Were there no returns to education in the USSR? Estimates from Soviet-period household data

Labour Economics 6 Ž1999. 417–434 www.elsevier.nlrlocatereconbase

Were there no returns to education in the USSR? Estimates from Soviet-period household data Katarina Katz

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Department of Economics, Stockholm UniÕersity, 106 91 Stockholm, Sweden Received 17 February 1996; accepted 7 December 1998

Abstract The relation between wages and schooling in the USSR is studied, by estimating log–linear wage-equations for a sample of a Russian city in 1989. Estimates, made separately for men and women, show that there were rewards to education, contrary to claims by many Soviet and Western scholars. The low wages of some professionals, relative to skilled workers, were partly caused by gender differentials, partly by excess demand for manual labour. In addition, private costs of schooling were low and there were important non-monetary incentives connected with higher education. q 1999 Elsevier Science B.V. All rights reserved. JEL classification: J31; P24; T20 Keywords: Returns to education; Wage differentials; Soviet Union; Gender discrimination

1. The standard view of Soviet wage compression When the causes of economic inefficiency were discussed in the former USSR, it was much the received wisdom that wage differentials during the Soviet period had been very small, too small for wages to function efficiently as incentives. In particular, education premia were considered insufficient ŽGordon, 1987; Rimashevskaia and Onikov, 1991.. Although opinions in western Soviet Studies were )

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less unanimous, as prominent a Western scholar as Granick Ž1987. even stated that returns to education in the USSR were negative while Newell and Reilly Ž1996. emphasize the smallness of returns to education. Small returns to schooling in the USSR are often explained as a result of an ideologically inspired policy of ‘levelling’ Ž uraÕniloÕka. or of favourable treatment of manual workers 1 Že.g., Redor, 1992, p. 165; Rimashevskaia, 1992, p. 18f.. Yet, according to official Soviet wage-doctrine wages should correspond to productivity or—in Soviet terminology—‘to the final results of labour’. There are striking similarities in how the productivity of labour and its determinants have been conceptualised in Soviet wage theory and Western ‘human capital’ theory ŽMcAuley, 1979; Redor, 1992.. The Soviet system played an important part in shaping the history of this century. The legacy of the USSR still leaves its imprint on the Russian labour market. To understand this system and the reasons for its failure will remain for quite some time an important task for social scientists, inside and outside the former Soviet Union. The argument of this article is, first, that the USSR was not egalitarian. Second, if differentials were kept within certain limits, it was not done against the pressures of demand and supply. Third, the discussion of this issue has suffered from the unavailability of representative and reliable micro-economic data. It is an irony of history that the Soviet economist and statistician Strumilin anticipated the human capital theory of Becker Ž1964. and Mincer Ž1974. by 40 years. Though lacking the technical means of performing regressions on a large data set, Strumilin, in effect, estimated a model of productivity, as a function of age, experience and schooling in 1921 ŽStrumilin, 1966, first published 1925.. Like many intellectual and cultural achievements of the early 1920s, such studies were discontinued under and after Stalin. Any further multivariate analysis of Soviet wages had to wait half a century. Even then, they were made in the West and, by necessity, based on samples of emigrants ŽGregory and Kohlhase, 1988; Ofer and Vinokur, 1992.. These provided a rich source of information, but posed a problem of selectivity. 2 This study is based on data from a sample survey made in a Russian city, in 1989. The data permitted the estimation of wage-functions and, hence, an empirical study of wage differentiation according to education. The estimated coeffi1

Nevertheless, several Western studies of earnings- and income-differentiation in post-war USSR did not find them strikingly small, compared to Western Europe ŽBergson, 1984; Atkinson and Micklewright, 1992; Ofer and Vinokur, 1992; Redor, 1992.. 2 The sampling for the Israeli and US emigre ´ ´ studies was carefully done and checked for bias as far as was possible ŽMillar, 1987; Gregory and Dietz, 1991.. Yet, in my opinion, the problems of selectivity were reduced, but not eliminated.

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419

cients reflect, partly, the official principles for evaluation of work, partly, the preferences or prejudices of managers and, partly, relative scarcities of labour power. Because the Soviet labour force was in most cases able to choose where to work, the estimates also indicate the incentives relevant for choices of jobs and schooling. The estimates will not be of returns to education in the strict sense of internal rates of return, based on models of life-time earnings, but wage-rewards measured by coefficients, in a log–linear wage-function, as in comparable studies ŽOfer and Vinokur, 1992; Newell and Reilly, 1996.. Equations are estimated and analysed separately for men and women. Sections 2–4 will sketch the relevant Soviet institutional context. In Sections 5–8, the sample and variables are described and estimates of wage differentiation according to education are reported. The concluding part discusses returns to education and educational choice for men and women in the USSR.

