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Immigration, Trade, and the Labor Market

A National Bureau of Economic Research Project Report

Immigration, Trade, and the Labor Market

Edited by

John M. Abowd and Richard B. Freeman

The University of Chicago Press

Chicago and London

JOHNM. ABOWDis professor of labor economics and management, Cornell University, and a research associate of the NBER. RICHARD B. FREEMAN is professor of economics at Harvard University and director of the Labor Studies program at NBER.

The University of Chicago Press, Chicago 60637 The University of Chicago Press, Ltd., London 0 1991 by the National Bureau of Economic Research All rights reserved. Published 1991 Printed in the United States of America 00999897969594939291 5 4 3 2 1

Library of Congress Cataloging-in-Publication Data Immigration, trade, and the labor market / edited by John M. Abowd and Richard B. Freeman. cm. - (National Bureau of Economic Research Project p. paper) “Papers presented at a conference held in Cambridge, Massachusetts, 11-12 September 198T-Pref. Includes bibliographical references and indexes. ISBN 0-226-00095-8 (acid-free paper) 1. Alien labor-United States-Congresses. 2. Alien labor-Canada-Congresses. 3. Alien labor-Australia-Congresses. 5 . Labor market-Can4. Labor market-United States-Congresses. ada-Congresses. 6. Labor market-Australia-Congresses. 7. Foreign trade and employment-United States-Congresses. 9. Foreign 8. Foreign trade and employment-Canada-Congresses. I. Abowd, John M. trade and employment-Australia-Congresses. 11. Freeman, Richard B. (Richard Barry). 111. Series. HD8081.A5153 1991 331.6’2-dc20 90-24954 CIP

8 The paper used in this publication meets the minimum requirements of the American National Standard for Information Sciences-Permanence of Paper for Printed Library Materials, ANSI 239.48-1984.

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(Resolution adopted October 25, 1926, as revised through Seprember 30, 1974)

Contents

ix

Preface

1

Introduction and Summary John M. Abowd and Richard B. Freeman I. IMMIGRANT FLOWSAND PERFORMANCE IN THE UNITEDSTATES

29

1. Immigration and Self-Selection George J. Borjas 2. Undocumented Mexican-born Workers in the United States: How Many, How Permanent? George J. Borjas, Richard B. Freeman, and Kevin Lang

3. The Effect of Policy Restrictions on Capital and Labor Flows in Mexico Juan Diez-Canedo R.

5. Migration, Ethnicity, and Labor Force Activity Marta Tienda and Franklin D. Wilson IMPACT OF IMMIGRATION, TRADE,AND CAPITALFLOWSON U.S. LABORMARKET

101 121

4. Internal Migration of U.S. Immigrants Ann P. Bartel and Marianne J. Koch

11.

77

135

THE

6. Labor Market Adjustments to Increased Immigration Robert J. LaLonde and Robert H. Tope1

167

viii

Contents

7. The Effects of Immigration on the Labor

Market Outcomes of Less-skilled Natives Joseph G. Altonji and David Card

20 1

8. Industrial Wage and Employment Determination in an Open Economy Richard B. Freeman and Lawrence F. Katz

235

9. Foreign-Owned Businesses in the United States Jonathan S. Leonard and Rachel McCulloch

26 1

10. Immigration, International Ikade, and the Wages of Native Workers Peter Kuhn and Ian Wooton

285

11. Immigrants, Labor Market Pressures, and the Composition of the Aggregate Demand Susan M. Collins

305

EXPERIENCES: CANADA AND AUSTRALIA 111. COMPARATIVE 12. An Analysis of the Earnings of Canadian Immigrants David E. Bloom and Morley Gunderson

13. The Effects of International Competition on Collective Bargaining Outcomes: A Comparison of the United States and Canada John M. Abowd and Thomas Lemieux 14. Male Immigrant Wage and Unemployment Experience in Australia John J. Beggs and Bruce J. Chapman 15. Why are Low-skilled Immigrants in the United States Poorly Paid Relative to Their Australian Counterparts? Some of the Issues Illustrated in the Context of the Footwear, Clothing, and Textile Industries R. G. Gregory, R. Anstie, and E. Klug

321

343

369

385

Appendix: The NBER Immigration, Ikade, and Labor Markets Data Files John M. Abowd

407

List of Contributors

423

Author Index

425

Subject Index

429

Preface

This volume consists of papers presented at a conference held in Cambridge, Massachusetts, 11-12 September 1987, and is part of the National Bureau of Economic Research Labor Studies program. Support for the project came from the Ford Foundation. We are grateful to Jennifer Amadeo-Holl, Jane Konkel, and Jean Brown for help in getting these papers ready for publication. Any opinions expressed in this volume are those of the respective authors and do not necessarily reflect the views of the National Bureau of Economic Research or the sponsoring organization.


Introduction and Summary John M. Abowd and Richard B. Freeman

During the 1970s and 1980s, immigration, trade, and foreign investment in the United States became increasingly important in the U.S. labor market. The number of legal and illegal immigrants to the country increased, altering the size and composition of the work force and substantially raising the immigrant share of labor in “gateway” cities such as Miami, Los Angeles, and New York. The national origins of immigrants changed from primarily European to Mexican, Latin American, and Asian. Foreign trade rose relative to gross national product, and a massive trade deficit developed in the 1980s, turning the United States into a substantial debtor nation. Because the composition of employment shifted from manufacturing to nontraded services, the immediate burden of adjusting to trade-induced changes fell on a decreasing segment of the work force. As the flip side of the trade deficit, foreign investment in the United States grew rapidly, with foreign direct investment increasing until 3% of American workers were employed in foreign-owned firms. While at one time labor market analysts could look on the United States as a largely closed economy, the changes of the 1970s and 1980s brought about the internationalization of the U.S. labor market. What are the interrelations among the flows of foreign outputs and inputs that have caused such a change in the way we look at the U.S. labor market? How have the flows changed over time? Which industries or areas are most John M. Abowd is professor of labor economics and management, Comell University, and a research associate of the National Bureau of Economic Research. Richard B. Freeman is professor of economics, Harvard University, and director of the Labor Studies Program of the National Bureau of Economic Research. The authors acknowledge financial support from the Ford Foundation and the National Science Foundation (grant 88-13847 to Abowd). The authors thank George Bojas, Lawrence Katz, and Robert Topel for their help in preparing this paper. Daniel Kessler, Laura Leete, and Ana Revenga served as research assistants.

2


heavily affected by the movements in goods, the influx of immigrants, or foreign direct investment? What have we learned from studying the effects of these increased flows in the U.S. labor market? This paper presents background information about the growing internationalization of the U.S. labor market and summarizes the results of the NBER studies contained in this volume. The paper highlights several aspects of the internationalization of the U.S. work force. 1 . Although the number of immigrants relative to the population increased from the 1950s to the 1980s, the immigrant share of the growth of the work force was relatively moderate. The rapid growth of the labor force due to increased female participation and entry of the baby boom generation to the labor market kept pace with the influx of immigrant workers. 2 . The trade content of the U.S. economy, measured by exports plus imports relative to sales or GNP, has increased markedly, but the share of labor in traded sectors, notably manufacturing, has fallen, so that a smaller fraction of workers are directly affected by trade than in the past. Those workers are, however, more closely tied to world markets than in the past. 3. Direct foreign investment was substantial in the 1980s, reaching 34% of gross U.S. investment in 1988. Three percent of the private U.S. work force was employed in foreign-owned enterprises by the mid- 1980s. 4. The immigrant share of the labor force differs largely across geographic areas, whereas the trade share of product markets differs largely across industries. This motivates the research strategy for the studies of the effects of immigration and trade on the U.S. labor force: studies concerned with how immigration affects labor market outcomes contrast wages and employment in local areas with different immigrant shares of the work force; studies concerned with how trade affects labor market outcomes contrast wages and employment in industries with different trade shares of output. 5 . Industries are related to the open economy in a variety of ways. Industries in which there are considerable imports employ a disproportionate share of immigrants, whereas high export industries employ relatively few immigrants. Direct foreign investment is concentrated in manufacturing. Overall, the first-order effects of the internationalization of the labor market fall on manufacturing. 6. There are significant differences in the characteristics of workers between export-intensive, import-intensive, and immigrant-intensive sectors. Women workers and lower-paid, less-skilled workers are highly concentrated in sectors where imports are significant and where relatively many immigrants work. Perhaps surprisingly, foreign-owned enterprises have a comparable unionization rate to domestically owned enterprises: they are concentrated in traded goods sectors and have higher wages than domestic producers. The paper is divided into four parts. Section 1 deals with the aggregate

3

Introduction and Summary

flows of people, goods, and capital from overseas to the United States. Section 2 describes the industrial pattern and regional dimensions of the labor, goods, and capital flows as shown in the NBER data files developed for this project.' Section 3 turns to the characteristics of workers in sectors most affected by trade, immigration, and foreign direct investment. In sections 1-3, we have compiled statistics from a wide variety of sources in order to present comparable figures for 1960, 1970, 1980, and the most recent year available. Section 4 summarizes the findings of the papers included in this volume. 1. The Aggregate Flows of People, Goods, and Capital In this section, we review the basic data on each of the three flows under study: labor, goods, and capital. In contrast to demographic studies that focus on the immigrant share of the population and the increase in population, we focus on the immigrant share of the labor force and the increase in the labor force. In contrast to trade studies that focus on balance of payments issues, we focus on the proportion of workers in traded sectors and the ratio of exports plus imports to output in those sectors. In contrast to financial studies that consider international capital mobility broadly defined (and equal to imports minus exports by definition), we focus on direct foreign investment in plant and equipment. Flows of Labor Table 1 presents the basic data on the flows of immigrants entering the country (pt. A) and the stock of immigrants in the United States (pt. B) from the 1940s through the 1980s. The table provides figures for legal immigrant flows and legal plus estimated illegal immigrant flows in absolute numbers and relative to the population, labor force, and change in the labor force. The data in the first two columns of the table show that the number of immigrants coming into the United States and the number per one thousand inhabitants rose in the 1970s and 1980s, consistent with the increased public concern about immigration. When we consider the immigrant share of changes in the population and labor force, however, a different story emerges. Because the baby boom increase in the U.S. population occurs in the early postwar years (through 1960), the legal immigrant share of population growth is relatively small during this period. Because of the increased participation of women, the influx of baby boomers into the labor markets, and the fact that many legal immigrants enter for family unification reasons rather than for labor market reasons, the estimated immigrant flow share of the growth of the labor force

1. For a description of these data, see Abowd (in this volume).

4


Table 1

Flows and Stocks of Immigrants Relative to the Population

and Labor Force

A. Flows of Immimants

Period

DeCadal Flow of Immigrants (thousands)

Legal flows only:" 1941-50 1951-60 1961-70 1971-80 1981-90 Legal and illegal flowsb 1971-80 1981-90

Inflow Per 1,Ooo U.S. inhabitants

Immigrant Flow Share of Change: In Population (8) In Labor Force

1,035 2,515 3,322 4,493 5,900

.7 1.5 1.7 2.1 2.5

5.2 8.9 13.6 19.8 26.8

7.3 14.5 11.1 9.3 16.2

5,800 8,400

2.7 3.6

25.6 38.2

12.0 23.1

B . Stocks of Immigrants

Census of Population

Number of Foreign Born Counted (thousands)

As reported: 1940 1950 1960 1970 1980 Adjusted for undercount:c 1980

Population

Number of Foreign Born in Civilian Labor Force (thousands)

11,657 10,431 9,738 9,619 14,080

8.8 6.9 5.4 4.7 6.2

4,838 4,134 4,223 7,001

8.2 6.1 5.2 6.7

15,380

6.8

7,647

7.3

% of

Immigrant % of Labor

Force

Sources: Part A Flow of immigrants from U.S. Bureau of the Census, Statistical Abstract of the United States, 1989, table 7 (from the Statistical Yearbookof the Immigration and Naturalization Service), with the 1981-90 flow estimated by extrapolating the 1981-87 flows. Immigrant flow shares of changes were obtained by dividing flows by changes in decadal population from the relevant decades (tables in Council of Economic Advisers, Economic Report of the President, 1990). To obtain immigrants in the labor force, we assumed that the labor force participation rate of the decadal flow of immigrants was the same as the ratio of foreign-born workers in the civilian labor force to the foreign-born population (see pt. B). Part B: Foreign-born count and percentage of population from Sruristical Abstracr of the UnitedStates. 1988, table 44 (from the U.S. Census of Population). Foreign-born in the civilian labor force from various Censuses of Population. Qfficial counts from the Immigration and Naturalization Service summed over the indicated years. bAdjustedfor illegal flows using estimates from Borjas, Freeman, and Lang (in this volume) and Warren and Passel (1987), as described in the text. cAdjustedby adding the 1.3 million estimated uncounted illegal immigrants to the 1980 Census counts.

5


actually falls from the 1950s to the 1970s, raising serious doubts about the labor market basis for concern over immigration until the 1980s. Then the number of immigrants rises substantially, and the contribution to both population and labor force growth reaches a postwar high. The figures in part B for actual counts of the stock of immigrants (which depend not only on inflows of immigrants but also on emigration and the death or retirement of persons who immigrated decades earlier and which include some illegal immigrants) tell a generally similar story. While declines in the immigrant share of the population and labor force are reversed for the decade 1971-80, the immigrant proportion of the population or labor force in 1980 remains below the 1950 proportion. What happens to this picture when adjustments are made for the widely publicized illegal immigration into the United States? We have made adjustments in the table based on the methods of Borjas, Freeman, and Lang (in this volume) and earlier research on illegal immigration (Warren and Passel 1987). The bases for our adjustments are Warren and Passel’s estimate that the 1980 Census included about two million illegal immigrants and Borjas, Freeman, and Lang’s estimate that approximately 6 1% of illegal (Mexican) immigrants were counted in the Census. Taken together, these estimates suggest that there were on the order of 3.3 million illegal aliens in the United States in 1980. Warren and Passel estimate that 75% of the illegals counted in the Census came in the 1970s. Assuming, conservatively, that 75% of the uncounted illegal immigrants also came in the 1970s, we get 2.5 million as the estimated flow of illegal immigrants in the 1970s. Adding this number to the number of legal immigrants reported by the Immigration and Naturalization Service (INS) in the rows giving “legal and illegal flows” changes greatly the picture of immigrant flows given in part A. Immigrant flows now rise sharply in the 1970s compared to the 1960s. Similarly, adding 1.3 million uncounted immigrants to the 1980 Census count raises the immigrant’s share of population and labor force in part B of the table to levels close to those of 1950. If illegal flows proceeded in the 1980s at the same rate as in the 1970s, then, given the INS estimates of legal immigrant flows, we estimate that some 8,400,000 immigrants came to the United States in the 1980s. This raises the immigrant inflow per one thousand United States inhabitants and the immigrant share of the change in population and labor force above the levels of the 1970s. The 1970s and 1980s were periods of marked acceleration in immigration, in large part because of illegal flows. Another aspect of the flow of immigrants to the United States deserves attention. The change in the geographic origins of immigrants following the 1965 Immigration Act has produced a dramatic shift in immigrant origins from Europe and Canada to Asia. Figure 1 illustrates this change. If we adjusted the proportions in the figure for illegal immigrants (largely Mexican), the share from Latin America would also rise.

6


Fig. 1 Distribution of immigrant origins Source: Immigration and Naturalization Service Statistical Yearbook, 1987, table 2,

“Immigration by Region and Selected Country of Last Residence.”

Flows of Goods Figure 2 shows the widely heralded increase in the role of trade in the U.S. economy in terms of two related measures-exports ( X ) plus imports (M) relative to GNP, which we will call the trade content of the economy and the ratio of the trade balance (exports minus imports) to GNP. In the 1950s and 1960s, the overall trade content of GNP was roughly 10%-11%, with U.S. exports exceeding imports. In the 1970s, the trade content jumped, particularly after 1978, reaching a peak in 1981, then hovered around this level for the rest of the decade. The balance of trade diverged modestly from year to year until 1983, when it became negative. Large negative trade balances characterize the rest of the decade and are unlike any other postwar period. While the trade content of the U. S. economy has risen sharply, the proportion of workers employed in the traded goods sectors-manufacturing , mining (including crude oil), and agriculture-has fallen, so that relatively fewer workers are directly imported by foreign competition. Table 2 shows the ratio of exports plus imports to sectorul GNP for traded goods (agriculture, mining, and manufacturing), all other sectors, and the entire U.S. economy for the


7

a,

Y

8 -

a

6 4 -

-4

-

,

-6

+

I

I

,

,

, ,

1

,

1

1

1

1

1

1

1

,

1

,

1

1

1

,

I

,

I

,

,

r , , , ,I

,

(Exports - Irnports)/GNP

Fig. 2 Openness of the U.S. economy Source: U.S. National Income and Product Accounts, July 1989.

years 1960, 1970, 1980, and 1987. The table also shows the percentage of GNP originating in the sector and the percentage of full-time equivalent employment in the sector. Exports plus imports as a percentage of sectoral GNP rise sharply in the traded goods sectors, but the share of GNP and the share of employment in the traded goods sectors fall. Whereas in 1960 33% of the work force and 35% of GNP were in the traded goods sectors, by 1987 only 21% of employment and 23% of GNP were in those sectors. A smaller fraction of the labor force is directly affected by foreign competition by 1987 than in the earlier decades. The table also shows the employment-weighted exports plus imports as a percentage of sectoral GNP (last row) and the comparable ratio for the overall economy (“total all sectors”). The traded portion of the entire U.S. economy (goods and services) rose from 10% in 1960 to 22% in 1987 by either overall measure (also shown in fig. 2). The economy-wide trade ratios rise by much less than the ratios in the traded goods sectors. In terms of direct competition from foreign-produced goods, a decreasing proportion of the labor force faces the consequences of increased traded goods flOWS.2

2. This assumes that exports plus imports is a good measure of trade dependence. Under some circumstances it will be. Under others it may understate trade dependence: e.g., when prices are determined by the world market but there are no trade flows.

8

John M. Abowd and Richard B. Freeman The Changing lkade Content of the U.S. Labor Market (%)

Table 2

1960

1970

1980

1987

28.6 22.6 17.5 19.2 5.8 10.5

27.8 25.0 26.6 26.6 6.9 12.7

53.5 76.6 56.8 59.3 11.0 24.5

33.8 45.3 64.4 60.0 11.2 22.3

4.2 2.5 28.0 34.7 65.3

2.9 1.8 24.8 29.6 70.4

2.8 3.9 21.3 28.0 72.0

2.1 1.9 18.9 22.8 77.2

3.1 1.2 28.6 33.0 67.0

1.8 0.9 26.6 29.2 70.8

1.8 1.2 22.8 25.7 74.3

1.6 0.7 19.0 21.3 78.7

10.1

12.7

22.9

21.8

+

(Exports Imports)/GNPin sector? Agricultureb Mining' Manufacturing Total traded goods' All other sectorsf Total all sectors' Percentage of GNP in sector: Agriculture Mining Manufacturing Total traded goods All other sectors Percentage of employment in sector:h Agriculture Mining Manufacturing Total Traded Goods All other sectors (Exports Imports)/GNP in sector (employment weighted)'

+

Sources: Exports and imports 1960, 1970, and 1980 from Bureau of the Census, U.S. Commodiiy Exports and Imports as Related to Output 1981/80 (1983), table A. Exports and imports 1987 from U.S. Department of Commerce online data base of official statistics. GNP in sector from National Income and Product Accounts, table 6.1 (extracted from CITIBASE). Full-time employment in sector from National Income and Product Accounts, table 6.7B (extracted from CITIBASE).

*Exportsplus imports as a percentage of GNP originating in the industry group. bAgricultureis SIC industry groups 01-09. cMiningis SIC industry groups 10-14. dManufactureis SIC industry groups 20-39. Traded goods are agriculture, mining, and manufactures. 'All other sectors include SIC industry groups 15-17 and 40-99. Exports (imports) in all other sectors are defined as the difference between total exports (imports) and traded goods exports (imports). SExports and imports from the National Income and Product Accounts. Traded goods sectors consist of manufacturing (SIC 20-39), mining (SIC 10-14), and agriculture (SIC 01-09). hFull-timeequivalent employees from the National Income and Product Accounts. 'Exports plus imports as a percentage of GNP originating in the industry group weighted by employment in the industry group.

Capital Flows The flow of capital across international borders is the most difficult flow to measure and analyze. Net capital flows should equal the balance on current accounts (plus allocations of special drawing rights), but, in fact, the two differ significantly, requiring a statistical discrepancy line to produce the definitional equality. In terms of the effects on labor markets, we want to distin-

9


guish a foreign capital investment that is a long-term job creating flow from a short-run financial flow. If all net capital flows were of the former kind, public focus on the disemployment effects of an imbalance on the current account would be erroneous. If all the net capital flows were of the latter kind, by contrast, such concerns might be valid, although the imbalance would eventually alter the real exchange rate and, in principle, correct itself. It is not easy, however, to determine the degree to which capital flows fall along a spectrum from long-term job-creating to short-term financial flows. Presumably, direct foreign investment is job creating, while currency transactions are likely to be short run, though we still need to know the “motive” and likely holding period of these intermediate investments. A foreigner who buys stocks, corporate bonds, or U.S. Treasury obligations or even leaves money in a U.S. bank account for a long time can, through the flow of funds, produce as much longterm investment in the United States as a foreigner who builds a plant. We distinguish in table 3 between direct foreign investments in plant and equipment, likely to be long run, and other forms of capital flows. As can be seen in the table, both direct and indirect capital flows increased dramatically in recent years. Net U.S. investment abroad (the change in U.S. assets abroad from the international transactions accounts) increased from $4,099 million in 1960 (shown as a negative number in the table to reflect a capital outflow) to $82,110 million in 1988. Net foreign investment in the United States (the change in foreign-owned assets from the international transactions accounts) increased from $2,294 million in 1960 to $219,299 million in 1988. Direct U.S. investment abroad and direct foreign investment in the United States also increased dramatically since 1960. By 1988, over a quarter of foreign investment in the United States consisted of direct foreign investment. Are the international capital flows sizable or negligible in the context of the U.S. economy? Table 3 also compares net foreign investment in the United States and direct foreign investment in the United States to GNP and U.S. gross investment. Direct foreign investment in the United States rises from . l % of GNP and .4% of gross investment in 1960 to 1.2% of GNP and 9.2% of gross investment in 1988. While Japanese investment in the United States has received the most public attention, the percentage of direct foreign investment by country of ultimate beneficial ownership in table 3 shows that European direct investment is quantitatively much larger, although Japan increased its share dramatically in the late 1980s.

2. Industrial and Geographic Patterns Flows of goods, people, and capital occur differently by sector and area of the economy. Some industries produced traded goods, while others do not. Immigrants are overrepresented in some sectors and underrepresented in others, and immigrants go to some areas of the country, and not to others. For some long-term general equilibrium purposes, the sectoral division of the

10 Table 3

John M. Abowd and Richard B. Freeman Capital Market Flows between the United States and the Rest of the World Investments (millions of dollars)

Net U.S. investment abroad' Direct investment abroad Net foreign investment in the United Statesb Direct foreign investment Investment outlaysc

1960

1970

1980

1988

- 4,099

- 9,337 - 7,590

- 86,118

-82,110

- 2,940

- 19,222

- 17,533

2,294 315 NA

6,359 1,464 NA

58,112 16,918 12,172

219,299 58,436 65,019

Relative Figures (% of base) 1960

Net foreign investment in the United StatesiGNP Net foreign investment in the United States/gross investmentd Direct foreign investment'GNP Direct foreign investment'gross investment Percentage of direct foreign investment by country' Canada Japan Europe West Germany The Netherlands United Kingdom Rest of the World

1970

1980

1988

.4

.6

2.1

4.5

2.8 .1

4.1

.I

12.9 .6

34.7 1.2

.4

I .o

3.8

9.2

100.0 NA NA NA NA NA NA NA

100.0 NA NA NA NA NA NA NA

100.0 16.1 4.9 62.9 11.7 13.6 25.2 16.1

100.0 16.0 21.8 52.5 2.1 3.0 33.1 9.7

Sources: U.S. International Transactions accounts from the Survey of Current Business (June 1989). Percentage distribution by country of ownership from Survey of Current Business, U.S. Business Enterprises Acquired or Established by Foreign Direct Investors, 1980 and 1988. National Income and Product Account data extracted from CITIBASE. Note: NA = not available on a comparable basis. *From U.S. assets abroad, net (increaseicapital outflow [ -1). in the U.S. International Transactions accounts. Negative numbers indicate a net outflow. Direct investments abroad is a subaccount of U.S. private assets, net. bFrom foreign assets in the United States, net (increaselcapital inflow [+I), in the U.S. International Transactions accounts. Positive numbers indicate a net inflow. Direct foreign investments is a subaccount of other foreign assets in the United States, net. CInvestmentoutlays from the U.S. Department of Commerce, Bureau of Economic Analysis, Survey of New Foreign Direct Investment in the United States (1983). Cross investment series from the U.S. National Income and Product Accounts, annual data. 'Percentage of Bureau of Economic Analysis survey investment outlays by country of ultimate beneficial owner. Figures for 1988 are preliminary.

11


flows is unimportant. For many short- and intermediate-term questions, however, sectoral flows are critical. To deal with this issue, the NBER developed the Immigration, Trade, and Labor Markets Data Files (see Abowd, in this volume). These data allow us to examine the pattern of trade across industry lines over time, to contrast the industrial distribution of trade and the employment of immigrants, to determine the characteristics of workers in industries with more or less trade and with sizable or limited employment of immigrants, and to compare the geographic and industrial patterns of trade and immigration effects. Table 4 uses the NBER immigration and trade data files to assess the variability of trade ratios across manufacturing industries. The table records the mean, standard deviation, and coefficient of variation of trade ratios among the 450 four-digit SIC manufacturing industries and of immigration ratios Table 4

Variation in Trade and Immigratino Ratios for Manufacturing Industries (employment weighted)

+

(Exports (Exports Import/ Imports)/ Imports)/ Shipments New Supply Shipments Shipments

Export/

1960: Mean Standard deviation Coefficient of variation 1970: Mean Standard deviation Coefficient of variation 1980: Mean Standard deviation Coefficient of variation 1985: Mean Standard deviation Coefficient of variation Change, 196W30: Mean Standard deviation

(%)

(%'.)"

4.27 5.91

2.30 4.61

7.00 10.17

1.38

2.01

1.45

5.62 7.07

4.59 6.11

11.08 12.91

1.26

1.33

1.17

10.31 11.38

7.41 8.51

19.94 23.59

1.10

1.14

1.18

8.48 11.09

10.94 11.38

24.95 52.17

1.31

1.04

2.11

5.50 8.37

5.38 6.42

12.62 17.34

Immigrants/ Labor Force (%)

1.56 9.87

8.48 3.66 .43

.20 11.40

7.13 3.37 .47

.75 18.56

1.96 4.34 .55

- 7.95 50.78

- 1.58 14.03

- .40 2.82

Source: NBER Immigration, Trade, and Labor Markets Data Files (see Abowd, in this volume). Nofe: All ratios are stated as percentages of the relevant base. The statistics are averages over four-digit SIC industries using the annual employment in the industry as the weight. There are 450 SICs with valid immigrant ratio data and 430 SICs with valid import and export data. *New supply is the sum of shipments and imports.

12


among those industries. The table shows considerable variation in ratios of trade (functions of exports X and imports M) to shipments (S) across industries. The relatively stable coefficient of variation in the exports-to-shipments ratio contrasts with a declining coefficient of variation in the imports-tc+new supply ratio, implying an unchanged concentration of the former compared to an increasing concentration of the latter. Among the sectors with the largest increase in trade content are footwear except rubber, electrical equipment, and electronic resistors. Imports grew especially rapidly in footwear, and exports grew especially rapidly in electrical equipment. Both exports and imports increased in electronic resistors. Comparing the trade and immigration ratios across industries, one striking fact emerges: trade ratios are much more variable among sectors than are immigration ratios. This has important consequences for the way in which NBER and other researchers study the effects of trade on the labor market: focusing on differences across industries. A very different pattern emerges when we consider regional differences in trade ratios, immigrant flows, and foreign direct investment in the United States. Here we find exactly the opposite: immigration ratios vary much more across regions than across industries. This fact leads NBER and other researchers to study the effect of immigration on the labor market by focusing on differences across areas. The geographic concentration of immigration is documented in table 5. The table shows INS figures on the number of immigrants declaring selected standard metropolitan statistical areas (SMSAs) as their intended residence from 1976 to 1979 and the contribution of that flow to the growth of the labor force. Table 5 displays the ten SMSAs with the largest percentage of foreign born in the area. These gateway cities absorbed a very substantial fraction of all immigrants who entered the United States in the four-year period illustrated (comparable data were not collected for 1980). Table 6 shows Census of Population figures on the percentage of the labor force that are immigrants by SMSAs in 1970 and 1980. What stands out in these tables is the substantial concentration of immigrant flows by SMSA. How might the flow of illegal immigrants into the United States change the picture of geographic concentration shown in tables 5 and 6? Given the concentration of illegal aliens in California, where Warren and Passel estimate that 50% of illegals counted in the Census are located, the concentration of immigrants would become even more dramatic. Table 7 presents data on all our flows by state. Columns 1-3 give data from the Census of Population for immigrants as a percentage of the population in 1970 and 1980 and in 1980 adjusted for the likely undercount of illegals in the Census on a state-by-state basis using the estimates in Passel and Woodrow (1984). While the geographic diffusion of the stock of immigrants is lower than the diffusion of the flow of new immigrants among SMSAs, there is still considerable variation across areas. Columns 4-6 of the table turn from immigrant to trade figures. As data on

13


Table 5

SMSA Miami Los Angeles New York City El Paso Newark Washington, D.C. Houston Cleveland Philadelphia Dallas

Flows of Immigrants into Selected Standard Metropolitan Statistical Areas (SMSAs) Immigrants Declaring SMSA as Intended Place of Residence’

Change in Labor Force from 1976 to 1979b

Estimated Immigrant Contribution to Labor Force Growth (%)

79,099 74,515 247,052 13,053 8,879 8,359 23,868 3,800 10,571 10,735

54,233 254,000 38,000 8,836 40,738 166,193 255,367 38,108 85,016 220,331

73.3 14.7 326.8 74.3 11.0 2.5 4.7 5.0 6.3 2.4

Sources: Number of immigrants from the Statistical Yearbook of the Immigration and Naturalization Service, 1976-79: table number varies; table title “Immigrants Admitted by Specified Countries of Birth and Rural and Urban Area and City.” Change in the labor force from the Bureau of the Labor Statistics, Employment and Earnings, various issues. The immigrant labor force-tc+immigrant population ratio was estimated from the 1980 Census of Population Detailed Population Characteristics U.S. Summary, sec. A-U.S. PCSO-1-D1-A. Total immigrants is from table 254, “Citizenship and Year of Immigration for Foreign Born Persons by Country of Birth.” Immigrants in the labor force is from table 255, “Selected Economic and Social Characteristics by Nativity.” ‘Number of immigrants who declared the SMSA as the intended place of permanent residence during the period from 1 October 1975 to 30 September 1979. SMSAs are listed in descending order of percentage foreign born in the area. Thange in the size of the labor force from 1976 to 1979, inclusive. CEstimated as 50.3% of col. 1 divided by col. 2.

exports or imports by geographic location are unavailable, our estimates of the trade content of a state’s industry mix are obtained by weighting industry trade ratios according to the industrial distribution of state labor forces as follows: T, = C W i j T i , i

where T = relevant state trade ratio, W , = proportion of workers in state j who work in industry i, and Ti = national trade ratio in industry i. In contrast to the wide variation in immigration ratios across states, the trade ratios differ relatively moderately, except for the net export ratio ([X - M ] / S ) . For example, the five states whose industry structures have the highest import ratios ( M / [ S M I ) have an average value of 8.8, compared to an average figure of 3.8 for the five states with the lowest import ratios. While there are surely individual localities that are greatly sensitive to trade, the implication of the table is that trade flows are unlikely to have great effects on local labor markets, except, possibly, where there is a substantial net export ratio ([X - M ] / S ) , as in Alaska.

+

14

Table 6

John M. Abowd and Richard B. Freeman Immigrants as a Percentage of the Labor Force Selected SMSAs ~

Atlanta Baltimore Boston Chicago Dallas-Fort Worth Detroit Houston Los Angeles Miami New York City Philadelphia Pittsburgh St. Louis San Francisco Washington, D.C.

