.2 19.6 8.5 120.8 71.3 84.15

6.1 13.3 0.3 18.3 0.5 5.2

10.5 13.0 1.6 25.7 19.4 32.6

8.9

2000

16.7

19.1 55.3 ⫺5.5 43.6

1.2 ⫺4.5 ⫺5.2 ⫺0.8

3.7

⫺12.7 ⫺17.0 4.0 98.6 87.6 78.8

⫺0.6 ⫺4.5 ⫺1.5 ⫺5.9 2.1 6.5

5.4 2.4 17.3 ⫺2.8 14.5 ⫺19.9

0.3

2001 4.4

22.0

19.0 52.4 ⫺5.6 45.6

1.6 13.4 16.6 ⫺0.4

3.5

8.2 9.9 3.5 108.8 101.2 79.2

2.8 4.1 4.3 4.3 2.0 8.6

5.8 4.4 10.4 0.3 11.2 ⫺15.1

2002

19.8

19.0 52.3 ⫺5.3 47.8

1.2 3.8 4.3 1.2

3.6

17.4 19.1 11.2 110.1 106.0 79.6

5.5 7.5 5.8 8.6 1.5 4.8

7.7 6.6 11.4 2.7 3.9 0.4

5.5

2003

16.0

20.3 31.6 ⫺4.3 48.1

2.2 3.4 3.5 2.4

3.6

6.4 6.4 6.1 113.3 110.9 81.5

5.0 7.9 3.9 9.8 ⫺1.5 6.0

9.5 10.5 6.1 3.1 ⫺11.0 30.2

7.2

2004

13.1

19.7 31.2 ⫺3.8 46.2

3.3 9.9 11.8 0.8

3.6

5.6 7.4 ⫺0.8 112.3 103.3 83.0

2.5 3.9 0.8 5.1 ⫺1.6 5.8

8.3 9.2 5.4 4.7 1.9 8.5

5.2

2005

10.3

19.7 33.2 ⫺3.5 44.3

3.1 5.0 4.9 4.7

3.3

7.9 7.5 9.7 103.2 105.0 81.3

6.4 5.3 ⫺0.2 7.0 ⫺0.5 ⫺6.8

7.2 7.0 7.9 7.9 — —

5.9

2006


51

Asian Economic Papers 6.0 1.0 ⫺4.8 27.0 38.7 25.7 36.9 6.6 2.5

1,238 807 318.1

21.2 129.9 10.1 366 3.7

1996

0.3 0.2 ⫺5.3 20.8 43.9 25.2 53.7 5.5 2.8

594 376 133.4

18.5 138.7 10.6 438 4.1

1997

⫺6.9 ⫺25.9 13 25.6 42.6 19.9 33.2 6.7 3.9

586.1 375 132.4

2.8 141.7 12.3 406 13.6

1998

17.0 13.0 15.9 30.6 42.1 14.3 19.1 5.9 3.8

812.3 553 183.9

8.2 145.0 8.5 406 11.0

1999

17.0 26.3 9.4 28.3 46.9 10.9 16.1 5.5 3.8

679.6 616 179.5

5.1 133.5 7.7 415 9.7

2000

⫺10.6 ⫺10.3 8.3 29.5 51.9 13.9 21.2 6.6 3.8

696.1 428 127.9

2.9 141.1 7.1 405 11.5

2001

6.1 8.1 8.4 33.4 51.3 17.5 25.3 6.4 3.8

646.3 504 139.1

6.9 139.3 6.5 400 10.2

2002

12.4 5.4 12.8 43.8 47.3 17.9 20.0 5.9 3.8

793.9 551 135.9

9.7 140.0 6.3 412 8.9

2003

20.6 25.0 12.6 65.9 44.6 21.8 17.4 4.2 3.8

907.4 684 151.9

12.3 137.9 6.1 414 7.5

2004

18.7 26.4 17.1 82.1 33.5 22.8 14.4 4.4 3.5

1096.2 753 137.9

13.0 139.2 6.5 443 4.8

2006

100). The weight attached to each category in the total index

12.0 9.6 15.3 69.9 39.9 23.7 17.7 5.0 3.6

899.8 703 141.9

8.4 135.9 6.0 431 5.8

2005

only. Based on a “6-month” non-performing period. h. Excluding gold reserves. —

Data not available. MIER

Malaysian Institute of Economic Research

is given in brackets. d. Net of non-performing loans (6-month deªnition). e. End of period. f. End-of-the-year stock of outstanding loans deºated by the GDP deºator. g. Non-performing loans of commercial banks

a. All growth rates on a year-on-year basis. b. Sectoral share in GDP in 1996 are given in brackets. c. Based on manufacturing production index (1993

Note:

Source: Compiled from Bank Negara Malaysia, Monthly Statistical Bulletin (bnm.gov.my), IMF, International Financial Statistics, and MIER, Monthly Economic Monitor (various issues).

External transactions Merchandise exports (US$, FOB, percent) Merchandise imports (US$, FOB, percent) Current account balance as percent of GDP Foreign reserves (US$ billion)e, h Total external debt as percent GDPe Short term foreign debt as percent of total debte Short-term foreign debt as percent of foreign reservese External debt service ratio Average exchange rate (ringgit per US$)

Share market performance KLSE Composite index (1977 ⫽ 100) Market capitalization (ringgit billion) percent of GDP

Money and credit (end of period) M3 growth (percent) M3/GDP (percent) Average bank lending rated (percent) Bank credit to the private sector (1990 ⫽ 100)e, f Non-performing loan (NPLs) percente, g

Table 3. (continued)



The consumer sentiment and business conªdence index of the Malaysian Institute of Economic Research regained the pre-crisis level by late 1999. Public expenditure led the way to recovery. Following a 7.8 percent contraction in 1998, public consumption recorded double-digit growth from the ªrst quarter of 1999, contributing to over 70 percent of total consumption growth of 6.7 percent in that year. Public ªxed investment contracted by only 10 percent in 1998 compared to a 58 percent contraction in private ªxed investment. In 1999 the public ªxed investment expanded by 14 percent in a context of continued contraction in private investment (though at a lower rate), slowing contraction in total annual investment to 6 percent compared to 45 percent contraction in the previous year. Private consumption was seen stabilizing in the ªrst half of 1999 and grew strongly in the second half of the year. In the ªrst quarter of 2000 private consumption grew by 14 percent, yielding a 12 percent expansion in total consumption despite a slowing down of public consumption to a mere 1 percent (compared to over 10 percent growth in the four previous quarters). Private investment continued to contract in 1999, albeit at a much slower rate (12 percent) compared to a massive contraction (57 percent) in the previous year, and began to recover from mid 2000. On the production side, signs of recovery emerged ªrst in the services sectors (particularly in ªnancial services) and domestic-market oriented manufacturing. By the second quarter of 1999 recovery had become more broad-based, with exportoriented manufacturing playing a leading role. In 1999 and 2000 growth of exportoriented manufacturing was almost two times faster than domestic-oriented manufacturing. Of the total increment of manufacturing production during this period, 68 percent originated in export-oriented manufacturing. Of the total increment in GDP during this period, 70 percent came from the manufacturing sector, with almost 47 percent coming from export-oriented manufacturing alone. Thus, the Malaysian experience through the crisis is consistent with the conventional wisdom that greater export-orientation is an important facilitator of economic rebound following a crisis. However, in line with recovery in domestic demand, domesticoriented manufacturing also regained pre-crisis levels by 2000. The agricultural sector (including forestry and ªshing) recorded negative growth in 1997 and 1998, reºecting world market conditions for the major primary export products (in particular rubber and palm oil), adding to the crisis-driven growth collapse. This sector began to record positive growth from the second quarter of 1999, underpinned by a sharp rebound in palm oil output from a decline of 8.3 percent in 1998 to an estimated increase of 19.4 percent in 1999. The mining sector, however, re-

52



corded a marginal decline of 1.2 percent in 1999, reºecting a decline in domestic demand for gas in the depressed economy and some curtailment of crude oil production under the Government’s National Depletion Policy. The services sector grew by 6 percent in 1999, with all sub-sectors showing strong growth, reºecting across-the-board improvements in ªnal demand, in particular robust trade performance and strong recovery in consumer demand. The construction sector was the hardest hit by the crisis. In 1998 value-added in this sector contracted by a staggering 23.5 percent, accounting for over one-third of total GDP contraction (of 7.5 percent) in the year. Reºecting the severity of asset market collapse, the construction sector contracted by 23 percent in 1998 and the process of contraction continued well into the third quarter of 1999. Although the initial impetus for recovery came from ªscal pump priming, over time the recovery has become increasingly private-sector led, with private consumption and net exports providing much of the stimulant for output growth. Thus, Malaysia had been able to ride the crisis without building up a massive debt overhang. The end-of-year stock public debt as a share of GDP increased from 32 percent in 1996 to 36 percent in 1998 and remained at around the same level in 1999 and 2000 (Table 3). Almost 85 percent of the addition to total debt stock in 1998 and 1999 came from domestic borrowing. The share of foreign debt in the total stock did increase from 12 percent to 16.6 percent between 1996 and 1999. Much of foreign borrowing (over 80 percent), however, was long-term concessionary loans obtained from multilateral ªnancial organizations and foreign governments. The turnaround in the economy was accompanied by a notable strengthening of the balance of payments position, driven by a more favorable external trade balance and signiªcant inºow of long-term capital. By the end of 1999 Malaysia’s foreign exchange reserves stood at US$ 31 billion, and they provided 300 percent cover for total outstanding short-term debts and 200 percent cover for the stock of volatile capital (outstanding short-term debt ⫹ cumulating portfolio investment, as deªned previously) of the country.

6. Have capital controls worked? We observed in the previous section that the Malaysian economy recovered nicely following the introduction of the capital-control based reform package. But, precedent does not necessarily imply causation; the recovery could well have been the outcome of the working of natural market forces. Moreover, even if the controls were instrumental in engineering a rapid recovery this could have been at the ex-

53



pense of long-term growth. In particular, capital controls could have had a longterm damaging effect on capital inºows, both FDI and portfolio capital, in particular the former, which have been a pivotal element of the country’s pre-crisis economic dynamism. Inept practices (e.g., helping cronies) as part of the banking and corporate restructuring operations undertaken under the cover of capital controls could have resulted in costly resource misallocation. 6.1 Capital controls and economic recovery A number of observers have attempted to understand the role of capital controls in Malaysia’s recovery from the crisis through simple comparisons of sources of vulnerability and recovery experiences of crisis-hit countries using readily available performance indicators (Hiebert 1999; IMF 1999b; Lim 1999; Miller 1999; Dornbusch 2002). These comparisons have led to two common inferences. The ªrst is that Malaysia did not have a crisis in the ªrst place, it was an innocent victim of speculative capital exodus and the economy would have recovered swiftly with the help of the standard market-friendly policies. The inference is that capital controls did not make a “distinct” contribution to the recovery process in Malaysia. At the time Malaysia made the policy U-turn, capital had already left the country and speculative pressure for capital outºow from the Asian region was coming to an end. So the imposition of capital controls was simply a ritualistic locking of the barn door after the horse was stolen. More speciªcally, it is emphasized that not only Malaysia but also the other crisis-hit Asian countries, which maintained open capital accounts throughout under IMF-centered reform packages, began to recover at about the same time. But these views ignore the important fact that the economies under consideration are vastly different in terms of the sources of vulnerability to the crisis, and the nature of the economic structure, which determine ºexibility of adjustment to a crisis. Put simply, details differ in important ways from one country to another, and readily available performance indicators do not capture these differences (Cooper 1998; Corden 2003, chapter 11; Eichengreen 2003, chapter 9). The view that Malaysia did not have a crisis to warrant a drastic policy U-turn is primarily based on the relatively low levels of foreign debts and non-performing loans in the Malaysian banking system at the onset of the crisis. This view ignores the explosive mix of share market bubble and domestic credit boom that had developed in Malaysia in the lead-up to the crisis (Athukorala 2002).14 Be that as it may, there is no logic behind 14

54

Interestingly, on these grounds, the international ªnancier George Soros (1998, chapter 7) treated the economic situation in Malaysia in the lead-up to the crisis as untenable as (if not more untenable than) that in Korea, Thailand, and Indonesia. (Soros wrote this book before the Thai contagion hit Malaysia!)



the argument that the severity of a speculative attack on the currency of a country is proportional to the degree of vulnerability. If foreign lenders suspect an impending crisis, they do not expect to be told how serious the problem may become. They will simply withdraw their funds as rapidly as possible, thus turning a suspected ªnancial problem into a ªnancial rout (Cooper 1998). Given these considerations, an inter-country comparison can yield meaningful inferences only if economic adjustment under alternative policies is studied carefully while placing emphasis on fundamental differences in economic structures and original sources of vulnerability to the crisis. The “barn door” analogy misses the point that the purpose of capital control was to set the stage for monetary and ªscal expansion by preventing outºow of funds, both local and foreign-owned, in response to lowering the domestic interest rate relative to world market rates under the new expansionary macroeconomic policy stance. In any case, the available performance indicators are not consistent with the view that Malaysia has lagged behind the recovery process compared to the IMF-program countries. Whereas all these countries started to show signs of recovery from about late 1998, among the three countries under consideration only Korea has so far recorded a faster recovery rate than Malaysia. But Korea is a mature industrial nation with a diversiªed manufacturing base. Moreover, the dominant role played by a few national companies (chaebols) in manufacturing production and trade seems to have placed Korea in a uniquely advantageous position in the recovery process (Booth 1999; Corden 2007). In terms of the stage of development and the nature of the economic structures, undoubtedly the better comparator for Malaysia is Thailand. So far, Malaysia’s recovery rate has been much faster and steadier compared to Thailand (Table 2). The difference between the recovery experiences of the two countries becomes even more signiªcant when one goes beyond the aggregate GDP growth ªgure and looks at other performance indicators. For instance, even by the mid 2000 recovery in the Thai economy, Thailand continued to rely on massive public sector demand, with private consumption remaining well below pre-crisis levels. By contrast in Malaysia the recovery process had become broad-based by late 1999, with rapid recovery in private sector consumption and investment. Unlike Malaysia, problems in the ªnancial sector still remained a major source of uncertainty in Thailand. Even by early 2000, the NPL ratio of the Thai ªnancial system continued to remain stubbornly high (nearly 40 percent), and the volume of real outstanding credit was still falling. Reºecting mostly continuing ªnancial sector weaknesses, recovery of the share market in Thailand began to falter from about early 2000, compared to an impressive continuing recovery of the Malaysian share market. As a re-

55



sult of share-market related capital outºows, Thailand’s foreign reserve levels had begun to be depleted by this time, causing policy concerns about the sustainability of recovery (Siamwalla 2000). Kaplan and Rodrik (2002) examine whether the choice of the capital-control based crisis management route versus the conventional IMF recipe made a difference to the recovery process in Malaysia. They compare economic performance in Malaysia during 12 months following the radical policy shift with performances in Korea, Thailand, and Indonesia during the 12 months after each country began to implement an IMF-assisted crisis management package. Their results, derived by applying the time-shifted difference-in-difference regression methodology to monthly data on a number of key variables including industrial production, manufacturing employment, real wages, stock market indices, and foreign reserves, are consistent with the view that the Malaysian recovery experience under capital controls was superior to that of the three IMF-program countries. This ªnding is, however, dictated by the choice of the particular counterfactual (that is, performance of the other three countries during the ªrst year immediately after choosing the IMF route) for the econometric test. When the test is recast to examine how Malaysia performed relative to the other three countries during the same period after the imposition of capital controls in Malaysia, “capital controls look bad” (p. 429). The authors maintain that the counterfactual used in the former comparison is the valid one because by September 1998 Malaysian policies had become unsustainable and the realistic alternative to capital controls was an IMF program of the type that other countries had already undertaken. However, one can argue that the choice of this counterfactual would have stacked the deck in favor of Malaysian capital controls; various developments in the international economic environment (such as the sharp cut in the U.S. Federal Reserve interest rates and improved world demand conditions faced by some key export products (in particular electronics from countries in the region) had begun to aid the recovery process in all crisis-hit East Asian economies from mid 1998 (Rojas-Suarez 2002). Economists will continue to debate whether the Malaysia recovery record under capital controls was superior to that of the IMF-program countries. But there is little justiªcation for using the “superiority” yardstick in examining the outcome of the Malaysian experiment and making inferences about the suitability of capital controls as a crisis resolution strategy—failing an early and gracious arrival of the IMF and/or socio-political resistance to going along the IMF path. As we have already noted, the September policy U-turn in Malaysia was basically a policy choice made in desperation. There is no evidence to suggest that Malaysian policymakers antici-

56



pated this move to generate a superior outcome. Moreover, the almost unanimous view of the critics at the time was that Malaysia’s non-conventional approach was doomed to fail. The appropriate question is therefore whether this unorthodox policy shift was a viable alternative strategy for Malaysia to recover from the crisis. In the remainder of this section we attempt to broaden our understanding of this issue by probing how capital controls were instrumental in achieving this recovery. Our approach is to examine whether the original expectations (mostly negative) about the fate of the reform program were in fact consistent with the actual experience. Policy autonomy A major doubt about the effectiveness capital controls as a crisis management tool relates to presumably ample scope for avoidance and evasion, which can simply negate the expected monetary policy autonomy (Hale 1998; Edwards 1999). The general argument here is that, the more extensive trade and investment links are, the more difªcult and costly it is to control capital account transactions because of the multiplication in the number of arbitrage possibilities that arise in the course of normal business. The problem with this argument is that it is based on a misleading mixing of “placing funds abroad retail” [retail transfer of funds abroad] by manipulating current account transactions and “exporting capital wholesale” (Williamson 1993, p. 36). There is ample evidence from both developed and developing countries that capital controls are in fact effective in substantially reducing, if not preventing, capital ºows of the latter type, in particular placement abroad of institutional savings (Radelet and Sachs 1998; Larrain and Laban 2000; Eichengreen 2003). The evidence from capital controls in Malaysia is consistent with this evidence. Controls seem to have helped lowering interest rates and are encouraging a revival of domestic consumption and investment without precipitating capital ºights. Unlike the situation before the imposition of capital controls, short-term capital ºows stabilized in the ªrst quarter of 1998. Therefore, the foreign reserve position began to improve in tandem with the surplus in the current account. Total foreign exchange reserves, which remained around US$ 20 billion from the third quarter of 1997, surpassed the pre-crisis level of US$ 30 billion by the end of 1999. The “errors and omission” item in the balance of payments, which is widely considered to be a convenient indicator of “unofªcial” capital ºows, in fact shrank following the imposition of capital controls. As foreign exchange controls were targeted carefully on short-term investment ºows, and trade- and FDI-related transactions continued to remain liberal, the policy shift did not result in the emergence of a black market for foreign exchange.

57



Figure 2. Differential between domestic and international money market interest rates in Malaysia, Korea, and Thailand (January 1996–June 2000) (percentage points)

Source: Bank Negara Malaysia, Monthly Statistical Bulletin and IMF, International Financial Statistics (various issues). Note: Domestic money market rate used for each of the 3 countries is: Malaysia: 3-month Treasury bill rate; Thailand: 3-month repurchase rate on government bonds in the inter-bank market; Korea: 91-day beneªcial certiªcate rate. The 3-month Treasury bill rate in the United States is used as a proxy for international money market rate.

The effectiveness of capital controls in bringing in expected monetary policy autonomy is evident from the dramatic turnaround in the differential between domestic and international interest rates in Malaysia following the imposition of these controls (Figure 2). The differential remained positive and varied in the range of 0.6 percent to 2 percent during the period before the onset of the crisis. Then it increased reaching a peak of 8 percent at the height of the crisis in mid 1998. Following the imposition of capital controls in September 1998, it tended to decline, entering the negative territory by March 1999. From then the differential has remained around ⫺2.5 percent with little monthly ºuctuations. Both the dramatic decline in the differential and its remarkable stability in recent months clearly attest to the effectiveness of controls in insulating the domestic interest rate from international ªnancial market developments. This inference (based on simple visual inspection of relative movement in interest rates) is supported strongly by systematic econometric analyses of Edison and Reinhart (2000), Kaminsky and Schmukler (2001), and Doreisami (2004). Banking and corporate restructuring The breathing space provided by capital controls, exchange rate stability, and the resultant monetary policy autonomy were instrumental in the speedy implementation of banking and corporate restructuring. 58



Many feared that under the Danaharta/Danamodal program bailing out of the wellconnected would come at the expense of the poor and the middle class using the printing press backed by capital controls. This suspicion has given way to a virtual general consensus among the Malaysian observers that the shelter has been successfully used to implement the most effective and far-reaching ªnancial system cleanup among the crisis countries. The program is considered to have been more effective and fair than many of similar efforts in the region, notably those of Thailand and Indonesia but also that of Korea (Ogus 2000). The IMF, in its 1999 Public Information Notices on recent Article IV Consultation with Malaysia, commended the Malaysian authorities for “using the breathing space [provided by the policy measures introduced in September 1998] to push ahead with a well-designed and effectively implemented strategy for ªnancial sector restructuring” (IMF 1999a, IMF 2000). The Economist, in a dramatic reversal from previous pessimistic views, recently commended the Malaysian banking and corporate restructuring: In Malaysia there are doubts about the government’s handling of debtors; wellconnected ones have emerged in better shape than some analysts think they should have done. But the government’s success in holding down the level of bad debts, and in cleaning up the ones that did emerge, has been undeniable. That is one reason why Malaysia’s short-term prospects are so good (The Economist, 2000, p. 74). By mid 2000 Danaharta had successfully carved out bad debts to the tune of US$ 12 billion or 42.2 percent of total NPLs of the entire banking system. Through operation of Danamodal, the capital base of the banking system had been raised well above the international BIS requirement. The Corporate Debt Restructuring Committee had resolved bad debt problems of 25 ªrms with loans totaling US$ 4.7 billion, and was resolving another 26 cases with debt amounting to US$ 4.3 billion. As a result of the support provided by low interest rates and rapid recovery in containing NPL growth, performance of the banking and corporate sectors improved at a faster rate than originally envisaged. Consequently, Danamodal required considerably less funding than originally envisaged. Danaharta had ceased purchasing non-performing loans by mid 2000 and entered the workout phase of managing the acquired assets. The carving out of bad debts and the recapitalization of weak banks was instrumental in avoiding contraction in bank lending (Figure 3). This seems to have been a factor behind the broad-based recovery. A moribund credit market essentially constrains recovery by discouraging creditworthy businesses who would have been willing to spend more if they had access to credit. Moreover, as Kruger and Tornell (1999) have demonstrated in the context of the Mexican economy following the 1994

59



Figure 3. Real bank credit to the private sector: Malaysia, Korea, and Thailand, 1990–06 (1990 100)a

Source: Based on data compiled from IMF, International Financial Statistics database. Note: a. Total end-of-year balances deºated by the GDP deºator.

crisis, a continuing credit crunch caused by delayed banking restructuring could act as a major constraint on ªrms in the non-traded-goods sectors, which are normally the most affected by the crisis, and small ªrms in traded-goods sector, which normally do not have favored access to limited domestic lending sources or to foreign borrowing despite improved proªtability of operation. This resulted in a lopsided recovery process involving predominantly traded-goods industries dominated by large (mostly export-oriented) ªrms. Malaysia seems to have avoided this “Mexican syndrome” through early action in the sphere of banking restructuring. 6.2 Impact on FDI and other growth implications Many commentators expressed fear that capital controls would hamper the economic recovery by adversely affecting FDI in Malaysia (Hale 1998; Hill 1998; Hiebert 1999; Miller 1999). It was argued that this signiªcant departure from Malaysia’s long-standing commitment to economic openness could certainly have an adverse impact on the general investment climate of the country. Moreover, in Malaysia, the decision to impose controls appeared so sudden and arbitrary that it called into question the general credibility of the government’s whole framework for foreign investment. The pessimistic view was based on a false aggregation of FDI with portfolio investment and short-term bank credits. It ignored the time-honored dictum in the balance of payments theory that, “in terms of underlying determinants of mobility, FDI is quite different from hot money” (Meade 1951, p. 298). FDI ºows are

60



Figure 4. Net FDI ºows to Malaysia, Korea, and Thailand, 1985–04 (US$ million)

Source: UNCTAD, World Investment Report database. Note: a. Annual average.

determined by long-term considerations governing international production decisions of MNEs, not by ªnancial panics and related short-term economic changes, which underpin hot money movement. Therefore, regarding a country’s external economic policy, what is primarily important for attracting FDI is a ªrm commitment to the maintenance of an open current account (Bhagwati 1998). Was the continuity of the impressive record of Malaysia as a favored host of FDI broken by the capital controls? Net FDI ºows to Malaysia declined from US$ 7.9 billion in 1996 to US$ 3.0 billion in 1998. During the ensuing 6 years the average level of net annual inºows amounted to only 55 percent of that during 1990–95. It could well be that the prolonged period of policy and political uncertainty following the onset of the crisis, and widespread market scepticism about the fate of Malaysia’s unorthodox reform package introduced in September 1998, may have played a role. However, Malaysia’s post-crisis record of attracting FDI has been comparable to, if not more impressive, compared to Thailand (Figure 4). Only Korean has maintained a superior record. But one should be cautious in deriving inferences from a comparison of Malaysia’s post-crisis FDI experience with that of Korea (or Thailand) for a number of reasons.

61



Table 4. Mergers and acquisitions by foreign ªrms in Asian crisis countries, 1990–01, announced value (US$ million) 1990–94a

1995

1996

1997

Indonesia Korea Malaysia Philippines Thailand

747 676 1,221 1,446 778

809 192 98 1,208 161

530 564 768 462 234

332 836 351 4,157 633

683 3,973 1,096 1,905 3,209

1,164 10,062 1,166 1,523 2,011

819 6,448 441 366 2,569

3,529 3,648 1,449 2,063 657

“Crisis ªve” total

4,868

2,468

2,558

6,309

10,866

15,926

10,643

11,346

1998

1999

2000

2001

Source: UNCTAD 2002, Annex Table B.7. Note: a. Annual average.

First, the FDI series (derived from balance of payments accounts) depicted in Figure 4 provide only partial coverage of FDI in these countries. According to the standard deªnition, FDI has three components: equity capital, inter-company debt, and reinvested earnings. As in many other countries, data series on FDI in these countries capture only equity capital and inter-company debt. The omission of the third component (retained earnings) can lead to an underestimation of the actual magnitude of FDI in a given host country depending on the history of MNE involvement and the source country proªle of FDI.15 The degree of underestimation could well be greater for Malaysia and its long history as an attractive destination of FDI. Second, in Thailand and Korea acquisition by foreign companies of assets or equity of domestic companies has been an important component of foreign capital inºows during this period (Table 4). Despite the severity of the downturn, corporate distress was far less widespread in Malaysia than elsewhere, and there were simply fewer “bargain assets” for mergers and takeovers. Moreover, unlike Korea and Thailand, Malaysia did not resort to promoting acquisition and takeovers by foreign companies as part of the ongoing process of corporate and banking restructuring. During 1998–00, capital inºows relating to mergers and acquisitions amounted to US$ 20.5 billion in Korea (203 percent of total net FDI inºows) and US$ 7.7 billion (50 percent) in Thailand, compared to US$ 2.7 billion (17.2 percent) in Malaysia.16 It is also important to note that, accordingly to the FDI classiªcation system adopted by BNM in its balance of payments account, “purchase of existing plant and equipment” by foreigners is treated as “changes of ownership” not as “new investment.”

15

There is evidence that the component “retained earnings” in FDI is positively related with the age of operation of ªrms in a given country, and that U.S. MNEs have a general tendency to rely more on retained earnings for investment expansion compared to MNEs from other countries (Lipsey 2000).

16

Figures computed from UNCTAD World Investment Report database.

62



Because of this [sound] accounting practice, whatever takeovers that have happened are not reºected in ofªcial FDI ªgures, where as in Thailand and Korea value of these deals are captured in FDI data together with “true” investment (Ogus 2000). Third, compared to Korea and Thailand (particularly the former), Malaysia’s foreign investment regime had remained much more liberal for a long time, and in some sectors the presence of MNEs had already reached very high levels by the onset of the crisis. For instance, during 1990–96, FDI accounted for nearly 20 percent of gross domestic ªxed capital formation in Malaysia, compared to 4.4 percent in Thailand and a mere 0.8 percent in Korea (Athukorala 2007, Table 5.5). Thus the post-crisis increase in FDI in the former countries compared to Malaysia may, to a signiªcant extent, reºect “catching-up” entry by foreign ªrms following the new FDI liberalization initiatives. Third, in the immediate pre-crisis years, intra-regional inºows (particularly from Korea and Taiwan) accounted for over one-third of total FDI inºows to Malaysia. These ºows dwindled following the onset of the crisis. Portfolio investment Have portfolio investors deserted Malaysia as a punishment for its recalcitrant act? This question is important because, despite the disruptive role they played in the onset of the crisis, foreign portfolio inºows have important positive effects, when harnessed in an appropriate macroeconomic setting. They contribute to expansion in domestic investment by reducing cost of equity capital and helping ªrms to reduce their reliance on bank-based ªnancial intermediation (Williamson 1999). When the capital controls were ªrst introduced (and even after the new levy was introduced on 15 February) many observers were concerned about potential massive outºows of short-term foreign debt and portfolio investment after 1 September 1999. However, the ending of the 1-year moratorium turned out to be a non-event. Total net portfolio capital outºow in the fourth quarter of 1999 amounted to only US$ 2.2 billion, compared to a total stock of about US$ 10 billion potentially movable foreign portfolio investment remained in the country at the time the restriction was lifted (IMF 1999a, p. 98). Net outºows turned out to be positive by mid January 2000 and the ªrst quarter of the year recorded a total net inºow of US$ 2.4 billion. This investment pattern suggests that investors do not ªnd it difªcult to factor in the new proªt tax on portfolio investment, as ground rules are now more transparent in a context where signs of economic recovery are already clearly visible. The resurgence of portfolio ºows also may be because new inexperienced investors replace the ones who have been buried, or because memories of all investors are generally short (DeLong 1999).