2. Soviet wages To summarise very briefly, the Soviet wage-setting system: Manual workers had their jobs classified according to centrally set scales with, usually, six skill grades. Within each scale, the grade determined the basic wage. For white-collar workers, there was a similar, but more complex, system of grading and a range within which firms could set the wage. There were different scales for different sectors. In high-priority sectors, like heavy industry and mining, rates for similar jobs were higher than in low-priority sectors, such as light industry and services. Take-home-pay also included premia and bonuses related to the individual’s norm-fulfilment, to tenure and to how well the enterprise met its plan targets. There were also additions, for piecework, for hazardous or physically heavy work or for working in certain regions Žfor relatively recent and comprehensive accounts, see Oxenstierna, 1990; Rofe et al., 1991; Katz, 1994.. As noted above, the official Soviet principles for wage setting resembled the neo-classical theory of productivity. These principles, however, applied to the centrally set wage scales, and bonus-regulations. Since enterprises had certain means—some legal, some not —of adjusting earnings to demand and supply, actual relative wages in the USSR were not necessarily those intended by central planners. 3 Even so, in a Soviet-type economy, wages were not market wages. Firms were not profit-maximisers. Under central planning, enterprises were not free to set prices or wages, to choose their mix of either outputs or inputs, including labour. In this context, no direct relation between marginal costs and prices, or between 3

See Nove Ž1977. and Katz Ž1994. ŽChap. 3. and references therein.

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marginal productivity and wages can be assumed. 4 No textbook on the Soviet economy omits to mention the pervasive hoarding of labour in enterprises ŽNove, 1977..

3. The Soviet system of education After 8 years of compulsory schooling, Soviet pupils could go on to general secondary education, to secondary education combined with vocational school ŽPTU 5 which trained mainly for skilled blue-collar occupations. or to ‘specialised’ secondary school, which qualified students for semi-professional work Žmedical nurses, nursery teachers, technicians.. It was also possible to have a 1-year PTU-training after the eighth form, without complete secondary school. The main route into higher education 6 was from general secondary school. All forms of schooling beyond the first 8 years, were available through evening or correspondence courses. Tuition was free of charge and most full-time university students received non-refundable grants at subsistence level. The level of education of the population increased dramatically from the 1920s onwards, and the difference between men and women narrowed. By the 1970s, between two thirds and three quarters of 15-year olds went on to general secondary school and about nine tenths of the others to PTU ŽSwafford, 1979; Rutkevich, 1984.. According to the 1989 census, about three out of four employed, both of men and of women, had attained full secondary education and one in seven had higher education. The education of women and men were of about equal length, but in different fields, and a degree in a female dominated field led to lower-paying jobs than one in a male-dominated. Girls were barred from many PTU-programs for industry, mining and construction and found it difficult to be accepted into others ŽSwafford, 1979; McAuley, 1981.. In 1989, 20% of employed men had PTU-schooling and 11% of women.

4. Education and shortages of labour power The Soviet economy was characterised by pervasive excess demand, for consumer goods as well as for inputs to production, including labour. The available evidence indicates that there was less excess demand for highly educated 4 Strumilin recognised this. Having detailed information on the occupations of the workers in his Soviet sample, he used pre-Soviet wage scales for these jobs as a measure of productivity, rather than the non-market wages of 1921. 5 Professional’no-tekhnicheskoe uchilishche. 6 ‘Higher education’ in Soviet terminology always meant at university level.

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labour than for less educated. Generally speaking, in the later parts of the Soviet period, it was not difficult to find formally qualified staff for professional and semi-professional occupations except in the far north and remote rural areas Žsee Katz, 1994 and references therein.. When only a small part of each cohort could go to general secondary school, the bulk of those who did continued to university and highly qualified occupations. When the large majority of teen-agers acquired full secondary schooling, they could not all become professionals, but the aspiration to a large extent remained ŽMarnie, 1986.. Many more young people wanted higher education, than could get it, but more were educated than could find jobs that made use of their schooling ŽGloeckner, 1986; Malle, 1986; Granick, 1987.. The Soviet authorities encouraged students to train for skilled blue-collar occupations, partly, because there had traditionally been a shortage of skilled manual labour and, partly, because it was hard to find suitable jobs for those who had only general education. Although there is still some evidence of excess demand for skilled workers from the 1970s and 1980s, there are also a number of reports of workers having to take jobs beneath their level of qualification because there were not enough job openings corresponding to it ŽPravda, 1982; Oxenstierna, 1990; Komozin, 1991.. I have not, however, found any author who questions the severe shortage of labour for unskilled manual jobs. Malle Ž1987., for example, writes that in industry vacancies ‘occur predominantly in the least attractive jobs’ Žp. 374.. Thus, the bargaining position of young workers who demanded compensation for dirty, heavy, dangerous, boring unskilled jobs, was rather strong and this must have exerted an upward pressure on relative wages of unskilled workers.