~~

~

I970

1980

1.3 3.9 10.3 10.2 2.4 8.4 3.3 13.6 27.9 18.0 6.1 4.3 2.3 13.7 6.0

2.9 3.5 10.6 11.6 4.8 6.3 8.3 24.2 41.2 24.0 5.0 2.7 2.3 16.2 9.0

~~~

Sources: Based on individual data from the 1970 Census of Population and Housing 1/100 Public Use County Group Sample and the 1980 Census of Population and Housing Public Use Microdata A Sample. Note: The numerator is the number of immigrants in the labor force in the SMSA indicated. The denominator is the number of individuals in the labor force in the SMSA. SMSA definitions in the 1970 and 1980 Censuses of Population were made comparable by selecting the appropriate area and subarea codes (1970) and SMSA codes (1980).

The popular and business press are filled with stories about the decision of Japanese and other foreign investors to locate plants in certain regions of the country as opposed to others. Column 7 of table 7 presents data from the 1980 Benchmark Survey of Direct Foreign Investment in the United States (U.S. Department of Commerce 1983) on the proportion of the private work force employed in foreign-owned enterprises among the states. It shows considerable variation in employment in foreign-owned affiliates, with a range far exceeding that for trade shares, and a regional pattern differing greatly from that for immigrant employment.

3. Characteristics of Workers in Sectors Affected by Internationalization To evaluate the type of workers most likely to be affected by trade or immigration, we have performed a two-part analysis. First, we tabulated the average characteristics of workers by employment. Second, we calculated correlation coefficients between worker characteristics by industry and the relevant trade or immigrant worker ratio. Table 8 presents the results of the first analysis with sectors divided between traded goods and nontraded goods, between export- and import-intensive

15


Table 7

Geographic Distributionof Immigration, Bade, and Direct Foreign Investment Immigrant % of Labor Force

Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware District of Columbia Florida Georgia Hawaii Idaho Illinois Indiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri Montana Nebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee (conrinued)

% of U.S. % Net

Ratio

Export Ratio X - M/S

Affiliate Employment in Total Private Employmentb

%

1970

1980

Adjusted 1988

Export Ratio XIS

.7 5.9 5.4 .5 11.4 3.8 10.6 2.7

1.1 4.7 6.1 1.1 16.3 4.0 8.9 3.1

1.2 4.9 6.9 1.2 19.3 4.5 9.0 3.2

8.6 14.9 13.8 8.4 11.8 10.4 14.8 12.3

7.1 2.9 6.6 7.5 6.4 6.7 7.2 6.6

.8 11.7 6.5 -.2 4.8 2.9 6.8 4.9

2.0 6.9 1.7 2.2 2.5 1.9 2.7 3.9

8.0 10.0 1.2 11.4 1.8 7.3 2.2 1.8 1.3 .2 1.4 5.8 4.4 10.6 6.7 3.1 .2 1.8 1.6 1.4 6.2 5.1 10.6 3.3 13.9 .8 2.7 3.5 1.1 4.4 4.1 8.8 .7 1.2 1.1

6.9 11.5 1.9 15.3 3.0 8.0 1.9 1.5 2.1 .9 2.3 3.5 5.0 8.5 4.3 2.3 1.0 1.8 2.2 1.9 7.9 4.3 11.2 4.1 14.8 1.4 2.0 2.7 2.0 4.1 3.1 8.8 1.6 1.3 1.2

8.7 12.2 2.1 15.2 3.5 8.9 2.0 1.6 2.3 1.1 2.4 3.4 5.7 8.8 4.4 2.4 1.1 1.9 2.2 2.1 8.6 4.3 11.6 4.9 15.8 1.5 2.1 2.8 2.3 4.5 3.1 9.0 1.7 1.3 1.3

2.5 10.2 9.2 5.5 9.4 11.5 10.3 13.1 14.6 9.9 9.1 8.8 8.2 12.1 11.3 12.5 8.1 10.1 11.4 9.8 8.6 13.4 9.6 7.5 10.7 7.7 8.2 10.9 11.6 11.0 9.1 11.1 9.5 9.4 9.0

1.6 6.2 7.2 7.1 5.4 6.8 8.2 6.6 6.0 7.2 6.1 9.4 6.6 7.6 10.4 6.0 7.6 7.5 4.8 6.6 7.6 7.8 6.4 7.6 1.3 6.2 5.5 7.5 6.7 6.4 7.3 8.1 6.8 4.5 7.7

.8 3.2 1.2 - 2.6 3.3 4.0

.6 2.1 3.6 4.6 1.3 2.8 2.6 2.0 1.7 2.3 2.9 2.7 3.1 4.0 2.3 2.0 1.4 1.8 .8 1.o 1.3 3.6 4.6 2.0 2.9 3.2 1.2 2.2 2.0 1.2 2.8 2.0 5.3 .6 2.9

% Import

MIS

+M

1.1

5.8 7.9 1.9 2.4 -2.3 .8 3.5 - .9 5.9 - .4 1.5 6.2 2.5 - .2 4.6 2.4 - 1.2 2.5 .9 2.1 2.4 4.2 3.9 1.o 2.0 2.0 4.3 .3

16


Table 7

(continued) Immigrant % of Labor Force

Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming Summary: Mean of top 5 Mean of bottom 5 Difference

% Net

%

Adjusted 198W

Export Ratio XIS

% Import

Ratio MIS + M

Export Ratio X - MIS

% o f U.S. Affiliate Employment in Total Private Employmentb

1970

1980

3.5 2.9 7.4 2.4 5.9 1.2 3.3 1.6

6.5 4.0 4.5 3.7 6.1 1.1 2.5 1.9

7.6 4.5 4.5 4.2 6.5 1.1 2.6 2.1

12.0 11.8 14.2 8.5 15.2 10.5 11.5 9.6

6.6 6.6 6.5 6.6 6.1 7.1 7.1 6.0

4.7 4.5 7.1 1.3 8.5 2.5 3.5 3.0

2.7 2.2 3.6 2.2 1.5 3.6 3.4 1.7

13.5

13.8

14.8

14.7

8.8

8.4

5.1

.5 13.0

1.0 12.8

1.1 13.7

6.3 8.4

3.8 5.0

1.5 9.9

1.o 4.2

-

Sources: Immigrant, export, and import ratios are from the NBER Immigration, Trade, and Labor Markets Data Files (see Abowd, in this volume). Employment in U.S. affiliates is from U.S. Department of Commerce (1983), Foreign Direct Investment in the United States, 1980. T h e adjustment is based on Passel and Woodrow’s (1984) table 1, “Estimates of Undocumented Aliens Counted in the 1980 Census and Legally Resident Aliens by State of Residence and Period of Entry.” bFrom the 1980 Benchmark Survey of Foreign Direct Investment in the United States, “U.S. Affiliate Employment by State.”

manufacturing (reported separately for high-import and high-export industries), and by high and low immigrant-worker ratios. The differences between traded and nontraded sectors reflect basic economic differences between characteristics of workers in goods and services industries: workers in nontraded goods are better educated, younger, more likely to be female, and less likely to be union than workers in traded goods. In addition, wages and GNP per worker in this sector are lower than in traded goods. While immigrant ratios are nonnegligible in nontraded sectors, they are lower than in the traded goods sector, indicating that the traded goods sector is more directly tied to the international economy by flows of people as well as by flows of goods. Decomposing manufacturing into high (top quintile) export and import to shipments sectors, we find striking differences in the characteristics of the work forces. These differences indicate which workers are more or less likely to be directly positively or negatively affected by trade. The principal differences among workers revealed by the table are that export sectors have proportionately more educated workers, fewer blacks, and strikingly fewer female workers than import-intensive industries; that high-export manufactur-

17


Table 8

Average Characteristics of the Labor Force in 1980 (industry averages weighted by employment) Production Workers Earnings With W o Years of Black 16-24 Female Immigrant Who Are per College (8)(%) (%) (%) (%) Union(%) Worker

Traded goods Manufacturing High exports (top 20) High imports (top 20) Nontraded goods and services All industries Low immigrant /labor force High immigrant /labor force

18.7 19.3

9.3 21.4 10.2 20.2

32.4 35.9

7.8 8.2

36.8 43.7

25.5

1.5

18.3

29.9

7.5

41.6

15.4

10.9

20.2

44.8

10.4

40.6

31.2 28.6

10.2 24.9 10.0 24.1

50.6 46.8

6.1 6.5

21.8 25.0

36.2

9.4

17.4

41.2

3.4

33.0

20.9

14.1

22.6

52.4

12.0

24.1

Value Added per Worker

16.9 17.4

32.6 28.6

13.0

21.8

~

Source: Calculated from the NBER Immigration, Trade, and Labor Markets Data Files (see Abowd, in this volume). Thousands of dollars per worker.

ing sectors have lower proportions of immigrants than high-import manufacturing (though their ratio still exceeds the economy-wide average). The fraction of blue-collar workers unionized in the sectors does not, by contrast, show any noticeable differences. Turning to the characteristics of workers by immigrant ratios, we find that high-immigrant-ratio sectors tend to have less educated workers, relatively more blacks, relatively more women, relatively more young workers, and relatively fewer union workers. With the exception of the unionization pattern, these differences mirror those between export- and import-intensive industries in manufacturing. Table 9 records the correlation coefficients between mean characteristics of workers and export and import ratios and the net export ratio in manufacturing and between the mean characteristics of workers and immigrant ratios in manufacturing, nonmanufacturing, and all industries. The correlations confirm the evidence given in table 8, revealing a strikingly high positive correlation between the percentage of workers who are women and the percentage of workers who are immigrants in manufacturing industries. The correlations also show that female, black, and immigrant workers tend to be concentrated in industries with negative net exports and that educated workers tended to be in industries with positive net exports. These calculations suggest that both trade and immigrant flows may have an especially large effect on the female work force, especially in manufacturing.


18

Table 9

Correlation Coefficients for Immigration and lkade Ratios with Various Labor Force Characteristics, 1980 (employment weighted) ~~~~

Nonmanufacturing

Manufacturing Only

XIS 2 Years of college % Black % 16-24 % Female % Immigrant % Unionized Eamingsiworker Shipmentsiworker

.31 - .25 -.29 - .22 -.16 .05 .32 .I3

Mi(S

+ M)

All Industries

Immigrant/ Immigrant! Immigrant/ Labor Force (X - M ) / S Labor Force Labor Force

-.I3 .08

-.14 .I1 .22

.oo

-.06 -.09

- .21

.38

- .01

.06 .I6

- .29

.I2 .29 .20

.I1

1 .oo - .36 - .53 - .29

- .18 - .32 - .30 .05 .35

- .35

- .21 .15 .18 .25 -.18

.I9

Source: NBER Immigration, Trade, and Labor Markets Data Files (see Abowd, in this volume). Note: The statistics are painvise correlation coefficients computed using the percentage of total employment in the industry as the weight.

Finally, we consider the characteristics of workers in foreign- and U.S.: owned businesses operating in the United States. Table 10 displays a collection of comparisons from the 1974, 1980, and 1987 Benchmark Surveys of Foreign Direct Investment. The 1980 survey is the most detailed, and it reveals that employees of nonbank U.S. affiliates of foreign companies are about as likely as employees of U.S.-owned companies to be unionized. Further, 1980 hourly earnings levels are somewhat higher, though sales per employee levels are similar in affiliates. Because the benchmark surveys are not comparable in the universe (banking affiliates are included in 1974 but excluded in 1980 and 1984) and in the summary data tables (employment is not reported by industry in 1974), it is difficult to discern trends in the comparisons of foreign-owned to U.S.-owned businesses. It seems likely that the differences are not substantial, and this conclusion is supported by other research (Leonard and McCulloch, in this volume). 4.

Findings of the NBER Project

Motivated by the internationalization of the American labor market described in sections 1-3, the NBER undertook the research project whose results are given in this volume. The first part of the project studied the factors that influence the number and characteristics of immigrants and their location in the country, including the undocumented, largely Mexican aliens who have aroused so much public concern. The second part of the project examined how immigration and trade affect the wages and employment of American workers. The third part of the project added an international comparative dimen-

19


Table 10

Selected Business and Employment Characteristicsof U.S. Affiliates of Foreign Companies, by Industry

Number of employees (thousands)’ Percentage of civilian labor force Percentage union in U.S. affiliates Percentage union in all U.S. businesses Average hourly earnings for production work: Workers in U.S. affiliates (manufacturing) Workers in all U.S. businesses (manufacturing) Sales per employee (thousands) of dollars):b U.S. manufacturing affiliates U.S. manufacturing businesses Percentage of total employment: Traded goodsc manufacturing Selected nontraded goodsd

1974

1980

1987

1,083 1.2

2,034 1.9 29.3 25.2

3,160 2.6

7.85 7.27

3.3 2.8

9.9

88.86 91.27

137.65 125.63

5.4 5.0 3.0

1.7 8.4 3.6

Sources: Survey of Current Business, “Benchmark Survey of Foreign Direct Investment in the United States, 1974” (May 1976). U.S. Department of Commerce (1983), Foreign Direct Investment in the United States, 1980. Survey of Current Business, “U.S. Affiliates of Foreign Companies: 1987 Benchmark Survey Results” (July 1989). U.S. shipments and employment data from Survey of Current Business, various issues. ”11 U.S. affiliates in 1974; nonbank U.S. affiliates in 1980, 1987. bManufacturingindustries only, thousands of dollars per employee. ‘Agriculture, mining, and manufacturing only. dWholesale trade, retail trade, finance (except banks), insurance, and real estate.

sion by studying immigration, trade, and the labor market in two other countries that are major immigrant recipients, Canada and Australia. Canada is of particular interest for several reasons: Canadian immigration policies historically have stressed job skills as a condition for entry to a greater extent than the United States does; Canada had a significant balance of trade surplus with the United States in the 1980s; and Canada has long depended on foreign (largely U.S.) capital to employ a large share of its work force. Australia is of interest because immigrants constitute an exceptionally large proportion of its work force, raising issues about how immigration affects the macroeconomy, and because its protectionist trade policies contrast with the free trade policies of the United States. The differing experiences among the United States, Canada, and Australia indicate the degree to which different labor market institutions and economic policies can condition the effect of immigration and trade on economic outcomes. The project researchers used different strategies to study the flow of immigrants, the effects of immigration on labor market outcomes, and the effects of trade on those outcomes. The studies that focus on the flow of immigrants

20


compare the labor market and migration behavior of individual immigrants since it is the immigrant decisions and performance in the labor market that are at issue. The studies of the effects of immigration on wages and employment compare local labor markets that have different immigrant shares in the work force. The principal reason for focusing on local markets is that immigrants are concentrated by geographic area, constituting large and increasing proportions of the work force in gateway cities but negligible proportions elsewhere, as indicated in tables 5 and 6. By contrast, the studies focusing on trade examined the effect of trade on industry labor markets. This is because the export and import components of economic activity vary and change greatly among industries, suggesting that the first-order labor market effects of trade occur at the industry level. Most of the findings are based on data from government surveys such as the Census of Population and the Census of Manufactures. To answer certain questions, however, researchers developed new data sets, ranging from one that links import prices to collective bargaining contracts in Canada (Abowd and Lemieux) to a survey of illegal Mexican immigrants in the San Diego area (Borjas, Freeman, and Lang). Because trade, immigration, and labor market data are collected using different standards by diverse government surveys, researchers developed the industry-based trade and labor markets data file for U.S. manufacturing industries from the 1950s through the 1980s and the areabased immigration and labor market data file for the 1960s, 1970s and 1980s (Abowd; Altonji and Card; and LaLonde and Topel). Studying immigration and trade by comparing outcomes across individuals, areas, or industries differs from most studies in international trade, where researchers use general equilibrium models to make inferences about the economic effects of immigration, trade, and capital flows. While there is no inherent conflict between these two types of research approaches (some of the studies use input-output and trade models; (e.g., Kuhn and Wooton; Collins), our decision to concentrate on individuals and markets was a conscious one that conditions the issues we address and our major findings. Our approach pins down the first-order effects of trade and immigration on the economic well-being of the groups most affected by the internationalization of the U.S. labor market but does not yield estimates of the broader benefits of trade or immigration to the overall society. The approach has the advantage of basing inferences on the great variation in the experiences of individuals, areas, or industries and of requiring less formal structure than general equilibrium analyses, at the cost of being unable to answer questions about how things may work out for the society as a whole in the long run. As a broad generalization, the American labor market adjusted well to immigrant flows, absorbing immigrants into local area work forces with little redistributive losses to natives, but it had greater difficulty adjusting to the surge of imports, which produced some noticeable losses to natives in affected industries. Still, industry wages were as flexible to changes caused by trade as

21


to changes caused by domestic factors, falling where imports reduced domestic production and thus buffering employment to some extent. By contrast, in Australia, industry wage responsiveness to imports was limited, and the government sought to protect labor through import restrictions. The research highlights the supply responsiveness of immigrants to economic and political conditions and to foreign as well as to American immigration policies in the context of a “world market for immigrants.” Immigrant Flows 1. The flow of illegal immigrants to the United States, while sizable, falls far short of the huge numbers often reported in the media. NBER estimates of Mexican-born illegal immigrants based on the number of deaths and births of Mexican-born persons in the United States, Mexican surveys of returned migrants, and analyses of apprehension statistics that take account of the fact that apprehensions are determined by Border Patrol activity as well as by immigrant flows support the claim of Census Bureau demographers that the 1980 Census enumerated over half the illegal immigrants. The number of illegal Mexican immigrants in 1980 was on the order of two million rather than ten to twelve million. Moreover, most of the likely illegal Mexican immigrants counted in the Census have a family composition and type. of employment similar to those of legal immigrants (Borjas, Freeman, and Lang; DiezCanedo). Consistent with a factor endowment explanation of immigration, most illegal aliens are unskilled. 2. The characteristics of immigrants are influenced significantly by the economic and political situation in the home countries and by the attractiveness of the United States in the “world market for immigrants,” where the United States competes with other immigrant-recipient countries such as Canada and Australia. All else the same, workers with a high earnings potential are especially likely to migrate to the United States from a country with an egalitarian wage structure (where they cannot easily make high earnings), while workers with a low earnings potential are especially likely to migrate from a country with great wage inequality. The 1965 changes in U.S. immigration policy produced a wave of immigrants whose labor market skills were lower relative to those of native Americans than was true of earlier waves of immigrants, who did especially well in the labor market relative to natives (Borjas). Changes in Canadian immigration laws produced a similar pattern of declining skills in the late 1970s. Australia, by contrast, attracted immigrants who did well compared to natives through 1980 (Borjas; Beggs and Chapman; Bloom and Gunderson). 3. New immigrants to the United States are as mobile across geographic areas as natives, on average, but their mobility has not led them to spread out across the country. Instead, they move to cities where their fellow countrymen reside in large numbers. The tendency of immigrants to cluster dominates such economic incentives as differences in unemployment rates or welfare

22


benefits across areas in determining immigrant migration flows (Bartel and Koch). Cuban, Mexican, and Puerto Rican immigrants and natives who move from cities with a high proportion of persons of their ethnic background to cities with a low proportion of persons with their ethnic background have roughly similar earnings and employment experiences as their peers who move from cities with a low proportion of persons of their ethnic background to cities with a high proportion of persons of that ethnic background (Tienda and Wilson). The direct advantages and costs of immigration thus continue to be borne by gateway cities, while the persistent geographic concentration of immigrants may reduce their economic progress and rate of long-run assimilation into the broader society. The Effects of Trade and Immigration on Labor Markets 4. Increased immigration has a modest adverse effect on the wages of the immigrants themselves and on the wages of earlier waves of immigrants, but it has only a modest effect on the wages of the young black and Hispanic Americans who are likely to be the next closest substitutes (LaLonde and Topel). Neither the employment nor the wages of less educated black and white natives worsened noticeably in cities where immigrant shares of the population rose in the 1970s. On the positive side, there is some evidence that, in cities with more immigrants, employment grew more rapidly or declined more slowly in low-wage industries where immigrants tended to find jobs and that less-skilled natives moved into better jobs (Altonji and Card). The broad implication is that immigrants have been absorbed into the American labor market with little adverse effect on natives. 5. “General equilibrium analysis” of the potential effects of immigration on the labor market through changes in sectoral outputs and prices further supports the claim that immigration has not harmed American labor. Indeed, the concentration of immigrants in import-intensive, traded goods manufacturing industries and the distribution of capital and native labor among export, import, and nontraded goods sectors suggests that increased immigration may actually benefit native labor, at least in the short run (Collins; Kuhn and Wooton). 6 . Wages in industries where sales are adversely affected by trade tend to decline relative to wages in other industries, just as do wages in industries where sales are adversely affected by domestic market developments, buffering to some extent the loss of jobs in industries facing large increases in imports. Unionized sectors make greater wage adjustments than nonunion sectors, apparently because workers in those industries earn above-market wages that can be reduced to save jobs whereas nonunion wages are closer to competitive levels (Abowd and Lemieux; Freeman and Katz). Once workers are dislocated by trade, however, they appear to have greater difficulty finding work than workers displaced for other reasons (Kruse 1988). 7. Foreign-owned firms employ nearly 3% of American workers. Despite

23


the concern about foreign ownership, wages of production workers appear to be higher in foreign-owned enterprises, and rates of unionization are not different from domestic-owned companies. Moreover, notwithstanding all the attention given to Japanese firms, the bulk of direct foreign-owned enterprises in the United States are European. Foreign-owned firms use substantially more highly educated research-and-development employees (Leonard and McCulloch). Comparative Experiences: Canada and Australia 8. Canadian and Australian immigration policies, traditionally based on labor market skill considerations, have moved toward admitting immigrants for reasons of family unification, as in the United States. Since 1974, Canada has given preferential treatment to persons with close relatives in the country as well as to those who fulfill certain labor-market criteria. As a consequence, immigrants coming to Canada after the mid-1970s apparently do worse in the labor market relative to natives than earlier immigrant cohorts (Bloom and Gunderson). Australia admitted immigrants on the basis of a labor market point system from the 1970s through the early 1980s, with the result that the labor market skills of Australian immigrants did not drop in the 1970s relative to those of native workers, as in the United States and Canada (Beggs and Chapman). The implication is that immigration policies significantly affect the type of immigrants and their labor market performance. 9. Low-skill immigrants are relatively more highly paid in Australia than in the United States. There are three reasons for this: (1) wage differentials by occupation are smaller in Australia than in the United States; (2) immigrants are more highly unionized in Australia than in the United States; and (3) Australia has enacted trade policies that protect industries employing low-skill immigrants. Australian protection of immigrant-intensive industries produces relatively higher prices for the outputs of those sectors and extracts a sizable social cost on the order of 50% to 100% of the wage bill in footwear, clothing, and textiles (Gregory, Anstie, and Klug). 10. In Canada, changes in import and export prices, which reflect the pressure of the international economy on producers, have significant effects on the employment of workers covered by collective bargaining agreements. Increases in import prices, which shift demand to domestic producers, and in export prices, which reflect greater returns from increasing sales overseas, are associated with increases in employment of sizable magnitudes. In both the United States and Canada, unionized employment is more sensitive to import competition than to a comparable reduction in domestic production (Abowd and Lemieux) .

Concluding Remarks Perhaps the most intriguing finding of the Immigration, Trade, and Labor Markets studies is the apparently different direct effect of immigration and

24


trade on workers in the affected labor markets. Whereas immigration does not discernibly reduce the wages and employment of less-skilled native workers in immigrant-intensive localities, imports reduce the pay as well as employment of workers in heavily affected industries. Why? What might account for this differential effect? While we cannot give a conclusive and quantifiable answer, the general factors likely to underlie the differences do seem clear. First, differences in the concentration and magnitude of imports and immigration in affected areas certainly have an influence. In the ten industries with the largest growth of import shares of sales from the 1960s to the mid-l980s, import shares rose by 14% of domestic sales to 73% of domestic sales on average. By contrast, in the ten standard metropolitan areas with the greatest 1970-1980 growth of immigrants relative to the work force, new immigrants averaged 20% of the 1970 work force. Employment fell by 56% in the tradeaffected industries, while employment of natives increased in all the immigrant-affected localities save for New York City. Second, immigration has potential offsetting effects on the demand for labor in affected areas, while trade has no such effects on demand for labor in affected industries. Immigrants purchase goods and services in the area in which they work, raising demand for labor. Immigrant skills are also likely to complement the skills of some native workers, raising demand for them. By contrast, even with balanced trade, workers in an industry facing a surge of imports are unlikely to benefit directly from offsetting export-created demand for labor or from complementary demands for native labor in retail and wholesale trade. Third, it is possible that the concentration of immigrants in gateway cities did not increase the labor supply in those areas by as much as the immigration numbers would suggest. This would be the case if natives adjusted their choice of location of residence to take account of the immigrant flows. The flow of immigrants to, say, Los Angeles could have deterred midwesterners or southerners from migrating there or impelled natives to move elsewhere, so that the labor force in the city was not all that different from what it would be absent immigration. No such mitigating response exists for trade-affected industries. All these considerations suggest that the 1980s import surge caused a greater "shock" in affected labor markets than did the influx of immigrants and, thus, created greater difficulties of labor market adjustment. Trade upset the demand-supply balance in industry labor markets more than immigration upset the demand-supply balance in local labor markets. One additional factor may also contribute to the greater effect of imports than immigration on affected workers. In some industries, worker skills and earnings are industry specific, so that shocks cause greater economic losses to the affected employees. Consequently, labor mobility may be easier for work-

25


ers facing immigrant competition in a local labor market than for workers facing import competition in a trade-affected industry. In summary, while trade and immigration may have the same long-run economic effects on an economy, there are good reasons (and, more compelling, empirical evidence) that they have different transitional costs for affected workers.

References 1978. Citibank Economic Database [machine-readable magnetic data file, 1946 to present]. New York: Citibank. Council of Economic Advisers. 1990. Economic Report of the President. Washington, D.C.: U.S. Government Printing Office, February. Kruse, Douglas. 1988. International Trade and the Labor Market Experience of Displaced Workers: Evidence from the Displaced Worker Survey. Industrial and Labor Relations Review 41, no. 3 (April): 402-17. Passel, Jeffrey S., and Karen A. Woodrow. 1984. Geographic Distribution of Undocumented Immigrants: Estimates of Undocumented Aliens Counted in the 1980 Census by State. Washington, D.C.: U S . Bureau of the Census, Population Division. U.S. Department of Commerce. Bureau of the Census. 1983. US.Commodify Exports and Imports as Related to Output 1981180; Washington, D.C.: U.S. Government Printing Office, November. . Annual. Statistical Abstract of the United States. Washington, D.C.: U S . Government Printing Office. . Decennial. Census of Population Detailed Population Characteristics U.S. Summary. Washington, D.C.: U.S. Government Printing Office. U.S. Department of Commmerce. Bureau of Economic Analysis. 1983. Foreign Direct Investment in the United States, 1980. Washington, D.C.: U.S. Government Printing Office. . Monthly. Survey of Current Business. Washington, D.C.: U.S. Government Printing Office. U.S. Department of Labor. Bureau of Labor Statistics. Monthly. Employment and Earnings. Washington, D.C.: U.S. Government Printing Office. U.S. Department of Justice. Immigration and Naturalization Service. Annual. Statistical Yearbook of the Immigration and Naturalization Service. Washington, D.C.: U S . Government Printing Office. Warren, Robert, and Jeffrey S. Passel. 1987. A Count of the Uncountable: Estimates of the Undocumented Aliens Counted in the 1980 Census. Demography 24 (August): 375-93. CITIBASE.


Immigrant Flows and Performance in the United States


1

Immigration and Self-selection George J. Borjas

The insight that migrants may be systematically different from persons who do not choose to migrate has long played an important role in sociological and historical studies of the immigration phenomenon (see, e.g., the studies contained in Jackson 1969). The selectivity hypothesis has also played a major role in the modem economic literature that analyzes how immigrants do in the U.S. labor market. For example, the early studies of Chiswick (1978) and Carliner (1980) invoke the assumption that immigrants are positively selected from the population of the countries of origin to explain the cross-sectional empirical finding that immigrant earnings (after a short time period) “overtake” the earnings of natives with the same observed socioeconomic characteristics, such as age and education.’ My recent work in this area (Borjas 1985, 1987) has addressed two related questions raised by the early studies. Since most of the literature analyzing immigrant earnings focuses on the study of single cross-sectional data sets, my 1985 paper raised the possibility that the overtaking findings could be due to the fact that cross-sectional regressions confound aging and cohort effects.2 The positive correlation between immigrant earnings and years of residence in the United States observed in the cross section could arise because immigrants “adapt” rapidly to the U.S. labor market or because earlier waves of immigrants differ in substantial ways (labor market productivities, unobserved George J. Bojas is professor of economics at the University of California, San Diego, and a research associate of the National Bureau of Economic Research. The author is grateful to Richard Freeman for many helpful discussions of the ideas presented in this paper, to Charles Brown for insightful suggestions and comments, and to Bemt Bratsberg for excellent research assistance. He is also grateful to the National Science Foundation (grants SES-8604973and SES-8809281) and to the National Institute of Child Health and Human Development (grant R01-HD22344) for financial support.

29

30

George J. Borjas

abilities or skills) from more recent waves. Borjas (1985) adapted well-known techniques (see, e.g., Heckman and Robb 1983) to separately identify aging and cohort effects using the 1970 and 1980 U.S. Censuses. This methodology, which “tracks” synthetic cohorts of immigrants over time, showed that ( a ) immigrant assimilation was not as fast as the cross-sectional studies indicate, (b) the more recent immigrant waves performed substantially worse in the labor market than the early postwar waves, and ( c ) there was little likelihood that the most recent immigrant waves would ever earn substantially more than natives of comparable age and education. An important insight provided by the study of synthetic cohorts is that invoking the assumption of positive selection, though it may be correct for some cohorts of immigrants, may be completely wrong for other cohorts of immigrants. This raises the important question of exactly which factors determine whether immigrants are positively or negatively selected from the population in the countries of origin. Borjas (1987) presents an initial attempt to address this problem and derives a simple economic model of selection on the basis of unobserved characteristics (which, after all, form the focus of much of the literature on immigrant earnings). This model, which will be discussed in detail below, shows that there is no general law stating that immigrants must be positively selected. In fact, under a reasonable set of conditions, it is likely that immigrants are negatively selected (i.e., persons who have below-average earnings and productivities are the most likely to migrate to the United States). My empirical analysis revealed that positive selection was more likely to characterize immigrants from the advanced industrial countries and negative selection was more likely to characterize immigrants from the Third World countries, who form the bulk of migration to the United States in the post-1965 period. This paper expands my earlier work in a number of significant ways. The theoretical analysis below will argue that, although most of the literature has focused on the role that selection in unobserved Characteristics plays in determining immigrant earnings, there is also selection in observed characteristics such as education. The theoretical framework clearly shows that it is completely possible for the most educated persons to migrate to the United States (i.e., positive selection in education) but for these persons to be the least productive in the population of highly educated persons (i.e., negative selection in unobserved characteristics). The analysis below presents a number of propositions that yield insights into the process that determines the selection of immigrants in these separate dimensions of “quality.” The empirical analysis in this paper expands my previous work in two ways. First, it presents a detailed analysis of the U.S. earnings of immigrants by focusing on the roles played by selection in both observed and unobserved characteristics. It will be seen that a number of the theoretical predictions are confirmed by the data. Second, it is clear that potential migrants can choose from a number of potential host countries. The empirical analysis below will

31

Immigration and Self-Selection

present a systematic study of the selection biases generated by the sorting of migrants among three potential countries of destination: Australia, Canada, and the United States. The evidence indicates that both country-of-origin and country-of-destination characteristics play an important role in determining the performance of immigrants in any labor market.

1.1 Theory of Immigration 1.1.1 The Roy (1951) Model Migration is assumed to flow from country 0, the country of origin or the “home” country, to country 1, the country of destination or, for concreteness, the United States. This simple framework ignores three potential complications. First, it is likely that persons born in the United States also consider the possibility of migrating to other countries, and perhaps many of them do so. Second, even persons choosing the United States as a country of destination may find that things do not work out (or perhaps work out much better than expected), and some return migration is thereby generated. Third, individuals contemplating migration in a particular country of origin enter the “immigration market” in which a number of other host countries (such as Australia and Canada) compete for the immigrant’s human and physical capital. Little is known about the size and composition of the migrant flows from the United States to other countries; hence, these possibilities are ignored in what follows. Much more, however, is known about the size and composition of the flows from any given home country to each of three potential host countries (Australia, Canada, and the United States), and the implications of the simpler two-country model will be applied below to the more general framework where potential migrants not only decide whether to migrate but also choose a country of destination. Residents of the home country face an earnings (w)distribution given by (1)

In w,

=

XS,

+

E ~ ,

where X is a vector of socioeconomic characteristics with value 6, in country 0, and the disturbance E~ is independent of X and is normally distributed with mean zero and variance @. The earnings distribution facing individuals in the United States is given by (2)

In w,= (1 - M ) X S ,

+ MX6, +

E,,

where M is a dummy variable indicating whether the individual is foreign born or native. The vector 6, gives the value that the U.S. labor market attaches to the socioeconomic characteristics X for natives. This valuation may differ because of discrimination or other unobserved factors from the value 6 , that the labor market attaches to the characteristics brought in by potential migrants. The disturbance E, is again independent of X (and M )and is normally distrib-

32

George J. Borjas

uted with mean zero and variance 0:.Finally, the random variables E~ and E , have correlation coefficient p. Equations (1) and (2) completely describe the earnings opportunities facing a potential migrant (as well as U.S. natives). Three questions are raised by this simple framework. First, what factors determine the size of the migration flow generated by the income-maximization hypothesis? Second, what types of selection in the unobserved characteristics E are created by the endogenous migration decision? Third, what types of selection in the observed characteristics X are created by the endogenous migration decision? The migration decision is determined by the sign of the index function:

where C gives the level of mobility costs, and 7~ gives a “time-equivalent” measure (T = C/wo)of the costs of migrating to the United States. The level of migration costs C is likely to vary among individuals for two reasons. First, there are time costs associated with migration, and these time costs are likely to be higher for persons with higher opportunity costs. Second, there are transportation costs associated with migration. These direct costs include not only the air fare (which is likely to be constant across individuals) but also moving expenses of family and household goods, and it is reasonable to suppose that these expenses may also be a positive function of w,. These assumptions give little hint as to how the time-equivalent measure of mobility costs, T ,varies across individuals. It is instructive to assume first that T is constant across individuals since the main implications of the Roy model are clearest in this special case. The analysis below will show that the treatment of T as a random variable in the population does not substantially alter the analysis and will, in some instances, reinforce the conclusions of the simpler model. Since migration to the United States occurs when I > 0, the emigration rate from the country of origin for persons of given characteristicsX is given by

(4)

P ( X ) = pr{v > - [ X ( S , - So) -

T]}=

1

-

@(z),

where v = E , - E ~ z, = - [ X ( S , - So) - T]/u,, and 0 is the standard normal distribution function. If the characteristicsX have a joint density function given byf(x), then the emigration rate from country 0 is given by

P

P(x)f(x)dx.

= X C i l

Equations (4) and (5) summarize the (rather obvious) economic content of the theory of migration proposed by Hicks (1932) and further developed in Sjaastad (1962). In particular, the emigration rate is a negative function of mean income in the home country (po = XS,), a positive function of mean

33


income in the United States (p, = XS,), and a negative function of migration costs. Much of the literature on the internal migration of persons in the United States is devoted to testing these theoretical predictions (see the survey in Greenwood 1975). The immigration literature, on the other hand, has historically focused on explaining not the size of migration flows but their composition or labor market quality. As far back as 1919, for example, Douglas was asking whether the skill composition of immigrant cohorts was constant across successive immigrant waves. The theory of migration contained in equations (l)-(5)has important implications about the selection biases that characterize the pool of migrants in terms of both unobserved and observed characteristics. Consider initially the selection mechanism in the unobserved characteristics E. In particular, consider the conditional expectations E(1n w, I X, I > 0) and E(1n w ,I X, I > 0). Note that these means condition on two dimensions: the observed characteristics X and the decision to migrate. Under the normality assumptions, these conditional means are given by

(7)

+

where A = + (z) /P (X ), and is the density of the standard normal. The variable A is inversely related to the emigration rate and will be positive as long as some persons find it profitable to remain in the country of origin (i.e., P [ X ] < 1). Let Q, = E ( E , I X, I > 0), Q, = E ( E , I X , I > 0), and k = U,/U,. The variables Q, and Q,measure the “quality” (in terms of unobserved characteristics) of the migrant pool. The Roy model identifies three cases of substantive interest. Positive Selection, Q,

> 0 and Q,> 0.

This type of selection exists when migrants have above-average earnings in the country of origin (for given characteristicsx) and also have U.S. earnings that exceed the earnings of comparable U.S. natives (ignoring the possibility that immigrant earnings may be reduced because of their ethnic or racial background). Inspection of equations (6) and (7)shows that the necessary and sufficientconditions for this type of selection to occur are

If p is sufficiently high, and if income is more dispersed in the United States than in the country of origin, immigrants arriving in the United States will be selected from the upper tail of the home country’s income distribution and

34

George J. Borjas

will outperform comparable natives on arrival in the United States. Intuitively, this occurs because the home country, in a sense, is “taxing” high-ability workers and “insuring” low-ability workers against poor labor market outcomes. Since high-income workers benefit relatively more than low-income workers from migration to the United States (regardless of how much higher mean incomes in the United States may be relative to the country of origin), a brain drain is generated, and the United States, with its greater opportunities, becomes a magnet for persons who are likely to do well in the labor market. Negative Selection, Q, < 0 and Q , < 0 This type of selection is defined to exist when the United States draws persons who have below-average incomes in the country of origin and who, holding characteristics constant, do poorly in the U.S. labor market. The necessary and sufficient conditions for negative selection to occur are (9)

p>k,

k < 1.

Negative selection also requires that p be “sufficiently” positive but that the income distribution in the country of origin be more unequal than that in the United States. Intuitively, negative selection is generated when the United States “taxes” high-income workers relatively more than the country of origin and provides better insurance for low-income workers against poor labor market outcomes. This opportunity set leads to large incentives for low-ability persons to migrate, since they can improve their situation in the United States, and to decreased incentives for high-ability persons to migrate, since income opportunities in the home country are more profitable. Refugee Sorting, Q, < 0 and Q , > 0 This kind of selection occurs when the United States draws below-average immigrants (in terms of the country of origin) but migrants have aboveaverage earnings in the U.S. labor market. The necessary and sufficient condition is p

< min

(k,

k).

In other words, if p is negative or “small,” the composition of the migrant pool is likely to resemble a refugee population. For instance, it is likely that p is negative for countries that have recently experienced a Communist takeover. After all, the change from a market economy to a Communist system is often accompanied by structural changes in the income distribution and by confiscation of entrepreneurial assets and redistribution to other persons. The Roy model suggests that immigrants from such systems will be in the lower tail of the “revolutionary” income distribution but will outperform the average U. S. native worker. The basic Roy model thus provides a useful categorization of the factors

35


that determine the quality or composition (in terms of unobserved characteristics) of the migrant pool. Even at this level, several important implications are generated that give some insight into a number of empirical findings in the literature. For example, many studies have documented the fact that refugee populations perform quite well in the U.S. labor market when compared to native workers of similar socioeconomic characteristics. These empirical results are explained by the income-maximization hypothesis and by the fact that these refugee populations, prior to the political changes that led to a worsening of their economic status, were relatively well off in the country of origin. It is, therefore, unnecessary to resort to the arbitrary distinctions between “economic” and “noneconomic” migrants to explain the refugee experience. The Roy model also provides an interesting explanation for the empirical finding that the quality of migrants to the United States has declined in the postwar period (where quality is defined by the wage differential between migrants and natives of the same measured skills). Prior to the 1965 amendments to the Immigration and Nationality Act, immigration to the United States was regulated by numerical quotas. The distribution of the fixed number of quotas across countries was based on the ethnic population of the United States in 1920 and thus encouraged migration from (some) Western European countries and strongly discouraged immigration from other continents, particularly Asia. The favored countries have one important characteristic: their income distributions are probably much less dispersed than those of countries in Latin America or Asia. The 1965 amendments abolished the discriminatory restrictions against immigration from non-European countries, established a twenty thousand numerical limit for legal migration from any single country (subject to both hemispheric and worldwide numerical limitations), and led to a substantial increase in the number of migrants from Asia and Latin America. The new flow of migrants thus originates in countries that are much more likely to have greater income inequality than the United state^.^ It would not be surprising, therefore, if the quality of immigrants declined as a result of the 1965 amendments. The theoretical analysis yields two equations that can guide empirical analysis. These equations are given by

(1 1)

Q,

= g b 0 , CL,,n, u,,, u,, P),

Equation (1 1 ) gives a “reduced-form’’ equation, where immigrant quality in the United States (i.e., the wage differential between migrants and natives of equal measured skills) is a function of all the primitive parameters of the model (i.e., the parameters of the two income distributions and migration costs). My earlier paper (Borjas 1987) provides a detailed analysis of the theoretical restrictions implied by the income-maximization hypothesis on the direction of the effects of the various variables in the model. These effects are

36

George J. Borjas

usually ambiguous and can be categorized in terms of “composition effects” and “scale effects.” In particular, a change in any variable OL will create incentives for a different type of person to migrate (the composition effect) and for a different number of persons to migrate (the scale effect). Equation (12) is a “structural” equation and states that, if knowledge of A is available, a subset of the parameters of the model enters multiplicatively through the h function (see eq. [7]). By holding A constant, the structural equation essentially nets out the scale effect and leads to more unambiguous predictions of the effect of the exogenous variables on the quality of immigrants. It is important to note that the h function in (12) does not depend on mean income levels in the countries of origin and the country of destination or on the level of migration costs since these factors play a role only through the selectivity variable A. Three comparative statics results are implied by analysis of the A-constant structural quality equation. 1. An increase in the variance of the income distribution in the home country leads to a decrease in the quality of migrants in the United States. 2. An increase in the variance of the income distribution in the United States leads to an increase in the quality of migrants in the United States.’ 3. An increase in the correlation coefficient between earnings in the home country and earnings in the United States increases immigrant quality if there is positive selection and decreases immigrant quality if there is negative selection. The ambiguity arises because, the larger the correlation coefficient, the better the “match” between the two countries. The improvement in the match increases the quality of the immigrant flow if there is positive selection and decreases it if there is negative selection. 1.1.2 Random Mobility Costs These insights have been derived from the simplest version of the Roy model, which treats mobility costs (defined as a fraction of potential income in the country of origin) as a constant in the population. This assumption may be restrictive, and it is important to ascertain how its relaxation affects the results of the model. Suppose that mobility costs are normally distributed in the population and can be written as

(13)

Tr= P,

+

E,,

where p, is the mean level of mobility costs in the population, and E, is a normally distributed random variable with mean zero and variance u;. The random variable E, may be correlated with E, and E, and the correlation coefficients are given by pno and p,, , respectively. The conditional expectations of migrant incomes in the home and destination countries are now given by

(14)

E(ln w, I X, I > 0) = X 6 ,

+u

u

Ud

[(P

):

-

- Pd

3,

37

(15)

Immigration and Self-Selection E(ln w , I X, I

> 0) = X 6 ,

+

where v’ = E , - E, - E,. Equations (14) and (15) show that the addition of migration costs does not affect any of the substantive results of the simplest version of the Roy model if migration costs are uncorrelated with earnings opportunities. However, if migration costs are correlated with earnings opportunities, the type of selection that is generated may change in either direction. Suppose, for example, that migration costs are positively correlated with earnings opportunities. For instance, high-ability persons may take longer to find appropriate jobs. This positive correlation makes both Q, and Q, more negative and hence increases the likelihood of negative selection. Conversely, if migration costs (measured in time units) and earnings opportunities are negatively correlated, the likelihood of positive selection is increased. Two additional points about this more general model are worth stressing. First, the importance of variable migration costs in the analysis will diminish greatly if the variance in migration costs is relatively small compared to the variance in the income distributions. Second, regardless of how important migration costs are, the key result that negative selection is more likely from countries with high levels of income inequality and positive selection is more likely from countries with more equal income distributions is unaffected. 1.1.3 Selection in Observed Characteristics Equation (4), the probit equation determining the migration rate, contains an additional insight: the migration rate is a function of X through the parameter (6, - 8,). Hence, the migration of persons with larger levels ofX is more likely if X has a higher return in the United States than in the country of origin, and the migration of persons with lower levels of X is more likely if the country of origin values the characteristicX more than the United States. A complementary analysis to the Roy model can be derived if it is assumed that the vector X consists of only one variable, say education (s), that this variable is uncorrelated with the disturbances in the earnings functions, and that this variable, too, is normally distributed in the population. The assumption of only one variable in the vector X is irrelevant since the results can be easily generalized to any number of variables. The assumption of normality, though unrealistic for some socioeconomic characteristics, does simplify the mathematics substantially and allows a useful extension of the Roy approach to the study of selection in observed skills and the analysis of the actual wage differential between immigrants and natives (as opposed to the standardized wage differential). Suppose the earnings functions in the two countries are given by (16)

In w, = po + 6,s

+ E,.

38

George J. Borjas

In w , = p,

(17)

+ 6,s + E,,

and that the education distribution in the population of the country of origin can be written as (18)

s =

+

Er,

where E, is normally distributed with mean zero and variance US. Assuming that mobility costs are constant, the emigration rate for the population in the country of origin is given by

(19)

p

=

pr{(El -

Eo)

+ (4

+ (6, - 6,)p$

- $)E, > - [(PI - Po) - TI} = 1 - @(z*),

+

where t = (E, - E ~ ) (6, - SO)&,, and z* = -[(p, - po) + (6, - 6JPs - TI/U,. Two interesting questions can be addressed within this framework. First, consider the conditional expectation of schooling of persons who do migrate. It is easy to show that

Hence, the mean schooling of migrants will be less than or greater than the mean schooling of the population depending on which of the two countries values schooling more. Positive selection in schooling will be observed when (6, - 6,) > 0 so that the U.S. labor market attaches a higher value to schooling, while negative selection in schooling will be observed when (6, - 6,) < 0 so that highly educated individuals have little incentive to leave the country of origin. It is important to stress that these selection conditions seem to have little to do with the conditions determining selection in unobserved characteristics. Any permutation of selection mechanisms in unobserved and observed characteristics is theoretically possible. Hence, negative selection in unobserved characteristics (or ability) may be jointly occurring with positive selection in education, or vice versa. Simply because the United States attracts highly educated persons from some countries does not imply that these highly educated persons are the most productive highly educated persons in that particular country of origin. At a more fundamental level, however, the determinants of the two types of selection are not all that different. The sorting in observed characteristics is guided by international differences in the prices So and 6,. In the case of unmeasured skills, the sorting is guided by the variances ui and a:. In a sense, these variances measure the “prices” of unmeasured skills in the respective countries since these abilities are more highly rewarded in countries with higher levels of income inequality. The sorting in all the dimensions of skills,

39


therefore, is guided by the same basic process: skills flow to whichever country offers the highest price for them. The actual mean earnings of the migrant pool in each of the two countries are given by (21)

E m w, I I > 0 ) =

Il.0

+

6oPs

(6, - 6,)6,

+ -(p *OU1

u,

-

)]A,

Mean earnings of migrants depend on the mean education of migrants, as given by (20), and on the mean level of their unobserved characteristics. Since the two kinds of selections are independent, nothing can be said about how the average migrant performs in the host country unless the kinds of selections that occurred in each of the two dimensions of quality are known. Nevertheless, it is of interest to document the net effect of the selection in all the various dimensions of skills on immigrant earnings, and the empirical analysis below presents a detailed study of the unstandardized earnings differential between immigrants and natives in the host country. Equations (21) and (22) show that generalizations about the quality of immigrants based solely on observed education levels (or other measures of X) are extremely misleading. In addition, it is well known that observed characteristics such as education, age, marital status, health, etc. explain a relatively small fraction of earnings variation across individuals. It is not uncommon, for example, to find that the observed characteristics explain much less than a third of the variance in wage rates or weekly earnings. The selection in unobserved characteristics, therefore, is likely to be much more important empirically than the selection in observed characteristics. A number of comparative statics results can be generated by analysis of equation (20). Perhaps the most interesting of these results is

That is, a one-year increase in the mean education level of the country of origin will increase the mean education level of persons who actually migrate to the United States, but this increase will be by less than one year. The intuition for this result follows from the fact that an increase in kSwill change the size of the immigrant flow. Suppose, for concreteness, that (6, - 6,) > 0 so that there is positive selection in education. The increase in p, makes it worthwhile for more persons to migrate and thus dilutes the mean education level of the population of migrants. Hence, the increase in the conditional expectation is less than the increase in the population mean. An important implication

40

George J. Borjas

of this theoretical prediction is that the variance in education levels across immigrants (from different countries) in the United States will be smaller than the variance in education levels of the actual populations across countries in the world. In other words, the population of migrants in the United States is more homogeneous (in terms of education) than the populations of the different sending countries. In general, equation (20) implies the existence of observable quality equations analogous to (1 l ) and (12): (24) (25)

QT

= g * b 0 , CL.,,m, uo, ul, P, F,, us,(6, - 6,,)1,

QT

= h*[uo, ul, P, IJ-~, us,(8, -

6,)lL