63



Figure 5. Net portfolio capital ºows to Malaysia, Korea, and Thailand, 1996Q1–2005Q2 (US$ million)

Source: Based on data compiled from IMF, International Financial Statistics database.

Immediately after the imposition of capital controls, Morgan Stanley Capital International (MSCI), International Finance Corporation (IFC) (the investment arm of the World Bank), and Dow Jones removed Malaysia from their capital market indices. Lack of transparency in new measures at the time controls were imposed and uncertainty about future growth prospects of the economy were as much an issue as the nature of the controls themselves. Following the introduction of market-friendly changes to capital controls and as the economy began to show clear signs of recovery, IFC and Dow Jones reinstated Malaysia in their global indices by the end of 1999. MSCI reinstated Malaysia in its global indices by June 2000. Following the sharp decline in the second half of 1997, net portfolio capital inºow to Malaysia remained virtually negligible for the next 5 years (Figure 5). There has, however, been a notable recovery from about mid 2003. Interestingly there is no notable difference between Thailand and Malaysia in terms of overall trends in portfolio inºows during the post-crisis years. From about mid 2003 in every quarter net inºows to Malaysia have exceeded those to Thailand. Hidden costs of capital controls Crisis management behind closed doors could well impede growth through various forms of resource misallocation. If the Malaysian authorities have made use of the breathing space provided by capital controls to rescue companies and banks that were rendered illiquid by the ªnancial panic (unable to rollover short-term credit) but were otherwise viable, then the underlying

64



growth prospects of the economy will remain intact. Alternatively, if bailouts assisted inefªcient (mostly politically linked) ªrms whose insolvency hastened by the high interest rates and lower aggregate demand, then growth prospects would have been impaired. Such rescue operation may also induce moral hazard by encouraging ªrms/banks to continue engaging in risky acts, in the hope that they will be rescued in the event of similar future crises. There is indeed anecdotal evidence of inappropriate rescue operations (Ariff 1999; Yap 2001). Signiªcant differences in discount rates applied to various assisted banks by Danahartha remain unexplained. Similarly, the criteria used by Danamodal in its decisions as to which banks should be given priority in injecting capital remain largely unexplained. But whether these opaque practices are unique to the capitalcontrol based crisis management in Malaysia is a debatable issue. Similar concerns have been raised relating to banking and corporate restructuring processes in Thailand, Korea, and Indonesia—countries that are riding the crisis without capital controls. Moreover, one can reasonably argue (along the lines of Krueger and Tornell 1999, for instance) that economic gains associated with the speedy implementation of banking and corporate restructuring in Malaysia might have compensated signiªcantly, if not totally, for these alleged costs.

7. Concluding remarks Given the Malaysian policymakers’ reluctance to an IMF-sponsored reform process, which was rooted in the country’s policy history, the capital control-based crisis resolution policy package was a logical policy choice. Once the Malaysian authorities decided to deviate from the IMF route and follow the conventional Keynesian recipe for crisis management, capital controls seems to have provided a conducive setting for the effective pursuance of such policies. The new policy prevented massive capital outºow and permitted sustaining signiªcant interest rate differential with the rest of the world. Against the popular perception that short-term capital ºows cannot be controlled in an economy that is highly integrated in the global economy, the Malaysian evidence suggests that this can in fact be effectively done (at least on the margin)—provided the controls are speciªcally targeted at short-term capital transactions and are forcefully implemented. The ªxed exchange rate has helped the recovery process by preventing premature exchange rate appreciation as part of improved market sentiments about the recovery prospects. There is no evidence to suggest that controls on short-term capital ºows have adversely affected Malaysia’s image as a favorable location for FDI. The time-honored (and yet much-neglected in the current debate on crisis management) dictum that

65



the long-term investment is determined by quite different factors compared to “hot money” movements is reconªrmed by the Malaysian experiment. Nor have foreign portfolio investors permanently deserted Malaysia as a punishment for the restraints imposed on them during the crisis. Here, the lesson is that the use of capital control is unlikely to have an adverse lingering effect on foreign portfolio investment, provided timely steps are taken to infuse greater ºexibility and transparency to the regulatory mechanism and the reform process brings about speedy economic recovery and the controls are implemented in a transparent fashion strictly in line with the original plan. One can still dispute the argument that controls have played a special role in delivering a superior recovery outcome for Malaysia (compared to the IMF-program countries) for want of counterfactuals. However, the fact remains that the new policy measures enabled Malaysia to achieve recovery while minimizing social costs and economic disruptions associated with a more market-oriented path to reform. This is a signiªcant achievement because maintaining social harmony is an overriding concern (quite apart from economic efªciency consideration) of economic policymaking in ethnically diverse Malaysia. Even if the bloody racial riots in Kuala Lumpur in 1969 are ignored as a distant event, the imminent ethnic conºict brought about by the modest economic downturn in Malaysia in the mid 1980s cannot be entirely overlooked. There is indeed anecdotal evidence of inappropriate rescue operations. But whether these costly practices are unique to the capital-control based crisis management in Malaysia is a debatable issue. Moreover, one can reasonably argue that economic gains associated with the speedy implementation of banking and corporate restructuring in Malaysia might have compensated signiªcantly, if not totally, for these alleged costs. It is pertinent to end this paper with an important caveat. The inference that capital controls have helped crisis management in Malaysia by no means implies that Malaysia’s radical policy shift should be treated as a ready-made alternative to the conventional IMF recipe by other developing countries. It is of course hazardous to draw general policy lessons from the study of an individual country case. With the beneªt of hindsight, one can reasonably argue that a number of factors speciªc to Malaysia as well as to the timing of policy reforms may have signiªcantly conditioned the actual policy outcome. Thanks to long-standing prudential controls on foreign borrowing, Malaysia succumbed to the crisis with limited foreign debt exposure. With a vast domestic reve-

66



nue base and ready access to “captive” domestic ªnancial sources (in particular the Employees Provident Fund [EPF] and the oil-rich Petronas), the Malaysian government was relatively better positioned than perhaps any other crisis country to make a decisive departure from the conventional, IMF-centered approach to crisis management. Additionally, the implementation of new controls was aided greatly by a well-developed banking system, which was able to perform most of the new functions smoothly in the normal course of their business. In the management of the controls it also helped that Malaysia had both a disciplined banking system and a competent central bank. Not all countries are so blessed. The imposition of capital controls coincided with a signiªcant upturn in manufactured exports (mostly as a result of dramatic recovery in the world electronics trade). Therefore there was no shortage of foreign exchange. The particular exchange rate parity, presumably chosen arbitrarily by the Malaysian policymakers, eventually turned out to be a highly realistic rate, as the market panic against Asian economies began to subside and the yen began to appreciate against the U.S. dollar. Given the availability of ample foreign exchanges for trade and FDI-related activities at a realistic exchange rate there was no panic buying leading to foreign exchange manipulation. Any policy inference from the particular Malaysian experience needs to be appropriately qualiªed for these speciªc circumstances.

Appendix 1. Malaysia: A chronology of crisis management, 1997–05 1997 14–15 May 2 July 8 July 14 July 24 July 4 August 28 August 3 September 5 September 15 September 17 October 5 December 27 December

Bank of Thailand intervened to defend baht from attack by speculators. Bank of Thailand abandoned the long-standing peg of the baht to the US$. The new market determined rate (US$/Baht ⫽ 30) reºected about 18 percent depreciation against the US$. Bank Negara Malaysia (BNM) (the Malaysian Central Bank) intervened aggressively to defend the ringgit, boosting the currency to a high of 2.5100 after a low of 2.5240. BNM gives up the defense of the ringgit after unsuccessfully defending it by jacking up interest rates to 50 percent and spending an estimated US$ 3 billion. The Malaysian ringgit plunged to a 33-month low. Ringgit hit 38-month low of 2.653 to US$, and Prime Minister Dr. Mahathir launches bitter attack on “rogue speculators.” BNM instructed commercial banks to observe a US$ 2 million limit on non-commercial ringgit offer-side transactions with each foreign customer. KLSE banned short selling of 1,000 blue-chip stocks. A plan was announced to use funds from the Employees Provident Fund (EPF) to prop up share prices by buying stocks from Malaysian shareholders, but not from foreigners, at a premium above the prevailing prices. Ban on short selling of KLSE-linked stocks was lifted. BNM increased the 3-month intervention rate from 6 percent to 7.55 percent. Finance Minister Anwar Ibrahim unveiled the 1998 budget-cutting infrastructure spending, increasing import duties, and including breaks for expenditures aimed at narrowing the current account deªcit. The corporate tax rate was cut from 20 percent to 28 percent to stimulate investment. Finance Minister Anwar Ibrahim announced an austerity package. He announced a reduction of the growth forecast for 1998 from 4–5 percent to 7 percent and that for 1997 to 7.5–7.7 percent from 8.0 percent. BNM increased its 3-month intervention rate from 7.55 percent to 8.7 percent.

1998 1 January 2 January

67

BNM reduced the period in arrears (default period) for classifying a loan as non-performing by banking institutions for six months to three months, with a view to strengthening prudential supervision. BNM instructed Malaysia’s 39 ªnance companies to begin merger talks.



Appendix 1. (Continued) 1998 (continued) 7 January

9 January 6 February 9 February 16 February 24 March 1 June 1 July 23 July

31 July 1 August

3 August 10 August 17 August 25 August 31 August 1 September

2 September

3 September 4 September 7 September 9 September 10 September 16 September 23 September

5 October 23 October 9 November

68

The National Economic Action Council (NEAC) was established as a consultative body to the Cabinet to deal with the economic crisis. Daim Zainuddin, who was ªnance minister during the economic crisis of the late 1980s (and a close conªdant of Dr. Mahathir), was appointed as the executive director of NEAC. The Finance Minister conªrmed that BNM had moved to stem the slide in ringgit. Government claimed sufªcient foreign reserves and ruled out an IMF rescue. BNM increased its 3-month intervention rate from 10 percent to 11 percent. BNM lowered the statutory reserve requirement (SRR) from 13.5 percent (a rate which had prevailed from 1 January 1996) to 10 percent to help check the extraordinary rise in bank lending rates. The SRR for commercial banks, ªnance companies, and merchant banks was reduced from 13.5 percent to 10 percent of their eligible liabilities. BNM introduced new measures for banks to shore up capital-adequacy position at the ªrst sign of trouble. BNM also announced that three ªnancial institutions—Bank Bumiputera, Abrar Finance, and Cempaka Finance—might need to be recapitalized by a total of 782 million ringgit (US$ 2.08 billion). Pengurusan Danaharta Nasional Berhad (National Asset Management Company) was set up to acquire and manage NPLs of the banking institutions. SRR was reduced from 10 percent to 8 percent. NEAC launched the National Recovery Plan. Its recommendations (in particular an exchange rate system that reduces volatility, a shift away from high interest policy, and easing of ªscal and monetary policy) did indicate signiªcant departure from conventional IMF lines, but there was no hint about imposition of capital control. A new framework for liquidity management in banking institutions was introduced. Danamodal Nasional Berhad (Banking Recapitalization Agency) was set up to recapitalize banking institutions. The Corporate Debt Restructuring Committee, a joint public and private sector steering committee, was set up to facilitate and expedite corporate debt restructuring. BNM’s market intervention rate was reduced from 11 percent to 10.5. BNM’s market intervention rate was reduced from 10.5 percent to 10 percent. BNM’s market rate was reduced from 10.5 percent to 10 percent. BNM reduced market intervention rate was reduced from 10 percent to 9.5 percent and ruled out any possibility of introducing capital controls. Tun Diam Zainuddin was appointed Minister of Special Functions to oversee the Malaysian economy. The media interpreted this as a calculated move to reduce Anwar’s role in crisis management. Overseas trading of Malaysian securities was banned. KLSE announced that trading on Singapore’s over-the-counter Central Limit Order Book (CLOB) market was no longer recognized. A wide range of foreign exchange and capital controls were introduced, substantially insulating Malaysia’s ªnancial markets from external inºuences and effectively closing down the offshore ringgit market (See Appendix 2). The SRR was reduced from 8 percent to 6 percent. The base lending rate (BLR) framework was revised to ensure a more rapid transmission of changes in monetary policy on lending rates charged by banks. The exchange rate for the ringgit was ªxed at 3.80 per U.S. dollar, a rate which was stronger than the average rate of 4.18 for the previous two months, but signiªcantly below its pre-crisis level of about 2.49. Prime Minister Mahathir sacked his deputy and the Finance Minister Anwar Ibrahim on grounds of immorality. Liquidity-asset ratio requirement for commercial banks was reduced from 17 percent to 15 percent with immediate effect. BNM’s market intervention rate was reduced from 9.5 percent to 8 percent. Dr. Mahathir became the Acting Minister of Finance. BNM relaxed ceilings on bank lending to the property sector. Dr. Mahathir became First Finance Minister and Mustapa Mohamed (former Minister of Entrepreneur Development) was appointed Second Finance Minister. BMN instructed the banks to aim at achieving a minimum annual loan growth of 8 percent by the end of the year. SRR was reduced from 6 percent to 5 percent. Salomon Smith Barney was appointed ªnance advisor to the government and Danamodal Nasional Berhad. SRR was reduced from 5 percent to 4 percent. Liquidity asset requirement of commercial banks was reduced from 17 percent to 15 percent. Ceiling on loans for the purchase of shares and unit trust funds was raised from 15 percent to 20 percent of total outstanding loans for commercial banks and ªnance companies while leaving the ceiling on merchant banks at 30 percent. Default period for classifying a loan as non-performing by banking institutions was increased from 3 months to 6 months. BNM’s market intervention rate was reduced from 8 percent to 7.5 percent. Limits on bank lending to housing and real estate were relaxed. The 1999 budget proposed a signiªcant ªscal stimulant package involving an increase in the budget deªcit. BNM’s market intervention rate was reduced from 7.5 percent to 7 percent.



Appendix 1. (Continued) 1998 (continued) 20 November 21 November 5 December

The minimum monthly repayment on outstanding credit card balances was reduced from 15 percent to 5 percent. Banking institutions were instructed to establish Loan Rehabilitation Units to manage problem loans. Hire-purchase guidelines were abolished, allowing banking institutions to determine their own hirepurchase loans. The maximum annual lending rate under the Fund for Small and Medium Industries and the Scheme for Low and Medium Cost Houses was reduced from 10 percent to 8.5 percent.

1999 4 January 10 January 14 February 18 February 26 May 29 July 7 August 9 August 21 September 21 October

Banking institutions were instructed to achieve a minimum loan growth of 8 percent by the end of 1999. BNM took control of MB Finance Berhad, the biggest ªnance company (with assets amounting to about US$ 5 billion, one-fourth of total assets of all ªnance companies) on grounds of weak management. The 12-month holding rule on repatriation of foreign portfolio capital was replaced with a three-tier exit levy on the principal and proªt. Repatriation of funds relating to investment in immovable property was exempted from the exit levy. BNM raised US$ 1 billion through a global bond issue. The issue was oversubscribed by 300 percent. BNM unveiled a plan to combine the country’s 58 ªnancial institutions (22 commercial banks, 11 merchant banks, and 25 ªnance companies) into six large banking groups. Residents were allowed to grant overdraft facilities in ringgit not exceeding RM 200 million for intra-day and not exceeding RM 500 million for overnight to foreign stock-brokering companies subject to certain conditions. BNM’s intervention rate was reduced from 7 percent to 5 percent. The three-tier levy on repatriation of portfolio capital was replaced with a ºat 10-percent levy on proªt repatriated. Commercial banks were allowed to enter into short-term currency swap arrangements with non-resident stockbrokers for a maturity period not exceeding 5 working days with no rollover option.

2000 14 March 30 September 27 October 15 December 20 December

Funds arising from sales of securities purchased by non-residents on the CLOB market were permitted to be repatriated without paying exit levy. Licensed offshore banks in the Labuan Offshore Financial Centre were allowed to invest in ringgit assets from their own account only and not on behalf of clients. The investment could not be ªnanced by ringgit borrowing. Proªt earned from foreign portfolio investment in Malaysia for a period of more than 1 year was exempted from the 10-percent repatriation duty. The 10-percent levy on proªts earned from foreign portfolio investment repatriated within 1 year was abolished. Licensed commercial banks were allowed to extend intra-day overdraft facilities not exceeding RM 200 million and overnight facilities not exceeding RM 10 million to foreign stockholding companies and foreign custodian banks.

2001 6 January 21 November

All controls on the trading of futures and options on the Malaysian stock exchange were abolished. Licensed banks were allowed to extend credit facilities to non-residents up to an aggregate of RM 5 million to ªnance projects undertaken in Malaysia.

2002 12 March 3 August

The RM 10,000 ceiling on foreign currency loans to residents for investment overseas was removed. The requirement for using only ringgit for settlement of transaction on ringgit-denominated assets between residents and non-residents and between non-residents was abolished. Banks were permitted to extend ringgit overdraft facilities not exceeding RM 500,000 in aggregate to nonresidents provided the credit facilities are fully covered at all times by ªxed deposits placed by the nonresident customer with the lending bank.

2003 4 January

The maximum amount of repatriation of proªts, dividends, rental income, and interests on all bona ªde investments without prior approval was increased from RM 10,000 to RM 50,000, or its equivalent in foreign currency. Residents who have foreign currency funds were permitted to invest freely in any foreign currency products offered by onshore licensed banks. The ceiling on bank loans to non-residents (excluding stock-brokering companies, custodian banks and correspondent banks) was raised from RM 200,000 to RM 10,000,000.

2005 21 July

BNM announced abolition of the ringgit peg to the US$ in favor of a managed ºoating system.

Source: Compiled from Bank Negara Malaysia, Annual Report (various years), press releases and exchange notices (www.bnm.gov.my), and IMF, Annual Report on Exchange Arrangements and Exchange Restrictions (various years).

69



Appendix 2. Malaysia: Exchange control measures prior to and after 1 September 1998 Transaction

Prior to 1 September 1998

New

(1) Transfers based on external accounts

Transfer between external account holders freely allowed

Transfer of any amount between external accounts requires prior approval. Source of funding external accounts are limited to: (a) proceeds from sale of ringgit instruments, securities registered in Malaysia, or other assets in Malaysia; (b) salaries, wages, commissions, interests, or dividends; and (c) sales of foreign currency. Use of funds in accounts is limited to purchase of ringgit assets in Malaysia.

(2) General payments

Residents were freely allowed to make payments to non-residents for any purpose. Amounts of RM 100,000 and above were permitted provided the resident does not have any domestic borrowing (if the payment is for investment abroad), or the payment is made in foreign currency (for non-trade purposes).

Residents are freely allowed to make payments to non-residents for any purpose up to RM 10,000 in ringgit or foreign currency, except for imports of goods and services. Amounts exceeding RM 10,000 require approval and are allowed in foreign currency only.

(3) Export of goods

Payments to be received in foreign currency or ringgit from an external account.

Payments are to be received from an external account in foreign currency only.

(4) Credit facilities to nonresidents

Non-resident correspondent banks and stock-brokering companies were permitted to obtain credit facilities up to RM 5 million from domestic banks to fund mismatch of receipts and payments in their external accounts.

Domestic credit facilities to nonresident corresponding banks and non-resident stock-brokering companies are no longer allowed.

(5) Investment abroad

Corporate residents with domestic borrowing were allowed to invest abroad up to the equivalent of RM 10 million per calendar year on a corporate group basis.

Residents with no domestic borrowing are allowed to make payment to non-residents for investment abroad up to an amount of RM 10,000 or its equivalent in foreign currency per transaction.

(6) Credit facilities from nonresidents

Residents were allowed to obtain ringgit credit facilities of below RM 100,000 in the aggregate from nonresident individuals.

All residents require prior approval to make payments to non-residents for investing abroad an amount exceeding RM 100 equivalent in foreign currency. Residents are not allowed to obtain ringgit credit facilities from any non-resident individual.

(7) Trading in securities

There were no restrictions on secondary trading of securities registered in Malaysia between residents and non-residents and among non-residents. For transfer of securities registered outside Malaysia from a nonresident to a resident, the resident was subject to the rules on investment abroad.

Ringgit securities held by nonresidents must be transacted through an authorized depositor. All payments by non-residents for any security registered in Malaysia must be made from an external account (in foreign currency or in ringgit). All proceeds in ringgit received by a non-resident from the sale of any Malaysian security must be retained in an external account at least for 1 year before converting to foreign currency. All payments to residents for any security registered outside Malaysia from non-residents must be made in foreign currency.

70



Appendix 2. (Continued) (8) Import and export of currency notes, bills of exchange, insurance policies, etc.

A resident or non-resident traveler was free to import or export any amount of ringgit notes or foreign currency notes in person. Export of foreign currencies required approval. Authorized currency dealers were allowed to import any amount of ringgit notes, subject to reporting to Bank Negara Malaysia on a monthly basis.

A resident traveler is permitted to bring ringgit notes up to RM 1,000 only and any amount of foreign currencies. A resident traveler is permitted to export ringgit notes only up to RM 1,000 and foreign currencies up to the equivalent of RM 10,000. A non-resident traveler is permitted to import ringgit notes up to RM 1,000 only and any amount of foreign currencies. A non-resident traveler is permitted to export ringgit notes up to RM 1,000 only and foreign currencies up to the amount brought into the country.

(9) Transaction in the Labuan Offshore Financial Centre

Licensed offshore banks were allowed to trade in ringgit instruments up to permitted limits.

Licensed offshore banks are no longer allowed to trade in ringgit instruments.

Source: Compiled from Bank Negara Malaysia, Quarterly Bulletin, Second Quarter 1998; Kuala Lumpur; and IMF (1997).

References Ariff, Mohamed. 1999. The Financial Crisis and the Reshaping of the Malaysian Economy: Trends and Issues. Unpublished paper. Kuala Lumpur: Malaysian Institute of Economic Research. Athukorala, Prema-chandra. 1998. Malaysia. In: East Asia in Crisis: From Being a Miracle to Needing One?, edited by Ross H. McLeod and Ross Garnaut, pp. 85–101. London: Routledge. Athukorala, Prema-chandra. 2003. FDI in Crisis and Recovery: Lessons from the 1997–98 Asian Crisis. Australian Economic History Review 43 (2):197–213. Athukorala, Prema-chandra. 2007. Multinational Enterprises in Asian Development. Cheltenham, UK: Edward Elgar. Athukorala, Prema-chandra, and Peter G. Warr. 2002. Vulnerability to a Currency Crisis: Lessons from the Asian Experience. World Economy 25 (1):33–57. Bhagwati, Jagdish. 1998. The Capital Myth: The Difference between Trade in Widgets and Dollars. Foreign Affairs 77 (3):7–12. BIS (Bank of International Settlement). 1998. International Banking and Financial Market Development. Basel, Switzerland: BIS. BNM (Bank Negara Malaysia). 1999a. Annual Report of the Board of Directors for the Year Ended 31 December 1998. Kuala Lumpur: BNM. BNM (Bank Negara Malaysia). 1999b. The Central Bank and the Financial System in Malaysia. Kuala Lumpur: BNM. Booth, Anne. 1999. Initial Conditions and Miraculous Growth: Why Is Southeast Asia Different from Taiwan and South Korea? World Development 27 (2):301–321. Bruton, Henry J. 1993. The Political Economy of Poverty, Equity and Growth: Sri Lanka and Malaysia. New York: Oxford University Press.

71



Cooper, Richard. 1998. Comments on the paper by Radelet and Sachs. Brookings Papers on Economic Activity 1:90–92. Cooper, Richard N. 1999. Should Capital Controls Be Banished? Brooking Papers on Economic Activity 1:89–141. Corden, W. Max. 2003. Too Sensational: On the Choice of Exchange Rate Regimes. Cambridge, MA: MIT Press. Corden, W. Max. 2007. The Asian Crisis: A Perspective after Ten Years. Asian-Paciªc Economic Literature 21 (1):1–12. Crouch, Harrold. 1998. The Asian Economic Crisis and Democracy. Public Policy 11 (3):39–62. DeLong, J. Bradford. 1999. Financial Crises in the 1980s and the 1990s: Must History Repeat? Brookings Papers on Economic Activity 2:253–294. Doreisami, Anita. 2004. From Crisis to Recovery: The Motivations for and Effectiveness of Malaysian Capital Controls. Journal of International Development 16 (2):241–254. Dornbusch, Rudigar. 2002. Malaysia’s Crisis: Was It Different? In: Preventing Currency Crises in Emerging Markets, edited by Sebastian Edwards and Jeffrey A. Frankel, pp. 441–455. Chicago: Chicago University Press. Edison, Hail J., and Carmen M. Reinhart. 2000. Capital Controls During Financial Crises: The Case of Malaysia and Thailand. International Finance Discussion Papers No. 62, New York: Federal Reserve Bank of New York. Edwards, Sebastian. 1999. A Capital Idea? Reconsidering a Financial Quick Fix. Foreign Affairs 78 (3):18–22. Eichengreen, Barry. 2003. Capital Flows and Crises. Cambridge, MA: MIT Press. Fisher, Stanley. 2004. IMF Essay from a Time of Crisis: International Financial System, Stabilization and Development. Cambridge, MA: MIT Press. Haggard, Stephen. 2000. The Politics of the Asian Financial Crisis. Journal of Democracy 11 (2):130–145. Hale, David. 1998. The Hot Money Debate. The International Economy November/December:8– 12; 66–69. Hiebert, Murray. 1999. Capital Idea? Far Eastern Economic Review 162 (26):55. Hill, Hal. 1998. Malaysian Roulette. Asian Wall Street Journal, 9–10 October. Hood, Ron. 2001. Malaysian Capital Controls. Policy Research Working Paper No. 2536. Washington, D.C.: World Bank. IMF. 1999a. Public Information Notice No. 99/88: IMF Concludes Article IV Consultation with Malaysia. Washington, D.C.: IMF (www.imf.org/external/np/sec/pn/19999/pn9988.htm). IMF. 1999b. International Capital Markets: Developments, Prospects, and Key Policy Issues. Washington, D.C.: IMF. IMF. 2000. Public Information Notice No. 00/63: IMF Concludes Article 1V Consultation with Malaysia. Washington, D.C.: IMF (www.imf.org/external/pn/sec/pn006.3.htm). Jomo, K. S. 2004. Were Malaysia’s Capital Controls Effective? In: After the Storm: Crisis, Recovery

72



and Sustaining Development in Four Asian Economies, edited by K. S. Jomo, pp. 173–203. Singapore: Singapore University Press. Kaminsky, Graciela, and Sergio Schmukler. 2001. Short-lived or Long Lasting? A New Look at the Effects of Capital Controls. In: Brooking Trade Forum 2000, edited by Susan Collins and Dani Rodrik, pp. 125–178. Washington, D.C.: Brookings Institution. Kaplan, Ethan and Dani Rodrik. 2002. “Did the Malaysian Capital Controls Work.” In Preventing Currency Crises in Emerging Markets, edited by Sebastian Edwards and Jeffrey A. Frankel, pp. 393–441. Chicago: University of Chicago Press. King, Mervyn. 1999. Reforming the International Financial System: The Middle Way. Finance Stability Review 7:203–211. Krueger, Anne O., and Aron Tornell. 1999. The Role of Bank Restructuring in Recovering from Crisis. NBER Working Paper No. 7042. Cambridge, MA: National Bureau of Economic Research. Krugman, Paul. 1998. Saving Asia: It’s Time to Get Radical. Fortune 138 (5):74–80. Krugman, Paul. 1999. The Return of Depression Economics. New York: Norton and Company. Larrain, Felipe, and Raul M. Laban. 2000. Private Capital Flows to Chile in the 1990s: Causes, Effects and Policy Reactions. In: Capital Flows, Capital Controls, and Currency Crises: Latin American in the 1990s, edited by Felipe B. Larrain, pp. 223–250. Ann Arbor, MI: University of Michigan Press. Lim, Linda. 1999. Malaysia’s Response to the Asian Financial Crisis. Statement before the Subcommittee on Asia and the Paciªc Committee on International Relations, U.S. House of Representatives, June, www.house.gov/international_relations Lipsey, Robert E. 2000. The Role of Foreign Direct Investment in International Capital Flows. In: International Capital Flows, edited by Marin Feldstein, pp. 307–330. Chicago: University of Chicago Press. Mahani, Zainal Abidin. 2002. Rewriting the Rules: The Malaysian Crisis Management Model. Singapore: Prentice Hall. Mahathir, Dato’ Seri Bin Mohamad. 1998. Currency Turmoil: Selected Speeches and Articles by Prime Minister of Malaysia. Kuala Lumpur: Lomkokwing Integrated. Meade, James. 1951. The Theory of International Economic Policy, Volume 1: The Balance of Payments, chapter XXII. London: Oxford University Press. Miller, Merton H. 1999. Reºections of a Retiring Keynote Speaker. Keynote address at the 10th Annual Conference of PACAP/FMA, 25–26 July, Singapore. Narayanan, Suresh. 1996. Fiscal Reform in Malaysia: Behind a Successful Experience. Asian Survey 36 (9):869–881. NEAC (National Economic Action Council). 1998. National Economic Recovery Plan: Agenda for Action. Kuala Lumpur: Prime Minister’s Department. Ogus, Simon. 2000. Malaysian Twilight Zone. The International Economy May/June:44–47. Radelet, Steve, and Jeffrey Sachs. 1998. The East Asian Financial Crisis: Diagnosis, Remedies, Prospects. Brookings Papers on Economic Activity 2:1–89.