5. The data This study uses survey data from a random sample of 1200 households in Taganrog, a city of 300 000 inhabitants, located in the South of Russia and dominated by heavy industry. 7 The sampling was made from a stratified register of dwellings. ŽStudent and worker hostels were excluded.. Non-response was 1%. In each household, one person was chosen as ‘the respondent’, in a nonrandomised manner. To check whether this selectivity biased estimates, a wagefunction including variables that were available for all working household members was estimated for them and for working ‘respondents’. A Chow-test indicated

7

For comparisons of the sample with Russian and Soviet population in terms of labour market, social and demographic characteristics, see Katz Ž1994. ŽChap. 4..

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that the function did not significantly differ between the larger sample and the ‘respondents’. 8 A total of 935 respondents had earned a wage from the state sector during the preceding month, and 4% of partial non-response left 371 male and 526 female observations available for estimates. It is, of course, problematic to generalise from a local sample. There are some differences between the labour force of Taganrog and that of the USSR, in terms of sector of employment and of education. Nevertheless, if we limit the reference population to the urban European, the results obtained from this sample are of considerable interest, considering the limited amount of material that has been available for econometric study. Note also that this was a centralised economy, and that we want to draw conclusions about wage-functions, not about wage-levels. Centralised set wage scales and bonus-regulations implied a higher degree of uniformity than in a market economy. If the wage-function was similar, regional disparities in terms of the characteristics controlled for in the model, do not invalidate the estimates.

6. Variables and models Wage-equations were estimated for men and women separately. The dependent variable is the logarithm of wage per hour. 9 For a full list of independent variables and variable means, as well as the years of schooling corresponding to the different forms of education, see Section A.1. In Table 1 estimates of a wage-function is reported, allowing us to assess the wage-premia for education, net of the effect of other factors which may be correlated with education, such as age and experience or physical conditions of work. Education is measured by dummy variables for highest achieved level, rather than as years of schooling, since the return per year of education varies considerably between forms of schooling. The reference group has 8 years of school. 10

8

This equation for monthly wages, estimated for men and women separately included age, eight education dummies, marital and pensioner status. Equality of the parameters for respondents and other household members was not rejected at the 5% level. Estimates and test-statistics are reported by Katz Ž1994. and available from the author. 9 The wage-measure is discussed in Section A.1 below. Estimates of models for monthly wages are available from the author. 10 Eight years of school Žincomplete secondary. was compulsory when all but the oldest respondents went to school. This makes it a suitable reference. Using a more numerous category, such as specialised secondary education, as reference decreased standard deviations of education parameters, but only marginally.

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Table 1 Men

Interc. Age Agesq Stazh Stazhsq Tenure Tensq Pens Mts Unskilled Physqual Nonman-lowed Nonman-seced Nonman-highed Highqual Highed1 Highed2 IncHigh Specsec Gensec PTU1 PTU2 Incsec Lowed Heavyind Transp Constr Light Serv Trade Teach Health Art Science Govt Other Heat Heavy Nervous Othercond N Adj. R 2 †

Women

Coefficient

Standard deviation

Coefficient

Standard deviation

0.072 y0.015 Ey4 0.017 y2Ey4 0.015U U y4Ey4 y0.298UU 0.064 – 0.038 y0.511 0.064 y0.026 0.189 0.353UU 0.004 0.102 0.128† 0.128† 0.181U 0.213U – 0.090 – y0.024 y0.071 y0.032 y0.229UU y0.323U y0.023 0.081 0.039 y0.084 y0.224 y0.083 0.121† 0.013 y0.143UU 0.081† 370 0.21

0.422 0.025 3Ey4 0.013 3Ey4 0.006 2Ey4 0.086 0.052

y0.332 y0.011 Ey4 0.007 y6Ey5 0.016U U y4Ey4 y0.333UU 0.048 – 0.074 y0.045 y0.011 0.281UU 0.536UU 0.141 0.086 0.229U 0.152U 0.115† 0.189U 0.487UU – 0.217† – y0.051 y0.078 y0.024 0.016 y0.318UU y0.069 y0.167 0.161UU y0.206U y0.071 0.238UU 0.143 0.173UU y0.114UU 0.114U 526 0.24

0.340 0.020 2Ey4 0.011 2Ey4 0.007 2Ey4 0.093 0.036

0.067 0.364 0.099 0.101 0.133 0.094 0.110 0.112 0.073 0.076 0.092 0.092 0.174 0.064 0.070 0.085 0.082 0.148 0.126 0.152 0.085 0.084 0.235 0.077 0.066 0.053 0.041 0.042

Significant at 10%, U significant at 5%, UU significant at 1%.

