The Global Wage System: A Study Of International Wage Differences 1594540942, 9781594540943

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THE GLOBAL WAGE SYSTEM: A STUDY OF INTERNATIONAL WAGE DIFFERENCES

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THE GLOBAL WAGE SYSTEM:

A STUDY OF INTERNATIONAL WAGE DIFFERENCES

Gernot Kohler, Ph.D.

Nova Science Publishers, Inc. New York

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The authors and publisher have taken care in preparation of this book, but make no expressed or implied warranty of any kind and assume no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS

JOINTLY ADOPTED BY A COMMITTEE COMMITTEE OF PUBLISHERS. Printed in the United States of America

OF THE

AMERICAN

BAR

ASSOCIATION

AND

A

CONTENTS

About the Author

Vii

Chapter 1.

Introduction

Chapter 2.

A Glance at the Literature

Chapter 3.

Facts and Figures

Chapter 4.

International Wage Differences and Unemployment/Underemployment

25

International Wage Differences and Labour Productivity

31

International Wage Differences and Exploitation

45

Chapter 7.

Wage Discrimination

49

Chapter 8.

Policy Discrimination

=i)

Chapter 5.

Chapter 6.

Chapter 9.

Structural Discrimination

a

Chapter 10.

Ideological Discrimination

69

Chapter 11.

Factors Affecting National Wage Levels and International Wage Differences

71

Praxeology

aS

Chapter 12. References

79

Index

83

ABOUT THE AUTHOR Gernot Kohler writes on issues of global economics. Book publications: Global Keynesianism: Unequal Exchange and Global Exploitation (with Tausch, 2002);

Globalization:

Critical

Apartheid (1978). Numerous College, Oakville, Canada.

Perspectives

(with

Chaves,

2003);

Global

journal articles. Formerly professor at Sheridan

Chapter 1

INTRODUCTION My approach to the study of the global wage system is influenced by the feminist critique of wages, which has established that wage differences between men and women tend to be due to a mixture of discrimination and other factors. There may be “ ‘pure’ wage discrimination, which means that women are paid less than equally productive men are . . .The second line of argument is that women are paid less . . . because they are less productive. (Johansson et al. 2001) Similarly, my investigation revolves around the theme of discrimination versus productivity. Are international wage differences due to productivity differences or are they due to discrimination? The following issues will be examined: Data on international wage differences; long-term changes of national wage rates; wage differences due to gender, race, ethnicity, culture, and region; the relationship between wages and GDP per capita; the relationship between national wage levels and unemployment; the relationship between international wage differences and labour productivity; the issue of exploitation; and four kinds of discrimination — namely,

wage

discrimination,

policy discrimination,

structural

discrimination,

ideological discrimination.. Some policy recommendations conclude the book.

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Chapter 2

A GLANCE AT THE LITERATURE

In the classical European economic literature international wage differences were noticed but did not stir much debate. Adam Smith (Wealth of Nations, 1776, Book 1, Chapter 8) noted that wages in China had been stagnating since Marco Polo’s time due to population growth and abundance of workers. David Ricardo (Principles, 1817) does not investigate international wage differences, even though he mentions labourers in England, Portugal, Poland, America, and so on. (Neither are international wage differences investigated in his chapter 7 “On foreign trade”, in which he formulates the principle of comparative advantage.) Karl Marx (Das Kapital, 1869) devotes a short chapter to international wage differences; there he makes the observation that wages in different countries are related to the development of capitalist production in those countries (Marx 1976:

702). In the field of international economics, textbooks tend to teach the theorem of

factor price equalization. The factor-price equalization theorem states that “international trade will cause the wages of homogeneous labour (i.e. labor with the same level of training, skills and productivity) to be the same in all trading nations” (Salvatore 1987: 104), but the causes of international wage differences are not subjected to a thorough analysis in the same texts. Siebert’s neoclassical text on the world economy links international wage differences to two major factors, namely, the fact that labour tends to be “internationally spatially immobile” and the fact that labour in different countries tends to be “inhomogeneous with respect to qualifications”’(Siebert 1997: 71-72), but does not expand on the issue. Neoclassical economists who address the issue tend to explain international wage differences in terms of factor endowment and productivity (e.g., Bhagwati 1984; Falvey and Gemmell 1996). In recent years,

4

Gernot Kohler

wages in the world economy have received extensive empirical attention in the research project by Galbraith with a focus on pay inequity (e.g., Galbraith 2002) and the research project by Williamson on historical wage developments and historical price shocks to the global wage system (e.g., Williamson 1998; Hadass and Williamson 2001). The world-systems literature discusses wages from a macro-sociological perspective; for example, Chase-Dunn observes about the global core-periphery wage differential that: “The wage differential between core and peripheral workers is a dynamic and reproduced feature of the system. Core states . . . are induced to provide some protection for the wages of their citizens, as well as supplemental benefits composing the social wage. The core/periphery wage differential is greater than that which would be due to differences in productivity alone, and this differential is maintained by restrictions on international labour migration from the periphery to the core. The great differences in capital intensity between the core and the periphery also account for a good portion of the wage differential.” (Chase-Dunn 1998: 77-78). Further important factors affecting international wage differences mentioned by Chase-Dunn include coercion, ideological factors, and political and ethnic stratification (Chase-Dunn 1998: 41, 96, 86). International wage differences have been discussed in the context of the debate on “unequal: exchange”. For example, Arghiri Emmanuel discusses various causes of the inequality of wages between high-wage and low-wage countries - including physiological wage, historical wage, market wage, equilibrium wage, moral element, trade-union factor, wage zones and so on. (Emmanuel 1972: 109-122) Samir Amin stated that “super-exploitation” of workers at the global periphery exists “because the differential of wages (and incomes from non-wage labour in general) [sc., between global periphery and center] is much higher than the differential of productivities.” (Amin 1990: chapter 6) The recent activist literature in Europe and North America, notably, that of the anti-sweatshop movement, has raised alarm about wages and working conditions around the world.

Chapter 3

FACTS AND FIGURES

INTERNATIONAL WAGE DIFFERENCES To give some background, international wage differences are presented in Table 1. Table 1 shows wages in manufacturing, for men and women combined, year 1995, for 87 countries. The first data column shows wages as percent of U.S.

wages, based on purchasing power parity values. The countries are ordered in terms of this column, from highest to lowest, where USA = 100%. The second

data column shows the monthly wage in PPP values (purchasing power parity values, at current “international dollars”, as opposed to U.S. dollars at nominal exchange rates). Subsequent columns show the wage in local currency for varying pay periods (days, weeks or months). The categories of labour vary between “salaried employees”, “employees,” > 66. “wage earners”, and “unskilled” and are, thus, not fully identical between countries. The table shows, for example, that wages in the manufacturing sector in China and Indonesia are about one tenth of manufacturing wages in USA. The ratio between the highest real wage (Belgium) and lowest real wage (Malawi) in the table is 72 : 1.

6

Gernot KG6hler Table 1 - Wages in manufacturing, year 1995, men and women

Country

A Yo of

Monthly Wage, at current

Wage, men __ Local

Pay

Category

yee

international

and women

currency

period

of labour

wage

dollars (PPP Francs

value)

Belgium

140

2940

104773

Denmark Virgin Islands (US) Canada Cyprus

137 123

2893 2589

152.76 15.82

117-2455 105 2203

FAZTS 710

United States

100

2105

514.59

Netherlands Germany, Former FRG United Kingdom Norway

100 99

2105 2075

4184 25.46

98

2061

7.92

Month _ salaried employees Kroner Hour employees Dollars, US Hour Wage earners Dollars Week employees Pounds Month _ salaried employees Dollars Week Wage earners Guilders Month employees Mark Hour Wage earners Pounds Hour employees

oF

2037

109.83

Kroner

Hour

Italy Austria Australia Luxembourg

96 94 92 91

2025 1986 1946 1920

3059000 26020 15.59 478.0

Lira Schilling Dollars Francs

Month Month Hour Hour

Finland

91

1918

11004

Markka

Month

Israel Sweden

88 87

1856 1835

5113 106.95

New Shekel Month Kronor Hour

Gibraltar

86

1817

279,29

Pounds

86 = 85 82

1809 1785 1728

1123900 6.86 10.56

Won Month Pounds Hour Dollars, US Hour

82

1722

1263

Peseta

Korea Isle of Man Guam

Spain

Week

Hour

Wage earners — employees employees employees Wage earners Wage earners — employees Wage earners Wage earners employees employees Wage

earners employees

Facts and Figures Monthly Wage, at %

Country

of

USS. wage

current

international

Wage, men and women

Local

Yen

Emalangeni Dollars Cordobas Pound

currency

Pay period

Category

Month Month

employees skilled employees employees Wage earners Wage earners

of labour

dollars (PPP value)