where QT gives the mean level of the observed characteristics of immigrants in the United States. The estimation of (24) and (25), of course, is likely to be extremely difficult in practice since they introduce a number of primitive parameters (e.g., 6, - 6,) that are unobservable and likely to remain so.

1.2 Empirical Framework Recent empirical research on the earnings of immigrants stresses the importance of disentangling the cohort and aging effects that are confounded by a single cross section of data. In the analysis presented in this paper, two Censuses in the country of destination will be pooled (e.g., the 1970 and 1980 U.S.Censuses), and the following regression model will be estimated: (26) (27)

In wo = Xjai

+ a l y j + azy,’ + 2 p,C, + yimj + E ~ , In wnl = X,S, + y,ml + t

E,~,

where wii is the wage rate of immigrantj, wnris the wage rate of native person 1, X is a vector of socioeconomiccharacteristics (e.g., education, age, etc.), y is a variable measuring the number of years that the immigrant has resided in the country of destination, C is a vector of dummy variables indicating the year in which migration occurred, and IT is a dummy variable set to unity if the observation is drawn from the 1980 Census and zero otherwise.6The vector of parameters (a,, az),along with the age coefficients in the vector X, provides a measure of the assimilation effect (i.e., the rate at which the ageearnings profile of migrants is converging to the age-earnings profile of natives), while the vector of parameters p estimates the cohort effects. The period effects are given by yi for immigrants and by y, for natives. The model in equations (26) and (27) is underidentified. In particular, some of the right-hand-side variables in the immigrant earnings function are perfectly collinear. Suppose, for example, that the immigrant arrived in calendar year 8 so that C, = 1. Then

41

Immigration and Self-Selection y = (T

(28)

- k - 8)

+ nk,

where T is the calendar year in which the latest cross section is observed, and k is the number of years separating the two cross sections. The variable capturing the period effect, therefore, is a linear combination of the cohort variable and of the years-since-migration variable. Obviously, two cross sections cannot be used to identify three separate effects: period, cohort, and aging effects. In order to estimate the structural parameters describing the extent of immigrant assimilation and cohort quality change, a restriction must be imposed on the size of the period effect in the migrant population. A reasonable, though unverifiable, assumption is that the period effect experienced by immigrants (y,) is identical to the period effect experience by natives (y,). In other words, changes in the wage rate due to shifts in aggregate economic conditions affect the immigrant and native wage levels by the same relative magnitude. It is easy to show that this restriction is sufficient to identify all the structural parameters in equations (26) and (27) exactly. This theoretical restriction leaves some amplitude for its empirical implementation since the choice of the native base is essentially arbitrary. The choice of a native base for the various immigrant groups under study will be discussed in detail below. There are two dimensions of migrant quality that can be calculated from the estimated regressions in (26) and (27): (a) the entry wage of immigrants when they arrive into the United States and (b) the rate at which this wage changes over time. To simplify the empirical analysis, the two measures will be combined into a single measure of immigrant quality. In particular, let G,(8) be the entry wage of an immigrant cohort that arrives in the United States at age twenty in calendar year 8, and let Gnbe the entry wage of a comparable (in terms of all observable economic variables) native person who enters the labor market at age twenty. Similarly, let gi be the rate at which the earnings of immigrants grow over their lifetime, and let g, be the growth rate for natives. Finally, let r be the rate of discount (assumed to be the same for migrants and natives). If persons are infinitely lived, the present values associated with the earnings streams of migrants and natives are given by

(29)

vj(e) =

I

Gi(8)e-@-gJ'df =

wi(8)/ (r - g i ) ,

0

(30)

=

@

(r- g).

The percentage difference in present values between immigrants of cohort 8 and natives is defined by

42

George J. Borjas

and a first-order approximation (using the assumption that earnings growth rates are small relative to the discount rate) yields In [Vi(e) / V,] = [In ai(8)- In

an]+ .-gi

-g,

r

Hence, the percentage difference in the present value of the earnings streams faced by immigrants and natives is an additive function of the wage differential at the time of entry and of the difference in earnings growth rates over the life cycle.7 The present value differential in (32) can be easily evaluated from the estimates of equation (26) and (27) if two assumptions are made. First, the rate of discount is assumed to be 5 percent. Clearly, the assumption of any higher discount rate would lead to a worsening of relative immigrant earnings since the latter part of the working life cycle (where immigrants tend to do better) would be more heavily discounted. Second, the growth rates g, and g, must be evaluated from the age and years-since-migration coefficients in the earnings functions in (26) and (27). The quadratic specification for age and years since migration in the earnings functions implies that the growth rate is not constant over time. The empirical analysis below will define the growth rate g, and g, by (33) (34)

2,

= ( Y , [ X ,50, 30, 81- Y , [X,20, 0, 8])/30,

2,

=

( Y , [ X , 501 - Y,[ X , 20])/30,

where Y , [ X ,A , y, 81 is the predicted (In) earnings for an immigrant with characteristics X , at age A, with y years of residence in the United States, and who migrated in cohort 8 . Similarly, Y J X , A] gives the predicted earnings for a native with characteristics X at age A . In other words, the average growth rate experienced by immigrants and natives between ages twenty and fifty (evaluated at the mean characteristics of the migrant population, X) is used for estimation of the growth rate in the present value expressions. This approach has the useful property that the growth rates (for both immigrants and natives) are a linear function of regression coefficients, and, since the entry wages are given by Y,[X, 20,0, 01 for immigrants and Y J X , 201 for natives, the present value expressions in (33) and (34) are also linear functions of regressions coefficients; hence, a standard error can be easily evaluated. This approach makes a departure from the tradition in the empirical literature that analyzes immigrant earnings. The entire literature essentially focuses on the estimation of entry wage levels and on the calculation of “overtaking” points (if they exist). This type of analysis is not useful if overtaking points occur rather late in the life cycle (or if they do not occur at all), as some recent evidence suggests. The empirical use of the present value of earnings is much more consistent with the theoretical content of the theory of migration and

43


deemphasizes the somewhat misleading concept of overtaking points. The analysis of the success of migrant groups in the United States, to borrow from the human capital theory that guided much early research on immigrant earnings, should be based not on the calculation of wage differentials at given ages but on the life-cycle wealth accumulated by migrants and natives. Hence, the present value approach used in the empirical sections of this paper is much more in the tradition of the human capital literature and of the Roy model of immigration developed in the previous section.

1.3 Earnings of Immigrants in the United States 1.3.1 Data and Descriptive Statistics This section analyzes the relative earnings of immigrants in the U.S. labor market. The data are drawn from the 1970 2/100 U.S. Census (obtained by pooling the 5% SMSA and County Group Sample and the 5% State Sample) and the 1980 5/100 A sample. The complete samples are used in the creation of the immigrant extracts, but random samples are drawn for the native “baseline’’ populations.8 The analysis is restricted to men aged 25-64 who satisfied five sample selection rules: (1) the individual was employed in the calendar year prior to the Census; (2) the individual was not self-employed or working without pay; (3) the individual was not in the armed forces (as of the survey week); (4) the individual did not reside in group quarters; and (5) the individual reported annual earnings exceeding $1,000. Throughout this section, the dependent variable is the logarithm of the individual’s wage rate in the calendar year prior to the Census. The individual’s wage rate is defined as the ratio of annual earnings to annual hours worked. In the 1970 Census, annual hours worked is given by the product of weeks worked in 1969 and hours worked in the Census week, while, in the 1980 Census, annual hours is the product of weeks worked in 1979 and usual hours worked per week in that calendar year. Forty-one countries were chosen for analysis. These countries were selected on the basis that both the 1970 and the 1980 Censuses contained a substantial number of migrants from that country. In particular, it is necessary to have at least eighty observations of persons born in a particular foreign country in the pooled UlOO 1970 Census to enter the sample of forty-one countries. The countries thus chosen account for over 90 percent of all immigration to the United States between 1951 and 1980. It must be noted, however, that this restriction omits some countries that during the late 1970s became important source countries (e.g., Vietnam). Since two Censuses are required for the complete identification of the parameters of the model presented in section 1.2, however, a systematic analysis of the relative earnings of these migrants will have to await the 1990 Census. Table 1.1 begins the empirical analysis by presenting the unstandardized

44

George J. Borjas

differential between the log wage rate of the various migrant groups and ‘hatives.” In these statistics, the native population is defined as the group of U.S.born white, non-Hispanic, non-Asian men aged 25-64. Perhaps the most striking finding in the table is the fact that migrants from European countries tend to have wage rates that often exceed the wages of white natives, while migrants from Asian or Latin American countries tend to have wage rates that are substantially below those of white natives. Table 1.1 also presents the relative earnings of the 1965-69 cohort of migrants as of 1970, the relative earnings of the same cohort in 1980, and the relative earnings of the 1975-79 cohort as of 1980. These statistics yield important insights into the process of assimilation (the rate at which the earnings of migrants and natives are converging) and into the extent of productivity differences across successive cohorts. The “tracking” of the 1965-69 cohort across Censuses shows that the relative earnings of this cohort of migrants improved over time for most national groups. At the same time, the comparison of successive immigrant cohorts (i.e., the comparison of the 1965-69 cohort as of 1969 and the 1975-79 cohort as of 1979) shows that for some countries the relative earnings of migrants increased while for other countries the relative earnings of migrants decreased substantially. For example, the most recent migrant from France in 1970 was earning about 8 percent less than natives at the time of entry, while the most recent migrant from France in 1980 was earning about 22 percent more than natives at the time of entry. Conversely, the most recent migrant from India in 1970 earned about 4 percent more than white natives at the time of entry, but the most recent migrant from India in 1980 was earning 21 percent less than white natives at the time of entry. Table 1.2 continues the descriptive analysis by presenting the mean (completed) education level of four different cohorts of immigrants that arrived in the 1960-80 period. Since the education data available in the Census does not differentiate between education obtained prior to immigration and education obtained in the United States after immigration, the mean education levels for the 1970-74 and 1975-79 cohorts are obtained from the 1980 Census, and the mean education levels for the 1960-64 and 1965-69 cohorts are obtained from the 1970 Census. This use of the available data is designed to minimize the contamination of the education variable by postmigration schooling. The statistics in table 1.2 are consistent with the well-known secular increase in education levels over time for practically all migrant cohorts. It is worth noting, however, that for some countries the increase in education has been quite small (e.g., Portugal) while for others (e.g., Norway) it has been amazingly large. As the theoretical analysis in section 1.1 shows, these truncated education means can be understood only in terms of the population means of the education distribution in the countries of origin. To provide some insights into the extent of self-selection on the basis of education, table 1.2 also presents mean education levels calculated for the population in the countries of origin. The mean education level for the 1960s is calculated using

45


Table 1.1

Unstandardized U.S. Earnings of Immigrants Relative to White Natives

1970 Country Europe: Austria Czechoslovakia Denmark France Germany Greece HwwY Ireland Italy Netherlands Norway Poland Portugal Romania Soviet Union Spain Sweden Switzerland United Kingdom Yugoslavia Asia and Africa: China Egypt India Iran Israel Japan Korea Philippines Americas: Argentina Brazil Canada Colombia Cuba Dominican Republic Ecuador Guatemala Haiti Jamaica Mexico Panama Trinidad and Tobago

1980

All 1965-69 Immigrants Cohort

Sample All 1965-69 Size Immigrants Cohort

1975-79

Sample Size

,1969 ,1229 ,1208 ,1109 .I600 - ,1722 .1304 - .0369 - ,0150 .OW3 ,1653 .0392 - ,1913 .1153 ,0813 - ,1572 ,1485 ,2424 .I669 ,0353

,2182 ,0466 ,1803 - ,0766 ,1095 - .3704 - ,0631 - ,0260 - ,1707 .1412 ,2629 - ,0952 - ,2406 - .I915 - .I048 - ,3480 .2573 .0095 .1902 - .I382

380 398 141 317 2,399 634 650 754 3,068 430 243 1,629 349 259 907 210 221 177 2,231 646

,2108 ,1483 .2387 ,1071 ,1577 - .I874 ,1059 ,0688 - ,0124 ,1717 26.96 ,0165 - .2104

-.1258 .0273 ,4241 ,2237 ,2646 - ,3392 - ,1805 - ,1421 - ,1616 ,2824 ,2444 - .3698 - ,3240 - ,2913 - ,2856 - ,2143 ,1617 .4735 .1924 - ,1706

746 872 291 952 6,499 2,328 1,356 1,580 7,236

- .0533 - .0417 ,2392 ,3307 ,2111 ,0546

,3598 ,1141 ,3570 ,1158 ,2350 - ,2556 ,1027 ,0737 - ,0790 ,2179 .4183 .0207 - .1949 .0928 - .0578 - ,0184 .4570 ,2121 ,3188 - ,0191

408 3,278 2,213 614 2,104 730 335 397 5,475 1,967

- ,1543 - ,0073 ,1667 - ,0116 ,0707 ,0535 - ,0781 - ,1920

- .3459 - .2127 ,0413 - ,3556 -.I951 .0519 - .2183 - ,2389

880 136 363 121 141 228 142 816

- ,2212 ,0737 ,1221 - .0545 - ,0274 ,1362 - ,0881 - ,0707

- .1324 .3222 ,4050 ,1375 - ,0392 ,1492 ,2409 ,0694

- S372 - ,2892 - .2085 - ,2237 - .2483 .2020 - .3007 - ,3143

3,875 6% 3,629 1,027 789 1,634 2,013 4,955

,0319 .0212 ,1072 - .I452 - ,2822

- ,1644 - ,0993 ,1084 - ,2337 - .4461

218 - ,0096 101 ,0485 ,1258 3,430 254 -.2313 1,960 - ,1828

.0086 ,1407

- .2027 - .2698

,1440

- .1428 ,0481 ,1739 - ,4464 - .5392

834 345 7,083 1,760 6,837

- .3576 - ,2343 -.I940 - ,3041 - .I645 - ,4094 - ,0187

- ,5157 - ,4511 - ,5372 - ,3061 - .2462 - .6021 - .1899

210 174 82 130 263 3,122

- ,4768 - .2473 - .3425 - ,3726 - ,2132 - .3975 - ,0761

- ,4319 - ,2858 - ,2182 - ,2296 -.I245 - .3431 - ,1263

- ,6785 - ,5229 - s977 - ,6536 - .3604 - .6402 - ,3663

1,605 1,097 723 1,133 2,061 24,955 584

- ,1561

- .2909

86

- .1488

- ,0685

- ,4150

782

101

.0551

Cohort

1,161

46

George J. Borjas

Table 1.2

Completed Years of Schooling in Immigrant Cohorts Mean Education in Population

Year of Arrival Country Europe: Austria Czechoslovakia Denmark France Germany Greece Hungary Ireland Italy Netherlands Norway Poland Portugal Romania Soviet Union Spain Sweden Switzerland United Kingdom Yugoslavia Asia and Africa: China Egypt India Iran Israel Japan Korea Philippines Americas: Argentina Brazil Canada Colombia Cuba Dominican Republic Ecuador Guatemala Haiti Jamaica Mexico Panama Trinidad and Tobago

1975-79

1970-74

1965-69

1960-64

1970s

1960s

14.8 15.4 15.5 15.6 15.2 11.1 13.6 13.8 10.6 15.9 15.2 12.7 6.6 13.7 14.3 13.2 15.4 15.4 15.1 11.0

13.9 14.5 13.6 14.8 14.2 9.9 13.5 13.1 8.5 15.1 15.6 11.9 6.7 14.5 13.5 11.3 15.8 15.4 14.7 10.6

13.4 14.1 16.1 14.5 13.3 8.8 12.3 12.9 6.8 14.1 14.0 10.7 5.2 11.6 10.5 10.3 15.5 14.5 13.7 10.7

12.8 12.5 11.6 12.8 12.0 10.9 12.6 11.3 7.5 12.3 11.7 9.5 5.8 11.9 11.3 9.9 14.4 13.6 13.1 9.4

8.7 10.2 11.2 11.1 10.7 9.2 10.6 9.1 9.1 10.4 9.9 11.2 8.2 9.5 11.4 8.0 10.3 8.7 10.8 9.7

6.7 9.1 8.5 7.0 10.1 6.2 7.2 8.1 5.6 8.8 7.2 7.0 3.5 5.3 8.1 4.4 8.7 6.7 9.9 3.5

11.3 15.9 16.1 15.2 14.2 15.7 14.0 14.2

12.8 16.2 17.6 16.3 13.8 14.7 14.9 14.9

12.8 15.5 16.7 15.3 13.5 15.4 15.8 14.8

13.2 15.1 17.0 15.5 14.0 15.0 16.5 13.9

8.4 5.7 4.9 3.6 9.8 11.2 8.0 8.2

4.3 4.0 2.2 1.3 7.0 9.2 5.0 5.1

13.6 15.4 14.6 11.9 11.3 8.9 10.9 9.0 10.2 11.3 6.5 13.1 11.7

12.1 13.1 13.7 11.3 9.9 9.1 11.0 9.7 12. I 10.9 6.8 12.7 12.0

12.0 12.6 12.9 10.6 9.5 8.4 10.4 9.9 12.0 10.7 6.1 12.4 11.0

12.6 12.8 11.4 11.5 11.9 7.9 11.3 12.0 11.2 10.6 6.0 11.1 14.4

8.7 8.6 10.3 5.0 8.3 6.2 6.2 2.9 3.2 9.5 6.1 10.1 7.9

6.3 2.8 8.5 2.2 4.1 3.6 3.4 1.5 1.7 4.5 2.9 5.9 7.1

47


enrollment data in the various countries of origin during the 1950s, while the mean education level for the 1970s is calculated using enrollment data in the various countries of origin during the 1960s. The “lagged” construction of the variable giving mean education levels in the country of origin is designed to account for the fact that, in the samples used here, the average person migrated at about age 20. The relevant education distribution, therefore, is given by that of persons enrolled in school a few years earliec9 The means in table 1.2 present a remarkable picture. Even after allowing for the substantial errors involved in calculating the population means for each country of origin, the truncated means are almost always much greater than the population means. For example, the mean of education in Haiti is about three years, but the most recent Haitian immigrants report ten years of education in the 1980 Census. Surprisingly, the two statistics are most similar for Mexico, where both immigrants and the Mexican population have 6-7 years of education. Overall, table 1.2 suggests that immigrants are positively selected on the basis of education. The model presented earlier implies that this result is consistent with the hypothesis that the “rate of return” to education is greater in the United States than in most countries of origin. However, it is also consistent with the hypothesis that migration costs are lower for persons with higher education levels. This conjecture has received intensive study in the internal migration literature (Schwartz 1968). 1.3.2 Basic Regression Results The regression model in equations (26) and (27) was estimated on each of the 41 countries under analysis using the pooled 1970 and 1980 Census data. As noted earlier, the choice of the native baseline is an important step in the estimation procedure. In this section, the reference group is chosen according to the racial/ethnic background of the population of each country of origin. The estimation uses the white, non-Hispanic, non-Asian sample of native men as the reference group for migrants from Europe, Canada, and the Middle East. The group of Asian natives is the reference group for migrants from all other Asian countries. The group of Mexican natives is the reference group for Mexican migrants, and the group of “other Hispanic” men is the reference group for persons from all other Spanish-speaking countries in the American continent. Finally, the group of black natives is the reference group for migrants from countries with predominantly black populations (i.e., Haiti, Jamaica, and Trinidad and Tobago). The definition of the reference group in terms of the racial/ethnic background of the immigrant population is a simple way of specifying different period effects for the various immigrant groups. Presumably, the effect of changes in aggregate economic conditions on immigrant earnings is likely to be better approximated by the period effects experienced by populations that closely resemble the immigrant group. It is important to note, however, that, although the baseline populations differ across the 41 countries, the calcula-

48

George J. Borjas

tion of the present value differentials defined in equation (32) will always be relative to white, non-Hispanic, non-Asian natives (as in table 1 . 1 ) . In other words, the use of alternative reference groups is simply used to “net out” the period effect in the 1980 Census, and, after controlling for period effects, all comparisons between migrants and natives are conducted with respect to the “white” population. Initially, the regression model is estimated using a detailed list of demographic controls. These include education, marital status, health status, and metropolitan residence (as well as age and age squared). The calculated present value differentials estimated from the 41 runs of the model are presented in table 1.3 for each of the 6 cohorts identifiable in the Census data. It is worth stressing that these present value differentials measure the differences in earnings among migrants and white natives of equal measured skills and hence are empirical counterparts to the quality measure Q, defined in terms of unobserved characteristics. Table 1.3 shows that there are substantial differences in the “abilities” of migrant groups across the 41 countries of origin. Immigrants from European countries (particularly Western European countries) tend to do quite well relative to white natives of comparable socioeconomic characteristics. Recent immigrants from the United Kingdom, for example, can expected about 10 percent larger earnings over their lifetime than comparable white natives, recent immigrants from France will earn about 8-19 percent more than comparable white natives, and recent immigrants from Sweden will earn about 1020 percent more than white natives over their lifetime. On the other hand, immigrants from most Asian and Latin American countries do not perform well in comparison to white natives of equal observable skills. Recent immigrants from Taiwan, for example, will earn about 16-34 percent less over their lifetime than comparable white natives, immigrants from Israel will earn about 20-30 percent less, immigrants from Argentina about 20 percent less, and immigrants from Colombia about 24-38 percent less. An immigrant’s birthplace plays an important role in determining the type of selection that characterizes the migrant flow. In addition, table 1.3 shows that, even within a given country of origin, there are sizable differences in the unobserved quality of immigrants across the various cohorts. The quality of immigrants from some countries has been increasing rapidly, while the quality of immigrants from other countries has been declining rapidly. For instance, the most recent French immigrants have a higher earnings potential than earlier cohorts (particularly those arriving before 1970), while the most recent Polish migrants have much lower earnings potential than migrants of earlier cohorts. Similarly, the most recent Canadian immigrants earn about 8-15 percent more than most of the earlier cohorts, while the most recent Mexican immigrants earn about 9-13 percent less than the earlier Mexican cohorts.I0

49


lhble 1.3

Present Value Differentials between Immigrants and Natives Year of Arrival

Country

Europe: Austria Czechoslovakia Denmark France Germany Greece Hungary Ireland Italy Netherlands Norway Poland Portugal Romania Soviet Union Spain Sweden Switzerland United Kingdom Yugoslavia

(continued)

1975-79

1970-74

1965-69

- ,0841 ( - .74) - ,0141 (-.14) ,4432 (2.76) .I879 (2.29) ,0733 (1.69) -.lo60 (-2.00) ,1542 (- 1.94) ,1267 (1.58) .0498 (1.30) .2815 (3.66) ,1880 (1.45) - .1926 (-4.11) .0293 (.5u - ,2030 (-2.12) - ,2641 (-4.42) ,1047 (1.17) ,1141 (1.01) ,2395 (2.15) ,1052 (3.11) ,0602 (.93)

,1344 (1.25) - ,0546 ( - .73) ,0623 (.35) ,0829 (1.15) .0638 (1.50) - .I818 ( - 5.08) -.I132 (- 1.81) ,0817 (1.39) ,0424 (1.75) - .O917 (-1.11) .2468 (1.56) ,0727 (1.95) ,0348 (.82) .0911 (1.21) - .03W

,1945 (2.84) - .0036 (-.lo) ,1522 (1.45) - .0415 ( - .74) ,0385 (1 .a) -.I344 (-4.40) - ,0128 ( - .26) .1758 (3.75) .0693 (3.77) .0936 (1.69) .1757 (1.74) .0784 (2.65) ,0785 (2.31) - .0050 (-.lo) - .0332 ( - .57) .0518 (.96) ,2205 (1.97) .I407 (1.69) ,0948 (4.19) .0625 (1.94)

( - .55)

,1287 (2.01) ,1621 (1.15) .lo71 (.93) .0910 (2.88) .0746 (1.84)

1960-64 ,0707 (1.33)

.m

1950-59

< 1950

- .o004

- .0312 (-

( - .01)

,0182 ~42) - ,0434 ( - .65) - ,0626 (-1.56) ,0150 (.97) - .0402 - .0381 (-1.10) (-1.39) - ,0389 .0380 ( - .86) (1.45) - .0252 .0676 ( - .84) (2.14) .0839 .0695 (5.10) (5.04) ,0264 - ,0422 ~ 7 0 ) (-1.40) ,2017 ,1437 (2.55) (2.48) .0387 ,0526 (1.66) (2.44) .0954 ,0871 (2.18) (2.44) - ,0253 ,0534 ( - .39) (1 .04) - ,0456 ,0203 (-1.06) (.68) - .0022 - ,1186 ( - .01) (-2.22) ,0721 .o001 (.78) (.01) ,0967 ,0594 (1.41) (1.05) ,0449 ,0098 (2.32) (.%I ,1389 ,1089 (3.90) (4.15) (1.01) - ,0010 ( - .01) -.I179 (-2.57) ,0115

.05%

(.W . I 105 (.94) .0539 (.81) ,1174 (4.26) - ,1230 (-2.28) ,1441 (2.55) - ,2171 (-4.82) ,0627 (2.48) -.I736 ( - 2.77) - .0290 ( - .35) .0764 (2.31) .1746 (2.11) - ,0041 ( - .01) ,0322 (.67) - ,1001 ( - .94) ,0153 (.14) .0264 (.26) - ,0432 ( - 1.47) .0237 (.39)

50

George J. Borjas

Table 1.3

(continued) Year of Anival

Country Asia and Africa: China Egypt India Iran Israel Japan Korea Philippines Americas: Argentina

1975-79

1970-74

1965-69

- ,3662 - ,3362 (-7.58) ( - 10.26) -.I186 - ,1597 ( - 1.70) ( - 1.46) - .3365 - .1635 (-5.28) (-2.94) - .0084 .075 1 (-.lo) (.77) - ,3304 - ,2346 ( - 3.50) ( - 3.32) ,0741 - ,0145 ( - .22) (.95) - ,1840 -.I162 ( - 2.00) ( - 1.51) - ,0778 -.I884 ( - 2.53) (-4.01)

- ,2274 ( - 8.62) - ,0588 ( - .82) - ,0497 ( - .98) .0215 (.32) - ,2766 ( - 4.18) - ,0496 ( - .81) - ,0966 ( - 1.31) - ,0689 ( - 2.75)

- ,1842

- ,1908 ( - 3.10) .0623 (.75) ,1149 (5.27) -.I562 ( - 2.65) - .I366 ( - 6.08) - .0399 (-2.75) - .2348 (-3.07) - ,2551 (-3.32) - .1227 ( - 1.56) - ,1078 (-2.72) - .0399 ( - 2.75) - ,1267 ( - 1.80) - .0438 (-.W

- ,0822 (-1.47) .0782 (.88) ,0681 (4.24) - ,0614 ( - 1.11) - ,0687 (-3.52) - ,0338 (-2.39) - ,0657 ( - .89) - ,2085 (-2.33) - ,0189 ( - .22) - ,2182 (-4.51) - ,0338 (-2.39) - ,0972 (-1.47) - ,0002 (- .01)

- .2537 (-2.97) Brazil .0679 (.54) Canada .1440 (4.07) Colombia - ,3764 (-4.99) Cuba - ,2711 (-5.89) Dominican Republic - ,1566 ( - 5.73) Ecuador - ,2965 (-3.16) Guatemala - ,3163 ( - 2.68) Haiti - ,4721 (-4.81) Jamaica - ,2958 ( - 4.48) Mexico - ,1566 ( - 5.73) Panama - ,2717 ( - 2.20) Trinidad and - ,2433 Tobacco ( - 2.15)

- .2723 (-3.69) - ,0944 ( - .91) ,0497 (1.51) - .2372 ( - 3.62) - .0850 ( - 2.92) - ,0628 ( - 3.52) -.1742 ( - 2.08) - ,2695 (-3.17) - ,2447 ( - 2.85) - ,1505 ( - 3.24) - ,0628 ( - 3.52) - ,0221 ( - .24) - .0774 ( - .95)

Note: The t-ratios are presented in parentheses

1960-64

(-6.88) - ,0980 (-1.34) ,0391 (.78) - ,0470 ( - .71) - ,1978 (-3.10) - .0401 ( - .71) - ,0738 (-1.04) - ,1075 ( - 3.26)

1950-59

0) = 13.02 .23 p, -9.76r, (9.78) (1.90) ( - 2.44)

R2 = .236,

E(s I I > 0) = 8.01 + .66p,, -9.82r - 1.44 AGNP, R2 = .421 (4.81) (4.51) (-2.79) (-4.24)

where the dependent variable is the mean educational attainment of the immigrant group in the United States, ps is the mean educational attainment in the source country, r is the rate of return to education in the source country, AGNP is the percentage difference in per capita GNP between the source country and the United States at the time of migration, and the t-statistics are presented in parentheses. The most important finding in these regressions is the significant negative effect of the rate of return to schooling in the source country on the educational attainment of the immigrant pool. A 10 percentage point increase in the rate of return to schooling decreases the mean educational attainment of the immigrant group by almost one year. The key implication of the Roy model, therefore, is confirmed by the data. The educational composition of the sample of immigrants is determined by the relative payoff to schooling in the source country. The regressions also indicate that the mean level of educational attainment in the country of origin has a positive effect on the mean educational attainment of immigrants and that the coefficient, as predicted by the theory, is between zero and one. I should add, however, that this confirmation of the theory-like the results regarding the rate of return to schooling-should be treated with some caution because the data on international differences in mean education levels and rates of return are measured with substantial error.

55


1.3.5 The Unstandardized Wage Differential between Immigrants and Natives Up to this point, the empirical analysis has focused on ascertaining the types of selection that occur on the basis of unmeasured skills and on the basis of educational attainment. As noted in the theoretical section, selection occurs along every single dimension of skills that is valued by the labor market, with particular skills flowing to countries that value those skills the most. As a result of these selection processes, the actual earnings of the typical immigrant in the United States are likely to differ substantially from the earnings of the typical white native. To determine the effect of self-selection in the various dimensions of skills on the earnings gap between immigrants and natives, the 41 regression models were reestimated without including any demographic characteristics in the vector X (except, of course, age and age squared so as to trace the ageearnings profile over the working life). The resulting present value differentials are presented in table 1.6. As can be seen by comparing tables 1.3 and 1.6, the relative earnings of some immigrant groups are lowered significantly when no standardization for demographic variables is conducted. For instance, the most recent immigrant cohort from Mexico earns about 47.8 percent less than white natives, but they earn only about 15.7 percent less than demographically comparable white natives. On the other hand, the most recent cohort of immigrants from the United Kingdom earns about 18.8 percent more than white natives, but only about 10.5 percent more than demographically comparable white natives. In other words, some immigrant groups have demographic characteristics that are much less valuable than those of natives, while other immigrant groups have demographic characteristics that are much more valuable than those of the typical native. It turns out that the same country-specific variables that explain the variation in standardized earnings differentials among source countries also explain the variation in the unstandardized differentials. Table 1.7 analyzes the determinants of the intercountry variation in unstandardized immigrant earnings (analogous to the second-stage regressions on standardized earnings presented in table 1.5). It is worth noting that, as before, immigrants originating in countries with high levels of income inequality have lower earnings than immigrants originating in other countries. This is not surprising since the level of income inequality can be interpreted as a summary index of skill prices. Thus, the key prediction of the Roy model is confirmed by the analysis of the actual earnings (as opposed to the standardized earnings) of immigrants.

1.4 Immigrant Sorting among Host Countries The last section showed that the labor market performance of immigrants currently living in the United States is strongly influenced by the economic and political characteristics of the country of origin at the time of migration.

56

George J. Borjas

Table 1.6

Unstandardized Present Value Differentials between Immigrants and Natives Year of Arrival

Country

Europe: Austria Czechoslovakia

1975-79

1970-74

1965-69

1960-64

1950-59

0). As time in the United States accumulates for an arrival cohort, the earnings disadvantage caused by being a member of a large cohort evaporates (p. > 0), as do the cross effects of cohort size on adjacent

+

The Effects of Immigration on Wages: Linear Restrictions on Own and Intercohort Substitution (dependent variable: log average weekly earnings in 1979)

Table 6.8

Parameter Model

Y

A

CL

4

F-Statistic for Restrictions

Nore: Model 1 controls include cohort, origin, occupation, and industry effects in addition to the

demographic controls listed in table 6.5. Model 2 drops occupation from the set of controls, and model 3 drops industry and occupation. The reported F-statistics test the restricted four-parameter model relative to the completely unrestricted model with thirty parameters. Dependent variable is log weekly wages; standard errors are in parentheses. For definitions of parameters, see the text.

184

Robert J. LaLonde and Robert H. Tope1

arrivals (4 < 0).I9All these estimates are consistent with immigrant crowding in local markets, tempered by assimilation and imperfect substitution. 6.3.2 Annual Earnings versus Wages: Do Quantities Matter? The analysis to this point has focused only on market clearing price adjustments with inelastic labor supply. However, if immigration also causes quantity adjustments in terms of unemployment, hours, or weeks worked, then annual earnings may be a more appropriate measure of welfare. A detailed analysis of adjustments on each of these margins is beyond the scope of this paper (see Altonji and Card, in this volume). Yet quantity and price adjustments are likely to be correlated, so the effects of immigration on annual earnings may be larger than on wages. The estimates in tables 6.5-6.8 would then underestimate the distributive effects of immigration. To explore this possibility, table 6.9 reproduces the estimates in tables 6.6 and 6.8 when log annual earnings in 1979 instead of log average weekly earnings is used as the dependent variable. The estimates from the unrestricted model of the effect of a proportional increase in all immigrant groups, in part A of the table, are slightly larger than the corresponding estimates in table 6.6 (the most recent arrival cohort is an exception). For the specification in row 2 of table 6.9, part A, the effects on earnings exceed those on wages, on average, by about a third, though the standard errors are large enough that equality of effects cannot be rejected. Thus, it appears that the main distributive effects of immigration operate through price flexibility rather than through adjustments in unemployment or participation. This conclusion is reinforced by a comparison of the estimates in part B with those of table 6.8, which report restricted estimates of substitution parameters. The estimates for wages and annual earnings differ only trivially. On this evidence, we conclude that the main actor in market adjustments to immigration must be wage flexibility. Adjustments in unemployment or participation are negligible. 6.3.3 The Effects of Immigration on Young Native Blacks and Hispanics

To this point, we have treated all nonimmigrants as a single aggregate, while focusing on substitution possibilities among immigrants. For these groups, the effect of immigration on measures of welfare are quite small. Even so, some groups of native Americans may be more sensitive to the crowding effects of immigration than others, and for them the implied redistributive effects are of some concern. Here, we focus on two identifiable groups who may face the most important crowding effects of immigration: young (aged 16-34) blacks and Hispanics. We treat young blacks and Hispanics as separate inputs that interact with immigrants in local production (see eq. [ 13 above). The unrestricted matrix of estimated substitution effects now contains fifty-six parameters, and it is not very informative. As above, we may calculate the effect of a scale (d In M i=

185

Labor Market Adjustments to Increased Immigration

Table 6.9 A. Effects on Log Annual Earnings of a Proportional Increase (d In M i= 1) in All Immigrant Cohorts, Unrestricted Substitution Effects (dependent variable: log annual earnings in 1979) Years Since Immigration Model

0-5

6-10

11-15

16-20

2 1-30 ~~

1 2 3

- .089

(.032) - .093 (.033) - ,091 (.033)

- .064 (.032) - .079 (.033) - ,074 (.033)

- .077 (.034) - ,085 (.034) - .078 (.034)

- ,066 (.036) - .071 (.037) - ,062 (.037)

~

- ,008 (.032) - .014 (.033) - ,008 (.033)

B. The Effects of Immigration on Earnings: Linear Restrictions on Own and lntercohort Substitution (dependent variable: log annual earnings) Parameter Model 1

2 3

Y - .026 (.008) - ,030 (.009) - ,028

(.ow

A ,008

CL

.009

(.ow (.ow ,009 .011 (.ow (.ow ,009 .011 (.ow (.ow

4


- ,003

,721

- ,004

,756

- ,004

,822

Note: See notes to tables 6.6 and 6.8. Standard errors are in parentheses.

1) increase in all immigrant cohorts on the wages or earnings of blacks and Hispanics. These estimated effects are shown in part A of table 6.10 for two specifications of the model.20Overall, there is only weak evidence that immigration reduces the wages and earnings of these natives. The largest estimates that we obtained are shown in row 1: the point estimate of the effect of a 170 percent increase in the size of all immigrant cohorts on black wages is only 2.4 percent, though the estimate is smaller than its standard error. The corresponding estimate for Hispanics is less than 1 percent. Surprisingly, in light of our previous results, the effects on earnings are slightly larger than on wages. Thus, there is some evidence of reinforcing adjustments on time worked, especially among young blacks. Again, however, these effects are not precisely estimated. An alternative strategy for examining these effects is to impose the restrictions given by (7) and (8) on the matrix of intercohort substitution terms among immigrants, while leaving own and cross effects for blacks and Hispanics as free parameters. To impose some structure, we allow black and Hispanic wages to be affected separately by immigrant cohorts that arrived before and after 1965. The hypothesis is that crowding effects of immigration are

186


Table 6.10

The Effects of Immigration on Wages and Earnings of Young Blacks and Hispanics

A. The Effects of a Proportional Increase in All Immigrant Cohorts (unrestricted models) Effect On: Model

Black Wages

Black Earnings

Hispanic Wages

1

- .024

- ,059

- .009

2

(.030) - .020 (.030)

(.035) - ,046 (.036)

(.032) - ,008 (.032)

Hispanic Earnings

- ,015 (.037) - .012 (.038)

B. Estimated Cross Effects of Immigrants on Blacks and Hispanics (linear restrictions imposed) Effect on Blacks of an Increase in:

Model Earnings: 1 2 Wages: 1

2

Native Blacks

Post-1965 Immigrants

Effect on Hispanics of an Increase in:

Pre-1965 Immigrants

Native Hispanics

Post-1965 Immigrants

Pre-1965 Immigrants

- .042 (.018) - .028 (.018)

- ,005

- ,014 (.018) - ,008 (.017)

,015 (.010) ,018 (.OlO)

- ,025

- ,042 (.015) - ,031 (.015)

,008 (.010)

- ,020 (.015) - ,014 (.015)

.007

- ,013

(.OlO) ,010 (.009)

- ,016

(.012)

- ,008

(.012)

,005 (.010)

(.015)

- ,030 (.015)

(.013) (.013)

Note: Part A parameter estimates refer to the effect of a unit change in log employment of all immigrant cohorts (d In M,= 1 for all i) on log wages or earnings of blacks and Hispanics. Part B estimates represent the effect of a unit change in log employment of the indicated group. Model 1 contains all demographic controls listed in table 6.8. Model 2 adds industry and occupation controls. Standard errors are in parentheses. Part B models constrain intercohort substitution matrix for immigrants to follow eqq. (7) and (8). Black and Hispanic effects are free parameters.

concentrated on these demographic groups and that recent immigration is the most important factor. We report (in pt. B of table 6.10) the own effects for both blacks and Hispanics as well as estimated cross effects with immigrants. In each case, we find crowding effects of blacks and Hispanics on their own wages; increases in the labor force shares of these groups reduce their wages, though only the estimate for blacks is significant. We also find that recent immigrants are substitutes for young blacks, though the effect is small ( - .01 is the largest estimate we obtained) and imprecisely estimated. It is substantially smaller than the own effect on black wages ( - .042). The estimates for Hispanics are more mixed. Finally, for neither group do we find important differences between the wage and the earnings estimates, suggesting that employment and hours adjustments are also minor concerns. Overall, these estimates do not suggest to

187


us that immigration is a prime factor affecting labor market outcomes for these young natives. 6.3.4

Results from the 1970 Census

According to Census data, immigration to the United States in the 1970s was roughly double its level in the 1960s. Because this sharp increase in the flow of new immigrants was highly geographically concentrated (see sec. 6. l ) , it is plausible that short-run labor market adjustments would generate a stronger relation between immigration and relative wages in the 1980 Census than in the 1970 Census. We examine this point in table 6.11, which summarizes estimates of the substitution effects from the 1970 Census. Because the story is not much different in these data, we report only the substitution effects of a proportional increase in all immigrant cohorts from the unrestricted model (eq. [5]) in part A of table 6.11 and the restricted form of intercohort substitution effects (eq. [S]) in part B. Table 6.11 A. Effects on Relative Log Weekly Wages of a Proportional Increase in All Immigrant Cohorts (unrestricted substitution effects, 1970) Years Since Immigration Model 1

2 3

0-5 ,012 (.022) .008 (.023) ,009 (.023)

6-10

11-15

16-20

2 1-30

- ,024

- ,007

- ,017

.ow

(.024) - ,025 (.024) - ,028 (.024)

(.027) - ,003 (.028) - ,006 (.028)

(.028) - ,024 (.029) - .017 (.029)

(.024) ,012 (.025) ,015 (.025)

B. Estimated Substitution and Assimilation Parameters for Log Weekly Wages of Immigrants (linear restrictions imposed, 1970) Parameter Model 1 2 3

Y - ,019

h

P

,008

,001

(.007)

(.003) .008 (.003)

- ,021 (.007)

,008 (.003)

- ,020

4J


- .0005

,861

- .o004

,834

(.001)

,001

(.001)

,001

- .oO04

,814

(.001)

Nore: Part A calculated from estimated substitution matrix for unrestricted models analogous to those in columns 1-3 of table 6.5. These results are comparable to those in table 6.6. For other controls in each model, see table 6.5. Calculations are based on a sample of 17,158 immigrants in 119 large SMSAs from the 1970 Census. Standard errors in parentheses. For part B, see notes to table 6.8. Dependent variable is log weekly wages; standard errors are in parentheses. The results when the dependent variable is log annual earnings are similar.

188


The estimates in part A should be compared to the corresponding estimates for 1980 in table 6.6. Whereas the 1980 estimates implied sharply lower earnings among new immigrants, the corresponding estimates for 1970 are negligible. For earlier arrivals, the estimates are negative though generally smaller than in 1980, and none are significant by conventional standards. These points are also apparent in part B; while all the substitution relations take the anticipated sign, only y is significant. The key point is that all these effects are substantially smaller than in the 1980 data (see table 6.8). The relation between the estimates generated by the 1970 and 1980 cross sections raises an important issue. Did the increased immigration of the 1970s generate the substantial crowding effects that seem to show up in the 1980 cross section? To answer this question, we create a pseudo-panel from the combined 1970 and 1980 Census files and analyze within-market changes in immigration, wages, and earnings. 6.3.5 Panel Estimates: Relative Wage Adjustments within SMSAs, 1970-80 The preceding econometric results rely on cross-sectional differences in labor force shares to generate price adjustments. Since labor is mobile in the long run, the existence of these wage differentials appears inconsistent with spatial equilibrium, so our interpretation of these results may be suspect. In light of this problem, we estimate equation (6), which pools the data from the 1970 and 1980 Censuses. We add to the model six hundred fixed effects that control for entry cohort (time in the United States) within each SMSA. Thus, the variation used to estimate substitution effects occurs over time and within SMSA-cohort cells. In effect, we ask whether areas that experienced unusually rapid immigration over the decade also experienced falling relative wages and earnings of recent immigrants and whether there were spillover effects of these changes on other groups.21 Results are summarized in tables 6.12 and 6.13. In table 6.12, we report models for the determination of log weekly wages and annual earnings that constrain intercohort substitution effects to follow (7). Each row of the substitution matrix is summarized by two parameters: an “own” effect of increasing cohort size on members of the cohort and a cross-cohort substitution effect that allows each cohort to have the largest effects on adjacent arrival cohorts. As above, we expect the former effect to be negative and the latter to be positive. The results are surprisingly similar to the cross-sectional estimates (see table 6.7), though standard errors are somewhat larger. In four of five cases, the point estimate of the own effect of cohort size is negative, with smaller effects on adjacent cohorts. Differences between the estimates for log weekly wages and annual earnings are small, which indicates again that the main effects of immigration are on wages rather than employment (weeks worked). Furthermore, the estimates show a tendency to “die out” as time in the United

189


Table 6.12

Wages Changes within Locales: The Effect of Immigrationon Changes in Wages and Earnings within SMSAs, Linear Restrictions on Intercohort Substitution, 1970-80 Dependent Variable Log Weekly Wage

Cohort: Years Since Immigration 0-5: Own effect

Cross effects

6-10: Own effect Cross effects 11-15: Own effect

Cross effects

16-20: Own effect Cross effects 21-30: Own effect

Cross effects Origin effects Cohort x SMSA effects Industry effects Occupation effects R2

(1)

- ,039 (.014) ,018

- .041 (.014) ,020

- .007

(2)

- ,034 (.014) ,016 (.006)

- ,036 (.014) ,018

- .009

Log Earnings (3) - ,049

(.016) .021

(.ow

- ,065

(.015) .036 (.009)

- ,002

(4)

- .045 (.015) ,020 (.006)

- ,061 (.015) ,020

- ,003

(.013) ,006 (.009)

(.013) ,007

(.014)

(.009)

(.010)

(.014) .003 (.010)

,032 (.018) - ,011 (.011)

,027 (.018) - ,008 (.011)

,047 (.021) - .019 (.013)

,042 (.021) - ,015 (.012)

- .010

- .010

- ,006

- .005

(.015) ,003

(.014) .001

,002

(.016) (.007)

(.016) .001 (.007)

,002

Yes Yes

Yes Yes

Yes Yes

Yes Yes

no no

Yes no

no no

Yes no

,258

,272

.243

,257

Note: For other regressors, see note to table 6.5. The models include a dummy variable for 1980. Standard errors are in parentheses.

States accumulates: effects of within-city changes in shares are stronger for more recent arrivals. In light of the last point, table 6.13 shows estimates for the most parsimonious specification, which restricts substitution terms to follow (8). These “panel” estimates should be compared to the cross-sectional results reported

190


Table 6.13

Wage Changes within Locales: The Effect of Immigration on Changes in Log Wages and Earnings within SMSAs: Linear Restrictions on Own and Cross-Substitution Effects, 1970-80 Log Weekly Wage (1)

(2) - ,020

(.009)

,012 (.004) ,003 (.002) - ,002 Origin effects Cohort x SMSAeffects Industry effects Occupation effects R2

Log Earnings (3)

(4)

- ,029 (.014) ,016 (.004) ,005

- ,027

- ,002

- ,002

(.010) ,015

,005

(.001)

(.001)

(.001)

Y e5 Yes no no

Ye5 Yes Yes no

Yes Yes no no

Yes Yes Yes no

.257

,272

,243

.256

Note: See note to table 6.8. Standard errors are in parentheses. N = 44,004

in tables 6.8 and part B of table 6.9. In light of our previously stated concerns, we are surprised that the panel and cross-sectional results are almost identical. All parameters are of the anticipated signs, with relative magnitudes that accord with theory. Our point estimates imply that a rough tripling (d In M = 1) of the rate of new immigration to an area would reduce the relative wages and earnings of new immigrants by 2-3 percent. Again, this crowding effect of membership in a large cohort dies out as U.S. experience accumulates, which indicates assimilation. Effects of new immigration on previous immigrants are smaller than the direct effects, which is indicative of imperfect substitution. 6.4

Conclusion

This paper has examined the effect of immigration on the labor market. Our basic finding is that increased immigration reduces the wages and earnings of immigrants and their close substitutes, though in our view the effects are not large. For immigrants themselves, a sustained doubling of the rate of new immigration may reduce relative earnings of new immigrants by about 3 percent, but even this effect tends to die out over time as immigrants assimilate to the American market. Labor market effects on nonimmigrants appear to be quantitatively unimportant: the wages and earnings of young blacks and Hispanics are not very sensitive to immigration. In short, our estimates imply that immigrants are rather easily absorbed into the American labor market. There

191


is little here to indicate that the redistributive effects of immigration should be a major policy concern. These conclusions are tempered by at least two points. First, our analysis has relied heavily on differences in wages across geographic areas. These differentials are difficult to rationalize as an element of a long-run equilibrium of the labor market. We argued that the upsurge of immigration in the 1970s was a change in labor supply that generated short-run wage adjustments among areas, and comparison of time-series and cross-sectional results tended to support this assumption. Second, our analysis mainly treated immigrants as a homogeneous group, and so we ignored the effect that specific immigrant groups may have. For example, in light of our results, it is plausible that illegal immigration from Mexico affects mainly young Hispanics. These points deserve attention, but we defer them to later research.

Data Appendix Selection and Construction of Variables The data used in this study were drawn from the 1970 and the 1980 U.S. Census of Population and Housing, Public Use Samples (see U.S. Bureau of the Census 1973, 1983). The samples include males sixteen to sixty-four years old, who were not attending school, who were currently in the labor force at the time of the Census, who had worked for pay during 1979, who were not institutionalized, and who were living in SMSAs identified on both the 1970 and the 1980 Public Use Samples. For 1980, we used the 1%-B Public Use Sample. For 1970, we used the l%5% questionnaire-county Group Public Use Sample. SMSA Definitions During the 1970s, the Office of Management and Budget changed the definitions of many SMSAs based on population and commuting patterns in the 1970 Census. These changes are published in “Standard Metropolitan Statistical Areas” (Office of Management and Budget 1976). We used this information to make the SMSA definitions in the 1980 and 1970 samples as comparable as possible. In principle, there are two ways to make these adjustments: (i) the SMSA definitions in the 1980 sample can be adjusted so that they conform to the 1970 definitions (see Altonji and Card, in this volume); (ii) the SMSA definitions in the 1970 sample can be adjusted so that they conform to those in 1980. Neither Public Use Sample provides enough information so that a user can redefine the SMSA definitions to make the two years exactly comparable. However, for most SMSAs, the changes do not add or

192

Robert J. LaLonde and Robert H. Topel

subtract many persons from the sample. The first procedure (i) is a little more precise, although it leads to a smaller sample size, while the second procedure (ii) is less precise but leads to a larger sample size. We tried both procedures and found that the results were robust to either method. All the results reported in the paper are based on the second procedure, where we redefined the 1970 SMSA definitions to make them comparable to the 1980 definitions. Adjusting the SMSA definitions is difficult because the Public Use Samples do not provide enough information on a household’s county group. Therefore, a user often does not know for sure whether some households are in a particular SMSA after a county (or a portion of a county) has been either added or subtracted between two Census years. In many cases, we drew a random sample of persons from a particular county (or group of counties if this was the finest level of identification) that corresponded to the share of persons in the area that was actually added or subtracted from the SMSA definition. This task is particularly difficult in New England and eastern Virginia. In a few cases, it was simpler and more precise to use the 1970 SMSA definitions as opposed to the 1980 definitions as the standard. This poses no problems for the analysis as the important thing is to have comparable SMSA definitions for the two years. Table 6A.1 presents a list of the 119 SMSAs used in the analysis, along with the shares of all immigrants and recent immigrants in both 1970 and 1980 and the 1980 shares of young (16 to 34 years old) blacks and Hispanics in each SMSA’s employed labor force. Note that the share of employed young blacks seems small in large SMSAs. This fact, however, is due to the concentration of blacks in the central cities. For example, in Chicago, blacks are concentrated in the city, whereas there are fewer blacks in heavily populated suburban Cook, Lake, and DuPage counties. In southern SMSAs, a much larger share of the outlying population is black. Variable Definitions We used two measures of earnings as dependent variables, weekly wages and annual earnings. Annual earnings is the sum of wage and salary income and self-employment income. We excluded persons who reported that their self-employment earnings where negative. Weekly wages are defined as annual earnings divided by weeks worked in 1969 and 1979. Two potential problems with these earnings data are that (i) earnings are reported up to a maximum of $50,000 in 1970 and $75,000 in 1980 and (ii) in 1970 weeks worked is reported in discrete intervals. For practical purposes, the “top coding” problem seems to be minor. In 1980, 1.2 percent of the immigrants, ,1 percent of the young black males, .1 percent of the young Hispanic males, and 1.2 percent of all other native workers had either wage or salary income or self-employment income that was greater than $75,000. In 1970, .6 immigrants had wage or salary income or self-employment income that was greater than $50,000. To resolve the problem in the weeks worked data for 1970, we inputted weeks worked for each person based on the mean

193


Table 6A.1

Share of Immigrants and Young Native Blacks and Hispanics in Large SMSAs Proportion of Employed Male Labor Force ~~

Immigrants in

Immigrants in

1970

1980

Natives 16-34 Years SMSA

All

Recent Arrivals

AKRON,OH

,045 .046 ,027

,013 ,010 ,012

.030 .040 ,043

,003 ,012 ,021

.03 1 .012 ,005

,002 ,003 .183

,034

,005

,036

,010

,008

,012

,082 ,023 ,009 ,006 ,021 ,066 ,034 .008

,033 ,007 ,004 ,002 .005

,161 ,022 .025 ,017 ,036 ,097 ,033 ,018

,096

,007 .Ooo ,102 ,126 ,041 ,016 ,089 ,123

.036 .Ooo ,002 ,006 ,078 ,077 ,002 ,003

.077 ,001 ,099 ,016 ,020 ,023 .020 ,122 .092 ,056 ,060 .049 ,055 .143 ,047 .013 .058

,012 ,001 ,002 ,005 .036 ,005 ,003 ,007 ,001 ,001 .019 .002 .007 ,006 .002 .228 .029

.019 .035 .02 1 ,021

,014 .001 .040

ALBANY-SCHEN-TROY ,NY ALBUQUERQUE,NM

All

Recent Arrivals

Blacks

Hispanics

ALLENTOWN-BETHEASTON ,PA-NJ ANAHEIM-SANTA ANA-GRDN GVE,CA APPLETON-OSHKOSH ,WI ATLANTA,GA AUGUSTA,GA-SC AUSTIN,TX BAKERSRELD,CA BALTIMORE,MD BATON ROUGE,LA

,018 .010 ,003

,009

,011

.006

,014 .047 .012 ,006

BEAUMONT-FT ARTHURORANGE,TX BINGHAMPTON ,NY-PA BIRMINGHAM,AL BOSTON,MA BRIDGEPORT,CT BUFFAL0,NY CANTON,OH CHARLESTON,SC CHARLOTTE,NC CHATTANOOGA,TN

,014 ,041 ,003 ,089 ,100 .059 ,023 ,014 ,015

.004

CHICAGOJL

,096

CINCINNATI ,OH-KY-IN

,018 ,067 .003 ,012 ,031 ,020

CLEVELAND,OH COLUMBIA,SC COLUMBUS,OH CORPUS CHRIST1,TX DALLAS-FORT WORTH ,TX DAVENPT-ROCK I S MOLINEJA-IL DAYTON'OH DENVER,CO DES MOINES,IA DETROIT,MI DULUTH-SUPERIOR,MI-WI EL PAS0,TX ERIE,PA FLINT,MI

(continued)

,019 .014 ,036 ,021 ,074 ,033 ,196 .025 .045

,001 ,013 ,002 ,029 .033 ,011

,006

.002 .007 .001 .034 ,005 .017 .Ooo .003

,006 ,006

.004 ,006 ,013

.004

,016 .002 .049 ,005 ,017

,023 ,034 ,010 ,095 ,086

,064

.018 ,015 ,022 ,010 ,132 ,024 ,059 .014 .021 .063 .054

,027 ,018 ,046 ,027 ,065 ,023 ,233 .017 .014

,013 ,012 ,003 .037 .026 .017

,004 ,004 ,010 ,007 .067 ,007 .018 .006 .009 .026 ,035 ,012

,004

,020 ,020 ,017 ,005 ,127 .005

,004

,068

.Ooo ,006 .019 ,061

.004 ,005 .Ooo .209 ,002 ,010

194

Robert J. LaLonde and Robert H. Topel ~

Table 6A.1

~____

~

(continued) Proportion of Employed Male Labor Force

Immigrants in 1970

Immigrants in 1980

Natives 16-34 Years SMSA

All

Recent Arrivals

Recent All

Arrivals

Blacks

,015

Hispanics

FTLAUDERDALEHOLLYWOOD,FL FRESN0,CA

,053 ,080

.026 ,019

.084 ,137

.034 ,069

.062 ,032

,012 .004

,055 .03 1

,016 .007

,071

,005 .016 ,019 ,114 ,032

.001

.016 ,019 .019 ,095 .I07

.007 .007 .011 .028 ,074

,017 .055 ,026 ,020 .075

,005 .014 .043

,044

.013 .099

GARY-HAMMONSEAST CHICAGOJN GRAND RAPIDS,MI

.026

.026 ,007

GREENSBOReWSTN-SLMHIGH PT,NC GREENVILLE,SC HARRISBURG,PA HARTFORD,CT HOUSTON,TX

,005 .001 ,040

,014

.002 ,001

HUNTINGTON-

LANSING,MI

,007 .013 ,006 ,020 ,193 .009 ,016 .005 ,011 ,023

LAS VEGAS,NV

,040

ASHLAND,WV-KY4H JNDIANAPOLIS,IN JACKSON,MS JACKSONVILLE,FL JERSEY CITY,NJ JOHNSTOWN,PA KANSAS CJTY,M*KA KNOXVILLE,TN LANCASTER,PA

.002

,003

,002 .006 ,128 .Ooo .006 .Ooo .003 .Ooo ,020

.012

.003

,009

.004

,013 ,015

,004

,051 .I64

,001

.019 ,294 ,009 .022 ,018 .023 ,025 .085

,005

,006 ,153 ,004 .009 ,007 ,010

,009 ,036

,074 ,037 .004 ,044

.017 ,003 ,019 ,046

,002 .007 ,059 .002 .008 .002 .012

,012 ,024

LITTLE ROCK-N LITTLE ROCK, AR LORAIN-ELYRIA,OH

,009 ,027

LOS ANGELES-LONG BEACH,CA LOUISVILLE,KY-IN MADISON ,WI MEMPHIS,TN-AR MIAM1,FL MILWAUKEE,WI MNPLS-ST PAUL,MN MOBILE,AL NSHVL-DAVIDSON,TN NEW HAVEN,CT NEW ORLEANS,LA NEW YORK,NY NEWARK,NI NWPTNWS-HAMPTON,VA NFOLK-F"TSMTH,VA OKLAHOMA CITY,OK ORLAND0,FL

.I35 ,008 ,038 ,013 ,264 .039 ,027 .011 .006 ,092 ,032 ,158 ,098 ,010 ,009 .009 .032

,002 ,005

.010

,004

,033

,005

,202 ,004 ,011 ,003 .230 ,009 ,011 ,003 .006 .020 ,020

,067

,266

.001

.008

,014 ,004 .249 ,006 ,008 .Ooo .003 ,029 ,016 ,065 ,036 ,002 ,001 ,003 .016

,033 ,010

.420 ,039 ,027 ,008 .013 ,072 ,044 ,205 ,137 .024 ,023 ,019 .044

.I01 ,064 .007 .009 .011 .021

,102 ,026

.044 ,041

,006 ,168 ,062 ,034 ,009 ,120 ,060 ,043 . I13 ,038 ,059 ,110 ,134 ,043 ,042

,003 .020 ,055

.Ooo ,006 ,001

.025 ,010 .004 .006 ,001 ,008

,013 ,032 .015 ,003 ,005

,009 ,011

195


Table 6A.1

(continued)

Proportion of Employed Male Labor Force

Immigrants in 1970

Recent Arrivals

Immigrants in

1980 All

Natives 16-34 Years

Recent Amvals

Blacks

SMSA

All

Hispanics

OXNAR>VENTURA,CA

,105

,053

,161

,078

,011

.053

,127 ,009 ,049 .041 ,036 .038 ,072 .026 .015 ,072 ,040

,046 ,001 ,011 ,010 ,006

,177 ,021 ,048 ,059 ,025 ,048 ,085 ,027 .023 ,056 ,031 ,068 ,019 ,204 ,032 ,085 ,138

,084 ,012 ,018 ,025 ,006 ,019 .041 ,012 ,007 ,014 .009 ,021 ,006 ,101

.044 .Ooo

.036 ,065

.033 ,020 ,054 .010 ,022 ,011 ,007 .006 ,128 ,028 ,018 ,020 ,055 ,010 ,007 ,023 .02 1

.027 ,200 .040

,164 ,142 ,107 ,065 .013 .042

.076 .076 .038 .021 .001 ,006

,040 ,017 .016 ,015 ,140 ,007

.032 ,063 .072 ,007 ,008 .006

,021 ,064 ,007 ,015 ,016 ,007 ,011 ,016

.010 ,011 ,020 ,019 ,038 .022

.014 ,066

PATERSON-CLIFTONPASSAIC,NI PEORIAJL PHILADELPHIA,PA-NI PHOENIX,AZ PITTSBURGH ,PA PORTLAND,OR-WA PROVIDENCE, RI READING,PA RICHMOND,VA ROCHESTER,NY ROCKFORD,IL SACRAMENT0,CA ST LOUIS,MC+IL SALINASMONTEREY ,CA SALT LAKE CITY,UT SAN ANTONI0,TX SAN DIEG0,CA

.064 ,016 ,121 .037 ,061 .079

,008 ,025

,008 ,005

,027 .006 ,018 ,004 ,038

