Oldrich Kyn*

 

Education, Sex and Income Inequality in Soviet-type Socialism

 

 in:   Income Distribution and Economic Inequality Edited by Zvi Griliches, Wilhelm Krelle, Hans-Juergen Krupp, and Oldrich Kyn (CampusVerlag, Halsted Press, John Wiley & Sons.)

 

There is no doubt that the social, political and economic transformation of East European countries after the Communist takeover has led to considerable changes in their income distributions. It is also quite apparent that these changes significantly diminished1, but did not fully eliminate, the economic inequality among various population groups.

The purpose of this paper is to study empirically two aspects of income distribution in Poland and Czechoslovakia, namely the inequality resulting from different levels of education and from differentials in incomes of men and women. Other aspects of income distribution will be mentioned briefly to allow for an overall comparison of the reality of income distribution with the normative statements of Marxian economic theory.

 

The Normative View of Marxian Theory on Income Distribution under Socialism

The original Marxian view on distributive justice is very far from crude egalitarianism. Marx and Engels never argued for absolutely equal incomes of all people, although they did believe that the capitalist distribution of income is too unequal and unjust and must therefore be replaced by a new socialist or communist form of distribution. The best known reference for Marx's own view on income distribution under socialism and communism can be found in his ‘Critique of the Gotha Program’. There he stated that under socialism, which he defined as a lower stage of communist society, people should be rewarded according to their work or contribution to society, while in the second stage or ‘full communism' people should be rewarded according to their needs. Neither of these principles requires a full equality of incomes.

The Marxian principle ‘equal amount of product for equal amount of labor' must necessarily produce quite considerable income differentials2 especially if it is interpreted in the context of the labor theory of value. The ‘contribution to society' must be measured in some way, if it is to he a basis for the distribution of income, and for Marxists it would be natural to measure it -- at least in the case of the so-called productive workers -- by the value created by labor. But according to the labor theory of value the amount of value created depends on the skill of the worker and on the complexity of his labor. More complex labor is supposed to create more value in the same interval of time than simple labor3 The socialist principle of distribution therefore implies that a person with higher skills should receive a higher wage than a less skilled worker. As long as higher skills are obtained by individual effort, schooling or experience such income differentials may be ‘deserved'. But income inequality under socialism may also result from variation in inborn physical and mental abilities. 'One man is superior to another physically or mentally and so supplies more labor in the same time or can labor for longer time.'4 Socialist equality is therefore only the equality of the right to income and not the equality of income. "It recognizes no class differences, because everyone is only a worker like everyone else; but it tacitly recognizes unequal individual endowments and thus productive capacity as natural privileges"5.

The principle 'from everybody according to his abilities to everybody according to his needs' which was designed by Marx for the second stage of communism does not imply full income equality either. As long as people remain physically and intellectually different, they will continue to have different needs so that unequal incomes would be retained even under 'full communism'. 

In the section called 'Private Property and Communism' of his 'Economic and philosophical Manuscripts' Marx distinguished two forms of communism: 
The first one, which he called a 'crude communism' seems to be a simplistic and spontaneous reaction of the formerly oppressed and under-privileged against the rich and powerful. Marx argued that 'crude communism' called for 'equality of wages only because it was an expression of "envy and the desire to reduce everything to a common level" 6.Obviously Marx did not like this kind of crude communism because "it aims to destroy everything which is incapable of being possessed by everyone," because "it wishes to eliminate talent etc. by force" and because it "negates the personality of man in every sphere"7 - He even compared the crudely egalitarian view on income distribution with a similar crudely communist attitude toward women: 'This tendency is expressed in an animal form, marriage is contrasted with the community of women -- just as women are to pass from marriage to universal prostitution, so the whole world of wealth is to pass from the relation of exclusive marriage with the private owner to the relation of universal prostitution with the community".8

In contrast to crude egalitarian communism, Marx developed his vision of true communism which is to be more than a simple negation of private property; it is to be 'a positive abolition' which 'assimilates all the wealth of previous development’. True communism should, of course, bring distributive justice, but Marx's vision goes far beyond that, it is to be a society where man becomes a true human being, free not only from all forms of external, (i.e. economic, political, cultural, etc.) oppression and manipulation, but also free from internal self-oppression and self-manipulation. "Communism is the abolition of human self-alienation, and the real appropriation of human nature through and for man".9 As Erich Fromm stresses: "For Marx the aim of socialism was the emancipation of man and the emancipation of man was the same as his self-realization … The aim of socialism was the development of the individual personality." Or in Marx's words: 'The suppression of private property is therefore the complete emancipation of all the human qualities and senses."10

These extensive quotations are intended to demonstrate that although Marx was very critical of the injustices and inequalities of the capitalist income distribution, his view of socialist and communist income distribution did not imply an egalitarian leveling off all incomes. Marx never specified exactly which income inequalities should be eliminated and which should remain, but it may not be difficult to draw some inferences from his views. Generally, Marx would probably argue that all types of income inequality based on artificial, man-made stratification of society into classes, racial or ethnic groups as well as inequalities resulting from the usurpation and exercise of political and social power and from the specific forms of the operation of the capitalist market economy, should be eliminated. On the other hand, the income differentials which are based on natural differences in physical and mental abilities, in acquired skills and knowledge, and possibly also differentials resulting from personal preferences (e.g. between work and leisure) would remain.