Japan Swaziland New Zealand Nicaragua Ireland

80 78 78 76 75

1680

1598 1573

278800 2134 14.56 2443.6 6.47

Germany,

66

1385

17.00

Mark

Hour

France

62

1313

52.78

Franc

Hour

Puerto Rico

58

1213

7.41

Dollars, US Hour

1650 1649

Hour

Month Hour

former GDR

Wage

earners Wage

earners employees employees employees employees Wage earners employees

Singapore Malta Slovenia Brazil SE Greece

58 56 51 49 48

1212 1174 1080 1023° 1018

Z1S733 68.29 92877 631.0 1243.30

Dollars

Month

Pounds Tolars Reais Drachma

Week Month Month Hour

Trinidad and Tobago

42

880

790.59

Dollars

Week

Peru

4)

866

1137.0

Month

Wage

Month

earners employees

Hour

Wage

41 41

863 863

6654.0 3.72

Nuevos Soles Pesos Pesos

41 Portugal 38 Seychelles Malaysia 37 36 Paraguay "136 Zimbabwe 35 Czech Republic 33 Hong Kong, China 33 Algeria 32 Hungary

860 809 769 767 764 730

100700 20135 1002 610414 1702.54 7854

Escudo Rupees Ringgit Guaranies Dollars Koruny

Month Month Month Month Month Month

earners employees employees employees employees employees employees

704

278.0

Dollars

Day

Wage

Dinars

Month

Forint

Month

earners employees employees

Philippines Argentina

699 671

10462 39554

8

cr

Gernot Kohler

% of

Monthly Wage, at current

Wage, men __ Local

Pay

Category

U-S.

international

and women

currency

period

of labour

54365 159085 225995 176.4 7194 2421.00 656.65 268436 16.6

Colones Pesos Pesos Dinars Koruny Kroons New Zlotys Lei Pesos

Month Month Month Month Month Month Month Month Hour

wast

dollars (PPP value)

Costa Rica Chile Colombia Jordan Slovakia Estonia Poland Romania Dominican

32 ai 30 30 30 27 26 26 24

664 659 635 633 630 568 543 543 512

Republic

employees employees employees employees employees employees — employees employees Total

— — — — —

employmen t

Solomon Islands Bolivia Croatia Guatemala Latvia Bulgaria Kazakhstan Lithuania Thailand Swaziland Mexico

23

484

632

Dollars

Month

employees

22 Papp Dip 22 21 21 Zt ZA 20 20

473 461 457 456 452 448 sad 439 43] 428

959 1672 1138.04 86.47 8448 6520 323 4994.0 558 1220.6

Month employees Month — employees Month employees Month — employees Month employees Month ~— employees Hour employees Month — employees Month _ unskilled Month employees

Guinea Swaziland Kenya Botswana Belarus Pakistan Moldova Mauritius

19 18 17 L? 17 16 ie) 15

39] S15 363 36] 361 334 325 321

130000 483 6228.7 Bio oa 821238 2970.00 209.5 132.00

Bolivianos Kunas Quetzales Lats Leva Tenges Litas Baht Emalangeni Nuevos Pesos Francs Emalangeni Shillings Pula Roubles Rupees Leu Rupees

El Salvador

14

303

6.88

Colones

Month employees Month _ unskilled Month employees Month — employees Month — employees Month — employees Month — employees Day Wage earners Hour Wage ecamers

Facts and Figures

Country

% of US. ee

Egypt

13

Monthly Wage, at oun ; international dollars (PPP value) 275

Uzbekistan China Sri Lanka

1] 11 10

Kyrgyzstan Indonesia

2

Wage, men __ Local and women _ currency

hey period

84

Pounds

Week

235 227 216

1529 430.75 145.35

Sums Yuan Rupees

Month Month Day

10 9

213 191

377.07 39300

Soms Rupiahs

Month Week

India

8

177

1211

Rupees

Month

Tajikistan Azerbaijan Armenia Malawi

4 6 5 2

141 134 105 41

1559.0 95556.7 7680 195.79

Roubles Manats Dram Kwacha

Month Month Month Month

Source |

(b)

©)

(c)

(c)

(c)

Category of labour

Wage earners — employees — employees Wage earners — employees Wage earners Wage earners employees ~— employees employees — employees

(c)

Notes: (a) countries are included on the basis of data availability (b) own calculation based on (c) and using World Bank data on purchasing power parities (c) source = ILO, LABORSTA database; ISC3 category D=manufacturing; ISC2 category 3 = manufacturing

WAGE DIFFERENCES RELATED TO GENDER In most countries wage rates for women are lower than wage rates for men (see, Table 2). Table 2 shows that women’s wages in manufacturing are, on average, 71 % of men’s wages. The most equitable country in the table is Sri Lanka, having a female wage rate in manufacturing that is 95% of the male wage rate, i.e. a ratio of approximately 1 : 1 . In other countries women’s wages are lower; for example in Kenya women’s wages are 48 % of male wages, which is a ratio of 2: 1.

Gernot Kohler

10

Table 2 Gender Wage Differences, 1995 Wages in the manufacturing sector, Selected countries

Swaziland Australia Kyrgyzstan France

gaa Wage aoe of men’s wage 95 87 85 81 1f:

Ukraine

78

USA

76

Egypt Philippines Costa Rica Hong Kong Jordan: Brazil Malaysia Japan Peru (Lima) Kenya

74 74 71 65 62 58 58 56 55 48

Sn Lanka

Men

Women

Local Currency : Unit

17.08

16.2

Rupees

Hour

558 16.07 158.5 So)

483 13.67 128.6 44.31

Emalengeni Dollars Soms Francs

Month Hour Month, note 2 Hour

9287.2

7280.5

thousands

87 1529 60273 3577 a 569.7 1242 318200 29.04 6867.7

64 5592 42739 233:5 3.4 333 719 177900 15.9 3314.4

Pounds Pesos Colones Dollars Dinars Reais Ringgit Yen Nuevos Soles Shillings

Pay period

Hrivna,

Average

Month, note 3 Note 4, source b

Week Month Month Day Day Month Month Month Week Month

70.7

Sources: (a) ILO, LABORSTA database (2003), for all countries, except USA (b) for USA: AFL-CIO, Fact Sheet 2003, online at:

http://www.dpeaflcio.org/policy/factsheets/fs_2003_prowomen.htm Note 1 — Source (a) does not give breakdowns by gender for China, India, Russia, USA, and others

Note 2 - Kyrgyzstan - wages for agricultural sector Note 3 - Ukraine - wages for all non-agricultural sectors Note 4 - USA — wages for all sectors, year 2002

Facts and Figures

1]

WAGE DIFFERENCES RELATED TO RACE, ETHNICITY, CULTURE Wage differentials may exist along racial, ethnic and cultural lines. Here are some examples:

Example 1: USA “In 2002, women earned 76% as much as men. For women of color, the gap earned 67%, and Latina women 55%, of men's weekly earnings. While Asian Pacific American women do better, they still earn 83.5% as much as men.” (AFL-CIO 2003) was wider. African American women

Example 2: Canada According to a study by Matthews and Lian of the University of British Columbia, non-white Canadians earned less than their similarly educated white counterparts. The study has been summarized, as follows (Orient Times 1999): The two sociologists analysed 425,107 Canadians and concluded that the pattern of wage discrimination against non-whites is “startling”. They attributed this income gap to “discrimination against visible minorities”. “We took 1991 census figures as this was the first time that about 500,000 Canadians received detailed census questionnaires that expected them to answer about their age, gender, how long in labour force, period of immigration to Canada, education, ethnicity and their annual earnings,” Mathews explained. “We found that people of European origin - British, French, Italians, Portuguese - doing identical work, with same level of education, receive same wages for similar kind of work,” he said. Contrary to the general perception in Canada, the two found no wage difference between the British and French Canadians. “But there is a difference between white Canadians and visible minorities in terms of wages,” they found. The study claimed that among non-whites, discrimination pervaded every ethnic group. Those of Asian, African and Latin American descent earned less than Anglo-Canadians in nine out of 10 educational categories, concluded Mathews and Lian. South Asians earned 6.8 per cent less than the white Canadians.