,008

,014 ,033

,005

,008

.055 ,001

,006 ,002 .009 ,007 ,005 ,006 .037 ,003 ,058

SAN FRANCISCOOAKLAND,CA SAN JOSE,CA SANTA BARBARA,CA SEATTLE-EVERETT,WA SHREVEPORT,LA SPOKANE,WA

.126 ,098 ,104 ,066 ,007 ,045

,054 ,044

.042 ,020 .003 .012

SPRINGRELHHCPEESTOCKTON,CA

,069 ,121

SYRACUSE,NY

,040

TACOMA ,WA

.049 ,038 ,026 .076 .057

HLYKE,MA 0 in 1980) and earnings in the previous year (P37 = 0-500 in 1970; PlOl > 0 in 1980), two additional variables are defined: weeks worked in the previous year and earnings per week in the previous year. For 1980, these variables are constructed directly: weeks worked is measured by variable P95; and earnings per week is measured by P101/P35. (These calculations make no adjustments for allocated responses or truncation of the reported earnings figure.) For 1970, only interval measures of weeks worked and total annual earnings are available. We assigned midpoints of the intervals to the weeks and earnings figures and then constructed earnings per week as the ratio of the assigned values.

228

Joseph G . Altonji and David Card

Appendix B Matching SMSA Dejinitions between I970 and 1980 The Public Use Samples of the 1970 Census identify 125 individual SMSAs (see pp. 123-26 of the Description and Technical Documentation for the Public Use Samples of Basic Records from the 1970 Census). A total of 120 of these are used in our statistical analysis. Four SMSAs were deleted because of difficulty matching between 1970 and 1980 or because of too small sample sizes: Lorain-Elyria, Ohio; Johnstown, Pennsylvania; San BernadinWRiverside, California; and Wilkes Barre-Hazelton, Pennsylvania. The Fort Worth SMSA was merged with Dallas (see below). The Census Bureau publication Geographic Ident$cation Code Scheme (1983, 11-17) gives a detailed list of changes in the county-level definitions of SMSAs between 1970 and 1980. In most cases, these changes involve the addition of surrounding counties or parts of these counties to the SMSA. The major exceptions are (1) the combination of Dallas and Fort Worth into a single SMSA; (2) the creation of a separate SMSA consisting of Nassau and Suffolk counties of New York State (formerly part of the New York SMSA); and (3) the reclassification of Bergen County, New Jersey, from the PatersonClifton-Passaic SMSA to the New York SMSA. Our general matching strategy was to redefine 1980 SMSA boundaries to the 1970 boundaries. With only a few exceptions, this involved deleting individuals from the 1980 Census file who resided in counties that were classified as part of the SMSA in 1980 but not in 1970. For example, Montgomery County, New York, was added to the Albany-Schenectady-Troy SMSA in 1973. Individuals in this county were therefore deleted from the 1980 file. County-level information for each household is coded in the variable COGRP (location 6-8 of the household record) of the Public Use “A” Sample of the 1980 Census. County group codes are obtained from the 1980 County Group Equivalence File (1980 Census of Population and Housing, Public Use Micro Data Sample, part 77) and Appendix M of the 1980 Census Public Use Microdata Samples Technical Documentation. In most cases, individual counties are identified by one or more county group codes. For these cases, the deletion is accomplished by specifying the county group code(s) of those counties added to the SMSA after 1970. In some cases, only parts of a surrounding county group were added to the SMSA. In these cases, we randomly deleted a fraction of individuals from the added county or county group. The fraction of individuals deleted was set equal to the relative population of the part of the county added to the SMSA. Estimates of population for county subgroups were obtained from the 1980 County Group Equivalency File. In all, a total of forty-nine counties or county subgroups were deleted from the definitions of the 120 SMSAs. Another forty counties or county subgroups

229

Immigration and the Labor Market Outcomes of Less-skilled Natives

were partially deleted. The number of individual records actually affected by these deletion procedures is small. For example, of 244,941 immigrants identified on the 1980 Public Use A Sample using the 1980 SMSA definitions, 2,609 (1.07 percent) were deleted in the change to the 1970 definitions. A copy of the computer instructions that performed the deletions is available from David Card on request. To account for changes in the classification of Nassau and Suffolk counties in New York State, we added individuals in the Nassau-Suffolk SMSA in 1980 to the New York SMSA sample. To account for the changes in definition of the Paterson-Clifton-Passaic SMSA, we added individuals in the 1980 sample living in Bergen County, New Jersey (classified as part of the New York SMSA in 1980), to the Paterson-Clifton-Passaic SMSA sample and deleted them from the New York SMSA sample. To account for the reclassification of Dallas and Fort Worth into a single SMSA, we combined individuals from the Dallas and Fort Worth SMSAs in the 1970 Census file into a single DallasFort Worth sample. No attempt was made to deal with minor reclassifications affecting the Boston and Providence SMSAs and the Detroit and Flint SMSAs.

Appendix C Industry Definitions Matching of 1970 and 1980 Three-Digit Codes Our procedure was to reclassify the three-digit industry codes of individuals in the 1970 Census to 1980 industry codes. The Census Bureau provided us with cross-tabulations of 1970 and 1980 three-digit industry codes for samples of males and females who had been coded under both systems. These cross-tabulations were used to estimate the probability that an individual with a given 1970 code would be classified in a particular industry under the 1980 coding scheme. Using these probabilities, a computer program was developed that reclassifies individuals probabilistically from their 1970 three-digit industry to a particular 1980 three-digit industry. The computer program processes males and females separately. A copy of the program is available from David Card. Industry Classifications Used in Tables 7.3-7.6 Using the three-digit industry titles in Appendix H of the Public Use Microdata Samples Technical Documentation, we developed a “two-digit” classification consisting of seventy-six individual industries. (There are 23 1 separate industries in the 1980 Census industry coding system.) This classification combines many smaller three-digit industries: for example, “agricultural ser-

230

Joseph G. Altonji and David Card

vices except horticulture” (industry 020) and “horticultural services” (industry 021). A listing of the computer instructions used to classify three-digit industries into this two-digit system is available from David Card.

Appendix D Classification of High- and Low-Immigrant Cities In order to determine average immigrant skill levels by SMSA, a regression equation was fit to the log of average weekly earnings for the 1980 sample of male immigrants. The equation included the same flexible function of age and education used to regression adjust native outcomes (see the text description) as well as a set of forty-six countryhegion dummy variables and their interactions with an indicator variable for having entered the United States after 1970 and a variable representing years in the United States. (Chiswick [ 19781, Borjas (1985, 19871, and others have shown that country of origin, immigration cohort, and years since immigration affect earnings in the United States.) This equation was then used to assign a predicted wage to each male immigrant. Immigrants with a predicted wage less than the median predicted wage for the entire United States were classified as “low skill.’’ Finally, the fraction of low’lkenty Cities with Highest Fraction of Low-Skill Immigrants

Table 7D.1 City

Miami El Paso Los Angeles Salinas Jersey City Oxnard-Ventura New York Honolulu Paterson Fresno San Diego Anaheim Bakersfield Stockton Santa Barbara San Francisco San Jose Houston San Antonio Providence

Fraction Immigrants

Fraction Low-Skill Immigrants

.36 .21 .22 .19 .24 .13 .21 .15 .15 .ll .13 .13 .09 .ll .12 .16 .14 .08 .07 .09

.20 .20 .16 .16 .15 .10 .10 .10 .09 .09 .08 .08 .08 .08 .07 .07 .07 .06 .06 .06

231


Table 7D.2

Forty Cities with Lowest Fraction of Low-Skill Immigrants

City Huntington-Ashland, KY Chattanooga Birmingham Knoxville York, PA Canton Jackson, MS Cincinnati Dayton Flint Appleton Louisville St. Louis Nashville Indianapolis Richmond Duluth Memphis Akron Greensboro South Bend Utica-Rome, NY Erie, PA Pittsburgh Harrisburg Binghampton Greenville Peoria Wilmington Fort Wayne Mobile Madison Lancaster Toledo Youngstown Lansing Columbus Atlanta Minneapolis Shreveport

Fraction Immigrants .01 .01 .01 .01 .01

.02 .01

.02 .02 .03 .02

Fraction Low-Skill Immigrants

.oo

.oo .oo .oo .oo .oo

.oo .01 .01 .01 .01

.01

.01

.02

.01 .01

.01

.02 .02 .03 .01 .03 .01

.03 .04 .03

.01 .01 .01 .01 .01 .01 .01 .01 .01

.03 .02 .04 .02 .02 .03 .02

.01

.01

.01

.03 .02

.01 .01 .01 .01

.03 .04 .03 .02 .02 .03 .02

.01

.01 .01

.01 .01 .01

.01

.01 .01 .01 .01

skill immigrants in each SMSA was determined by multiplying the fraction of immigrants in the SMSA by the fraction of immigrants who are classified as low skill. Table 7D.1 lists the twenty cities with the highest fraction of lowskill immigrants. Table 7D.2 lists the forty cities with the lowest fraction of low-skill immigrants.

232


Notes 1. Most of the available evidence is summarized by Greenwood and McDowell (1986), General Accounting Office (1988), and Papademetriou et al. (1989). Two studies of particular relevance to ours are Grossman (1982) and Borjas (1987). Lalonde and Topel (in this volume) provide a parallel study to ours, focusing on the effects of recent immigrants on the labor market outcomes of earlier immigrants. Muller and Espenshade (1985) analyze the effect of immigrants on various California cities. 2. A similar conclusion is reached by Kuhn and Wooton (in this volume) and Papademetriou et al. (1989, ch. 4). 3. The average change in the percentage of immigrants between 1970 and 1980 in the 120 SMSAs in our sample is 1.4 and ranges between 0 and 11.4 percent. 4. If the price of output is exogenous, it is more convenient to work with the elasticities of factor prices with respect to factor quantities, holding constant marginal cost. These are usually known as elasticities of complementarity (see, e.g., Hamermesh 1986). 5. This depends, of course, on constant returns to scale and on perfectly elastic supplies of capital and other inputs. 6. In order to avoid the theoretical prediction of factor price equalization across cities, it is necessary to assume that the number of goods produced within a city is less than the number of locally supplied factors. For further discussion of this point, see Kuhn and Wooton (in this volume). 7. We ignore land or any other locally supplied factors. 8. For notational simplicity, we suppress the dependence of c(.) on the prices of nonlabor inputs. 9. In the notation of eqq. (1) and (2), dD,(9, w,)/dw, = 0, and dL,(w,, 9)/d9 = 0, for j = ( u , s). 10. Johnson (1980a) makes the further assumption that the elasticity of labor supply among existing immigrants is zero, so that the effective supply elasticity in the market for unskilled labor is ( I - f,)~, wheref, is the fraction of immigrants in the existing pool of unskilled workers, and E is the labor supply elasticity of natives. 11. That is, O p , + Osust= .6(O, + Os), where 8, represents the value share of labor in thejth skill group. 12. No entries are included in the first row under the column for a,, = .25. In this row of the table, a,, is strongly negative ( - S25). Thus, skilled and unskilled labor must be relatively strong substitutes (i.e., a,, > .8) to satisfy the restrictions on the matrix of partial elasticities. 13. If a,* = ask, eq. (5) implies that the value of the coefficient b, is independent of the substitutability between skilled and unskilled labor. 14. The elasticities of demand for unskilled labor with respect to its own wage rate (qJ implied by the parameter choices in table 7.1 range from - 1.O (in the lower-lefthand entries of the table) to - 2.6 (in the upper-right-hand entries of the table). 15. Estimates of the fraction of output produced in a city that is consumed locally are not easily obtained. Roughly 35 percent of consumer e tures are allocated to personal, health, business, and education services, public s, transportation services, and other goods with a high local content. 16. If the immigrants are primarily unskilled, then one might expect out-migration of unskilled natives and in-migration of skilled natives. 17. Filer (1988) shows that the net migration rate of natives to an SMSA between 1975 and 1980 is negatively related to the migration rate of immigrants into the SMSA between 1970 and 1974 and to the migration rate of immigrants into the SMSA be-

233


tween 1975 and 1980. The negative relation appears to be strongest for low-skilled and less-educated natives. 18. Papademetriou et al. (1989, chap. 4) summarize evidence from a few industry studies suggesting that in some cases immigrant labor has been used to undercut union firms paying higher wages and employing native workers. 19. By “Census week” we mean the week immediately preceding the administration of the Census, for which individuals report their major activity. The Census is administered on 1. April. 20. Our two-digit industry classification is explained in App. C. 21. Of the ten highest-immigrant-share industries in 1980, seven were in the top ten industries by immigrant share in 1970. The rank-order correlation across industries between the 1970 and 1980 immigrant shares is .86. 22. The average fraction of immigrants in the total population in our sample of cities in 1970 was .044 and ranged from .003 to .242. The average fraction of immigrants in the total population in 1980 was .058 and ranged from .008 to .357. 23. It should be pointed out that the index is computed from the industry distribution of existing immigrants and cannot be used to assess the effects of an inflow of immigrants that are much different from the existing stock. 24. The number of white males in private household services is so low that the index cannot be calculated. 25. For example, many high-immigrant-share cities are also major transportation centers (New York, Los Angeles, Miami). This fact may partially explain the relatively high share of the transportation services industry in the high-immigrant-share cities. 26. It is interesting to note that total employment growth rates between 1970 and 1980 for the twenty high-immigrant-share cities and the forty low-immigrant-share cities were virtually identical-the ratio of 1980 to 1970 employment was .92 for the high-immigrant-share cities and .91 for the low-immigrant-share cities. The relative growth rates of less-skilled native employment, however, were somewhat different in the two sets of cities. The relative ratios of 1980 to 1970 employment totals in highversus low-immigrant cities were .96 for white males, .90 for white females, 1.02 for black males, and .87 for black females. 27. A similar approach is used by Borjas (1987). 28. An alternative strategy is to study the effect of immigrant flows to particular SMSAs that one can identify as exogenous. For example, Card (1990) examines the effect of the Maria1 boat lift on the Miami labor market and finds little effect on the wages and unemployment rates of less-skilled blacks and other non-Cuban groups. His results for wages are somewhat at variance with the instrumental variables estimates we report below. 29. The instrumental variables estimation of the first-difference equation also uses these weights. 30. The implied per capita labor supply elasticity is roughly minus one. An alternative explanation, which might be consistent with an extended version of the model allowing for heterogeneity within the population of less-skilled natives, is that a downward shift in the wage distribution induced by immigration results in the exit from the labor force of natives with the lowest skill levels. However, given that the decline in the employment population ratio is small, a compositional shift cannot explain the results even if the wages of those who left employment were essentially zero prior to their departure. 31. In contrast, the correlation between the fraction of immigrants in 1970 and the change in fraction of all immigrants in the SMSA is .60. These correlations refer to the unweighted sample of 120 SMSAs.

234


References Bartel, Ann. 1989. Where do the new U.S. immigrants live? Journal of Labor Economics 7 (October):371-91. Borjas, George. 1985. Assimilation, changes in cohort quality, and earnings of immigrants. Journal of Labor Economics 3(0ctober):463-89. . 1987. Immigrants, minorities, and labor market competition. Industrial and Labor Relations Review 40(April):382-93. Card, David. 1990. The impact of the Mariel boatlift on the Miami labor market. Industrial and Labor Relations Review 43(January):245-57. Chiswick, Barry. 1982. The impact of immigration on the level and distribution of economic well-being. In The gateway: US. immigration issues and policies, ed. Barry Chiswick. Washington, D.C.: American Enterprise Institute. Filer, Randall. 1988. The impact of immigrant arrivals on migratory patterns of native workers. Typescript, Department of Economics, Hunter College-CUNY. General Accounting Office. 1988. Illegal aliens: Influence of illegal workers on wages and working conditions of legal workers. Washington, D.C.: U.S. Government Printing Office. Greenwood, Michael, and John McDowell. 1986. The factor market consequences of U S . immigration. Journal of Economic Literature 24(December): 1738-72. Grossman, Jean. 1982. The substitutability of natives and immigrants in production. Review of Economics and Statistics 64(November):596-603. Hamermesh, Daniel. 1986. The demand for labor in the long run. In Handbook'of labor economics, ed. Orley Ashenfelter and Richard Layard. Amsterdam: NorthHolland. Johnson, George. 1980a. The labor market effects of immigration. Industrial and Labor Relations Review 33(April):33 1-41. . 1980b. The theory of labor market intervention. Economica 47(August): 309-30. Muller, Thomas, and Thomas Espenshade. 1985. Thefourth wave: California's newest immigrants. Washington, D.C.: Urban Institute Press. Papademetriou, Demetrios, et al. 1989. The effects of immigration on the U.S. economy and labor market. U.S. Department of Labor Bureau of International Labor Affairs Immigration Policy and Research Report no. 1, May.

8

Industrial Wage and Employment Determination in an Open Economy Richard B. Freeman and Lawrence F. Katz

The increasing internationalization of the U.S. economy, evinced in the growth of trade, immigration, and (post- 1982) trade imbalance-induced capital flows, raises questions about the responsiveness of the labor market to shocks produced by open economy developments. How do trade-induced changes in product demand and immigration-induced changes in labor supply affect relative wages and employment? Do industrial labor markets respond to shocks generated by international flows of goods and labor as they do to those generated by domestic developments? Does a decline in demand due to international trade (and other factors) reduce wages in an industry relative to those elsewhere? To what extent do wages respond differently in union than in nonunion settings? To what extent do wages respond differently to increases as opposed to decreases in relative demand? To answer these questions, we analyze cross-section time-series data on imports, exports, immigrant shares of employment, annual and hourly earnings, and employment for detailed U.S. manufacturing industries over the period 1958-84 and contrast the responsiveness of the industry earnings in more and less highly unionized industries and between industries facing greater and lesser shocks in sales. In contrast to studies that focus on the direct and indirect effects of the trade balance or immigrant flows on the aggregate economy (using general equilibrium models or input-output analysis), our concern is with direct trade effects on disaggregated industries. I The principal finding is that the industry wage structure responds to Richard B. Freeman is professor of economics at Harvard University and director of the Labor Studies program at the National Bureau of Economic Research. Lawrence F. Katz is associate professor of economics at Harvard University and a research associate of the National Bureau of Economic Research. The authors are extremely grateful to Dan Kessler for expert research assistance and to Lawrence Summers for helpful comments.

235

236

Richard B. Freeman and Lawrence F. Katz

changes in product market sales, with trade-induced changes in sales having approximately the same effect on earnings as sales due to domestic market developments: on average, a 10% annual change in relative industry revenues resulting from trade or other factors alters relative earnings by about .5% over the long run. In addition, we find enough variation in changes in wages, sales held fixed, to trace out a demand curve “trade-off’ between wages and employment across industries. Surprisingly, perhaps, we also find that wages respond more to sales in unionized industries than in nonunionized industries and more to relative declines in sales than to relative increases in sales. Finally, industries with growing or large immigrant shares of employment tend to fall in the industrial wage structure, apparently for reasons beyond any immigrant-native pay differential within industries.

8.1 Potential Labor Market Responses to Ikade-induced Changes in Product Demand When product demand changes in an industry, one expects employment to change in the same direction, with wage adjustments “buffering” the magnitude of job losses or gains. The extent of wage responses to shifts in demand is likely to depend on the mechanisms for wage setting-in particular, on whether wages are set in a decentralized fashion in industry labor markets by supply and demand to clear labor markets; by collective bargaining/administered wage setting that produces premium wages in some industries; or on a national basis with little scope for industry variation. In competitive decentralized wage setting, the extent of wage response to shifts in demand or supply of labor depends on elasticities of demand and supply.*Formally, write the industry demand for labor curve in first-difference form as (1)

dE = -kdW

+ dX,

where E = In employment, W = In wage, X = In shift in the derived labor demand curve due to shifts in product demand, and d = the difference operator. Let the industry labor supply curve be represented as dE

(2)

=

edW

+ dS,

where S = In factors that shift supply. Market clearing produces the following reduced-form relations: dW

(3b)

=

(dX - dS)/(k + e )

dE = (edX - kdS)/(k + e )

Since elasticities are likely to be greater in the long run as factor mobility increases and as firms move in and out of industries, wage responses will be smaller and employment responses greater to any exogenous shock as time

237

Industrial Wage and Employment Determination in an Open Economy

proceeds. In the extreme, when the elasticity of labor supply to an industry approaches infinity, there is no wage response to shifts in either schedule, and the sole change is in employment. Under collective bargainedladministered decentralized wage setting, wages in particular industries diverge from market-clearing rates for any of a number of possible reasons (efficiency wages, rent sharing, collective bargaining), producing a queue of workers at going rates and an effective infinitehear infinite elasticity of labor supply to the ~ e c t o r .Since ~ neither employers nor unions are directly constrained by labor supply conditions, there are several possible wage responses to shifts in demand. Some argue that, in markets where senior workers have a disproportionate influence on wage setting, wages are less responsive to trade-induced changes in demand than competitively determined wages. Grossman (1984) analyzes the conditions for this to be true in a model in which wages are set (subject to a labor demand constraint) by a majority-voting union with a seniority layoff rule and free entry into the union. Modeling an increase in international competition as a exogenous decline in the world price of the product produced by the unionized sector, he shows that international competition has two offsetting effects on the wage. For a union of a given size, a lower wage will be desired because greater international competition increases the risk of layoffs. On the other hand, declines in union membership will raise the average seniority level, which, he argues, produces a median member who wants higher wages. The net effect is ambiguous in general, but, in the case of a constant elasticity labor demand schedule, his model predicts that the union wage will be completely unresponsive to the international price.4 Others note the possibility (and existence in some industries in some periods) of “endgame bargaining” in which unions, seeing little future to an industry, seek to extract as much as they can in a short period (Lawrence and Lawrence 1985). Our analysis emphasizes the possibility of greater-than-competitive downward wage adjustments when wages exceed outside alternatives in a unionized-administered wage sector, so that we expect larger downward wage adjustment for union than for nonunion workers. The view that wage responses to increased product market competition may be greater under unionism is consistent with research on the effects of trucking deregulation on wages that finds substantial relative wage reductions for union truckers and much less wage response for nonunion truckers following deregulation (Rose 1987). Consider, finally, industry wage responses under a centralized system in which wages are set nationally (corporatist economies) or in which there is considerable “spillover” or “flow on” of changes in wages across industries. In these settings, we would anticipate shifts in demand to have little effect on wages but substantial effects on employment. The existence of such wagesetting systems outside the United States provides potentially fruitful controls for evaluating the effect of decentralized wage setting in the United States on wage responsiveness.

238


8.1.1 Modeling Union Behavior Consider first the wage policy of a union concerned with both wages and employment that is subject to a labor demand constraint. In simplest form, its decision making can be viewed as maximizing a utility function U ( W , E ) subject to a labor demand constraint E = E (W). Then the maximizing condition is to set wages so that the ratio of the marginal value of employment to the marginal value of wages equals the elasticity of labor demand. If trade (or other factors) increases the elasticity of demand, wages are likely to drop, as the wage-employment trade-off facing the union is worsened. Huizinga (1987) shows that, in imperfectly competitive product markets, an increase in international competition is likely to increase the elasticity of the product demand elasticity facing domestic firms and lead to wage concessions by a monopoly union. If trade (or other factors) leaves the elasticity unchanged but shifts the demand curve downward, the union is also likely to lower wages when demand declines and raise them when demand increases. Similar implications can be derived for the standard utilitarian union model (McDonald and Solow 1981) in which a union with a fixed membership maximizes the welfare of the representative member subject to the labor demand ~ o n s t r a i n tThe . ~ union maximizes

where N is the fixed membership, u(.) is the utility function of the representative member, e is the level of employment, w * is the opportunity (or alternative) wage, and w is the wage level. The maximand can be rewritten as e(w)[u(w)- u(w*)]dropping a constant term. This formulation yields the familiar optimizing condition in which the elasticity of the gain from employment is equated to the elasticity of labor demand:

-u’(w)w/[u(w)- u(w*)]= e’(w)w/e. Here, the union wage depends only on the elasticity of labor demand and the degree of risk aversion of the representative union member. Changes in international competition that affect the elasticity of labor demand will affect wages in the same direction, while shifts in labor demand not changing the elasticity will not affect wages. In the case of efficient bargains between the union and firms, the level of labor demand will affect wages as well as the elasticity.6 When senior workers play an especially important role in the union, there is a strong possibility of a more complex response pattern. Faced with a positive shock in demand, existing union members are likely to weigh wage gains highly relative to employment gains, producing sizable increases in wages. Faced with modest negative shocks, they are less likely to sacrifice rents to save the jobs of marginal employees, producing wage inertia. Faced with siz-

239


able negative shocks and threats of plant shutdowns, on the other hand, existing unionists may be willing to offer large wage concessions. As there is reasonable a priori logic for expecting unions to respond less, more, or even “perversely” to shocks due to trade or other factors, the question of which response pattern dominates actual wage setting in the United States is an empirical one.

8.1.2 Sales and Shifts in Demand The models described thus far relate changes in wages and employment to exogenous shifts in demand and supply. To apply them to data, it is necessary to measure the exogenous shifts, which, given our focus on trade, requires that we obtain appropriate indicators of shifts in product demand and the contribution of trade to such shifts. In this study, our primary indicator of shifts are industry sales and its price and quantity components and sales decomposed into domestic market sales, exports, and imports, appropriately weighted to take account of their relative magnitudes. As sales depend on industry supply as well as demand conditions, however, simply replacing the X terms in (3a)-(3b) with sales and regressing wages/employment on sales does not yield the desired response parameters. There is a potentially important simultaneity bias due to the effect of wages on industry prices and output. As a first cut at the simultaneity problem, assume that the supply curve of industry output is flat, so that prices depend solely on costs of production. Then we can model the simultaneous relation between wages and sales with the following simple market model: a) product demand: (4)

dQ = -hdP

+ dX,

where Q = In output, P = In price of output, and X = In shifts in demand, as in (3a); b) the effect of wages on cost of productiodproduct price: dP

=

adW,

where a is a labor’s share of cost; c ) wage determination equation: dW

=

qdX,

where q is the parameter of interest to us ( = l/[k

+ el in eq. [3a]).

Note that this equation makes wage changes depend not on observable changes in prices or quantities (which are affected by wages) but rather on the unobserved exogenous shift in product market conditions. Substituting (5) into (4) yields a relation between output and wages: (7)

dQ = -hadW

+ dX,

240


which, in turn, yields a relation between sales (dS = dQ dS = (1 - h)adW

+ dP) and wages:

+ dX.

Solving for dX in (8)' and substituting into (6) yields an equation between changes in wages and changes in sales?

(9)

dW

=

{q/[l

+ q(1 - h ) ~ ] } d =S AdS.

Adding an error term to (9) with the usual properties, we can estimate the parameter A by least squares regression of observables on observables. We are interested, however, not in A but in q , the response of wages to changes in product market conditions. Rearranging terms we see that, for any estimate of A: (10)

= A/[1 - Aa(1 - h)].

Equation (10) shows us that the estimated parameter of wages on sales yields the correct response coefficient only if the product demand elasticity is unity. If h is less than unity, A will understate q, while, if h is greater than unity, it will overstate q , with the magnitude of the difference between A and q dependent on the magnitude of Aa(1 - h). For reasonable values of the parameters, however, it turns out that the difference between A and q will be small. For example, with the mean value of a (labor's share of cost of sales) in our data of .25 and estimated values of A below .lo, the bias is modest for anything short of huge elasticities of product demand.9 In the context of the model of equations (3a)-(3b), moreover, the difference between the parameter relating employment to shifts in demand and the regression coefficient of employment on sales will also be small.'O The econometrics gets more complicated, however, if, rather than adding an error term to equation (9), we allow for error terms in each of the underlying equations as well. As we substituted for dX to get (9), the error terms in the price and wage determination equations become part of the error structure in (9), with the result that dS is correlated with the error.II In this case, it is necessary to instrument dS to obtain a consistent estimate of A.I2 As an alternative way of modeling the relation between wages, prices, and output in an open economy, consider the situation when prices are determined on world markets so that an industry in a given country can sell as much as it produces at the going world market price. Here, there is still likely to be a feedback of wages on sales, as increases in wages increase costs of production and reduce output, thereby reducing sales. We model this market pattern by assuming an upward-sloping industry supply curve with a fixed elasticity. Following a logic analogous to that in (4)-(10) above, we can show that the regression of wages on sales leads to an understatement of the parameter of wage responsiveness of exogenous shifts in market demand, essentially because the reverse causality is negative.


241

8.1.3 The Trade Component of Sales Turning to the effect of changes in trade, we decompose sales into its component parts-the size of the domestic market (DOM = sales - eximports); exports; and the import share of domestic market sales ports (MSHR = imports/DOM)-and use a first-order approximation to obtain

+

(11)

dS = w,d In (DOM)

+ w,d In exports - w,d(MSHR),

where w, = (sales - exports)/sales, w z = exports/sales and, w, = DOWsales. The weights are obtained by considering the effect of small changes in domestic-generated revenues, export-generated revenues, and the import share of revenues on changes in total revenues in a decomposition that ignores interaction terms, and will, accordingly, be more accurate for small than for large changes. The purpose of the weighting is to adjust the relevant changes for the difference in absolute magnitude of sales generated by domestic demand (90% or so of sales) and trade. When there is trade balance in an industry, the weight on changes in the import share of the domestic market becomes unity [DOWsales = (sales imports - exports)/sales = sales/ sales]. When, as in the 1980% imports exceed exports, the weight placed on this term exceeds unity. Substituting (1 1) into (9), we obtain a relation between wages (or employment) and weighted In changes in the domestic and foreign components of revenues:

+

(12)

dW

=

+ +

Aw, d In (DOM) Aw,(d In exports) - Aw,[d(MSHR)] other factors.

Note that this model makes a strong implicit assumption about market behavior: it postulates that the labor market responds similarly to (weighted) changes in sales due to trade-related factors as to those due to domestic factors. While in the short run there may be some differences in market responses to trade-generated as opposed to domestic market-generated changes due, say, to differing assessments of whether changes will persist over time (e.g., because foreign competition depends on highly volatile exchange rates), in the long run we see no compelling argument to expect industry labor markets to react any differently to changes in revenues from different sources: a 10% shift in demand is a 10% shift in demand. If the assumption that foreign- and domestic-based changes have the same effects on the labor market is valid, then the coefficients on the trade and domestic revenue terms will be similar in regression analysis.

8.1.4 Shifts in Supply and Immigrant Labor The impact of immigrant labor on industry wage levels is twofold. To the extent that immigrants are paid differently than otherwise comparable native-

242


born workers, average wages in an industry will depend on the immigrant share of labor with a coefficient equal to the wage-differential between immigrants and native workers. Changes in the immigrant share of labor in an industry will, accordingly, be associated with changes in industry wages:

(13)

dW = bd(IMS),

where IMS = immigrant share of the work force and b = wage differential between native and immigrant labor. In addition, however, if immigrant labor is a good substitute for native labor in immigrant-intensive industries, supply-induced changes in immigrant shares will alter the wages of natives in the industry as well, further reducing the position of the industry in the industry wage structure.

8.2 Cross-Industry Analysis for U.S. Manufacturing We estimate wage-sales and employment-sales equations using the NBER Trade and Immigration Industry data set for manufacturing production workers.I4 The data set provides information on the wages and employment of production and all workers, trade flows and immigrant shares of employment, as well as other control variables in 428 four-digit SIC manufacturing industries from 1958 to 1984.15We examine the data in three ways. First, we analyze changes over the twenty-six-year period from 1958 to 1984, which can be viewed as reflecting changes in long-run comparative statics for the “average” industry. Second, we relate annual changes in wages and employment across industries to changes in revenues and the part of those changes due to trade and domestic demand, with individual year dummy variables entered in our regressions to capture economy-wide cyclic-type phenomena. I 6 Third, we explore responses over different time periods to see whether responses in the 1980s period of large trade imbalances differ from those in earlier periods. As a check on the results from our establishment-based analysis, we also estimate wage change equations utilizing industry wage differentials estimated from the 1974 and 1984 Current Population Surveys (CPSs) for the fifty-eight three-digit 1980 Census industries (CICs) that can accurately be matched to the 1970 CIC system used in the 1974 CPS and to the NBER trade and immigration figures. Our pooled cross-section time-series industry analysis differs, it should be noted, from the time-series analyses for particular industries that other researchers have used to investigate the effect of trade on the labor market.” We examine the relative responsiveness of industries to the particular shocks that face them, exploiting the differential patterns of change among industries rather than the time-series patterns of change for a particular industry. As a consequence, our estimated response parameters are average elasticities of response across industries. Formally, in terms of the model of (12), if each industry has its own response parameter a ai, where a is the mean of the

+

243


industry response parameters, the form of our basic equation can be written as (14)

dW,, = (a

+ a,)dS,, = ads,, + a@,,,

Where the latter term becomes part of the error structure. This term reflecting the heterogeneity in industry responses creates heteroskedasticity in the errors but does not bias estimation of the average response coefficient as long as the individual industry component of the response (a,) is independent of other variables in the equation.I8 In section 8.3, we consider potential differences in response coefficients among industries. 8.2.1 Data Description Table 8.1 gives the 1984 level of variables, 1958-84 changes in variables, and standard deviations for variables of concern to us (pt. A) and selected correlations of the changes for the period 1958-84 (pt. B). The descriptive statistics reveal several characteristics of industry labor and product markets that underlie the ensuing econometric results: The shares of imports and exports relative to the size of the domestic market are relatively modest even in 1984 after two or so decades of rapid growth of trade, with imports averaging 14% of domestic demand and exports averaging 8% of sales. Immigrant shares of labor are also modest, averaging 8% in 1984.19 The principal dependent variables of concern to us-changes in In annual earnings (obtained by dividing payroll by employment) and in In hourly earnings (obtained by dividing payroll by person-hours)-show nearly identical industry variation over the period 1958-84, indicating virtually identical patterns of change in hours per employee. The correlation in the 1954-84 change in In annual earnings and change in log hourly earnings is .95. When, by contrast, we examine short-run year-to-year changes, we find considerable variation in hours per worker across industries and thus differences in changes in annual and hourly earnings. The standard deviation of In changes of industry employment exceeds the standard deviation of In changes in hourly earnings by a factor of 3.9 (.67/ .17), documenting the fact that quantity adjustments dominate industrial labor markets, possibly because workers are good substitutes across industry lines. In the goods market, the standard deviation of In changes in physical output (deflated sales) exceeds the standard deviation of In changes in prices by a similar proportion (3.0 = .73/.24). The standard deviations of our major independent variables-weighted changes in domestic demand, foreign demand, and the import shareshow considerable interindustry variation, as is necessary if we are to estimate their effects on the labor market with any precision. Turning to the correlations in part B of the table, note first the .42 positive correlation between changes in the level of imports and changes in In employ-

244


Table 8.1

Descriptive Statistics for NBER 'kade and ImmigrationData Set, 428 U.S. Manufacturing Industries A. Major Variables

Mean Levels, 1984: Imports/domestic demand Exports/sales ImmigrantslEmployment Changes, 1958-84: DLN(hour1y wages for production workers) DLN(annua1 wages for production workers) DLN(production employment) ImmigrantsiEmplo yment DLN(sales) DLN(price) DLN(output) Weighted changes, 1958-84: * DLN(domestic demand) DLN(foreign demand) Import share

SD

.14 .08 .08

.I6 .10 .05

1.43 1.43 - .02 - .OO 1.74 1.06 .67

.I1 .18 .67 .03 .72 .24 .73

1.71 .16 .I5

.65 .23 .43

B. Correlations, 1958-84: Log Changes Weighted Changes Production Hourly EmployWages ment Hourly wages Production employment % Immigrantsb

1.00

.04

.04

1.00 .06

-.I2

%I&grants

Domestic Foreign Import Sales Imports Exports Demand Demand Share

-.12

.26

-.08

.I0

.13

.I5

.90 .04

.42 .13

.44 .05

.83 .10

.23 - .04

.06 1.00

-.23 -.18 .06

Note: DLN(.) = change in logarithm of the variable. a Weights utilized are the average of the 1958 and 1984 weights. Absolute change in percent immigrant.

ment. If industries with increasing imports expand employment, why is there such public concern over the effect of imports on jobs? The reason for the seeming paradox is that imports and domestic production tend to increase in the same industries-namely, those where domestic demand is growing. This highlights the need to control for total demand or, alternatively, to focus on the import share of the domestic market, in estimating the effect of imports on the labor market. Indeed, the (weighted) change in the import share of domestic sales is negatively correlated with changes in employment at a highly significant - .18. Similarly, with respect to earnings, while the correlation between changes in imports and earnings is a modest - .08, the correlation between changes in earnings and the (weighted) change in import share is a

245


hefty - .23. Note finally that changes in the immigrant share of employment in an industry are negatively related to changes in wages and positively related to employment changes, indicating that immigrants have gone into industries with growing employment but declining wages.*O 8.2.2

Basic Regression Results

Table 8.2 presents coefficients and standard errors on the major variables of concern from our regression analyses of long-term (1958-84) changes in hourly earnings and personhours worked. All the regressions contain the set of controls listed at the bottom of the table, including two-digit SIC industry dummies (to allow for broad industry differences in responses). Columns 1-3 show the estimated effect on hourly earnings of sales, prices, and physical output taken separately and weighted trade and domestic revenues; columns 4-6 show the estimated effects on hours of sales, price, and quantities and of weighted trade and domestic revenues. In addition, we report the coefficients and standard errors on the change and base year level of immigrant shares in each regression. We do not report the results of comparable regressions for Table 8.2

Coefficients(standard errors) for Effects of Changes in Sales, Trade Variables, and Immigration Ratios on Wages and Employment of Production Workers in U.S. Manufacturing Industries: Long Period Log Changes (N = 428), 1958-84 Hourly Wages

Variables

(1)

(2)

Annual Hours (3)

(4)

(5)

(6)

~

Change in log sales Change in log output Change in log price Change in % immigrant % Immigrant in 1960

,886 (.017)

,049 (.011)

-.445

(.286) -1.164 (441)

,049 (.011) ,108 (.035) -.389 (.287) -1.198 (41)

Weighted log change in domestic demand Weighted log change in foreign demand Weighted change in import share

RZ

- ,432 (.286) -1.164 ( ,440) ,040 (.011) .076 (.034)

1.082 (.462) ,900 (.713)

,886 (.017) .884 (.057) 1.080 (.466) ,901 (.714)

.89

.89

- ,064 (.017)

.38

.39

.39

1.002 (.497) ,545 (.765) ,894 (.020) ,710 (.059) - .479 (.030) .88

~~

Nore: The reported regressions include two-digit SIC dummies, change in percent union, change in percent production workers, and the initial (1958) values of the following variables: percent union, percent production workers, and log of value added per worker. Weights utilized in weighted log changes are means of 1958 and 1984 weights.

246


annual earnings and production worker employment as they yielded virtually identical coefficients to those in the table because of the lack of industry variation in hours per employee over the long run pointed out on page 243.21 There are three principal findings. First, the calculations show that changes in revenues significantly affect relative wages, implying that the industry wage structure is “flexible” with respect to changes in the market conditions in particular industries. Using the reduced-form model of (3a)-(3b) to interpret the results, the ratio of the estimated effect of sales on hours to the estimated effect of sales on earnings provides a measure of the magnitude of the elasticity of labor supply across industries: from columns 4 and 1, it is 18.0, which implies considerable labor mobility across industry lines in response to changes in wages. Second, despite the long period under study, changes in the (weighted) import share of the domestic market and in exports have roughly comparable effects on‘wages, as do weighted changes in domestic shipments, indicating that, as a first approximation, the industrial wage structure in the United States responds to open economy developments to the same extent as to domestic developments. In addition, the trade-generated and domestic market-generated changes in sales have significant effects on person-hours, though with noticeably different estimated coefficients. Third, the long period change estimates show that both the change and the initial level of the immigrant ratio are negatively related to changes in hourly earnings and positively related to changes in employment. The magnitudes of the coefficients on immigrant shares are, however, too large to be attributed to pure immigrant-native wage differentials given the small proportion of immigrant workers and likely modest differentials correcting for worker skill (Borjas 1985; Chiswick 1978). They are more likely to reflect the concentration and movement of immigrants into low- and declining-wage industries. That immigrants find jobs in industries that are increasing employment but falling in the wage structure is consistent with the basic fact that employment in the United States has been growing in industries with low and relatively declining wages. 8.2.3 Annual Changes Next we examine the effect of domestic- and foreign-generated changes in sales on earnings using annual rather than long period changes in the variables. Because annual earnings and hourly earnings differ in the short run, owing to short-run variations in person hours worked relative to employees, we report results for both earnings variables. Because our focus is on interindustry responses, we include year dummies in these regressions to control for general cyclic phenomena. As can be seen in table 8.3, we obtained somewhat different results between annual and hourly earnings. First, changes in sales had much larger effects on annual earnings than on hourly earnings, owing to the implicit re-

247


Table 8.3

Coefficients(standard errors) for Effects of Changes in Sales and Wade Variables on Wages in U.S. Manufacturing Industries: Annual Log Changes (N = 11,165), 1959-84 Annual Earnings

Variables Change in log sales

(1)

(2)

(3)

(4)

,056

(.007)


,066

.029

(.004)

.008

,071 (.015) - ,059 (.011) .22

(6)

,026 (.@J7) .032 (.007)

,069

(.004)

Change in log price

(5)

,027 (.004)

,068 (.004)

Change in log output

R2

Hourly Earnings

.22

.22

(.016) - .01I (.012)

.19

.20

.20

Note: The reported regressions include year dummies, two-digit SIC dummies, change in percent union, and change in percent production workers as well as the variables listed above.

sponse of hours per employee to changes in sales (the difference in the effect of a variable on annual and hourly earnings is its effect on hours per worker) as firms responded to relative declines in sales by reducing work hours and/or temporarily laying off some workers and responded to increases in sales by increasing work hours, including over time. Comparing the effect of sales on hourly earnings in table 8.3 with the effect of sales on hourly earnings in table 8.2, we find that hourly earnings adjustments are greater in the long run, contrary to the purely competitive model of (3a)-(3b). This can be taken as evidence for collectively bargained/administrated wage settlements as opposed to spot market settlements. Second, and more disturbing to our analysis, table 8.3 shows that, while annual changes in sales due to open economy developments have substantial and well-defined effects on changes in annual earnings, they have statistically insignificant effects on changes in hourly earnings, contrary to the findings of table 8.2. To reconcile the findings on the magnitude of the effect of sales and tradeinduced changes in sales in the short run and long run, we made two further calculations. First, we examined the determination of hourly earnings over three intermediate periods: 1958-70, which covers the 1960s strong job market; 1970-80, when the economy was sluggish; and 1980-84, when the country developed an extraordinary trade imbalance. The results of this analysis are given in table 8.4. Consistent with the results for the entire period, they show substantial and significant effects for import-induced changes in sales on

248


Table 8.4

Long Period Log Hourly Wage Change Regressions by Time Period, U.S. Manufacturing Industries, 1958-70, 1970-80, 1980-84 Time Period

Variables Change in log sales Weighted log change in domestic demand Weighted log change in foreign demand Weighted change in import share Change in % immigrant R2

N

1958-70

1970-80

.022

.035

(.ow (.015)

1980-84

1958-70

1970-80

1980-84

,084 (.018)

- .148 (.271)

.031 (.017) ,043 (.033) -.076 (.057) - ,342 (34)

.28 428

.26 428

.086 (.016) ,017

(.009)

,095 (.039) - ,141 (.055)

- ,263 (.269)

-.325 ( .262)

.27 428

.26 428

.. . .20 428

,040

(.066) -.067 (.018)

... .19 428

Note: The reported regressions include two-digit SIC dummies, change in percent union, change

in percent production workers, and the initial values of the following variables: percent union, percent immigrant, percent production workers, and log of value added per worker. Weights utilized in weighted log changes are mean of initial and final period weights. The 1958-70 regressions do not include change in percent union, and the 1980-84 regressions do not includz change in percent immigrant because the required data are not available for these variables over these periods.

hourly earnings but weaker effects for export-induced changes. The period regressions also show a marked pattern of differences in wage responsiveness among the periods, with the effect of sales on hourly earnings greatest in the 1980s, as might be expected given the wage concessions of that period.22Second, we have explored the timing of the effect of sales on hourly and annual earnings by including lagged sales variables in our regressions of annual changes in hourly earnings on annual changes in sales. The results of these calculations (see table 8.5) suggest that within three years the effect of changes in sales on hourly earnings rises to the long-run level and is roughly equal to the effect of changes in sales on annual earnings. The differences in timing of the effect of sales on hourly and annual earnings suggest that hours worked (which vary because of both layoffs and overtime or short time) may be an important indicator to workers of the need to adjust hourly pay in the face of demand shocks. For our purposes, what matters is that these calculations show that the long period hourly earnings results are the valid ones for assessing adjustments beyond a year or so.

8.2.4 Current Population Survey Data

As a check on our findings from establishment data, we have also estimated the effect of trade and immigration on industry wages using household data from the CPS tapes. These calculations have the advantage of letting us con-

249

Industrial Wage and Employment Determination in an Open Economy Regression coefficients (standard errors) for the Effect of dS on dw

Table 8.5

Hourly Earnings

Annual Earnings

.029

dS

,069

(.ow

(.ow - ,027 (.ow .005 (.ow

- .001

&(-I)

(.ow

ds( - 2 )

.017

(.ow

Sum

,045

.

,047

Nore: The coefficients are based on the same specifications as in table 8.3, with the addition of the lagged sales variables.

Table 8.6

Long Period CPS Industry Wage Differential Change Regressions, 1974-84: Fifty-Eight Three-Digit 1980 CIC U.S. Manufacturing Industries All

Union

Variables Change in log sales

(3)

R2

N

-.869 (.409) .21 58

(4)

- .ow (.031)

.I31 (.047)

,057 (.025)

Weighted log change in domestic demand Weighted log change in foreign demand Weighted change in import share Change in % immigrant

Nonunion

,010 (.029) ,019 (.095) - ,228 (.050) -.523 (.383) .39 58

.097 (.055) -.180 (.181)

- 1.053 (.751)

- ,367 (496) - .737 (.731)

.18 58

.31 58

-.612 (.501) .09

58

- ,089 (.034) ,067 (.112) - ,221 (.059) -.071 (.451) .35 58

Note: Reported regressions include change in percent union and change in percent production workers.

trol for individual characteristics that affect earnings at the cost of limiting the sample to fewer and more aggregated industries. We proceeded in a two-part analysis. In step 1, we estimated industry wage effects by regressing the In of average hourly earnings of individuals on their characteristics and dummy variables for the In step 2, we regressed the change in the estimated industry effect on changes in sales, immigrant ratios, and sales decomposed between trade and domestic factors. The basic results, shown in columns 1 and 2 of table 8.6 (we will discuss the findings in cols. 3-6 shortly), confirm the table 8.2 finding that, over an extended period of time, changes in revenues due to trade substantially aEect industry hourly earnings and also confirm the finding that industries with growing immigrant shares of the labor


250

force fall in the wage structure. Indeed, in these calculations, the dominant factor in changes in wages in the period 1974-84 is the import part of sales. 8.2.5

The Wage-Employment Trade-off

While our analysis shows that wages respond to changes in sales resulting from trade and other factors, there is also a significant independent or unexplained component to changes in wages as well. Does this component of the change in wages affect the quantity of labor used, sales held fixed? To what extent do cross-industry data show a trade-off between wage responsiveness and the employment of labor? To answer these questions, we have performed the regression calculations summarized in table 8.7, in which we relate annual and long-run changes in In annual production hours to changes in wages and sales (cols. 1, 4),product wages and deflated output (cols. 2, 5), and wages and sales decomposed into trade and domestic market determinants (cols. 3, 6), using both annual change and long period change data. The results offer strong support for the notion that, sales held fixed, there is a significant wageemployment trade-off across industries in the U.S. labor market. Employment Table 8.7

The Wage-Employment 'kade-off in U.S. Manufacturing Industries: Dependent Variable = Change in Log Annual Hours of Production Workers Annual Changes, 1959-84

Variables Change in log wage

(1)

(2)

- ,629 (.013)

- ,628

(.013) Change in log product wage Change in log sales Change in log output

N

(4)

- ,612 (.079) (.045)

.921 (.016)

,670

,921 (.016)

,699 (.005)

,922 (.018) ,754

.649 (.006) ,676 (.022) - ,545 (.017) .71 11,165

(6)

- .887

(.008)

(.om

(5)

- ,683 (.071)

- ,545


R'

(3)

Long Period Changes, 1958-84

.72 11,165

.69 11,165

(.055)

- ,524 (.028) .9 1 428

.91 428

.89 428

Nure: The reported annual change regressions include year dummies, two-digit SIC dummies, change in percent union, and change in percent production workers as well as the variables listed above. The reported long period change regressions include the same variables except for the time dummies and in addition include the change in percent immigrant. The wage variable utilized in all the regressions is the hourly wage of production workers. The change in the log product wage is given by the difference in the change in the log wage and the change in the log shipments deflator.

251


growth is lower, sales fixed, by roughly 5%-9% in industries where wages rise by lo%, relative to other industries. While we recognize that one cannot interpret the estimated relation as a demand curve, particularly in light of our analysis of wages as dependent on sales, the inverse relation is nonetheless impressive. Unfortunately, we lack good measures of shifts in labor supply to industries and of factors that lead to different union/employer wage-setting policies to estimate a structural demand equation given our model, in which wages are endogenous.

8.3 Differences in Wage Responses among Sectors Are the wage responses found in section 8.2 the same across all industries, or do different wage-setting institutions or economic conditions produce different responses to changes in sales? Do the more heavily unionized industries respond more, or less, to changes in sales due to trade and other factors than do the less unionized industries? Is there evidence of asymmetric wage responses to increases and decreases in sales? 8.3.1

Union and Nonunion Responsiveness