It seems clear that Marx would not opt for income equality if it were to limit personal freedom and the full development of individual potential or if it sacrificed talents to barrack type uniformity. Also, ascetic self-deprivation would not be acceptable as a tool for eliminating inequality, because it would almost surely have to be achieved by ideological mass manipulation, rather than by a truly voluntary manifestation of personal preferences.

There are three basic reasons why Marxist justify income inequality:
1.Personal differences in the quantity of work measured either by its duration or by energy expenditures that each individual contributes to society. These differences may result from different physical endowments of individuals i.e. from biological or genetic factors, as well as from differences in attitudes toward work and preferences between work and leisure, i.e. primarily from cultural or 'social environment’ factors.

2Personal differences in the quality or complexity of work. These may result from different mental endowments of individuals, which may be due both to biological or genetic factors as well as differences in skills and knowledge acquired by experience or education.

3.Differences in the costs of reproducing labor power of a particular kind. According to the Marxian theory, labor which creates value is divided into two parts: necessary and surplus labor. Necessary labor is used to cover the reproduction costs of labor power and as such should be the main determinant of wages. This is relevant especially for income differentials of workers with different levels of education. It is more costly to reproduce more educated labor power therefore wages and salaries of people with more years of schooling should be higher. However, the fact, that a considerable part of the cost of education in socialist countries is paid by the government rather than by individuals, may weaken this line of reasoning. It may seem surprising, but probably fair to conclude, that the Marxian normative view on income distribution under socialism, although based on totally different theoretical and ideological postulates, leads to conclusions very similar to those reached in human capital theory.

Let us now turn to those sources of income inequality, which according to Marxian theory should not exist in socialism.
1.Probably most objectionable to Marxists is income inequality based on unequal distribution of wealth. First, Marxists regard the income from owning property as a truly undeserved, exploitative return. Second, for functional reasons Marxists believe that under socialism private property should not exist. Third, they object to private property as a source of income because it tends to maintain or increase income inequality. Wealthier people have access to better schools and to jobs which bring them higher incomes, and people with higher incomes accumulate wealth faster than those with lower incomes.

2It seems that Marxists would object to income inequality which results from the power structure of society. The communist party apparatchik, government official or central planner may deserve higher incomes than average workers if their jobs require more experience and higher level of education, but they should not earn more simply because they belong to the upper layers of the power hierarchy.

3.Marxists should also find objectionable income inequality based purely on sex, race or ethnicity. Such income differentials are discriminatory, and have nothing to do with a person's contribution to society.

4.Finally Marxists would probably object to income differentials resulting from persistent disequilibrium between supply and demand in the labor market. According to the original Marxist view all parts of a socialist economy should be rationally planned ex ante so that supply and demand for individual categories of labor should always be in equilibrium.

 

Empirical Evidence from Czechoslovakia and Poland

It would be beyond the scope of this paper to attempt an exhaustive study of all aspects of income distribution. In any case, such a task could not be accomplished because the original source data on personal income in Poland and Czechoslovakia are not available to Western scholars. The only source of information available to us are data already processed by simple statistical routines, i.e. averages, frequency tables or cross-tabulations. For these reasons we shall concentrate our attention only on a few selected aspects of economic inequality, while other aspects may be mentioned only casually, if at all.

Although we have almost no data on the distribution of personal wealth in Poland and Czechoslovakia, we may quite safely conclude that this source of income inequality has been almost totally eliminated. This does not mean that wealth is distributed equally - we know that considerable inequalities in this area still persist - it simply means that most of the privately owned property is 'unproductive’ and as such cannot generate income. There are a few exceptions. such as private farming in Poland for instance, or interest paid on personal savings, but private farmers are not very rich, and the interest rate on personal savings is only nominal.

Poland and Czechoslovakia also seem to have very few problems with income inequality based on race and ethnicity, and this is of course mostly due to the fact that both countries are in this respect relatively homogeneous. There are some indications that Czechoslovakia's policy of assimilating the gypsy population has not been very effective, but unfortunately no data on the current situation of the gypsies are available.