0.051 0.359 0.059 0.099 0.122 0.101 0.114 0.111 0.067 0.066 0.094 0.173 0.124 0.083 0.079 0.066 0.068 0.064 0.070 0.130 0.055 0.081 0.123 0.083 0.103 0.065 0.041 0.051

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The data allow us to distinguish between biological age, general work experience Ž stazh. and tenure Žfirm-specific human capital.. 11 These variables are included, and as in standard human capital models, their squares ŽWillis, 1986.. Older cohorts, on average, have less education, but more experience and tenure, which increase their wages. Not controlling for these factors would, thus, lead to a downward bias in the estimated education coefficients. Soviet wage regulations provided for compensation for poor or dangerous physical conditions of work ŽRofe et al., 1991.. The incidence of such conditions is negatively correlated with education. It is therefore important to control for compensating differentials, again in order to avoid downward bias in education parameters. This is attempted by the inclusion of some self-reported work-conditions in the model. These are far from ideal controls, but the best that the data allow. Estimates in Table 1 indicate that the variables included do have an effect on wages. The model includes, as is usual, marital status. It also controls for being a working pensioner. This is not standard, but justified by the Soviet context in which pensioners tended to take or be given low-paying jobs. Another ‘Soviet institutional’ trait is the group of sector variables, included to control for the different degree of priority, and different wage-scales, accorded to different spheres of activity. The omitted category is heavy industry. For men, this sector dominates the sample, and there are relatively few observations for each of the others. The model also includes job-types, defined by the Soviet scholars who designed the survey. The two highest, managerial work and very qualified creative, intellectual work, were very small, and have therefore been fused into one, ‘highqual’, in this study. All other non-manual work was in one category, ranging from unqualified typists to physicians or civil engineers. This very broad group was divided into three, ‘nonman-highed’, ‘nonman-seced’ and ‘nonman-lowed’ depending on whether the person had higher education, secondary education or less. The educational requirements for the jobs would have been a better criterion than the actual education of respondents, but this information was not available. Skilled blue-collar work made up ‘physqual’. Reference group was unskilled manual workers. The correlations between job-type variables and education categories reduce the precision of estimates. Also, if certain jobs are open only to those with a certain education, part of the wage-premium for the job-type can be considered as an

11 We use ‘stazh’, or work-record as defined by Soviet legislation as a close proxy for work-experience. ‘Tenure’ is ‘stazh in the enterprise’. Age, general work experience and firm-specific experience, though strongly correlated are not reducible to one another ŽWillis, 1986.. In the Soviet context, it is interesting to distinguish tenure, since advantages given to ‘stayers’ were used by enterprises to reduce labour turnover.

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education premium. This should be kept in mind in assessment of incentives for education, based on wage-equation estimates.

7. Education premia for men Estimates are reported in Table 1. For men, the coefficient for full-time higher education of 0.35 is significant and corresponds to a wage-premium of 42%. 12 The interaction effect for nonmanual work 13 and higher education is very small negative, with very low precision. Does this means that workers generally earned as much as professionals and that a university graduate might as well forget his education and take a blue-collar job? A more likely explanation is that there were a limited number of well-paid, very qualified jobs in production, which were labelled blue-collar but often held by university graduates. This agrees with the observation of Malle Ž1987. that some of the most attractive, very highly skilled, blue-collar jobs in industry were taken by engineers. Part-time university training has no effect on wages. It is quite likely that correspondence or evening courses had low pay-off. The education was considered inferior to full-time studies and the wage premium should be smaller, though not necessarily nil. In the comparison between respondents’ wages and those of all household members, only two parameters in the male equation differed at 10%level. These were those for higher education from evening or correspondence courses and for incomplete higher education, both of which were smaller for respondents. The number of observations, in both cases, is small. The parameter for ‘highqual’—managerial and highly qualified intellectual jobs —of 0.19 is not significant. Again, there are few observations and, perhaps, collinearity. ŽThis category is correlated with full-time higher education and with the Art and Science sectors.. In wage-models with job-types, but without education variables, the parameter for the most qualified jobs is significant at 0.1%-level and has a parameter of about 0.4. Thus, it would be precipitate to conclude that there was no wage-differential. If, despite the low precision, we add the parameters for university education and for a job in this category, the sum is 0.54, corresponding to a wage differential of over 70%. The parameter for white-collar worker with secondary education, of 6%, is not significant. Neither are those for general and specialised secondary school of about 14%, but they have probability-values below 10%. When the interactions with job-types are not included in the model, the coefficient for specialised secondary 12

expŽ0.35.y1s 0.42. In Sections 7 and 8, ‘non-manual work’ excludes the highest qualification category, unless otherwise indicated. 13