73



Rojas-Suarez, Liliana. 2002. Comment [on Kaplan and Rodrik 2002]. In: Preventing Currency Crises in Emerging Markets, edited by Sebastian Edwards and Jeffrey A. Frankel, pp. 432–437. Chicago: Chicago University Press. Searle, Peter. 1999. The Riddle of Malaysian Capitalism: Rent-seekers or Real Capitalists? Sydney: Allen & Unwin. Siamwalla, Amar. 2000. Anatomy of the Thai Economic Crisis. Bangkok: Thailand Development Research Institute. Snodgrass, Donald R. 1980. Inequality and Economic Development in Malaysia. Kuala Lumpur: Oxford University Press. Soros, George. 1998. The Crisis of Global Capitalism. London: Little, Brown and Company. Stiglitz, Joseph E. 2002. Globalization and Its Discontents. New York: W.W. Norton. Williamson, John. 1993. A Cost Beneªt Analysis of Capital Account Liberalisation. In: Financial Opening: Policy Issues and Experiences in Developing Countries, edited by Melmut Reisen and Bernhard Fischer, pp. 25–34. Paris: OECD. Williamson, John. 1999. Development of the Financial System in Post-Crisis Asia. Paper presented at the High-Level Dialogue on Development Paradigms, 10 December, Tokyo. Williamson, John, and Molly Mahar. 1998. A Survey of Financial Liberalisation. Essays in International Finance No. 211. Princeton, NJ: International Finance Section, Princeton University. World Bank. 1996. Managing Capital Flows in East Asia. Washington, D.C.: The World Bank. Yap, Michael. 2001. The 1997 Currency Crisis and Subsequent Policy Response. In: Modern Malaysia in the Global Economy: Political and Social Change into the 21st Century, edited by Colin Barlow, pp. 45–58. Cheltenham, UK: Edward Elgar. Yousof, Zainal Aznam, Awang A. Hussain, Ismail Alowi, Lim Chee Sing, and Sukhdav Singh. 1994. Financial Reforms in Malaysia. In: Financial Reforms: Theory and Evidence, edited by Gerald Caprio, Izak Atias and James A. Hanson, pp. 276–322. Cambridge: Cambridge University Press.

74


Comments

Comments

Hongzhong Liu: The author contributes to the debate about whether Malaysian capital control is successful or not by paying more attention to the economic and sociopolitical circumstances. My comment is based on some broad questions. What is the criterion to judge success? Who is asked to answer the question? Of course, it is Malaysia’s Prime Minister Mahathir who made the ªnal decision, and he thought he was successful. However, today, the debate asks which policy—IMF-designed or capital control—is more effective when making comparisons among Malaysia, Thailand, and South Korea. I would like to raise two questions followed by my understanding. First, why did Malaysia, actually Mahathir, take the unorthodox policy? What are its implications to other countries, such as China? Second, from what perspectives should comparisons be made? (Or what is the criterion?) First, the crisis began in May 1997 and the capital control policy was implemented in September 1998. Compared with the conventional policy, designed by the IMF, the capital control policy was more risky. That is why, as Professor Zainal Aznam Yusof explained in his comments on this paper, there was a long debate on this topic in Malaysia before September 1998. As the theory of regret tells us, in order to avoid the pain of regret, people tend to follow a safe route instead of being innovative. However, in the case of Malaysia, it did not take the less risky approach proposed by IMF, which Thailand, Korea, Indonesia, and the Philippines followed. Theoretically, if Malaysia chose the same policy, and if it failed in controling the crisis, then the IMF should be blamed. Why not? Asian Economic Papers 7:1


Comments

The ªrst possible explanation might be related to the “representative heuristic” hypothesis: people tend to make decisions under uncertainty based on speciªc (arbitrary) experiences. In the mid 1980s, Malaysia succeeded in managing that crisis on its own. During January and February 1994, the Bank Negara Malaysia (the central bank) imposed a number of restrictions on capital inºow, which were quite successful. Therefore, the representative heuristic might lead to overconªdence, which might result in adopting the risky policy. The second possible explanation might be related to the mental accounting or framing effect. As Professor Peter Blair Henry argued, Mahathir seemed to link the stepping down of President Suharto in May 1998 with Indonesia’s signing of the IMF agreement. On 24 June 1998, Mahathir placed Daim Zainuddin in charge of economic policy. On 1 September, Mahathir imposed capital control. The next day, he ªred Anwar, the Finance Minister. If that is the case, Mahathir might think of failing to handle the crisis as a loss instead of treating the successful management of the crisis as a gain. This is in line with prospect theory which argues that people tend to be risk-seeking when making decisions concerning loss, but tend to be risk-averse when making decisions concerning gain. There might be other behavioral explanations, such as disjunction effect. Now I address the second question that asks from what perspective the policies should be compared. The paper argued that Malaysian capital control is successful mainly from the macroeconomic perspective. Other studies, such as Simon Johnson’s, argued that the capital control did not yield major beneªt but were not costly in macroeconomic terms. However, Simon Johnson also argued that the stock market interpreted the capital control as beneªting the politically connected ªrms relative to unconnected ªrms. I wonder whether the author’s conclusion will be different if he were to take the microeconomic perspective of judging success and pay more attention to the performance of the banking industry in Malaysia’s bank-dominated ªnancial system. In so doing, we may have better understanding of (1) why private investment has recovered so slowly in Malaysia, and (2) why portfolio capital came back to Malaysia so quickly. For the ªrst question, I would guess, the answer might be due to the restructuring of the banking sector followed by the tightening of loan standards to prevent large non-performing loans in the future, and the tightening of credit. As to the second question, it might be attributed to overreaction of the portfolio capital before and after the capital control policy. Once investors regained their conªdence, portfolio capital outºow turned into inºow suddenly and quickly.

76


General Discussion

Summary of General Discussion on “The Malaysian Capital Controls: A Success Story?” by Prema-chandra Athukorala

Bhanupong Nidhiprabha thought that the paper was interesting because it provided the analytical background to Thailand’s recent attempts at capital controls, although the objective in Thailand was not to avoid currency depreciation. He noted that foreign investors recently rated Thailand as the worst country in the region for investments and wondered whether this could have been caused by the capital controls. Other participants suggested that this negative assessment could instead be explained by factors like the turbulent political development associated with the military coup and the trouble in the south of Thailand. Muhammad Chatib Basri made the interesting point that Indonesia did consider the imposition of capital controls during the Asian ªnancial crisis, but ªnally decided against them. The decision was based on the judgment that capital controls needed a clean and competent administration to implement them, a condition that could not be met in Indonesia. Is it credible that the Malaysian administration was so much less corrupt? Athukorala replied that an incorruptible administration was obviously an advantage but that one should note that there are different types of capital controls, each with different requirements on the administration. Pakorn Vichyanond added that capital controls were not only difªcult to implement and sustain, but that one should also not underestimate the psychological impact of such controls on future risk premia. Donald Hanna said that from the perspective of an investment banker, the uncertainty and nervousness among foreign investors were probably higher than portrayed in the



General Discussion

paper. Athukorala agreed that this could be the case, but nervousness in his experience was usually short-lived. In fact, just a few weeks after the introduction of the controls, most foreign investors applauded capital controls. Hanna also expressed the view that it was not a coincidence that capital controls were immediately introduced before Anwar Ibrahim was sacked from the government. If not, the sacking would presumably have led to a massive capital ºight. Finally, Hanna noted that Malaysia did pay a price for the capital controls in the ªrst 6 to 8 months because substantial capital returned to East Asia, but not to Malaysia. Maria Socorro Gochoco-Bautista said that oftentimes capital controls are discussed as an emergency measure to make the economy recover from a crisis, but maybe they could be implemented to prevent a crisis. Athukorala was skeptical and stressed that there were likely to be long-term negative effects of capital controls on economic growth.

78


Indonesia’s Slow Recovery

Indonesia’s Slow Recovery After Meltdown

Iwan J. Azis Cornell University West Sibley Hall 213 Ithaca, New York 14853 [email protected]

Abstract

Although signs have emerged that some of the forces that caused the 1997–98 Asian financial crisis have begun to diminish and progress has been made in macroeconomic affairs, 10 years after the meltdown Indonesia’s recovery is still among the slowest in Asian crisis countries. During the last few years, the relatively rapid growth of the financial sector (inadequately restructured) reflects the presence of excess liquidity and the sector’s vulnerability. The slow growth of investment explains the economy-wide effects on the real sector and the stagnancy or deterioration of some social indicators. This paper focuses on two issues related to the slow recovery: the financial structure of lenders and borrowers that dampened credit, and the dismal performance of regional growth following the 2001 decentralization policy. There is some evidence indicating that agency costs have slowed credit and investment growth (credit channel), and that institutional constraints produced a lack of growth incentives among local governments. Efforts to raise the sub-national welfare post-decentralization have also been constrained by national policies such as a tight budget and the relatively conservative monetary policy despite the fact that they are not too effective at controlling inflation. The decomposition analysis also shows that an aggregate demand expansion would have been effective to stimulate growth.

1. Introduction Since the Asian ªnancial crisis (AFC), Indonesia has made some progress in macroeconomic affairs. The government made early repayments to the IMF, and corporate leverage ratios and currency mismatches have declined. However, the persistently growing excess liquidity combined with low investment makes the country’s resilience to withstand shocks unclear. On the one hand, low investment fails to halt the rise of unemployment; on the other hand,




the high concentration of funds in the ªnancial sector raises this sector’s vulnerability. The share of short-term borrowing remains high especially among local corporations, and, as market volatility in mid 2006 has shown, the vulnerability associated with default probabilities and credit spreads is still high. Fears of a repeat of the 1997 crisis surfaced as capital inºows began to surge in 2006. Policymakers and pundits are quick to denounce the possibility, citing the ºoating exchange rate and the sizeable foreign reserves, unlike the conditions in 1997. Interestingly, however, forceful arguments rarely are made as to what happens with the strength of the corporate and ªnancial sector. Although inºows of capital are also faced by other East Asian countries, the challenge for Indonesia is compounded by the fact that its post-crisis recovery has been the slowest among those stricken by the AFC. Indeed, GDP growth since the crisis has been tepid. The comparative trend of the PPP-based GDP clearly shows that Indonesia’s recovery has lagged behind those affected by the AFC (Figures 1a and 1b). Following a major policy change toward decentralization in 2001, the growth performance in many sub-national regions has been also disappointing. The socio-economic repercussions of the slow growth (e.g., rising unemployment rate, poverty) are predictable, but the precise reasons are debatable. Certainly, conservatism in ªscal and monetary policy stands out. What has become the “menu of the day” among policymakers is to argue that non-economic factors such as weak legal framework, corruption, complicated bureaucracy, red tape, labor laws, and lack of awareness on the part of regional governments about ways to attract investors are all to blame. Arguments are even made about the adverse effect of democracy such as interventions by the parliament that caused delays and other problems in policy implementation. Although it is true that non-economic factors play a role, some explanations also originate in the economic policy. The latter could in fact inºuence the non-economic factors. What the economy needs during 10 years following the crisis is a real signiªcant push. The government’s budget, however, continues to lean toward tightness. Also puzzling is the conservatism in monetary policy until recently, despite the fact that it fails to lead the inºation rate to converge to trading partners’ rates. This paper explores the sources of Indonesia’s slow recovery by focusing on two accounts: the slow growth of credit and investment, and the sluggish performance of regional sub-national economies that induced a disappointing national growth. On the ªrst account, the role of agency costs is explored by simulating a ªnancial general equilibrium (FGE) model that merges the balance sheets of agents with the real sector and the aggregate demand. A decomposition analysis of aggregate supply

80



Figure 1a. PPP-based GDP index (1995

1)

Source: Processed from IMF calculations of PPP-based GDP.

Figure 1b. PPP-based GDP index (1995

1)

Source: Processed from IMF calculations of PPP-based GDP.

81



and demand is conducted in order to understand the policy implications of the slow recovery and high inºation more fully. It is revealed that the aggregate supply curve is relatively ºat, suggesting that a positive shock of aggregate demand would have been more effective to stimulate growth, but less effective to control inºation. This is contrary to the implicit assumption reºected in government policies. On the dismal performance of regional growth, the discussion focuses on economic and institutional factors, particularly their role after the 2001 decentralization policy.

2. Post-crisis socio-economic performance The post-crisis GDP growth in Indonesia has been less than 5 percent annually. Looking at the domestic demand, the 2002–04 performance produced the highest growth with accelerated rates of private consumption and investment. The exchange rate also strengthened. During the last 2 years, GDP grew faster but the rates of private consumption and investment (key variables for a sustained recovery) fell badly, and only exports and government spending rescued the overall growth (Figure 2). In the case of exports, favorable commodity prices and a weaker rupiah supported the surge, whereas the increase in government spending was driven by a series of natural disasters,1 rising debt payments (especially the domestic debt), and regional decentralization. This trend can be explained by the relationship between interest rates, credit, and investment during 2000–01, 2002–04, and 2005–06. At the early stage, slow growth was partly due to restrictive monetary and ªscal policies. The policy rate (Sertiªkat Bank Indonesia or SBI rate, later known as Bank Indonesia or BI rate) rose to reach almost 18 percent in December 2001. Interestingly, the lending rates did not change much (two rates converged) such that real credit and investment continued to grow well. However, starting in early 2002, sluggish growth began, despite persistent declines in the SBI rates. The disconnect between the monetary and real sectors lasted

1

Since the 2004 Tsunami, the country has been hit by several calamities, forcing the government to increase spending. Even assuming that there will be no more major disasters in the coming years, more spending is still needed to cover the damage caused by the mud-gushing disaster in East Jawa that started last year when a shaft probing for natural gas pierced a pressurized aquifer about 9,000 feet underground. About 1 billion cubic feet of mud has spread across two square miles, burying 12 villages and 20 factories, destroying thousands of houses and buildings, and damaging many infrastructure facilities and rice ªelds. No less than 15,000 people have been displaced, and the economic impact on the region (one of the most important provinces in the national economy) could be devastating. The estimated impact gets larger every day (in the trillions of rupiah). Since the company that caused this disaster (Lapindo Brantas) will unlikely cover the estimated damage, most spending will come from the government’s budget.

82



Figure 2. Annual growth of GDP and its components (percent) and exchange rate

Source: Author’s calculation based on data from BPS and Bank Indonesia.

until mid 2004. During this period, the stickiness of the lending rate widened the gap between the lending and the SBI rates (Figure 3).2 Real investment recovered only during the third quarter of 2004, but the trend did not continue. The credit growth remained sluggish despite the continued fall of lending rates until mid 2005. Amid a falling investment rate and the slow growth of credit, the monetary authority raised the SBI rate. There was no signiªcant pressure on core inºation, but the rupiah depreciated quickly reportedly due to the rising demand of imported oil by the state oil company Pertamina, which led to a surge in the dollar requirement. It is puzzling that the monetary authority continued to react to such a short-term exchange rate movement, despite the formal adoption of inºation targeting. The subsequent pressure came after the controversial policy to remove the domestic fuel subsidy in October 2005. Conªdent that the policy would have no serious socioeconomic impact (which was not the case), the subsidy was slashed drastically, causing the domestic fuel price to surge by more than 120 percent. As expected, prices of other goods jumped, forcing the monetary authority to continue its conser2

Note that during the ªrst two quarters of 2003 the growth rate of investment was negative, and close to zero in the ªrst quarter of 2004.

83



Figure 3. Interest rates and real growth of credit and investment

Source: Author’s calculations based on data from BPS and Bank Indonesia.

vative stand. The episode demonstrates that despite the government’s rhetoric, stimulating growth was not high on the priority list. Credit and investment fared poorly during 2005–06, restraining the already sluggish recovery process. Low credit and investment occur despite excess liquidity. The increased number of ªnancial assets and instruments available in the market, and the remaining uncertainties and difªculties in the investment climate have drawn the excess liquidity into ªnancial assets. The Flow-of-Fund (FOF) provides a clear picture of this. As shown in Figure 4, based on FOF data the share of ªnancial investment in total investment among business and household sectors has increased dramatically since the AFC. This explains the lackluster performance of investment in the real sector during the period. Figure 4 also shows that investment is ªnanced increasingly through capital market and self-ªnancing, not from bank lending. Regional decentralization, which ofªcially began in 2001, has also been blamed for the slow growth performance. As shown in Table 1, the majority of provinces experienced a declining growth trend during post-decentralization. The growth of total gross regional domestic product (GRDP, the regional counterpart of GDP) fell by almost half from 8.13 percent during 1993–96 to 4.84 percent in 2001–06. Some of the problems originate in national policies; others are within the jurisdiction of regional

84



Figure 4. Business and households’ investment in ªnancial assets, and non-bank ªnancing (share of total assets and total liabilities)

Source: Flow-of-Fund, various years. Note: Fin Investment and Non-Bank Financing consist of securities (short term and long term), shares, and equity.

governments (e.g., collection of unnecessary fees, misuse of funds, white-elephant projects, and widespread local capture). With a dismal growth performance, the country’s social conditions have either not improved or deteriorated. As shown in Figure 5, the falling trend of poverty incidences since 1999 was reversed in 2006; the number of poor has increased from 35.1 to 39.3 million.3 It is estimated that about 9.5 percent of Indonesians are chronically poor and 14 percent are transient poor. Disappointing economic growth has also caused the unemployment rate to rise. By 2006, the proportion of labor force who are out of work reached a record high—over 10 percent. On health conditions, measured by seven indicators including the life expectancy and infant mortality rates, Indonesia has been ranked lower than the Philippines and Vietnam. Access to clean water has declined, and access to sanitation remains low. With the deteriorating infrastructure and quality of education, the real beneªt of rising enrollment rates becomes limited. Many, although not all, of the social indicators are related to income, hence economic growth. A recent multi-country survey found that 19 percent of Indonesian teachers were absent from the classroom, and the main reason for the 3

Based on March 2007 data, the poverty ªgure declines slightly, but the number of poor (37.2 million) remains higher than in 2005 (35.10 million). This conªrms the devastating impact of the 2005 fuel subsidy cut on the poor.

85



Table 1. Pre- and post-decentralization GRDP annual growth rates

Provinces

Pre-decent growth 1993–96 (%)

Post-decent growth 2001–06 (%)

NAD Sumut Sumbar Riaua Jambi Sumselb Bengkulu Lampung DKI Jabarc Jateng Yogya Jatim Bali

1.74 9.18 8.08 4.76 8.49 8.26 6.66 8.51 8.99 8.15 7.19 7.91 8.26 7.86

2.67 5.58 5.46 3.94 5.54 5.08 5.45 5.04 5.62 4.95 4.87 4.52 5.34 4.41

Provinces

Pre-decent growth 1993–96 (%)

Post-decent growth 2001–06 (%)

Kalbar Kalteng Kalsel Kaltim Sulutd Sulteng Sulsele Sultra NTB NTT Maluku Malukuta Papuaf Total

9.25 9.58 9.25 8.00 8.10 8.51 7.94 6.59 7.80 8.57 6.82 5.23 14.19 8.13

4.54 5.61 6.33 2.27 4.59 6.90 5.47 7.35 3.41 4.54 4.44 4.31 0.04 4.84

Source: Author’s calculation based on BPS data. Note: a. Includes Kepri. b. Includes Babel. c. Includes Banten. d. Includes Gorontalo. e. Includes Sulbar. f. Includes Irjabar.

absenteeism is low salaries (World Bank 2007). Indonesian primary school teachers have to take up second jobs as they earn only half of what their counterparts in the Philippines and Thailand earn (in terms of a proportion of per capita GDP). Many of the environmental indicators also point to stagnancy or worsening conditions during the 10 years after the crisis.

3. Disconnect between monetary and real sectors One of the features commonly shared by many East Asian economies during the last few years is excess liquidity. Indonesia seriously suffers from this predicament. Bank credit has been low but the ªnancial market is ºush with liquidity. The problem gets deeper as the interest rates remain relatively high, the government budget continues to be tight, and bank and corporate sector restructuring are inadequate. The macroeconomic policy continues to adopt a conservative approach using the aggregate demand (AD) policy to control inºation, and the aggregate supply (AS) to improve growth. There was an episode (2002–04) when the interest rates lowered, albeit slightly, but credit and investment growth remained low (even until now). This led policymakers

86



Figure 5. Poverty (index: 1997Q1

3), unemployment, and CPI (index: 1997Q1

1)

Source: Processed from BPS data.

and analysts to question the effectiveness of expansionary monetary policy. Conceptually, the latter can be associated with the problem of credit allocation.4 It has been known that the ºuctuations of credit growth are due to changes in the supply and demand for funds, both of which are inºuenced by the ªnancial structure of lenders and borrowers. When ªrms are also lenders to other ªrms, frictions in the credit market are likely to amplify, propagating real and nominal shocks to the economy (Stiglitz and Greenwald 2004). In a principal–agent problem, the credit and investment cycle can be affected by a depressed ªrm’s collateral value due to either falling asset prices, or a worsening ªrm’s balance sheet caused by a double mismatch.5 Both can raise the agency costs imposed by asymmetric information between borrowers and lenders. In such circumstances, there is an incentive for borrowers to pass off risky or potentially bad projects as good projects to lenders. This can lower the probability that the loan is repaid, or raise the probability that the ªrm will go 4

The basic premises are: (1) credit, not interest rate, plays a central role in determining economic activities. The actual allocation of credit, however, is critically dependent on the judgments of lenders concerning the risk associated with borrowers, not on an auction market. (2) The presence of asymmetric information implies that there is a cost for acquiring information (agency cost) and this cost is sunk; thus, the credit market is inherently imperfectly competitive. (3) The relation between money and output is not necessarily stable (only some of the transactions are related directly to income and output generation), and the relation between the two is endogenous.

5

Like other Asian crisis countries, Indonesia suffered from double mismatches during the AFC. See Azis (2006).

87



bankrupt. Either way it will lead to a higher cost of external ªnance (higher interest rate).6 What if the balance sheet problem is located in the banking side (e.g., large holding of non-liquid assets or recap bonds), and there is a considerable size of nonperforming loans (higher defaults)? In such a case, the collateral of ªnancial intermediaries is likely to fall. This will force lenders to undertake portfolio reallocations that may result in credit rationing; that is, at any given interest rate fewer funds are made available. The bank’s balance sheet is depressed due to the high exposure of foreign debt and large holding of non-liquid assets (recap bonds). The expected increase of credit following the issuance of a huge amount of government bonds did not materialize. Instead, banks have greater incentives to hold central bank certiªcates (SBI), and are content with holding bonds and other non-risky assets to secure high capital adequacy ratio. The whole premise of the recapitalization program (i.e., bonds will eventually be converted into credits) did not materialize, and the ªrms’ ability to invest declined. All these hypotheses essentially suggest that credit and investment are sensitive to the net worth if agency costs associated with asymmetric information are present. This will not only propagate the initial shock to the economy but it can also make monetary policy ineffective. Hence, loan or credit is inºuenced by the net worth of borrowers, as well as lenders’ asset composition and net worth: é æWEALTH ù ö Corp ÷WFK ú CREDITi ⫽ si LBL Combank ê ç ÷ ç P K ( K )Corp ø úû êë è

m1

é æWEALTH ù ö ComBank ÷÷WFK ú ê çç P K ( ) K ComBank ø ëè û

é BComBank ù ASComBank úû êë m3

m2

(1)

where LBL is the size of loan-able fund, WFK is the unit value of capital used to measure the value of agent’s wealth; PK and K are the price of and demand for capi6

The cost difference between external ªnance and internally generated ªnance is a measure of agency cost, which likely increases in recessions and decreases in booms. In a multi-period framework, the high rate of bankruptcy can be a consequence of high interest rates. The initial upward pressure on the rates may be independent of economic fundamentals or the underlying real event (it can simply come from the lenders’ belief, e.g., through a coordinated signal that the economy—hence the project—has no good prospect and that the probability of bankruptcy is high). At any rate, the causality between the interest rates and bankruptcy can work both ways.

88



tal, respectively. The ªrst bracket on the RHS reºects the balance sheet position of the corporate sector, the second denotes the proportion of bank’s risk-free asset including recap bonds (B as a proportion of AS); si is positive constant, and ␮1, ␮3 ⬎ 0, and ␮2 ⬍ 0. The last term on the RHS captures the bank’s net worth. Thus, a depressed value of net worth leads to a lower amount of credit, so does a high proportion of illiquid asset in the lender’s total asset. Given that the considerable amount of recap bonds and agents’ balance sheets are still weak, this equation is consistent with the postulate of credit market frictions, in which interest rates do not necessarily perform as the equilibrating factor in the supply and demand markets for loans.7 To corroborate, this speciªcation is used to simulate an FGE model that uses Indonesia’s ªnancial social accounting matrix (FSAM). Two scenarios showing with and without agency costs (ªnancial sector with agency cost or FSAC, and ªnancial sector without agency cost or FS, respectively) are compared. In the case of FS, the amount of funds made available by banks is found overestimated. The weak balance sheet reºecting the unªnished restructuring of the corporate sector has raised the agency costs imposed by asymmetric information between borrowers and lenders. This lowers the probability that the loan is repaid, or raises the probability of bankruptcy. Either way it will lead to higher costs of external ªnance. On the banking side, the large holding of non-liquid assets (recap bonds) and central bank certiªcates (SBI), combined with fears of higher nonperforming loans may have affected lending behavior that caused fewer funds to be made available for loans at any given interest rate. Some banks translate this into higher lending rates while others simply undertake credit rationing. This agency costs component, absent in FS, plays an important part in FSAC. Figures 6 and 7 show that by incorporating agency costs, the resulting estimates of credit and investment are much closer to the actual data. Thus, when Bank Indonesia lowered slightly the interest rates on SBI in 2002, such a policy was insufªcient to stimulate credit.8 The fact that the loan rates under FSAC are higher than in FS, and that the actual loan rates did not decline as fast as the SBI rate (see again Figure 3) suggest that banks have been inclined to keep the loan rates high. Figure 8 clearly shows that the loan rates under the agency cost model are closer to the actual data 7

In these cases, the credit constraint is associated with the lender’s decision and hence the supply curve for intermediated ªnance moves to the left. However, the demand side can also be an important explanation for real credit crises, one mechanism of which works through the corporate manager’s decision not to invest especially when they believe that the depressed balance sheet can raise the bankruptcy cost.

8

The yearly average of the exchange rate has actually strengthened—namely, from 9,595 rupiah per U.S. dollar in 2000 to 8,465 and 8,978 in 2003 and 2004, respectively.

89



Figure 6. Trends of real investment: with and without agency costs

Source: Results of model simulations.

Figure 7. Trends of credits: with and without agency costs


90



Figure 8. Trends of loan interest rates: with and without agency costs


compared to those generated by the model without agency costs (a sensitivity analysis displaying the robustness of the model is shown in the Appendix). To verify the role of agency costs, counterfactual simulations were conducted in which the policy-based (BI) rates are lowered from the actual data by 6 full percentage points in 2001 (a one-time shock). Imposing this scenario on FS and FSAC models reveals that the stimulating effect of lower interest rates on credit, investment, and GDP under FSAC is smaller than under FS (Figures 9 and 10). This is due to a combination of banks’ behavior associated with the agency costs and different magnitude of the exchange rate effect on ªrm’s investment that works through the changes in the balance sheet position. Simulation results also show that the FSAC estimates of loan interest rates are higher, as is the unemployment rate. The gap between unemployment rates in FS and FSAC gets wider over time, implying that the cumulative error of using models that ignore the role of agency costs could be large, and the resulting unemployment rates will be underestimated. Thus, there is indeed some evidence that the supply and demand for funds in postcrisis Indonesia are inºuenced by the ªnancial structure of corporate and ªnancial sectors. The combination of continued high leverage and weak balance sheet of the corporate sector, asset prices that have not fully recovered, and the disappearance of known large investors may have raised the agency costs imposed by asymmetric in-

91



Figure 9. Trends of credit following a reduction of interest rate: with and without agency costs


Figure 10. Real GDP and investment following a reduction of interest rate: with and without agency costs


92



formation between borrowers and lenders. Along with a bank’s large holding of bonds and SBI, this causes a leftward shift in both the supply and the demand curves for intermediated external funding. Under such circumstances, a relatively small decline in interest rates will be unable to raise credits and investment, as the “negative” ªnancial accelerator counters the expansionary effects of the policy. Thus, with agency costs present, in order to stimulate growth the monetary policy needs a supporting push from other AD sources, namely, expansionary ªscal policy. The following analysis conªrms the vital role of AD shock in Indonesia. By decomposing AD and AS using the structure vector auto regression (SVAR) with restrictions à la Blanchard and Quah (1989) and Gamber (1996), the impulse responses show that, given the AD shock, no clear patterns of output and price relation are detected. The implicit slope of the AS curve is relatively small (0.1756) with low R2 (0.1165; see Figure 11). A ºat AS curve implies that controlling inºation through AD contraction is not always effective. On the other hand, the implicit slope of the AD curve, as shown in Figure 12, is large (⫺1.4075) compared to those in other Asian crisis countries (e.g., ⫺0.0623, ⫺0.111, ⫺0.14, and ⫺0.29 in Korea, Malaysia, the Philippines, and Thailand, respectively).9 Hence, price changes in Indonesia are more sensitive to AS shock than to AD shock. Cost-push policies are more inºationary. The evidence from my visits to several regions, especially in the eastern part of Indonesia, supports this postulate. Many of these areas (islands) rely on basic goods and commodities transported from other islands. Disruptions in transportation due to either weather or technical problems quickly translate into higher prices. A deliberate policy such as a fuel subsidy reduction is another supply-side shock that easily raises inºation. Looking at the glass half-full, higher growth can be more effectively achieved through an AD shock; and such a policy is not inºationary. Thus, the recent trend of the softening of monetary policy is in the right direction, albeit rather too late, but the persistent tightness on the ªscal front is inconsistent with the AS–AD decomposition analysis. Expanding AD through budget is virtually non-existent since 2001 (the deªcit never exceeded 2 percent of GDP, although the State Finance Law and Government Regulation 23/ 2003 prescribes that the consolidated national and local government budget deªcits be limited to 3 percent).