12

Gernot Kéhler

REGIONAL WAGE DIFFERENCES Regional wage differences may exist within countries. For example, in China (see Table 3) the wages in manufacturing differ between the province of Shanghai, which has the highest regional wage (2486 RMB), and the province of Heilongjiang in the interior with the lowest regional wage (1019 RMB). Manufacturing wages in Heilongjiang are 41 % of those in Shanghai. The ratio is ps se We Table 3. China, Wage Differences Across Provinces in 1995

(RMB/person)

Wage in Manufacturing Sector** (RMB)

Average

1480

1437

Coastal Shanghai Guangdong Beijing Zhejiang Tianjin Fujian Jiangsu Hainan Shandong Liaoning

5315 2350 3132 2256 2889 1918 2049 1413 1614 1945

2496 Daan 2126 1701 1691 1666 1593 1443 1328 1270

Interior Yunnan Gansu Guangxi Xinjiang Qinghai Ningxia Guizhou

850 TNS 993 931 638 965 502

1594 154] 149] 1466 1337 1333 1321

Sichuan

889

1298

Province

GDP per capita

Facts and Figures

13

Province

GDP per capita

Wage in Manufacturing Sector**

Hunan Hebei Hubei Anhui Jilin Shaanxi Shanxi Henan Jiangxi Inner Mongolia Heilongjiang

wf 1243 1164 938 1243 655 997 at 861

1274 1268 1258 1192 1160 1149 1148 1133 1106

Nes 1529

1034 1019

Source: China Statistical Yearbook, 1989 — 1999. Quoted from: Songhua Lin (2003), “International Trade, Location and Wage Inequality in China.”, Paper, University of California at Davis,

Department of Economics, p30, Table 1 Note ** Wage in the manufacturing sector is the real wage rate of formal employees manufacturing sector

in the

DYNAMISM AND CHANGE OF NATIONAL

WAGE LEVELS, 1975 - 95 National

wage

levels are not static but, rather, exhibit a fair amount

dynamism and change over time - both up and down. See, Figure 1.

of

14

Gernot Kohler

Figure 1 Changing National Wage Rates, 1975-1995

2000 5

100)1800-

1600 +

\

i

(international 1985 dollars, monthly wage ]

1975

1995 year

Figure 1 shows that the global wage structure is not crystallized but, rather, keeps changing dynamically. Figure 1 shows how national wage rates changed in 38 countries from 1975 to 1995, in manufacturing, for men and women. The wages are monthly wages and are expressed in constant PPP values (international dollars, 1985=100). Figure 1 is based on Table 4, which allows us to examine the dynamic changes more closely. See, Table 4.

Facts and Figures

15

Table 4 (supporting Figure 1) - Changing National Wage Levels, 1975 - 1995 Monthly wages in manufacturing, men and women, Constant international dollars (PPP values), 1985=100 Country Singapore Chile Cyprus Hong Kong, China Mauritius Belgium Spain Greece Finland United Kingdom Denmark Norway Pakistan Hungary Italy Ireland Canada Australia France Netherlands Sri Lanka Austria Luxembourg Japan Portugal Sweden Egypt United States New Zealand Costa Rica Dominican Republic India

1975 189 179 424 233 139 954 613 321 816 924 1256 981 206 PES 951 820 1420 1139 709 1156 167 1024 1045 1036 479 1242 180 1422 1189 436 330 154

1995 860 510 1200 574 293 1811 1149 540 1314 1458 1900 1417 286 351 1298 1074 1825 1465 911 1402 200 1205 1143 1108 514 1321 189 1458 1177 426 307 138

Change % 355 185 183 146 11] 90 87 68 61 58 el 44 39 38 36 3] 29 29 28 21 20 18 9 / 4 6 5 3 -] -2 -7 -10

Zimbabwe Swaziland

410

355

-13

WAT

603

=17

16

Gernot Kohler

eee

1975 399 921 309 196

1995

Change %

330 516 160 43

-17 -44 -48 -78

Median Average

1975 661 667

1995 886 864

Change % 2 40

Maximum Minimum ratio max : min

1422 139 LOrer

1900 43 44:1

5fe -78

Country Kenya Solomon Islands El Salvador Malawi

N=38

Sources and Notes (a) countries are included on the basis of data availability (b) source = ILO, LABORSTA database; ISC3 category D=manufacturing; ISC2 category 3 = manufacturing; and World Bank data for calculation of purchasing power parities

Figure | and Table 4 show that: (a) There was a rising global trend in wage levels between 1975 and 1995. The

median wage in manufacturing increased from 661 constant international dollars per month in 1975 to 886 constant international dollars in 1995 (1985=100). (The average changed from 667 to 864 constant PPP dollars in the same period.) (b) There is inertia and dynamism. There was a moderately high correlation between the starting position of a country in 1975 and its position in 1995. The correlation between manufacturing wages in 1975 and in 1995 is r = 0.86 . Thus, there is both constancy (74 percent of common variance) and change (26 percent of non-common variance). (c) The gap between the highest and lowest wage was widening - namely, from a ratio of 10: 1 in 1975 and to a ratio of 44 : 1 in 1995. The maximum monthly wage changed from 1422 constant PPP dollars in 1975 (1985=100) to 1900 constant PPP dollars in 1995. The minimum monthly wage changed from 139 constant PPP dollars in 1975 to 43 constant PPP dollars in 1995. (d) Of the 38 countries in the chart, there are 23 countries (61% of 38 countries) whose wage level increased during the 20-year period; there are 5 countries

Facts and Figures

17

(13% of 38 countries) whose wage level stagnated over the 20-year period (“stagnation” defined as 7% or less wage increase in 20 years); and there are 10 countries (26% of 38 countries) whose wage level decreased over 20 years. (Wage levels measured in constant PPP values, 1985=100).

The five countries with the greatest percentage gains in manufacturing wages in this group of 38 countries (in descending order) were: Singapore, Chile, Cyprus, Hong Kong, Mauritius. The five countries with wage stagnation included USA and Japan. The five countries with the greatest percentage decreases in manufacturing wages in this group of 38 countries were: Zimbabwe, Kenya, Solomon Islands, El Salvador, Malawi (with Malawi having the greatest wage decrease). Reasons for wage decline in manufacturing in these five countries in the period 19751995 appear to include: El Salvador - 12 years of civil war to 1992 Solomon Islands - chaotic political situation, ethnic strife, government bankruptcy Zimbabwe - political problems, racial conflict, embargo by UK and Commonwealth, 60% unemployment, Kenya -- reliance on exports of primary goods whose prices are declining

Malawi - the economy is 86% agricultural These five countries, thus, had either political problems (civil war, ethnic or

racial conflict, bad government) or relied heavily on agriculture and agricultural exports.

85-YEAR CHANGE OF NATIONAL WAGE LEVELS, 1910 - 1995 Williamson’s historical research provides wage data for the time prior to World War I. With the use of those data we can construct a few historical wage trends over an 85-year period. Table 5 shows wages of selected countries for the period 1910 - 1995. The wages are shown in relative terms, namely, as a percent of the British wage (British wage = 100%). The table exhibits considerable dynamism of national wage levels over that 85-year period. Some countries were

Gernot Kohler

18

catching up with British wages (Korea, Japan, Brazil, Philippines) and others were falling back (Argentina, Mexico). Others, while rising in absolute terms, retained about the same position in relation to British wages over that period

(Egypt). Table 5 Manufacturing wages, 1910-14 and 1995 As a percent of British wage, Selected countries 1910-14

1995

change

% of UK wage 15 19 28 23 28 16

% of UK wage 9 9 82 88 42 21

-6 -10 +54 +65 +14 a5

Egypt

13.8

13

0

Argentina Brazil SE / Brazil Brazil NE / Brazil Colombia Mexico

100.8 Zod 74 24.8 60.6

42 50 50 3] 23

-59 +26 +43 +6 -40

100

100

Asia India Indonesia Japan Korea / South Korea Philippines Thailand Middle East

Latin America

UK

Sources: (a) for 1910-14, Williamson (1998) File No. 1855, Tables 1,2 (b) for 1995, ILO (2003), my conversion to PPP values and percent of UK wage, using World Bank data Notes: Brazil 1995 is for all of Brazil; Korea 1995 is for South Korea

INTERNATIONAL WAGE DIFFERENCES AND

GDP PER CAPITA The wage level of a country tends to correlate with the GDP per capita of that country. For example, the correlation between wages in manufacturing and GDP per capita is r= 0.86 in 1995 (N=80 countries); and the correlation between the

Facts and Figures

19)

wages of educators and GDP per capita is r= 0.85 (year 1995, N=20) (Kohler and Tausch 2002: 188).

The relationship between wages and GDP per capita can also be expressed as a ratio. For example, if the monthly wage in manufacturing was exactly the same as GDP per capita, pro-rated for one month, then the monthly wage in manufacturing would, theoretically, amount to 1/12 of annual GDP per capita = 8.3 % of GDP per capita. However, we find that the average monthly wage in manufacturing is 12.2% of GDP per capita (in 1995); and the median is 9.2% of GDP per capita (my calculations, based on N=84). That is slightly above the theoretical ratio of 8.3% of GDP per capita and indicates that the wage in manufacturing is, on average, higher than GDP per capita, but - as one may note, not much higher. The ratio of wage to GDP per capita exhibits great variability between countries. For example, the monthly manufacturing wages as a percent of annual GDP per capita were (in 1995): USA 8%, Kenya 36%, Hong Kong 3% (minimum in the dataset). This very loose relationship between wage rates and GDP per capita is due to the fact that various factors, like income distribution, labour force participation rates, and others, impinge on this ratio, as has also been observed by Williamson (e.g., Williamson 1998). The high ratio of 36% for Kenya, and a similarly high rate for Zimbabwe, can possibly be explained by the extremely high rates of unemployment in those countries (60-70%). The very low ratio for Hong Kong might, conceivably, be due to high rates of retained profits. Here are three model calculations that illustrate the influence of three alternative conditions on the wage-to-GDP per capita ratio - namely, unemployment, profit rate (surplus rate), and unequal wage distribution. See, Tables 6, 7, 8. The three model calculations are constructed in such a way that in each model two situations are compared. In each of the two situations we start with the same assumptions and calculate their implications, as we go down in the table. Then, at some point, differences are introduced — namely, no unemployment versus high unemployment; low retained earnings (surplus) versus high retained earnings; and equal wage distribution versus skewed wage distribution. The implications of these alternative additional assumptions are then calculated, leading to a result line at the bottom of each model table.