To evaluate the effect of trade unionism on the degree of wage responsiveness, we have performed two related analyses. First, we estimated earnings and employment response equations separately for industries whose union density made them high (upper third), medium (middle third), or low (lower third) in the distribution of union density as of 1973-75. Second, we added interaction terms between changes in sales and dummy variables for high, medium, and low union status to our basic regressions. As the findings for the two analyses were quite similar, we present for purposes of parsimony the separate union-density category regression results in table 8.8. Row 1 of the table records the estimated effects of sales on In hourly earnings by union class from annual change regressions. It shows that earnings responses tend to be higher in the more highly unionized Given that the scope for union wage responsiveness is likely to be greater the greater the gap between union and other wages, we take the analysis a step further in rows 2-5, by estimating a single change in In hourly earnings regression with more complex interaction terms that distinguish not only between high, medium, and low union density but also between industries with high (upper third), medium (middle third), and low (lower third) hourly earnings in 1958. These calculations show that it is the responsiveness of union wages to changes in sales in high-wage industries that underlies the greater elasticity of union wages to sales in row 1. To the extent that wages are high in these industries because of large union wage effects, this finding supports the notion that wage adjustments are greater where wages exceed competitive market levels. Finally, we note that regressions comparable to those in table 8.8 with employment as the dependent variable show that the pattern of change in employment


252 Table 8.8

Wage Responsiveness to Changes in Sales by Union Density: Annual

Log Wage Change Regressions, Hourly Wages, 1959-84 Union Density

Wage Class 1. All industries

2. High initial wage 3. Medium initial wage 4. Low initial wage

High ,041

(.ow

,088 (.025) .035 (.026) ,027 (.022)

Medium

Low

,021 (.007) ,066

.019 (.007) - ,010

(.019)

(.013)

,003 (.022) .023

,032 (.014)

,031

(.012)

Note: The union density and initial wage classes are derived by dividing the industries into thirds on the basis of initial union density and initial hourly wage. Row 1 presents the coefficients (standard errors) on change in long sales for separate log wage change regressions by union density class, which also include change in percent production workers, change in percent union, and time dummies. The regression used for rows 2, 3, and 4 included the same controls plus initial wage class and union class dummies, two-digit SIC dummies, and a full set of interactions of the union class and wage class dummies with change in log sales. Numbers in parentheses in rows 2, 3, and 4 are standard errors for difference between reported coefficient and the coefficient on the base group, low union, low initial wage.

responsiveness is opposite that for wage responsiveness, with employment responsiveness declining with union density, as would be expected if the wage adjustments serve to “buffer” employment in the market.25 Because the establishment-based data set does not permit us to differentiate between union and nonunion firms or workers within an industry, it is possible that the differences found in table 8.8 are not due to genuine differences in behavior between union and nonunion firms.26Accordingly, we have also estimated response parameters for union and nonunion parts of industries using our CPS data set, where it is possible to distinguish between union and nonunion workers. Here, we estimated industry wage effects by regressing In wages on worker characteristics and industry dummy variables for union and nonunion workers taken separately and then regressed the change in the industry effects on changes in industry sales, immigrant ratios, etc. These results, given in the regression coefficients in columns 3-6 of table 8.6, support the finding that unionization increases rather than reduces wage responsiveness: the industry differentials for unionized workers are significantly influenced by changes in sales, and the import component of changes in sales has an especially large effect in the union sector. By contrast, there is no noticeable effect of changes in sales or the import component of those changes on the wages of nonunion workers.

8.3.2 Responses of Industries at the Extremes The finding that wages as well as employment respond to open market shocks does not mean that those responses are a major element in industrial

253


wage and employment determination. Since the bulk of revenues are generated in domestic markets and most workers are native born, changes in trade and immigrant flows cannot possibly be a dominant force in altering the industrial wage structure or the composition of employment. Still, the wage responses to trade flows can have nonnegligible effects on wages and employment particularly at the extremes. A one standard deviation change in the import share (.43 by table 8.1), for example, induces a .028 change in wages and a .21 change in industry personhours, according to the coefficients in table 8.2. More strikingly, industries that faced massive changes in sales for either trade or domestic market reasons experienced large changes in wages as well as in employment. Figure 8.1 documents this by contrasting wage and employment changes from 1958 to 1984 between the ten industries experiencing the greatest positive and negative weighted changes in import shares, exports, and domestic market sales and changes in In total sales. The figure shows a wide spread in wage responsiveness between the extremes. For imports, the industries with the greatest increase in import share had wage increases some nineteen In points below the average for all industries and some thirty-three In points below wage increases for the industries with the largest decrease in import shares. Industries with the most/least rapid growth of domestic market sales show a smaller though still pronounced range of variation, while industries with the most/least rapid growth of exports show the least pronounced range. As for employment, the figure shows declines of .81 In points in industries with the most rapid rises in import shares, but it also shows above average declines in employment in industries where import shares fell-a seeming paradox that is due to the fact that import shares dropped most in industries with falling domestic market sales.27By contrast, the figure shows a monotonic relation between extreme changes in exports and in domestic market sales and employment: here, employment rises at rates far above average in the ten industries where exports or domestic sales increase most while falling at rates far below average in the ten industries where exports or domestic sales increase the least. Finally, putting all the components of change in sales together, we see sizable differences in changes in wages as well as in employment between industries experiencing the extremes of the change in sales. 8.3.3 Asymmetric Responses to Changes in Sales An important issue in decentralized labor markets where wages are flexible to industry conditions is whether wage responsiveness is symmetrical to declines in demand and increases in demand. In an economy in which wages are above the reservation wages of unemployed workers, greater responsiveness of wages to declines in demand than to increases in demand can increase employment.28To examine the symmetry of response, we have divided the change in the In sales variable into two parts: changes in excess of the mean change and changes below the mean change. We then regressed changes in In hourly wages over the twenty-six-year period 1958-84 on changes in sales interacted with a dummy for above average changes and a dummy for below


254

-

.... ....' ..... .'*.*.... .*. ..... .... ..... .... ..... .... '..... :.:.:.' ....

-0.15 -

7

'

-'I3'

Low

High

Low Sales 'Domestic' Foreign Demand Demand

'

'

''

Import Share

Deviations from average LN change in production employment

1

0,4 -02 -0,8

Low

- 1,4

-2 Demand

Demand

Share

Fig. 8.1 Mean changes for the ten industries with the highest and lowest changes in each category relative to the overall mean change-1958-84

'

255


average changes. The coefficients on the two sale interaction terms (standard errors) given below show greater responses to the below-mean than to abovemean changes on changes in In hourly earnings: effect of below-mean changes: .080 (.017); effect of above-mean changes: .011 (.020). This finding suggests that relative wage flexibility in U.S. manufacturing has taken the form of “concessions” in industries doing more poorly to a greater extent than it has of large relative wage gains in industries doing better and thus may have contributed to the job growth in the country. We did not find any evidence that industries paid exceptionally large wage gains in booming markets: most of the adjustment is on the down side. 8.4

Conclusion

This paper has documented with two different data sets that, in detailed manufacturing industries in the United States, wages respond significantly to changes in industry sales, whether generated by domestic market or traderelated developments, and found that changes in immigrant shares are also related to industry wages. It has also found that the wage-setting institutions in the labor market condition wage responsiveness, with unionized high-wage industries showing the greatest response to changes in sales. While far from the dominant force in altering the industrial wage structure, shifts in product demand due to changes in sales from trade as well as domestic market developments have contributed to changes in earnings by industry. As the observed inverse relation between changes in the immigrant share of the work force and changes in industry wages cannot be readily explained by compositional factors, this finding evidently merits more detailed analysis of the market for immigrant labor.

Notes 1. Dickens et al. (1985) provide a detailed review of aggregate studies of the employment effect of trade in the United States. Lawrence (1984) provides a good example of a study utilizing an input-output framework to analyze the effect of trade on employment in U.S. manufacturing. 2. We define competitive decentralized wage setting to be a system in which wages are set to equate labor supply and labor demand, as in a textbook perfectly competitive labor market. We defer until later in this section a discussion of the possibility that efficiency wage considerations may yield above market-clearing wages even with decentralized wage setting in competitive labor markets. 3. Katz (1986) surveys efficiency wage and rent-sharing models of wage determination and discusses some of the implications of these models for interindustry wage differences. Dickens and Katz (1987), Katz and Summers (1989), Krueger and Sum-

256


mers (1988), and Murphy and Topel (1987) provide evidence on the role of efficiency wage, rent-sharing, and standard competitive factors in explaining interindustry wage differentials. Lewis (1986) and Freeman and Medoff (1984) survey the vast literature on the effect of collective bargaining on relative wages. 4. Grossman assumes that the unionized sector faces an infinitely elastic product demand schedule at the exogenous international price. Labor demand elasticity depends only on the production technology and factor substitution possibilities. International competition is not viewed as changing product market structure and/or the elasticity of product demand. 5. A key assumption implicitly made in this model is that the membership is greater than the employment level that would prevail at the competitive wage rate w*. 6. In the case of Nash bargains with a utilitarian union and strongy efficient bargains, the wage is the mean of the average product of labor and the opportunity wage (McDonald and Solow 1981). In this case, a decrease in rents from increased international competition will lead to wage decreases in the union sector. 7. We can also substitute for dQ using (7) to obtain a relation between wages and output, dW = [ q / ( l + haq)]dQ. 8. While we have data on prices and quantities, the likelihood that price indices are inaccurate leads us to focus on sales. In empirical work, however, we also examine the separate effect of measured quantities and prices. 9. Consider the value of q when A is .I0 and n is .25. By eq. ( l l ) , we have q = .10/[1 - . l o . .25 . (1 - h) = .10/[1 - .025(1 - h)]. For the minimum value of h of zero, q differs from A by a bare 2.5%. For a high elasticity of, say, five, the difference is less than 10%. 10. To see this, we substitute dS - (1 - h)ndW for dX in eq. (3b) and then substitute (1 1) for dW.This yields the following: dE = {v/[l

+ q(1

- h)a]}dS,

where v is the desired parameter. 11. Rewriting eq. (5) and (6) as dP = udW + u and dW = qdx v, where u and v are error terms, one can easily derive a relation between wages and sales analogous to eq. (9):

+

dW = AdS

+ [V

-

q(1 - h)u]/[l

+ q(1 - h ) ~ ] .

12. Revenga (1989) uses an instrumental variables technique with the change in the (import-source-weighted) industry exchange rate used as an instrument for dS to reanalyze the data set that we examine in sec. 8.2. Her findings are qualitatively quite similar to those we report in sec. 8.2 and suggest that our least squares estimates may understate the response of industry wages to changes in industry sales. 13. This decomposition of change in log sales can be derived by writing sales as S = [(S - x)/DOM]DOM x, where x equals exports. The application of the difference operator to this decomposition of sales yields

+

dS = [S - x)/DOM]d(DOM) - DOM . [d(MSHR)] - d(D0M) . d(MSHR)

+ dx.

Equation ( I 1) in the text can then be derived from the above expression by dividing through by S to yield an expression in percentage changes, approximating percentage changes as In changes, and dropping the interaction term. This approximation is almost exact for annual changes. We have experimented with the exact decomposition using percentage changes and including the interaction term in several of our specifications and have found results in all cases quite similar to those obtained with our In change approximation.

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14. We have also examined all workers and nonproduction workers. As production workers are the majority of workers, the results for all workers are quite similar to those for production workers. For nonproduction workers, there are some modest differences, but nothing substantial enough to change the tone of the findings. 15. Abowd (in this volume) provides detailed descriptions of the data set and its construction. 16. We have also estimated annual change equations replacing the time dummies with observed cyclical variables, such as the aggregate unemployment rate, and allowed different industries to have different cyclical sensitivities. The results are quite similar to the reported estimates based on equations with time dummies. 17. Branson and Love (1988) analyze the effect of time-series changes in the real exchange rate on employment in U.S. manufacturing industries. Eichengreen (1988) performs a similar time-series analysis of the effect of the real exchange rate on employment in four U.S. basic industries with the industry-specific wage treated as exogenous and included as an explanatory variable. Grossman (1987) takes changes in the price of import substitutes as exogenous and analyzes the effect of time-series changes in import prices on wages and employment in nine trade-affected U.S. manufacturing industries. The lack of import price data for a large number of industries at the level of aggregation of available wage and employment data prevents us from following a similar strategy. Grossman (1986) also uses a similar methodology to analyze the effect of international competition on employment in the steel industry but treats the wage as exogenous. 18. We have computed White (1980) heteroskedasticity-consistentstandard errors for several of our specifications. The White standard errors typically differ from the reported standard errors by less than 5%. 19. The immigrant share data for 1984 are based 1980 Census of Population data. We utilize 1960, 1970, and 1980 Census of Population data on immigrant shares of population in the ensuing empirical analysis. 20. The change in immigrant share from 1960 to 1980 has a correlation of - .25 with the initial (1958) industry In hourly wage level, and the initial (1960) immigrant share has a correlation of - .35 with the change in the In hourly wages over the period 1958-84. Immigrants have moved into low-wage industries, and initially immigrant intensive industries have experienced relatively low wage growth. 21. For example, the estimated effect of sales on annual earnings was ,045with a standard error of .011, while the effect of sales on employment was ,890 with a standard error of ,018. 22. We have also analyzed annual changes within the periods and found the same result: greater responses of hourly earnings to sales in the 1980s than in earlier periods. 23. These regressions utilized samples from the May 1974 CPS and the full-year 1984 CPS for workers in the fifty-eight three-digit CIC manufacturing industries with consistent industry classifications over this period. The controls included in the In earnings regressions in addition to industry dummies were education and education squared; experience and experience squared; nonwhite, female, SMSA, region, part time, marital status, married times female, and occupation dummies; and interactions of the education and experience variables with the female dummy. The regressions for all workers (both union and nonunion) included a union dummy. Industry differentials for all workers from regressions without a union dummy yield quite similar results. 24. The same pattern of larger wage responses to sales of the high-union density class is also apparent when long changes for 1958 to 1984 are analyzed rather than annual changes. 25. In separate annual change regressions by union category for 1959-84 of changes in In annual production worker hours on changes in In sales and our usual set

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of controls, the coefficients (standard errors) on change in In sales were .63 (.01) for high union density, .61 (.01) for medium union density, and .71 (.01) for low union density. 26. For a discussion of the potential pitfalls in interpreting estimated coefficients on union density in industry regressions, see Lewis (1986). 27. The industries with the lowest growth in import shares also experienced well above average wage growth, suggesting possible endgame behavior of the type modeled by Lawrence and Lawrence (1985). Still, fig. 8.1 does show that the industries with the biggest declines in domestic market size had below average wage increases, indicating that endgame is far from the norm for declining industries. 28. This assumes no difference in the size of sectors with increasing/decreasing demand.

References Borjas, George J. 1985. Assimilation, Changes in Cohort Quality, and the Earnings of Immigrants. Journal of Labor Economics 3 (October): 463-89. Branson, William H., and James P. Love. 1988. U.S. Manufacturing and the Real Exchange Rate. Misalignment of Exchange Rates: Effects on Trade and Industry, ed. Richard C. Marston. Chicago: University of Chicago Press. Chiswick, Barry R. 1978. The Effect of Americanization on the Eamings of Foreignborn Men. Journal of Political Economy 86 (October): 897-921. Dickens, William T., and Lawrence F. Katz. 1987 Inter-industry Wage Differences and Theories of Wage Determination. NBER Working Paper no. 2271. Cambridge, Mass.: National Bureau of Economic Research, June. Dickens, William T., Phillip Shapira, Laura Tyson, and John Zysman. 1985. The Employment Effects of International Trade: A Review of the Literature. University of California, Berkeley, February. Mimeo. Eichengreen, Barry. 1988. International Competition in the Products of U.S. Basic Industries. In The United States in the World Economy, ed. Martin Feldstein. Chicago: University of Chicago Press. Freeman, Richard B., and James L. Medoff. 1984. What Do Unions Do? New York: Basic. Grossman, Gene M. 1984. International Competition and the Unionized Sector. Canadian Journal of Economics 17 (August): 541-56. . 1986. Imports as a Cause of Injury: The Case of the U.S. Steel Industry. Journal of International Economics 20: 201-23. . 1987. The Employment and Wage Effects on Import Competition in the United States. Journal of International Economic Integration 2: 1-23. Huizinga, Harry. 1987. Union Wage Bargaining in the International Economy. Harvard University, April. Mimeo. Katz, Lawrence F. 1986. Efficiency Wage Theories: A Partial Evaluation. In NBER Macroeconomics Annual 1986, vol. 1, ed. S. Fischer, 235-75. Cambridge, Mass.: MIT Press. Katz, Lawrence F., and Lawrence H. Summers. 1989. Industry Rents: Evidence and Implications. Brookings Papers on Economic Activity: Microeconomics, 209-75. Krueger, Alan B., and Lawrence H. Summers. 1988. Efficiency Wages and the Interindustry Wage Structure. Econometrica 56 (March): 259-93. Lawrence, Colin, and Robert Z. Lawrence. 1985. Relative Wages in U.S. Manufactur-

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ing: An Endgame Interpretation. Brookings Papers on Economic Activity, no. 1: 47106. Lawrence, Robert 2. 1984. Can American Compete? Washington, D.C.: Brookings. Lewis, H. G. 1986. Union Relative Wage Effects: A Survey. Chicago: University of Chicago Press. McDonald, Ian, and Robert Solow. 198 1. Wage Bargaining and Employment. American Economic Review 7 1 (December): 896-908. Murphy, Kevin M., and Robert Topel. 1987. Unemployment, Risk, and Earnings. In Unemployment and the Structure of Labor Markets, ed. K. Lang and J. Leonard. London: Blackwell. Revenga, Ana L. 1989. Wage Determination in an Open Economy: International Trade and U.S. Manufacturing Wages. Harvard University, December. Mimeo. Rose, Nancy L. 1987. Labor Rent-Sharing and Regulation: Evidence from the Trucking Industry. Journal of Political Economy 95 (December): 1146-78. White, Halbert. 1980. A Heteroskedasticity-consistentCovariance Matrix Estimator and a Direct Test for Heteroskedasticity. Econometrica 48 (July): 8 17-38.


9

Foreign-Owned Businesses in the United States Jonathan S. Leonard and Rachel McCulloch

For the United States, concern about foreign direct investment (FDI) has historically centered on the costs and benefits to the nation from the establishment of subsidiaries abroad by U.S. multinational firms. Since the mid-l970s, however, the United States has emerged as the world’s leading host to inward direct investment. Along with record purchases of U.S. securities, individual and institutional investors across the globe have purchased U.S. farmland, department stores, and luxury hotels. Foreign manufacturing firms have expanded U.S. distribution and service facilities and local production capacity. Japanese and European banks have opened branch offices in New York, Chicago, and Los Angeles. By 1984, the total value of foreign direct investments in the United States amounted to about 10 percent of the value of all New York Stock Exchange stocks. Moreover, these holdings had increased at a dramatic rate, from $35 billion in 1977 to $160 billion (nominal) in 1984. This flood of inward foreign direct investment represents a dramatic shift from the established pattern of the earlier postwar period. Until the 1970s, FDI globally was dominated by the outward thrusts of U.S. firms: the multinational corporations doing the investing were viewed by many writers as a peculiarly American phenomenon. But now foreign direct investment appears to be one more area in which the nation’s industrial competitors have been catching up to the United States. After years of relative stability, the ratio of inward to outward direct investments rose from less than one-quarter in 1977 to more than three-quarters by 1985 (Lipsey 1987). Jonathan S. Leonard is associate professor in the Organizational Behavior and Industrial Relations Group of the Haas School of Business Administration, University of California, Berkeley, a research associate of the Institute of Industrial Relations at Berkeley, and a faculty research fellow of the National Bureau of Economic Research. Rachel McCulloch is Rosen Family Professor of Economics and director of the Lemberg Program in International Economics and Finance at Brandeis University, and a research associate of the National Bureau of Economic Research.

261

262

Jonathan S. Leonard and Rachel McCulloch

As the foreign presence grows, U.S. policy concerns shift accordingly. Policymakers in the United States once focused primarily on the effects of direct investments abroad by U.S.-based corporations. The central issue in that policy debate was the relationship of outward investments to U.S. trade and domestic employment. Researchers assessed the degree of complementarity or substitution between U.S. exports and host-country production by U.S. subsidiaries and, to a lesser extent, between U.S. production for domestic markets and imports from subsidiaries abroad (e.g., Musgrave 1975; Dewald et al., 1978; Bergsten, Horst, and Moran 1978, chaps. 3,4). From a theoretical perspective, the key question was whether U.S. investments abroad were “defensive”-necessary to secure markets that would otherwise be lost to foreign rivals. Sympathetic observers like Vernon (1971) inferred from case studies that investments abroad by U.S. multinationals were largely defensive, but most American labor unions and some academic researchers (e.g., Frank and Freeman 1978) took a less optimistic view. Empirical testing was complicated by the product-cycle character of most U.S. direct investments abroad; the industries and firms with above-average propensities to invest abroad were also those with above-average propensities to export. Today, while the potential loss of U.S. jobs associated with establishment of foreign subsidiaries by U.S. firms remains an important policy concern, attention has moved to issues raised by inward direct investment-issues that are novel from the U.S. perspective but widely discussed during the postwar period in other major host countries, both industrialized and developing. The fundamental concern is the extent to which the investing firm, rather than the host country, derives the lion’s share of economic benefits from controlled local production. The distinguishing feature of foreign direct investment is an ownership stake sufficient to permit a local management role for the investor.’ In dollar terms, direct investment inflows from abroad remain small relative to the nation’s total foreign borrowings. However, this measure may understate the potential economic, political, and social impact. By definition, direct investments represent the extension of foreign firms’ managerial control into the U.S. economy, just as U.S. direct investments abroad have allowed American firms to control significant parts of the domestic economies of other nations.2 The recent pattern is controversial in the United States for much the same reasons that the former one has been controversial in host nations abroad. What accounts for the rapid increase in foreign direct investments in the United States? Is the flood of capital from abroad linked to U.S. trade problems? Should the United States welcome foreign investors, or are there reasons to limit the sale of U.S. assets? This paper considers the theoretical explanations for the growing foreign direct investment in the United States and explores some of the empirical regularities associated with foreign-owned companies. Section 9.1 discusses the

263

Foreign-Owned Businesses in the United States

many motivations underlying FDI in general and in the United States specifically. It provides a synthesis of theoretical explanations for the effects of these investments on the domestic labor market. Section 9.2 presents our data sources and methods. Section 9.3 analyzes the differences and similarities between U.S. and foreign-owned businesses in the United States. Section 9.4 offers some conclusions from the analysis.

9.1 The Motivations for Foreign Direct Investment 9.1.1 Foreign Investment and Internationalization Once largely insulated from developments abroad by its size and distance from other industrial powers, the United States has in recent years been drawn into increased economic intimacy with other nations. Foreign direct investments, first outward and more recently inward, have played a central role in establishing these linkages, bringing production and sales of enterprises in the United States and other nations under the control of a single management. The internationalization of the U.S. economy can be measured in a number of ways. The most obvious is that U.S. markets for goods and services are far more open than in the past. Almost every manufacturing industry has experienced a dramatic rise in the ratio of imports to domestic production-perhaps no surprise at a time when the nation has run trade deficits of record proportions. Less well known is that almost every U.S. manufacturing industry has also experienced a rise, albeit not as dramatic, in its exports. The same is true for agriculture, for the extractive industries, and for many of the service activities that now dominate U.S. employment. Even more striking than the increased flow of goods across U.S. borders is the growth in the volume and variety of asset transactions with other nation^.^ U.S. investors have long dominated international financial markets as lenders. In the 1980s, however, the United States became a major international borrower. Indeed, as a result of increased foreign borrowing and reduced foreign lending, the nation is today billed as the world’s leading debtor nation-an unfamiliar role, and one that many Americans find troubling. While the nation’s overall dependence on capital from abroad is itself womsome, the primary focus of concern is direct investment and the associated control by foreign enterprises over U. S. productive activity. The increased trade in financial assets reflects several independent developments. The United States and most other industrial nations have greatly reduced legal barriers to both inward and outward financial flows, part of a more general trend toward deregulation of financial markets. The recycling of large petrodollar surpluses in the 1970s contributed to the growth of institutions capable of handling huge international capital flows. Revolutionary changes in global communications have facilitated the integration of national financial markets into a single worldwide network of lenders and borrowers.

264


Together with expanded options for international communication, reduced costs of transporting goods and people have promoted a shift by many firms from national to global management of innovation, production, and distribution. Although the recent cries of alarm suggest otherwise, U.S. borrowings abroad are themselves nothing new. The United States was a borrower and net debtor for most of the period between the nation’s founding and World War I. What is new is the emergence of the United States as the major host country for inward foreign direct investments by multinational corporations based in other countries. From 1961 through 1967, less than 3 percent of the world’s flow of new FDI came to the United States. That fraction rose gradually during the 1970s and peaked in 1981, when the United States attracted nearly half of total direct investment inflows worldwide and two-thirds of total inflows going to developed countries (Lipsey 1987). Around 1980, the United States displaced Canada as the world’s leading host to foreign subsidiaries. The growth in U.S. trade and the growth in foreign direct investment have been linked developments. Transactions between multinational firms and their foreign subsidiaries have accounted for a major share of the overall rise in the volume of U.S. trade.4 Coordinated by a single global management, trade in intermediate inputs as well as completed goods allows international comparative advantage to operate not only at the level of individual products but also in determining the location of different steps in the production of a single product. As of 1985, about 15 percent of all foreign direct investments in the United States (and nearly two-thirds of all Japanese investments) were in the wholesale trade sector. The main task of such investments is to promote the parent’s imports to or exports from the United States. Even when local production by a manufacturing subsidiary replaces goods previously imported, as has occurred in autos and electronics, the resulting fall in imports of finished goods is typically offset by an associated increase in imports of component^.^ 9.1.2 Why Firms Go Abroad To understand why foreign firms have been increasing their ownership stakes in the U.S. economy, it is useful to review the basic conditions required for profitable foreign direct investment. These conditions provide a context for identifying specific changes in the global environment that may underlie the recent surge in establishment of U.S. subsidiaries by firms abroad. In one sense, the central puzzle concerning FDI is why it takes place at all, given the significant competitive disadvantages faced by a firm entering a foreign market. To make the strategy viable, the investing firm must possess an “advantage” in terms of product, process, or management sufficient to outweigh its obvious disadvantages relative to actual or potential domestic competitors in the host country. The existence of additional hurdles and risks fac-

265


ing a foreign entrant suggests that foreign subsidiaries should on average yield higher profits than domestic operations in the same industry. These conditions imply that direct investments will be concentrated in markets that do not conform to the paradigm of perfect competition-markets characterized by incomplete information, barriers to entry, or other “imperfections.” A rent-generating competitive advantage is still only a necessary condition for the viability of foreign direct investment. Since the firm’s competitive advantage could in some circumstances be better exploited by exporting from the home country, an additional requirement for setting up foreign operations is a locational advantage. This could reflect the usual considerations of production and transport costs as well as advantages arising from national policy at home and abroad, for example, taxation, regulation, and barriers to trade. In the absence of a significant locational advantage, the potential investor is likely to choose exporting over the more costly and risky option of establishing a local subsidiary. Like the competitive advantage, the locational advantage is necessary but not sufficient. Even with a locational advantage, there must be an organizational advantage of FDI over alternative strategies such as licensing or other long-term contractual arrangements with firms in the host country. In the language of industrial organization, there must be an internalization advantagean advantage to substituting internal modes of resource coordination within a single firm for an external market-based arrangement between independent firms. In other words, there must be an advantage of integrated global management .’

9.1.3 Internalization and Internationalization Foreign direct investment is precisely a firm’s internalization of economic activity across a national boundary-internalization of management. The underlying motives are essentially the same ones that promote expansion of a firm’s activities within a single domestic market, but with a larger anticipated benefit required to offset the larger costs of international expansion. This perspective is supported by the empirical finding that firms investing abroad are on average larger in their domestic operations than other firms in the same industry. Multinational firms based in small countries are also typically smaller in absolute size than their counterparts from large countries. Both observations are consistent with the hypothesis that firms should exhaust the gains from domestic internalization before going abroad. Since the investments abroad of U.S. multinationals dominated the global picture for several decades after World War 11, most of the empirical research has focused on these. Studies of I 3 1 by U.S. manufacturing firms have verified that investment activity is clustered in the industries where research and development and advertising expenditures are important. Such expenditures presumably create the competitive advantage necessary for a U.S. firm to operate profitably in a foreign environment. Competitive advantages interact

266


with potential gains from internalization. For example, high-technology firms tend to exploit their newest technologies via subsidiaries, while older products and processes are licensed to independent foreign producers. Evidence on locational advantage is less compelling, except for resourcebased industries. Predictable factors such as availability of suitable labor at lower cost, a large and protected domestic market, favorable tax or regulatory treatment, and stable political environment all appear to have some influence on location decisions. Other locational considerations cannot be separated from the benefits of a single global management structure (internalization). Some FDI is primarily for the purpose of enhancing local sales of goods imported from a firm’s production facilities elsewhere, as with distribution and service facilities.* Local operations can also enhance exports by the parent to the host market by providing the parent with up-to-date market information when conditions are changing rapidly, thus keeping the parent in close touch with market trends. In a concentrated industry, the establishment of local production capacity may be central to the investing firm’s competitive strategy (e.g., Graham 1978). The subsidiary represents the firm’s precommitment to a substantial presence in the local market. Other advantages of multinational activity are associated with being multinational rather than with any specific host location. A global production network permits the firm to diversify risk and, more generally, increases its options when conditions are volatile (e.g., Kogut 1983). The risk-handling motive may be relevant in explaining not only investments in the post-BrettonWoods era of volatile exchange rates but also the classic foreign direct investments in extractive industries. Enhanced opportunities for tax avoidance are another widely cited benefit of multinational operations. The modest reported financial success (and correspondingly low tax burdens) of many foreignowned U.S. plants may reflect such accounting manipulation.

9.1.4

Exchange Rates and Direct Investment

Other things equal, a lower dollar makes U.S. products a better buy in world markets. Should the same hold for assets? If a U.S. asset is seen as a claim to a future stream of dollar-denominated profits, and if profits will be converted back into the domestic currency of the investor at the same exchange rate, the level of the exchange rate does not affect the present discounted value of the investment. Thus, dollar undervaluation (or overvaluation) is irrelevant unless a major motive for the investment is speculation on future movements in exchange rates. Speculative motives may influence portfolio investments but are unlikely to play an important role in direct investments, where the planning horizon usually extends over years or even decades. A more relevant consideration is that a weak dollar makes the United States more attractive as a production site. By lowering U.S. production costs rela-

267


tive to those in Europe or Japan, a fall in the dollar might shift locational preference for direct investors toward the United States. Even so, advantages of internalization would be required to make direct investment a profitable response to the new currency values. In the absence of such advantages, foreign firms would be unable to compete with U.S. firms in exploiting the locational advantage of lower production costs. A more basic problem with attributing investment flows to exchange-rate levels is that the post-Bretton-Woods regime of generalized floating has been characterized by large swings in key rates, from apparent undervaluation to apparent overvaluation and back again.9 Thus, the motive for increased U.S. investments may lie less in the specific level of the exchange rate around the time of the investment than in the high probability of future large movements. Here internalization does play a key role-allowing increased costs in one location to be offset by reduced costs elsewhere and permitting some flexibility in shifting marginal production between locations on different sides of a major rate alignment. 9.1.5 The Role of Protection No other incentive for foreign direct investment has received as much attention as import barriers. It seems almost self-evident that tariffs or quotas will stimulate direct investments in the protected markets. Recent developments in the U.S. auto and electronics industries offer visible support for the proposition. Yet statistical analyses of Canadian and U.S. data have failed to confirm a systematic relationship between direct investment and protection. The likely reason for the weak empirical findings is that protection by itself confers only a locational advantage. Whether that locational advantage leads to inward investment or simply alters conditions of domestic entry and exit depends on other industry characteristics. In the absence of a firm-specific competitive advantage optimally exploited through internalization, domestic producers will be better able than subsidiaries of foreign companies to capture the benefits of local production. Important though they are in their own right, autos and electronics may be exceptions to the general rule. In these industries, technological know-how and managerial know-how are firm-specific advantages that allow foreign producers (notably Japanese) to compete effectively with established domestic firms.l0 By contrast, the highly protected U.S. apparel and footwear industries have seen almost no direct investments from abroad. For these lowtechnology industries, firm-specific advantages are apparently too small to offset the greater costs incurred by foreign investors. Evidence at the country rather than the industry level also casts doubt on the hypothesis that protection is a strong magnet for inward direct investments. Among the less-developed countries, open, export-oriented economies have been more successful in attracting new investments than nations pursuing import-substitution strategies. For U.S. outward investments, Canada, the

268


United Kingdom, and Germany, all with relatively liberal trade regimes, have been the most important host countries. Bhagwati (1985) suggests a more subtle link between protection and direct investment using the concept of “quid pro quo” foreign investment-investment made to defuse protectionist pressure rather than to circumvent actual current or anticipated protection. On this interpretation, Japanese investments in the automobile industry were intended, at least in part, to avoid future increases in protection (e.g., local-content requirements) rather than to circumvent existing import restrictions. Presumably, such investments would lessen the perceived need for protection and also would shift the domestic political balance toward a more liberal trade stance. An alternative “strategic” interpretation is suggested by the oligopolistic structure of the auto industry and the extensive publicity surrounding Japanese entry into U.S. production. Japanese firms may wish to demonstrate to their U.S. rivals that Washington cannot protect them from yielding part of their customary shares in the U.S. market. The new Japanese entrants could actually benefit from future increases in protection if their competitive advantage translates into lower costs in U.S. production while trade barriers prevent the Big Three from putting their own nameplates on captive imports from Japan or elsewhere. 9.1.6 Foreign Investment and U.S. Labor For host countries worldwide, the most important anticipated benefit from foreign investment is the creation of new jobs. In this, the United States has been no diEerent. Holding out the prospect of hundreds or even thousands of new jobs, U.S. state and local officials have mounted formidable campaigns to lure foreign plants, usually offering sizable financial incentives to supplement the region’s other attractions.’’ Yet, as with the presumed job losses associated with outward investments of U.S. multinationals, the overall effects of inward investments on domestic employment are far from clear. First, while there are obviously “new jobs” created by new subsidiaries, to some extent these new jobs will be offset by employment losses elsewhere. In the most optimistic scenario, local production will simply substitute for goods previously imported.’* At least for the industry, the effect on total employment should be positive. But local production by foreign affiliates can also cut into the market share of domestic competitors, so that the new jobs are matched by layoffs elsewhere in the same industry. If the affiliates use more imported inputs than their domestic counterparts, production and employment may be reduced accordingly in the supplier industries. A second concern is about the types of jobs created. Will foreign multinationals use U.S. labor for routine assembly operations, keeping the “good jobs” at home? Reich and Mankin (1986) interpret Japanese joint ventures in the United States as “part of a continuing, implicit Japanese strategy to keep the higher paying, higher value-added jobs in Japan and to gain the project

269


engineering and production process skills that underlie competitive success.” A related concern shared with other host countries is that foreign affiliates allow little opportunity for local workers to rise into management ranks. While the Japanese presence in U.S. manufacturing is still too small and too new to offer much evidence on this issue, most analysts agree that U.S. operations abroad have benefited U.S. managers and skilled workers at the expense of less-skilled U.S. production workers, whose jobs have moved offshore. 9.1.7 U.S. Competitiveness and Inward Direct Investment The close link between FDI and the investing firm’s competitive advantage suggests that the rise in inward foreign investments in the United States as well as the slowing of U.S. direct investments abroad reflect the industrial catch-up of other nations to the United States. Where the competitive advantages were once controlled almost exclusively by U.S. companies, new rivals have emerged in Europe, Japan, and even some of the developing countries. As with the successful U.S. multinationals of earlier decades, these firms have exploited their competitive advantages first through exports and later through direct investment in the market countries. Like other host countries over the years, the United States is reevaluating the potential gains and losses from allowing free entry to foreign subsidiaries.

9.2 Data Sources and Methods Section 9.3 compares foreign-owned U.S. firms with their U.S.-owned counterparts. Most of the data used are derived from foreign direct investment series published by the Bureau of Economic Analysis of the U.S. Department of Commerce. Extensive cross-sectionaldata for 1980 are from Foreign Direct Investment in the United States, 1980, a survey of U.S. business enterprises in which foreign ownership, either direct or indirect, was at least 10 percent. A number of caveats apply to these data, particularly where comparisons are made to domestic aggregates. First, FDI data are reported in consolidated form for the U.S. affiliates. The activities of each establishment within multiestablishment enterprises are not classified separately by their own industry (as in the National Income and Product Accounts) but, rather, are classified by the industry group accounting for the largest percentage of the enterprise’s sales. This undercounts data for industries with many “owned” establishments and overcounts for industries with many “owner” establishments. It overstates cross-industry variance in sales. Second, FDI data aggregate petroleum-related activities including extraction, refining, and retailing. These have been removed from their respective industries and aggregated into a separate category that has been suppressed in the industry detail presented here. In consequence, these foreign-owned activities are undercounted in their respective subindustries.

270


Third, enterprises that are entirely foreign owned but in which no single foreign person owns at least 10 percent are not classified as foreign owned. For this purpose, “person” is defined to include any individual, partnership, associated group, or corporation, including members of a syndicate or joint venture. Fourth, compensation and employment data are collected only for U.S. affiliates whose assets, sales, or net income exceeded $1 million or whose land ownership exceeded two hundred acres. Fifth, data are annual averages for each enterprise’s fiscal year. Sixth, employment is reported as annual average number of employees, not as full-time equivalents. This will cause an understatement of compensation or wages per employee as reported here, but it should not affect the comparison of foreign direct with domestic because analogous concepts are used for domestic. Compensation per employee is also understated in some cases by the use of part-year compensation and year-end employment in some newly acquired enterprises and establishments. Finally, all the data discussed here are aggregates of enterprise data. The composition of the underlying sample changes over time, as the section on new acquisitions and establishments shows. Any change in, say, compensation per employee may then be due to (1) pay raises within previously sampled establishments, (2) deletion of low-wage establishments from the sample, (3) addition of high-wage establishments, or (4) purchase of a high-wage establishment by an enterprise with greater sales in another industry.

9.3 Buying American Foreign direct investment in the United States includes all firms in which 10 percent or more of the equity is foreign owned. The stock of FDI increased more than fourfold (in nominal terms) from $34.6 billion in 1977 to $159.6 billion in 1984. In 1977, the value of FDI in the United States was equal to 23.6 percent of the value of U.S. direct investment in foreign countries built up during earlier decades. By 1984, the reversal of net investment flows was well along. FDI in the United States was 68.4 percent of U.S. FDI abroad. Between 1977 and 1984, FDI in the United States more than doubled in proportion to the total value of all stocks listed on the New York Stock Exchange, the proportion rising from 4.3 percent to 10.1 percent. In part, this reflects growing foreign investments in all forms of U.S. assets. However, table 9.1 also shows that foreign direct investments increased in value relative to foreign-owned stocks and to U.S. investment abroad. Both developments indicate that potential foreign investors see greater competitive, locational, and organizational advantages to establishment of U.S. subsidiaries than in earlier periods. High interest rates affected many foreign investments in U.S. financial in-

271 Table 9.1

Foreign-Owned Businesses in the United States International Assets, 1977-84 1977

1. Private foreign investment in private U.S. assetsa 2. Row 1 as % of private U.S. fixed nonresidential gross capital 3. FDI in the US.” 4.Row3as%ofNYSEValue 5. Row 3 as % of foreignowned U.S. stocks 6. Row 3 as % of U.S.direct investment abroad

1978

1979

1980

1981

1982

1983

1984

157.9 189.8 242.1 308.7 380.1 477.0 559.2 630.5

5.3 34.6 4.3

5.6 42.5 5.2

6.2 54.5 5.7

6.9 7.7 9.0 10.1 10.9 83.0 108.7 124.7 137.1 159.6 6.7 9.5 9.6 8.7 10.1

86.9 101.0 112.8 128.5 168.3 162.4 140.9 166.4 23.6

26.1

29.0

38.5

47.6

56.2

60.4

68.4

Sources: Survey of Current Business, various issues; Economic Report of the President, various issues; Statistical Abstract of the United States. a Billion current dollars.

Table 9.2

Flows of Foreign Investment in the United States, 1960-86 (billions of U.S. dollars)

Year

Total Inflow

1960 1970 1972 1974 1976 1978 1980 1981 1982 1983 1984 1985 1986

2.3 6.4 21.5 22.5 42.7 65.4 84.7 78.2 109.7 96.9 108.2 127.1 213.3

Total FDI

FDI as % of Total

Japanese FDI

% of FDI from Japan

.3 1.5 .9 4.8 4.3 7.2 13.7 22.0 10.4 11.9 25.4 17.9 25.6

13.7 23.0 4.4 21 .o 10.2 12.1 16.1 28.1 9.5 12.3 23.4 14.0 12.0

NA

NA 3.6 2.0 4.4 13.5 12.5 5.3 12.6 16.8 13.8 17.2 17.3 18.5

~

.o .o

.2 .6

1.a .7 2.8 I .7 1.7 4.4 3. I 4.7

Sources: Survey of Current Business, various issues (for 1972-86); Business Statistics, 1984 (for 1960 and 1970). Note: Percentages calculated from unrounded flow data. Data for 1986 are preliminary.

struments. The composition effect created by the increase in foreign ownership of U.S. financial assets overshadows a less noticed shift in foreign investment toward direct corporate ownership. Since 1977, foreigners have increasingly been purchasing control of U.S. corporations. The value of inward FDI, as a percentage of foreign ownership of U.S. stocks, rose from 87 percent in 1977 to 166 percent in 1984. Table 9.2 shows the trends in the flow of new FDI. The total inflow of foreign investments increased in the 1970s and 1980s. Although FDI as a

272


percentage of the total inflow of investment does not show a distinct trend over this longer period, Japanese FDI has increased substantially. The flow statistics offer less support for the hypothesis of changing competitive, locational, or organizational advantages to establishment of U.S. subsidiaries by foreign-based multinational corporations. The absence of a trend in FDI as a percentage of total investment inflow indicates that the growth in FDI may be simply a manifestation of the growth in all forms of foreign investment in the United States. The trend in Japanese FDI in the United States suggests that economic advantage arguments may, however, apply to Japan and to the industries in which Japanese companies are highly visible. 9.3.1 Acquisitions and New Establishments Newly acquired or established enterprises show one form of increased investment by foreigners in the United States (see table 9.3). The Bureau of Economic Analysis (BEA) classifies as a new acquisition an existing U.S. enterprise in which foreign ownership (directly or through U.S. affiliates) passes 10 percent. However, this is only a small part of total investment, because additional equity investments in existing U.S. affiliates are not counted once the 10 percent threshold has been passed, and because only enterprises with assets exceeding $1 million or two hundred acres of U.S. land are included. To illustrate, of the 2.1 million U.S. employees of foreign-owned companies in 1980, 13 percent were in newly acquired enterprises, and .6 percent were in newly established enterprises. Compared to analogous rates for total domestic industry, the acquisition rate is high and the start-up rate low. Of the $522 million of foreign-owned 1980 assets, 8 percent were newly acquired and 1.4 percent newly established. Roughly 80 percent of these investment funds came through existing U.S. affiliates. In 1980, 37 percent of these investments were financed by U.S.-source funds. Only 2 percent of Table 9.3

Outlays and Employment in U.S. Enterprises Newly Acquired or Established by Foreign Direct Investors, 1979-85 Outlays ($ million current)

1979 1980 1981 1982 1983 1984 1985

15,317 12,172 23,219 10,817 8,091 15,197 19,547

Employment New Establishments 15,467 13,022 14,072 8,169 5,556 4,139 7,772’

Employment New Acquisitions 314,548 279,459 428,745 225,673 102,557 168,406 235,667’

Sources: Outlays: Shea (1986, 47, table 1). Employment: same 1979, 1980, 1981, 1982, 1983, 1984, and 1985. preliminary

273


these investments (1982) were reported to receive specific state or local investment incentives or subsidies. Measured either by employment or assets added in new acquisitions or establishments, inward FDI has been volatile and shows no clear trend. This is misleading. Indeed, both assets and employment in foreign direct investments have been growing steadily in the 1980s. The difference arises because most of the growth has occurred in ongoing foreign-owned businesses. This is similar to the growth process of domestic industry generally, which is also dominated by the expansion of ongoing concerns. 9.3.2 Foreign Ownership by Industry The nature of the industries in which foreigners invest does differ substantially from domestic industry as a whole. Table 9.4 shows the industrial distribution of employment in foreign-owned businesses. It bears greater resemblance to U.S. direct investment in other developed economies. Overall, foreigners invest predominantly in U.S. manufacturing industries. While 22.1 percent of 1980 U.S. employment was in manufacturing, this sector accounted for fully 54.3 percent of FDI. The service and retail trade sectors show the fastest growth in FDI, but manufacturing still dominates. Employment in foreign-owned manufacturing doubled between 1977 and 1984. Measured as a percentage of total industrial employment, foreign ownership has advanced farthest in chemicals, where 39 percent of all employment is in foreign-owned establishments; stone, clay, and glass (1 1 percent); primary metals (1 1 percent); food (9 percent); and electrical machinery (8 percent).I3The chemical industry stands out as a case in which foreign ownership is approaching a majority of the industry. These are all manufacturing industries in which the foreign parent may have a competitive advantage due to the importance of technology in determining business success. Of the sectors Table 9.4

Industrial Distribution of Employment in Foreign-owned Business, 1977-84. (%) 1977

1978

1979

1980

1981

1982

1983

1984

Mining Manufacturing Wholesaletrade Retail trade Construction Services Residual

1.31 56.28 12.55 11.65 1.07 3.04 14.11

1.12 56.22 12.03 11.96 1.61 3.57 13.50

1.03 57.39 11.18 13.46 1.60 3.76 11.58

1.23 54.33 10.67 14.95 2.11 4.18 12.54

1.65 53.79 10.51 14.23 2.40 5.13 12.29

1.67 50.74 11.44 16.26 2.12 5.43 12.34

1.46 51.54 10.61 16.51 1.98 5.34 12.55

1.18 50.76 10.79 16.72 1.55 7.07 11.93

Total (thousands)

1,219

1,430

1,753

2,034

2,417

2,448

2,526

2,715

Sources: U.S. Department of Commerce (1983) and Shea (1986). Note: Foreign employment in industry i over total foreign employment (in %), 1977-84.

274


with highest foreign ownership, two (electronics and chemicals) are commonly considered to embody advanced and rapidly progressing technology. In most other industries, the share of employment in foreign-owned businesses remains under 5 percent. Foreign employment as a share of total employment is notably low in communications and public utilities (.4 percent), services ( . 8 percent), agriculture (.5 percent), and construction (.9 percent). Regulation limits access to the first of these markets. The others are all nonmanufacturing industries with low domestic sales concentration. Foreign ownership rates are highest within manufacturing. Foreign ownership is increasing in almost every industry, including stagnant industries such as primary metals. In recent years, it has increased fastest in such home-goods industries as service, real estate, and retail trade.

9.3.3 Location Decisions As new entrants to the U.S. employment market, foreign direct investors have at times been characterized as locating in the low-wage South, the growing West, or the technologically advanced Northeast. From the perspective of any of these stories, the surprising fact is just how closely the geographic distribution of FDI employment parallels that of all domestic firms. Table 9.5 shows, for each of the nine major Census geographic divisions, the shares of foreign direct and of domestic employment for all sectors and for manufacturing. The largest difference between FDI and domestic location occurs in the Middle Atlantic states (New York, Pennsylvania, and New Jersey), which account for 21 percent of FDI employment but just 17 percent of doTable 9.5

Geographic Distribution of Employment, 1980 (in thousands) Fraction of Total

Geographic Region New England Middle Atlantic South Atlantic East North Central East South Central West North Central West South Central Mountain Pacific Total

Manufacturing

Fraction of Total Manufacturing

Foreign Domestic Foreign Domestic Foreign Domestic Foreign Domestic 122.9 5,474.5 414.5 15,011.6 363.1 14,625.2

.06 .21 .I8

.06 .17 .16

72.2 217.2 192.1

1,524.6 3,554.2 3,041.5

.07 .20 .I8

.07 .I7 .I5

368.1

16,826.8

.18

.I9

226.4

4,687.6

.21

.23

99.5

5,145.5

.05

.06

61.5

1,362.7

.06

.07

103.2

6,903.0

.05

.08

63.2

1,381.3

.06

.07

209.8 64.0 274.8

9,313.3 4,488.1 13,058.8

.10 .03 .14

.10 .05 .14

93.2 29.1 142.7

1,669.5 563.2 2,569.0

.08 .03 .13

.08 .03 .13

2,019.9 90,846.8

1.00

1.00

1,097.7 20,353.6

1.00

1.00

Sources: U.S. Department of Commerce (1983, table F-7); U.S. Bureau of Labor Statistics.

275