9

By generating the time series of output component (setting the supply innovation to zero) and the time series of inºation (setting the demand innovation to zero) due to AD shocks, it is revealed that the supply shock dominates output ºuctuations, including the sharp fall in 1997. Similarly, the AS shock dictates price ºuctuations. Compared to other Asian countries, the dominance of AS shocks in Indonesia lasted the longest.

93



Figure 11. AS curve: output growth and inºation due to AD shock

Source: Calculated by using Blanchard–Quah decomposition technique.

Figure 12. AD curve: output growth and inºation due to AS shock

Source: Calculated by using Blanchard–Quah decomposition technique.

94



It is generally argued that public expenditures can no longer be an effective source of growth, and, as part of the efforts to secure macro stability, a tight budget is needed to maintain low inºation and restore market conªdence. The decentralization policy is also blamed for making things worse (e.g., low budget absorption at the local level). Although the validity of these is still questionable, the budget capacity for expansion has been increasingly constrained by rising debt payments, especially for the domestic debt. Indeed, what has changed dramatically in the debt composition since the AFC is the surge of domestic sovereign debt, beginning with recap bonds, followed by regular government bonds. Measured as a share of central–regional transfers, interest payments for domestic debt reached 74 percent in 1999, exceeding interest payments for foreign debt. After 2001, as the central–local transfers surged following the decentralization policy, the share declined, and since then it has ºuctuated. As some recap bonds started to mature in 2002–03, principal payments began to rise. Pressured by this mounting repayment, the government had no choice but to implement the following policies: buy-back program, re-proªling, debt switching, reªnancing matured bonds, and reducing government’s contingent liabilities.10 Even with these efforts, however, as a share of total development expenditures the principal payment for domestic debt increased sharply during the last 2 years, to reach a comparable level with the foreign debt payment in 2006. The interest payment for domestic debt, however, far exceeded the foreign payment (Figure 13). Thus, the domestic debt repayments that include recap bonds, especially their interest payments, have signiªcantly reduced government budget capacity to expand. Along with the fact that recap bonds also remit perverse incentives for banks to extend credits (discussed earlier), and the reproªling policy implies greater burdens of future payments, the management of recap bonds clearly needs to be re-examined, not only in relation to growth but also in the context of ªscal sustainability.

4. Regional growth and decentralization As shown in Table 1, the slow recovery is consistent with the post 2001 decentralization trend of GRDP.11 Six years may be too short to expect the full beneªts of the policy. In addition, given the big-bang nature of Indonesia’s decentralization, adverse 10

As far as the amount is concerned, the re-proªling scheme has been the most signiªcant one. For example, at the early stage of the program the value of bonds held by one statebank alone, Bank Mandiri, amounted to Rp 130 trillion.

11

Post-decentralization growth is usually inºuenced by two important shifts: regional shift (less regional concentration), and sectoral shift (from economic production to social services and human capital formation, and to areas of greatest need).

95



Figure 13. Share of debt payments in total development expenditures

Source: Author’s calculations based on data from the Ministry of Finance.

repercussions are numerous, and there is nothing unusual about it. The focus of this section is to analyze the sources of slow GRDP growth after decentralization. International experience suggests that reasons behind diverse regional growth performances go beyond just policy differences. Institutional, political, and historical factors play a far more important role.12 Certainly, efforts to reduce poverty would have been more effective in faster-growing regions than in poorer or slowergrowing regions. The latter will have more difªculties to generate private sector jobs, and will generally experience greater growth volatility. On the premise that national growth is nothing but the summation of regional growth, it is of interest to understand the causes of regional growth deceleration. 12

96

The case of China versus Russia has been used by some authors to clarify this point. China’s regional growth accelerated during the post-decentralization policy, although the reverse was evident in post 1990 Russia. Unlike Russia, the initial rent holders and seekers were weaker in China because it started its transition from a low level of economic development such that the potential for local capture (e.g., by oligarchs) was more limited. Also, unlike Russia’s transition, which came with the emergence of a partly dysfunctional democracy, China’s transition has taken place under the tight control of the Communist Party with a strong position to reward local governments with “carrots” or to punish with “sticks” (Shleifer and Treisman 1999).



Many of Indonesia’s post-decentralization problems are rooted in institutional constraint—more speciªcally, the lack of a pro-growth incentive system. Let pg ⫽ probability that the local government stays in power if it fosters growth; and pc ⫽ probability if it kills growth by intensifying the “local capture.”13 The latter could happen because local governments have had few incentives either to resist capture or to rein in competition for rents (e.g., Bardhan and Mookherjee 2002). Denote C for the beneªts accrued to local ofªcials through local capture; and Rr for regional-own revenues, the size of which is determined by local rates that include both tax rates and other revenue collection rates, ␪, and regional output Yr. The share of central government revenues (from additional growth) going to local governments is denoted by ␣. Thus, ␣.t.Y is the actual revenues received by local governments where t.Y is the central government’s total revenue determined by the revenue transformation rate t (e.g., tax rate) and the national output Y. In this context, how much the local government values growth is proportional to ␣.t.Y. The central government can use ␣ as the “carrot” in promoting regional growth. Deªne PROB ⫽ pg/pc, the value of which depends on whether local ofªcials are appointed or elected. If they are appointed, then presumably the central government can choose PROB freely and make it as high as it wants. If they are elected, the outcome depends on the ability of the central government to affect the outcome of the election, through non-endorsement and non-support of speciªc candidates. Thus, the central government can use PROB as the “stick.” If, however, the center has little control over the election outcome, and capture is important, PROB may be less than unity, namely, the local government may be more likely reelected if it kills growth than if it fosters it. Under the above speciªcations, local governments choose growth if pg.(␣.t.Y ⫹ Rr) ⬎ pc.C or PROB.(␣.t.Y ⫹ ␪.Yr) ⬎ C (see Blanchard and Shleifer 1999)—that is, if the incentives for growth are higher than the incentives to obtain private beneªts from local capture. Thus, local governments are more likely to choose growth when the “stick” is strong (high PROB), the “carrot” is large (higher ␣), national growth potential Y is high, and revenue generation t & ␪.Yr is high.14 Efforts to lower the 13

14

97

The spread of local capture as a result of direct election for local leaders (Pilkada) raises the vulnerability of local government to capture by local elites, especially in regions with a high degree of income disparity. Because the possibility of power sharing between contesting parties is typically smaller at the local than at the national level, in general the likelihood of capture by elites is greater at the local level. One could, however, erroneously imply that by raising ␪, ceteris paribus, stronger growth incentives will be generated. In fact, in many cases the contrary holds: a higher ␪ deters investment ºows that could reduce the growth of Yr. Thus, the level of Yr can be inversely related to the size of ␪.



beneªts of local capture, C (e.g., corruption, weak legal system, ineffective law enforcement), help to stimulate growth as well. One approach suggested by Handerson and Kuncoro (2004) is to create inter-jurisdictional competition for private ªrms that would limit the extent of local regulation. This approach is based on the premise that bribes by ªrms in Indonesia arise principally from regulations— licenses and levies—imposed by local government ofªcials. Because in Indonesia a ªxed proportion of national revenues for regional development has been more or less set, a new fraction, say ␭, may need to be introduced to allow the center to use it as a “carrot.” Thus, the following relation holds: PROB.[(␭ ⫹ ␣).t.Y ⫹ ␪.Yr] ⬎ C. The regional growth variable Yr can also be attached to ␭ so that stronger growth incentives are imposed: PROB.[(␭(Yr) ⫹ ␣).t.Y ⫹ ␪.Yr] ⬎ C. The earmarked regional development fund, known as dana alokasi khusus (DAK), is ␣ in the above relation, and ␭ is another instrument the center can create and use to motivate regional governments to foster growth. This yields the following fundamental regional growth relation: PROB.[(␭(Yr) ⫹ ␣).t.Y ⫹ ␪.g(K or (1 ⫺ ␦) ⫹ ƒ(␪))] ⬎ C. In Indonesia’s post-decentralization period, PROB, C, and ␣ have been low, and ␭ is nonexistent. Note that according to this relation a higher Y could help raise growth incentives at the regional level. Thus, slow GDP growth is consistent with a lack of growth incentives at the local level. The likelihood of C being large is greater at the local level especially when the operational details (not just the functions) of what the region should do under the new decentralization law are either nowhere to be found or unclear. Although the tasks of the center and the province are clearly speciªed in law 22/1999, those of the region are not, making it difªcult to evaluate the effectiveness of decentralization. Worse, no clear performance indicators are imposed on regional governments. As a result, controls on how local governments allocate their resources are virtually nonexistent. A case in point is the budget allocation at the local level where the health sector only receives approximately 7 percent, and in average about one-third of the total budget is spent on government apparatus (in some regions the latter constitutes more than 80 percent). Although there are numerous problems at the local level, some national policies make efforts to raise local people’s welfare even more difªcult. National policies could have signiªcant repercussions on a regional government’s capacity to foster growth, which is seldom realized by the center. Some argue that expansionary ªscal policy will not work because local government’s capacity to spend is relatively low (recently only two-thirds of the revenue increases could be spent). This is partly due to delays in central–regional transfers, and local ofªcials being nervous about the

98



Figure 14. SBI holding by different institutions (trillion rupiah)

Source: Author’s calculations based on data from Bank Indonesia.

anti-corruption move. Although this may be true, other factors cannot be ignored. With so many deªciencies in infrastructure, there is much room for central government expenditure to make improvements that would lure the private sector to invest (crowding-in effects). Another case in point is the high SBI interest rates that create an incentive for regional governments to invest regional funds in this interestbearing central bank certiªcate. Although such portfolio allocation is legal (no law has been broken), the impact on regional development and growth is devastating, and it diminishes the beneªts of decentralization. As shown in Figure 14, high SBI rates during 2005 and the ªrst half of 2006 had caused not only falling investment and GDP growth, but also a sharp increase in SBI holding by regional development banks (BPDs) in many regions.15 Another example of a national policy that has deleterious effects on regional welfare is the drastic fuel subsidy cut, which was discussed previously. The policy was taken presumably due to the mounting pressures on the government budget, as the world’s oil prices surged. Despite the fact that there is still a considerable amount of subsidy for the banking sector (i.e., interest and principal payments for recap bonds), the government opted to cut only fuel subsidies. Consequently, the size of the cut was huge, causing the average domestic fuel prices to surge by more than 15

99

The funds used to purchase the SBI are mostly from the center–regional transfers associated with natural resources, as indicated by the fact that the holding is highest in the natural resource–rich regions.



120 percent. Contrary to the government’s original prediction, the socio-economic conditions throughout many regions deteriorated (see again Figure 5).

5. Conclusion As the title suggests, Indonesia’s recovery has been slow during the 10 years following the crisis. The socio-economic repercussions of it have been predicted (e.g., high unemployment and poverty), but the precise reasons of the tepid recovery are debatable. Non-economic factors such as a weak legal framework, corruption, a complicated bureaucracy, incompetent local governments, and labor laws have surely played roles. However, conservatism in ªscal and monetary policy also stands out. During the last few years, the rapid growth of the ªnancial sector reºects the presence of excess liquidity and the sector’s vulnerability, while the slow growth of real investment explains the unºattering trend of social indicators. There is some evidence indicating that agency costs have slowed credit and investment growth (credit channel). Based on ªnancial general equilibrium model simulations, where balance sheet data from the FOF are merged with detailed information of the real sector and the aggregate demand, it is revealed that the supply and demand for funds in post-crisis Indonesia are inºuenced by the ªnancial structure of the corporate and ªnancial sectors. The combination of continued high leverage and weak balance sheet of the sector, asset prices that have not fully recovered, and the disappearance of large borrowers have raised the agency costs imposed by asymmetric information between borrowers and lenders. Along with the bank’s large holding of bonds and SBI, this made monetary policy less effective. Under such a condition, a slight fall of interest rates will not be able to spur growth, unless it is supported by expansionary ªscal policy. The decomposition analysis indeed shows that an aggregate demand expansion would have been effective to stimulate growth and less inºationary. What the economy needed during the 10 years following the crisis was a signiªcant push, yet the government budget continued to lean toward tightness. Also puzzling is the conservatism in monetary policy until recently, despite the fact that it fails to make the inºation rate converge with the trading partners’ rate. The slow recovery is also consistent with the dismal trend of regional growth after the 2001 decentralization policy. The latter has been due largely to a lack of stick and carrot and the absence of growth incentives among local governments, as well as to numerous incidences of local capture. Efforts to raise the sub-national welfare have also been constrained by local and national policies (tightness in macroeconomic

100



policy). National policies could have signiªcant repercussions on regional government’s capacity to foster growth, which is seldom realized by the center. Looking ahead, with the current stronger foreign reserves, lower inºation, investment reform, better security, and more stable political conditions, the opportunity for a better performance in the next 10 years should be great. However, reform must continue and a less conservative macroeconomic policy needs to be implemented if the opportunity is to be realized. References Azis, Iwan. 2006. Indonesia’s External Liberalization: Policy Dynamics and Socio-Economic Impact. In: External Liberalization In Asia, Post-Socialist Europe, and Brazil, edited by Lance Taylor, pp. 180–206. New York: Oxford University Press. Bardhan, Pranab, and Dilip Mookherjee. 2002. Relative Capture of Local and Central Governments: An Essay in the Political Economy of Decentralization. IBER Paper No. C99–109. Berkeley, CA: University of California. Blanchard, Oliver, and Danny Quah. 1989. The Dynamic Effects of Aggregate Demand and Supply Disturbances. American Economic Review 79:655–673. Blanchard, Oliver, and Andrei Schleifer. 1999. Federalism with and without Political Centralization: China versus Russia. MIT Unpublished Manuscript. Gamber, Edward. 1996. Empirical Estimates of the Short-Run Aggregate Supply and Demand Curves for the Post-War US Economy. Southern Economic Journal 62 (4):856–872. Handerson, J. Vernon, and Ari Kuncoro. 2004. Corruption in Indonesia. NBER Working Paper No. 10674. Cambridge, MA: National Bureau of Economic Research. Shleifer, A., and D. Treisman. 1999. Without a Map: Political Tactics and Economic Reform in Russia. Cambridge, MA: MIT Press. Stiglitz, Joseph, and Bruce Greenwald. 2004. Towards a New Paradigm in Monetary Economics. Cambridge, MA: MIT Press. World Bank. 2007. World Bank Supports Indonesia’s Teacher Improvement Program through New US$86 million Program. Press Release No. 2007/011/EAP. Jakarta.

Appendix The following sensitivity analysis focusing on credit is conducted by changing the values of ␮1, ␮2, and ␮3 in equation 1. In Figures A.1, A.2, and A.3, different levels of credit are measured on the vertical axis, and different values of ␮s are measured on one of the horizontal axes. The other horizontal axis denotes the year. Figure A.1 shows that as ␮1 increases (greater sensitivity with respect to ªrm’s net worth) credit increases. Such a relation persists during the period under observation

101



Figure A.1 Sensitivity of ªrm’s net worth

Figure A.2 Sensitivity of illiquid asset holding

Figure A.3 Sensitivity of banks’ net worth

102



despite the fact that at any given value of ␮1 credit declines in 2001. A symmetrically opposite result is detected when the value of ␮2 is raised (sensitivity of the share of illiquid assets held by banks), except that the magnitudes of credit change—a decline, in this case—are larger than in the earlier case (Figure A.2). Testing the sensitivity of banks’ net worth by raising the value of ␮3 gives a rather different pattern. During 2000–03, higher weight of banks’ net worth leads to a larger amount of banks’ credit. However, at the end of the period a larger ␮3 generates a lower amount of credit (Figure A.3). The incentives to hold non-risky assets appear to be larger when the lending behavior is very sensitive to the bank’s net worth.

103


General Discussion

General Discussion on “Indonesia’s Slow Recovery after Meltdown” by Iwan Azis

Fredrik Sjöholm opened the discussion by noting that post-crisis economic performance in Indonesia was not disappointing, considering the massive economic collapse triggered by the Thai contagion and the hesitant policy response by the Indonesian government. The economy had been growing at a rate of over 5 percent over the past 5 years, which could be explained in terms of privatesector dynamism unleashed by the signiªcant liberalization reforms during the New Order era. Sjöholm argued that the mixed outcome regarding the decentralization program could be because most of the provinces are too small to be economically viable or are not well integrated in the global economy because of their geography and poor infrastructure. As for the latter point, he suggested that a systematic Indonesia–China comparison of interregional growth performance could yield important policy lessons for Indonesia. The major focus of the ensuing discussion was on the continued decline in the rate of investment (investment as a percentage of GDP) during the post-crisis period. Keun Lee questioned the validity of the agency-cost explanation of investment contraction in the post-crisis period. In the lead-up to the crisis, high agency cost was often referred to as a major problem of the investment climate in Indonesia, but private investment continued to expand, fuelling the economic boom. Liqing Zhang supported Lee’s argument by noting that agency cost is a long-term consideration affecting investment decisions, which is not relevant for explaining investment behavior in the aftermath of a crisis. Zainal Yusof pointed out the need for treating local private investment and FDI separately from government invest-



General Discussion

ment for a clear understanding of the causes of the poor investment performance, in particular persistently negative net FDI inºows. He noted that Indonesia’s ranking in terms of various indicators provided in the World Bank’s Cost of Doing Business Database has gone down in recent years. This was consistent with the available anecdotal evidence of shifting of foreign investors from Indonesia to Vietnam and other low-cost locations in the region. Prema-chandra Athukorala alluded to continuous contraction in real bank credit to the private sector as another possible explanation of the investment slump. Unlike in Malaysia and South Korea, in Indonesia the banking recurring process has not gone far enough to carve out bad debts and resuscitate lending capabilities of the banking system. Indonesia’s banking restructuring strategy of issuing recapitalization bonds to crisis-affected banks (rather than carving out bad debts and injecting new liquidity) turned out to be counterproductive; there was no incentive for banks to expand credit to the private sector because these bonds earned handsome interest income. Naoyuki Yoshino pointed out Indonesia’s half-hearted approach to corporate restructuring as another possible explanation. Keun Lee asked why the poverty level in Indonesia declined, not increased, in the immediate post-crisis period. On the same theme, Zainal Yusof wanted to know about the impact of the crisis on regional income disparities. Bhanupong Nidhiprabha questioned the plausibility of the empirical evidence reported in the papers as to the role of inelastic derogate demand as an explanation of Indonesia’s historically high inºation by regional standards. He pointed out the possibility that this ªnding might reºect an “omitted variable” problem in the econometric analysis. In responding to the issues raised in the open discussion, Iwan Azis emphasized that a lack of experienced/qualiªed leaders and administrators is the major obstacle to the smooth functioning of the decentralization program. To make matters worse, there are no incentives for the provincial governments to improve performance because of the “soft budget constraint.” On the issue of regional income distribution, Azis noted that according to his estimates of the Williamson index disparities had been deteriorating. Regarding his analysis of the causes of Indonesian inºation, he admitted the difªculties involved in delineating supply and demand factors underpinning inºation given the data limitations and the need for qualifying his inferences. On the continuous decline in the investment rate, Azis agreed that contraction in real bank credit was a major factor. Lack of incentives for aggressive credit expansion on the part of the bank given easy-life assured by recapitalization bonds and lack of demand for credit in an unfavorable investment environment could explain contraction in bank credit.

105


Financial Liberalization and Economic Growth

Financial Liberalization, Crises, and Economic Growth

Inkoo Lee Department of International Macroeconomics and Finance Korea Institute for International Economic Policy 300-4 Yomgok-dong Seocho-gu, Seoul 137-747 Korea [email protected] Jong-Hyup Shin Department of International Macroeconomics and Finance Korea Institute for International Economic Policy 300-4 Yomgok-dong Seocho-gu, Seoul 137-747 Korea [email protected]

Abstract

The paper computes the effect of financial liberalization on economic growth by combining the results of a panel model with those of a probit model. It finds a positive net effect from financial liberalization to growth. Surprisingly, we find that the net effect on growth is larger in the crisis-experienced country group than in the overall sample group. Our guess is that the crisis-experienced countries are mostly developing countries that usually enjoy higher growth rates than the developed countries because of the catching-up phenomenon. The paper also studies the link between financial liberalization and nominal interest rates, and finds, contrary to expectations, that the direct liberalization effect is positive. Our guess is that this reflected the overshooting of interest rates after crises.

1. The impact of ªnancial liberalization This paper investigates the impact of ªnancial liberalization on economic growth and the nominal interest rate. Financial liberalization affects economic growth through two channels: the direct liberalization effect and the indirect crisis effect. The direct liberalization effect is positive because ªnancial liberalization removes many frictions in ªnancial markets and reduces borrowing costs. The indirect crisis effect is negative because ªnancial liberalization may also cause ªnancial crises. Our deªnition of a crisis is the simultaneous occurrence of a banking crisis and a currency crisis, that is, a “twin crisis.” The sum of these two effects yields the net effect of ªnancial liberalization on economic growth. The direct liberalization effect is usually estimated by regressing economic growth on explanatory variables that




include a ªnancial liberalization dummy (or a ªnancial liberalization index), and a crisis dummy (Rodrik 1998; Bekaert, Harvey, and Lundblad 2005; Henry 2000, 2006). The indirect crisis effect is the product of (1) the marginal effect of ªnancial liberalization on the probability of a crisis (Kaminsky and Reinhart 1999; Glick and Hutchinson 2001) and (2) the coefªcient of the crisis dummy in the previous panel regression. The impact of ªnancial liberalization on the interest is studied by replacing the output growth rate with the interest in the panel equation and probit equation in section 2.

2. The empirical relationship between ªnancial liberalization and growth We use a two-step approach to estimate the effect of ªnancial liberalization on economic growth. The ªrst step makes use of a probit model to measure the marginal effect of ªnancial liberalization on the probability of a crisis. Crisisit ⫽ c ⫹ aFLit ⫹ ZitA ⫹ ␩it ,

(1)

where subscripts i and t denote an individual country and a time period, respectively. Crisisit represents a twin crisis dummy variable that is equal to one if the twin crisis occurs in country i at time t, and zero if the crisis does not occur. FLit is the ªnancial liberalization dummy variable, Zit is a set of explanatory variables, and ␩it is an error term such that ␩it _ N(0,1). The set of explanatory variables Zit consists of the bank liquid reserve ratio and the inºation rate. A, c, and a are constant parameters. The marginal effect of ªnancial liberalization on the probability of a crisis is given by the change in the predicted probability of crises occurrence that is conditional on the (discrete) explanatory dummy variable:1 Marginal Effect ⫽ Pr(Crisisit ⫽ 1|Z it , FLit ⫽ 1) ⫺ E(Crisisit ⫽ 1|Z it , FLit ⫽ 0). (2) In the second step, we run a panel regression model after testing the signiªcance of the group effect. yit ⫽ ␣ ⫹ ␤FLit ⫹ ␥Crisisit ⫹ Xit⌬ ⫹ ⑀it, 1

(3)

For computing marginal effects, one can evaluate the expressions at the sample means of the data.

107



where yit is a dependent variable that is the real per capita GDP growth rate, and Xit is a set of explanatory variables that includes log(initial real GDP), inºation, trade openness,2 and the investment/GDP ratio. ␣, ␤, ␥, and ⌬ are coefªcients on dependent variables, and ⑀it is an innovation. As the F-test cannot reject the null hypothesis that the constant terms are all equal, we choose a random effects model as the benchmark regression model. $ in the panel regression repThe coefªcient of the ªnancial liberalization dummy (b) resents the direct liberalization effect. The indirect crisis effect is computed by combining the results of the probit regression with those of the panel regression: the coefªcient on the crisis dummy in the panel regression is multiplied by the marginal effect of ªnancial liberalization from the probit regression, ( g$ ⫻ Marginal Effect). The net effect of ªnancial liberalization is given by: E(yit | FLit ⫽ 1) ⫺ E(yit | FLit ⫽ 0) ⫽ b$ ⫹ g$ ⫻ Marginal Effect.

(4)

2.1 Data We use the annual data of 58 countries between 1980 and 1999, with the number varying with the availability of data for each regression. Because most banking and currency crises have occurred during 1980s and 1990s, we believe that the sample period is not too short to catch the effects of ªnancial liberalization and the crisis on economic variables. We borrow the twin crisis dummy from Ranciere, Tornell, and Westermann (2006), who calculate this dummy using data from Caprio and Klingebiel (2003) and Glick and Hutchison (2001). The investment/GDP ratio comes from the Penn World Table. We have two proxies for the ªnancial liberalization variable. The ªrst proxy is the ªnancial liberalization dummy from Beckaert, Harvey, and Lundblad (2005), which is based on the dates of ofªcial equity-market liberalization in each country, and we call this variable FL(1). The second proxy is the ªnancial liberalization index from Wyplosz (2001) who made this index by combining the results of Demirguc-Kunt and Detragiache (1998), Mehrez and Kaufmann (2000), and Wyplosz (2001)—and we call this variable FL(2). We call the sub-sample of countries that have values for both FL(1) and FL(2) the FL-sample. The rest of the variables are from International Financial Statistics. In addition to estimating the models over the full sample, the 58 countries that contain values for FL(1), we also conducted the estimations on three sub-samples: the 2

Openness is measured by (export ⫹ import)/GDP.

108



21 countries that experienced a twin crisis (the “crisis-experienced” countries), the 11 Asian countries, and the FL sub-sample. The Appendix provides the list of countries in the full sample and in the three sub-samples.

3. The results 3.1 The direct liberalization effect on economic growth The upper panel in Table 1 reports the coefªcient estimates of the ªnal speciªcations of equation (3), after dropping some of the statistically insigniªcant variables (e.g., the government size variable). The ªnal list of regressors is the ªnancial liberalization variable, the twin crisis dummy, the initial real GDP, inºation, trade openness, and the investment/GDP ratio. Financial liberalization, trade openness, and the investment/GDP ratio are positively associated with economic growth, whereas the crisis dummy, the initial GDP, and inºation are negatively related to economic growth. The trade openness variable is generally insigniªcant, and is dropped in the estimation of the FL-sample. The point estimates of the FL dummy, FL(1), and the FL index, FL(2), suggest a substantial impact of ªnancial liberalization on output growth through the deepening of a country’s ªnancial system. For example, ªnancial liberalization, FL(1), appears to increase the annual GDP growth rate by 1.0 percentage point for the whole sample, 1.3 percentage points for crisis-related countries, and 0.6 percentage points for Asian countries. The coefªcients are signiªcant at the 1-percent level except for the Asian countries sub-sample. The size of the coefªcient for FL(2) is similar that for FL(1). The occurrence of the twin crisis decreases annual GDP growth rate by 4.2 percentage points for the whole sample, 4.3 percentage points for the crisis-experienced countries, and 6.1 percentage points for Asian countries. The use of FL(2) as the alternative measure of ªnancial liberalization does not change the results much, the marginal effect of the twin crisis is 4.4 percentage points. The coefªcients for the crisis dummy are signiªcant at the 1-percent level in all cases. 3.2 Financial liberalization and crises The lower panel in Table 1 presents the results of probit regressions of equation (1). Only the FL dummy variable is consistently statistically signiªcant across samples and across speciªcations. The bank liquid reserve ratio and inºation turn out to be insigniªcant in the incidence of crises. We drop the investment/GDP ratio and the initial real GDP as independent variables because they are not statistically signiªcant.