20

Gernot Kohler Effect of the Unemployment Rate

Observation from Table 6: A higher unemployment rate has the tendency to raise the ratio of wage : GDP per capita. Table 6 — Unemployment and the wage : GDP per capita ratio (model) Situation A

Situation B

0% unemployment

60 % unemployment

GDP, "international $"

6,000,000,000

6,000,000,000

assumed

Population

2,000,000

2,000,000

assumed

GDP per capita, "international $"

3000

3000

70%

70%

Labour force participation rate % Number of persons in the labour force

1,400,000

1,400,000

Unemployment rate %

0%

60%

Number of workers actually working 1,400,000

assumed

assumed

560,000

Wage bill = all wages paid as % of GDP Wage bill = all wages paid, "international $"

70%

70%

4,200,000,000

4,200,000,000

Annual wage per worker Monthly wage per worker

3000 250

7500 625

Ratio, monthly wage : GDP per capita, as %

8.3

20.8

assumed

Result

Facts and Figures

21

Effect of the Surplus Rate (Profit, Retained earnings rate)

Observation from Table 7: A higher surplus (profit, retained earnings) rate has the tendency to lower the ratio of wage : GDP per capita. Table 7 - Surplus, profit, retained earnings and the wage: GDP per capita ratio (model) Situation A

Situation B

GDP, "international $" Population

With 30% surplus 6,000,000,000 2,000,000

With 60% surplus 6,000,000,000 2,000,000

GDP per capita, "international $"

3000

3000

70%

70%

assumed

1,400,000 0%

1,400,000 0%

assumed

1,400,000

1,400,000

Labour force participation rate % Number of persons in the labour force Unemployment rate %

assumed assumed

Number of workers actually working

Surplus, aggregate profit, % of GDP 30% Wage bill as % of GDP=100 - Surplus % 70% Wage bill = all wages paid, "international $" 4,200,000,000 Annual wage per worker Monthly wage per worker

3000 250

Ratio, monthly wage : GDP per capita, as % 8.3

60%

assumed

40% 2,400,000,000 1714 143

4.8

Result

Gernot Kohler

22

Effect of the Wage Distribution Observation from Table 8: Wage inequality between categories of workers tends to generate divergent ratios of wage : GDP per capita. Table 8 - Wage distribution and the wage : GDP per capita ratio (model) Situation A

Situation B

equal distribution of wages

unequal distribution of wages

6,000,000,000 2,000,000 3000

6,000,000,000 2,000,000 3000

assumed assumed

Labour force participation rate 70% % Number of persons in the labour 1,400,000 force Unemployment rate % 0%

70%

assumed

Number of workers actually working

1,400,000

1,400,000

Wage bill as % of GDP=100Surplus %

70%

70%

Wage bill = all wages paid,

4,200,000,000

GDP, "international $"

Population GDP per capita, "international Ch

1,400,000

0%

assumed

assumed

4,200,000,000

"international $"

Split of wage bill top 20% of workers earn x % of 20 wage bill

80

assumed assumed

Bottom 80% of workers earn y % of wage bill

80

20

Wages going to top 20% of

840,000,000

——_3,360,000,000

workers, "international $"

Facts and Figures Situation A

23 Situation B

equal

unequal

distribution of

distribution of

wages

wages

Wages going to bottom 80% of 3,360,000,000 workers, "international $" Annual wage of worker intop 3,000 20%, "international $" Annual wage of worker in 3,000 bottom 80%, "international $"

840,000,000

Monthly wage of worker in top 20%, "international $" Monthly wage of worker in bottom 80%, "international $"

250

1,000

250

63

Ratio 1 for top 20% of workers, 8.3 monthly wage : GDP per capita, as % Ratio 2 for bottom 80% of 8.3

workers, monthly wage : GDP

12,000

750

33

Result

2:1

Result

~

per capita, as %

(d) Sectoral GDP per capita and wages

It is interesting to decompose GDP per capita by main sectors of the economy and compare the “sectoral GDP per capita” and wages.

24

Gernot Kohler Table 9 Sectoral GDP per capita Belgium

Chile

Zimbabwe

GDP per capita 1995 for entire country

13,761

5,834

1,168

Agriculture

GDP per person in sector 6880 13210 13761

GDP per person in sector 3334 8210 5332

GDP per person in sector 214 1635 3649

1175

510

355

8.9%

6.2%

21.7%

Industry Services Monthly manufacturing wage

1995 Ratio wage : sectoral GDP per capita of industry

Notes and sources: (a) all numbers, except percentages, are given in constant international $ (1985=100) (b) wages from ILO, LABORSTA database, and using World Bank data for conversion

to

international $ (c) GDP, population, sectoral shares calculated from CIA, World Factbook 2002

Table 9 shows that in Belgium the monthly manufacturing wage was 8.9% of the sectoral GDP per capita for industry; in Chile it was 6.2%; in Zimbabwe it was 21.7%.. The sectoral GDP per capita figures vary considerably. Agriculture has the lowest sectoral GDP per capita in each of the three countries. In Belgium industry and services have virtually the same sectoral GDP per capita. The highest sectoral GDP per capita for Chile is found in industry; for Zimbabwe it is in services.

Chapter 4

INTERNATIONAL WAGE DIFFERENCES AND UNEMPLOYMENT/UNDEREMPLOYMENT Adam Smith observed that in a situation with surplus population wages tend to stagnate. “There would be a constant scarcity of employment, and the labourers would be obliged to bid against one another in order to get it.” (Smith 1776, Book 1, Chapter 8) Karl Marx spoke of a “reserve army” of the unemployed the existence of which permits capitalists to pay mere subsistence wages. The data show (see, Table 10) that there is a statistical relationship between the unemployment rate, on one hand, and wage levels, on the other hand, but it is

not as strong as one might expect. The correlation of the national unemployment rate with manufacturing wages (year 1995, measured in PPP terms) is r = -0.3 (i.e., negative 0.3). The relationship is in the expected direction - namely, the higher the unemployment rate, the lower the wage, but a correlation of -0.3 is a weak one. (In statistical terms, that explains only nine percent of the statistical variance.) I explored the data in Table 10 further by making alternative assumptions, e.g., by inserting drastically higher rates of unemployment /underemployment for countries where the source indicated “high underemployment” or similar. (That is not shown here.) However, the correlation for this group of 78 countries did not change very much as a result.

Gernot K6hler

26

Table 10 Unemployment (2002) and Wages

Country

bl aM rate (%)

Algeria Argentina Armenia Australia Austria Azerbaijan

31.0 DA 20.0 6.3 4.8 16.0

Belarus

Zl

Belgium ae gy be Botswana Brazil Bulgaria Canada Chile

Pee

12 40.0 6.4 18.0 7.6 92

China

10.0

Colombia Costa Rica Croatia Cyprus Greek Cypriot ypriot ar area Czech Republic Denmark Dominican Republic Egypt

17.4 6.3 pare

Year and Notes on unemployment data, fromthe source 2002 est.

re

2001 est. 2002 2002 est. official rate is 1.2% (2003 est.) officially registered unemployment (December 2000); large number of underemployed workers 2002 est. note: widespread underemployment (2000) official rate is 21% (2001 est.) 2001 est. 2002 est. 2002 est. 2002 urban unemployment roughly 10%; substantial unemployment and underemployment in rural areas (2002 est.) 2002 est. 2002 est. 2002 est. Turkish Cypriot area 5.6% (2002 est.)

= a

2002 2002

a: 12.0

2002 est. 2001 est.

Wage 1995, monthly, current PPP dollars per month 699 863 105 1946 1986 134 361

2940

ath 361 1023 452 2455 659 97

635 664 461 2203

_ 2893 ee 275

International Wage Differences and Unemployment...

Country

Year and Notes on Unemployment unemployment data, from the rate (%) source

El Salvador

10.0

Estonia Finland France Gibraltar Greece Guam Guatemala Hong Kong Hungary India Indonesia Ireland Isle of Man Israel Italy Japan

12.4 8.5 9.1 2.0 10.3 15.0 TES Wes) 5.8 8.8 10.6 4.3 0.7 10.4 931 5.4

Jordan

16.0

Kazakhstan Kenya

8.8 40.0 3.1 ae 7.6 5 4.1 3.8 7.0 8.8

Korea, South

Kyrgyzstan Latvia Lithuania Luxembourg Malaysia Malta Mauritius Moldova

8.0

Netherlands

3.0 33

New Zealand

but the economy has much underemployment (2001 est. 2001 2002 est. 2002 est. 2001 est. 2002 est. 2000 est. 1999 2002 est. 2002 est. 2002 2002 est. 2002 est. March 2003 2002 est. 2002 est.