mestic. FDI employment is also more prevalent in the South Atlantic states and less prevalent in the relatively depressed East North Central region. Aside from these differences, the location decisions of foreign direct investors in the United States look much like those of domestic employers. As we noted above, much FDI involves the creation of wholesale, retail, and service establishments to support the international trade of the parent. Apparently, the geographic distribution of population in the United States is more important than regional labor market differences in determining the location of these establishments. 9.3.4 Compensation Differences U.S. production workers do not appear to suffer under foreign ownership. In the manufacturing sector, the ratio of compensation per worker in foreignowned to the same measure for employees of U.S .-owned businesses increased from .94 in 1977 to 1.08 in 1984 (table 9.6). Foreign ownership has little effect on the mix of fringes to wages, so wages show a similar pattern. While compensation per worker is 10 percent lower in the foreign-owned establishments of the food, primary metals, and instruments industries, in general foreign gains in domestic industries have not been accompanied by relatively low-wage labor. Overall, workers in foreign-owned businesses enjoy a 20-30 percent advantage in compensation over employees of U.S.-owned businesses. The compensation differential can be decomposed into a within-industry differential and a composition effect. Only about a third of the overall difference is due to higher compensation in foreign direct employment within industry. For the most part, the higher compensation found in the aggregate in foreign direct employment is explained by the greater concentration of FDI in the highwage manufacturing sector. For manufacturing, the BEA provides separate data on wages, hours, and occupational structure. The hourly wages of production workers in foreignowned enterprises are 8 percent greater than the domestic average in 1980, although the hours worked are 8 percent less. For production workers, increased hourly pay is balanced by shorter hours. Foreign-owned firms in manufacturing appear more top heavy, employing 64 percent production workers in 1980 compared to the domestic average of 70 percent (see table 9.7).14 Since the overall 1980 compensation ratio is less than one, non-production workers in foreign-owned firms appear to be paid less than their counterparts in U. S .-owned firms. As table 9.6 shows, foreigners appear to invest in high-wage industries. In our discussion of the sources of advantage to FDI, we noted that locational advantages may be conferred by protective trade policies, as in the automotive industry, for example. However, successful direct investment requires that the foreign investor also possess competitive advantage. Otherwise, only domestic entry and pricing decisions are altered by the protection. When some trade

276 Table 9.6

Jonathan S. Leonard and Rachel McCulloch Compensation Ratios (foreign compensation per worker over domestic compensation per worker)

Industry

1977

1978

1979

1980

1981

1982

1983

1984

All industries All nonpetroleum industries Mining Manufacturing Durable Transportation & equipment Primary metal industries Fabricated metal products Machinery, except electrical Electric & electronic equipment Nondurable Textile products & apparel Lumber & furniture Paper & allied products Printing & publishing Rubber & plastics products Stone, clay, & glass products Food & kindred products Wholesale trade Retail trade Construction Services Finance Real estate Insurance Communication Transportation

1.20 1.17 .94 .94

1.23 1.20 1.02 .96

1.20 1.18 .98 .94

1.20 1.19 1.02 .95

1.27 1.42 1.09 1.06

1.30 1.28 1.16 1.11

1.29 1.28 1.16 1.08

1.26 1.24 1.14 1.08

.81 .83 .96 .96

1.07 .86 .98 .99

.93 .80 .98 .98

.97 .86 .90 .97

.97 .86 .98 .99

.98 .90 1.09 1.10

.90 .95 .96 1.05

.96 .99 1.12 1.01

.83

.86

.87

.90

.99

1.OO

.96

.98

1.21 1.01 .99 1.02 .82 .90 .89 1.03 1.22 .80 1.10 1.33 1.02 1.00 1.31 1.05

1.13 .93 1.00 1.03 .83 .97 .91 1.03 1.21 1.10 1.17 1.67 .96 1.02 .86 1.07

1.07 .81 .98 1.07 .87 .97 .78 1.01 1.29 1.16 1.11 1.68 1.40 1.01 1.13 1.09

1.07 .89 .96 1.07 .95 1.08 .83 1.02 1.24 1.06 1.04 1.54 1.38 .99 .74 .90

1.14 .84 1.22 1.10 .91 1.11 .89 1.01 1.18 1.16 .94 1.54 1.34 .94 .76 .88

1.21 .83 1.24 1.11 .94 1.21 .90 1.11 1.21 1.23 .97 1.91 1.19 .93 .63 .93

1.17 1.03 1.30 1.14 .92 1.17 .87 1.19 1.13 1.26 1.03 1.87 1.12 .91 .55 1.14

1.21 1.01 1.18 1.14 1.06 1.12 .91 1.13 1.05 1.14 1.05 1.71 1.12 .91 .57 1.07

Sources: U.S. Department of Commerce (1983); Survey of Current Business; National Income and Product Accounts.

flows are restricted through commercial policy, factor flows are magnified. A partial explanation of the tendency of foreigners to invest in high-wage U.S. industries (and to pay the U.S. wage in those industries) is the export of capital to the United States as an alternative to export of the products of their domestic high-wage industries.

9.3.5 Research and Development Just as U.S. direct investment in other countries is dominated by industries with substantial research and development (R&D), foreigners investing in U.S. domestic industries use R&D to generate a competitive advantage. While R&D scientists and engineers constitute only .5 percent of employees of domestic-owned firms, they are 2.1 percent of the employees in foreignowned companies (see table 9.8). By this measure, R&D intensity is half

277

Table 9.7


Proportion of Production Workers in Manufacturing, 1980 Ownership Foreign Manufacturing Food & kindred products Chemicals & allied products Primary & fabricated metals Primary metals Fabricated metals Machinery Machinery, except electrical Electric & electronic Textile products & apparel Lumber & furniture Paper & allied products Printing & publishing Rubber & plastics Stone, clay, & glass Transportation equipment Instruments & related products

.64 .68 .48 .72 .71 .74 .61 .57 .63 .81 .79 .74 .57 .75 .75 .66

Domestic .70 .69 .57 .75 .77 .74 .64 .64

.64

.64

.86 .82 .75 .56 .77 .71 .65

.60

Sources: U.S.Department of Commerce (1983); Statisrical Abstracr of the Vnired Srares.

Table 9.8

Research and Development Intensity, 1980 9% of Total Employees Who are R&D Scientists and Engineers

All industries Manufacturing Food & kindred products Chemicals & allied products Primary metal industries Fabricated metal products Machinery Electric & electronic equipment Other manufacturing Textile products & apparel Paper & allied products Stone, clay, & glass products Transportation & equipment Instruments & related products

Foreign

Domestic

2.13

.46

SO

.42 4.48 .78 .61 3.03 3.91

6.14 .72 2.40 3.62 3.78 .39 .43 .94 2.34 4.10

Sources: U.S. Department of Commerce (1983); National Science Foundation.

.09 1.09 .81

1.88 3.86

278


again as high in foreign-owned compared to domestic-owned manufacturing businesses (3.1 vs. 2.0 percent) and more than ten times greater in nonmanufacturing (.9 vs. .07 percent). One need not be xenophobic to wonder about the fate of U.S. comparative advantage in R&D-intensive industries when the declining share of U.S. citizens in U.S. graduate science and engineering education is coupled with the declining domestic-ownership share in U.S. R&Dintensive industries. 9.3.6 Collective Bargaining Among industrialized countries, the United States now has one of the lowest union representation rates. Among the many explanations proffered, some have pointed to differences in management attitude. Managers from some European countries and Japan are often surprised at the unquestioned and vehement antiunion animus of their U.S. counterparts. Higher unionization rates in home countries may be associated with greater management tolerance of unionization and perhaps with greater skill in developing cooperative arrangements with unions. If foreign owners really take a less antagonistic position toward unions, one might expect this to carry over to their U.S. operations and reveal itself in higher unionization rates than in U. S .-owned domestic operations. On the other hand, once geographically removed from home country approbation and leverage, the same cost considerations that drive U.S. companies may dominate. To the extent that cost disadvantages of union firms can be capitalized in the sales price of corporate assets, no difference in unionization is expected on the basis of foreign ownership. In 1980, 23 percent of U.S. employees were union members. Among foreign-owned companies, 29 percent of employees were covered by collecTable 9.9

Union Density at Home and Abroad, 1980 % Covered by Collective Bargaining in Foreign Owned

All Industry United States All foreign owned Canada United Kingdom Japan Netherlands Sweden France Germany Switzerland

29.21 32.05 26.07 20.26 24.52 31.90 47.49 30.96 17.35

Manufacturing

35.9 31.13 35.39 29.11 28.82 34.03 55.05 20.06 22.55

% Unionized Home Country

23.1 30.50 53.10 30.80 37.10 87.80 19.20 38.60 33.50

Sources: U.S. Department of Commerce (1983); Kokkelenberg and Sockell (1985); Troy and Sheflin (1985).

279


tive bargaining contracts (see table 9.9). Again, this difference is almost entirely due to the greater concentration of FDI in the manufacturing sector. Within manufacturing, foreigners leave any pro-union sentiments at home. Whereas 36 percent of U.S. employees are union members, only 31 percent of the employees in foreign-owned companies are covered by collective bargaining contracts (a more inclusive measure). Part of the difference in manufacturing union density may reflect union avoidance on the part of foreigners. Part may reflect compositional differences within manufacturing, and part may be due to a vintage effect. Newer firms and industries are less unionized, and foreign ownership is presumably concentrated among these. In any case, owner attitudes inferred from homecountry unionization can be dismissed as an important factor. The growing internationalization of the world economy has so far presented greater competition attacking local rents and greater opportunities for union avoidance than for the international application of union leverage to enforce union standards.

9.3.7 Home-Country Effects The characteristics of FDI in the United States may differ systematically by home country of the investor, although in general one may suspect that such differences either are transient or represent industry-specific or firm-specific effects. For the countries that are home to most of the ultimate beneficial owners of FDI in the United States, table 9.10 compares a number of characteristics. Of these countries, investments owned by Australians and Dutch appear the most successful. They show the highest return on assets and the highest pay. The Dutch investments are also R&D intensive. Countries such as Germany and Switzerland, with the greatest share of their investment in manufacturing, show the worst rates of return. Japanese investments stand out only in their avoidance of manufacturing, compared to the investments of other foreigners. In general, the measures in table 9.10 appear to tell more about the common characteristics of FDI in the United States than about differences systematically related to home-country factors. 9.4

Conclusions

The close link between foreign direct investment and the investing firm’s competitive advantage suggests that both the rise in inward direct investment in the United States and the slowing of U.S. direct investment abroad reflect the industrial catch-up of other nations to the United States. Where the competitive advantages that underlie successful foreign investment were once controlled almost exclusively by U.S. companies, new rivals have emerged in Europe and Japan, and even in some of the developing countries. As with the U. S .-based multinationals that dominated global direct investment flows in earlier decades, these foreign-based firms have exploited their competitive

Table 9.10

Characteristics of Foreign Direct Investment by Country of Origin, 1980

1. Net income/assets 2. Wagesiworkers* 3. Compensation/workers* 4. R&D employedlworkers 5. Manufacturing employed/workers 6. Wages/workers** 7. Compensation/workers**

All Foreign Countries ,031 16.28 19.69 .021 .54 16.92 20.67

Canada

United Kingdom

Japan

.022 16.92 20.68 .011 .52 17.19 21.90

,056 14.59 17.80 ,017 .52 15.13 18.50

,026 16.26 18.57 ,015 .31 16.35 19.25

Sources: U.S. Department of Commerce (1983, tables B-8, E-2, F-2, F-13, F-14). Nore: U.S. column gives comparable domestic averages. *In thousands of dollars. **In manufacturing.

Netherlands ,049 18.84 23.00 ,040

.55 17.60 21.13

France

Germany

,011 18.58 22.76 .016 .58 20.33 25.64

15.40 18.73 .024 .64 15.76 19.38

,006

Switzerland

Australia, New Zealand, South Africa

United States

.008 15.99 19.04 ,038 .65 17.88 21.50

,097 18.17 21.35 ,019 .29 17.97 21.12

.059 13.91 16.39 ,005 .20 17.36 21.68

281


advantages first through exports and later through direct investments in market countries. As with U.S. companies investing abroad, foreign firms establishing subsidiaries in the United States often rely on superior technology for the cornpetitive advantage necessary to make their investments profitable. Although these firms come to the United States to exploit an already-established cornpetitive advantage, their U.S. operations employ a larger proportion of scientists and engineers than U.S.-owned businesses in the same industry; foreignowned enterprises in the United States are on average more R&D intensive than their domestic counterparts. However, R&D intensity is the only large difference between foreignowned and US.-owned businesses that emerged from our statistical comparison. Indeed, it is striking how similar foreign-owned and U.S.-owned businesses appear statistically. Although foreign-owned companies have a different industrial mix favoring manufacturing, retail and service establishments are growing fastest. Wages and compensation of foreign-owned businesses are very similar to those of U.S.-owned businesses in the same industries.

Notes 1. In U.S. statistics, the line is drawn at 10 percent of equity, although most U.S. direct investments abroad and many foreign direct investments in the United States are wholly owned subsidiaries of the parent. Other asset purchases, e.g., of private or government bonds or smaller blocks of stocks, are termed portfolio investments. 2. The size of the ownership stake need not indicate the total size of the controlled activity. A fall in measured direct investment could, in principle, be accompanied by an increase in the extent of controlled activity. Alternative measures of foreign influence include sales, employment, and profits of the controlled enterprise. 3. Although appropriately classed as asset transactions, direct investments are distinctive in that no international transfer of financial capital, i.e., purchasing power, need be entailed. In many cases, the contribution of the investing firm to a joint venture consists primarily of proprietary technology or managerial expertise rather than financial capital. Even when financial capital is part of the investment “package,” the required funds may be borrowed locally in the host country. This was a common practice in the 1960s for the U.S. firms establishing European subsidiaries. 4. In 1983, US.multinational corporations accounted for more than three-quarters of U.S. exports and almost half of U.S. imports. However, these shares have been declining from their peaks in the 1970s (the comparable percentages for 1977 were 84 and 58), while the U.S. trade role of foreign multinational firms appears to have grown over the same period. (See Barker 1986.) 5. This has been alleged in recent years about Japanese investments in the United States but is also a longstanding complaint of less-developed host countries. These nations invite foreign investments with the hope of reducing chronic balance-ofpayments difficulties. The usual experience is that induced imports of machinery and components tend to offset any direct reduction in imports or rise in exports of the

282


product itself. This is, of course, consistent with the notion that balance-of-payments difficulties are fundamentally macroeconomic problems requiring macroeconomic solutions. On the macroeconomic roots of the U.S. trade deficit, see McCulloch and Richardson (1986). 6. The idea that FDI requires a significant departure from conditions of perfect competition was advanced by Hymer (1960) and expanded by Kindleberger (1969), Caves, and many others. For a comprehensive survey of the literature, see Caves (1982, chap. 2). 7. The three “necessary conditions” are elaborated by Dunning (198 1, and earlier papers). Dunning uses this classification to explain the distribution of investment by home and host country and industry. For a detailed analysis of direct investment as international internalization, see Rugman (1980). 8. The U.S. investments by Japanese firms are a case in point. While this kind of complementary relationship between exporting and direct investment was suggested by Bergsten, Horst, and Moran (1978) for manufacturing investments, it may be even more important in the case of service industries such as banking and insurance. 9. As long as rates are determined mainly by market forces, in one sense under- or overvaluation cannot occur. These descriptions usually refer to a deviation of marketdetermined rates from rates calculated using relative price levels (purchasing power parity). 10. As discussed below, local policies to attract new investment may also play a role. 11. These benefits include tax breaks, cheap loans, worker training, and free infrastructure. According to a Kentucky legislative study, the state will spend $125 million, or about $42 perjob, to attract Toyota’s new plant (Lore 1987). 12. In early 1987, the president of Ford Motor Co. called for further reductions in auto imports from Japan to compensate for increased production by Japanese plants in the United States. 13. Statistics are for 1984. Sources are U.S. Department of Commerce, Foreign Direct Investment in the United States, and the Survey of Current Business. 14. This difference may be understated by the use of full-time equivalent counting only for the domestic figure.

References Barker, Betty L. 1986. U.S. Merchandise Trade Associated with U.S. Multinational Companies. Survey of Current Business 66 (May):55-72. Bergsten, C. Fred, Thomas Horst, and Theodore H. Moran. 1978. American Multinationals and American Interests. Washington, D.C.: Brookings. Bhagwati, Jagdish. 1985. Investing Abroad. Esmee Fairbairn Lecture, University of Lancaster, 27 November. Caves, Richard E. 1982. Multinational Enterprise and Economic Analysis. Cambridge: Cambridge University Press. Dewald, William, Harry Gilman, Harry Grubert, and Larry Wipf, eds. 1978. The Impact of International Trade and Investment on Employment. Washington, D.C. : U.S. Department of Labor. Dunning, John H. 1981. Explaining the International Direct Investment Position of Countries: Towards a Dynamic or Developmental Approach. Weltwirtschaftliches Archiv 117:30-64.

283


Frank, Robert H., and Richard T. Freeman. 1978. The Distributional Consequences of Direct Foreign Investment. In The Impact of International Trade and Investment on Employment, ed. William Dewald, Hany Gilman, Harry Grubert, and Larry Wipf. Washington, D.C.: U.S. Department of Labor. Graham, Edward M. 1978. Transatlantic Investment by Multinational Firms: A Rivalistic Phenomenon? Journal of Post Keynesian Economics 1(Fall):82-99. Hymer, Stephen. 1960. The International Operations of Firms. Ph.D. diss., Massachusetts Institute of Technology. Kindleberger, Charles P. 1969. American Business Abroad: Six Lectures on Direct Investment. New Haven, Conn.: Yale University Press. Kogut, Bruce. 1983. Foreign Direct Investment as a Sequential Process. In The Multinational Corporation in the 1980’s ed. C. P. Kindleberger and D. B. Audretsch, Cambridge, Mass.: MIT Press. Kokkelenberg, E. C., and D. R., Sockell. 1985. Union Membership in the United States, 1973-1981. Industrial and Labor Relations Review 38:497-543. Lipsey, Robert E. 1987. Changing Patterns of International Investment in and by the United States. In The United States in the World Economy, ed. Martin Feldstein, 475-545. Chicago: University of Chicago Press. Lore, Dave. 1987. Japanese Auto Investments: Great Today but What about Tomorrow? MidAmerican Outlook 1O(Spring):2-4. McCulloch, Rachel, and J. David Richardson. 1986. U.S. Trade and the Dollar: Evaluating Current Policy Options. In Current US.Trade Policy: Analysis, Agenda and Administration, ed. R. E. Baldwin and J. D. Richardson. Cambridge, Mass.: National Bureau of Economic Research. Musgrave, Peggy B . 1975. Direct Foreign Investment Abroad and the Multinationals: Effects on the US. Economy. Washington, D.C.: Senate Foreign Relations Committee. Reich, Robert B., and Eric D. Mankin. 1986. Joint Ventures with Japan Give Away Our Future. Harvard Business Review 64(March/April):78-86. Rugman, Alan M. 1980. Internationalization as a General Theory of Foreign Direct Investment: A Re-appraisal of the Literature. Weltwirtschaftliches Archiv 116:36579. Shea, Michael A. 1986. U.S. Business Enterprises Acquired or Established by Foreign Direct Investors in 1985. Survey of Current Business 66(5):47-53. Troy, L., and N. Sheflin. 1985. Union Sourcebook. West Orange, N.J.: IRDIS. U.S. Department of Commerce. 1983. Foreign Direct Investment in the United States, 1980. Washington, D.C.: U.S. Government Printing Office. Vernon, Raymond. 1971. Sovereignty at Bay. New York: Basic.


10

Immigration, International Trade, and the Wages of Native Workers Peter Kuhn and Ian Wooton

The purpose of this paper is to develop and apply to U.S. data a theoretical model with the following features. First, it should yield a set of predictions regarding the effects of international factor movements, such as immigration, on the rewards of all factors employed in the country, including labor disaggregated by skill level. Second, it should be consistent with the following stylized facts: (i) the U.S. economy is “partially open” in the sense that it produces both internationally traded and nontraded goods; and (ii) international trade in goods has apparently not equalized factor prices between the United States and the rest of the world. These requirements play an important role in this paper because few of the existing models that consider general-equilibrium effects of factor endowments on factor prices satisfy them. For example, Hicks’s (1932, chap. 6) classic analysis assumes that no goods are traded internationally. This work predicted that the effect of factor quantities on factor prices was determined by a set of within-industry elasticities of substitution as well as substitution elasticities in consumption, and it stimulated several empirical attempts to estimate these parameters (e.g., Fallon and Layard 1975). On the other hand, the basic two-good, two-factor (2 X 2) trade model (Samuelson 1948) assumes that all produced goods are traded. It predicts, unrealistically, that trade alone should eliminate all factor price differentials between countries and thus that factor endowments should have no effect on factor prices. Finally, among the trade models that do allow for international factor movements to affect factor prices (e.g., the 2 X 2 models of Kemp 1966; Markusen and Melvin 1979; Brecher and Choudhri 1982; and Rivera-Batiz 1982; the two-good, three-factor (2 X 3) models of Batra and Casas 1976; Ruffin 1981; and Jones Peter Kuhn is associate professor of economics, Department of Economics, McMaster University. Ian Wooton is associate professor of economics, Department of Economics, University of Western Ontario.

285

286

Peter Kuhn and Ian Wooton

and Easton 1983; as well as the higher-dimensional treatments of Jones and Scheinkman 1977; or Chang 1979), the only one that includes any nontraded goods is Rivera-Batiz (1982). The model in this paper can be thought of as an extension of Rivera-Batiz (1982), which adds an extra traded good and an extra factor. We thus have three factors, two traded goods, and one nontraded good. The additional traded good allows us to have both an exporting and an import-competing industry in the analysis and to compare how these two sectors are affected by changes in factor endowments. The additional factor allows us to distinguish between workers with different investments in human capital. Specifically, we subdivide the labor force into skilled workers and unskilled workers. Immigrants of a particular type are considered to be perfect substitutes for native workers of that same category. Our model may also be thought of as the addition of a nontraded sector to Ruffin’s (1981) 2 X 3 model. The paper’s main theoretical results are twofold. First, we find that the directions of the effects of factor endowments on factor prices, while not zero as in the “standard” trade model, are still independent of the within-industry technical substitution elasticities between inputs in production. This independence property (which incidentally also holds in Rivera-Batiz’s lowerdimensional model) dramatically illustrates the effects of allowing international trade in even a subset of commodities on models of the functional distribution of income. It arises because, contrary to the closed-economy model, the fundamental determinants of factor price changes are not the ability to substitute factors in production; they are, instead, the tendency for factor prices to change in such a way as to maintain the international competitiveness of the country’s exporting and import-competing industries, as long as those industries continue to operate. Second, providing that a relatively weak “normality” condition holds, the directions of all the factor quantity-factor price effects in our model can be deduced directly from the relative intensities of factor use within the traded sector of the economy only, as follows. First, an increase in the supply of any factor lowers its own price. Second, with three factors, one will be “extremely” intensively used in exports, another in imports, and the third will be the “middle” factor in the traded sector of the economy. Our model predicts that an increase in the supply of either extreme factor lowers the price of the other extreme factor and raises the price of the middle factor. Third, an increase in the supply of the middle factor benefits owners of both extreme factors. Interestingly, the results given above are identical to those obtained by Ruffin (1981) without a nontraded sector. The paper’s main empirical result, based on factor intensities in 430 fourdigit U.S. manufacturing industries for the years 1960, 1970, 1980, and 1984, is the following. For all definitions of traded versus nontraded goods considered, and for all years except 1960, skilled labor is extreme in exports and unskilled labor is extreme in imports, with capital as the middle factor. Thus,

287

Immigration and Wages

our model predicts that, in the long run, the interests of both types of labor in immigration issues should coincide and should conflict with those of capital. Workers of both types should oppose all immigration but favor foreign investment in the United States, while owners of capital should favor immigration of both types of workers. Section 10.1 of the paper outlines the structure of the model. Section 10.2 solves the model for the effect of factor endowment changes on factor prices. Section 10.3 characterizes the properties of that solution. Section 10.4 presents our empirical estimates of factor intensities for the United States and their implications, while section 10.5 concludes.

10.1 The Model Each of the three goods X , , X,, and X , is produced using the services of the three factors of production, V , , V2,and V,, according to linearly homogeneous production functions. We adopt the convention that good 3 is nontraded and that, of the two traded goods, XI is imported and X , exported. Let qJbe the quantity of factor i required to produce a unit of good j , where a,Jdepends on the prices of the three factors w,,w,, and w,.Without loss of generality, we number factors in such a way that alliu12

‘2Iia22

u31iu32

in the initial equilibrium and assume the inequalities are strict. Thus, in Ruffin’s (1981) terminology, when comparing factor intensities of the two traded sectors, factor 1 is extreme in imports (XI),factor 2 in exports ( X J , and factor 3 is the middle factor in the traded sector. If the nominal prices of the three goods are p , , p,, and p , , then the zeroprofit conditions for production are (1)

allwl

+

%Iw2

+ ‘3Iw.3

=

(2)

a12wl

+

‘2Zw2

+

= p29

PI?

+ a23w2 + a33w3 = p3. It is assumed that all three goods are produced in positive amounts, so the market prices exactly reflect the costs per unit of output. Full employment of the stocks of the three factors of production would entail (3)

a13wl

Were all three goods prices exogenously determined, then equations (l), ( 2 ) , and (3) would uniquely determine the factor-price vector, and factor prices

288


would be independent of the factor endowments. However, while goods 1 and 2 are considered to be traded internationally at exogenously given world prices, it is assumed that good 3 is nontraded, its price being endogenously determined by domestic demand. In consequence, changes in factor endowments, through immigration, may influence the returns to factors in the economy. Let domestic demand for good 3 be represented by a Hicksian compensated demand function, that is,

Equilibrium in the market for the nontraded good occurs when domestic supply exactly meets domestic demand, (7)

and “national’ utility is a function of the quantities of goods consumed in the country by native and immigrant factors together:

u=

(8)

U [ C , ,c,, q.

National utility is maximized subject both to the balanced trade constraint,’ (9)

PI CI + P2

c 2

=

PIX, + PIX,,

and to the constraints of technology and endowments, (10)

x3

=

g [ X , ,x,; v,, v,,

v31.

Equations (I)-( 10) provide a complete description of the static general equilibrium of the economy.

10.2 Factor Migration Consider a change in the domestic supply of a factor of production as a result of migration. This will directly affect production activity through the change in the total factor supplies available for production. It will also affect the (endogenous) price of the nontraded good, with consequent further changes in output and induced changes in factor rewards. Differentiating equations ( l ) , ( 2 ) , and ( 3 ) reveals the way in which the equilibrium is disturbed by small changes in commodity prices: e l l G I + e2]G2 + e31G3= pI, e12Gl

+ e22G2+ e32G3= b2,

e13Gl+ ez3G2 + e33G3 = p3, where O,, =a,w,lp,, the distributive share of factor i in industry j , and a “hat” (*) over a variable denotes a relative change (e.g., = dw/w). Similarly, by ~

289


differentiating equations (4), ( 5 ) , and (6), the response to changes in factor endowments can be determined: (4’)

(5’) (6’)

+ A$, + AIg3 = C, A,$, + AzzX2 + AZg3 = Qz A31Xl + A3zrZz + A3g3 = A,,rZ,

QI

- {at GI

+ a: Gz + a: G3}, - {u; GI + U: Gz + G3}, - {a: GI + U: G, + U: G3},

where A, = a,Xj /Vi, the fraction of the total supply of factor i used in the jth industry, and @ denotes the economy-wide substitution toward or away from the use of factor i when factor k becomes more expensive, under the assumption that each industry’s output is held constant. That is, a: =EjA,Ei, where Ei is the elasticity of demand for factor i with respect to w, in industry j , holding output and other factor prices constant. Were commodity prices to remain unchanged after the factor movement (as would occur if all goods were traded at exogenously given world prices), then, from equations (1’), (2’), and (3’), factor earnings would also remain constant, and hence the bracketed terms of equations (4’), ( 5 ’ ) , and (6’) would all be zero, as there would be no substitution between factors in production. Output change in response to changes in factor endowments would be influenced only by the relative intensities with which factors are used in each of the three industries. This behavior results from a higher dimensional analogue of the familiar Rybczynski theorem (which was derived for a model with two factors and two goods), and we shall call it a “pure Rybczynski effect.” Good 3 is, however, not traded internationally, and the inflow of factors will induce changes in both the demand for and the supply of that good. Differentiating equation (8), and using equations (9) and (10) to determine the change in utility resulting from factor immigration at constant commodity prices,

where p is the marginal utility of money income, and I is national income:

Rewriting equation (1 1) in terms of relative changes yields (11‘)

0

= ox

8’ Qi,

where w = pl/U, and 8‘ is the share of factor i in national income,

290


By the appropriate choice of utility scale, let o = 1 locally. Then equation ( 1 1’) becomes

0 = 8lV1 1-

(12)

e2V2

+

e3V3.

Differentiating the market-clearing condition, equation (7), to determine the equilibrium responses to disturbances in the market for good 3,

x, = v,p, + v d 2 + v3p3+ KO,

(13)

where u I , v2, and v3 are the compensated price elasticities of demand for X,, and IJ. is the income elasticity of demand for X,. It will be convenient to rewrite (13), using (12), as

p,p, + pd2+ p3x3+ +{elV, + e2V2 + e3P3}, where PI -vlIv3, p, -v2/v,, p3 l/u3 < 0, and (if good 3 is normal) + .. - phJ3> 0. (13’)

p,

=

’’

”

“

Rewriting the output-response equations (4’)-(6’) in matrix form,

[

’I1

’I2

’13

’21

’22

’231

’31

‘32

’33

u; 0; a;

=

-

Solving this for X,, (14)

f!] [ [2 2 $1 [

x3 = s,Q,+ S2V2 + s3Q3 + ylGl + y2G2 + y3G3,

where (15)

yk

=

-{s,a; +

for k = 1, 2, 3; Sk = IAtl/lhl, for k lambda matrix

=

+ S,a:},

6,a;

1, 2, 3; \A1 is the determinant of the

‘I1

’I2

‘13

’31

’32

’33

and

from the definitions of the “extreme” and “middle” factors. Note that the pure Rybczynski effect (i.e., when factor prices are constant) of factor endowment changes on output of good 3 is then

Substituting equations (3’) and (14) into (13), and solving, yields

291


(17) A I G l+ A2G2+ A3G3=

PI@,+ Pd2+ Zlpl + Z2p2+ Z3p3,

where (18)

A, = (0,3 - P3YJ9

fori = 1, 2, 3; and (19)

z,= P,@, - CLO'),

for i = 1, 2, 3. The comparative statics of factor rewards in this economy are now completely determined by equations (1 '), (2'), and (17):

which is easily solved for G I , G2,and G3. Since, in this paper, we focus on the effects of factor endowments on factor prices, we henceforth set @ = @ = 0 and solve (20) for Gkin terms of pi,for i , k = 1 , 2 , 3 . The induced change in factor rewards when factor endowments change is then given by

for i, k = 1,2, 3,

The properties of equation (21) are analyzed in the following section.

10.3 Consequences for Factor Rewards The consequences of (21) for the effects of factor endowment changes on both nominal factor prices and the welfare of factor owners are analyzed in turn below. We begin with properties of price changes that are independent of a certain "normality" condition and then consider the additional restrictions imposed by that condition. Finally, welfare effects are considered. 10.3.1 General Results To develop an intuition for the effects of endowment changes on factor rewards, consider the effect of a change in the endowment of factor i on the rewards paid to factor k in equation (21). The Zj term gives the effect of im-

292


migration (pi> 0) or emigration (pi< 0) on the price of the nontraded good X,. Recalling equation (19), (19)

z, = p3(s,-

pet).

The first term in the parentheses is the supply effect of the change in output of good X , resulting from the immigration of factor i, that is, the pure Rybczynski effect of equation (16). The second term is a demand effect, reflecting the increased demand for good X, resulting from a higher level of national income created by the increase in the economy’s endowment. We call the difference between the terms the “modified Rybczynski effect.” Should this be positive, then an excess supply of the nontraded good has been induced by the migration, triggering a fall in its price, p 3 (because 6, < 0), and vice versa. The other component of the effect of pion w, in (21) is the ratio of the two determinants. Changes in commodity prices in tbe traditional two-factor, twogood trade model induce changes in factor prices according to the StolperSamuelson Theorem. Were all three commodities in our model traded internationally, then the response of factor prices to a change in p , would be a higher dimensional analogue to these familiar magnification effects. We shall call it the “pure Stolper-Samuelson effect.” The magnitude of this response can be measured by solving equations (l’t(3’) (letting$, = ~3, = 0), yielding

[ I

fork = 1, 2, 3, where 1 0 1 is the determinant of the theta matrix,

8

=

‘11

‘21

‘31

‘12

‘22

‘32

‘I3

‘23

‘33

.

Good 3 is not traded, however, and faces a less than infinitely elastic demand. As a result, changes in factor earnings have repercussions on the amount of X , supplied, through within-sector substitution among factors, and this will induce a further change in the commodity price. This effect is captured by the P3y, terms that modify the denominator from 181, in the pure StolperSamuelson effect of equation (22), to A in equation (21). We therefore call the ratio of determinants in (22) a “modified Stolper-Samuelson effect” of p , on w,. It can be shown, for any negative semidefinite economy-wide substitution matrix, that (23)

sign(A) = sign((81),

and so the modified Stolper-Samuelson effect will be qualitatively identical to the pure Stolper-Samuelson effect.2 The entire effect of a change in the endowment of factor i on the earnings

293


of factor k is thus simply the product of the modified Rybczynski effect of V , on p , and the modified Stolper-Samuelson effect of p , on w,. This interpretation and decomposition of (21)emphasizes a major feature of the model that is independent of any assumptions regarding the structure of demand and factor-intensity rankings and is true for changes in the rewards to all factors. The only way that factor endowment changes can affect factor prices is through inducing changes in price of the nontraded good, p,; that is, factor prices cannot change unless p , changes. Otherwise, were p , fixed, because either the good was traded internationally or domestic demand was infinitely elastic (p, = 0 ) , then factor prices would be uniquely determined by equations (1)-(3), independently of the endowment.,

10.3.2 The Normality Condition Consider more closely the excess supply of X, induced by immigration of factor i (6, - pel). The level of national income will always rise with factor inflows, and, if this is compounded by a negative Rybczynski effect (6, < 0) lowering the supply of X,, then there will undoubtedly be excess demand for the nontraded good, inducing an increase in its price. However, suppose immigration of factor V, induces an expansion of X, production because 6, > 0. The change in factor endowment has then raised both the demand for and the supply of the nontradable. Thus, the potential exists for what we shall call a “perverse demand” result that, with a sufficiently high national income elasticity of demand for the nontraded good, an increase in the output of good 3 induced by a change in endowment will be accompanied by an increase in its price. In this subsection, we develop a pair of sufficient conditions, together called the “normality condition,” that rule this out. One of these is a fairly weak restriction on the structure of demand, while the other is a constraint on supply effects that, as we shall see, is clearly satisfied by our empirical evidence on factor intensities. Together they ensure that (24)

sign(6, - kel) = sign@,);

thus, the modified Rybczynski effect, like the Stolper-Samuelson effect, is qualitatively the same as the “pure” effect. First, rewrite the expression for 8, in equation (16)in terms of distributive shares:

where 0, = pjXj/I,the share of good j in national expenditure. From our numbering of factors in terms of relative intensity of their use in the traded sector, we know that

(26)

sign(+,)

=

sign(+,) = -sign(+J.

294


Thus, immigration of either of the two “extreme” factors will have opposite effects to immigration of the “middle” factor on the production of the nontraded good. Furthermore, it is helpful to note that (27)

sign(6,)

=

signlo(,

since l0,l > 0. A sufficient condition for 6, > 0 is then that

-‘ 3 2< - ‘12 -‘22o r - 0. Noting that p = X3/f,this may be rewritten as 1 > k0,.

Multiplying by 0‘ and rewriting yields

Thus, the noninferiority of goods 1 and 2 ensures that the demand term modifying the Rybczynski effect is less than the wage bill of the immigrant factor relative to the value of output of X , . We can therefore rule out the perverse demand result if, when 6, > 0, 6, > 0Y0,. Using equation (25), this may be rewritten to state: sign 10.1= sign 101 Suppose that factor 3 has a positive Rybczynski effect, that is, S3 > 0. As are neganoted earlier, this means that 1 01 is positive. As both l0,l and

295


tive, we are assured that l9,l exceeds 191, and, hence, the perverse demand result cannot occur if goods 1 and 2 are noninferior. If, instead, immigration of factor 3 directly reduces the production of the nontraded good, that is, 6, < 0, then the potential for a perverse demand result arises with immigration of either factor 1 or factor 2. Now, as 191 < 0 and only I@,[ > 0, it is not possible to rule out l9,l/l9l 1 for 191 < 0 a n d j = 1 or 2, which ensures a large enough supply effect in the cases where the nontraded sector is extremely unintensive in the middle “traded” factor. The latter condition is clearly satisfied by the data on factor intensities that we consider below. 10.3.3 Effects of Factor Endowments on Factor Prices under the Normality Condition Under the normality condition, modified Rybczynski effects are qualitatively the same as the pure effects, as is also true for modified StolperSamuelson effects. Furthermore, it is straightforward to show that the Rybczynski effect of Vt on X , is always the same sign as the StolperSamuelson effect of p , on wi.This can be seen by manipulating (25) and substituting into (23), yielding (30)

sign(6,) = sign

[TI.

19.1

Substituting (24) and (30) into (21), and noting that p, < 0, we have

(31)

=

sign( - s,s,~J,

for i, k = 1 , 2, 3. Irrespective of whether 191 S 0, it is clear from (31) that any factor is its own “enemy,” in that immigration of a factor will always lower its own nominal wage, while emigration raises its earnings. Further, as w , and w, have the same sign, the extreme factors, 1 and 2, will be mutual enemies while being friends with the middle factor, 3. The qualitative effects of factor quantities on factor prices thus depend only on which factors are extreme in the traded sector of the economy, not on the relative factor intensities of the traded versus nontraded sector. The reason is that, whenever a factor supply shift raises the price of nontraded goods (by lowering supply), higher nontraded goods prices lower the equilibrium price of that factor, and conversely. 10.3.4 Welfare Effects The effectof the above nominal changes in returns on the welfare of a factor depends on the induced changes in p , ( p , and p , being fixed, by assumption). Assessment of welfare effects is, however, made easy if we note that, by substituting (22) into (21),

296


p3 = p, 101(6, A

kf?)+,.

Since the ratio of determinants is always positive, the general equilibrium effects of V, on p 3 are always the same sign as when factor prices are held fixed. In more detail, immigration of factor Viwhose 6, > 0 will result in a fall in p , , raising the real income of those factors whose nominal earnings have increased. The other factors are faced with both reduced nominal earnings and lower prices, with apparently ambiguous consequences for their welfare. However, consider the relative change in the ratio of earnings of factor k to the price of good 3, (33)

[3

= w -

p,

[m 1%

Substitute equations (21) and (32) into this expression and rearrange to find (34)

t]

=

8 3

- 1).

Using (29), (31), and (32), equation (34) implies that sign [6,1p3] = sign 6,. Thus, under the normality condition, changes in nominal earnings will outweigh changes in the price of the nontraded good, ensuring that changes in welfare will have the same signs as the changes in factors’ nominal returns. Table 10.1 gives a complete listing of the predicted responses of variables to changes in migration.

10.4 Empirical Estimates of Factor Intensities Our estimates of U.S. factor intensities are based on an analysis of 430 four-digit manufacturing industries during the period 1960-84. These indusTable 10.1

Predicted Effects of Factor Endowment Changes under the “Normality”Condition Endowment Change

297


tries constitute all the industries in the NBER Data Set on Trade, Immigration, and Foreign Direct Investments (450 in all) with, data on both exports and imports.6 This data set has the advantages of being at a finer level of aggregation than most existing empirical studies of factor intensities and trade and of being available for a long series of years, as recently as 1984.’ It has the disadvantage of being limited to manufacturing industries only. Thus, we view our results here as (a)likely to be quite accurate for the manufacturing sector relative to other studies but (b) relevant to the whole economy only to the extent that the pattern we identify in manufacturing generalizes to other industries. Our basic procedure begins by allocating the total value added in each fourdigit industry into payments to one of the three factors in the model. Total income of “unskilled” workers was defined as the industry’s total productionworker payroll for the year. Income of “skilled” workers is the difference between this and the industry’s total payroll.8 Finally, subtracting total payroll from total value added yielded the payments to “capital” in the industry. Thus, in keeping with our model’s assumptions, we ignore indirect factor inputs and assume no intermediate inputs in produ~tion.~ Next, the 430 four-digit industries were aggregated into three as follows. First, nontraded goods were defined as those industries whose level of “openness” to international trade, measured by the sum of exports plus imports divided by total industry output, was under k%, where k took on alternative values of 0, 5, 10, and 20. Then the remaining, traded goods were divided into import-competing goods and exports, depending on whether the net imports of the industry (imports less exports) were positive or negative, respectively. This yielded 3 X 3 table of factor incomes, from which it was then straightforward to calculate the matrix of factor intensities, 0. Table 10.2 shows the factor intensities calculated in this manner for the year 1984 and with k = 10%. This criterion yielded a nontraded sector producing $351 billion of value added, which is 36.6% of the total value added of $959 Table 10.2

Estimated Factor Intensities for U.S. Manufacturing, 1984 V,, Unskilled Labor

V,,Skilled Labor

V,, Capital

1. “Absolute” factor intensities (9J:

a. XI (import competing) b. X , (exporting) c. X, (nontraded) 2. Relative factor intensities (9,/9,J: a. 9,,/9,, (imports vs. exports) b. 8,,/9,,(imports vs. nontradeds) c. 9,,/9,, (exports vs. nontradeds)

,2615 ,2092 ,2205

,1504 ,1858

.I408

1.2502

.SO96

1.1861

1.0681

.9487

1.3192

,5881 ,6051 ,6387 ,9719 ,9208 ,9473

Sources: NBER Data Set on Trade, Immigration, and Foreign Direct Capital Investments. Note: Nontraded sector is defined as industries with (exports + imports)/output under 10%. Determinant of 9 equals ,0019528.

298


billion in the sample. Import-competing and exporting industries, respectively, produced $361 billion (37.6%) and $247 billion (25.7%) of value added. A list of industries producing more than $10 billion of value added, by trade category, is given in table 10.3. Row 2a of table 10.2 clearly indicates that, within the traded sector, unskilled labor is extremely intensive in imports and skilled labor is extreme in exports, with capital as the middle factor. This accords well with the widely held notion that the United States is well endowed with skilled labor and poorly endowed with unskilled labor, relative to the rest of the world (e.g., Baldwin 1971).1° The list of industries in table 10.3 supports this notion, with what are commonly considered “low-technology’’ industries such as paper and steel appearing only in the import category and “high-technology’’ industries like computers dominating in exports. Finally, row 2b of table 10.2 indicates that the middle “traded” factor, V,, is used extremely intensively in the nontraded sector relative to imports. Thus, in a well-defined sense, the nontraded sector is intensive in capital and unintensive in both types of labor, relative to the traded sector. By previous results, this means that 1 01 > 0 and that the “perverse demand” result cannot occur as long as XI and X , are normal goods (either individually or as a bundle). Thus, in combination with the results in table 10.2, our model predicts that immigration of either type of labor will, in the long run, hurt doTable 10.3

Four-Digit Industries with over $10 Billion of Value Added, by Wade Category, 1984

1. Exports:

2869 3573 3721 2. Imports: 2621 291 1 3312 3662 3674 3679 371 1 3714 3861 3. Nontraded goods: 271 1 2752 2834 3079 3761

Industrial organic chemicals, n.e.c Electronic computing equipment Aircraft Paper mills Petroleum refining Blast furnaces Radio and TV transmitting, signaling and detection equipment Semiconductors and related devices Electronic components, n.e.c. Motor vehicles and passenger car bodies Motor vehicle parts and accessories Photographic equipment and supplies Newspapers, publishing Commercial printing, lithographic Pharmaceutical products Miscellaneous plastics products Guided missiles and space vehicles

Note: Nontraded goods deflned as (exports where classified.”

+ imports)/output < 10%. N.e.c.

means “not else-

299


mestic owners of both types of labor. The reason is that immigration raises the price of nontraded goods, which in turn benefits owners of capital. This higher return to capital must be compensated for by a reduction in wages of both types of workers if U.S. exports and import-competing industries are to remain internationally competitive. How robust are these conclusions to changes in the definition of traded versus nontraded sectors and to changes in factor intensities over time in the U.S. economy? This question is explored in table 10.4, which summarizes our estimates of factor intensities for various levels of k and for other years. What is most striking about this table is that, for every year but 1960 and for every level of k, the factor intensity rankings in the traded sector are the same as in table 10.2. Also, (81is the same sign, indicating that the relative factor intensities of the traded versus nontraded sectors are unchanged and that normality of X , and X , in demand continues to be sufficient to rule out perverse factor-demand results. Only in 1960 are the results mixed; we feel that this is connected with changes in the U.S. economy over time that produced “Leontief paradox”-type results in earlier years but no longer do so, as the United States specializes more and more in knowledge-intensive industries. We conclude that our estimates of direct relative factor intensities (and the resulting predictions about the effects of immigration) reliably summarize a broad underlying pattern that has persisted in manufacturing since 1970 or even somewhat earlier. Table 10.4

Factor-IntensityRankings for Various Definitions of the Nontraded Sector, 1960, 1970, 1980, and 1984

+

Maximum Level of (Exports Imports)/ Output in the Nontraded Sector

.o 1960: Extreme factors in imports and exports, respectively (within traded sector) Sign I 0 I 1970: Extreme factors in imports and exports, respectively (within traded sector) Sign I 0 I 1980: Extreme factors in imports and exports, respectively (within traded sector) Sign I e I 1984: Extreme factors in imports and exports, respectively (within traded sector) Sign 10 I

.05

.I0

.20

K, S

+

u, s

u, s

+

NA

Note: K = capital, S = “skilled (nonproduction) labor, and U

u, s

+

u, s

+

=

“unskilled” (production) labor.

300


How robust are our estimates to the inclusion of nonmanufacturing industries in the analysis? This question cannot be answered with the NBER data but is explored using two-digit industry data from another source in the Appendix. Interestingly, this analysis indicates that the basic results do generalize. The basic reason for this is that services (which constitute the bulk of the excluded, nonmanufacturing industries), contrary to widespread beliefs, are not always nontraded and not always labor intensive. For example, real estate (a very important nontraded service) is highly capital intensive, while education (which is very intensive in skilled labor) is a substantial U.S. net export. Still, owing to the considerable practical and conceptual problems involved in estimating service trade (see the data sources listed in the Appendix), we view this finding with considerable caution. Clearly, there is an outstanding need for careful empirical analysis based on better-quality trade data for service industries. The consistency of our results across definitions of sectors and years is, however, suggestive and proves a useful illustration of the ease with which our theoretical model can be implemented. 10.5

Conclusion

This paper has developed a simple, general equilibrium model of how factor endowments affect factor prices when a subset of the goods produced in the economy is traded at internationally fixed prices. The result is a model that makes unambiguous predictions that are independent of estimated elasticities of substitution among factors in production. Our empirical analysis, based on 430 four-digit manufacturing industries in the years 1960, 1970, 1980, and 1984, indicated that, at least since 1970, factor intensities in U.S. manufacturing follow a very consistent pattern: skilled labor is used extremely intensively in exports, while unskilled labor is extremely intensive in import-competing industries. Furthermore, the middle factor in the traded sector, capital, is used intensively in the nontraded sector relative to the traded sector. Thus, to the extent that the relative factor intensities we find in manufacturing generalize to the whole economy, our model predicts the following. Increased immigration of either skilled or unskilled workers to the United States will, in the long run, hurt U.S. workers of both types and benefit owners of capital. These effects should be associated with an increase in nontraded goods prices, which, by reducing the international competitiveness of the country’s traded goods, causes the reduction in wages. They are also independent of the technical substitution elasticities in production that so many analysts have attempted to estimate (for a recent summary, see Hamermesh and Grant 1979). Our model can, of course, be extended and improved on in various ways. These include incorporating intermediate inputs (as is done in empirical work by Baldwin 1971; and Stem and Maskus 1981), allowing for capital mobility (as in Gerking and Mutti 1983), considering shorter run effects (as in Rivera-

301


Batiz 1987), relaxing the extreme assumption that U.S. consumption and production have no effect on the prices of traded goods, and considering nonbalanced trade. In addition, better data, which consider nonmanufacturing industries as well, may also be available in the relatively near future as more trade statistics on services are collected. All these extensions could, of course, change our specific predictions about the directions of factor price changes here, which we view as suggestive but tentative. It is clear that they will not, however, change what we view as the fundamental lesson of this paper. This lesson is that the effects of immigration in a partially open economy may be determined by a fundamentally different set of factors than in a closed economy, where technical substitutability between factors in production plays the key role. In the partially open economy, factor prices are constrained to change in a way that preserves the international competitiveness of its traded goods, as long as those goods continue to be produced. This places tight restrictions on the kinds of factor-price changes that can occur. These restrictions deserve, we feel, greater prominence in the work of empirical and policy-oriented researchers studying the effects of immigration to the United States.

Appendix Factor Intensity Estimates or the Entire U S . Economy from Two-Digit ndustry Data, 1983

f

This appendix explores the generalizability of our results based on manufacturing only to the entire U.S. economy by constructing a factor-intensity matrix for the entire U.S. economy from two-digit data for 1983. These data were obtained from the following sources: total value added and compensation of employees from the Survey of Current Business (66 [July 19861: 1986, table 6); merchandise trade data from the 1985 Statistical Abstract of the United States (tables 1448, 1449); and rough estimates of service trade from the Office of Technology Assessment (1986, table 2, using midpoints of intervals). Thus, some services are classified as exports (e.g., education), others as imports (e.g., insurance), and still others as nontraded (e.g., retailing). Skilled workers were defined as those who completed high school; the percentage of the work force that was skilled was then taken from a 1980 Census tabulation of occupation by industry (U.S. Department of Commerce series PC-80-2-70, pp. 1-80) and their relative wage rates in 1983 from U.S. Department of Commerce series Money Income of Husbands, Families and Persons in the United States (P-10, no. 146, table 48). Payments to land as an input are included by definition with capital, which functions as a kind of “residual” factor here. Because of the small number of industries. we chose

302


criteria of openness to trade and a critical balance-of-trade level that yielded nontraded, exporting, and importing sectors of roughly equal sizes (this meant that some industries with low trade deficits were classed as exports). This procedure yielded the following factor-intensity matrix, 8:

where the three factors are, respectively, unskilled labor, skilled labor, and capital, as in table 10.2. Noting that, as in table 10.2, the nontraded sector is again relatively intensive in capital, w e conclude that the factor-intensity rankings w e find in manufacturing may well generalize completely to the entire U.S. economy when better data on service trade are available.

Notes 1. Since this is an undistorted economy, we know that it maximizes a weighted sum of the utilities of its members subject to the balanced trade and resource constraints. Under certain conditions (see, e.g., Gorman 1953), this is equivalent to maximizing a community utility function of the type used here. We assume that such conditions are satisfied here, which does not necessarily imply that all agents (including immigrants) are equally endowed. This latter condition would of course make the analysis of immigration much less interesting. 2. To see this, note that the denominator can be written as

Through algebraic manipulation, the second term can be reduced to [p3/(0,10/)]u’Tu, where T is a negative semidefinite matrix derived from the economy-wide substitution matrix (see Jones and Scheinkman 1977), and u is a column vector. The product U‘TU is nonpositive, while p, is negative, and 8, is positive. By simple manipulation it follows that lel/A is always positive. 3. It is assumed that endowment changes do not move the economy out of the “cone of diversification.” 4. The national income elasticity of demand equals the national utility elasticity of demand since, locally, units of income and utility are equivalent. Also, it is easily shown that, in the “perverse demand” case, the demand curve for factor V ,in general equilibrium is upward sloping. While this might be a fortunate situation for owners of V,,it appears quite unlikely given the fairly weak sufficient conditions needed to rule it out. 5. In the standard 2 X 2 model of international trade, the Rybczynski effect of the immigration of one factor resulted in the output of one industry increasing while production in the other industry diminished. Thus, the value of output of the expanding

303