109



Table 1. Financial liberalization and economic growth Panel Regression Dependent Variable: Per Capita Real GDP Growth All countries Financial Liberalization Dummy,

FL(1)a

Crisisexperienced countries

0.010 (3.67)***

0.013 (2.92)***

Asian countries

FL-samplee

0.006 (1.36)

0.011 (3.30)***

Financial Liberalization Index, FL(2)b

FL-samplef

⫺0.042 (⫺7.68)***

⫺0.043 (⫺6.82)***

⫺0.061 (⫺6.63)***

⫺0.045 (⫺6.72)***

0.008 (1.91)* ⫺0.044 (⫺6.51)***

Initial Real GDP

⫺0.055 (⫺5.23)***

⫺0.040 (⫺2.18)**

⫺0.150 (⫺7.05)***

⫺0.078 (⫺5.61)***

⫺0.070 (⫺5.06)***

Inflation

⫺0.002 (⫺4.74)***

⫺0.002 (⫺4.21)***

⫺0.208 (⫺7.33)***

⫺0.002 (⫺4.60)***

⫺0.002 (⫺4.44)***

⫺0.001 (⫺0.07)

⫺0.000 (⫺0.03) 0.214 (7.87)***

0.222 (8.06)***

Twin Crisis

Trade

Dummyc

opennessd

0.008 (1.77)*

Investment/GDP

0.176 (8.36)***

R2 F-statistic

0.211 (6.47)***

0.145

0.311 (8.95)***

0.263

⫺1.324

0.479

—

0.234

—

0.222

—

—

Probit Regression Dependent Variable: Twin Crisis Dummy All Financial Liberalization Dummy, FL(1)

Crisis

0.272 (1.70)*

0.435 (2.37)***

Asia 0.779 (2.25)**

FL sample

FL sample

0.595 (1.99)**

Financial Liberalization Index, FL(2)

0.362 (1.73)* ⫺0.303 (⫺0.44)

⫺0.111 (⫺0.12)

⫺5.628 (⫺1.79)*

1.395 (1.46)

1.271 (1.35)

Inflation

0.017 (1.03)

⫺0.001 (⫺0.04)

2.405 (1.46)

0.014 (0.81)

0.010 (0.59)

Constant

⫺1.947 (⫺12.40)***

⫺1.527 (⫺8.30)***

⫺1.831 (⫺4.62)***

⫺2.186 (⫺7.59)***

⫺1.992 (⫺9.04)***

R2

0.005

0.016

0.063

0.010

0.007

Number of Observations

1,160

420

220

540

540

Number of countries

58

21

11

27

27

Marginal Effect of Financial Liberalization

0.020

0.073

0.062

0.054

0.036

Bank Liquid Reserve Ratio

Note: t-statistics are in parentheses. ***, **, and * indicate the significance at the 1%, 5%, and 10% level, respectively. a. FL(1) represents the financial liberalization dummy which is based on the dates of official equity-market liberalization in each country. b. FL(2) is the financial liberalization index from Wyplosz (2001). c. Dummy variable indicating a currency crisis and a banking crisis. d. Ratio of exports and imports to GDP. e. This column represents results when FL(1) is used as the regressor. f. This column represents results when FL(2) is used as the regressor.

110



Table 2. Liberalization and crisis effects on economic growth Liberalization effect Crisis effect Net effect

All (%)

Crisis (%)

Asia (%)

FL(1) (%)

FL(2) (%)

⫺1.00 ⫺0.08 ⫺0.92

⫺1.30 ⫺0.31 ⫺0.99

⫺0.60 ⫺0.38 ⫺0.22

⫺1.10 ⫺0.24 ⫺0.86

⫺0.80 ⫺0.16 ⫺0.64

The last row of Table 1 reports our computations of the change in the predicted probability that is conditional on the (discrete) explanatory dummy variable, namely, the marginal effects of ªnancial liberalization on the probability of a crisis. Financial liberalization is expected to increase the probability of crises by 2.0 percentage points in the full sample, 7.3 percentage points for the crisis-experienced countries, and by 6.2 percentage points for the Asian countries. For the FL-sample, the probability of crisis is increased by 5.4 percentage points when FL(1) is used, and by 3.6 percentage points when FL(2) is used. 3.3 The net effect of ªnancial liberalization on economic growth Table 2 reports the net effect of ªnancial liberalization calculated according to equation (4). In every case, ªnancial liberalization leads to a positive net effect on economic growth. The net effect ranges from a low of 0.22 percentage points in Asia to a high of 0.99 percentage points in crisis-related countries. Surprisingly, the net effect in the “crisis-experienced countries” sub-sample is greater than that in the whole sample. We guess this is because the crisis-experienced countries are mostly developing countries, and the developing countries usually enjoy higher growth rates than developed countries because of the catching-up phenomenon. Or, the liberalization effect is simply just greater in developing countries. Overall, the results in this section conªrm our view that ªnancial liberalization contributes to economic growth through an increase in ªnancial deepness, even after controlling for the probability of crisis occurrence. Our results are similar to those of Ranciere, Tornell, and Westermann (2006) in that our estimate of the net effect of ªnancial liberalization on economic growth is 0.92 percentage points in the full sample and their estimate is 0.86–0.91 percentage points. 3.4 Financial liberalization, crises, and interest rates There are two links between ªnancial liberalization and interest rates. On the one hand, one would expect that interest rates will increase due to the borrowing restriction in ªnancial markets after crises have occurred. On the other hand, the effect of ªnancial liberalization on the interest rates is likely to be negative because ªnancial liberalization makes it easier to borrow money from abroad.

111



Table 3. Financial liberalization and interest rates Panel Regression Dependent Variable: Nominal Interest Rate Financial Liberalization Dummy, FL(1)a

0.019 (3.76)***

Twin Crisis Dummyb

0.056 (6.93)***

Real GDP

⫺0.009 (⫺3.90)***

Inflation

0.609 (34.45)***

Trade opennessc

⫺0.042 (⫺4.40)***

R2

0.682 ⫺7.513

F-statistic

Probit Regression Dependent Variable: Twin Crisis Dummy FL Dummy

0.379 (1.59)

Bank Liquid Reserve Ratio

2.277 (1.86)*

Inflation

0.033 (1.29)

Constant

⫺2.207 (⫺8.84)***

R2

0.009

Number of Observations

680

Number of Countries

34

Marginal Effect of Financial Liberalization

0.026

Note: t-statistics are in parentheses. a. FL(1) represents the financial liberalization dummy which is based on the dates of official equity-market liberalization in each country. b. Dummy variable indicating a currency crisis and a banking crisis. c. Ratio of exports and imports to GDP. ***, **, and * indicate the significance at the 1%, 5%, and 10% level, respectively.

The results in Table 3 show that the crisis dummy has a signiªcantly positive relationship with nominal interest rates as expected, and the FL dummy is also positively related to nominal interest rates, contrary to our expectation. It turns out that ªnancial liberalization directly increases the interest rate by 1.9 percentage points, and the resulting net effect of ªnancial liberalization amounts to 2.05 percentage points (Table 4). The unexpected sign of the liberalization effect can be explained by some combination of the following four considerations. First, it is possible that the interest rate overshoots after crises, which may reverse the true (negative) liberalization effect by scaling up the average interest rate over the entire period. Second, the removal of

112



Table 4. Liberalization and crisis effects on interest rates, % Liberalization effect Crisis effect Net effect

1.90 0.15 2.05

ªnancial restraints may not improve allocation efªciency in credit markets because of imperfect information. Third, external factors such as the high and volatile world interest rate may have been transmitted to the domestic country that has undergone ªnancial liberalization. Last, but not least, ªnancial repression usually includes an interest rate ceiling that is set below the market-clearing interest rate; and so ªnancial liberalization will cause the interest rate to rise.

4. Conclusions It has been widely recognized that ªnancial liberalization plays an important role in economic development. Although an expanding body of literature has documented this effect across space and time, the channel through which ªnancial liberalization affects the economic growth remains unclear. This paper employs probit and panel regressions to show that ªnancial liberalization is positively associated with economic growth, with the (positive) direct liberalization effect dominating the (negative) indirect crisis effect. Financial liberalization is expected to increase GDP growth by 0.92 percentage points in the whole sample and by 0.99 percentage points in crisis-experienced countries, respectively. Financial liberalization contributes to economic growth even when the sample is restricted to countries that experienced ªnancial crises. We also test the effect of ªnancial liberalization on the interest rates. The result is contrary to our expectation: ªnancial liberalization increases the interest rates. We conjecture that the overshooting in interest rates after a crisis and the removal of interest rate ceiling after liberalization are the main reasons for this phenomenon. References Bekaert, Geert, Campbell Harvey, and Christian Lundblad. 2005. Does Financial Liberalization Spur Growth? Journal of Financial Economics 77:3–56. Caprio, G., and D. Klingebiel. 2003. Episodes of Systemic and Borderline Banking Crises. World Bank Working Paper. Washington, DC. Demirguc-Kunt, A., and E. Detragiache. 1998. Financial Liberalization and Financial Fragility. IMF Working Paper. Washington, DC.

113



Glick, Reuven, and Michael Hutchinson. 2001. Banking Crises and Currency Crises: How Common Are Twins? New York: Cambridge University Press. Henry, Peter. 2000. Stock Market Liberalization, Economic Reform, and Emerging Market Equity Prices. Journal of Finance 55:529–564. Henry, Peter. 2006. Capital Account Liberalization: Theory, Evidence, and Speculation. NBER Working Paper No. 12698. Cambridge, MA: National Bureau of Economic Research. Kaminsky, Graciela, and Carmen Reinhart. 1999. The Twin Crises: The Causes of Banking and Balance of Payment Problems. American Economic Review 89 (3):473–500. Mehrez, G., and D. Kaufmann. 2000. Transparency, Liberalization, and Bank Crises. World Bank Working Paper. Washington, DC. Ranciere, Romain, Aaron Tornell, and Frank Westermann. 2006. Decomposing the Effects of Financial Liberalization: Crises vs. Growth. NBER Working Paper No. 12806. Cambridge, MA: National Bureau of Economic Research. Rodrik, Dani. 1998. Who Needs Capital Account Convertibility? In: Should the IMF Pursue Capital Account Convertibility?, Essays in International Finance, edited by Stanley Fischer, p. 207. Princeton, NJ: Princeton University. Wyplosz, Charles. 2001. How Risky Is Financial Liberalization in the Developing Countries? G-24 Discussion Paper Series.

114



Appendix Table A.1 The countries in each sample used in the estimations

Country

All countries

Algeria Argentina Australia Austria Bangladesh Belgium Brazil Canada Chile Colombia Costa Rica Cote d’Ivoire Denmark Dominican Republic Ecuador Egypt El Salvador Finland France Germany Ghana Greece Guatemala Honduras India Indonesia Ireland Israel Italy Jamaica Japan Jordan Kenya Korea Malaysia Mexico Morocco Netherlands New Zealand Nigeria Norway Pakistan Paraguay Peru Philippines Portugal South Africa Spain Sri Lanka Sweden Switzerland Thailand Tunisia Turkey United Kingdom United States Uruguay Venezuela

— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — —

Crisisexperienced countries

Asian countries

— —

— — —

— —

— — —

—

— —

—

— — — —

—

— —

— —

— —

— —

— — — —

— —

— —

—

—

— —

— — —

— — —

—

— — — —

— —

— — —

— —

—

— —

— — — —

— — —

— — —

— —

—

—

— — —

— —

—

Note: The regression in each column includes countries with “—” mark.

115

FL-sample

Sample for studying effects on interest rates


— — — — — — — — — — —

Comments

Comments

Liqing Zhang: This is an impressive empirical study, which includes interesting points. I do not want to make substantial challenges, but would like to raise some questions. It seems to me that some issues probably need to be clariªed or studied further. First, let me brieºy repeat the main ideas in the study. The paper empirically explores the effect of ªnancial liberalization on economic growth by combining the results of a panel model with those of a probit model. There are two main ªndings in the paper: 1. Although conªrming the results of previous studies that show the positive net-effect of ªnancial liberalization on growth, the authors found that the net effect on growth is larger in the crisis-experienced country group than in the overall sample group. 2. The paper investigates the effect of ªnancial liberalization on nominal interest rates. The results, contrary to the usual wisdom, show that the direct effect of liberalization is positive because of the overshooting in interest rates after crises. Some points seem to be questionable. First, can we really get a reasonable explanation or persuasive economic logic for the outcome of the empirical study? The outcome of the empirical study seems to be contrary to intuition. A ªnancial crisis often has a negative impact on economic growth. Realistically, how can ªnancial liberalization have a more positive impact on the growth in crisis-experienced countries? The authors’ answer is the following: ªnancial liberalization tends to have a more positive effect on growth in the



Comments

crisis-experienced countries only because the crisis-experienced countries are mostly developing countries that enjoy higher growth rates than the developed countries. This point seems hard to accept. Maybe we can distinguish two situations. If the authors mean that ªnancial liberalization during or shortly after the crisis is a part of the crisis recovery blueprint, then a higher growth rate will be possible because the growth rate in crisis countries often recovers spectacularly from the trough of the crisis. However, if the authors mean that the ªnancial liberalization has a more positive impact on the growth rate of crisis-experienced countries in the long-term, then it does not seem plausible to me. Second, there are two technical questions: (1) What is the correct deªnition of crisisexperienced counties? Should there not be a difference between a “one-time-crisis” country and “many-times-crises” countries? There must be some differences between these two situations other than just differences in luck. (2) What is the correct deªnition of ªnancial liberalization? The authors used a ªnancial liberalization index and a dummy variable in their empirical work, and both measures were not constructed by the authors but from earlier studies by other authors. Lee and Shin should have discussed and evaluated the different measures of ªnancial liberalization—for example, isn’t the ªnancial liberalization index likely to be a more accurate reºection of the extent of ªnancial liberalization than a zero-one dummy? Third, theoretical studies and many country experiences have suggested that there are many differences between domestic ªnancial liberalization and capital account liberalization, in terms of preconditions and impact. Therefore it might be meaningful to distinguish between these two forms of ªnancial liberalization when we study the impact of the ªnancial liberalization on either economic growth or nominal interest rate. Lee and Shin should have related their study to the substantial literature indicating the relation between capital account liberalization and economic growth. Fourth, as to the increase of the nominal interest rate after ªnancial liberalization, one possible explanation is that ªnancial liberalization usually removes the domestic credit rationing and therefore tends to increase the demand for domestic credit, which will push the nominal interest rate to increase. Therefore, it seems to me that there is not necessarily a relation between ªnancial liberalization and the so-called “overshooting after the crisis.” Finally, what are the policy implications of the Lee and Shin study? Is it true that a country should create some kind of crisis in order to enlarge the positive effect of ªnancial liberalization on growth? Obviously not, but I hope the authors could give us some hints. 117


General Discussion

General Discussion on “Financial Liberalization, Crises, and Economic Growth” by Inkoo Lee and Jong-Hyup Shin

Much of the general discussion on the paper by Inkoo Lee and Jong-Hyup Shin focused on methodological issues. Kanhaiya Singh stated that he found it difªcult to interpret the results in the absence of a sufªcient discussion on variable deªnition and measurement. Singh also questioned the appropriateness of using a binary dummy variable to capture ªnancial liberalization episodes as liberalization episodes in individual countries have different attributes in terms of the composition of the policy package. Maria Socorro Gochoco-Bautista questioned the appropriateness of grouping countries by geography rather than by the degree of ªnancial market liberalization, which should have been the more logical choice. She also expressed concern that the reduced form approach adopted in the paper had naturally overlooked the transmission channels of the impact of ªnancial liberalization. Bhanupong Nidhiprabha noted that many variables found to be important in the recent empirical growth literature had been omitted in model speciªcation without giving any justiªcation. He also argued that the results would have been biased by the arbitrary choice of only some countries that have embarked on liberalization reforms. Relating to the same (omitted variable) issue, Naoyuki Yoshino expressed concern that the failure to include country size would have biased the results. Hyun-Hoon Lee alluded to the importance of explicitly allowing for the stage of development in model speciªcation; countries at an advanced stage of development are better placed to reap gains from liberalization. In particu-



General Discussion

lar, he questioned the appropriateness of putting together developed and developing countries as a uniªed sample for the purpose of the econometric analysis. Yoshino also emphasized the importance of allowing for time lagged in analyzing the impact of ªnancial liberalization. Kaliappa Kalirajan questioned the use of a random effect estimator without testing the sensitivity of results to other alternative estimation methods; in this case, the use of a random effect estimator could be problematic because the error term is highly correlated. Harry Wu wanted to know why, of the two alternative measures of ªnancial liberalization used in the econometric analysis—the index of liberalization and the binary dummy—that only the latter turned out to be a statistically insigniªcant explicator of economic growth. Conceptually, argued Wu, the liberalization index is the superior measure of the two because it captures the differences in the degree of liberalization achieved across countries whereas the dummy variable simply distinguishes liberalizing countries as a group from non-liberalizers. Iwan Azis argued that liberalization is a process (not a one-time event) and hence it was only though systematic case studies that one could come up with meaningful policy inferences. Keun Lee drew attention to the need for further research to ªnd out why some countries have been able to reap gains from ªnancial liberalization without facing currency crises and ªnancial collapse; does the answer lie in the accompanying macroeconomic polices, in particular a ªrm commitment to real exchange rate misalignment?

119


What Holds Back Bangalore Businesses?


Amar Bhidé Columbia Business School 3022 Broadway Uris Hall 722 New York, NY 10027 [email protected]

Abstract

This paper focuses on non–Information Technology businesses operating in the city of Bangalore, India. Data on firms compiled from statutory regulatory filings suggest that the number and proportion of businesses that expand rapidly are much lower than in the United States. In-depth interviews with over 100 entrepreneurs in Bangalore suggest that deficiencies in the performance of basic governmental functions (such as in collecting taxes and maintaining land records) play a significant role in discouraging businesses from starting at or expanding to an economically efficient scale of operation.

1. Introduction We know that in the United States and other advanced Western economies, high-growth ªrms represent a small but important category in the broad universe of businesses. Most businesses start small and remain small; their economic signiªcance is limited in that they simply replace other small businesses that used to do the same thing. Only a small proportion of businesses expands rapidly and presumably makes more signiªcant contributions to the growth of the economy. According to Birch (1993), Birch, Hagerty, and Parsons (1997), and Gailly et al. (2004), only about 3 to 5 percent of rapidly growing businesses account for about three-quarters of the jobs created through ªrm expansions. Bhidé (2000) suggests that rapid growth does not seem to be just a matter of chance—the relatively few fast-growing businesses appear to belong to a different category from the more numerous businesses that do not expand. We might expect Bangalore to provide a hospitable environment for high-growth ªrms because it is the epicenter Asian Economic Papers 7:1



of an “offshoring” boom. But in fact, I will report in this paper that the demographic analysis of ªrm births, expansions, and deaths shows that the incidence of rapid expansion of legitimate businesses in non–Information Technology (IT) sectors is considerably lower in Bangalore than in the United States. Furthermore our study of the individual performance and attributes of a sample of high-growth ªrms suggests that ªrms who are outstanding performers in Bangalore are laggards by U.S. standards. Our data analyses lead to the negative inference that Bangalore does not offer a model worthy of replication in other parts of the country. In all likelihood, many of these problems are not restricted to Bangalore, but rather are a feature of the Indian economy as a whole. It is more common to use multi-country surveys to assess the climate for entrepreneurship rather than the single-city approach used in this paper. These multicountry studies provide a more ready basis for statistical analysis, but in my view several factors limit their utility as a basis for making public policy choices. As Ghemawat (forthcoming) puts it, the indices in these multi-country studies assume that all countries and locations are structurally the same. Furthermore, some of the surveys implicitly assume that the more start-ups, the better, and that economic prosperity increases with the proportion of business-owners in the population when, in fact, high self-employment is a feature of economic backwardness. The high productivity and wages of advanced economies are in considerable measure due to businesses that create and exploit economies of scale and scope. Such businesses increase the proportion of the workforce that work for an employer rather than for themselves. Individual entrepreneurs certainly continue to play a signiªcant role in advanced economies, but it is the quality of their enterprise that matters and not their raw numbers. In contrast, this study of one city offered three advantages. First, it allows us to pay attention to its distinctive structures and relationships (e.g., a review of Bangalore’s history and geography, its infrastructure, educational system, civic organizations, and local government ªnances).1 Second, by studying individual ªrms we could at least attempt to distinguish between the signiªcant problems businesses face from the annoyances or incidental correlates of a bad environment. Third, we did not make the “more new businesses the better” presumption; rather, our interest was in policies that promote productive enterprises that make the best possible use of society’s scarce resources. This paper is organized as follows. Section 2 analyzes the overall pattern of ªrm and employment growth in non-IT ªrms in Bangalore, using prior research on U.S. pat1

Although we do not report all of our ªndings on these many relationships in this paper, they have informed our analysis and inferences.

121



terns as a basis for comparison. Section 3 contrasts our ªndings on high-growth businesses in Bangalore with earlier work on high-growth businesses in the United States. Section 4 discusses hypotheses about the relatively low rates of growth of businesses in Bangalore. The last section concludes with the public policy implications of the research.

2. Overall ªrm and employment growth 2.1 Prior research on U.S. patterns The U.S. job market is marked by a great deal of turbulence. According to U.S. Census Department data, employment increased from about 102 million at the start of 1996 to 115 million by the end of 2001; and this was the net result of about 93 million jobs created and 80 million jobs lost (see Table 1). Employment in the large Fortune 500–type ªrms (those with more than 10,000 employees) has stagnated or declined in the last quarter century. But, if we deªne large to be ªrms with more than 500 employees, employment in large ªrms has not declined. Whether or not large ªrms added jobs at a faster rate than small ªrms depends on the period under consideration. The distribution of employment by ªrm size has shown only a modest variation over time. Table 2 shows a slight upward trend for the share of total employment accounted for by large ªrms, growing from 46.9 percent in 1991 to 50.1 percent in 2001. The apparent stability, however, masks considerable dynamism. On average, during the 5 years from 1996–97 to 2000–01, about 6 percent of the establishments operated by large ªrms closed down and 32 percent contracted each year. Yet because of the expansion of existing large ªrms and small ªrms becoming large, total employment by large ªrms increased by about 10 million and the number of large ªrms grew by about 7 percent over this 5-year period. Relatively few ªrms have shown high rates of employment growth. On average, in the 5-year period between 1996–97 and 2000–01 about 29 percent of establishments expanded their employment each year. Given that one ªrm can operate multiple establishments, the proportion of ªrms that expanded in a year should be even lower. According to David Birch,2 just 3 percent of all companies in 1998 were gazelles (i.e., they had grown by at least 20 percent a year for the prior 4-year period with a minimum of $100,000 in revenues at the start of the period).

2

“Small business guru David Birch on why entrepreneurs are alive and well, the best places to start businesses, and, of course, gazelles.” Interview with David Birch by Justin Martin, Fortune Small Business, 1 December 2002.

122


123


b. Average of each year’s average for the 5-year period.

a. For the 5-year period from 1996–97 to 2000–01.

Note:

Source: Author’s analysis of data posted on Census Web site.

Number of establishments at the start of 1996–97 Percent: Net change in establishmentsa Total establishment birthsa Total establishment deathsa Number of employees at the start of 1996–97 Percent: Net change in employmenta Percent: Gross employment added due to birthsa Gross employment lost to deathsa Gross employment added due to expansionsa Gross employment lost due to contractionsa Employees added per birthb Employees lost per deathb Employees added per expansionb Employees lost per contractionb

5,970,420 100 324,979 3,691,599 3,318,470 102,151,341 100 12,864,587 100 33,590,526 ⫺29,361,916 59,507,116 ⫺50,871,139 9.09 ⫺8.85 6.58 ⫺6.33

TOTAL 2,611,353 44 160,512 2,201,147 2,014,003 5,477,345 5 4,791,545 37 3,989,971 ⫺3,518,761 6,519,790 ⫺2,199,455 1.81 ⫺1.75 2.26 ⫺1.26

1–4 1,011,039 17 25,202 418,569 386,542 6,533,356 6 1,364,083 11 2,666,186 ⫺2,433,479 4,694,926 ⫺3,563,550 6.37 ⫺6.30 2.87 ⫺2.01

5–9

Employment size of the enterprise (“ªrm”)

Table 1. Changes in employment and establishments of U.S. ªrms

623,167 10 ⫺2,204 204,577 208,506 7,847,568 8 856,268 7 2,520,174 ⫺2,508,279 4,939,908 ⫺4,095,535 12.49 ⫺12.06 4.19 ⫺3.25

10–19 633,233 11 9,938 205,568 199,495 18,634,030 18 1,442,065 11 5,076,597 ⫺5,109,449 10,633,733 ⫺9,158,816 27.80 ⫺25.91 8.02 ⫺7.11

20–99

274,988 5 44,407 146,931 96,473 14,646,771 14 1,034,431 8 4,317,131 ⫺3,562,272 7,969,005 ⫺7,689,433 30.25 ⫺37.01 14.18 ⫺14.49

100–499

816,640 14 87,155 514,807 413,451 49,012,271 48 3,376,195 26 15,020,467 ⫺12,229,676 24,749,754 ⫺24,164,350 29.20 ⫺29.60 17.03 ⫺16.83

500 What Holds Back Bangalore Businesses?


Table 2. Distribution of U.S. ªrms and paid employees by employment size of enterprise Firms by employment size of enterprise (%)

Paid employees by employment size of enterprise (%)

Year

Total

20

100

500

Total

20

100

500

2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991

5,657,774 5,652,544 5,607,743 5,579,177 5,541,918 5,478,047 5,369,068 5,276,964 5,193,642 5,095,356 5,051,025

11.0 10.9 10.7 10.6 10.5 10.4 10.5 10.2 10.2 10.3 10.3

1.8 1.8 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.6 1.6

0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

115,061,184 114,064,976 110,705,661 108,117,731 105,299,123 102,187,297 100,314,946 96,721,594 94,773,913 92,825,797 92,307,559

82.1 82.0 81.6 81.2 80.9 80.5 80.5 80.2 79.9 79.8 79.7

64.4 64.2 63.8 63.3 62.7 62.3 62.1 61.9 61.5 61.3 61.2

50.1 49.9 49.7 49.1 48.2 48.0 47.5 47.3 46.9 47.0 46.9

Source: Compiled from Census Web site.

2.2 Primary data collection on the Bangalore situation Compared to the extensive, albeit inconclusive, research about the United States, the literature on the dynamics of ªrm and employment growth in India is sparse, as is the availability of reliable data. The Government of India conducts a census only once every 10 years. Moreover, the results are highly aggregated and only available after a considerable time lag. They cannot be used to form the kind of estimates of job and ªrm growth discussed in the previous section. Data provided by the governments of individual states also suffer from the same limitations, and as we will see, do not inspire conªdence in their reliability. Our research team investigated a variety of sources for creating a database that would allow us to track individual ªrms. Eventually, we concluded that registration information required under three Acts—the Factories Act, the Shops and Commercial Establishments Act, and the Contract Labor (Regulation and Abolition) Act— could be treated as being mutually exclusive and collectively exhaustive. All three acts have the same broad purpose: to regulate working conditions. Most for-proªt businesses have to register, but only under one of the acts. Conveniently, a single Labor Commissioner, appointed by the government of the State of Karnataka (whose capital happens to be Bangalore), has responsibility for administering each act.3 Additionally, most of the employees of the Labor Department in Bangalore work out of (and keep their records in) one location. The types of businesses required to register under the three acts are as follows: the Factories Act covers manufacturing businesses that have more than 20 employees or have more than 10 employees and an “industrial” electricity connection (locally 3

The Labor Commissioner took a great deal of interest in our research. The cooperation of the inspectors who work for the Labor Commissioner’s ofªce and are responsible for maintaining the registration information was, however, somewhat variable.

124



called “power”). The Shops and Commercial Establishments Act covers all nonmanufacturing businesses, regardless of their size; its applicability to manufacturing businesses not covered under the Factories Act (e.g., because they employ less than 10 workers) is not clear. In my reading of the legislation, the deªnition of a “commercial establishment” is broad enough to cover a manufacturing business; and I think that it is reasonable to assume that establishments not covered under the Factories Act de facto fall under the Shops and Commercial Establishments Act. Businesses can supplement their normal workforce with “contract” employees who perform occasional or peripheral functions under conditions speciªed by the Contracts Act. The act also requires registration by “contractors” who provide more than 20 such contract employees to other businesses and by the “principal employers” who use their services. We ªnally decided to focus our study only on businesses registered under the Factories Act and the Shops and Commercial Establishments Act. We excluded businesses covered under the Contracts Act because of concerns about the reliability of the data. According to the Labor Commissioner the use of contract labor had become increasingly widespread—the number of registered principal employers in Bangalore had grown by 34 percent (from 2,345 in 1998 to 3,138 in 2002). Yet the number of licensed contractors had decreased by 21 percent (from 6,846 to 5,403) during this period and the corresponding number of contract workers covered had decreased by 19 percent (from 310,825 to 253,016). In other words, compliance had likely declined, and it is difªcult to monitor the compliance of contractors with the registration requirement because contractors generally do not have ªxed premises that Labor inspectors can visit. We decided to collect data on every 20th business in every register compiled under the Factories Act and the Shops and Commercial Establishments Act. We found, however, that many of the registration records, especially in the Shops and Commercial Establishments registers, were maintained haphazardly, creating two kinds of problems. First, many records did not have entries indicating either closure of the business or the renewal of its registration. We decided to attach a “presumed dead” tag to businesses in the year in which their registrations expired (and were not subsequently renewed). Second, data on the number of employees of the business (either at the outset or at the time of renewal or both) were not entered consistently. In such cases, we estimated the employment from the registration or renewal fees paid.4 4

For instance, according to the schedule for 2003, businesses with less than 10 employees had to pay Rs. 250. We also calculated that the modal employment of businesses that paid Rs. 250

125



We separated the data set into IT ªrms and non-IT ªrms. Then, we did a separate analysis of the IT sector, which we discuss in another paper. This paper focuses only on businesses that are not in the IT sector. 2.3 “Static” distributions There were substantial discrepancies between our data set and the ofªcial numbers published by the government. According to the most recent state government statistics, there are about 81,575 businesses registered under the Shops and Commercial Establishments Act in Bangalore. Therefore, we should have gathered at least 4,079 records from the Shops registers but we had only 3,893 records of which 429 were for businesses that had been ofªcially marked as closed; and 1,236 were businesses that had not been marked as ofªcially closed but whose registrations had expired and had not been renewed before 2003. So the number of businesses with current registrations that we found in our sample was 2,228 instead of 4,079. Similarly, the most recent ofªcial statistics report 4,855 factories operating in Bangalore. Our data set had 621 registrations under the Factories Act but 252 have closed ofªcially and 310 have not renewed. So we had only 59 factories that were of live units with current registrations instead of the expected 243 (5 percent of 4,855). However, we believe that the number of unregistered units is quite large. After we had presented preliminary ªndings about the large number of “lapsed” registrations to the Labor Commissioner, he ordered an intensive survey of one ward. This survey found 1,444 businesses, of which only 489 had ever been registered. And of those 489, 112 had not renewed their registrations. In other words, only about 26 percent of operating businesses had valid registrations.5 This is consistent with the common belief that a large subterranean economy comprising a multitude of small enterprises exists and operates outside the ofªcial purview of the state. Table 3 shows the distribution of employment in Bangalore by the employment size of non-IT registrants. Overall, employment by all registrants grew by 26 percent between 1998 and 2003, which is greater than the 13 percent U.S. growth rate in the (and whose fee and employment information had been properly recorded) was four employees. So if we encountered a company that paid Rs. 250 in 2003, but for whom there was no entry for the number of employees in its registration page, we assumed it had four employees. 5

Mysteriously, some 226 units in the ward that had current registrations were not recorded in the survey. This could be because the survey itself was not exhaustive or because some units exist only in the register. The latter possibility raises the question of who pays their registration fees.