2002 Official rate; actual rate is 25%-

30% (2001 est.) 2002 est. 2001 est. 2002 est. 1999 2001 est. 2001 est. 2002 est. 2002 est. 2002 est. 2002 est. roughly 25% of working age Moldovans are employed abroad (2002 est.) 2002 est. 2002 est.

at

Wage 1995, monthly, current PPP dollars per month

303 568 1918 1313 1817 1018 1728 457 704 671 LPL 19] 1373 1785 1856 2025 1680

633 448 363 1809 215 456 444 1920 769 1174 321

325 2105 1649

28

Gernot Kohler Wage 1995,

Country

Unguip yen rate (%)

Year and Notes on unemployment data, fromthe source

: EN Norway Daren

oY 3.9 78

Paraguay

18.2

Peru

9.4

2002 est. widespread underemployment (2002 est.)

Philippines Poland Portugal Puerto Rico Romania Singapore Slovakia Slovenia Spain Sri Lanka Sweden Tajikistan Thailand Trinidad and Tobago United Kingdom United States j ie Virgin Islands Zimbabwe

10.2 18.1 4.7 12.0 8.3 4.6 17.2 11.0 eS 8.0 4.0 40.0 29

2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002 2002

ee

2002

oe 5.8

2002 est. 2002 plus another 20%

te

underemployment (1999 est.)

4.9 70.0

(March 1999 2002 est.

N=78

lus considerable ae (2002 est.) 2002 est. Bigae underemployment

est.

est. est. est. est.

est. est. est.

monthly, current PPP dollars per month et 2037 334 767 866

863 543 860 1213 543 1212 630 1080 1722 216 1835 141 439 he “ee! 2105

2: 2589 764

International Wage Differences and Unemployment...

U Country

Year and Notes on nemployment rate (%) unemployment data, fromthe source

29

Wage 1995, monthly, — current PPP dollars per month

Combined unemployment and underemployment in many nonWorld 30% industrialized countries; developed countries typically 4%-12% unemployment Correlation unemployment (%) with wage r= - 0.34 Sources: (1) unemployment data and notes from CIA, World Factbook 2003 (2) wage data from ILO, LABORSTA database, and using World Bank data for conversion to international $ Note: countries are included on the basis of data availability

Many of the countries that are missing in Table 10 are poorer countries for which wage data were missing, while unemployment data were available. I experimented with estimated wage data for all missing countries, thereby raising the number of countries to N=235. The resulting correlation was r= -0.5 . That is still not as strong a correlation as one might expect on the basis of theory. When we plot the data of this exercise, we reach an interesting observation about the relationship between national unemployment and national wage levels. See, Figure 2. Figure 2 provides an excellent insight into the relationship between national unemployment /underemployment and wages. It confirms the familiar view that high unemployment /underemployment is related to low wages. In Figure 2 that can be seen in the fact that wage levels grow lower and lower as we go from left (low unemployment) to right (high unemployment /underemployment). For example, starting from 45% unemployment /underemployment and going right, we have the following low income countries (unemployment / underemployment shown in brackets): Lesotho (45%), Nepal (47%), Senegal (48%), (territory) West Bank and Gaza (50%), Djibouti (50%), East Timor (50%), Haiti (possibly 50%), Zambia (50%), Cocos (Keeling) Islands (60%), Angola (60%), Zimbabwe (70%). There is only one point in the graph that spoils the view a bit and is an “outlier” in statistical jargon - namely, Zimbabwe (unemployment rate 70% and monthly manufacturing wage 764 international $) (located at the right edge of the graph).

Gernot Kohler

30

oe Ne

It is possible that the wage given for Zimbabwe in the database is for the bestpaid category of manufacturing workers, rather than all categories.

Figure 2 Unemployment and

°

$

° °

° ° °

$e, o

°

0m f

8

°

oS

°

°

Md

@

Rea

°

ee.

R

°

oo oo

%, 9%

2 3°

mo

°

oo.

Ag rs wee © 0 %e mo 0% © ¢ 00 © oO Ghooo 900 000 oo 6 Ot eee @

10,

oo

15

20°

oo

° o 6 © 06 o 6

©

° o

° o 6 ry

« $

°

T

5

°

°

0

°

;

eo

T

T

a

0

° °

@

ieSe

25 3036 40) 4G 50 unemployment rate

SRT Tae

eae

roU

| a)

Gamer

The interesting insight stemming from Figure 2 is that the reverse of the theoretical view is not true. That is to say, whereas high unemployment and underemployment are associated with low wages, the reverse cannot be claimed namely, it cannot be said that low unemployment leads to high wages. For example, when we examine the area of the graph where unemployment is between 0% and 10%, we find countries with both very high and very low wages. The two highest manufacturing wages in the graph are paid in Belgium (wage 2949 international $ per month, unemployment 7.2%) and Denmark (wage 2893, unemployment 5.1%). However, in the same range of unemployment we find low-wage countries as well, for example, Cambodia (unemployment 2.8%), Laos (unemployment 5.7%), Burma (unemployment 5.1%), Madagascar (unemployment 5.9%), India (unemployment 8.8%).

Chapter 5

INTERNATIONAL WAGE DIFFERENCES AND LABOUR PRODUCTIVITY A standard explanation of international wage differences is in terms of labour productivity, broadly conceived. The hypothesis is that the higher labour productivity is, the higher will be the wage, and the lower labour productivity is, the lower will be the wage; and, in dynamic terms, that, as labour productivity increases, wages will tend to increase, and vice versa, as labour productivity decreases, wages will tend to decrease.

CONCEPT AND ALTERNATIVE DEFINITIONS OF LABOUR PRODUCTIVITY In the general definition, labour productivity is equal to output divided by labour input. Both components of the formula - namely, “output” and “labour input”, can be measured in a variety of ways. The differences in measurement are so severe, that they affect empirical observation in significant ways and raise important theoretical questions. The component of “labour input” may be measured in various ways - e.g., as hours (days, weeks, months, years) of labour, or as number of workers, or as

number of workers multiplied by time, or as labour cost. The component of “output” may be measured either in physical terms (e.g., tons of coffee harvested) or in value terms (e.g., the peso or dollar value of the tons of harvested coffee). That leads to two or more significantly different concepts of productivity that are different not only in measurement terms, but also in terms of theory - notably,

32

Gernot Kohler

physical productivity and value-productivity. In the physical concept of productivity, output is counted as objects produced or services rendered - e.g., number of cars built (per worker or per time interval) or tons of wheat produced (per worker or per time interval) or number of dentist operations performed (per dentist or per time interval) or number of students taught to a certain level (per teacher or per time interval), and so on. When, on the other hand, we use the value concept of productivity we count the monetary value produced - e.g., number of dollars worth produced (per worker or per time interval) or number of rupees worth produced (per worker of per time interval) etc. Each of the two concepts of productivity may be applied at different levels of aggregation namely, individual, sectoral, national, global, or other. Examples of physical labour productivity:

(1) Worker A produces twice as many objects per day as another worker B; the physical labour productivity of worker A is double the physical labour productivity of worker B. (2) In country A the rice production per worker is twice the rice production per worker in country B; the physical labour productivity in rice production of country A is double that of country B. (3) Factory automation in a car factory has the result that the same number of workers produces twice as many cars as before; physical labour productivity of the factory has doubled. Value productivity (money-valued labour productivity) depends on physical labour productivity and on product prices (selling price). The relationship between the two kinds and of labour productivity is: value productivity = physical productivity * product prices Thus, if product prices are identical or constant, then double physical productivity leads to double value productivity. However, if product prices are not identical or constant, then value productivity will behave differently from physical productivities. Examples of value productivity (money-valued labour productivity): Situation (1): double car production per worker + selling price of car is constant. Result: physical productivity per worker is doubled and value productivity per worker is doubled (2 * 1 = 2).

International Wage Differences and Labour Productivity

33

Situation (2): double coffee production per worker + reduce the coffee price to one half. Result: physical productivity per worker is doubled, but value productivity per worker is unchanged (2 * 4 = 1). Situation (3): double the number of computers assembled per worker + reduce the computer price to one quarter. Result: physical labour productivity is doubled, but value productivity per worker is reduced to one half (2 * “= 4).

WAGES AND PHYSICAL LABOUR PRODUCTIVITY As explained above, the analysis must clearly distinguish between the two kinds of productivity - physical productivity and value productivity (moneyvalued productivity). Here I will discuss the relationship between wages and physical productivity. In particular, I will establish the point that certain categories of workers in different countries may have identical physical productivities, while their wages may differ tremendously. It is generally considered fair to pay a higher wage if a worker produces more and/or better output, and to pay the same wage if a worker produces the same or equivalent output. Here are some examples in which workers around the world produce the same or similar output, yet their wages are vastly different.