Somewhat more serious is the discrepancy which still exists between incomes of Czechs and Slovaks. This is an outcome of the historically lower level of economic development in Slovakia, rather then of a deliberate discrimination of Slovaks. Actually the preferential treatment of Slovakia, especially in the allocation of investment, has led to an apparent closing of the gap between the two nationalities.

For example, a comparison of average monthly wages in the two Republics11 shows that in 1955 Czech wages were 6.1 % higher than Slovak wages, in 1960 -3.4%, in 1970 - 1.9 %, and by 1974 only 1.2% higher.

However, the comparison of average monthly wages may lead to an underestimation of income inequality between Czechs and Slovaks. The micro-census data (family budgets) indicate, that the per capita income in Slovakia is still almost 20 per cent lower than in Bohemia and Moravia.

Table 1  Distribution of Czechoslovak Households 
According to Net Money Income per Capita (%)

 

1970

1973

Income

Bracket

Czech 

Republic

Slovak

Republic

Czech

Republic

 Slovak

Republic

-7201 

12.5

23.3

6.6

15.2

7201-9600

15.2

19.8

12.6

17.9

9601-12000

18.8

20.4

 16.9

18.9

12000-14400

17.8 

14.8

17.9

17.7

14401-16800

12.9

 9.0

14.6

12.2

16801-19700

9.0

 5.3

10.5

7.6

19201-

 13.8

 7.4

20.9

10.5

 

100.0

100.0

100.0

100.0

Mean annual
 per capita income 
in Kcs

 12,936

10,701

14,463

12,102

Source: Statistical Yearbooks of CSSR 1972 pp. 475-476 and 1975 pp. 479-480

There have been two remarkable tendencies in the intersectoral income distribution in postwar Czechoslovakia:

1. Income differentials among broad sectors of the economy have been diminished and the ranking of sectors has changed considerably (see Table 2).

Table 2 Sectoral Monthly Average Wages in Czechoslovakia

 

Average wages in Kcs

Ranking of Sectors

 

1948

1970

1948

1970

Education and Culture

 1022

1832

 1

 4

Health and Welfare

990

 1776

 2

7

Transportation

896

 2239

3

1

Trade

840

1654

 4

8

Construction

 829

2195

5

2

Industry

 759

1967

 6

 3

Agriculture

657

 1806

7

5

Communication

656

1786

8

6

 

Source: J. Adam, Wage, Price and Taxation Policy in Czechoslovakia 1948-1970, Berlin, 1974.

2. Income differentials among branches of industry has not diminished and their ranking remained almost unchanged (see Table 3).

 

Table 3 Average Wages of Blue Collar Workers
 in Selected Branches of Czechoslovak Industry

 

Average wages in Kcs

Ranking of Sectors

 

1950

1970

1950

1970

Electric power

 1038

2109

3

3

Fuels

1093

2668

2

1

Ferrous metallurgy

 1126

2251

1

2

Machine building

940

1936

4

4

chemicals

894

1893

5

5

Wood working

836

1747

7

7

Glass porcelain and ceramics

755

1661

8

8

Textile

609

 1485

 9

 9

Food

 880

1816

 6

6

Source: Statistical Yearbooks of CSSR, 1966, p.210. and 1972, p.250.

3, Another interesting feature of income distribution in Czechoslovakia is the drastic change which has occurred among the relative incomes of three basic categories of workers in industry (see Table 4, page 280).

Table 4 Average Wages of Main Categories 
of Workers in Czechoslovak Industry

 

1948

 1960

1970

(1) Blue collar workers

734

 1406

 1902

(2) Engineering-technical personnel

 1214

 1868

2569

(3) Administrative staff

914

1225

1626

Ratio 2:1 in %

165

133

 135

Ratio 3:l in %

 125

87

86

Source J. Adam op.cit, p.83.

There is very little direct statistical information about the relation between the level of education and incomes in Czechoslovakia. Data in Tables 2 and 4 seem to indicate that income differentials between more and less educated people have diminished, and in some cases the relation has been reversed. For example the average monthly earnings in Czechoslovakia in 1965 of a skilled coal-miner was 3521 Kcs, that of a lathe operator 2422, a doctor 2243, a locksmith 2010, a grammar school teacher 1907, a bricklayer 1865, and a hospital nurse 1178.12

Similarly, the cumulative income at age 60 in Czechoslovakia in the early 1960's (in thousands Kcs) was 3125 for an assembler, 989 for an engineer, 949 for a farmer, 900 for a technician, 888 for a lawyer, 771 for an economist, and 692 for an unskilled worker13

Probably the most striking feature of income distribution in Czechoslovakia is the persistent discrepancy between the wages and salaries of men and women. Notwithstanding the facts that (1) Marxist ideology clearly condemns discrimination of women, (2) Czechoslovak law gives them the legal right to the same wages as men, (3) the excess demand for labor and governmental policies of assistance to working women (e.g. pregnancy leaves, subsidized day care centers etc.) has led to a very high level of women's participation in the labor force (47 % in 1974), and (4) equal educational opportunities are available to men and women, women nevertheless receive on average only two-thirds of the average income of men.