industry had to increase by a greater amount than national income. If both of the goods were normal, then the increase in the value of production for the expanding industry would then necessarily be greater than the increase in the value demanded of the good, ensuring a nonperverse demand result. Our results show that, when there are three goods and three factors, nonperversity is not guaranteed. To see this, suppose that immigration of both factors 1 and 2 increases output of X,, through the Rybczynski effect. Even with factor intensities unchanged (as product prices are constant), this need not result in a fall in the output of both of the traded goods. Indeed, the increase in the value of production of X , might be quite small relative to the increase in national income when output of one of the traded goods also increases. Thus, the potential does exist that the expansion in output of good 3 may be less than the increase in its demand, even when goods 1 and 2 are jointly noninferior in consumption. 6. The factor income information in the NBER data set was taken from the Annual Survey ofManufactures for various years. This could be matched with trade figures from the Trading Monitoring System of the Bureau of Labor Statistics for 430 of the 450 four-digit industries. The data were collected and made available to us by John Abowd and Richard Freeman. 7. See table 4.2 in DeardorfF (1984). The finest level of detail used in the studies cited there is in Stem and Maskus (1981), who use 128 three-digit industries. Our sample of 430 four-digit industries contrasts very favorably with all three studies. 8. An alternative, and perhaps preferable, definition of skilled vs. unskilled workers might be based on total years of education. Unfortunately, information on years of education by industry is not available in this data set. Our experiments using high school completion rates with two-digit data, reported in the Appendix, lead us to expect that this would not change the results. 9. Ideally, our theoretical model should incorporate (both traded and nontraded) intermediate inputs and specify how these should fit into the empirical analysis. Since it appears that this extension of our model would significantly complicate our analysis, it is not undertaken here. We feel that it is, however, an important area for further research on this topic. 10. This notion has often been advanced as a possible explanation of the “Leontief paradox” of capital-intensive U.S. imports (Leontief 1953). Interestingly, this paradox does not arise in table 10.2 here (the combined labor shares in imports exceed those in exports, and the capital share is greater in exports than imports). Three possible reasons for this are the fact that we use four-digit, not two-digit, data, the fact that our focus on manufacturing excludes natural resource industries, and the later time period. Of these, the “time” explanation seems to be most convincing, for the following reason: for the four different values of k considered in the experiments of table 10.3, the capital share in exports exceeded the capital share in imports three out of four times in 1984, four times in 1980, twice in 1970, and zero times in 1960. This seems fairly strong evidence of an increasing relative capital intensity of U.S. exports over time.

References Baldwin, R . E. 1971. Determinants of the Commodity Structure of U.S. Trade. American Economic Review 61:126-46. Batra, R. N., and F. R. Casas. 1976. A Synthesis of the Heckscher-Ohlin and the Neoclassical Models of International Trade. Journal of International Economics 6:21-38.

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Brecher, R. A., and E. U. Choudhri. 1982. The Factor Content of International Trade without Factor-Price Equalization. Journal of International Economics 12:277-84. Chang, W. W. 1979. Some Theorems of Trade and General Equilibrium with Many Goods and Factors. Econometrica 47:707-26. Deardorf€, A. 1984. Testing Trade Theories and Predicting Trade Flows. In Handbook of International Economics, vol. 1, ed. R. W. Jones and P. B. Kenen. Amsterdam: North-Holland. Fallon, P. R., and P. R. G. Layard. 1975. Capital-Skill Complementarity, Income Distribution, and Output Accounting. Journal of Political Economy 83:279-302. Gerking, S., and J. Mutti. 1983. Factor Rewards and the International Migration of Unskilled Labor: A Model with Capital Mobility. Journal of International Economics 14:367-80. Gorman, W. 1953. Community Preference Fields. Econometrica 21:63-80. Hamermesh, D. A., and J. Grant. 1979. Econometric Studies of Labor-Labor Substitution and Their Implications for Policy. Journal of Human Resources 1 4 518-42. Hicks, J. R. 1932. The Theory ofWages. New York: Macmillan. Jones, R. W., and S. T. Easton. 1983. Factor Intensities and Factor Substitution in General Equilibrium. Journal of International Economics 15:65-99. Jones, R. W., and J. A. Scheinkman. 1977. The Relevance of the Two-Sector Production Model in Trade Theory. Journal of Political Economy 85:909-36. Kemp, M. C. 1966. Gain from International Trade and Investment. American Economic Review 56:788-809. Leontief, W. 1953. Domestic Production and Foreign Trade: The American Capital Position Re-examined. Proceedings of the American Philosophical Society 97: 332-49. Markusen, J., and J. Melvin. 1979. Tariffs, Capital Mobility, and Foreign Ownership. Journal of International Economics 9:395-410. Office of Technology Assessment. Congressional Board of the Ninety-ninth Congress. 1986. Trade in Services: Exports and Foreign Revenues, Summary. Washington, D.C.: U.S. Government Printing Office. Rivera-Batiz, F. 1982. Nontraded Goods and the Pure Theory of International Trade with Equal Numbers of Goods and Factors. International Economic Review 23: 401-9. . 1987. Modeling the Short-Run Economic Effects of Immigration: Some General Equilibrium Simulations. In Modeling andSimulation, Vol. 17, ed. W. G. Vogt and M. H. Mickle. Research Triangle Park, N.C.: Instrument Society of America. Ruffin, R. J. 1981. Trade and Factor Movements with Three Factors and Two Goods. Economic Letters 7: 177-82. Samuelson, P. A. 1948. International Trade and the Equalization of Factor Prices. Economic Journal 58: 163-84. Stem, R. M., and K. E. Maskus. 1981. Determinants of the Structure of U S . Foreign Trade, 1958-1976. Journal of International Economics 11~207-24.

11

Immigrants, Labor Market Pressures, and the Composition of the Aggregate Demand Susan M. Collins

The purpose of this paper is to examine the effect of changes in the composition of aggregate demand on total labor requirements and on the requirements for jobs typically held by immigrants, using the input-output (10)tables for the United States. The paper asks two sets of questions. First, how are labor requirements affected by a dramatic turnaround in the trade balance? Does it matter whether the deficits are accompanied by an investment or a consumption boom, and how are the resulting labor market pressures distributed across industries? Second, how are immigrants distributed across domestic industries, are they differentially affected by shifts in the composition of demand, and are the recent changes likely to have made immigrant workers more “visible,” providing one explanation for the increased attention they have received in recent years?’ The basic approach is as follows. Changes in the composition of aggregate demand will alter the distribution of labor requirements across sectors and industries. While these shifts do not imply changes in labor demand or in actual employment, they can be interpreted as indicating labor market pressures in those sectors where demand has decreased. Because immigrants and native workers are distributed quite differently across jobs, these pressures will influence the two groups differently.* Input-output analysis provide a useful framework to explore the linkages between aggregate demand, labor market pressures, and immigrants because it integrates both microeconomic and macroeconomic aspects. On the microeconomic side, it considers the output and employment responses of particular industries. On the macroeconomic side, it incorporates the key identity from Susan M. Collins is associate professor of economics at Harvard University and a faculty research fellow of the National Bureau of Economic Research. The author would like to thank 1. Abowd, H. Bowen, R. Freeman, and K. Lang for comments and A. Revenga for excellent research assistance.

306

Susan M. Collins

the National Income and Product Accounts, which highlights the linkages between net exports and the other components of aggregate demand. The counterpart to an external imbalance (i.e., a deficit in U.S. goods and services vis i vis the rest of the world) must be an excess of investment over domestic savings. The macroeconomic focus is important because it points to a different set of issues and conclusions than many of the industry studies. For example, suppose that a researcher concluded that imports had been a “cause of injury” in a particular industry and that restricting imports would be likely to raise domestic output and employment. From the macroeconomic perspective, unless the policies to restrict imports were expected to reduce the savings investment imbalance, thereby reducing the total trade deficit, these policies merely shift the trade deficit between sectors. There are also some drawbacks to the I 0 analysis. By maintaining constant input-output coefficients and fixing the commodity composition of each component of aggregate demand, it rules out substitution on both the production and the consumption sides. A related point is that it does not specify why aggregate demand changes and how relative prices (including interest rates and exchange rates) are affected. The answers to these questions will in turn have implications for the composition of imports, consumption, and the other components of demand. In order to incorporate these factors, it would be necessary to imbed the I 0 framework into a macroeconomic model, which is beyond the scope of the current paper. The paper is composed of four remaining sections. Section 11.1 asks where the immigrants are and examines the distribution of immigrant workers across sectors. Section 11.2 turns to the key macroeconomic issues and discusses changes in the composition of aggregate demand. Section 11.3 analyzes the effect of shifts in the composition of aggregate demand on labor requirements by sector and for immigrants and nonimmigrants. The section first spells out the methodology and then discusses results. Concluding remarks are given in the final section.

11.1 Where Are the Immigrants? A number of authors have pointed out that immigrant workers tend to be concentrated in different industries than native workers. In particular, immigrants tend to enter the labor market in low-wage, relatively unskilled positions, but the distribution of immigrants over industries and occupations becomes more similar to that of natives the longer they remain in the United state^.^ Table 11.1 compares the 1982 employment distribution of foreign-born (immigrants plus refugees) and native workers across sectors. These data are derived from a special matched sample of respondents to supplementary CPS surveys conducted in March and April 1983. Unfortunately, only about 75%

307

Immigrants, Labor Market Pressures, and the Aggregate Demand

Table 11.1

Employment Distribution by Sector % of Total Group Employment

Total Agriculture Mining Construction Manufacturing Transportation and public utilities Wholesale trade Retail trade Finance, insurance, real estate Private household Other service Public administration Total (thousands)

Foreign

Native

Foreign as % of Total Sector Employment

4.7 25.3

3.4 1.o 6.0 19.2

8.4 5.7 6.4 10.2

1.0 4.3 16.3

4.2 3.9 16.5

1.2 4.4 16.3

4.8 7.1 8.1

6.2 1.3 30.3 4.7

6.6 1.8 30.5 2.4

6.2 1.2 30.3 4.9

8.4 11.5 8.0 4.1

109,064

8,694

100,370

7.97

3.4 .9 5.9 19.6

3.6

.I

Source: Sehgal(1985).

of the April sample matched with the March sample, and no corrections were made for missing value^.^ The first three columns of the table report the shares of total, foreign-born, and native employment in each of eleven sectors. The final column gives the percentage of foreign born in total sectoral employment for each sector. The table shows that foreign born accounted for 7.97% of total employment but that these workers were not evenly distributed across sectors. The largest difference between the two groups is in manufacturing, which accounted for 25% of foreign-born workers but only 19% of native workers. Furthermore, manufacturing has the second highest concentration of immigrants, behind private household services. Immigrants are relatively underrepresented in public administration. However, it is not surprising that natives are twice as likely to hold these jobs since many of them require citizenship. Immigrants are also underrepresented in construction and in transport and public utilities. Within manufacturing, immigrants are disproportionately located in nondurable goods: 9.5% of total employment in nondurables was immigrant compared to less than 7% of total employment in durables. Immigrants are disproportionately located in apparel, where they account for over 19% of total employment. They also account for large employment shares in textiles, footwear, leather, drugs, and cleaning and toilet preparations. They are relatively scarce in tobacco, petroleum refining, and chemical product industries. The various durable goods industries each account for a small share of total immigrant employment. Immigrants are relatively visible in some sectors, such as miscellaneous manufacturing.

308

Susan M. Collins

In summary, immigrants account for less than 8% of total employment. However, they are distributed across industries quite differently than native workers are. Furthermore, they are extremely visible in some industries, amounting to 12%-20% of the total work force. The remainder of the paper explores the implications of recent changes in aggregate demand on the distribution of job requirements across industries and asks whether reductions in job requirements have been concentrated in industries where immigrants are also concentrated. 11.2 Shifts in the Composition of Aggregate Demand In fact, there have been large recent changes in the composition of aggregate demand. The two identities from national income accounting given in (1) and (2) are very useful for documenting the shifts in key macroeconomic variables and for highlighting the linkages between the foreign sector and domestic demand:

C

+ 1 + G + (X - M),

(1)

Y

(2)

(X - M ) = S,

=

+ S, - 1 - R .

As usual, Y denotes GNP; C , I , and G denote private consumption, investment, and government spending, respectively; and X and M refer to exports and imports of goods and services, S, and S, to private and government savings, and R to net other international transaction^.^ Equation (2) says that foreign savings must equal the difference between domestic savings and investment. Table 11.2 shows the U.S. experience during 1973-86. The top panel gives the composition of aggregate demand as shares of GNP during each of four subperiods, while the bottom panel shows the domestic savings and investment counterparts to net export performance. As shown, net exports declined during 1977-79, improving somewhat during 1980-82 before the substantial deterioration during 1983-86. The two periods of poor trade performance differ in more than simply the magnitude of the deficit. In 1977-79, investment rose by 2.2% of GNP relative to 1973-76, requiring additional domestic and/or foreign savings. Approximately one-third was met by foreign savings, as the trade deficit declined by .8% of income. The remaining two-thirds was met by an increase in government savings. During 1980-82, investment fell, as did all three components of savings. However, because of the larger decline in government savings, foreign savings did not return to its 1973-76 level-the trade balance recovered only partially. The 1983-86 period stands in stark contrast to 1977-79. Although the trade balance deteriorated by 2.6% of GNP, investment rose by little more than .5 percent. Instead of increased domestic savings, government savings fell pre-

309


Table 11.2 Year

The U.S. Experience, 1973-86 Consumption

The shifting composition of aggregate demand (as a percentage of GNP): 1973-76 62.6 1977-79 62.7 1980-82 63.6 1983-86 65.2

Investment

15.8 17.9 15.7 16.3

Gov’t

20.2 18.9 19.6 20.1

(Defense)

Net Exports

(5.5)

1.3

(4.9) (5.6) (6.4)

1.o - 1.6

.5

Savings Private Decomposition of net exports: savings investment (as a percentage of GNP): 1973-76 1977-79 1980-82 1983-86

18.1 17.9 17.7 17.2

Gov’t

- 1.5 - .2 - 1.9 -3.3

Private Investment

15.8 17.9 15.7 16.3

Net Exports

Other

1.3 .5

.6

1.o

.9

- 1.6

.8 .8

Source: Economic Report of the President, 1987

cipitously, by 1.5%of GNP, while private savings continued its trend decline. From table 11.2, government spending rose by just .5 percent of GNP (although this figure masks the large shift toward defense spending). Private consumption, on the other hand, ranged from 64.8% to 65.6% of GNP during 1983-86. The jump is especially notable because private consumption has been relatively stable at 63% of income since 1950 and has exceeded 64% in only four years between 1950 and 1980. Thus, the 1983-86 trade deficit coincided with the large reduction in government revenues, which lowered government savings but raised private consumption. The next section of the paper explores the labor market implications of these compositional shifts in demand.

11.3 Final Demands and Labor Requirements 1 1 .3.1 The Framework The I 0 tables provide a useful way to link changes in the composition of demand to shifts in industrial output and labor requirements. As already discussed, the major shortcomings of the approach are that it does not consider whether the demand shifts are associated with relative price changes and that it rules out substitution-on both the demand and the supply side-by assum-

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Susan M. Collins

ing constant coefficients. The results provide information about the labor that would be required to produce the sectoral outputs consistent with a particular final demand. These labor requirements may be very different from sectoral employments-especially in the short run. Although the results of an I 0 analysis cannot be interpreted as indicting shifts in actual labor demands or employments, they do provide information about the likely labor market pressures. The analysis uses the eighty-five-industry-level disaggregation of the 1980 I 0 tables.'j Final demands and data used to compute the technical input-output coefficients are valued in producer prices. The vector of labor requirements per dollar output is matched to the 1977 10 tables.' The exercises discussed below will consider different compositions of final demand, so that the index k refers to the kth scenario, or aggregate demand composition. The central relation is given in equation (3):

(3)

L,

=

Y [ I ] . Q . dk9

where

L, = the total labor required in each industry, given the kth aggregate demand composition (85 x 1); y = the vector of labor requirements for a dollar of output industry

(85 X 1); I = the identity matrix (85 x 85); Q = the total requirements (direct and indirect) matrix of the output from each industry required to prduce a dollar's worth of each commodity (85 x 85); d, = the vector of final demands for each commodity, given the kth aggregate demand composition (85 X 1). Equation (4)divides final demand into three parts:

(4)

d, =

n

*

T,.D,

where

D = total aggregate demand;

TT, =

n=

the vector of shares in total demand of each of the nine componentsprivate consumption, investment, inventory accumulation exports, imports, and four types of government expenditures (9 X 1); the matrix of demands for each of the eighty-five commodities per dollar of each component of final demand (85 X 9).

To focus on the implications of recent shifts in demand composition, alternative final demand vectors were computed by varying the shares in aggregate demand (T,)while holding everything else constant. In other words, total demand and the commodity composition of each piece of final demand were held constant as the shares of investment, consumption, imports, etc. were varied.

311

1 1.3.2


The Components of Final Demand

Variations in the composition of aggregate demand will influence labor requirements even when total demand is held constant. This is because each type of demand has a different commodity basket so that each concentrates its spending on commodities with different labor requirements. To help interpret the results in the next sections, table 11.3 compares the total labor requirements to produce one million (1980) dollars worth of the commodities in each demand component. (Thus, the entry for imports is positive .) Inventories are excluded because the commodity composition of inventories varies substantially from year to year so that it is not particularly useful to think of them as a fixed basket of commodities. The first column of table 11.3 reports the total number of jobs required per million dollars. The second column gives the average expenditure per job to produce each commodity basket. The figures point out that there is substantial variation. Government spending requires the most labor per dollar spent. A million dollars of government expenditure requires 69 jobs when spent on education and 55 jobs when spent on defense. (Equivalently, the figures imply a total of $14,400 of educational expenditure per job and $18,200 of defense expenditure per job .) Government expenditures are followed by fixed investment and private consumption, which require 42 and 41 jobs per million dollars, respectively. It may seem surprising that the external sector has the smallest labor requirements and that labor requirements are slightly higher for exports than for imports. Because of the relative capital abundance in the United States, imTable 11.3

Labor Requirements and Final Demand All Workers

'Qpe of Demand Private consumption Fixed investment Exports Imports ( - ) Federal government: Defense Other State & local: Education Other Addendum:' Exports Imports ( - )

Immigrants

Jobs per $million Expenditure

$/Job

Jobs per $million Expenditure

% Total Jobs

40.7 42.0 26.6 23.0

24,600 23,800 37,600 43.500

3.5 3.2 2.1 2.1

8.5 7.7 8.0 9.2

54.9 61.8

18,200 16,200

3.2 3.5

5.8 5.6

69.4 59.7

14,400 16,700

3.1 3.4

4.6 5.7

38.3 44.2

26,300 22,700

Source: Tabulated by author from I 0 tables, as described in text. a Excludes petroleum, and noncomparable products.

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Susan M. Collins

ports would be expected to be relatively labor using. However, traditional trade theory has implications for the ratio of capital to labor embodied in trade, not for the absolute amount of any single factor. In fact, Leontief‘s paradox (that capital abundant countries have higher capital-labor ratios embodied in their imports than in their exports) has been a standard result in the empirical trade literature. For a review of this literature, see Deardorff 1984. Early resolutions to the paradox have included disaggregation of labor so as to include human capital as a separate factor and special treatment of natural resource industries. More recently, Learner (1980) has shown that it is not inconsistent with theory for capital abundant countries to have higher capital-labor ratios in imports when they are also running trade surpluses. In the 1980 I 0 tables, further disaggregation of imports and exports generates the more intuitive result that imports are labor intensive relative to exports. Both export and import final demands from the I 0 tables include substantial expenditures on commodities that have little or no domestic labor inputs. For example, 27.8% of exports are classified as commodities from the “rest of the world,” including labor remittances. The relevant “industry” uses no domestic labor. Nine percent of imports are from the rest of the world. In addition, 14% are “noncomparable imports,” also with no domestic labor usage, while 26% are on petroleum-related products that use relatively little labor. The total labor requirement per million dollars spent on imports excluding these special categories is 44. This exceeds the labor requirements for consumption and investment expenditures and the comparable figure for exports, which is only 38. Total labor requirements per dollar of expenditure depend on the type of final demand expenditure. It is also interesting to explore how type of expenditure is likely to influence the availability of jobs for immigrants versus nonimmigrants. To do this requires an additional assumption-that the share of immigrants in total industry employment remains relatively constant. Then, if immigrants account for 8% of agricultural employment, an increase of 100 agricultural jobs will generate approximately 8 jobs for immigrants and 92 for nonimmigrants. Using the data on immigrants as a share of total industry employment from table 11.1 in this way, it is possible to split the total labor requirements from column 1 of table 11.3 into immigrant and nonimmigrant. The third and fourth columns of table 11.3 report the number of “immigrant jobs” per million dollars of expenditure on each demand component together with the jobs “held” by immigrants as a percentage of total labor requirements. Because immigrants are not proportionally distributed across industries, the share of total jobs that are likely to be filled by immigrants changes with the component of demand. The last column of table 11.3 shows that the import commodity basket is the one with the highest immigrant labor concentration. Nine point two percent of the labor required to produce the (1980) import

313


basket was likely to have been immigrant. This is not surprising given the large percentages of immigrant workers in apparel, footwear, and other industries with strong import competition. Private consumption expenditures have the second highest immigrant concentration, followed by exports and fixed investment. Government expenditures, particularly on education, come at the other end of the scale because the labor requirements to satisfy these demands are concentrated in industries with relatively small shares of immigrants. Thus, shifts in the composition of final demand will tend to put different labor market pressures on immigrants and nonimmigrants. In particular, an investment boom will generate a larger rise in total labor requirements than a consumption boom, and fewer of those jobs are likely to go to immigrant workers. Overall, a consumption boom will be relatively less beneficial for native workers. However, these aggregate figures mask differences in the intraindustry labor requirements associated with the demand components and provide an incomplete picture of the likely labor market pressures associated with changes in the composition of aggregate demand. The next step is to consider particular aggregate demand vectors and to compare the implied labor requirements, disaggregating both by immigranthathe and by industry. 11.3.3 Alternative Scenarios The analysis below considers four final demand vectors corresponding to four compositions of aggregate demand. These are shown in table 11.4. The first column shows the base case-the actual composition in the 1980 I 0 tables.8 The second column shows a scenario like the 1983-86 period. The

Table 11.4

Composition of Aggregate Demand (percentages of GNP) % A Alt. 1 Trade Deficit Consumption Boom

From Alt. 2 Trade Deficit Investment Boom

1980 Alt. 3 Investment Slump Consumption Boom

63.1

65.6

63.1

65.6

16.1

18.6

14.1

- .3

- .3

12.6 -11.5

16.6 - .3 11.6 - 14.0

11.6 - 14.0

12.6 - 11.5

4.9 2.4

6.5 2.2

5.4 2.6

4.9 2.2

5.2 7.5

5.0 6.8

5.4 7.6

5.1 7.3

1980 Base Type of Demand % Total Consumption Investment: Fixed Inventory Exports Imports Government, federal: Defense Other Federal State & local: Education Other

- .3

Source: Tabulated by author from I 0 tables, as described in text

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Susan M. Collins

trade balance shifts from surplus to deficit,while private consumption soars. In addition, government spending shifts toward federal defense spending and away from state and local expenditures. The third column shows an alternative scenario with the same trade deficit as in column 2, but an investment instead of a consumption boom. The final column maintains the consumption boom from column 2 but assumes an investment slump instead of a trade deficit. (Inventories are the same share of output in all scenarios.) Because it is misleading to interpret the labor requirements as employment, the results for the three alternative scenarios are presented as percentage changes from the corresponding 1980 base. Table 11.5 provides an overview of the effect of the compositional shifts in demand on labor requirements. Even though total aggregate demand is held constant, total labor requirements rise in the first two scenarios and fall slightly in the third. The increases for trade deficits combined with either a consumption boom (alternative 1) or an investment boom (alternative 2 ) are not surprising given the relatively small labor requirements per dollar of total imports. The labor requirements rises somewhat more in scenario 2 because scenario 1 includes a shift toward defense and away from other types of government spending and because investment requires relatively more labor than consumption per dollar expenditure. This also explains the decline in the third scenario. It is again interesting to decompose the total labor requirements into immigrant and nonimmigrant segments. This is done in the second and third rows of table 11.5. The figures point out a more striking difference between the two trade-deficit scenarios. In the actual 1983-86 combination described in alternative 1, there is little difference between the growth of “immigrant jobs” and “nonimmigrant jobs.” However, alternative 2 , the historically typical combination of high investment and trade deterioration, implies a substantially larger expansion of “nonimmigrant jobs” than “immigrant” jobs. Similarly, a switch from investment to consumption with no trade change, as in alternative 3, implies an increase in jobs typically held by immigrants and a decline in jobs typically held by natives. To the extent that workers in declining sectors are laid off more quickly than workers are hired in expanding sectors, any demand shift that alters the secTable 11.5

Demand Shifts and Total Labor Requirements % A Alt. 1 Trade Deficit Consumption Boom


Immigrant Native

1.22 1.27

1.12

.I6

1.81

- .33

Total

1.27

1.75

- .30

1980 Alt. 3 Investment Slump Consumption Boom

315


toral distribution of labor requirements will tend to cause short-term unemployment, even if aggregate labor requirements have increased. A key implication of table 11.5 is that native workers will have a relatively easier adjustment to expanding imports when the trade deficit is associated with an investment boom. The very different distribution of jobs when the trade deficit coincides with a consumption boom places native workers more directly in competition with immigrants for the jobs in expanding sectors. The next step is to look at the distribution of the change in job requirements implied by each scenario across industries. Table 11.6 examines the sectoral decomposition of total labor requirements for all sectors and nondurable manufacturing where immigrants are overrepresented. The first column gives the share of each sector in the total labor requirements in the 1980 base. Columns 2-4 give the percentage change in sectoral labor requirements for each of the three alternative scenarios. The consumption boom-trade deficit alternative leads to a reduction of labor requirements in manufacturing but to increases in service and government sectors. (The decline in manufacturing, with its large concentration of immigrants, is offset by a spurt in household services and other particular industries.) In contrast, the investment boom-trade deficit scenario leads to increased labor requirements in manufacturing but slower growth in the service sectors. An important implication from the top panel of table 1 1.6 is that we should expect a trade deficit cum consumption boom to shift employment from manufacturing to service sectors, but it is incorrect to conclude that such an employment shift is “caused” by an expanding trade deficit. A trade deficit of equal magnitude (alternative 2) cum investment boom will tend to shift employment toward manufacturing and construction and away from services, finance, insurance, and real estate. The analysis has identified two sets of labor market pressures that are likely to arise from a consumption boodtrade imbalance but unlikely to be present in the more standard investment boodtrade imbalance combination. The first is that natives are relatively less concentrated in the sectors and industries with large increases in labor requirements. The second is that labor requirement decreases will be concentrated in the manufacturing sector and therefore more likely to increase pressures for protectionism. The bottom panel of table 11.6 presents further information about the change in labor requirements in (durable and nondurable) manufacturing under each alternative to make a third point. Within manufacturing, many of the industries with large declines under the consumption boom-trade deficit scenario also have high concentrations of immigrants. In particular, leather, footwear, and plastics, three of the industries suffering the largest losses, have immigrants accounting for 9.5%-16.5% of their work forces. Immigrants are extremely “visible” in these industries. Similarly, of the three industries en-

316 Table 11.6

Susan M. Collins Changes in Labor Requirementsin All Sectors and in Nondurable Manufacturing 1980 % A Alt. 1 Base Trade Deficit % Total Consumption Boom

All sectors: Agriculture Mining Construction Manufacturing Transportation, communication Wholesale & retail trade Finance, insurance, real estate Services Government Total Nondurable manufacturing: Food & kindred products Tobacco manufacturers Fabric Textile mill products Apparel & other textile products Miscellaneous textile products Paper & allied products Paperboard containers & boxes Pnnting & publishing Chemical & allied products Plastic materials & synthetics DNgS Paints & allied products Petroleum & coal products Rubber & miscellaneous plastic products Leather products Footwear except rubber products Total

1.95 .80 6.39 20.87


- .44 - 7.04 - .78 - .53

-2.78 -6.56 8.60 .54

1980 Alt. 3 Investment Slump Consumption Boom 2.53

- .69

-6.72 - 1.85

5.08 16.60

1.32 2.35

1.54

.63 .79

5.12 24.26 18.94 100.00

2.46 2.35 1.28 1.27

.15 .26 4.46 I .75

2.61 2.42 -2.14 - .30

20.60 .84 6.64 1.38

1.59 1.32 -2.01 - 1.02

- 1.80 - 1.94 - 4.40 .90

3.50 3.26 2.25 - 1.74

15.86

- .28

-4.77

4.31

1.89 6.24

.33 -2.36

- 1.62 -2.88

1.94 .92

2.53 15.09

.08 1.18

- .85 1.02

.93 .70

.55

6.36

-3.93

-4.11

- .03

2.68 3.78 .74

- 2.92 .23 - .27

-3.03 - 1.86 3.35

- .08

3.86

- .50

-2.44

8.36 .28

- 1.75 -8.08

- 1.25 - 12.67

- .70

2.86 100.00

-5.70 .I0

- 11.01 -2.20

5.31 1.92

Source: Tabulated by author from I 0 tables, as described in text.

2.64 -3.19 1.43

4.57

317


joying the largest increases, two (tobacco and printing) have relatively small concentrations of immigrants, while the third (food) is about average.

11.4 Concluding Remarks This paper has made two major points. First, changes in the composition of aggregate demand will shift the distribution of labor requirements across industries and sectors. It will also affect total labor requirements. Thus, it is important to identify changes in the components of domestic absorption when analyzing the likely labor market consequences of aggregate demand shifts. By the same token, it is inappropriate to examine changes in imports or exports in isolation. An increase in imports must coincide with an offsetting shift in some other component(s) of demand, and a consumption boom is not equivalent to an investment boom or to a rise in government expenditures. Instead, the total and the sectoral distribution of labor requirements will depend critically on which has occurred. The analysis showed that the 1980s consumption boom with trade deficit has implied that decreased labor requirements were concentrated in the manufacturing and construction sectors. In contrast, an investment boom with trade deficit would tend to raise labor requirements in both these sectors. Therefore, it is incorrect to ask whether trade deficits tend to“deindustria1ize” the economy by shifting employment from manufacturing to services because the answer also depends on the changes in other components of aggregate demand. The second set of points concerns the role of immigrant workers versus native workers. Immigrants are not evenly distributed across sectors but are concentrated in some manufacturing industries and in private household services. Thus, shifts in the distribution of labor market requirements should have quite different short-run implications for immigrants than for native workers. The analysis showed that an investment boom cum trade deficit implies that requirements for jobs typically held by natives will increase nearly twice as quickly as requirements for jobs typically held by immigrants. Immigrants are relatively better off in a consumption boom, in which case there is little difference between the job growth rates for the two groups. Furthermore, some of the industries with the largest declines under the consumption boom are also the ones with the heaviest concentrations of immigrants. Both these factors may tend to make immigrants more “visible” in the labor market and help explain the recent increase in concern over their presence.

Notes 1 . Another possible explanation, an increase in the number of legal and/or illegal immigrants entering the country, is discussed in other papers in this volume. 2. This approach is relevant for short-run analysis only. Over time, both immigrants and native workers will presumably move from contractingto growing sectors. 3. This issue is discussed in Sehgal(l985) and in Borjas (1987).

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Susan M. Collins

4. For additional discussion of these data, see Sehgal(l985). 5. Net other international transactions are capital grants, net transfers, and interest payments. This term also includes the statistical discrepancy. 6. For further description of these data, see “The Input-Output Structure of the U.S. Economy, 1977” in Survey of Current Business (May 1984). 7. These data are from the Survey of Current Business, November 1985 and May 1986. 8. These data do not correspond exactly to the figures in table 1 1.1 because output is measured in producer prices in the I 0 tables but in consumer prices in the National Income and Product Accounts.

References Abowd, J. M., and R. B. Freeman. 1990. Internationalization of the U.S. Labor Market. NBER Working Paper no. 3321. Cambridge, Mass.: National Bureau of Economic Research. Aho, C. M., and J. A. Orr. 1981. The Growth of Trade-Sensitive Employment. Monthly Labor Review 104; no. 2(February):28-35. Borjas, G. 1987. Self-Selection and the Earnings of Immigrants. American Economic Review 77, no. 4(September):531-53. Branson, W. H . , and J. P. Love. 1986. Dollar Appreciation and Manufacturing Employment and Output. NBER Working Paper no. 1972. Cambridge, Mass.: National Bureau of Economic Research. DeardorfS, A. V. 1984. Testing trade theories and predicting trade flows. In Handbook of International Economics, vol. 1, eds. R. W. Jones and P. B. Kenen. Amsterdam: North-Holland. DeardorE, A. V., and R. M. Stem. 1986. The Michigan Trade Model of World Production and Trade: Theory and Applications. Cambridge, Mass.: MIT Press. Dickens, W. T., P. Shapiro, L. Tyson, and J. Zysman. 1985. The Employment Effects of International Trade: A Review of the Literature. Department of Economics, University of California, Berkeley. Mimeo. Grossman, G. M. 1986. Imports as a Cause of Injury: The Case of the U.S. Steel Industry. Journal of International Economics 20, nos. 3/4(May):201-24. . 1987. The Employment and Wage Effects of Import Competition in the United States. Journal of International Economic Integration 2, no. l(Spring): 1-23. Krueger, A. 0. 1979. The Impact of Foreign Trade on Employment in U.S. Industry. In Current Issues in Commercial Policy and Diplomacy, ed. J. Black and B . Hindley. London: Macmillan. . 1980. Protectionist Pressures, Imports and Employment in the United States. Scandinavian Journal of Economics 82(2): 133-46. Learner, E. 1980. The Leontief paradox reconsidered. Journal of Political Economy 90, no. 3 (June): 495-503. Orr, A. C., and J. A. Orr. 1983. Employment Adjustments in Import Sensitive Manufacturing Industries, 1960-80. Department of Economics, Manhattan College. Mimeo. Schoepfle, C. K. 1982. Imports and Domestic Employment: Identifying Affected Industries. Monthly Labor Review 105, no. 8 (August): 13-26. Sehgal, E. 1985. Foreign Born in the U.S. Labor Market: The Results of a Special Survey. Monthly Labor Review 108, no. 7 (July): 18-24. U.S. Trade Related Employment, 1978-84. 1986. USITC Publication no. 1855, May. Washington, D.C. : United States International Trade Commission.

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Comparative Experiences: Canada and Australia


12

An Analysis of the Earnings of Canadian Immigrants David E. Bloom and Morley Gunderson

The purpose of this paper is to analyze immigrant labor market progress in Canada-a country that, as a matter of official policy, has screened most of its immigrants on the basis of their expected “ability to assimilate.” In particular, we compare earnings profiles for Canadian immigrants and natives and seek to determine whether immigrant earnings profiles reflect any “vintage effects” associated with year of immigration. Over the past ten years, a number of studies of immigrant earnings have focused on these same issues using data for U.S. immigrants. Among the best known is that by Chiswick (1978), which fits a standard wage equation to cross-sectional data on immigrants and natives in the 1970 Public Use Sample of the U.S. Census. Chiswick’s results support the conclusion that, when they first enter the labor market, immigrants earn approximately 25 percent less than natives with comparable years of schooling and experience, marital status, etc. However, Chiswick also finds that immigrants have steeper experience-earnings profiles than “comparable” natives, with immigrant earnings overtaking native earnings within roughly thirteen years of their entry into the United States. A number of other studies have fit the same basic model to similar data and have reached roughly identical conclusions (see, e.g., Carliner 1980; Long 1980; and Borjas 1982). David E. Bloom is professor of economics in the Department of Economics at Columbia University and a research associate of the National Bureau of Economic Research. Morley Gunderson is professor of economics and director of the Centre for Industrial Relations at the University of Toronto. An earlier version of this paper was presented at the NBER Conference on Trade, Immigration, and Labor in September 1987. The authors are indebted to McKinley Blackbum, Anne Hill, Robert Komfeld, David Neumark, Andrew Newman, and Marcus Rebick for assistance in the preparation of this paper and to Blackbum, Rebick, Richard Freeman, Mark Killingsworth, Jacob Mincer, and Glenn Withers for helpful discussions and comments. This research was supported by NIH grant HD18844-02 and by a grant from the Ford Foundation to the National Bureau of Economic Research.

321

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David E. Bloom and Morley Gunderson

The set of findings based on Chiswick’s approach to measuring immigrant assimilation has been challenged by Borjas (1985), who argues that the steepness of immigrant earnings profiles is inflated by cross-cohort declines in immigrant quality. Evidence supporting this argument is provided by using pooled data from the 1970 and 1980 U.S. Population Censuses to measure earnings growth in the intercensal period for individual entry cohorts of immigrants. On the basis of this analysis, Borjas concludes that “cross-section studies of immigrant earnings provide useless and misleading insights into the process of immigrant assimilation into the labor market” (p. 485). Borjas’s conclusion deserves further examination. A priori theoretical reasoning is perhaps more consistent with Chiswick’s empirical conclusions than with those of Borjas. Low entry wages for immigrants can plausibly be explained as a loss of (origin) country-specific human capital; rapid earnings growth can be viewed as reflecting positive self-selection into immigration (i.e., immigrants are above average in terms of their aggressiveness, ambitiousness, willingness to work hard, etc.). In contrast, sizable cross-cohort declines in immigrant quality are somewhat harder to accept given that it is not overall quality that is hypothesized to have declined but rather that component of overall quality that is unmeasured (i.e., the part of immigrant quality that is not measured by or correlated with variables such as schooling, experience, marital status, country of origin, etc.). Borjas’s results, as he recognizes, may also reflect differential patterns of underenumeration in the successive Censuses or nonrandom intercensal mortality and out-migration. Indeed, out-migration, death, or undercounting of immigrants who are relatively unsuccessful in the labor market are all alternatives to declining immigrant quality as an explanation of Chiswick’s cross-sectional results. We will also use the example of Canada as an opportunity to gain some insight into the importance of intercensal exiting from an immigrant population. Although there is little information on either the covariates of immigrant mortality or on differential Census undercounting of immigrants, there are several established lines of inquiry on the subject of out-migration. According to a group of imperfect information models, out-migration is an event that was unplanned ex ante and that occurs primarily among migrants whose labor market expectations are not satisfied (see Yezer and Thurston 1976; Allen 1979; Blejer and Goldberg 1980; and Lam 1986). These models suggest, at the margin, that out-migrants will tend to be selected from the lower end of the earnings distribution. On the other hand, intertemporal substitution models tend to view out-migration as a planned event among individuals who make short-term moves in order to take maximal advantage of temporarily favorable earnings opportunities (Stark and Bloom 1986; Fox 1987). These models suggest that out-migration will be most prevalent among individuals who are selectively active and successful in the labor market. Although the results are far from definitive, empirical research by Jasso and Rosenzweig (1987, 1989) and by Lam (1987) tends to support this view insofar as out-

323

An Analysis of the Earnings of Canadian Immigrants

migration of U.S. and Canadian immigrants is reported to be most prevalent among those who are relatively successful. Although they are extremely different in spirit, both the imperfect information and the intertemporal substitution models of out-migration share an important empirical implication, namely, that the variance of residuals in a migrant earnings equation will decline with duration of stay (i.e., under the imperfect information models, exit occurs at the lower end of the distribution, while, under the intertemporal substitution models, exit occurs at the upper end). In contrast, job matching or asymmetric information models imply that the residual variance in a wage equation will increase with duration of stay as employers are increasingly able to observe the true productivity of migrants (see Harris and Holmstrom 1982; Katz and Stark 1984). We attempt to infer which set of forces tends to be stronger by examining patterns in the variance and kurtosis of immigrant earnings by duration of stay. For example, we will interpret an increase (decrease) in the variance of earnings with duration of stay as evidence favoring the relative importance of the job-matching models (imperfect information models). Thus, we have four main goals in this paper. First, by fitting the models proposed by both Chiswick and Borjas to data for Canada, we hope to assess the extent to which it is generally true that cross-sectional studies of immigrant earnings are “useless and misleading.” Second, we hope that estimates of these alternative models will lead to clear substantive conclusions regarding the shape of immigrant earnings profiles and the importance of entry-cohort effects on earnings. Third, by comparing corresponding results under different Canadian immigration policies, we hope to shed some light on the significance of a nation’s institutions in determining the economic benefits of immigration. Finally, by analyzing the variance of immigrant earnings by duration of stay in Canada, we hope to assess the relative importance of selective intercensal exiting and job matching/asymmetric information in models of the labor market progress of immigrants. 12.1 Immigration Policy and Immigrants in Canada

In an effort to enrich our interpretation of statistics related to the labor market experience of Canadian immigrants, this section will present a brief review of the history of Canadian immigration policy and of immigration to Canada. 12.1.1 A Brief History of Canadian Immigration Policy From the nineteenth to the twentieth century, international migration to developed countries has been determined less and less by events and decisions of individuals in countries of origin and more and more by regulations established in countries of destination. In this regard, Canada is no exception. Until 1869, Canada’s immigration policy was simply one of free entry. But, begin-

324


ning that year, a series of legislative enactments established specific principles of selection and associated regulatory apparatus. Prohibitions were established on the landing of “criminals and other vicious classes” in 1872, paupers and destitute immigrants in 1879, and diseased persons in 1902. In 1904, an exorbitant head tax of $50 (Canadian) was established for Chinese immigrants. During these years, the central government also set up quarantine stations, specified legal responsibilities for companies involved in transporting immigrants, and began to require those companies to make deposits into a fund whose purpose was to cover the expenses of indigent immigrants before they were able to secure employment. The basic structure of Canadian immigration policy during much of the first half of the twentieth century was set forth in the Immigration Act of 1910. This act firmly established the principle of selective immigration by creating a proscribed class of immigrants: those “deemed undesirable because of climatic, industrial, social, educational, labour, or other conditions or requirements of Canada, or deemed undesirable because of their customs, habits, modes of life and methods of holding property and their probable ability to become readily assimilated.” In practical terms, this act led to a distinction between countries in the extent to which they were considered to be “preferred” or “nonpreferred.” The two most preferred countries were the United Kingdom and the United States (and France as of 1947). They were followed by several other countries in northern and western Europe that were “not too different [from Canada] in language and mode of life.” Countries in central and eastern Europe were considered to be nonpreferred, with the most nonpreferred countries being Greece, Italy, Syria, and Turkey. Immigrants from Asian countries were considered so undesirable that their admission was strictly regulated under separate acts. Subject to time-varying restrictions on total immigrant volume, applicants from the most preferred countries were admitted on almost a laissez-faire basis, while the admission of immigrants from other preferred countries depended to varying degrees on whether they possessed training and skills for which there was a need in Canada. Only immigrants in a relatively narrow range of occupations (e.g., agriculture) were admissible from nonpreferred countries, and the range of relatives they could bring with them was quite limited. One of the chief characteristics of twentieth-century immigration policy in Canada is its strong labor market orientation. In a broad statement outlining the principles that have guided Canadian immigration policy throughout the post-World War I1 era, Prime Minister MacKenzie King declared in 1947 that Canada would encourage immigration to meet its need for population. He said further that Canada would accept as many immigrants “as could be advantageously absorbed into the national economy,” with the admissibility of potential immigrants to Canada depending on, among other factors, labor conditions and requirements in Canada and each applicant’s “ability to assimilate.”

325


King also affirmed the discriminatory features of Canada’s immigration policy, stating that “the people of Canada do not wish, as a result of mass immigration, to make a fundamental alteration in the character of our population. . . . Canada is perfectly within her rights in selecting the persons whom we regard as desirable future citizens. It is not a ‘fundamental human right’ of any alien to enter Canada. It is a privilege. It is a matter of domestic policy.” Because control over the volume of immigrants to Canada and over their national and occupational composition resided in the hands of the Cabinet, immigration policy in Canada has been remarkably responsive to a variety of social, economic, and political situations throughout most of this century. For example, immigration was tightly restricted during the high unemployment years of the 1930s; immigrants were not accepted from Japan, Germany, or Italy during World War 11, although many displaced Europeans were admitted from other countries; and Canada actively assisted and accepted many immigrants from Hungary during 1957. Canadian immigration policy has often been referred to as a “tap-on, tapoff policy” because of its flexibility and its responsiveness to contemporary labor market concerns. For example, the admission of immigrants was increased sharply as a response to labor shortages in the 1950s but was curtailed during the years 1958-62 because of high rates of unemployment. Beginning in the 1950s, immigration officials treated professionals and entrepreneurs with capital quite favorably because of their potential to generate employment opportunities in Canada. Indeed, Canada abandoned its policy of national discrimination in the 1960s partly because it became increasingly clear that Canada would not be able to satisfy its need for skilled manpower via immigration from its list of preferred countries. In 1967, Canada substantially altered the mechanisms by which it administered its immigration policies. First, it eliminated all discrimination on the basis of race or nationality. Second, it defined four classes of immigrant applications: ( I ) sponsored relatives (i.e., dependent relatives); (2) nominated relatives; (3) independent applications; and (4) refugees. Sponsored relatives would be admissible merely if they could demonstrate that they were in good health and of good character. Refugees, a status defined by the United Nations, would be accorded preferential treatment in admission. Finally, nominated relatives and independent applications would be judged on the basis of a point system. The two key features of the point system are that it removed a good deal of subjective authority from the hands of immigration officers and assigned considerable weight in admissions decisions to labor market-related factors. In order to be admitted under the point system, an immigrant needed to receive at least fifty points out of a maximum of one hundred. Points were awarded according to the following nine criteria, with some minor differences in the evaluation of independent applications and applications from nominated relatives:

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1. Education and training: One point for each year of successful formal education or occupational training, up to a maximum of twenty; 2. Personal characteristics: Up to fifteen points awarded at the discretion of immigration officers on the basis of their perception of the applicant’s adaptability, resourcefulness, initiative, and motivation; 3. Occupational demand: Up to fifteen points, for both skilled and unskilled workers; 4. Occupational skill: Ranging from one point for unskilled workers to ten points for professionals; 5 . Age: Ten points for applicants below the age of 35, with one point less for each year above age 35 (with a minimum of zero points); 6. Arranged employment: Ten points for applicants with a definite job in Canada; 7. Knowledge of French and English: Up to ten points depending on an applicant’s fluency in French and English; 8. Relatives: Up to five points for applicants with relatives in Canada that could help them get established; 9. Employment opportunities: Up to five points for applicants moving to areas of strong labor demand.

The point system was amended in 1974, as a response both to the large number of immigrants admitted to Canada in 1972 and 1973 and to increases in the unemployment rate in Canada. A priority system was established for processing immigrant applications that gave preferential treatment to applicants with close relatives in Canada, to applicants with prearranged employment in high-demand occupations, and to entrepreneurs and refugees. A “Canadians-first” policy was also established under which applicants would receive no credit for prearranged employment unless they could show that no equally qualified Canadian citizen or landed immigrant was available to fill the position. In addition, an applicant would lose ten points if there was no evidence of prearranged employment or bona fide demand for their labor. The figures in table 12.1 indicate that a sharp increase occurred in the proportion of Canadian immigrants admitted on the basis of family ties following the 1974 policy changes. 12.1.2 Trends and Patterns in Immigration to Canada The foreign born have constituted a sizable fraction of the Canadian population throughout the twentieth century. In 1901, 13.3 percent of the Canadian population was foreign born. This fraction increased sharply during the first decade of the century and hovered around 22 percent into the 1930s, when difficult economic circumstances led to restrictive immigration policies that caused it to decline. Nonetheless, the foreign-born fraction of the Canadian population had not fallen below 15 percent through the early 1980s (see table 12.2).

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Table 12.1

Immigration to Canada by Category of Admission % Admitted as Sponsored or

Assisted Relatives

Year 1954-58 1960-64 1965-69 1970-74 1975-79 1980-84

Total Number of Nonrefugee Immigrants 839,045 456,143 909,882 785,079 593,862 468,731

Total

Sponsored Relatives

Assisted Relatives

% Admitted from Independent Applications (including refugees)

33.0 44.6 37.8 49.3 67.6 64.2

NA NA NA 24.7 45.2 54.2

NA NA NA 24.6 22.4 10.0

67.0 55.4 62.2 50.7 32.4 35.8

Source: Employment and Immigration Canada, Annual Report to Parliament on Immigration Levels, selected years. Note: NA, not available.

Table 12.2

Foreign-born Members of the Canadian Population, Stocks and Flows Total Canadian Population (in millions)

% Foreign Born

5.4 7.2 8.8 10.4 11.5 14.0 18.2 21.6 24.1

13.0 22.0 22.3 22.2 17.5 14.7 15.6 15.3 16.1

Stocks: 1901 1911 1921 1931 1941 1951 1961 1971 1981

Flows: 1901-1 I 1911-21 1921-31 1931-41 1941-51 1951-61 1961-7 1 1971-81

Population Increase (thousands)

Number of Immigrants (thousands)

Ratio of Immigrants to Population Increase

1,836 1,581 1,589 1,130 2,502 4,229 3,330 2,5 15

1,759 1,612 1,203 150 548 1,543 1,429 1,447

.96 1.02 .16 .13 .22 .37 .43 .58

Source: Author calculations based on data reported in Immigration Statistics, 1983(Ottawa: Supply and Services, 1985).