126



Table 3 Distribution of employees of Bangalore registrants by employment size of registrant Shops and Commercial Establishments Act Estimated totala employees

Percentage of total registrants with employees numbering

Year

0–9

10–19

20–29

30–39

40–49

50–99

100–499

500

2003 2002 2001 2000 1998 1994 1990

292,640 221,520 148,920 108,780 106,740 68,700 69,500

43.9 44.2 52.1 55.2 60.9 65.4 67.8

13.9 13.7 15.5 14.8 16.9 15.1 16.2

6.2 5.8 5.3 8.5 9.8 1.4 0.0

2.0 2.5 5.2 2.4 3.0 9.4 7.3

6.7 5.0 6.3 6.0 2.5 0.0 0.0

3.8 2.1 2.7 5.0 1.0 4.4 4.4

6.5 8.6 12.9 8.1 5.9 4.3 4.3

17.1 18.1 0.0 0.0 0.0 0.0 0.0

Factories Act Estimated totala employees


Year

0–9

10–19

20–29

30–39

40–49

50–99

100–499

500

2003 2002 2001 2000 1998 1994 1990

243,260 286,220 316,080 346,800 318,040 222,500 211,340

0.0 0.1 0.1 0.1 0.1 0.2 0.8

1.6 1.0 0.4 0.3 0.1 0.2 0.5

5.6 5.6 5.6 6.4 6.0 7.8 7.8

0.5 0.2 0.9 1.1 1.0 0.3 0.3

2.2 0.9 0.5 0.5 0.0 0.4 0.4

16.7 18.3 15.7 16.0 19.1 20.9 23.8

58.6 54.4 52.7 59.5 62.4 49.5 44.7

14.8 19.6 24.0 16.1 11.3 20.7 21.8

Shops and Commercial Establishments

Factories Acts

Estimated totala


Year

employees

0–9

10–19

20–29

30–39

40–49

50–99

100–499

500

2003 2002 2001 2000 1998 1994 1990

535,900 507,740 465,000 455,580 424,780 291,200 280,840

24.0 19.3 16.7 13.3 15.4 15.5 17.4

8.3 6.5 5.3 3.8 4.3 3.7 4.4

6.0 5.7 5.5 6.9 6.9 6.3 5.8

1.3 1.2 2.3 1.4 1.5 2.5 2.0

4.6 2.7 2.4 1.8 0.6 0.3 0.3

9.6 11.2 11.5 13.4 14.6 17.0 19.0

30.1 34.5 39.9 47.2 48.2 38.9 34.7

16.1 18.9 16.3 12.3 8.5 15.8 16.4

Source: Research team analysis of registration data. Note: a. Equals sample multiplied by 20.

5-year period between 1996 and 2001. However, the total absolute number of new jobs created in the United States is greater because the U.S. economy has a much larger base of employment in legitimate businesses. For instance, Massachusetts, which has a population comparable to that of Bangalore, had an employment growth rate of only 10 percent between 1996 and 2001, which translated into 285,000 new jobs compared to the 111,120 jobs created in Bangalore. Tables 3 and 4 together show that regardless of the category of registrant, the differences in the distribution of employment by size of ªrm in the United States and Bangalore remain large. In the United States at the end of 2001, ªrms with fewer than 20 employees accounted for 89 percent of the total number of ªrms with paid employment, but only 18 percent of total paid employment (Table 2). In Bangalore

127



Table 4. Distribution of Bangalore registrants by employment size of registrant Shops and Commercial Establishments Act Estimated totala registrants


Year

0–9

10–19

20–29

30–39

40–49

50–99

100–499

500

2003 2002 2001 2000 1998 1994 1990

35,940 28,120 21,720 17,020 18,340 14,240 8,900

88.3 88.8 88.7 89.2 90.3 93.3 90.6

7.3 7.1 7.0 6.5 6.3 4.8 7.2

1.9 1.8 1.5 2.2 2.2 0.3 0.0

0.4 0.6 1.0 0.5 0.5 1.3 1.6

1.2 0.9 0.9 0.8 0.3 0.0 0.0

0.4 0.2 0.3 0.5 0.1 0.3 0.4

0.4 0.5 0.6 0.4 0.2 0.1 0.2

0.1 0.1 0.0 0.0 0.0 0.0 0.0

Factories Act Estimated Totala Registrants


Year

0–9

10–19

20–29

30–39

40–49

50–99

100–499

500

2003 2002 2001 2000 1998 1994 1990

2,660 3,100 3,320 3,720 3,500 2,720 2,820

0.0 0.6 0.6 1.1 1.1 1.5 7.1

9.0 5.8 2.4 1.6 0.6 1.5 2.8

25.6 25.2 25.9 29.0 26.9 31.6 29.1

1.5 0.6 3.0 3.2 2.9 0.7 0.7

4.5 1.9 1.2 1.1 0.0 0.7 0.7

29.3 32.9 28.9 28.5 32.6 31.6 33.3

27.8 30.3 29.5 32.3 34.3 28.7 23.4

0.8 1.3 1.8 1.1 0.6 1.5 1.4


Factories Acts

Estimated totala


Year

registrants

0–9

10–19

20–29

30–39

40–49

50–99

100–499

500

2003 2002 2001 2000 1998 1994 1990

38,600 31,220 25,040 20,740 21,840 16,960 11,720

82.2 80.1 77.0 73.4 76.0 78.5 70.5

7.5 7.0 6.4 5.6 5.4 4.2 6.1

3.5 4.2 4.7 7.0 6.1 5.3 7.0

0.5 0.6 1.3 1.0 0.9 1.2 1.4

1.4 1.0 1.0 0.9 0.3 0.1 0.2

2.4 3.5 4.1 5.5 5.3 5.3 8.4

2.3 3.5 4.5 6.1 5.7 4.7 5.8

0.2 0.2 0.2 0.2 0.1 0.2 0.3

Source: Research team analysis of registration data. Note: a. Equals sample multiplied by 20.

in 2003, registrants with fewer than 20 employees accounted for nearly 90 percent of total registrants (Table 4) and for 32.3 percent of the total employment (Table 3). Conversely, U.S. ªrms with more than 500 employees accounted for just 0.3 percent of the total number of ªrms with paid employment, but for slightly over 50 percent total paid employment at the end of 2001. In our Bangalore sample, registrants with more than 500 employees accounted for 0.2 percent of total registrants and 16.1 percent of total employment in 2003. If we make the plausible assumption that unregistered units are predominantly small, the true gap between the employment accounted for by large businesses in Bangalore and the United States is likely to be even greater. Some indication of this is provided by comparing the absolute numbers employed by large ªrms. Bangalore ªrms with more than 500 employees had a total workforce of about 86,279 employ-

128



ees in 2003, whereas Massachusetts ªrms with more than 500 employees had a workforce of more that 1.5 million in 2001. 2.4 Underlying dynamics Table 5 presents data about the underlying births, deaths, expansions, and contractions of non-IT businesses in Bangalore. Births and expansions in our sample of Shops and Commercial Establishments registrants added 242,060 jobs between 1999 and 2003, but the net addition of jobs was only 184,540 because of the loss of 54,960 jobs due to closures and contractions. The net reduction of 116,060 jobs from 1999 to 2003 in our sample of Factories registrants resulted from the creation of 171,780 jobs and the loss of 287,840 jobs. And if we combine both categories of registrants, the net growth of 68,480 jobs in our sample was due to the creation of 413,840 jobs and the loss of 342,800 jobs. Put differently, every “net” job addition by registrants required the gross creation of 5.8 jobs. The sources of the gross increases in employment appear to be quite different in Bangalore and the United States. A noteworthy contrast lies in the role played by the births of new ªrms. Table 5 shows that the number of all new non-IT businesses registered in the 5 years between 1999 and 2003 represents 141 percent of the total number of registrants (with current renewals) at the start of 1999. Employment added by the new businesses at the end of 2003 represents about 101 percent of the total employment of all registrants at the start of 1999. In the United States, by contrast, employment added by the births of new establishments in 5 years after 1996 amounts to just 40 percent of total employment in 1995.6 Another noteworthy difference between businesses in Bangalore and the United States lies in the low contributions to job creation made by the expansion of existing businesses and the low incidence of high-growth businesses. The expansion of existing businesses in the United States between 1996 and 2001 has created at least a comparable number of jobs to those created by the births of new ªrms; but in Bangalore, births created 15 times more jobs in the 1999–03 period. Similarly in the United States, the total number of jobs created through 1996–01 expansions equaled 58 percent of the jobs existing at the start of the period, whereas in Bangalore, jobs created through the expansion of registrants between 1999 and 2003 represented just 7 percent of employment at the start of the period.

6

Because some establishments may have been started by businesses formed before 1995, the contribution made by the births of new businesses may be even lower. As the base numbers of employment by legitimate ªrms are much higher in the Unites States, the higher rates in Bangalore do not mean higher absolute numbers of jobs created.

129


130


b. Includes only those registrants who had registered before the start of the period and whose registrations were current at the end of the period.

a. Equals sample multiplied by 20.

Note:

Source: Research team analysis of registration data.

3,000 1,700 ⫺1,300 2,580 1,060 2,780 3,840 220 40 278,240 162,180 ⫺116,060 160,680 74,800 210,040 284,840 11,100 3,000 62.28 74.18 50.45 75.00

13,320 18,260 4,940 17,160 1,700 8,280 9,980 3,380 1,360 59,020 105,940 46,920 102,320 11,660 39,460 51,120 12,260 8,080 5.96 5.12 3.63 5.94

18,260 35,780 17,520 27,360 480 7,220 7,700 1,200 820 105,940 290,480 184,540 226,720 4,340 45,600 49,940 15,340 5,020 8.29 6.49 12.78 6.12

Number of registrants at the start of the period Number of registrants at the end of the period Net change in number of registrants over period New registrations (“births”) during period Registrations ofªcially closed during periodb Registrations not renewed during periodb Total “deceased” registrants during period Number of registrants who expanded in periodb Number of registrants who contracted in periodb Employment by registrants at the start of the period Employment by registrants at the end of the period Net change in employment during period Employment added due to “births” (new registrants) Employment lost due to ofªcial closures Employment “deemed lost” due to non-renewals Employment lost to “deaths” (closures ⫹ non-renewals) Employment added by registrants’ expansionsb Employment lost due to registrants’ contractionsb Employees added per birth Employees lost per death Employees added per expansion Employees lost per contraction

21,260 37,480 16,220 29,940 1,540 10,000 11,540 1,420 860 384,180 452,660 68,480 387,400 79,140 255,640 334,780 26,440 8,020 12.94 29.01 18.62 9.33

Shops and Commercial Establishments Act

Estimates for Bangalore Populationa

2,620 3,000 380 1,700 500 820 1,320 120 40 220,500 278,240 57,740 145,400 36,100 52,200 88,300 11,000 6,760 85.53 66.89 91.67 169.00

Factories Act

1994–98 (both calendar years inclusive) Both acts

Shops and Commercial Establishments Act

Factories Act

1999–03 (both calendar years inclusive)

Table 5. Employment changes due to births, closures, expansions, and contractions of Bangalore registrants

15,940 21,260 5,320 18,860 2,200 9,100 11,300 3,500 1,400 279,520 384,180 104,660 247,720 47,760 91,660 139,420 23,260 14,840 13.13 12.34 6.65 10.60

Both acts



Table 6 contains the distribution of Bangalore businesses that had current registrations at the start of 1999, categorized by the degree to which the number of their employees changed over the next 5 years. It shows that only 6.5 percent of Shops and Commercial Establishments registrants, 3.5 percent of Factories registrants, and 6.1 percent of all registrants recorded any increase in their employment. The proportion of establishments increasing their employment by more than 15 percent per year is even smaller—only 2.4 percent of all registrants. This is less than half the 5 percent or so of U.S. businesses that grew at 15 percent or more per year (over a 5-year period) in the National Commission on Entrepreneurship’s (2001) study. The differences in the actual number of high-growth business are sharper. About 520 registrants operating in Bangalore at the start of 1999 increased their employment by more than 15 percent a year for the next 5 years. According to the National Commission on Entrepreneurship’s study, the average number of ªrms that grew at this rate across all labor market areas in the United States with populations of more than 5 million is 6,277. This is about 12 times the number of high-growth ªrms in Bangalore.7

3. High-growth ªrms in the United States and Bangalore 3.1 Prior U.S. ªndings Bhidé (2000) investigated whether high-growth businesses in the United States were drawn from a different distribution of start-ups than the typical businesses that started small and stayed small. Founders of 100 companies from the 1989 “500” list (a compilation by Inc. magazine of the fastest growing privately held companies in the United States, henceforth, Inc. 500) were interviewed. The average company on the list had 1988 revenues of about $15 million, 135 employees, and a 5-year sales growth of 1,407 percent. The list of prospective interviewees was narrowed to companies founded in the previous 8 years (because the start-up history of older companies would be more difªcult to obtain), and hence the interviewees turned out to be a little smaller than companies on the full list. Comments about the Inc. list refer to this sub-list of 100 ªrms. The interviews suggested that new businesses that grow rapidly were businesses that provide high–unit price goods and services to other businesses rather than to consumers. Similarly, although popular start-ups are mainly found in industries 7

The smallest number of high-growth ªrms in any labor market area reported in the National Commission study is 3,569. This is for Bridgeport, Connecticut, whose population of about 3.5 million was about 30 percent lower than the population of Bangalore in 1995. The Boston Labor Market area, which has roughly the same population as Bangalore, had 5,498 highgrowth ªrms.

131


Percentage of registrants whose employment at the end of 2003 compared to the start of 1999 Total employment change due to

16,520 1,120 400 100 60 20 40 0 18,260

64,960 18,060 10,460 3,160 2,700 1,120 6,240 0 106,700

40.7 48.2 40.0 60.0 33.3 100.0 100.0 0.0 41.4

41.3 8.9 5.0 0.0 0.0 0.0 0.0 0.0 38.0

2.3 14.3 30.0 0.0 66.7 0.0 0.0 0.0 3.8

2.8 3.6 0.0 0.0 0.0 0.0 0.0 0.0 2.7

Percentage of registrants whose employment at the end of 2003 compared to the start of 1999

10.3 1.8 5.0 0.0 0.0 0.0 0.0 0.0 9.5

2.7 23.2 20.0 40.0 0.0 0.0 0.0 0.0 4.5

1,480 1,200 1,440 80 0 0 0 0 4,200

620 720 360 0 200 0 0 0 1,900

Total employment change due to

19,360 7,820 3,980 1,820 900 1,100 6,240 0 41,220

9,460 2,780 0 0 0 0 0 0 12,240

0–9 10–19 20–29 30–39 40–49 50–99 100–499 500⫹ All Units

40 20 940 100 0 1,140 1,200 20 3,460

360 300 19,000 3,100 0 60,900 198,380 36,000 318,040

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

100.0 100.0 83.0 40.0 0.0 86.0 85.0 0.0 83.2

0.0 0.0 14.9 20.0 0.0 10.5 13.3 100.0 13.3

0.0 0.0 0.0 40.0 0.0 3.5 1.7 0.0 2.9

0.0 0.0 2.1 0.0 0.0 0.0 0.0 0.0 0.6

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

360 300 15,800 1,200 0 52,580 161,280 0 231,520

0 0 0 0 0 0 0 0 0

0 0 0 700 0 2,000 2,800 0 5,500

0 0 600 0 0 0 0 0 600

Total Total Higher, Higher, by Closure Contraction Exapansion of ‘low to Exapansion of ‘high Employment registrants employment Nil (due less than 15% p.a. or of of med. growth’ (less than growth’ (by 15% p.a. Unavailable to closure) Unchanged 15% p.a. more Lower registrants registrants 15% p.a.) registrants range in rangeb in rangeb or more) registrants

Estimated employment for population at the start of 1999

Factories Acta

0–9 10–19 20–29 30–39 40–49 50–99 100–499 500⫹ All Units



Shops and Commercial Establishments Acta

Table 6. Changes in employment in the 5-year period 1999–03 for registrants operating with current registrations at the start of the period

Percentage of registrants whose employment at the end of 2003 compared to the start of 1999


Total employment change due to

16,560 1,140 1,340 200 60 1,160 1,240 20 21,720

65,320 18,360 29,460 6,260 2,700 62,020 204,620 36,000 424,740

10.3 1.8 1.5 0.0 0.0 0.0 0.0 0.0 8.0

40.8 49.1 70.1 50.0 33.3 86.2 85.5 0.0 48.1

41.2 8.8 11.9 10.0 0.0 10.3 12.9 100.0 34.1

2.3 14.0 9.0 20.0 66.7 3.4 1.6 0.0 3.7

b. Equals sample multiplied by 20.

a. Includes only those registrants whose employment was known (or could be estimated from fees paid) in 1999.

Note:

Source: Research team analysis of registration data.

0–9 10–19 20–29 30–39 40–49 50–99 100–499 500⫹ All Units

2.8 3.5 1.5 0.0 0.0 0.0 0.0 0.0 2.4

2.7 22.8 6.0 20.0 0.0 0.0 0.0 0.0 3.8

19,720 8,120 19,780 3,020 900 53,680 167,520 0 272,740

1,480 1,200 1,440 80 0 0 0 0 4,200

620 720 360 700 200 2,000 2,800 0 7,400

9,460 2,780 600 0 0 0 0 0 12,840


Factories Actsa


Table 6. (Continued)


where the technology has matured and demand has stabilized, more than a third of Inc. 500 companies were in the computer industry, which has long been in a state of technological ºux. Interviews also suggested ways in which Inc. 500 companies resemble low-growth businesses: they seldom have ªnancing provided by professional venture capitalists and they do not have “proprietary” technologies or topnotch founding teams who can raise large amounts of capital. Inc. 500 company founders also did not spend much effort on market research or writing business plans. 3.2 The Bangalore interviews One obstacle we faced in replicating the Bhidé (2000) study in Bangalore lay in identifying high-growth businesses and the fact that there are no Inc. 500–type lists for the city. We decided to look for founders of businesses that were not “marginal” in that they had grown to 10 or more employees. We started with a database of 30,000 businesses that had registered as Small Scale Industry units, which made them eligible for incentives provided by the government to encourage small-scale enterprise. We later supplemented this database with a smaller database provided by an association of small-scale businesses in the state, and with the information from the Labor Department registers. Restricting our interviews to businesses that were no more than 8 years old also proved challenging. The Small Scale Industry database records about registrants’ start dates (and much else) were simply wrong.8 Another problem was that the formation of a new legal entity in Bangalore does not necessarily correspond to what would normally be regarded as the launch of a new business in the United States. For instance, a small ªrm in the United States that starts a new manufacturing facility will usually not establish a new legal entity; but as we will see later tax breaks and other incentives for the small-scale sector make such proliferation of new businesses commonplace in India. In the ªnal list of 100 interviews, about 20 of our interviewees were not randomly located through a database, but were referred to us by prior contacts and by other interviewees we had found from the databases. We also relaxed our criteria of only interviewing founders of businesses that were less than 8 years old. Four of the interviewees were founders of IT ªrms, so the ªnal sample was almost a non-IT sample. All these adjustments naturally raised concerns about an unrepresentative sam8

We discovered that we were probably unable to locate businesses in the Small Scale Industries database because more than two-thirds of them had ceased to function or may have never existed except on paper. Then we found that only a few registrants in our sample had (from the start or through growth) 10 or more employees.

134



ple of founders. However, it is now my belief that I have ended up doing what did not seem feasible at the outset: focusing our interviews on the founders of highgrowth businesses. After we had compiled the results of the 100 interviews, we found that the growth rates of the businesses were well above the usual growth rates in Bangalore. The median increase in the number of employees from the year in which our interviewees started their business to 2004 was 700 percent. The median compound annual rate of increase was 20 percent. As shown in Table 6, the median growth rate for all Bangalore registrants is zero. About 63 percent of companies in our interview sample grew at an annual rate of 15 percent or more, whereas only 2.4 percent of all registrants recorded this rate of growth. It may well be the case that exceptional entrepreneurs who expanded their businesses at an out-of-the-ordinary rate were more willing to be interviewed. In other words, we may have beneªted from an unintended but desirable bias in our sample. 3.3. Performance vis-à-vis Inc. 500 companies Although employment growth in our Bangalore sample borders on the stellar by the standards of other local companies, it is considerably lower than in the Inc. 500 sample. The median annual growth of employees between 1984 and 1988 of the Inc. 500 companies was 64 percent, compared to the 20 percent growth of the Bangalore companies. The median number of employees (38) in our Bangalore sample in 2003 is just slightly over one-third of the median number of employees (100) in the Inc. 500 sample in 1988 despite the Bangalore companies being older than the Inc. 500 companies. Median annual revenues for the Bangalore companies in 2003 amounted to Rs. 30 million (or about US$ 666,000), whereas median annual revenues in 1988 for the Inc. 500 companies that we interviewed amounted to US$ 5.5 million. The median equity invested by the founders of Inc. 500 companies to start their businesses amounted to US$ 10,000; and for the Bangalore founders, it was Rs. 375,000, or about US$ 8,333. However, we should note that US$ 10,000 amounts to half or less of per capita GDP in the United States (in the years during which the investment was made) whereas Rs. 375,000 represents at least 10 times the estimated per capita GDP in the city of Bangalore. The median ratio of 1988 revenues to start-up equity (which we can think of as a crude output–input measure) for the Inc. 500 companies works out to 378, whereas the median ratio of 2003 revenues to start-up equity for the Bangalore companies works out to just 19.4. Our interviews do suggest the possibility of differences between the numbers that the entrepreneurs reported to us (as well as to the government agencies) and the ac135



tual numbers. For instance, in one case we observed 15 employees on the premises when the entrepreneur asserted that he had only 8 employees. In two other cases, the founders said they actually employed twice as many people as they had on their ofªcial books. Similarly, four of our interviewees said they reported only half the revenues they actually earned. Is it possible that there were in fact many more business owners who signiªcantly misrepresented their numbers to us, and could this eliminate the performance gap discussed above between Bangalore and Inc. 500 entrepreneurs? I do not believe it does. On the employment side, there are two thresholds that businesses have an incentive to avoid crossing. Businesses with more than 10 employees may have to contribute 4.75 percent of wages to an Employees’ State Insurance Scheme (a state operated health plan); and those with 20 or more employees have to contribute about 10 percent of wages to a Provident Fund scheme (a retirement plan). The Payment of Bonus Act requires businesses with more than 20 employees that have been operating for more than 5 years to pay (whether or not the business is proªtable) a minimum annual bonus to employees. Therefore, it is likely that in the overall population of businesses in Bangalore, many businesses that report fewer than 10 or 20 employees actually employ more than these numbers. In our sample, though, only about one-quarter of our interviewees reported fewer than 20 employees. For the remaining three-quarters, there is no obvious reason for them to have understated their employment numbers to us. Similarly, businesses with less than Rs. 10 million in revenues are exempt from paying excise taxes, levied at a base rate of 16 percent of sales. But only 30 percent of our sample reported revenues of less than Rs. 10 million (i.e., 70 percent did not have a strong incentive to under-report their revenues to us). 3.4. Nature of opportunities About one-half of the Inc. 500 founders started businesses in ªelds that were in a state of ºux or turbulence because of a new technology, regulatory regime, fashion, or other such external change. Starting a proªtable business in a stable market, where competitive forces have long shaken out weak technologies and ªrms, requires a signiªcantly better approach or a new “combination.” In a new or changing market, however, entrepreneurs do not require a signiªcant innovation to make a proªt. Customers and suppliers take some time to learn about their alternatives after a change occurs. Meanwhile, entrepreneurs can buy inputs cheaply from uninformed suppliers and sell them at higher prices to uninformed customers. In about 40 percent of Inc. 500 cases, start-ups had neither a unique product nor the beneªt of a market in disequilibrium. Here the proªtability of the venture appar136



ently derived from the founders’ capacity to differentiate their product or service through their personal effort. The personal efforts of the founders usually paid off in markets where customers placed a high value on what Sabini and Silver (1982) call “fuzzy” attributes, whose dimensions, such as trendiness, elegance, and responsiveness, which customers cannot easily measure or even articulate. Like the Inc. 500 founders, our Bangalore interviewees did not start their businesses to exploit a signiªcant innovation—roughly the same percentage in both groups claimed to have started with a unique offering. We also recorded some similarities in the nature of opportunities: only a small proportion (14 percent in the case of Inc. 500 companies and 23 percent of Bangalore start-ups) offered consumer goods or services. However, there is a noteworthy but unquantiªable difference. Interviews with the Inc. 500 founders provided some plausible (albeit unveriªable) explanation for why their businesses might have earned abnormal returns without a signiªcant innovation (e.g., by exploiting unsettled market conditions; their resourcefulness and “hustle” in satisfying fuzzy wants). This was not the case with many of our Bangalore interviews. Some interviewees had apparently taken advantage of unexpected changes in customer wants. For instance, one entrepreneur we interviewed had started a business making bottled soft drinks. The market was mature and the entrepreneur faced much larger and well-entrenched rivals. The venture was on the brink of failure when it suddenly started becoming fashionable to serve bottled water (instead of tap water) at weddings in Bangalore. The entrepreneur salvaged his business by switching to this new market where he did not face intense competition. Another difference between the Inc. 500 and Bangalore businesses lay in the extent to which they competed against larger companies. The great majority of the Inc. 500 interviewees competed against other small companies or start-ups, so they were not handicapped by their lack of scale economies. In Bangalore, however, nearly a quarter of the start-ups in our sample competed against much larger competitors. When we asked our interviewees about how they managed to do so, they acknowledged that their larger competitors probably enjoyed some economies of scale on the manufacturing side but that they had lower overhead costs. My explanation for our Bangalore ªndings is that our interviewees did not in fact earn “abnormal” returns. They invested their capital and their time on which they may have earned a “normal” or market-determined rate of return, but they did not get anything for their insight, ingenuity, or “hustle.” In Knight’s (1921) terminology, they did not earn a true proªt. This would help explain why the Inc. 500 interview-

137



ees were able to build much larger businesses starting with roughly the same amount capital—the high proªtability of the Inc. 500 companies helped ªnance much higher rates of growth. But why should entrepreneurs take the risk of investing their resources in a single business, only to earn market rates of return? One possibility is that the entrepreneurs made a mistake and thought they could earn high proªts without having a competitive edge. Or, entrepreneurs might face “frictions” or high “transactions costs” that would signiªcantly reduce the net return that they could earn on their labor and capital in other people’s businesses. The hypothesis that many Bangalore interviewees earned a return only on their capital and labor is consistent with the source of their start-up ideas. Seventy-one percent of Inc. 500 founders replicated or modiªed an idea they had encountered through previous employment and nearly half simply copied their previous employer’s business model (although they did not necessarily compete head-to-head with their previous employer). In contrast, just 35 percent of Bangalore interviewees got their idea in the course of their previous employment and less than 20 percent started their ventures in the same line of business as their previous employers. This suggests that fewer entrepreneurs in Bangalore were likely to have the prior knowledge (e.g., of fuzzy wants or opportunities for incremental innovation) that could sustain high proªts. 3.5 Diversiªcation Only about half of our interviewees in Bangalore anticipated rapid growth in the future. Yet more than 80 percent planned to start or already owned another business. Some of these businesses were in the same or related ªelds. In the majority of cases, the diversiªcation was unrelated (e.g., an entrepreneur who started in road construction then opened a business that sold ketchup). Many of the Inc. 500 interviewees had also entered new ªelds—for example, one company started as a broker for scrap plastic and morphed into a recycler of Styrofoam containers. Overall, about one-third of the Inc. 500 interviewees had signiªcantly altered their original business idea and another third reported moderate changes. Another quarter or so were so-called “serial entrepreneurs,” who had started one or more businesses before launching the venture that was in the Inc. 500 sample. The important difference of the Inc. 500 interviewees and the diversiªcation strategies of the Bangalore entrepreneurs is between the changes in business models and serial entrepreneurship. Serial entrepreneurs generally sell or shut down their prior business before starting their next venture, whereas the Bangalore interviewees usually continued to operate their prior business. The outcome is the creation of quasiconglomerates called business “groups” in India.