Table 5 Average Monthly income of 
Men and Women in Czechoslovakia

May of the Respective Year in Kcs

socialist sector

industry

1959

1968

1970

1959

1968

1) men

1596

 2106

2338

1689

2140

2) women

1046

 11400

1565

 1054

1350

 Ratio 2.1 in %

65.5

66.5

66.9

62.4

63.3

 Source: J. Adam, op.cit., p. 87.

In the breakdown of wages by industries (Table 6) we see that the overall discrepancy between the incomes of men and women can be explained partly by the fact that the women's participation ratio is higher in sectors with lower wages, and partly by the fact that in each sector the wages of women are lower than the wages of men.

Table 6 Monthly Average Wages 
in Sectors of Czechoslovak Economy

 May 1963

in Kcs

Average Wages

 Women’s
Wage
 as % of

Men’s
 Wage

Women’s
Participation
Ratio

  Men   

 Women

Construction   

1787

1161

65.0

12.0

Railway'

1778

1218

 68.5

18.2

Bus transportation

1757

1198

68.2

14.4

Industry

1903

1192

62.6

36.4

Administration

1867

 1218

65.2

45.7

Education and Culture

1759

 1236 

70.3

65.4

Forestry

1584

 1061 

67.0

33.0

Communication

1589

 1179

 74.2

47.2

State farms

1449

 1123

77.3

40.9

Health and welfare

1824

1123

61.6

74.2

 Retail trade

1565

 1150

73.5

 71.5

Municipal service

1463

  1042 

71.2

47.6

Public catering

1437

 1076

 74.9

67.8

Source: 3. Adam, op.cit.. p.89

Several facts are noticeable: First, there is a clear negative correlation between the women's participation ratio and the level of wages among sectors. Second. the sectoral differences in women's wages are much smaller than the differences in men's wages. The standard deviation of the women's wage is only 63.8 Kcs while the standard deviation of men's wage is 166.5 Kcs. Third, the sectors with the highest women's participation level (health. education and trade) are those which have lost most in terms of relative wages (see Table 2), whereas the sectors with the lowest women's participation are those which have gained. Fourth, the above also holds for differentials among branches of industry. It can be shown that the branches with the highest women's participation level are those where the wages are lowest (e.g. textile, food), while the women's participation level in the branches with highest wages (e.g. metallurgy and coal mining) is very low. These facts imply that in spite of a considerable increase in women's participation in the labor force, which was also accompanied by an increased level of education and training, the gap between wages and salaries of males and females has not diminished. Can this be explained rationally from the principles of socialist income distribution? Most likely not.

It may be argued. that the increased school enrollments and the increased employment of women are relatively recent phenomena. so that their average level of education and work experience is still lower than the levels of education and experience of men. This may be true, but are the differences in education and experience large enough to explain the entire gap between incomes of men and women, so that pure sex discrimination can be ruled out? To answer this questions we need data which would allow us to estimate simultaneously the role of education, experience and sex in the determination of personal incomes. Unfortunately, this kind of data cannot be obtained for Czechoslovakia. However, Poland statistics14 contain the following two types of data, which can be used for our purpose:

  1. The number of fully employed persons in the socialist sector of Polish economy in a 4-way breakdown according to 22 administrative regions (wojewodztw), 13 economic sectors, 5 levels of education, and sex.

  2. Four categories of wage funds and the total number of employees in a 2-way breakdown according to 22 administrative regions (wojewodztw) and 13 economic sectors.

Both sets of data are available for the years 1970 and 1971. The second set of data allows us to calculate average wages for each sector of each region for both years, by simply dividing the wage fund by the number of employees in the respective cells of the cross-tabulation. Two types of average wages will be used in the following regressions as left-hand variables.

  • W1 - average wages of type 1 include only payments from the so called 'Personal wage fund'

  • W2 - average wages of type 2 include (in addition to W1) all other payments to individuals from the so-called ‘nonpersonal wage funds', and other funds of enterprises. This would include bonuses, honoraria, per diem reimbursement of travel cost, etc.

The first data set was used to calculate the shares of individual education levels, as well as the women's participation ratio in each sector of each region for both years.