Example 1: Agricultural Workers Wage earners in agriculture, paid by the day, perform comparable (even if not identical) physical labour in different countries. The wage for migrant farm workers in Canada in year 2003 is, as follows: “Every year approximately 15,000 migrant farm workers from the Caribbean and Mexico arrive in Canada to work in our fields, orchards and greenhouses. Most workers are men but women also participate . . .. Commodities that workers are engaged in include: Tobacco Flue, Tobacco Black, Canning/Food Processing (fruit and vegetables), Nurseries, Vegetables, Greenhouse Vegetables, Fruit (including apples) and Flowers. The hourly wage increased to 7.50 /hr in all of these commodities except in Flowers where the hourly wage increased to $7.70. [Sc. these dollar figures are Canadian dollars] Mechanically harvested tobacco flue is also paid differently. The first kiln filled in a day is paid $75 and thereafter workers are to be paid hourly for the remainder of the workday. Tobacco Black harvesting hourly wage is $8.80.” (Justicia 2003)

Gernot Kohler

34

The wage mentioned in the quotation, namely, Canadian $7.50 per hour in

year 2003, is near the legal minimum wage of Canadian $ 7.40. Canadian $7.50 per hour in year 2003 is approximately US $5.60 per hour or US $56 for ten hours, which may be estimated as being equivalent to about $45 PPP international dollars in 1995. Thus, the daily wage for a migrant farm worker in Canada is similar to the daily wage of a casual farm worker in Japan, as shown in the table for year 1995 (see, Table 11). Table 11 Wages in Agriculture, 1995

Country

Daily Wage, Current Notes from 3 3 international — the source $ (PPP values)

id Skill category

: Daily Wage, LCU

Local currency : unit (LCU)

Japan

48

enits e earners

7963

Yen

Japan

36.3

ware S earmers

6028

Yen

Mauritius

15.9

wage earners

131.00

Rupees

Philippines

195

menand women

wage S earners

92227

Pesos

Sri Lanka

5.85

Tea

men and

wage

plantations

women _ earners

78.84

Rupees

Gender

casual day workers, excl men : fishing casual day workers, excl women : fishing menand women

‘Rice, com, coconut and sugar cane

Source: ILO, LABORSTA database (online) 2003 Notes: (a) wages in ISC2 category 1=agriculture, hunting, forestry, and fishing (b) inclusion criteria = if worker category is given as “wage earner” and pay period is given as

“day”

(c) my conversion of local currency values to PPP values, using World Bank data

The physical productivity of the agricultural casual labourers in these different countries is comparable, similar or identical, but the wages are very different. Thus, identical physical labour productivity does not necessarily lead to an identical wage.

International Wage Differences and Labour Productivity

35

Example 2: Shoe Manufacturing Workers A Canadian entrepreneur stated (in a radio interview, year 2000) that he has his shoes manufactured in Asia, where he pays 20 cents per hour of labour, he said, rather than in Canada, where he pays $20 per hour of labour. He implied that the quality of workmanship (skill) in Asia was the same as in Canada (homogeneous skills) and that he would provide the same machinery in both cases. In this example, the worker’s physical productivity is the same in Asia and Canada. However, the wages are vastly different. Thus, identical physical labour productivity does not necessarily lead to an identical wage.

Example 3: Textile Manufacturing Workers The wage for a textile worker in Nicaragua in a foreign industrial zone (“Zona Franca”), who makes clothes for export, was less than US $3 per a 10 hour day in 2003. (Sewell 2003) The effective wage for a home worker in the garment industry in Toronto, Canada in 2003 was, on average, Canadian $ 6-8 per hour (equivalent to approximately US $4.50 - 6.00 per hour). That results in approximately US $52.50 for a ten-hour day. (Maquiladora Solidarity 2003) The wage difference between Nicaragua and Canada is 17.5 : 1. Thus, assuming that the physical productivities are very similar, similar physical labour productivity does not necessarily lead to an identical wage.

Example 4: Automotive Workers The weekly pay for automotive workers at Volkswagen in Mexico in year 2002 was between US$230 and $250, “making the Volkswagen workers among the very highest paid autoworkers in all Mexico.” (UAW 2002) The annual wage for automotive manufacturing in USA in 2003 was between $40,175 (in the state of North Carolina) and $74,686 (state of Michigan) (U.S. Bureau of Economic Analysis 2003). That is a weekly wage in the range of US $773 to $1,436. The ratio of U.S. to Mexican automotive wages is thus between 3.4 : 1 and 5.7: 1, while the physical labour productivity is most likely identical. Thus, identical physical labour productivity does not necessarily lead to an identical wage.

36

Gernot Kohler

Example 5: Dentists Dentists perform procedures many of which are very similar in different countries. A tourist reported that she had dental work done in Hungary in 1998 for which she paid Hungarian Forints equivalent to Canadian $300 (three hundred). The fee for the same dental work in Canada might have been Canadian $ 3,000 (three thousand), as a rough estimate. Thus, identical physical labour productivity does not necessarily lead to an identical remuneration.

Example 6: Educators Table 12 shows monthly wages for teachers and other educators in 22 countries at purchasing power parity values (PPP values) for year 1995. The income gap for educators in this group of countries is 32 : 1. While wages differ enormously, the skills required for, and outcomes obtained from, teaching in elementary and high schools is more similar than different around the world.

Table 12: Wages 1995, Education Sector All employees, Men and Women, per month, averages’

Korea, Republic of United Kingdom Netherlands Finland Canada Isle of Man Netherlands Antilles Austria Israel Slovenia Czech Republic Poland Mexico Peru Hungary

Monthly wage, "International dollars" (PPP values) 2959 2880 2832 2279 2130 1813 1497 1484 1303 1170 705 496 482 448 434

Romania

363

Country

International Wage Differences and Labour Productivity Country China Slovakia Estonia Lithuania Latvia Kyrgyzstan

a

Monthly wage, "International dollars" (PPP values) 255 255 245 218 182 92

Sources: ILO (1999) for wage rates in local currency. My conversion to PPP values, using World Bank data

Summary of Examples These examples demonstrate quite clearly that there may exist no correspondence whatsoever between physical labour productivity and wage level in an international comparison. Therefore, it cannot be claimed that the workers around the world are remunerated in proportion to their physical productivity.

WAGES AND VALUE PRODUCTIVITY As stated above, there are two significantly different concepts of labour productivity, namely, physical productivity and value productivity (money-valued productivity). Value productivity (money-valued productivity) contains a price dimension, which is missing in physical productivity. In general terms, value productivity (money-valued labour productivity ) is defined as: value productivity = (physical output) * (product prices) / labour input or, value productivity = value added/ labour input The fact that product prices are included in the definition of money-valued productivity is very important; namely, for a given labour input, value productivity (money-valued labour productivity) is controlled by two variables, namely, physical output and market value (price) of the output, rather than by only one factor (physical output).

38

Gernot Kohler

Example: a garment worker produces 100 shirts in a given period of time. (a) The physical productivity of that worker for that time period is 100 shirts/ 1 worker. (b) The money-valued productivity depends on the market value of the shirts. If the shirts sell for 5 dollars each and materials cost 2 dollars for each shirt, then the value-added for each shirt is 5 - 2 = 3 dollars and the money-valued productivity of that worker is (100 shirts * 3 dollars)/ 1 worker = 300 dollars per worker. If, on the other hand, the shirts sell for 10 dollars each (and material costs

are the same as before, namely 2 dollars for each shirt), then the value-added for each shirt is10 - 2 = 8 dollars and the money-valued productivity of that worker is (100 shirts * 8 dollars) / 1 worker = 800 dollars per worker. The example shows the importance of the selling price for the calculation of the worker’s value productivity (money-valued productivity). In this case, we see identical physical productivity - namely, 100 shirts per worker per time interval, but two different money-valued productivities - namely, 300 dollars per worker or 800 dollars per worker. The difference between the two is controlled by the selling price of the product and not by the physical labour productivity of the worker. The world prices of agricultural products like wheat, rice, coffee, and so on

are subject to considerable changes over the years, related to changing market conditions. Whereas the product prices are changing, the physical labour input to produce those products does not change in the same way. Thus, with a given technology of production, value productivity (money-valued labour productivity) in wheat, coffee, etc. production changes in proportion to changing market prices, rather than in proportion to physical labour productivity.