138



3.6. Sources of funds A large proportion of interviewees from both groups said that their personal funds represented the main source of their start-up equity. The size of the minority that reported raising arms-length equity ªnancing (from parties who were not relatives or friends of the founders) was, however, larger in the Inc. 500 group: 11 percent of Inc. 500 founders versus 3 percent of Bangalore founders. Some of our interviewees in Bangalore said that the growth of their businesses was constrained by their lack of capital. So we asked whether they had tried to raise additional equity. One interviewee said that he had but that only unsavory characters were willing to invest and usually through a debt rather than an equity-like investment. Others expressed incredulity that anyone (who was not an active partner) would want to invest in the equity of a private company. One interviewee said: “How would the investor know I wasn’t cooking the books? Only public companies can have such investors.” Forty-four percent of Bangalore interviewees used borrowed funds to ªnance their start-ups compared to just 20 percent of the Inc. 500 interviewees.9 Approximately the same proportion of interviewees from both groups (a little over 60 percent) used debt to ªnance their growth. All but four of the Bangalore founders who borrowed from banks said that they provided real estate that was worth at least as much as the value of their loans as collateral. Some of our Bangalore interviewees who did not have enough collateral got unsecured credit from non-institutional lenders instead. Apparently, they faced a variety of choices such as using post-dated checks and borrowing from chit funds (informal credit union). 3.7. Uses of funds Our Bangalore interviewees said they made large outlays for the physical space they needed for their business. Excluding the 24 percent of founders who already owned their premises and two entrepreneurs who started out of their homes, our interviewees reported a median expenditure of Rs. 200,000. This compares to the median start-up equity of Rs. 375,000 and total capital (equity plus debt) of Rs. 500,000. The relatively high outlay on property partly reºects the deposits demanded by landlords in Bangalore—tenants generally have to pay a deposit equal to at least 11 months of rent, compared to 1 month in the United States.

9

Interviews with bankers in the United States who make loans to small businesses (see Bhidé, Stevenson, and Bilden 1990) suggest that banks generally do not extend credit to start-ups, which helps explain why 80 percent of the Inc. interviewees had no borrowing at the outset.

139



The high outlays of our Bangalore interviewees also reºected a preference for owning rather than renting property. As mentioned previously, 24 percent of founders already owned the premises they used for their start-ups; indeed, two of them said that having a place provided the impetus to search for business. Twelve percent simultaneously acquired property and started their business. And of those who started by renting, over one-third bought their own property later on. So when we interviewed them in 2004, only 40 percent of our subjects were in rented properties. In the United States, just one Inc. 500 interviewee—a Cuban immigrant in Miami who said he wanted to help out his father in the construction business—had purchased real estate to house his business.10 Bangalore entrepreneurs apparently used more funds to start or expand their businesses than their U.S. counterparts in other areas as well. They usually extended credit for 90 days or longer to their customers, compared to the 30 days that are customary in the United States. They received less credit and vendor ªnancing from large suppliers than did their U.S. counterparts (e.g., the owner of a copy shop told us that that the Xerox Corporation, which leases equipment to copy shops in other parts of the world, requires its purchase in Bangalore). Investments to create selfcontained businesses were commonplace in Bangalore (e.g., more than half of the entrepreneurs owned electrical generators), and 22 percent had uninterruptible power supply units as backups to the electricity supplied by the state-owned utility. To my knowledge, none of the Inc. 500 businesses interviewed had invested in electrical generators. 3.8 Organizational development In the Unites States, founders of archetypal venture capital-backed start-ups use equity options to recruit and motivate well-qualiªed employees. The founders of the Inc. 500 companies we interviewed generally did not. The questionable prospects of their ventures, which made it difªcult to raise outside equity, also made it hard to use options to attract top-notch employees. Instead, many founders provided most of the critical labor themselves. They also used their networks to hire “diamonds in the rough” (i.e., innately talented individuals who lacked veriªable credentials or had some taint), effectively paying an “efªciency wage” to such individuals. Later as the Inc. 500 companies grew and uncertainties about their prospects resolved, their hiring policies evolved toward the venture-capital archetype.

10

Until personal tax rates were reduced sharply in the early 1980s, it was more common for small business owners to personally own their premises in the United States as well.

140



Our Bangalore interviewees did not pay high wages to attract well-qualiªed employees. They made even less use of equity or equity options—only the four founders of IT-based businesses said they had. None hired professional managers at the outset, and tried to hire diamonds in the rough. We also found little evidence of post start-up “professionalization” (e.g., only one interviewee spoke of the importance of hiring good middle-managers and even in this case I believe that the entrepreneur was really referring to production supervisors in his garment factory). In only a few cases our Bangalore interviewees told us that they had to go to some trouble to ªnd the employees they needed. The norm was a passive and impersonal approach to recruiting (e.g., putting up a notice outside their factory gates). Interviewees who hired white-collar employees (including software programmers) used the impersonal mechanisms of advertisements and placement agencies in situations where their U.S. counterparts would have relied on personal referrals and relationships. We heard many complaints from the Bangalore interviewees about the difªculty of retaining employees, which were almost never expressed in the Inc. 500 interviews. It is common for Bangalore employers to ameliorate the consequences of turnover rather than to prevent it (e.g., they would overstaff software projects so that the disruption of departures would be minimized). None of our interviewees, however, spoke of paying an efªciency wage to discourage turnover. When I brought up the possibility in a few interviews, the entrepreneurs expressed the same incredulity as when I had asked them about raising equity from arms-length investors. Efªciency wages could not work in India our interviewees said, workers just did not think enough about the long term. 3.9 Founders’ backgrounds and family roles Our Bangalore interviewees were, like the Inc. 500 founders, well educated compared to the overall population of their countries. Sixty-three percent of the Bangalore interviewees and 81 percent of the Inc. 500 interviewees have university degrees. Of those with university degrees, over 50 percent of the Bangalore group had trained as engineers compared with only 15 percent of the Inc. 500 group. Only 5 percent of Inc. 500 founders reported that they came from poor backgrounds, and only 3 percent of the Bangalore founders had parents who had blue-collar jobs. About 40 percent of the Bangalore interviewees volunteered the information that they were Brahmans who constitute about 10 percent of the Indian population. What groups were under-represented? It seems likely that (as with the Inc. 500

141



founders interviewed in the United States) there were not many members of traditionally disadvantaged groups among our Bangalore interviewees.11 The Inc. 500 interviewees were, geographically speaking, restless rather than rooted; our Bangalore interviewees even more so. About half of the Inc. 500 interviewees had been not raised in or around the place where they had started their companies, and twothirds of the Bangalore sample were not natives of the city. Relatives appear to have played a somewhat more important role in the ventures of our Bangalore interviewees than in those of the Inc. 500 interviewees. Fifteen percent of the former raised their initial equity from their parents12 and other relatives compared to 7 percent of the latter. The proportion of businesses started by teams of founders (rather than by individuals) was lower in Bangalore. About 50 percent of Bangalore founding teams had members who were related compared to 14 percent of Inc. founding teams.

4. Hypotheses about differences between outcomes in United States and Bangalore 4.1 Proximate explanations Earlier we saw that the proportion of Bangalore non-IT businesses that show gazelle-like growth is about half of that in the United States and the number of such ªrms (adjusted for population) is many times smaller. Our analyses of registration data (and interview results) suggest that high-growth non-IT businesses in Bangalore do not expand as rapidly as Inc. 500 companies that occupy the highgrowth tail in the U.S. distribution. Crude measures suggest that high-growth Bangalore start-ups make less efªcient use of capital. Our interview ªndings suggest some ªrst-level or proximate explanations for the relatively low performance of Bangalore businesses. They pursue less-promising opportunities and therefore do not earn the high proªts that they could use to expand their businesses. Low proªtability also makes it difªcult to raise arms-length capital for ªnance expansions. They build mini-conglomerates comprising many small businesses instead of focusing on realizing economies of scale and scope in one 11

We asked our Bangalore interviewees a question that I could not have asked the Inc. 500 interviewees—whether they belonged to a “business” community (e.g., Vysya or Chettiar) or hailed from a region (such as the Marwar) that has been traditionally associated with business. About one-third said they did, which is almost certainly greater than their share of the population.

12

Parents and occasionally other relatives also pledged their property as collateral for bank loans.

142



large business. Economies of scale and scope may also be lost because of vertical integration. Vertical integration also ties up capital, as does purchasing real estate and extending very long credit terms. But what makes entrepreneurs pursue low-proªt opportunities? Why do so many diversify when their U.S. counterparts focus on growth? Why do they invest in real estate, extend 90 days or more of credit, and make in-house what they could buy? 4.2 Catching up on technological backwardness The following case can be made that wealth-constrained individuals in the United States have more opportunities than wealth-constrained individuals in Bangalore to start and grow proªtable businesses by helping to advance the technological frontier. In some ªelds (such as biotechnology) large capital requirements preclude any role for the small start-up. In many other ªelds, however, self-ªnanced entrepreneurs, who have advantages in conducting low-budget experiments on novel ideas, can play a signiªcant role. In advanced countries like the United States, most resources are already in or near their highest-valued use. Any increase in their productivity requires new technologies (broadly deªned). In an under-developed economy, however, increases in incomes and proªts do not require U.S.-style technological innovation. Almost by deªnition, the actual productivity of its resources is below that in developed economies because the technologies in wide use in it are inferior to the technologies already introduced and in extensive use in developed economies. Rapid growth can be achieved merely through the introduction into and diffusion through the economy of such superior technologies. Moreover, the returns from investing in new technologies are generally lower than the returns from acquiring and implementing existing technologies from the developed countries. This is because existing technologies can be acquired at lower costs since the outlays required for their development have already been incurred. And although there is some uncertainty about the ªt of transplanted technologies with the local environment, the basic technical and market risks are long gone. Therefore, the opportunities that U.S. entrepreneurs have to proªt from making incremental contributions in the early stages of the development of new technologies will be considerably less abundant for their counterparts in India. Individual entrepreneurs who cannot mobilize large amounts of capital face disadvantages in acquiring technologies from abroad because proven technologies usu-

143



ally require large-scale operation.13 In addition, the acquisition of proven technologies, even when it is just a matter of copying or reverse engineering, involves ªxed costs. These costs are more easily amortized by a large enterprise. Because large, established organizations (including wealthy family groups) have natural advantages in mobilizing the resources required to operate a large-scale enterprise, much of the low-hanging fruit offered by proven overseas technologies lies outside the reach of wealth-constrained individual entrepreneurs. And indeed, large organizations—or exceptionally well-credentialed individuals who are capable of mobilizing capital on a large scale—have been at the forefront of the visible manifestations of technological catch-up in India.14 In a previous era, the government reserved many sectors for small units in which large businesses were simply not permitted to operate. Although most of these reservations have been removed, as we will see subsequently, other features of the environment continue to encourage businesses to start small and stay small. 4.3 Indirect taxes and labor laws The system of indirect taxation has a powerful inºuence on the decision of entrepreneurs to operate many small businesses instead of one large business. Exemptions to taxes are numerous and complex. They fall under 70 broad categories, subdivided into 259 entries, 52 conditions, and 7 lists, with each list containing numerous items. Businesses located in certain troubled or backward areas do not have to pay excise taxes, nor do businesses with less than Rs. 10 million in total revenues.15 A large business (with more than Rs. 10 million in revenues) subject just to the basic rate would have to realize cost savings through economies of scale of 16 percent of the market price of its output in order to survive. Statutory contributions to health insurance and retirement schemes amount to about 15 percent of regular wages, and when other payments such mandatory bonuses are included, the total non-wage

13

New technologies often start out in niche markets; their subsequent application to mass use turns on the realization of signiªcant economies of scale, through mass production techniques, for instance. By the time many technologies become proven they are no longer suited for entrepreneurs’ small start-up businesses.

14

Many modern automobile and motorcycle plants have been started, but all by existing Indian and multi-national companies. Two chains of coffee shops, apparently based on the Starbucks model, have opened; both have been sponsored by powerful business houses and families. With the exception of Infosys (which like the typical Inc. 500–type start-up was started with modest funding provided by its founders), much of software off-shoring and call-center operations are controlled by diversiªed Indian conglomerates or groups, overseas companies, or individuals with venture-capital backing.

15

I relied on the report published by the Ministry of Finance (2004) for the facts on indirect taxes mentioned in this section.

144



costs faced by the typical employer amount to 50 percent of regular wages. However, businesses with fewer than 20 employees do not have to incur these costs. According to some of our interviewees, once a business registers with the excise tax authorities or enrolls in the provident fund scheme it cannot easily withdraw—even if its revenues or employment subsequently fall below the prescribed thresholds. Businesses who register for excise taxes also run the risk that tax ofªcials who have to satisfy a collection “quota” or expect a bribe may make excessively high assessments of the tax liability. Therefore, apprehensions about the future consequences of taxes may also play a role in discouraging businesses from expanding. Rules intended to protect workers also contribute to the reluctance to operate on a large scale. In particular, our respondents expressed concerns that employing more than 20 workers makes them liable for inspections under the Factories Act. 4.4 Infrastructure Power failures are an everyday occurrence in Bangalore, and no one can fail to notice the poor state of the roads and public transport. These deªciencies do not, however, necessarily penalize large ªrms. Indeed, they may increase the beneªts of scale—for instance, a large business can use a higher capacity and lower per-unit cost generator for its in-house supply of electricity than a small business. But generating instead of buying electricity does tie up capital, and the higher costs of generating electricity on a small scale reduce proªts. This makes it difªcult for wealthconstrained individuals to ªnance the expansion of their businesses. Deªciencies in the transportation system for physical goods are also likely impede the realization of economies of scale. In principle, India’s huge population should support large scale production; but in fact, for many goods and services the country comprises a collection of small local and regional markets. Manufacturers who produce on a national scale face high costs in transporting goods to their customers because large efªcient trucks cannot operate on narrow and poorly maintained roads. The railway system operates at more than its safe level of capacity utilization and goods transport has to compete with the politically more powerful demand for passenger trains. As a result, even companies that are large often produce their goods in dispersed regional plants. This in turn limits the optimal size of suppliers’ facilities. Similarly, bottlenecks in harbors and inefªciencies in loading and unloading sea containers limit scale economies of exporting companies in the manufacturing sector. The deªciencies of the Indian system compared to that of countries like China make it difªcult for Indian companies to compete in export markets for goods

145



where the cost and speed of sea-borne transportation matter to their customers. Typically, such goods involve high-volume manufacturing. Although interviewees did seem to be aware of its inºuence, the types of businesses they were in appeared to reºect the parlous state of the transportation infrastructure. About 40 percent of our interviewees manufactured physical goods; of these only three sold their goods to customers in India outside the immediate region. And in all three cases the transportation costs accounted for a small percentage of the value of the goods (namely smart cards and specialty chemicals). Similarly, most of the businesses that served export markets (15 percent of the sample) provided services rather than physical products. 4.5 Land Our interviewees believed that acquiring land and buildings was a good investment because prices “always went up.” But shouldn’t rising future demand and inelastic supply be fully reºected in current prices, thus erasing the incentive to buy instead of rent? Not necessarily. Government policies encourage businesses to purchase commercial and industrial properties by making offers to “owner-occupants” that they cannot refuse. For example, the government uses eminent domain–like rights to acquire raw land—this cleans up its title—and provides it with municipal amenities. Then, rather than selling it at the highest price the market will bear, the land is sold at an administered price to businesses (who are not supposed to sell or rent the property for a speciªed period). One of our interviewees estimated that his purchase price from the government was about 25 percent lower than the purchase price of an equivalent plot plus the bribes he would have had to pay to clean up the title and secure municipal amenities. In addition to selling land at subsidized prices, government agencies also provide ªnancing at favorable terms to small businesses. 4.6 Property rights Copy shops in Bangalore have to buy copiers because manufacturers are no longer willing to lease copiers. Apparently the market for copier leases collapsed because the legal system could not limit the disappearance of leased copiers to levels that would allow the market to function. Similarly, the legal system was apparently unable to adequately deter motorcycle dealers from “diverting” the inventory ªnanced by manufacturers. As a result, manufacturers now demand up-front payment. The judicial enforcement of private contracts in India is also almost certainly worse than it is in the United States. But to what degree does this explain the performance gap between the Inc. 500 and Bangalore interviewees? Certainly our Bangalore interviewees expressed great skepticism about the court system. They also rarely use

146



written contracts. Interestingly, the few interviewees who did offered reasons that had little do with trying to secure judicial protections for their rights. For instance, one interviewee said his company had started making more of an effort to write up agreements to avoid misunderstandings as its projects had become more complex. Another said that he started using contracts to improve internal controls—he did not want his salespeople making reckless oral promises to customers. 4.7 Common beliefs Bhidé (2000) argues that economies of scale and scope are created by driven entrepreneurs rather than automatically or accidentally discovered. Thus the ambition and ability of a Ray Kroc or a William Wrigley, rather than innate differences in product attributes, explains why hamburger and chewing-gum markets are highly concentrated when markets for hot-dogs and lollipops are not. In other words, the choices and mindset of individual decision makers matter. Similarly, in India entrepreneurs can choose whether or not to search for scale economies that will allow them to overcome the tax and labor cost penalty faced by large businesses. This choice in turn is inºuenced by the beliefs of the individuals. Our interviews in Bangalore suggest that many entrepreneurs start out with a presumption that favors a search for opportunities to diversify rather than for ways to expand their existing businesses. When I asked a software entrepreneur what he hoped to accomplish in the next 5 years, he said he wanted to build a “group.” When I asked why he had such a goal, he said that it was what all the successful businessmen he knew did. Beliefs and conventions also seem to be behind excessive vertical integration and stafªng. In the United States, ªrms routinely pay premium prices and efªciency wages to encourage their vendors and employees to provide reliable high quality goods and services. Many of the Bangalore entrepreneurs have as little faith in the reliability of private vendors and employees as they do in the State electricity board. They also believe that because vendors and employees place a low value on longterm payoffs, paying premium prices or wages cannot elicit responsible behavior. Therefore, entrepreneurs integrate vertically and hire ªve people to do the job of four.

5. Conclusions We should expect differences in the entrepreneurial activity of starting and expanding businesses in the technologically advanced U.S. economy and the considerably more technologically backward Indian economy. However, the differences we do

147



ªnd are not of the sort that we would expect to observe if the Indian economy was “catching up” quickly. If the catch-up process was proceeding quickly, we ought to see more births and expansion of businesses and more job creation than in a more mature advanced economy. The process ought to be particularly robust in Bangalore where the rapid growth of software development units, call centers, and the like are said to have signiªcant multiplier effects.16 In fact, outside of the IT industry, the net growth of legitimate businesses and jobs (scaled by population) is considerably lower in Bangalore than even in the relatively slow-growing labor market areas in the United States such as Massachusetts. Furthermore, businesses in Bangalore appear to be less efªcient in their use of capital. The indicators of sub-par performance do not appear to be an artifact of sampling procedures or defective data. Rather, we ªnd systematic features of the Bangalore environment that make it difªcult for businesses to start on a large scale or attain large scale through growth. And to the extent that scale helps businesses more efªciently acquire and implement proven modern technologies, these features retard the catch-up process. The wealth-constrained individual entrepreneur unwittingly becomes an obstacle to development rather than an agent of technological progress. The troublesome feature of Bangalore’s development that our study highlights is the degree to which it has fallen outside the “legitimate” sector. The low rate of unemployment in spite of relatively small absolute increases in jobs in the “legitimate” sector points to a substantial expansion of the so-called informal sector. But productivity in the informal sector tends to be lower than even the uninspiring level of productivity we found in many legitimate businesses. Moreover, whereas businesses and their employees in the informal sector of the economy consume public goods, they usually do not pay the taxes the government requires to provide these goods. A large informal sector therefore depresses the quantity and quality of public goods the state can provide which in turn can depress the rate of economic development. What, if anything, can be done to improve matters? Recent history suggests that the system does have a capacity for constructive change. Policy biases against economic efªciency have been considerably reduced. In the last decade most direct restrictions on ªrm size (e.g., through reservations) and on the importation of modern technologies have been removed. Domestic companies no longer have to apply for licenses to enter a new line of business. Although the system is still rife with exemptions, the 16

According to popular wisdom (and the software industry association) each job in these industries “indirectly” creates 2.5 jobs in other sectors.

148



number of excise tax rates has been reduced from 100 to 3. Some of the distortions have been moderated through the introduction of value-added principles (Ministry of Finance 2004). The political setting for these changes should also alleviate concerns that only totalitarian or single-party rule can produce signiªcant reforms. The dramatic reforms after 1991 came in a period when the country was governed by a succession of coalitions. Changes that would have been considered unimaginable 2 decades ago—and whose absence would have been cited as proof of public choice theories—were implemented with little popular backlash. Quite possibly this was because the gains from reform were of such a magnitude that those who were living off the “rents” under the prior regime could be easily bought off. It should also be noted that the need for reform itself was created because of policies adopted during the several decades after independence when the Congress party did not face any effective political opposition. Going back even further, when India was under British colonial rule, economic performance was worse than under the de facto one-party rule of the Congress Party. This points to mistakes and miscalculations, rather than the compulsions of electoral politics, as the primary culprit. The analysis of this paper also suggests, however, that changes considered dramatic by historical standards may nonetheless be insufªcient to sustain long-term development whose fruits are widely enough distributed for the process to be acceptable under a popular democracy and whose rate is high enough to accommodate the demand for gainful employment. More subtly, our study suggests a categorical difference between what has been done and what remains to be done. Most of the policy reforms that have been implemented to date appear to have remedied the State’s prior “sins of commission”—counterproductive interventions in the workings of a free market. The reforms were certainly politically challenging in that the resistance of the groups who saw their interests threatened had to be overcome by some combination of persuasion or side-payments. The reforms, however, did not require any upgrading of the state’s administrative capacities—they could be accomplished largely through legislation or changes in regulation. With a few exceptions, such as the restrictions on FDI (notably absent in the IT industry) most of the obvious sins of commission have now been rectiªed. Our interviews suggest that sins of omission—the failure of the state to provide the services and functions that do add value—now represent the binding constraint on more rapid growth and development. Moreover, these sins of omission represent a more serious challenge. As with the sins of commission, there are vested interests with a stake in the status quo whose resistance must be overcome. Moreover, sins of com-

149



mission cannot be corrected with the stroke of a pen: getting the state to do what it should do involves signiªcant administrative and implementation challenges. It is all very well to talk about establishing good “institutions” in the abstract; their effective functioning in fact turns on the gradual development of an intricate machinery comprising many speciªc organizations, tacit rules, incentives, and conventions. To make this discussion concrete, consider the enforcement of property rights, speciªcally of land. Few would contest the proposition that such enforcement is a natural function of the state and one which cannot be privatized. Our interviews also suggest that the failure to enforce rights leads to the misallocation of resources, for instance by encouraging entrepreneurs to invest in real estate instead of in growth opportunities. But although enabling legislation may be necessary, rights for land and other property cannot be enforced simply by passing a law. The courts have to give well-nuanced meaning to the rights that have been legislated—for instance by specifying what property owners can or cannot do with their land—and establish an effective process for settling disputes. The administrative machinery of the state has to keep good records about who owns what, and the coercive machinery of the state (i.e., the police) has to enforce the decisions of the courts (i.e., by evicting individuals whom a judge has said should be evicted). Reforming a system of property rights therefore requires much more than simply overcoming the not inconsiderable opposition of those who have developed a vested interest in the defects of the system. It involves a painstaking, protracted effort of changing many processes and activities. The “bandwidth” of those who are in a position to effectuate such changes is, however, constrained. Therefore, whereas many sins of commission may be effectively tackled at once, this is unlikely to be the case with sins of omission. Here, policymakers have to set priorities and focus on those initiatives that provide the highest return to their attention and effort. And arguably the sins of omission in Bangalore and other parts of India owe their continued existence to the failure of policymakers to set such priorities. For instance, the problems with the system of land records or the inadequacy of the transportation system are well known; however, the effort to solve these problems is often reduced to a token level because of a lack of focus. How should these priorities be set? In my opinion, the usual and apparently scientiªc approach of cross-sectional regressions is of limited use. Unless one assumes that outcomes are generated by an identical process operating in many places and times, regression coefªcients and measures of statistical signiªcance provide little guidance to what is the right thing to focus on at a particular place or a particular time. To the extent that history and speciªc circumstances matter, we can usefully

150



adopt the “pragmatic” approach of a common law judge. By this I mean setting priorities after a comprehensive examination of the facts pertaining to a speciªc situation, and after considering lessons from other similar situations that have occurred in the past. A pragmatic approach also involves treating judgments about priorities as provisional and subject to revision as more data become available or as circumstances change. The data we gathered on Bangalore suggest a focus on improving the performance of some basic governmental functions such as maintaining land records; making sure that goods can be easily transported; that the supply of electricity is reliable and municipal functions are available; deterring and punishing theft; and collecting taxes efªciently. The last item seems to be of particular importance. Without sufªcient tax revenues, the state cannot provide the public services needed for rapid economic development. Moreover, the manner in which taxes are now collected has engendered, besides a large underground economy, a variety of undesirable side effects such as an excessive tendency of businesses to rely on debt, invest in real estate, and to operate below economic scale. My reading of the facts also suggests that many of the initiatives that the government has embarked on, such as trying to provide a sound regulatory basis for the derivatives market or reforming the rules for the governance of publicly traded companies, do not attack the currently binding constraints on growth. Moreover, to the degree that organizations can develop only a few new capabilities at a time, the effort spent on second order problems also reduces the likelihood of solutions to the more critical issues. In the same vein, the data we gathered (some of which have not been presented in this paper) raise questions about the appropriateness of efforts by several state governments and the central government to promote IT and other knowledge-based industries. Employers in these industries have directly created a few hundred thousand jobs in Bangalore. They may have generated more employment for drivers, maids, cooks, gardeners, and so on in the informal sector, but there is no way of estimating the magnitude of this effect. We can, however, infer that the so-called multiplier effect in the “legitimate” economy cannot be substantial.17 We cannot blame the IT industry for its failure to create more jobs, or suggest that the growth of the industry should somehow be checked. However, I believe efforts 17

This is not to say that the growth of software development units and call centers has had no echo in the formal sector—as mentioned we did encounter interviewees whose businesses provided complementary services to software companies. Also, we cannot observe the contra-factual—what the rate of job and employment growth would have been in the absence of the off-shoring boom.

151



speciªcally targeted at the IT industry warrant skepticism. My concern is not so much with the ªnancial resources being used to promote the IT industry, but with the diversion of attention to it. It is worth noting that China has developed at a considerably more rapid rate without a “world-class” IT industry (or, for that matter sophisticated derivative or stock markets). Some argue that these shortcomings will cause China to fall behind in the long run. The alternative view, which I subscribe to, would be that China has followed, perhaps just by accident, a sensible sequence for attacking the binding constraints. If and when the absence of sophisticated ªnancial markets becomes the bottleneck in China, nothing prevents policymakers from focusing their attention on that arena. Arguably, if this had been done prematurely China might not have been able to develop the conditions necessary for the growth that it has enjoyed. Reasonable people with different prior beliefs and assumptions about the process of development may disagree with these interpretations. But I would hope that anyone with a pragmatic bent who believes in some degree of trial and error would be persuaded by my account about at least one critical need—namely, for much more accurate, real-time data. As things stand, policymakers are ºying blind. They cannot easily correct course, because they do not know when they are off-course. If, as in an advanced country, things were naturally going right, the lack of good data would not matter much, but this is far from the case in India.18 To conclude, India’s development glass is now partially full. Economic reforms of the last 10 years have signiªcantly reduced the state’s sins of commission. Given the extremity of these prior sins, reforms engendered signiªcant improvements in economic performance with little evident pain or disruption. But many sins of omission in some basic functions of the state, that the citizens of advanced countries have taken for granted for more than a century, remain. In order to ªll the still empty portion of the glass, policymakers should ªrst focus on these basic roles and avoid attempting tasks suited to a more advanced economy. References Bhidé, Amar. 2000. The Origin and Evolution of New Businesses. New York: Oxford University Press. Bhidé, Amar, Howard Stevenson, and Phillip Bilden. 1990. “Note on Acquiring Bank Credit,” Harvard Business School Note: 391–410. 18

Interestingly, the absence of good data leads not only to economic mistakes, but political ones as well. In the last election held in Karnataka for instance, the Chief Minister called a snap election in the apparent belief (but as far as I can tell, uninformed by any facts) that voters would reward him for the IT boom. This turned out to be a misjudgment.