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In order to maintain such a high fraction of foreign born among the Canadian population, immigration flows into Canada have been quite substantial. For example, there were 4.4 million immigrants to Canada from 1951 to 1981, a period during which the population of Canada increased from fourteen to twenty-four million. Although the ratio of new immigrants to the overall increase in the size of Canada’s population has been above 15 percent during every year in the post-World War I1 era, there has been a great deal of year-to-year variation in the number of immigrants. Especially large numbers of immigrants arrived in Canada in 1951 (194,391), 1957 (282,164), 1967 (222,876), and 1974 (218,465); in contrast, relatively few immigrants arrived in 1946-47 (roughly 68,000 immigrants per year), 1961-62 (roughly 73,000 immigrants per year), 1978 (86,300), and 1983 (88,800). From the mid-1950s to the mid-l970s, the percentage of Canadian immigrants intending to enter the labor force was just above 50 percent. That figure dropped to 44 percent starting in the mid-l970s, as the number of admissions from independent applications dropped from nearly 110,000 in 1974 to under 21,000 in 1984. Even more dramatic has been the shift in the distribution of occupations among immigrants expecting to enter the labor force. This shift has been notably in the direction of increased skill and training. Among immigrants entering Canada during 1954-58, only 12 percent listed their intended occupations as managers or professionals. In contrast, 37 percent listed agricultural worker, laborer, or service worker as their intended occupation. During the years 1979-83, the percentage of managers and professionals increased to 28 percent, while only 14 percent of immigrants reported Table 12.3

Distribution of Intended Occupations among Canadian Immigrants Planning to Work, by Year of Immigration (70)

Year of Immigration Occupation Managerial Professional C1erica1 Service Agriculture Construction Manufacturing and mechanical Laborers Other Total

1979-83

1974-78

1969-73

1964-68

1959-63

1954-58

6.3 21.5 11.4 8.2 4.1 4.2

7.5 22.5 14.7 9.1 2.2 7.1

5.1 26.1 15.0 11.1 3. I 6.1

2.4 25.3 13.5 9.6 3.2 8.8

2. I 17.3 11.9 16.5 7.5 7.1

10.2 14.4 9.2 9.8

19.0 1.3 24.0

22.2 1.5 12.6

20.9 2.4 9.0

23.9 7.2 6.1

18.4 12. I 6.5

21.8 13.9 8.5

100.0

100.0

100.0

100.0

100.0

100.0

1.4 10.8

Source: 1954-73: Employment and Immigration Canada, Annual Report to Parliament on Im-

migrurion Levels, 1980, 14. 1974-83: Author calculations based on information reported in annual issues of Immigration Srutisrics (Employment and Immigration Canada). a Includes transportation and communication, commercial and financial, logging, fishing, trapping and hunting, mining and quarrying, and unspecified.

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that they intended to work as agricultural workers, laborers, or service workers. While some portion of these changes undoubtedly reflects sectoral shift in the Canadian economy, the bulk of the changes reflect the increased emphasis on skill and training in Canada's immigration policy (see table 12.3). Table 12.4 presents a cross-tabulation of the foreign-born population of Canada by country of origin and year of immigration. The data are taken from the 1981 Canadian Census. The figures clearly show that British and American immigrants dominated the immigration flow to Canada before 1946 (i.e., in 1981, 61 percent of all pre-1946 immigrants in Canada were from the United Kingdom or the United States). That dominance ended immediately following World War I1 as immigration from Europe (excluding the United Kingdom) expanded sharply. From 1946 to 1955,68 percent of all immigrants to Canada were from Europe (excluding the United Kingdom), up from just 37 percent prior to 1946. Germany, Italy, and the Netherlands alone supplied an especially large proportion of immigrants in the ten years following the war (36 percent). Even in absolute terms, no European country increased the number of immigrants it supplied to Canada during the postwar period, whereas the number of immigrants from most European countries actually declined (i.e., based on numbers of immigrants actually in Canada in 1981). Table 12.4

Canadian Immigrants by Country of Origin and Year of Immigration, 1981 Year of Immigration

Country of Origin

1976-80

1971-75

Africa Asia Belgium/ Luxemburg Britain France Germany Greece Ireland Italy Latin America Netherlands Other Europe' Other NonEuropeb Poland Soviet Union United States

233 1,975

390 1,720

20 690 65 73 36 20 86 793 53 399

Total

1966-70

1956-60

255 889

106 245

39 209

16 20 1

14 75

1,053 5,314

12 874 67 92 139 26 182 1,080 46 778

25 1,209 122 213 229 29 703 633 999

10 646 67 176 169 12 718 149 64 456

55 1,104 69 473 176 31 1,068 95 255 869

72 1,833 102 764 84 19 1,030 68 848 848

34 2,324 19 84 18 23 127 35 31 718

228 8,680 511 1,875 851 160 3,914 2,853 1,387 5,067

89 58 80 394

113 59 34 529

95 78 28 466

31 87 21 191

25 121 49 155

29 54 1 614 200

12 473 476 1,007

394 1,417 1,302 2,942

5,064

6,141

6,063

3,148

4,793

7,269

5,470

37,948

90

1946-55 pre-1946

Total

1961-65

Source: 1981 Canadian Census of Population, 1/100 sample. Data include all immigrants (place of birth other than Canada) except inmates, members of the armed forces, and immigrants who arrived in 1981. a Includes Spain, Portugal, Scandinavia, and non-Soviet and Eastern Bloc countries. Includes Australia, Pacific Islands, and other areas not otherwise listed.

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Table 12.4 also shows that the pattern of immigration to Canada changed rather dramatically when Canada stopped discriminating among immigrants on the basis of country of origin. For example, between the first and second half of the 1960s, immigration to Canada from Asia and Latin America increased nearly fourfold. Although these regions of the world supplied only 2 percent of Canada’s immigrants prior to 1946, they supplied 46 percent from 1971 to 1975 and 55 percent from 1976 to 1981. 12.1.3 Immigrants in the Canadian Labor Market We next present a brief descriptive analysis of the employment, unemployment, and earnings experience of male immigrants represented in the 1971 and 1981 Canadian Population Censuses. Table 12.5 reports selected labor market characteristics of immigrants and natives based on data contained in the 1981 Canadian Census of Population. Judging merely on the basis of labor market activity measures, it would not be unreasonable to conclude that Canadian immigrants are well assimilated in the labor market. The labor force participation rate of male immigrants (aged fifteen and over) was 72.4 percent in 1981, just slightly below the rate of 73.6 percent for native Canadians. The closeness of native and immigrant labor supply extends beyond labor force participation rates to hours and weeks worked as well. Ninety-three percent of employed immigrant males worked thirty-five or more hours during the 1981 Census reference week, compared to 92 percent of employed native males. Similarly, 70.6 percent of the male immigrants reported having worked forty-nine to fifty-two weeks in 1980, just .2 percentage points higher than the figure for native males. Despite the closeness in these measures of immigrant and native labor supply, unemployment rates for immigrants were notably lower than for natives in 1981 (i.e., among males, the unemployment rates were 8.5 percent for natives and 5.3 percent for immigrants). These differentials could reflect a variety of factors, including differences in reservation wages, human capital, and demographic composition (for an analysis of immigrant labor supply and unemployment in Table 12.5

Selected Labor Market Characteristicsof Immigrants and Natives Aged Twenty-five to Sixty-four in 1981

Labor force participation rate (ages 15 and over, %) % Who worked 49 or more weeks in 1980 % Working 35 or more hours during the Census reference week % Self-employed Unemployment rate (%) Average wage and salary income in 1980 among those not primarily self-employed (in thousands of dollars) Source: Authors’ tabulations of 1981 Census data

Immigrants

Natives

12.4 70.6 93.3

73.6 70.4 91.9

15.3 5.3 20.4

13.3 8.5 19.0

331


Canada, see Fox 1987). Presumably, such factors also underlie the explanation of the difference in average income between male immigrants and natives (i.e., the immigrants had a 7.4 percent advantage). It is also worth noting that rates of self-employment are slightly higher among immigrants than among natives, with 15 percent of immigrant males reporting that they were selfemployed in 1981 (compared to 13 percent for native males). Table 12.6 compares labor force participation rates, unemployment rates, and levels of average income among different entry cohorts of Canadian immigrants-using data from both the 1971 and the 1981 Canadian Censuses. The statistics show that the more recent immigrants have relatively low labor force participation rates, relatively low average income, and relatively high unemployment rates. Labor force participation rates are also relatively low in both Censuses for pre- 1946 immigrants, presumably because many immigrants in that cohort had reached retirement age by 1971 and 1981. Although labor force participation rates are quite flat across the cohorts of immigrants that entered Canada between 1946 and 1975, it does appear that unemployment rates are higher for the more recent cohorts. Average immigrant earnings also tend to be lower for immigrants in the more recent entry cohorts. Thus, while there do not seem to be major differences in the employment and earnings experiences of immigrants and natives in Canada, there are notable differences between immigrants in different entry cohorts. Whether these differences represent genuine vintage effects or simply reflect the influence of immigrant labor market characteristics or other variables cannot be determined from these tables. Making such a determination requires that we control for a variety of variables in a multivariate manner, which we turn to in the following section. Table 12.6

Immigrant Labor Force Participation, Unemployment, and Income, by Year of Immigration, for Males in the 1971 and 1981 Censuses Labor Force Participation Rate

Unemployment Rate (%)

Average Income (in thousands of current dollars)

1981

1971

1981

1971

1980

1970

Pre- 1946 1946-55 195MO 1961-65 1966-70 1971-75 1976-80

29 77 84 84 82 81 73

47 89 86 85 81 NA NA

4.4 3.6 3.9 5.3 6.1 6.7 8.0

4.6 4.1 5.0 5.3 6.1 NA NA

23.4 22.9 21.1 19.9 21.1 18.2 15.0

18.8 17.5 17.0 15.9 14.8 NA NA

Total

72

74

5.3

4.9

20.4

16.8

Year of Immigration

Source: Author calculations using 1981 and 1971 Census data. Nore: NA, not applicable.

332


12.2 Empirical Analysis of Immigrant Earnings Profiles In this section, we will analyze earnings patterns among Canadian immigrants using data contained in the 1971 and 1981 Canadian Censuses. Our goal is to answer the following three questions. (1) On average, do employed immigrants receive higher wages than employed natives who are comparable in terms of observed productivity-related characteristics? (2) On average, do employed immigrants who have been in Canada for a total of X years receive higher or lower wages than employed immigrants who have been in Canada for X + Y years but who are otherwise comparable in terms of observed characteristics? (3) Does the dispersion of immigrant earnings tend to vary with duration of stay?

12.2.1 Empirical Models and Data Issues The standard model used to compare earnings profiles for immigrants and natives was proposed by Chiswick (1978). The basic regression model, which is fit to cross-sectional data for a pooled sample of both immigrants and natives, is a simple extension of the standard human capital earnings function: (1)

log Y = a,

+ a,(SCH) + a,(EXP) + a,(EXPSQ)

+ a,(IMMIG) + a,(YSM),

where Y is earnings, SCH is years of schooling, EXP is years of labor market experience, EXPSQ is years of labor market experience squared, IMMIG is an indicator variable for immigrants, and YSM is years since migration interacted with the immigrant dummy variable. The estimate of a, measures the average percentage difference between the earnings of natives and newly arrived, but otherwise comparable, immigrants. The estimate of a5measures the average percentage increase in immigrant earnings with each year that immigrants spend in their new home country, beyond the increase in earnings associated with the fact that their human capital stock may have changed during that year (e.g., EXP may have increased). Thus, a positive estimate of a, has been taken to indicate that the average experience-earnings profile of immigrants is steeper than that of natives, which is suggestive of labor market progress and assimilation. Borjas (1985) has recently pointed out that interpreting the coefficients in equation (1) in this manner requires one to assume that there are no omitted variables that are correlated with YSM. This assumption may be difficult to defend because YSM also measures “date of entry into the new country” in a cross-sectional regression. If unmeasured factors relevant to labor market success vary systematically across entry cohorts of immigrants, the coefficient a5 will measure both immigrant labor market progress and the effect of the average difference in unmeasured factors across successive entry cohorts (i.e., it may be a biased measure of the labor market progress experienced by different entry cohorts over time).

333


The most straightforward way to overcome the fact that YSM is a linear combination of a vector of year-of-immigration dummy variables in crosssectional data is to make use of data that follow cohorts over time. Since such data provide observations on each entry cohort at two or more points in time, it is possible to estimate the effect of time spent in the new country on earnings without the potentially confounding influence of entry cohort effects (i.e., a regression model can be specified with year-of-immigration dummy variables and YSM on the right-hand side because the same individual in a particular entry cohort, with an immutable “year of entry,” will have different values of YSM when he or she is observed at different points in time). To our knowledge, there are no longitudinal data for Canada that are suitable for conducting such an analysis. Thus, following Borjas, we will construct a cohort data set for different entry cohorts of immigrants using data contained in the 1971 and 1981 Canadian Population Censuses. We will fit the following regression model to pooled data from these two Censuses: (2)

+ b,(SCH) + b,(EXP) + b,(EXPSQ) + b,(IMMIG) + b,(YSM) + c,(COH,) + . . . + c,(COH,),

log Y = b,

where COH, through COH, are indicator variables reflecting immigrant membership in different entry cohorts. In principle, fitting this regression provides estimates of cohort-specific effects on earnings as well as an estimate of the average rate of earnings growth that is free of entry-cohort bias (i.e., an estimate of earnings growth within-and not across-entry cohorts). Several features of this econometric approach should be kept in mind. First, unlike longitudinal data, cohort data cannot be used to estimate individualspecific effects on earnings because there is no information to link the same individuals in the different cross sections. Second, a particular entry cohort sample observed in 1971 is not necessarily representative of the same population as the corresponding sample that is observed in 1981. As noted earlier, nonrandom patterns of out-migration, mortality, and differential undercounting-of which we find some evidence in our data as well as in Lam (1987)will tend to undermine the comparability of the samples. Changing patterns of employment and self-employment pose similar problems insofar as our regressions are fit to samples of working individuals who earned their income primarily from wages and salaries. Third, because only two cross sections are available for the present analysis, we will not be able to control for period effects that may affect the earnings of different cohorts differently (e.g., the business cycle). Also deserving mention are two issues raised by the pooling of data from two cross-sectional samples. First, in order meaningfully to compare earnings in the 1971 and 1981 Censuses, it is necessary to make an adjustment for inflation. We do this by using the Canadian Consumer Price Index to transform earnings in the 1971 Census (which refer to the year 1970) into 1980 inflation-adjusted dollars (the multiplication factor is 2.17). Second, intercen-

334


sal changes in the real earnings of immigrants may be partly due to changing capital-to-labor ratios, technological change, or business-cycle fluctuations. Since real wage growth due to these factors does not reflect labor market progress that is immigrant specific, we make an adjustment to the real earnings of immigrants in the 1971 Census that transforms those data into “productivityconstant” terms. These adjustments highlight our central interest in this section: measuring the component of intercensal earnings growth for different entry cohorts of immigrants that is independent of human capital accumulation, overall economic growth, business-cycle effects, inflation, etc. In order to explore the robustness of our results, we make two distinctly different types of productivity adjustments. First, we simply multiply immigrant earnings reported in the 197 1 Census by the ratio of real earnings received by native Canadians in the 1981 and 1971 Censuses (the multiplication factor is 1.20). This simple adjustment assumes that immigrants would have experienced the same real wage growth as natives in the absence of any assimilation effects. At a somewhat deeper level, it assumes that average levels of human capital did not change among natives relative to immigrants and that the structure of returns to different types of human capital also did not change between Censuses. To avoid these assumptions, we also employ a slightly more complex productivity adjustment that takes account of changes in the human capital profile of the immigrant and native labor forces and of changes in the returns to different types of human capital. We do this by (a)estimating real wage equations for natives in 1971 and 1981 and (b)using the difference in the estimated coefficients to adjust immigrants’ 197 1 wages for intercensal changes in the returns to different types of human capital. We report results based on both sets of adjustments below. 12.2.2 Empirical Results on the Level of Earnings The immigrants we analyze represent 1/100 samples of individuals born outside Canada, while the natives represent 1/600 samples of individuals reporting Canada as their place of birth. Both the immigrant and the native samples are restricted to individuals aged 20-64 who are not predominantly self-employed and who worked at some point during the year prior to the Census for a wage in excess of fifty cents per hour in the 1971 Census and one dollar per hour in the 1981 Census. The variables IMM46.50-IMM7680 refer to immigrant entry cohorts (e.g., year of entry from 1946 to 1950, etc.). Table 12.7 reports descriptive statistics for the various samples we analyze. Table 12.8 presents ordinary least squares estimates of wage equations using cross-sectional data from 197 1 and 1981 separately. The dependent variable in all equations is the natural logarithm of an individual’s wage and salary earnings in inflation-adjusted (1980) dollars during the year preceding the Census (i.e., 1970 and 1980). These equations were specified with schooling (SCH), experience (EXP and EXPSQ), marital status (MST), and vectors of categorical variables (not reported in the table) measuring hours worked per

335


Table 12.7

Variable Definitions and Descriptive Statistics for Male Immigrants and Natives in 1971 and 1981 Immigrants

Variables

1971

Definitions

YSM

Log previous year’s wage and salary income, nominal dollars Years since immigration

SCH

Education in years

EXP

Work experience in years

MST

Marital status dummy (1 currently married) Immigrated 197680 Immigrated 1971-75 Immigrated 196670 Immigrated 1961-65 Immigrated 1956-60 Immigrated 194655

LNWAG

IM7680 IM7175 IM6670 IM6165 IM5660 IM4655 Sample Size

=

1981

Natives

1971

1981

8.795 8.710 9.754 9.667 (.622) (.691) ( .689) (.6@) ... ... 15.121 18.383 (8.884) (10.151) 10.277 10.515 11.237 11.088 (3.426) (3.269) (2.963) (2.67 1) 21.320 23.708 19.079 23.730 (13.162) (12.371) (13.380) ( 12.806) ,725 ,762 .802 .806

...

...

.218 ,111 ,187 ,338

,104 ,165 .189 .lo3 ,164 .230

...

... ...

8,290

9,368

5,119

6,295

...

... ... ...

Note: The immigrant samples represent lil00 samples of all immigrants in each Canadian Census aged 20-64 who report working positive weeks and hours, who earned more than 50 cents per hour in the 1971 Census (one dollar per hour in the 1981 Census), and who are not primarily self-employed. Inmates, members of the armed forces, and immigrants arriving during the year in which the Census was taken are excluded from the sample. The native samples represent l i 600 samples of all natives in each Canadian Census. The samples were constructed using the same criteria as those used to construct the immigrant sample, of those criteria that are relevant. The regressions include vectors of categorical variables with information on hours worked per week and weeks worked per year.

week and weeks worked per year as right-hand-side variables. Depending on the specification, the wage equations may also include an indicator variable for immigrants (IMMIG) and a variable measuring the number of years an immigrant has been in Canada (YSM). Although the results are not reported in these tables, we also estimated models with additional right-hand-side variables reflecting an individual’s religion and language ability and the square of YSM. Since these latter variables had little explanatory power either individually or jointly, these specifications are not reported here in the interest of parsimony. The first two columns of table 12.8 report wage equation estimates for the samples of male immigrants in 1971 and 1981. The estimated equations have the basic structure one might expect: a 4-5 percent rate of return to schooling, an earnings-experience profile that increases at a decreasing rate, and a 15-25 percent positive wage differential for married men. In addition, the “years since migration” coefficients are positive and significantly different from zero,

336


Table 12.8

Estimated Wage Equations for Immigrants and Natives Using Single Cross Sections: Males Immigrants

Natives

Immigrants and Natives

~~

1971

Variable

YSM

.0044 (.0007) ,0427 (.0022) ,0376 (.0016) - ,068I (.0030) ,1569 (.0137)

IMMIG

(X

IMMIG)

SCH

1981 .0053 (.OO06) ,0447 (.0018)

1971

1981

.0054

(.oow

(.0017) - ,0692 (.0033) ,1820 (.0132)

,0670 (.0025) ,0405 (.0021) - ,0655 (.0042) .1963 (.0169)

,0574 (.OO25) ,0436 (.0019) - ,0701 (.0039) .I920 (.0147)

...

...

...

...

Constant

6.6074 (.0751)

6.6777 (.0683)

6.1279 (.0676)

6.5665 (.0613)

RZ RSS N

,485 1,651.O 8,290

,554 1,089.9 5,119

,543 1,364.6 6,295

EXP EXPSQ(- 100)

MST

.woo

,509 2,004.9 9,368

1971

1981 ,0077

(.OOOS)

,0528 (.0014) ,0385 (.0013) - ,0658 (.0024) .I754 ( .O 107) - ,0693 (.0123) 6.3749

,0486 (.0014) ,0421 (.0013) - ,0705 (.0025) .I875 (.0099) - .I657 (.0120) 6.6566 (.M) (.0399)

,508 2,790.5 13,409

,516 3,433.9 15,663

Note: See note to table 12.7. Estimated standard errors are reported in parentheses below the coefficient estimates.

although the coefficients are rather small in magnitude (i.e., about .5 percent per year). The third and fourth columns of table 12.8 report estimates of similar equations for native Canadians. The pattern of results corresponds quite closely to those for the immigrants, although the rate of return to schooling is slightly higher for natives than for immigrants, probably indicating that schooling has an important country-specific component. Even the residual variances for the immigrant and native wage equations are quite close in magnitude (e.g., .20 for the immigrants and .21 for the natives in the 1971 Census data). Given the closeness of the estimated wage equations for the immigrants and the natives, a simple way to compare the wage profiles is to follow the work of Chiswick (1978) by pooling the data for the two groups and fitting a wage equation that includes a dummy variable for immigrants, both by itself and interacted with the “years since migration” variable. The results of this exercise are presented in the last two columns of table 12.8. Based on the 1971 data, the estimate of the immigrant coefficient indicates that immigrants earn roughly 7 percent less than comparable natives when they first arrive in Canada; the estimate of the coefficient on YSM indicates that immigrants’ wages increase an average of .54percent per year spent in Canada, beyond the increase associated with the acquisition of experience. These estimates imply

337


that the earnings profiles of comparable immigrants and natives cross at roughly 12.8 years. In contrast, the 1981 data indicate that entering immigrants earn 16.6 percent less than otherwise comparable natives, although their wages increase at the rate of .77 percent per year spent in Canada, suggesting that the immigrant and native earnings profiles do not cross until the immigrants have been in Canada for 2 1.6 years. In order to investigate whether the various cross-sectional estimates of immigrant labor market progress are biased by entry-cohort effects on wages, we now estimate alternative models from pooled 1971 and 1981 data on immigrants. The first column of results in table 12.9 reports estimates of the simple Table 12.9

Wage Equations for Male Immigrants in Canada Based on Pooled 1971 and 1981 Data

Unadjusted Cross-Cohorts Model YSM SCH EXP EXPSQ(/ 100) MST CONSTANT

.0076 (.0005) ,0460 (.0013) .0401 (.0012) - ,0719 (.0022) ,1711 (.0096) 6.6467 ( ,0508)

IM7680 IM7175 IM6670 IM6 165 IM5660 IM4655

R2 RSS N

,494 37.663 17,658

Unadjusted Within-Cohorts Model

,0202

(.W8)

,0437 (.0013) ,0383 (.0012) - ,0675 (.0022) ,1723 (.0095) 6.1493 (.0564) .4713 (.0302) .5062 ( ,0261) ,4548 (.0241) .3684 (.0224) ,2730 (.0189) ,1676 (.0155) .506

36.757 17,658

Within-Cohorts Model Using Sample Average Productivity Adjustment

Within-Cohorts Model Using Weighted Average Productivity Adjustment

,0024

,0042

(.OOW

(.O@W

,0437 (.0013) .0383 (.0012) - .0675 (.0022) .1723 (.0095) 6.8620 (.0564) - ,1874 (.0302) - .0634 (.0261) - ,0259 ( ,0241) - ,0233 (.0224) - .0296 (.0189) - ,0104 (.0155)

.0389 (.0013) .0401 (.0012) - .0703 (.0022) ,1680 (.0095) 6.8828 (.0563) -.1163 ( ,0302) - ,0066 ( ,0260) ,0142 ( ,0240) ,0116 (.0223) - ,0034 (.0188) - ,0026 (.0154)

,496 36.757 17,658

,501 36.612 17,658

Note: See note to table 12.7 Estimated standard errors are reported in parentheses below the coefficient estimates.

338


wage equation fit to the pooled data. As one would expect, these estimates are quite similar to the estimates computed for the separate years’ samples (i.e., they are simply a matrix-weighted average of the results in cols. 1 and 2 of table 12.8). In the second column, we include a vector of dummy variables that reflect an immigrant’s entry cohort. The coefficient of YSM in this equation therefore represents a weighted average of within-cohort real earnings growth between 1971 and 1981 for the different entry cohorts, other things equal. The estimate of this coefficient is quite substantial in magnitude (2.02 percent) and is quite well determined (i.e., the standard error is .08 percent). Thus, the within-cohort growth rate of real earnings is three to five times larger than the cross-cohort growth rate of real earnings. In addition, estimates of the cohort fixed effects suggest that the average unmeasured quality of immigrants increased across all entry cohorts until the cohort that entered from 1976 to 1980. As noted earlier, the coefficient on YSM captures both the true “assimilation effect” in which we are interested and any wage growth associated with changes taking place in the economy over time (e.g., increasing capital-tolabor ratios). In order to isolate the assimilation effect, we adjust the 1971 earnings data for changes in labor productivity that occurred among native Canadians from 1971 to 1981. Estimates of the wage equation fit to these productivity-adjusted data are reported in the third and fourth columns ‘of table 12.9. The third column applies the simple adjustment described above based on the ratio of real wages received by native Canadians in 1971 and 1981; the fourth column applies the more complex regression-based adjustment that accounts for human capital changes among the immigrant and native labor forces as well as changes in the returns to different types of human capital. The coefficient of YSM reported in column 3 is .24 (with a standard error of .08). Although it is statistically significant, this estimate is substantially smaller than the estimate of 2.02 computed using non-productivity-adjusted data. Thus, it appears that the effect of assimilation on the earnings of cohorts of immigrants is quite small, indeed, almost negligible. Put another way, within-cohort growth in immigrant earnings is primarily due to economic forces that affect both immigrants and natives. Further confirmation of this result is provided in the fourth column of table 12.9. Although the estimated coefficient of YSM is somewhat larger when we apply the more elaborate productivity adjustment, the assimilation effect of .42 percent per year is still substantially small. Moreover, it is sufficiently close in magnitude to the estimates computed from the individual cross sections to conclude that Borjas’s assertion that estimates of the latter type are “misleading and useless” does not necessarily generalize beyond the data he analyzed. It is also worth examining the estimates of the cohort fixed effects in columns 3 and 4 in table 12.9. There are no statistically significant cohort effects among any of the five pre-1971 entry cohorts of immigrants. However, the

339


estimates do suggest that average unobserved quality among immigrants arriving from 1976 to 1980 (and perhaps also among the 1971-75 arrivals) was significantly lower than for previous cohorts, the same conclusion suggested by a comparison of the cross-sectional coefficient estimates of IMMIG in table 12.8. This result is not especially surprising given the relatively high fraction of immigrants admitted into Canada as relatives of Canadian citizens and landed immigrants in the late 1970s (see table 12. I). Under the immigration policy in effect in Canada in the 1970s, applications from relatives did not receive the same degree of labor market screening as independent applications did. 12.2.3 The Dispersion of Immigrant Earnings In this subsection, we present and review statistics on the dispersion of income among immigrants. In particular, we are interested in determining whether immigrant incomes tend to become more or less disperse as the duration of their stay in Canada increases. A tendency for dispersion to decline with duration of stay is consistent with the hypothesis that intercensal outmigrants tend to be selected from the tails of the distribution (i.e., immigrants whose earnings expectations were not met selected out of the lower tail and migrants who planned temporary stays to take advantage of favorable earnings opportunities selected out of the upper tail). In contrast, a tendency for dispersion to increase with duration of stay would be consistent with the view that the labor market has more information about the true productivity of immigrants the longer they have stayed in the country. The third and fourth columns of table 12.10 report the raw standard deviations of immigrant incomes by the duration of their stay in Canada as of both 1971 and 1981. Although the standard deviation of income is highest for the oldest entry cohort of immigrants, there is little evidence of a pattern across the more recent entry cohorts in either Census year. There is some tendency for income dispersion to decrease for individual entry cohorts from 1971 to 1981, but this may not be due to increased duration of stay since dispersion among native Canadians also decreased between 1971 and 1981. In addition, it is worth noting that the standard deviation of immigrant incomes is remarkably close to the standard deviation of native incomes in both Census years. Table 12.10 also reports the standard deviation of the estimated residuals for different entry cohorts of immigrants in the 1971 and 1981 Censuses. These statistics are more appropriate measures of dispersion than the raw standard deviations because they do not reflect the influence of variations in observable factors that are associated with earnings. Nonetheless, they tell basically the same story as the unconditional measures of dispersion: there is no substantial evidence of a difference between natives and immigrants, or among immigrants with different durations of stay in Canada, in the dispersion of income. Thus, the statistics in table 12.10 are equally supportive of two conclusions: either selective out-migration and job matching are both empirically unimpor-

340


Table 12.10

Dispersion and Kurtosis in Conditional and Unconditional Distributions of Wage and Salary Income, for Male Immigrants, by Census Year

SD of Immigrant Earnings (OOO dollars)

SD of Residuals in Log Earnings Equation

Kurtosis of Residuals in Log Earnings Equation

SamplelEntry Cohort

1971

1981

1971

1981

1971

1981

All pre- 1970 immigrants Native Canadians Pre- 1946

12.2 11.8 15.7

11.0 10.9 12.2

11.3

10.8

.44 .47 .46 .42

2.9 2.3

1946-55 1956-60 196145 1966-70

3.7 2.8 2.4 4.6

11.0

10.4 11.4 10.9

.45 .46 .48 .42 .41

10.3 12.6

.44

.55

.44 .46 .47

3.0 3.6 2.5 2.6 2.1

5.0 3.5

3.3

Note: The equation used to estimate the residuals and to calculate their standard deviation and kurtosis is reported in col. 3 of table 12.9.

tant influences on immigrant earnings, or they are important influences whose effects tend to cancel out. In an attempt to distinguish between these alternative views, we examine higher-order moments in the distribution of residuals from the earnings equation. If the tails of the distribution are thinning as a result of out-migration, and if the variance of earnings is increasing among Canadian immigrants who stay in Canada, we would expect increased kurtosis in the distribution of residuals for particular entry cohorts; that is, the distributions should “thicken” from one Census to the next. This pattern of results is clearly revealed in the last two columns of table 12.10. However, because kurtosis also increases among native Canadians, a finding we had no reason to expect, we are reluctant to view our results for the immigrants as conclusive. It would thus appear that a fuller understanding of the dynamics of immigrants’ labor market outcomes and their out-migration decisions awaits the advent of true, large-scale, longitudinal surveys of immigrants.

12.3 Conclusion This paper has reported estimates of simple wage equations fit to crosssectional and cohort data for Canadian immigrants in the 1971 and 1981 Canadian Censuses. The estimates are used to assess (1) the usefulness of crosssectional analyses for measuring the pace of immigrant earnings growth, (2) the labor market implications of admissions policies that place different weights on the work skills possessed by prospective entrants, and (3) the relative effect of selective out-migration and job matching on the shape of immigrant earnings distributions as duration of stay increases.

341


The estimates provide evidence of a small to moderate assimilation effect that suggests that immigrants make up for relatively low entry wages, although the wage catch-up is not complete until thirteen to twenty-two years after entry into Canada. These results are revealed clearly in both the pseudolongitudinal and the cross-sectional analyses. The estimates also provide evidence that the unobserved quality of immigrants’ labor market skills declined following changes in Canada’s immigration policies in 1974 that led to a sharp increase in the proportion of immigrants admitted on the basis of family ties. Finally, since there is no evidence that the variance of immigrant earnings increases with their duration of stay in Canada, and since there are no differential immigrant-native changes in higher-order moments of the earnings distribution as duration of stay increases, the results are inconclusive with respect to the importance of selective out-migration and job matching in the evolution of immigrant earnings distributions over time.

References Allen, J. 1979. Information and Subsequent Migration: Further Analysis and Additional Evidence. Southern Economic Journal 45: 1274-84. Blejer, M., and I. Goldberg. 1980. Return Migration-Expectation versus Reality: A Case Study of Western Migrants to Israel. In Research in Population Economics, vol. 2, ed. J. Simon and J. DaVanzo. Greenwich, Conn.: JAI. Borjas, George. 1982. The Earnings of Male Hispanic Immigrants in the United States. Industrial and Labor Relations Review 35:343-53. . 1985. Assimilation, Changes in Cohort Quality, and the Earnings of Immigrants. Journal of Labor Economics 3:463-89. Carliner, Geoffrey. 1980. Wages, Earnings, and Hours of First, Second, and Third Generation American Males. Economic Inquiry 18237-102. Chiswick, Barry. 1978. The Effect of Americanization on the Earnings of Foreignborn Men. Journal of Political Economy 86:897-921. Fox, Marc. 1987. Remittance and Labor Supply Behavior of Immigrants. Ph.D. diss., Harvard University, Department of Economics. Harris, Milton, and Bengt Holmstrom. 1982. A Theory of Wage Dynamics. Review of Economic Studies 49:315-33. Jasso, Guillermina, and Mark R. Rosenzweig. 1987. How Well Do Immigrants Do? Vintage Effects, Emigration Selectivity, and the Occupational Mobility of Immigrants. In Research in Population Economics, vol. 6, ed. T. Paul Schultz. Greenwich, Conn.: JAI. . 1990. Self-selection and the Earnings of Immigrants: Comment. American Economic Review 80:298-304. Katz, Eliakim, and Oded Stark. 1984. Migration and Asymmetric Information: Comment. American Economic Review 74:533-34. Lam, Kitchun. 1986. Imperfect Information, Specificity of Schooling and Rate of Return-Migration. Economics Letters 21 :283-89. . 1987. An Analysis of the Outmigration of Foreign-born Members in a Population. Ph.D. diss., Harvard University, Department of Economics.

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Long, James E. 1980. The Effect of Americanization on Earnings: Some Evidence for Women. Journal of Political Economy 88:620-29. Stark, Oded, and David E. Bloom. 1986. The New Economics of Labor Migration. American Economic Association Papers and Proceedings 75: 173-18. Yezer, Anthony, and L. Thurston. 1976. Migration Patterns and Income Changes: Implications for the Human Capital Approach to Migration. Southern Economic Journal 42:693-702.

13

The Effects of International Competition on Collective Bargaining Outcomes: A Comparison of the United States and Canada John M. Abowd and Thomas Lemieux

The decade of the 1970s heralded the latest era of product and labor market globalization with renewed internationalization of the U.S. economy. Canada, which has long been a very open economy, also experienced substantially increased internationalization during this decade.2 The two countries have very substantial bilateral trading activity. Canada is the largest single destination of U.S. exports and the second largest source of U.S. imports (after Japan). In the 1970s, Canada was the largest destination and origin of U.S. traded goods. The growing importance of internationally traded goods in the U.S. economy and the continuing importance of such goods in the Canadian economy are displayed in figure 13.1. The figure shows exports plus imports as a percentage of gross domestic product in 1960, 1970, and 1980 for both countries. That the Canadian economy is three or four times more open than the U.S. economy is directly evident from examining figure 13.1. 4 The figure also indicates that the Canadian economy experienced very significant increases in its openness over this time period-imports plus exports grew from less than 40 percent of GDP to almost 60 percent. In the United States, imports plus exports grew from about 7 percent of GDP to over 20 percent during this period. The 1960s and 1970s, therefore, represent a period of increasing international economic activity for both the U.S. and the Canadian economies. John M. Abowd is professor of labor economics and management, Cornell University, and a research associate of the National Bureau of Economic Research. Thomas Lemieux is assistant professor of economics, Massachusetts Institute of Technology. The authors acknowledge financial support from the Ford Foundation, the National Science Foundation (grant 88-13847 to Abowd), and the Industrial Relations Section at Princeton University. This research was begun while the authors were at the Industrial Relations Section, Princeton University. They thank David Card for providing a clean version of the Labour Canada wage tape. They thank Charles Beach, David Card, Henry Farber, Richard Freeman, Harry Gilman, Lawrence Katz, W. Craig Riddell, and Gregory Schoepfle for comments on previous drafts.

343

344

r

John M. Abowd and Thomas Lemieux

60% 50%

40%

30%

20%

10%

0%

1960

1980

1970

=

USA

Canada

Fig. 13.1 Openness of the U.S. and Canadian economies Sources: United States, National University Data Base, CANSIM.

Income and Product Accounts,

CITIBASE

1978. Canada,

Both countries experienced changes in economic openness during this period large enough to have detectable effects on the domestic labor market. For the last two decades, the highly unionized Canadian manufacturing sector and the less unionized U.S. manufacturing sector have become more integrated into both the North American and the world market^.^ The share of imported manufactured goods in apparent Canadian consumption has increased from 22 to 3 1 percent over this period. Similarly, exports as a share of Canadian manufacturing production have more than doubled. Much of the increased integration of the North American economy occurred in the transportation equipment industry, which is covered by a 1965 bilateral agreement between the United States and Canada that eliminates most tariffs in both directions. Canadian transportation equipment imports rose from 28 percent of apparent domestic consumption in 1960 to 51 percent in 1968 to 65 percent in 1983. Canadian transportation equipment exports also rose, from 14 percent of domestic production in 1960 to 48 percent in 1968 to 67 percent in 1983. In the United States, transportation equipment imports increased from 2 percent of apparent domestic consumption in 1960 to 18 percent in 1983, while exports increased from 6 percent of domestic production to 14 percent in 1983. The increased bilateral trade in many other U.S. and Canadian industries, although not as substantial as the increased trade in transportation equip-

345

International Competition and Collective Bargaining Outcomes

ment, supports our premise of increased integration of the North American economy. In Canada, the effect of international competition on unionized domestic workers is regarded as a macroeconomic question susceptible to analysis using the tools of open economy international trade theory (see Cousineau 1987; Riddell 1986a, 1986b, 1986~).In the United States, the effect is generally regarded as a microeconomic question susceptible to analysis on an “effected industry” basis6 This paper is an attempt to blend these two views. Like the Canadians, we will estimate equations for the average effect of international competition on unionized wages and employment^.^ Like other U.S. researchers, we will model the microeconomic basis for the trade effects.8 Our study addresses the relations among international product market competition and the outcomes of domestic collective bargains. We have three specific goals. First, we quantify the effects of import and export competition on the wages and employment of unionized domestic workers using comparable data for the United States and Canada. Second, we compare the magnitude of the estimated effects to a reference value-the effects of a comparable change in domestic shipments-to determine if the trade effects are relatively large. Finally, for Canada we compare the estimated effects of value-based measures of import and export activity to the estimated effects of price-based measures, which may be more appropriate from a theoretical viewpoint. Using value-based trade measures, the estimated effect of an increase in import domestic market share, holding constant the rate of growth of the domestic market, is negative for employment in both countries and exceeds the effect of a comparable change in the size of domestic shipments. The import effect on realized real union wage rates is also negative for the United States, but not for Canada. The import effect on real wage rates in the United States is also larger than the effect of a comparable change in the domestic market size. The estimated effect of increased export growth is positive for bargaining unit employment in both countries. The export effect on employment is comparable in magnitude to the effect of a change in the size of the domestic market. The export effect on real wage rates is mixed-weakly positive for the United States and ambiguous for Canada. Increases in world export price indices are associated with increased union employment in Canada. Increases in world import price indices are associated with increased union employment and lower wage settlements in Canada. All the estimated world price effects on the Canadian unionized labor market are consistent with the estimated effects of value-based export and import measures for that economy.

13.1 The Role of International Competition in Collective Bargaining International competition may influence domestic collective bargaining in two ways. First, to the extent that foreign manufactured products are good

346


substitutes for domestically manufactured products, domestic firms must compete for global market share. Within the domestic product market, import competition reduces the effective extent of union organization in an industry and may reduce the quasi rents available to existing bargaining units. Within foreign product markets, exports work in the opposite direction to increase the effective extent of union organization in an industry and possibly to increase the quasi rents available to domestic bargaining units. Second, as substitute foreign manufactured products gain market share within the domestic product market, complementary services-wholesale distribution, retail distribution, and repair-also gain market share. The expansion of service employment opportunities may create quasi rents that could promote the formation of new bargaining units in these industries. This paper deals only with the direct competition effects of imports and exports on unionized workers in domestic labor markets. We do not consider the indirect effects of service sector expansion (see Leonard and McCulloch, in this volume). The major direct effects of international competition on unionized workers occur because either the union wage rate falls (relative to what it would otherwise have been) or there are employment displacements, which may be associated with unusually long unemployment spells andor wage reductions on reemployment. We consider only direct wage costs and direct bargaining unit employment effects. We do not examine unemployment spell length or reemployment wage rates. The major direct benefit to worldwide consumers is lower average product prices in the markets where there is substantial international competition. We do not measure this benefit to the consumer, although this is certainly an essential component of any policy prescription arising from this research. A bargaining unit is an ongoing relationship between a union and a financially viable e m p l ~ y e r The . ~ union represents the interests of the organized employees. The management represents the interests of the shareholders and other ultimate beneficial owners. For simplicity, the claims of other factors of production are ignored. During the negotiations that accompany the expiration of an existing collective bargaining agreement, management and the union use current information to form an estimate of the total value of the productive enterprise for which they represent competing interests. A collective bargaining outcome consists of explicit and implicit rules concerning the allocation of resources (employment) and the division of the resulting quasi rent between union members and shareholders (wage rates) that is expected to remain in force for some fixed term. If international competition is expected to have an adverse effect on the firm’s future profitability, then the current collective bargaining agreement will reflect that expectation. l o If the expected effects of international competition are too severe, the bargaining unit may disappear, and the evidence on surviving bargaining unit settlements will not reflect a complete analysis of either employment or wage effects. If the international competition is ex-

347


pected to improve the firm's future profitability, current bargaining units should be favorably affected. In this paper, we measure the expected effects of increased foreign competition on the future value of the firm using the relation between future revenues (of organized employers) and current information on domestic shipments, apparent domestic consumption, exports, and imports in the employer's product market. We consider two collective bargaining outcomes-bargaining unit employment and wage settlements. Bargaining unit employment is measured as the ex post growth rate of workers in the bargaining unit over the life of the new collective bargaining agreement, excluding retirees and including members with recall rights." Wage settlements are measured as the realized growth rate of real wage rates over the life of the agreement for the largest group of workers in the bargaining unit. We measure the effects of predictable increases in international competition on bargaining outcomes by relating employment growth and real wage growth during a collective bargaining agreement to the growth of apparent domestic consumption, exports, and imports expected to prevail during the life of the agreement. We measure the effects of unpredicted changes in international competition by relating the same outcomes to unexpected changes in domestic consumption, exports, and imports over the life of the agreement. In an open economy, union employment and wage rates within internationally competitive industries should respond to changes in the world market for manufactured goods. In general, we would expect these changes to depend on the world prices of traded goods. Although the importance of using world prices in modeling the effects of international trade on domestic labor markets has been recognized for some time (see Grossman 1982, 1986, 1987), most empirical analyses of the United States use import penetration ratios and export supply ratios as the main indicators of changes in the international environment.I* In this paper, we also consider the effects of changes in the world price of exports and imports on union employment and wage rates using our Canadian sample. We use Canadian data for two reasons. First, properly constructed price indices exist for a much longer time period in Canada than in the United States. Second, the Canadian economy is substantially more open than the U.S. economy. Our analysis thus permits examination of the consequences of using a variety of measures of international trade-value and price based-on the resulting estimates of employment and wage sensitivity.

13.2 A Model for the Effects of International 'lkade on Union Wages and Employment We begin at the bargaining unit level. Consider the effects of increased international trade on the present value of the quasi rents accruing jointly to the employer firm and union members. The quasi rents are measured as the difference between net revenues and the cost of employment.I3The cost of employ-

348


ment is evaluated at an external or market wage rate, not at the negotiated wage rate. For firmj in year t, let

R,, = net revenue of firm j in year t; Ljr = union employment of firmj in year t; MI, = total employment of firm j in year t; wJr= negotiated wage rate of firmj in year t; ,z, = any exogenous variable for firmj in year t; r, = one-year discount rate in year t; x, = annual opportunity cost of employment in year t; q = length of collective bargaining agreement. If year t is the initial year of a new collective bargaining agreement, then over the next q years the present value of the quasi rents may be expressed as

Since V,, measures the total quasi rent available to the employer firm and union, if increased import or export activity affects V,,, then wage settlements and union employment will be affected by this activity. Alternatively, if increased international trade has no effect on V,,, then neither wage settlements nor union employment should be affected. The present value of the quasi rents accruing to the bargaining unit captures the relevant total value of the enterprise, which may be divided among various claimants, including union members.I4 If the total value increases because of increased export activity, then the potential exists for greater union employment or wage settlements as a consequence of this export activity. Alternatively, if increased import competition lowers Vjr,then there is a presumption that lower employment and/or wage settlements should occur. To make these arguments concrete, consider the effects of a change in real industry shipments. Changes in real industry shipments due to external demand shocks should cause industry employment and total quasi rents to move in the same direction as industry shipments. If existing firms each represent a constant fraction of industry output, then firm level employment and total quasi rents should also change in the same direction as the change in industry output. The effect of a change in industry output on the negotiated wage rate is less clear. An increase in industry output could be associated with an increase or decrease in quasi rents per worker. Since the negotiated settlement divides the quasi rents per worker between the firm and the union members, an increase in quasi rents per worker ought to be associated with higher wage settlements, while a decrease in quasi rents per worker ought to be associated with lower wage settlements, all other things equal. These predictions can be derived explicitly from a simple version of an efficient bargaining relation between the employer firm and the union mem-

349


bers. Assume that the firm is fully unionized. Suppose the revenue that accrues to the firm is given by the function

0 R(L) = aL - -L*. 2 An efficient bargain chooses L to maximize R(L) - xL and sets w to divide the maximized quasi rents in the proportion y to the union and 1 - y to the owners of the firm. The resulting values for total quasi rents, employment, and the negotiated wage rate are

yo a - x

w = x + - 2 (

0

i.

In this simple model, any increase in demand for the firm’s product would be modeled as an increase in a.As the equations for L, and w show, increases in a are associated with higher total quasi rents, higher employment, and higher negotiated wage rates. Notice that the wage settlement awards each union member the same percentage of the quasi rents per worker (y) as overall negotiation determined. In the quadratic revenue model, an increase in quasi rents is always associated with an increase in quasi rents per worker so that the negotiated wage rate must increase; however, general functional forms for the revenue equation do not imply this particular result. Consider next the effects of a change in the world prices of imports and exports on the output of a particular industry. Assume that the domestically produced goods are Hicks substitutes for the imported goods and that the exported goods are identical to the domestically produced and consumed goods. Then, an increase in the world price of industry imports results in substitution away from the imported goods and into the domestically produced goods within the industry. This should increase domestic output within the industry. Hence, employment and total quasi rents should increase for the existing firms within the industry. Negotiated wage rates will increase or decrease depending on whether quasi rents per worker increase or decrease. An increase in the world price of exports results in expansion of domestic industry output along the industry supply curve. Hence, employment and total quasi rents should increase for the existing firms within the industry. Again, the movement in negotiated wage rates will depend on what happens to quasi rents per worker. IJ

350


13.3 An Empirical Specification for Bargaining Unit Level Data In order to give our model empirical content, we must specify relations connecting the exogenous economic factors (industry output, value-based trade measures, import prices, and export prices) to the total quasi rent, employment, and wage rate outcomes. We will not use comparable quasi-rent data for the United States and Canada; hence, our empirical models will consider only bargaining unit employment and wage rates. We deal with three important practical problems in developing our estimating equations. First, since the model is developed for application to bargaining unit data, the employer firms may have heterogeneous shares of output within the domestic industries and may face heterogeneous industry demand elasticities with respect to import and export price changes. There are insufficient data to attempt estimation of separate elasticities for each major industry. Instead, we formulate the model to permit estimation of the employment-weighted average elasticities across all domestic manufacturing firms in the sample for each country. Second, since nominal wage rates are renegotiated infrequently relative to changes in the economic environment, we distinguish between the effects of expected and unexpected changes in the exogenous variables. Expected changes in the exogenous variables are movements forecast in advance of the current negotiation. The effect of expected changes is captured by including the forecasted value of the exogenous variable, conditional on information available at the time of contract renegotiation, among the explanatory variables in the wage and employment equations. Unexpected changes in exogenous variables are the difference between movements realized over the life of the new agreement and the forecast of these movements formed during the negotiation of the agreement. The effect of unexpected changes is captured by including the forecast error among the explanatory variables. Third, since the important outcomes are not observed at frequent, equally spaced, synchronous intervals, we specify a set of relations that can be estimated using observations on the relevant variables that are measured in contract time. This allows us to estimate the models using vector autoregressions linking the annualized rates of change in the dependent and exogenous variables. In order to distinguish between the expected effects of economic factors on collective bargaining outcomes and the realized effects of these changes over the life of an existing agreement, we decompose exogenous economic variables into expected and unexpected components. For any exogenous variable z,~, we assume that the level follows a discrete martingale, so that for any positive q. Then write the logarithm of union employment and wage settlements as of the end of a contract that begins at date t and expires at date t + qas

351


In L,r+q = EUnL,r+q I In In

w,r+q

=

z,rI +

ul,t+qt

EUn w,r+q I In L,r?In wjr, z,r+q, z,,I +

u2,r+q.

In w,rT

Z,r+q,

If the conditional expectations are log linear in the levels of the underlying variables, then 1' In

4r+q

w,r+q

=

P,11

+

P,I4',r

=

P,21

+

1' L,r +

P,12

+

+

P,15(z~r+q

PI22

PI13

-

In r j r +

'1)

PI23

1' w,r

+

*l/r+q'

In w,r

+ Pj24jr + P,25(z,r+q + u2,r+q9 where the average of the coefficients P,rs must be estimated and the error vector yr should be vector white noise. Our specification allows for firm-specific factors, summarized by the average elasticities in the log-level equations. We difference this specification across collective bargains for the same firm and correct for the differences in contract length. This produces the following estimating equations:

+

A In L,r = bI2AIn -

(la)

Az,r-q)

+

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