The following variables were thus obtained

UN ... share of employees with university education
SP ... share of employees with secondary professional education
SG ... share of employees with secondary general education
EP ... share of employees with elementary education and professional (vocational) training
WOM.. women's participation ratio

Our main task now will be to check whether, and how much of the sectoral and regional variation of average wages can be explained by the above defined five explanatory variables. We shall therefore attempt to estimate the following regression equation

W = b + b1UN + b2SP + b3SG + b4EP + b5W0M + e

In the actual run several alternatively defined dummy variables and a special corrective variable which was designed to capture the impact of differences in the definition of total employment in the two sets of data were also included. The coefficients of this corrective variable as well as those of the dummy variables and the constants were in almost all cases significant, but they are not very interesting and therefore will not be reported.

 

 

Table 7

Left-hand variable: W1

Mean

Wage

Region 

UN  

SP 

SC

EP

WOM 

R2

 

1.

 

Warszawa

(city)

314

(4.62)

   437 

(2.32)

225 

 (2.90) 

296 

(2.08) 

-304 

(-6.13)

.866 

 

12.88 

 

35,111

 

2.

 

 Krakow

(city)

169

(2.77)

  427 

 (2.94)

-.27

 (-.00)

213

(1.46)

-232 

(-6.47)

.818 

 

8.97 

 

32,138

 

3. 

 

Lodz 

(city)

162  

(2.08)

71

 (.61)

 61

 (1.17)

 49 

 (.27)

-150 

(-5.91)

.827 

 

9.58 

 

31,088

 

 4.

 

 Poznan

(city)

186 

(2.98)

163

 (.77)

  88 

 (1.12)

126 

 (1.20)

-180  

(-6.19)

.832 

 

9.87

 

32,336

 

5.

 

Wroclaw

(city) 

  153

(2.60) 

 302

 (1.86) 

78 

(.86) 

118 

(.89) 

-218 

(-5.92)

.832 

 

9.93 

 

32,307

 

6.

 

Bialostok

 

74 

(1.50) 

146  

(3.08) 

80

(2.24) 

204

(2.60) 

 -132

(-6.35)

 .842 

 

13.71

 

27,072

 

7. 

 

Bydgosc

 

307

(6.83) 

 131 

(2.47) 

155 

(3.37)  

266

(3.43)

 -159

(-6.76)

.880 

 

18.86 

 

28,150

 

8.

 

Gdansk

 

242 

(7.00) 

114 

(1.28)

124 

 (2.57)

303 

 (3.50) 

-192 

(-7.54)

.933 

 

35.81 

 

30,267 

 

9. 

 

Katowice

 

314

(2.47)

 190 

 (1.35) 

169 

(1.63)

419 

 (2.80) 

-245

(-6.05)

.833 

 

12.79 

 

31,347

 

10.

 

Kielec 

 

231 (5.13) 

 57 

(.97) 

57 

(.99) 

130 

(.91) 

-113

(-3.97)

 .829 

 

12.49

 

27,528

 

 11. 

 

Koszalinsk

 

 219 

(6.09)

128

 (3.39) 

146 

(4.01)

345

 (4.30) 

 -151 

(-7.45)

.906 

 

24.89

 

27,804

 

12.  

 

Krakow

 

-28

(-.30) 

 170 

(2.00)  

12

(.19)

 217 

(1.76) 

-136 

(-3.99)

.759

 

8.09 

 

27,965

 

 13. 

 

Lubelsk

 

237 

(4.04) 

37 

(.59)

41 

 (.87) 

117

(1.07) 

 -132 

(-4.83)

.850 

 

14.84

 

27,570

 

14. 

 

Lodz 

(region) 

200 

(3.24) 

47

(.62) 

 52 

(.70)

183 

 (.94) 

-62

(-1.60)

 .659 

 

4.96 

 

27,079

 

15.

 

 Olsztyn 

 

221 

(5.41)

163  

 (3.41)

184

 (4.46) 

376 

(4.03) 

-151

(-6.54)

 .806 

 

10.70 

 

27,374

 

16. 

 

Opolak 

 

192 

(4.72)

184 

(5.76)

187 

(7.63) 

310

(8.35)

 -193 

(-17.50)

.970 

 

84.00 

 

28,940

 

17. 

 

Poznan 

(region) 

456 

(4.43) 

52 

(4.04)

266 

 (4.04) 

648 

(4.71) 

-158

(-5.20)

 .759

 

 8.11

 

27,321

 

18. 

 

Rzesow 

 

286

(5.81)

 234

 (3.49) 

 123 

(3.04) 

298 

(3.63) 

-200

(-7.16)

 .910

 

25.91

 

28,097

 

19. 

 

Szcecin 

 

230

(3.19)

 267 

 (2.42)

191

 (3.36)

 538

(4.91)  

-l88 

(-5.94)

.791

 

9.73 

 

29,670

 

20. 

 

Warszawa

(region)

 357 

(5.40)

152 

 (2.55)

117 

 (2.15) 

443

(3.43) 

 -139 

(-4.69)

.848 

 

14.37 

 

27,886

 

21. 