152



Birch, David L. 1993. “The Job Generation Process Revisited: An Interview with David L. Birch,” International Council on Small Business Bulletin. Birch, D., A. Hagerty, and W. Parsons. 1997. Corporate Almanac. Cambridge, MA: Cognetics, Inc. Gailly, Benoit, Damien François, Mahamadou Biga Diambeidou, Michel Verleysen, and Vincent Wertz. 2004. The Growth Trajectories of Start-up Firms: An Exploratory Study. ESU’2004 proceedings—ESU entrepreneurship conference, Twente, The Netherlands, pp. 1–16. Ghemawut, Pankaj. Redeªning Global Strategy: Crossing Borders in a World Where Differences Still Matter. Boston: Harvard Business School, forthcoming. Knight, Frank. 1921. Risk, Uncertainty, and Proªt. Boston: Houghton Mifºin. Ministry of Finance, Government of India. 2004. Report of the Task Force on Implementation of the Fiscal Responsibility and Budget Management Act. New Delhi: Ministry of Finance. Sabini, John, and Maury Silver. 1982. Some senses of subjective. In: Explaining Human Behavior, edited by P. E. Second, pp. 71–91. Beverly Hills, CA: Sage.

153


Comments

Comments

Kanhaiya Singh: 1. Introduction “What Holds Back Bangalore Business” is an interesting topic and the author raises doubts regarding the “catching up” of businesses in Bangalore with those of the United States. The author has selected the non-information technology (non-IT) sector of Bangalore for comparison with the industrial growth and development in Massachusetts in particular and in the United States in general. This appears to me to be a strange comparison. When looked at through the prism of revealed comparative advantage, the fact that Bangalore has specialized over time in the IT sector means that other business opportunities are less likely to be stellar in performance even inside Bangalore, leave aside in comparison with the best businesses internationally. For any comparison to be meaningful, a ªrst step would have been to ensure the compatibility of the comparison (i.e., ensure a like-to-like comparison). Of late, Bangalore has been compared with the Silicon Valley of the United States. Possibly, given the structure of industrial development of Massachusetts, Mumbai or some other city of India could have been more compatible for comparison rather than Bangalore. Bangalore is largely a software services powerhouse and naturally, “software is the most attractive opportunity for Bangalore-centric ªrms” (Murthy, Narayana, and Raju 2002). The city resembled a conventional industrial district during early 1990s (Heitzman 2004) but has been dominated by the IT sector since the end of 1990s.



Comments

In addition, a more useful and policy-relevant discussion could have emerged from the paper, if Bhidé had used time series of developments of the two or more cities. The paper abstracts from the history of changes in Bangalore. A comparison of the growth history of Silicon Valley during its early periods with that of present Bangalore would have given more insight. In short, the effect of initial conditions, which is the fundamental concern of conditional convergence theory, is completely ignored in Bhidé’s analysis. One important hypothesis of the paper appears to be that the IT boom in Bangalore should have enough of a multiplier effect in developing other business sectors in Bangalore to make them comparable to the IT boom itself or, for that matter, comparable to Massachusetts. This is an important aspect of the development issue and the role of the IT sector. But, again for meaningful policy prescriptions, an outcome from a cross-sectional analysis of Indian or international cities with a varying degree of IT development could have given better insight rather than this analysis of Bangalore alone. Moreover, the author neither calculates the expected multiplier nor presents the actual multipliers for the two cities under comparison. The author offers surprisingly strong conclusions about public policy, “our negative inference is that Bangalore does not offer worthy of replication in other parts of the country.” However, the author himself agrees that “many of these problems are not restricted to Bangalore, but rather are a feature of the Indian economy as a whole.” Therefore, the analysis falls short of pinpointing what is holding Bangalore back and what policies should be adopted to unshackle the growth of Bangalore. It is also not clear why Bhidé expects Bangalore to be the role model for India. The speciªc problems of Bangalore are those of a fast-growing city where the pace of infrastructure developments has lagged behind demand. This paper is the product of two exercises: 1. An analysis of birth, death, and employment of registered ªrms during 1999–03 based on the sample data obtained from registers of records under (a) the Factory Act and (b) the Shops and Commercial Establishment Act. 2. A study of the “climate for entrepreneurship” in Bangalore based on interviews (possibly structured?) with 100 founders of high-growth businesses. The key ªndings can be summarized as follows: • The differences in the “entrepreneurial” activities of starting and expanding business between the technologically advanced U.S. economy and the technologically

155


Comments

•

•

• • •

•

backward Indian economy are not of the sort that we would expect to observe if the Indian economy was “catching up” quickly. The multiplier effect of rapid growth of software development units, call centers, and the like is considerably lower in Bangalore than even in the relatively slowgrowing labor market areas in the United States such as Massachusetts. The owners of high-growth ªrms in Bangalore (a) pursue less-promising opportunities; (b) believe in diversiªcation rather than specialization; and (c) mostly use their own funds for investment. The policy environment encourages business to start small and stay small, and the main culprits are tax exemptions, indirect taxes, and labor laws. Infrastructure (e.g., power, roads) are inadequate and the situation is getting worse. The legal protection of land titles and property rights is inadequate and getting worse: “our interview also suggests that the failure to enforce rights leads to misallocation of resources, for instance by encouraging entrepreneurs to invest in real estate instead of in growth opportunities.” The low growth of the legitimate sector points to substantial expansion of socalled informal sector (un-registered manufacturing).

My assessment is that many of these conclusions are too general and do not appear to be driven by the data. They are too sweeping in nature because they were stated without substantial discussion in the main body of the text. The strong conclusion by Bhidé that “I believe efforts speciªcally targeted at the IT industry warrant skepticism” appears to suggest that policymakers should not be persuaded about the development of the IT sector as a driver of growth. It is, however, a fallacy to assign too much credit or discredit to the government policies in the development of the IT sector in Bangalore during this early period. Much of the growth had to do with the ignorance of the ofªcials about software development and the related business. “If limited understanding of the software industry allowed Indian ªrms to begin bodyshopping,1 it also prevented policymakers from taking decisive steps to actively promote software industry” (Saxenian 2000). In fact, Bangalore had pre-existing capabilities that attracted IT companies (Rajan 2006). The existence of large public sector companies in the area of electronics (e.g., CMC Limited, Electronics Corporation of India), several laboratories of defense research and development (e.g., units of Hindustan Aeronautics Limited), and tertiary 1

Body-shopping is deªned narrowly: it refers to offering on-site programming services (in the United States, for example) on the basis of “time and material” contracts. On-site services accounted for approximately 90 percent of the value of Indian software exports in 1990, and for 61 percent in 1995 (Saxenian 2000).

156


Comments

Table 1. Reasons for not registering; small industries, in percent

Nature of activity

Not Complicated interested procedure

Clearance not available from municipality, etc.

Local laws do not permit

Not aware of such provision

Manufacturing/Assembly/Processing Repair and Maintenance Services

15.5 7.3 17.1

1.4 0.8 1.7

0.4 0.3 0.7

1.3 0.2 0.4

17.7 9.9 25.6

36.1 18.5 45.4

Total

39.9

3.9

1.4

1.8

53.1

100.0

Total

Source: Development Commissioner (SSI), 2003.

Table 2. Employment per birth and per expansion in India and United States, in percent

Employees added per birth Employees added per expansion

Bangalore 1999–03 (Both acts India)

USA (1996/97– 2000/01

12.94 18.62

9.09 6.58

Source: Bhidé 2007, Tables 1 and 5.

institutions (e.g., Indian Institute of Science) provided the seeds for the development of the IT sector in Bangalore or similar places elsewhere in India.

2. On registration data used for the analysis and conclusions Bhidé used data from government registers of ªrms, which could be grossly underreported in the Indian case. The point is that the quality of Indian data cannot be compared to the quality of U.S. data (e.g., it is less likely that most of the Indian ªrms would have updated themselves every year). The 2001–02 Census of Small Scale Industries provides several reasons, summarized in Table 1, for why ªrms are not getting registered (e.g., more than half of the small businesses were not aware of the registration process). Bhidé should have supplemented his analysis with broadbased data collected by the central agencies, such as the Annual Survey of Industries (ASI), NSSO (rounds), Census for SSI (rounds), Economic Census (rounds), a survey of the unorganized sector, NSSO survey on employment and unemployment, and the Central Statistical Organization (CSO)/State Statistical Ofªces data sets available on domestic products at national, state, and district levels (in some cases). Sub-regional analysis must also take into account the economic environment of the regions where district-level data are available. Even the data collected by Bhidé tell a story, which is grossly in contrast to what is inferred in the paper. Table 2 shows that a comparison of the data in Bhidé’s Table 1 and Table 5 reveals that the number of employments per birth and expansion is actually higher in Bangalore than in the United States. Bangalore added 12.94 employ-

157


Comments

ees per birth of ªrm compared to 9.09 employees in the United States; and Bangalore added 18.62 employees per expansion of ªrm compared to 6.58 in the United States (see Table 2).

3. On the use of interview data and related analysis A major part of the paper is devoted to discussing the interview results of 100 U.S. companies and 100 Bangalore companies; both samples were selected from the highgrowth segment. Clearly, by ignoring the IT sector in Bangalore, Bhidé must have missed most of the (actual) high-growth ªrms in Bangalore. The IT sector has been growing at more than 28 percent on an average annually (CAGR between 1999–00 and 2005–06), which happens to be a many times higher growth rate than obtained in other sectors. In addition, the analytical table of survey data related to this exercise and results thereof are missing from the paper. This omission makes Bhidé’s comments highly subjective, difªcult to read, and sometimes appear to be based on responses of one or two people. Incorporating the statistical analysis of the interview data could have saved substantial space besides providing transparency, instead of phrases such as “many said,” “some said,” “few said,” and so on, which are vague and meaningless. The paper also fails in providing concrete examples for potential business opportunities in the Bangalorees non-IT sector, which must be clearly deªned for policy intervention. Nevertheless, the comparison of sources of funds, uses of funds, diversiªcation, and founders’ backgrounds are interesting. Clearly, the government’s afªrmative action policies (“reservation policies”) in favor of the lower castes have forced Brahmins to take up business in large numbers in Bangalore and possibly elsewhere also. About half of the Bangalore interviewees had university degrees.2 Bhidé contends that the relatively low performance of Bangalore business is mostly due to the fact that their entrepreneurs were pursuing opportunities with lower proªtability, for example, they build mini-conglomerates instead of specializing to achieve economies of scale. At the same time, however, Bhidé also observed that entrepreneurs were diversifying horizontally from one business to another in search of better proªts. Therefore, it is difªcult for me to believe that entrepreneurs are not already choosing the best option, and that it is the competitive environment that is pushing proªtability down. 2

I share Bhidé’s concerns about the distorting effects of taxation policies, which at times lure entrepreneurs to remain small or at least declare them small.

158


Comments

Table 3. Average annual growth in gross domestic income (GDI) at 1993–94 prices during 1999–00 to 2002–03, in percent District

Primary

Secondary

Tertiary

Total

Per capita

Bangalore Urban Bangalore Rural

0.29 8.08

11.56 24.82

10.14 19.86

9.96 14.56

5.01 14.35

Source: Planning Commission (2007), Appendix A-1.4: Primary Agriculture, Forestry and Fishing Mining and Quarrying; Secondary Manufacturing Electricity, Gas, and Water Supply and Construction; Tertiary GSDP-primary-secondary.

4. The multiplier effect of Bangalore’s IT boom Bhidé does not present credible tests to demonstrate that there is no spillover effect from the IT boom in Bangalore to the other parts of the local economy. Table 3 reports aggregate-level data from the 2007 Karnataka Development Report that indicate that from 1999–00 to 2002–03 the per capita income of Bangalore Urban grew at 5.01 percent whereas the per capita income of Bangalore Rural grew at 14.35 percent income. It is interesting to note that the annual population growth of Bangalore during this period was almost 5 percent. Such a huge population growth was not the outcome of natural growth but the outcome of an inºow of migrants from all over the country. Naturally, the rush of migrants to Bangalore was caused by the fast increase in employment opportunities there. The fact that rural Bangalore was also booming in all the sectors at the same time means that important factors have been omitted in Bhidé’s comments on the multiplier of Bangalore’s boom. Bhidé’s analysis based on data on births and deaths of ªrms is just too simplistic to capture the complete and accurate picture of economic development in Bangalore. It is also not correct to say that the legitimate formal sector is not growing in Bangalore; for example, a new Apparel Park at Doddaballarpur, Bangalore, alone is expected to provide 50,000 new jobs. Aggregate-level data from the CSO shows that the growth performance of registered (organized) manufacturing ªrms in Karnataka State—in which Bangalore is located3—has been much better in recent years as compared to overall Indian performance. Table 4 reports the annual growth rate of the value-added in registered manufacturing activities between 2001 and 2004 to be 13.1 percent for Bangalore and 7.2 percent for India.

5. The growth pains of Bangalore Bangalore is preferred by investors, not only because it has the seeds of a vibrant IT sector, but also because it has been a better place to operate in several other respects. Table 5 shows that Bangalore has one of the smallest slum populations, one of the lowest rates of sexual crimes, and one of the lowest poverty rates. The biggest issues 3

Bangalore constitutes almost half of the factories in Karnataka.

159


Comments

Table 4. Annual compounded growth in manufacturing GDP India

Karnataka

Mfg

mfg_registered

mfg_unregistered

1991–92 to 2003–04

6.4

8.2

3.7

Mfg 5.1

mfg_registered 4.8

mfg_unregistered 5.5

2001–02 to 2003–04

6.8

7.2

6.0

10.5

13.1

5.8

Source: Central Statistical Organisation (various) and Central Statistical Organisation (various-a). Note: Bangalore constitutes almost half of the factories of Karnatka.

are the inadequate infrastructure and the rising rates of murder, kidnapping, and theft. Trafªc volume is reported to be growing at 7–10 percent, making road congestion a perennial problem. In 2004, WIPRO Chief Azim Premji threatened to move out of Bangalore unless there were dramatic improvements in infrastructure. Several IT ªrms announced a boycott of the annual IT exposition slated to be held in Bangalore from 26–29 October in 2005 in protest against the inaction of the government to solve infrastructure problems and the boycott was supported by NASSCOM (National Association of Software and Service Companies), the country’s apex body of the IT industry.4

6. Slow reforms and counterproductive afªrmative actions Bhidé’s interviews appear to suggest that labor laws are not a big problem. This conclusion is too simplistic according to the work of other researchers. For example, Basu (2006) notes that too many laws (45 laws at the national level and close to four times that at the state level) govern the labor market, hence creating inºexibility in the relationship between labor and the employer. 4

It would be wrong to think that no progress has been made on the infrastructure situation, notably (a) about 40 km of the ªrst phase of Metro Rail project, costing about Rs. 64 billion, is under construction. It is designed to carry 40,000 passengers per hour in each direction; (b) under JUNRUM (Jawaharlal Nehru Urban Renewal Mission) investment, about Rs. 80 billion is proposed to improve the storm water drain network, environmental management, road networks, solid waste management and basic services to the poor; (c) Bangalore Development Authority (BDA), Bangalore Metropolitan Regional Development Authority (BMRDA), and Bangalore Metropolitan Transport Corporation (BMTC) have promising programs in terms of high-quality roads, and high volume modern vehicles; (d) twelve ºyovers including India’s longest 5.35 km Hebbal Flyover have already come up. Many are under construction; (e) the Bangalore–Mysore highway nearing completion will reduce a 4-hour journey to just about 90 minutes; and (f) the International Airport under construction (consortium leader Siemens Group) is proposed to be redesigned to cater to 4 million passengers per annum (currently the HAL airport handles 5 million passengers).

160


Comments

Table 5. Living conditions across Indian metropolitan areas (mostly census 2001) Description

Bangalore

Chennai

Delhi

Mumbai

Kolkata

House condition (percent of total household) Good Livable Dilapidated

63.5 33.7 2.8

90.4 9.02 0.6

58.04 36.6 5.35

58.8 38.1 3.09

56.2 38.9 4.96

Proportion of population living in slums (Series-1), (Municipal Corporations) Census of India 2001

10

18.9

18.6

54.1

32.5

Migrant population (duration 0–9-year) share in total population, Urban Agglomerates (UA) city, Census of India 2001

13.4

6.6

16.4

15.1

6.2

Population (million) (2001 census): municipal corporation area

4.3

4.34

9.88

11.98

4.56

Population (million) (2001 census): urban area

5.7

6.6

12.9

16.4

13.2

510.8 4.3 0.7 2.9 0.7 2.3

313.3 1.9 0.7 1.3 1.6 0.9

398.8 2.9 4.4 5.1 1.5 10.2

192.0 1.3 1.2 2.4 0.6 1.2

71.1 0.3 0.1 1.1 0.3 0.6

6

10.8

Cognizable crime (Indian Penal Code) rate in ªve metropolitan areas (2005) (number per 100,000; 2001 census population) All IPC offences Murder Rape Molestation Sexual harassment Kidnapping Poverty Ratio (share of population below poverty line of the respective states as deªned by the Planning Commission) in which the metropolitan area exists (1999–00): Urban area of districts

9.22

10.31

10.8

Source: Most data come from Census of India (2001a, 2001b, 2001c). Poverty Ratio is calculated by the author, crime data come from National Crime Records Bureau (NCRB 2005). Available from http://ncrb.nic.in/crime2005/home.htm

The Indian Government has sought to address existing social inequalities by adopting afªrmative action policies. Caste-based quotas are being implemented in university enrollment and in public sector employment. There is in fact growing demand for such “reservation policies” in private-sector employment as well. Because these reservation policies can impart negative effects on efªciency, they warrant more research.

7. Conclusions Although Bhidé has done extensive work on data collection and made comparisons between business in Bangalore and the United States, he has made much fewer efforts to reconcile the ªndings with aggregate data. Furthermore, the heart of this paper is a comparison of performance in entirely incomparable environments, namely comparing Bangalore ªrms in an area where they are not strong with U.S. ªrms in all sectors. Finally, Bhidé’s key ªnding that “efforts speciªcally targeted at the IT industry warrant skepticism” does not appear to be theoretically sound or empirically grounded. This paper thus provides large scope for future research.

161


Comments

References Basu, Kaushik. 2006, BBC News, Thursday, 18 May 2006. Census of India. 2001a. Housing Atlas of India: Census 2001. Ofªce of Registrar General of India, Government of India. New Delhi: Government of India. Census of India. 2001b. Migration: Census 2001. Ofªce of Registrar General of India, Government of India. New Delhi: Government of India. Census of India. 2001c. Slum Population (Series-1): Census 2001. Ofªce of Registrar General of India, Government of India. New Delhi: Government of India. Central Statistical Organisation. various. National Accounts. The Central Statistical Organisation (CSO), Ministry of Statistics and Program Implementation, Government of India. Central Statistical Organisation. various-a. State Domestic Product. The Central Statistical Organisation (CSO), Ministry of Statistics and Program Implementation, Government of India. Development Commissioner (SSI). 2003. Quick Results: Third All India Census of Small Scale Industries (2001–02). Development Commissioner (SSI), Ministry of Small Scale Industries, Government of India. New Delhi: Government of India. Heitzman, James. 2004. Network City: Planning the Information Society in Bangalore. New Delhi: Oxford University Press. Murthy, N., R. Narayana, and Sandeep Raju. 2002. Comments on “Bangalore: The Silicon Valley of Asia.” In Economic Policy Reforms and the Indian Economy, edited by Anne O. Krueger, pp. 194–203. Chicago and London: The University of Chicago Press. NCRB. 2005. Crime in India 2005. National Crime Records Bureau (NCRB 2005). Available from http://ncrb.nic.in/crime2005/home.htm Planning Commission. 2007. Karnataka State Development Report. New Delhi: The Academic Foundation. Rajan, Raghuram. 2006. India: The Past and its Future. Asian Development Review 23 (2):36–52. Saxenian, AnnaLee. 2000. Silicon Valley’s New Immigrant Entrepreneurs. The Center for Comparative Immigration Studies Working Paper 15. San Diego, CA: University of California. Saxenian, AnnaLee. 2002. Bangalore: The Silicon Valley of Asia In: Economic Policy Reforms and the Indian Economy, edited by Anne O. Krueger, pp. 169–212. Chicago and London: The University of Chicago Press.

162


Comments

Comments

Keun Lee: 1. Pattern of ªrm growth comparison between Bangalore and the United States In general, economic development is eventually about the growth of private sector ªrms. This paper deals with this important issue in the context of India—Bangalore in particular. The issue is part of a more general question of what determines the growth of ªrms in developing countries. To answer this question, the paper compares the attributes of high growth ªrms in India (Bangalore) and in United States, based on ªrm interviews. As is well known, Bangalore is the center of an “offshoring” boom, representing the fastest growing region in the Indian economy. Thus, one would expect to see many high-growth ªrms in Bangalore. There are three main ªndings in this paper. One, the expansion of ªrms (in the relative sense) is considerably lower in Bangalore than in the United States. Two, there is less job creation when an existing ªrm in Bangalore expands. Three, in terms of diversiªcation patterns, ªrms in Bangalore start as small specialized ªrms and then become small diversiªed ªrms rather than becoming large specialized ªrms as in the United States; that is, ªrms in Bangalore become small business groups where they produce many things by purchasing diverse inputs from many markets.

2. Interpreting the Bangalore experience The pattern of ªrm growth in Bangalore is quite understandable from the viewpoint of transaction cost economics. The degree of specialization depends on the size of



Comments

transaction costs (or the general level of development of the market mechanism). The higher the transaction costs, the more diversiªed (the less specialized) the ªrms—namely, the more likely the emergence of group ªrms (conglomerate-style ªrms). The growth rate of ªrm size also depends on market development, especially on market segmentation. The larger and more integrated markets in the United States allow successful small ªrms to grow bigger faster. This is the logic of the market failure argument for the existence of business group– style ªrms in developing countrie s (Leff 1978): Because of market deªciencies, business groups have emerged to ªll the gap by relying upon internal markets within the groups. Similar logic has been invoked using the term of “institutional voids” (Khana and Palepu 1997). The less developed an economy is, the larger are the instances of market failures in capital markets, labor markets, and commodity markets. Due to these kinds of market failures, we see less use of equity options and less use of professional workers/managers in Bangalore, and hence no evidence of post start-up “professionalization,” as found in this study by Bhidé.

3. Getting out of the size barrier to ªrm growth Now, let us think about how to make ªrms grow in Bangalore, or how to make them large specialized rather than just being small specialized or small diversiªed. In this regard, I do not think it is feasible or desirable to make a direct transition from being small diversiªed to being large specialized. Rather, it should be more natural or realistic to go through an intermediate stage of being large diversiªed. There are many examples of such types of the path and ªrms in the latecomer countries. For example, we have Korean chaebols as one of the most representative cases of the large diversiªed ªrms or conglomerates. They also used to be smaller ªrms in the past and they are facing a higher possibility of being large specialized ªrms in the future after going through restructuring after the Asian ªnancial crisis. However, the fact that they can now afford to be specialized because they have accumulated some level of technological capabilities and associated global competitiveness in some sectors is noteworthy (Lee and He 2007). In this regard, one interesting point made by Bhidé is that in less-developed countries (LDCs), the increase in incomes and proªts does not require U.S.-style highlevel technological innovation. A related fact is that returns from investing in new technologies are lower than the returns from acquiring and implementing existing technologies from the developed countries (DCs). But, proven technology often requires large-scale operation with higher ªxed costs in mass markets, compared to new technology requiring smaller or niche markets. This implies one important re-

164


Comments

quirement to growth and that is a need to have some minimum size to grab this natural advantage of using proven technology. This again implies serious difªculty for the latecomers in the LDCs such that “low-hanging fruits” (proven technology) lie outside of the reach of most of the wealth-constrained individuals in LDCs. In other words, a stark reality in most LDCs is that they face serious size barriers in catchingup. Unfortunately, Bhidé’s paper does not pursue much on this important question of how to dodge this trap. Let me try to suggest two lines of thought about how to escape the “smallness” trap and become large diversiªed ªrms. The ªrst line of thought comes from transaction cost economics that tell us that the key is to improve general market mechanisms and related institutions, including more engagement with international ªrms and learning opportunities. The second line of thought applies when the ªrst line of thought fails and intervention is needed to make a break-through—for example, measures to mobilize scarce resources for a few ªrms (promotion of selected business groups) by providing some subsidies or protection during the initial growth periods. Korea has done this in the past. Such invention also takes the form of generating spin-off ªrms from the public research institutes, as in the case of Taiwan (Mathews 2002). If a country focuses more on the ªrst line of thought then its path would be more like the path of Taiwan, namely, existence of many middle-size groups. And, if a country does both, then the outcome will be more like the path of Korea with much larger-size business groups than in Taiwan.

4. Concluding remarks A key contribution of Bhidé’s paper is that it identiªed the phenomenon of “size barriers” and points out the importance of nurturing big ªrms in LDCs. Somehow, the development literature has tended to focus on the small and medium enterprises, a bias that is based on the implicit assumption that increasing the size of ªrms is unrealistic and undesirable. I think, however, that having large ªrms is critical because they are the ones who can pick the low-hanging fruits and thereby generate an initial breakout of the smallness trap. Only when there is a critical mass of large ªrms can a large number of small ªrms survive by providing complementary goods or services to the large ªrms. I can go further to say that sometimes (when the required size and externalities are huge) it is necessary to start state-owned enterprises (SOEs) that can be privatized later. Korea has SOE examples such as POSCO, Korean Air, KT (telephone services), and even several shipbuilding companies, which were SOEs in the past but have now become global players. Present-day China is also a good example of this development approach. 165


Comments

References Khanna, Tarun, and Krishna G. Palepu. 1997. Why focused strategies may be wrong for emerging markets. Harvard Business Review July–August:41–51. Lee, Keun, and Xiyou He. 2007. Project Execution and Vertical Integration in Capability Formation of the Business Group in Korea: Samsung Created in Korea, Replicated in China. Paper presented at the Cicalics Workshop in Beijing. Leff, Nathaniel H. 1978. Industrial Organization and Entrepreneurship in the Developing Countries; The Economic Groups. Economic Development and Cultural Changes 26 (July):661–675. Mathews, John A. 2002. The Origins and Dynamics of Taiwan’s R&D Consortia. Research Policy 31:633–651.

166


Asian Economic Papers. Volume 7, Issue 1, Winter 2008

Spatial Economic Analysis - Volume 1, Issue 1

Spatial Economic Analysis - Volume 1, Issue 2

OECD Economic Surveys: South Africa - Economic Assessment - Volume 2008 Issue 15 (Oecd Economic Surveys 2008)

OECD Economic Surveys: Denmark Volume 2006 Issue 7

Asian Music - Journal Volume 42, Number 1, Winter - Spring 2011

Mathematics Magazine—Volume 81 issue 1-5, 2008

sports medicine Volume 39 Issue 1 January 2008

OECD Economic Outlook, Volume 2009 Issue 1 (No. 85, June)

Hippocampus (Volume 21, Issue 1)

Appetite, Volume 50, Issue 01 (January 2008)

OECD Economic Outlook, Volume 2010 Issue 2

Medcurso Gastroenterologia 2008 - volume 1

Asian Review of Accounting - Volume 15 Issue 1 (2007) - Special Issue: Chinese accounting

Groundings, issue 2, 2008

Mad, April 2008 Issue

East Asian Economic Regionalism

epoc - Ausgabe 2008-1 - Magisches Ägypten issue 1

Вопросы краеведения issue 7

Physiotherapy, Volume 97, Issue 1 (2011)

Applied Linguistics, Volume 31, issue 1, 2010

Cultural Studies: Volume 4, Issue 1

Manual Therapy Journal - Volume 13, Issue 1, Pages 1-90 (February 2008)

SIAM Journal on Numerical Analysis ~ Volume 47, Issue 1, 2008-2009 pp. 1-803

Photoshop User, June 2008 Issue

Callaloo (28:1, Winter 2005) Special Issue on Derek Walcott

Знамя Журнал 7 (2008)

Photoshop User, September 2008 Issue

Papers in Philosophical Logic: Volume 1

Philosophical Troubles: Collected Papers, Volume 1

Asian Economic Papers. Volume 7, Issue 1, Winter 2008

Spatial Economic Analysis - Volume 1, Issue 1

Spatial Economic Analysis - Volume 1, Issue 2

OECD Economic Surveys: South Africa - Economic Assessment - Volume 2008 Issue 15 (Oecd Economic Surveys 2008)

OECD Economic Surveys: Denmark Volume 2006 Issue 7

Asian Music - Journal Volume 42, Number 1, Winter - Spring 2011

Mathematics Magazine—Volume 81 issue 1-5, 2008

sports medicine Volume 39 Issue 1 January 2008

OECD Economic Outlook, Volume 2009 Issue 1 (No. 85, June)

Hippocampus (Volume 21, Issue 1)

Appetite, Volume 50, Issue 01 (January 2008)

OECD Economic Outlook, Volume 2010 Issue 2

Medcurso Gastroenterologia 2008 - volume 1

Asian Review of Accounting - Volume 15 Issue 1 (2007) - Special Issue: Chinese accounting

Groundings, issue 2, 2008

Mad, April 2008 Issue

East Asian Economic Regionalism

epoc - Ausgabe 2008-1 - Magisches Ägypten issue 1

Вопросы краеведения issue 7

Physiotherapy, Volume 97, Issue 1 (2011)

Applied Linguistics, Volume 31, issue 1, 2010

Cultural Studies: Volume 4, Issue 1

Manual Therapy Journal - Volume 13, Issue 1, Pages 1-90 (February 2008)

SIAM Journal on Numerical Analysis ~ Volume 47, Issue 1, 2008-2009 pp. 1-803

Photoshop User, June 2008 Issue

Callaloo (28:1, Winter 2005) Special Issue on Derek Walcott

Знамя Журнал 7 (2008)

Photoshop User, September 2008 Issue

Papers in Philosophical Logic: Volume 1

Philosophical Troubles: Collected Papers, Volume 1

Recommend Documents