THE RELATIONSHIP BETWEEN WAGES AND

THE TWo TYPES OF PRODUCTIVITY Wage is measured as dollars (or rupees etc.) of remuneration paid per hour (or day etc.) of labour. Money-valued productivity is measured as dollars (or rupees etc.) of value-added per labour hour (or day etc.). Wages of labour and money-valued productivity of labour are thus measured in the same dimension namely, as monetary unit per unit of labour. In practice a relationship between value added and wages develops. The relationship is not precise, but exists. When the value-added is high, employers can afford to pay high wages and workers tend to demand high wages. When the value-added is low, employers can afford to pay only low wages and workers tend to have lower wage expectations. For example, the value-added per hour of

International Wage Differences and Labour Productivity

39

labour is high in Germany and low in Haiti. German employers can pay higher wages than employers in Haiti; and workers in Germany expect higher wages than workers in Haiti. As a consequence of that, we find a correlation between money-valued productivity and wages. Money-valued productivity and wages are both functions of value-added. Value-added is a function of the market value (price) of the output. Namely: Value-added = market value of output less production costs Wage rate = function of (value-added) Money-valued labour productivity = function of (value-added)

The important point is that (a) money-valued productivity is not the same as physical productivity; (b) if productivity is defined and measured as moneyvalued productivity, then the claim that wage corresponds to money-valued productivity tends to be true in international comparisons, since both wage and value productivity are a function of value-added; (c) if, alternatively, productivity is defined and measured as physical productivity, then it cannot be claimed that wages correspond to productivities in international comparisons,

INTERNATIONAL WAGE DIFFERENCES VERSUS

INTERNATIONAL PRODUCTIVITY DIFFERENCES International wage differentials may be greater than international productivity differentials, as has been observed by several world-system scholars. For example: “The wage differential between core and peripheral workers is a dynamic and reproduced feature of the system . . .. The core/periphery wage differential is greater than that which would be due to differences in productivity alone, . . .” (Chase-Dunn 1998: 77-78) and: “Workers at the periphery are super-exploited . . . because the differential of wages (and incomes from non-wage labour in general) is much higher than the differential of productivities.” (Amin 1990, chapter 6)

40

Gernot Kohler

One mechanism that leads to such results has been identified, as follows: “According to . . . Prebisch, Singer, and Myrdal . . .productivity increases that take place in developed nations are passed on to their workers in the form of higher wages and income, while most or all of the productivity increases that take place in developing nations are reflected in lower prices” (Salvatore 1987: 273) Depending on which definition of productivity we use, this observation makes much or little sense. When we use the concept of “value productivity”, the statement is not overly plausible. However, when we use the concept of “physical labour productivity”, then the statement is plausible and appears valid - namely, then we can re-write the statement, as follows: “increases in physical labour productivity that take place in developed nations are passed on to their workers in the form of higher wages and income, while most or all of the increases in physical labour productivity that take place in developing nations are reflected in lower prices.“

IMPACT OF WORLD PRICES ON VALUE PRODUCTIVITY The case of Saudi Arabia illustrates the influence of world market conditions on national value productivity (also sometimes called “measured productivity”, as opposed to physical productivity). The development of Saudi Arabian (aggregate) labour productivity is shown in Figure 3.

International Wage Differences and Labour Productivity

4]

FIGURE 3. Saudi Arabia, Aggregate Labour Productivity, 1960 - 1989

z GDP/Werker

EEE EPIL EC PPS Year Notes: (1) Source: Penn World Tables; (2) GDP/worker is measured in constant 1985 "International Prices”

What explains the reversal of Saudi Arabia's (aggregate) labour productivity after 1982? According to a common view, productivity growth is propelled by technological progress and/or improvement of "human capital" and other inputs. In the Saudi case, these explanations appear inadequate for explaining the decline in aggregate productivity after 1982; another explanation appears more plausible - please examine Figure 4.

42

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FIGURE 4. Saudi Arabia -- GDP, Aggregate Labour Productivity and Labour Force, 1960 - 1989

GDP per worker ~e

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~ Number of workers

NOTES: (1) source: Penn World Tables; (2) GDP and GDP per worker measured in constant 1985 "International Dollars" (PPP values)

Figure 4 shows that (1) the labour force (number of workers) increased steadily; (2) labour productivity (GDP per worker) first increased and then declined after 1982; and (3) GDP increased and, then, declined after 1982. There is a correlation between aggregate labour productivity (GDP per worker) and output (GDP). The correlation between GDP and aggregate productivity is not surprising, but causality requires some examination. It is commonly assumed that variation in productivity (independent variable) causes variation in output (dependent variable) [or, productivity > output]. This assumption is not fully plausible in this case. If Saudi enterprises and Saudi workers achieved a given level of technological sophistication up to 1982, it is not plausible to assume that such sophistication suddenly reversed itself after 1982. Another causal explanation is

International Wage Differences and Labour Productivity

AS

attractive -- namely, that the causation goes the other way, from GDP to productivity (or, value of output > value of productivity); in other words: after 1982, GDP declined and, therefore, the aggregate measured labour productivity declined. This view makes sense when we take world petroleum prices into consideration. Petroleum is the major export of Saudi Arabia; and petroleum revenue is a major component of Saudi GDP. When world oil prices and world oil consumption increased, Saudi GDP increased handsomely. When the world petroleum price declined in the 1980's, Saudi GDP slumped. Saudi Arabian aggregate measured labour productivity declined concomitantly with GDP. The example of Saudi Arabia shows that the rise and fall of world prices and world effective demand may induce a rise or fall of national GDP, which may, in turn, induce a rise or fall in (national) aggregate (money-valued) labour productivity. National aggregate labour productivity, as commonly measured, may thus be a result of world-system conditions, rather than being of purely national origin.

THE IMPACT OF WORLD PRICES ON WAGES Williamson’s historical studies are important in this context. They show how worldwide price shocks have, in the past, affected wages. Williamson compiled extensive historical data on real wages and relative factor prices for the 19" and 20" century for countries of various world regions and found that real wages and relative factor prices are determined by (1) external price shocks, (2) factor endowment changes, and (3) technological change. Of the three, external price shocks were the most important determinants in his assessment, especially, “commodity price convergence” around the world, which was induced by declining transport costs. Williamson explains: “Two profound shocks occurred in this environment [sc. of the 19"" century] still hostile to liberal globalization policy: early industrialization in Britain which then spread to a few countries on the European continent; and resource "discovery" in the New World, set in motion by sharply declining transport costs linking overseas suppliers to European markets, so much so that real freight rates fell by an enormous 1.5 percent per annum between 1840 and 1910... .. These two shocks triggered a divergence in real wages and living standards across the Atlantic economy that lasted until the middle of the century . . .” (Williamson

1998:1)

44

Gernot Kohler

In addition to historical international price shocks of the described kind, contemporary global competition, which is very intensive and exacerbated by global free trade policies, reinforces international wage differences (Shaikh 1999: 2). More than that, an examination of the wage trends in Figure 1 (above) indicates that international wage differences have not only been “reinforced,” but have increased over the past few decades.

THE WAGE-PRODUCTIVITY ARGUMENT AS IDEOLOGY The wage-productivity argument lends itself to be abused as a justification for the payment of low wages, irrespective of whether the workers’ productivity is really lower or not. International wage differences share this problem with gender wage differences. Thus, the claim that a woman should earn less than a man with the explanation that “women are less productive” is useful for employers as an ideology (a myth) that he/she may use to justify his/her unequal payment for a woman, even though her work output may be equal or superior to that of a man. Similarly, the claim that a worker in a poor country should earn less than a worker in a rich country with the explanation that “workers in poor countries are less productive” is useful for employers as an ideology (a myth) that they may use to justify their low payment for workers in poor countries, even though their work output may be equal or superior to that of a worker in a rich country.

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Chapter 6

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INTERNATIONAL WAGE DIFFERENCES AND EXPLOITATION In a discussion of exploitation and international wage differences we have a choice of two major definitions of “exploitation” - namely, a generic definition or a specific Marxist definition. (1) Using a generic definition of exploitation: The dictionary defines “exploitation” as (a) “utilization;” (b) “the successful application of industry on any object, as in the cultivation of land, the working of mines, etc.;” “(c)’“‘selfish or unfair utilization” (New Webster 1980). Accordingly, the term exploitation has certain neutral meanings and a negative meaning (i.e., selfish or unfair utilization), as when someone is accused of being an exploiter. It is a fact that global corporations (TNCs, MNCs) use labour in low-wage countries. That constitutes “utilization” and “successful application of industry”, in the sense of the dictionary, i.e., exploitation in the neutral sense. Is this also a case of “selfish or unfair utilization”? The use of low-wage labour is certainly selfish on the part of global corporations, but is it also unfair? Let’s examine an example (the example is from a labour education film,

2003): Women work in a textile factory in Haiti, making sweatshirts. The wage is 0.50 US dollars per hour (50 cents). One woman sows over 1,000 sweatshirts per day. The factory is owned and rn by a local Haitian entrepreneur. The sweatshirts are shipped to the Disney chain in USA (and have a Mickey Mouse picture in front). The US retail price for one shirt is US$18.00. Let’s assume that the various kinds of labour employed on the US side for transportation, retail sales, and so on, are paid between USD 4.00 per hour and USD15.00 per hour.

46

Gernot Kohler

Does the wage of 0.50 dollars per hour for the Haitian worker constitute “unfair utilization” and, thus, exploitation, in the sense of the dictionary? The answer is twofold, namely: (1) in relation to other Haitian workers: The Haitian

minimum wage as per legislation of April 2003 is “70 gourdes per day (about $1.70 today) or about 20 U.S. cents per hour. Various categories of workers in Haiti earn less than minimum wage, for example, street cleaners. Moreover, 70% of Haitians are unemployed (Bracken 2003). Therefore, the Haitian wage of 50 cents per hour for the textile worker in the example is fair in relation to other Haitian wages. (2) In relation to US wages: The Haiti wage is less than the U.S. wages in the example. The work of the Haiti worker is productive and comparable to the work of the U.S. workers. Therefore, the Haiti wage is unfair in relation to the U.S. wages of the example. The example shows that global corporations which use labour of low-wage countries engage in “unfair utilization” and, thereby, exploitation, in the sense of the dictionary. This kind of exploitation has been called “transnational corporate exploitation”. (Chase-Dunn 1998: 236). (2) Using a Marxist definition of exploitation: In his 1848 speech on free trade Marx used the term “cosmopolitan exploitation” with reference to the world economy, but the concept is not in general use. Samir Amin has been criticizing the “super-exploitation” of workers of the Third World (e.g., Amin 1990). In addition to those concepts, I find the concept of “differential exploitation” useful, which has been employed, for example, by Lebowitz (2002: 8). Differential exploitation can be defined as a situation where two different rates of exploitation exist while outputs are identical. If “s” is surplus value and “vy” is wage (“variable capital”) and “v1” is wage rate 1 and “v2” is wage rate 2, then there may be a situation with two different rates of exploitation, namely, s/v1 and s/v2. For example, a woman may produce the same surplus value as a man, but if the two receive two different wages, then there is a case of differential exploitation based on gender difference. The world system is full of situations of differential exploitation both within countries and between

countries.