 

Wroclan 

(region) 

-169 

(.77)

206 

 (2.33) 

169 

(2.40)

352 

 (2.79)

-147 

(-4.14)

.646

 

 4.68

 

27,609

 

22.

 

 Zielena 

Gora

83 

 (.73) 

14 

(-.22) 

23

(.37)

 22 

 (.17)

-82

 (3.08)

 .680 

 

5.48 

 

28,186

 

The first regressions were run for each region separately across sectors. To increase the number of observations, data for the two years were pooled and a dummy variable for 1971 was included in the regressions. The total number of observations was 22 for each of the five cities, and 26 for each of the remaining regions so that the residual number of degrees of freedom is relatively small: 35 for cities and 19 for the other regions. Although the regressions were run for both types of wage variables, only those for W1 are reported, because W2 gave the same kind of results.

  1. A relatively high share (.7 - .9) of sectoral differences of average wages in each region are explained by the explanatory variables of the model.

  2. Consistently, the most significant explanatory variable is the women's participation ratio. The negative coefficients of WOM indicate that the average wage is 100 - 200 zlotys lower for each percent of women employed in the sector. From this type of regression however, we cannot conclude whether the lower average wage is due to the fact that women have lower wages than men, or to the fact that wages, including the wages of men, are generally lower in sectors with higher women participation. Probably both are true.

  3. The majority of coefficients of educational variables are positive, but they vary quite a lot and are frequently insignificant. The only safe conclusion from these regressions seems to be that the average wages in sectors are positively related to the percentage of employees with a higher than elementary education. It cannot be conclusively established, however, that a secondary education will yield higher wages than an elementary education with vocational training, or that a university education always results in a higher income than a secondary education.

 

 

Table 8

Left hand variable: W2

Mean

Wage

Sector 

UN 

SP 

SC  

HP

 WOM

R2

F  

1. 

 

Industry 

 

5564 

(10.73)

-1291

 (-4.21)

 -2407 

 (-3.80)

-590 

 (-6.30)

-514

 (-7.50)

 .843 

 

27.61 

 

34,009

 

2. 

 

Construc-

tion 

 

1014

(3.57) 

 -457 

(-2.24)

336 

 (.61) 

391

(4.41) 

 236 

(1.57)

.852 

 

29.52 

 

37,672

 

3. 

 

Agriculture 

 

372 

(2.31)

-l

 (-.02) 

 35

(.07)

 59

(.05)

 59  

 (1.64)

 .776

 

12.88

 

30,707

 

4.

 

 Forestry 

 

850

(1.48)

 -653 

(-2.49)

 -1237 

(-2.01)

725 

(2.82)

88 

 (.43)

.546

 

 4.47 

 

23,073

 

5.

 

 Transport, Communi-

cation

1358 

(1.60)

   l14 

(.26)

-317  

 (-.88)

76

(.51)

40

  (.35)

 .539 

 

6.02 

 

32,187

 

6.

 

 Trade 

 

650 

(4.77)

  -7 

 (-.08)

74 

(.48)

-66

(-1.11)

 11   

 (.20)

.769

 

17.13

 

31,276

 

7. 

 

Housing,

Communal Services

2048 

(8.89)

  -150

(-.96)

 -806 

  (2.49)

-229

 (-2.65)

 2 

 (.15)

.792 

 

19.58 

 

29,799

 

 

Science

 

 204 

(2.78)

 385 

(3.32)

42 

 (.15)

5  

 (.03)

-105

 (-1.18)

 .611

 

8.07 

 

41,913

 

9. 

 

Education 

 

176 

(1.73)

 -89 

(-1.08)

286

 (1.48)

 22 

(.14)

 -108

(-1.43)

.932 

 

 70.57

 

30,292

 

10. 

 

Culture, 

Arts

674 

 (4.06)

   -175  

(-.61)

170

 (.47)

180

(.29)

 -199 

(-.70)

.697 

 

11.82 

 

41,239

 

11.

 

 Health ,

Sport

444 

 (8.40)

-89 

(-2.98)

 -237   

(-2.25)

-168 

(-2.28) 

74 

(1.89)

.922 

 

60.91 

 

27,455

 

12.

 

Finance,

Insurance

524

 (5.35)

-33

(-.57)

      -8  

(-.09)

22

(.13)

146 

  (2.25)

.805 

 

21.19

 

38,605

 

13.

 

 Admini-

stration 

 

371 

(5.19)

-78 

(-.37)

  -83 

  (-.80) 

-208

(-.71) 

  -139 

(-1.54)

.874

 

35.65 

 

33,457

 

 

The second set of regressions (see Table 8) was run for each sector separately and across regions. Again, data for both years were pooled, and a dummy variable for 1971 was included in the equation. In each of the 33 sectoral regressions there were 44 observations so that the residual number of degrees of freedom was 36. This time, results for the left-hand variable W2 are reported; the regressions with W1 on the left-hand side gave very similar results. Although the regression equation in this case contains the same types of variables as the first case, the meaning of the regression coefficients is not identical. For example the coefficient of WOM will no longer contain the effect of the fact that in sectors with higher women's participation, wages of men may be lower. It may seem at first glance, that the regressions should give better results because they include more homogenous units. This however, is not necessarily true, because the results may be biased by the fact that in regions with a higher general level of wages (i.e. in cities) is concentrated a higher proportion of managerial, clerical and research staff.

The results in Table 8 do actually present a somewhat different picture than the results in Table 7. With the sole exception of industry, the coefficients of WOM are small, insignificant and sometimes have positive signs. The estimated coefficients of the educational variables also seem to be unreliable - they frequently take on strange values and signs and are very rarely significant. Only UN (the university education variable) has consistently positive, very high and rather significant coefficients.

 

Table 9 reports results of regressions which were run on pooled regional an sectoral data. Because pooling guaranteed a sufficient number of observations (276), it was possible to run the regression for each year separately. Two sets of dummy variables were introduced to control for the effects of regional an sectoral differences in the wage levels, therefore the coefficients reported in the table should in the clearest possible way represent the contributions of different levels of education, and pure sexual discrimination to the wage level. It is interesting to see, that university education seems to have quite a sizable and clearly significant positive impact on wages. Secondary professional education also has positive, but significantly lower effect than university education. Elementary professional education apparently contributes not much less than secondary professional education to wages, while secondary general education has no recognizable effect on the level of wages. Finally, the large negative and significant coefficients of WOM indicate that women who have the same level of education as men are paid less.

 

Table 9

Year

WT

UN

 SP

 SG

 EP

WOM

R2

 F

1970

W1

 232

(7.48)

 123

(4.92)

-33

  (-.68)

 79

(1.94)

 -126

(-5.28)

.898

53.2

1971

 W1

283

(8.73)

 143

 (5.62)

-45

(-.88)

 103

(2.46)

  -66

(-3.59)

.897

52.6

 1970

 W2

350

(9.32)

149

 (4.92)

 91

 (1.54)

  34

(.69)

  -194

 (-6.70)

 .91

61.5

1971

 W2

450

(10.06)

 156

(4.46)

47

 (.68)

115

(2.00)

-85

(-3.36)

 .905

57.2

 

A summary review of the contribution of individual factors to the explained variation in average wages is given in analysis of covariance tables (Tables 10  and Table 11) which were constructed from the regression Table 9. (year 1970 only). The sets of variables were added successively in hierarchical manner, which may overstate the explanatory power of those which entered earlier and understate the explanatory power of those which enter later. Nevertheless even the last entering factor (regions) appears to be highly significant.

It is interesting that education (when entered first) explains 40 - 45 percent variation in both W1 and W2 and that sex alone (after controlling for five educational categories) explains an additional 16 percent of variation in W1, but only 7 percent of variation in W2. After controlling for education and sex, and correcting for the discrepancy in data, sectoral differences in average wage account for about 15 percent of variation in W1 and 25 percent of variation in W2. Finally 7 to 8 percent of variation in both W1 and W2 is attributable to remaining regional differences in average wages.

 

Table 10 Analysis of Covariance Table for W1 and 1970

Source of variation

df

Sum of Squares

Mean Square

F statistic

Education

4

2.2030 E9

550.75 E6

232.94

Sex

1

.8591 E9

859.10 E6

363.35

Education and sex

5

3.0621 E9

612.42 E6

249.02

Corrective variable

1

.6609 E9

660.09 E6

279.52

Sectors

12

81098 E9

67.58E6

28.58

Regions

21

.373068 E9

17.76E6

7.51

Sub total

39

4.90705 E9

125.82 E6

53.22

Residual

236

.55799 E9

2.36 E6

 

Total

275

5.465 E9

19.87 E6

 

 

Table 11 Analysis of Covariance Table for W2 and 1970

Source of variation

df

Sum of Squares

Mean Square

F statistic

Education

4

4.6789 E9

1169.6 E6

337.74

Sex

1

.7142 E9

714.2 E6

206.24

Education and sex

5

5.3926 E9

1078.5 E6

311.43

Corrective variable

1

.6551 E9

655.1 E6

189.17

Sectors

12

2.4619 E9

205.2 E6

59.25

Regions

21

.6152 E9

29.3 E6

8.46

Sub total

39

 9.1248 E9

233.9 E6

67.562

Residual

236

.81737 E9

3.463 E6

 

Total

275

9.942 E9

36.15 E6

 

 

Conclusions

This study attempted to identify some of the primary factors which determine personal income distribution under Soviet-type socialism, and to compare the reality of the distribution with the normative statements of Marxian economic theory. The empirical evidence was based on scattered (and not always consistent) data for Czechoslovakia and Poland.