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women

receive

lower wages than men. Part of that is due to differential exploitation, since the women may produce the same or more surplus value than the men. Differential exploitation may also take place within countries on the basis of ethnicity and other demographic factors. Differential exploitation also takes place between countries. The most common situation is that of a global corporation or globally vertically integrated chain of production in which different wage rates are paid to workers with identical skills in different countries, even though they all contribute to the same surplus value (“bottom line”) generated by the global corporation. In

International Wage Differences and Exploitation

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Chapter 7

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WAGE DISCRIMINATION I am distinguishing between four types of discrimination, namely, (a) wage discrimination (the narrower concept), (b) policy discrimination, and (c) structural discrimination, (d) ideological discrimination. Wage discrimination is discrimination in the process of paying wages. Policy discrimination is discrimination resulting from public policies. Structural discrimination is discrimination resulting from the existence of social structures, broadly conceived. Ideological discrimination is discrimination embedded in our way of thinking. While wage discrimination affects wages directly, the other three kinds of discrimination may affect wages indirectly, but “indirect” does not necessarily mean “insignificant”. Wage discrimination based on gender is a familiar issue. The prohibition of wage discrimination based on gender has been written into law in various countries. We can use that legal language and adapt it to the problem of international wage discrimination. Here is an example of legal language prohibiting wage discrimination based on gender. The example is taken from the law of the state of Kentucky in the United States, valid in year 2003, namely:

“PROHIBITION OF THE PAYMENT OF WAGES BASED ON SEX: The employer is prohibited from discriminating between employees of opposite sexes in the same establishment by paying different wage rates for comparable work on jobs that have comparable requirements. This prohibition covers any employee in any occupation in Kentucky. Any employer violating this Act shall not reduce the wages of any employee in order to comply with the Act.

Gernot Kohler

50

No employer can discharge or discriminate against any employee for the reason that the employee sought to invoke or assist in the enforcement of this Act.” (Source: Kentucky 2003) This legal text applies to the situation within a firm (“in the same establishment”) and not to relations between firms and not to the macro-economy. The criterion for wage discrimination in this text is: “paying different wage rates for comparable work on jobs which have comparable requirements”. Can this principle be applied to international wage differences? Can we claim the existence of wage discrimination based on geographical location? Here is a precedent in which someone claims the existence of wage discrimination based on geographic location. The example is from a flyer of a political group in the state of Arkansas in the United States:

“Wage Discrimination! Pay Us What You Pay Up North! The Raw Deal Every day millions of people are discriminated against because of the region they live in. Southern workers routinely make 20-30% less than workers doing the same job in other parts of the country. Yet, no one in Washington seems to care about this kind of discrimination. The price of food, clothing, and other basic necessities is just as high in Arkansas as it is in New York or California. Help us fight for wage parity. After all, They owe it to you!” (Source: Southern Arkansas League 2002)

When we adapt the text of the Kentucky law (above) to international wage differences, then we might re-write it, as follows: PROHIBITION location:

OF THE PAYMENT

OF WAGES

BASED

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The employer is prohibited from discriminating between employees at different geographic locations in the same establishment by paying different wage rates for comparable work on jobs, which have comparable requirements. This prohibition covers any employee in any occupation in the world. Any employer violating this Act shall not reduce the wages of any employee in order to comply with the Act. No employer can discharge or

Wage Discrimination

51

discriminate against any employee for the reason that the employee sought to invoke or assist in the enforcement of this Act.

This proposed legal definition of wage discrimination prohibits wage discrimination based on geographic location of the employee. Thus, no matter where the employee is located, be it Canada, Congo, or China, the firm (i.e., “employer,” “establishment”) would have to pay the same wage rate. The proposed text requires additional refinement of the legal language since it contains certain legal loopholes that need to be fixed. The wage discrimination

discussed above applies to “establishments” - i.e., firms (notably, multinational corporations) or structures of transnationally integrated production or service. Given that definition, we can claim, in view of public knowledge about sweatshops and so on, that wage discrimination based on geographic location exists within transnational corporations and transnational vertical structures of

production and service. There remains another problem, as follows. When we consider the wage level of low-wage country L with the wage level of high-wage country H, can it be claimed that there exists wage discrimination between the two countries? These are wage levels that are not within “the same establishment” but, rather, two different national aggregates. It would be too far fetched to apply the existing legal concept of “wage discrimination” to these national aggregates. However, there may exist other kinds of discrimination between the two countries that may have a bearing on the international wage differences between the two countries. Those other kinds of discrimination between collectivities are at a structural and

policy level.

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POLICY DISCRIMINATION It is so much the nature of foreign, international, and global policies to be discriminating that it appears almost futile to apply the concept of discrimination in this area. Various policies, processes, and structures affect global wage differentials, including North-South policies, uneven development, international trade policy, and so on, but in the literature they are usually not discussed under the heading of “discrimination.” These forms of structural and_ policy discrimination between countries may have an influence on international wage differences, but do not constitute direct wage discrimination, as defined in the

previous section, but may have an *indirect* effect on wage differences between countries. To say “indirect” is not saying “insignificant”. On the contrary, the indirect effects on wages stemming from various kinds of structural and policy discrimination may be very powerful. Some aspects of policy discrimination that appear to be of particular relevance for the discussion of international wage differences include the following.

RESTRICTION OF INTERNATIONAL LABOUR MIGRATION The control of international labour migration (a) has a significant impact on international wage differences, (b) tends to be discriminatory. That kind of discrimination is condemned by some and welcomed by others. Siebert links international wage differences to the fact that labour tends to be “internationally spatially immobile” (Siebert 1997: 71-72). In other words, workers of any country cannot freely migrate to wherever they see their best chances. If they could,

Gernot Kohler

54

international wage differences would tend to diminish. Similarly, Chase-Dunn writes: “The core/periphery wage . . . this differential is maintained by restrictions on international labour migration from the periphery to the core” (Chase-Dunn, 1998: 77-78) In the history of European migration to North America, Europeans were free to migrate across the Atlantic. As a result, as Bukharin observed in 1917, “in the framework of world economy the process of equalizing the various wage scales is taking place with the aid of migration.” (Bukharin 2003: 39).

ECONOMIC SANCTIONS Economic discriminatory against Cuba, UN economic impoverish an

sanctions and embargoes against selected countries are clearly policies. Recent examples include the economic blockade by USA economic sanctions by the Commonwealth against Zimbabwe, and sanctions against Libya and Iraq. To the extent that these sanctions economy, they also affect wage levels in the targeted economy.

MILITARY AND COVERT COERCIVE POLICIES Military and covert intervention in a foreign country contributes to the destabilization or destruction of that country’s economy. Stating the obvious, such interventions are highly discriminatory, as only some countries receive that treatment, and affect the wages, if not the lives, of the workers in the affected

country.

POLICIES OF POSITIVE DISCRIMINATION Policies of positive discrimination are particularly noticeable in connection with hegemonial leader countries. During the Cold War 1945 - 1990, both USA and USSR bestowed economic favours on countries that were important to them. South Korea, Taiwan, Western Europe and Israel were of geopolitical importance to the United States during that era and prospered economically with the help of the United States. The Soviet Union helped Cuba with massive aid. Such policies have also an impact on wages in the affected countries.

Policy Discrimination

55

INTERNATIONAL TRADE POLICY In the field of international trade policy discrimination has been discussed for a long time under the heading of free trade versus protectionism. Advocates of free trade support the principle of non-discrimination. The principle of nondiscrimination is also central in the policies of the World Trade Organization (WTO), as follows: “The principle of non-discrimination built into the WTO agreements avoids that complexity. The fact that there is a single set of rules applying to all members greatly simplifies the entire trade regime.” (WTO 2002) Critics of free trade include classical economists like Friedrich List of the jgth century who argued for infant industry protection - i.e., if a country is developing a new industry, this infant industry may require protection from foreign competitors in order to be able to mature. Modern critics of free trade include Anwar Shaikh who argues that free trade reinforces international wage differences. Shaikh writes: “persistent international wage and technological differences still characterize the world today . . .. The point is to explain how free trade is grounded in them, and in turn reproduces them.” (Shaikh 1999: 2, my emphasis)

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