In conformity with Marxian theory, we find that income inequality has diminished and that wealth has ceased to be an important source of income differentials (this conclusion is based on evidence presented elsewhere). We also find that income inequality based on ethnic and regional differences has been diminishing since World War II, although some differences in personal incomes among Czechs and Slovaks, and among regions (wojewodztwa) in Poland, still persist.

It was shown in the first part of this paper that income differentials based on education (human capital) are considered by Marxists to be healthy and necessary for socialism. The evidence from Czechoslovakia seems to indicate that the role of education as a source of income differentials has diminished, and in some cases was reversed. The evidence from Poland, however, shows that education - primarily university education - is an important source of income differentials.

The empirical data demonstrate that considerable sectoral differences exist both in Czechoslovakia and Poland. This can hardly be justified in light of the Marxian normative theory of income distribution. However, the most striking conclusion is the fact that pure sex discrimination still remains as a major source of income inequality under Soviet-type socialism. This phenomenon is in clear contradiction with the normative Marxian view on income distribution.

 

Footnotes and references

 

* I am indebted to Ludmila Kyn and Ruth Polak - Getter for research assistance and editorial help on the final version of the paper. (back)

1

The Gini coefficients calculated recently by F. Paukert (ILO 1973), S. Jain (World Bank 1976), J. Slama (this volume) and Peter Wiles (Distribution of Income: East and West, North Holland Publishing House 1974) show that socialist countries of Eastern Europe have considerably smaller differences in personal incomes than other countries.   (back)

2

 "The first phase of communism, cannot yet provide justice and equality: differences, and unjust differences in wealth will still persist . . . the mere conversion of the means of production into the common property of the whole of society. . . does not remove the defects of distribution and the inequality . . . which continues to prevail so long as products are divided ‘according to the amount of labor performed’ ". (Lenin: State and Revolution, chapter V. paragraph 3, quoted from Lenin: On Politics and Revolution, New York 1968, pp. 222, 223).   (back)

3

"Skilled labor counts only as simple labor intensified, or rather, as multiplied simple labor, a given quantity of skilled labor being considered equal to a greater quantity of simple labor." (K. Marx: Capital vol. I, chapter 1 quoted from R. C. Tucker: Marx-Engels Reader, New York,1972, p. 206).  (back)

4

K. Marx: Critique of the Gotha Program, part I quoted from R. C. Tucker, op. cit. p. 387.  (back)

5

K. Marx: Critique of the Gotha Program, part I quoted from R. C. Tucker, op. cit. p. 387.   (back)

6

K. Marx: Economic and Philosophical Manuscripts quoted from E. Fromm: Marx’s Concept of Man, New York 1974, p. 125.  (back)

7

K. Marx: Economic and Philosophical Manuscripts quoted from E. Fromm op.cit. pp. 124,125.   (back)

8

 K. Marx: Economic and Philosophical Manuscripts quoted from E. Fromm op.cit. p. 125.    (back)

9

K. Marx: Economic and Philosophical Manuscripts quoted from E. Fromm op.cit. p. 127.    (back)

10

 K. Marx: Economic and Philosophical Manuscripts quoted from E. Fromm op.cit. p. 132.   (back)

11

Average monthly wages in the socialist sector of the Czechoslovak economy (in Kcs)

 

1955

1960

1970

1974

Czech Republic

1201

1375

1946

2243

Slovak Republic

1132

1330

1910

2203

Source: Statistical Yearbook of CSSR 1966, p.115 and 1975, p. 141.    (back)

12

Earnings in other selected professions in Czechoslovakia 1965

Profession

Average
monthly
earnings
in Kcs

Leading manager in engineering industry

4 692

Chief doctor of a regional hospital

3 381

Scientific worker (graduate)

3 022

Locomotive driver

2 363

Foreman in heavy engineering

2 149

Lawyer (graduate)

1 937

Labourer (5th wage class)

1 757

Dairywoman

1 632

Elementary school teacher

1 288

Shop assistant

1 011

Charwoman

940

Nursery nurse

 802

 Source: Jaroslav Krejci, Social change and Stratification in Postwar Czechoslovakia, London 1972, p. 72.    (back)

13

 J. Adam, op.cit. p. 86    (back)

14

Zatrudnienie w gospodarce narodowej 1970, Statystyka Polski Nr 95;
Zatrudnienie w gospodarce narodowej 1971, Statystyka Polski Nr 123;
Zatrudnienie i place wedlug wojewodztw 1971, Statystyka Polski Nr 125.  
(back)