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education is significant. 14 Note the low pay for the most unqualified white-collar jobs. Both coefficients for PTU-training, of about 20%, are significant while the coefficient for skilled blue-collar work is not. Nearly all men with PTU training are skilled workers Ž86%., but skilled workers may have many different kinds of schooling.

8. Education premia for women For women, all but two education parameters have a probability-value below 10%, when wages are compared with those for women with incomplete secondary school. The only exceptions are for higher education, and this is due to the very high correlation between ‘higher education’, on the one hand, and the category of ‘non-manual staff with higher education’, on the other. Among female non-manual workers nearly 90% of those with higher education have full-time university schooling. When the interactions with job-types are not included in the model, the effect of full-time university education of 42% and that of part-time university education of 28% are both significant. If we do add the parameter for non-manual work combined with higher education and the insignificant coefficient for full-time higher education, the sum is a 52% effect. We do not find an effect of nonmanual work combined with secondary education on wages. The effects of specialised secondary education Ž16%., incomplete higher Ž26%. and PTU with secondary Ž21%. are significant. That for general secondary is not, but has a probability-value below 10%. ŽWith a larger sample, the result would have been more conclusive.. Thus, when combined with some professional training, secondary school had a pay-off. The high coefficient for PTU without full secondary schooling for women is very unlikely to be representative. Yet, it illustrates some interesting characteristics of Soviet wage setting. There are only five female respondents in this category. While PTU trained for skilled manual jobs, these women work in unskilled ones. They have low monthly wages, but three work very few hours per week. At least two are cleaners. It was hard for enterprises to find cleaners. They could not pay more than the low, centrally set monthly wage, but they could make the job more attractive by turning a blind eye if the cleaner went home after 4 or 5 hours while being paid for eight. The official ideology determined a low wage for this job, because it was unskilled, while demand and supply considerations would have implied a higher rate. 14

A model created from the one reported here by omission of the job-type variables was estimated. For reasons of space estimates are not reported here, but are available from the author.

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Respondents with higher education earn more than those with less, if they are of the same sex. Nevertheless, women with university degrees earn less than men, on average even less than male unskilled workers. When examples are given to demonstrate that, in the USSR, high education led to low earnings, these examples are of highly female dominated occupations, such as school teacher and physician. Yet, for a woman, the alternative to such jobs, was not to become a high paid miner or skilled worker in heavy industry, but to do unskilled manual work, perhaps heavy or hazardous, and with even lower pay. The estimates in this study are of hourly wages. Analysis of monthly wages indicates a lower gender wage gap and lower wage premia for education, particularly for women. This reflects a particular feature of Soviet labour legislation. According to the Labour Code, certain jobs involved particularly high levels of mental strain and exertion ŽTerebilova, 1981.. Therefore, the statutory workweek in these occupations was shorter than the standard 41 h. Most of the jobs were highly female-dominated. They rarely required less than specialised secondary education and, in most cases, university. Thus, education enabled women to increase their hourly earnings and work a shorter workweek, if they accepted relatively low monthly wages. With a very unequal division of labour in the home, this was one way for these women to cope with a heavy ‘double burden’ of paid work, childcare and housework. Hence the advantages to women of higher education are underestimated when only monthly earnings are studied.

9. Returns to education in the USSR It is a question of judgement whether a given education premium is ‘large’ or ‘small’. Yet, it would be hard to dismiss the estimated wage differentials according to education found in Taganrog as ‘negligible’. Given the centralised system of wage setting, there is no obvious reason to expect these differentials to have been smaller in the USSR as a whole. If the sample had included large cities, where very highly qualified jobs, as well as people with higher academic degrees, were concentrated, we are likely to have found higher returns to academic education. 15 A comparison with other studies must be sketchy since both the variables available and the models differ very much. No earlier estimations of Soviet wage-models ŽGregory and Kohlhase, 1988; Ofer and Vinokur, 1992; Newell and Reilly, 1996. include PTU as a separate category. Further, all these studies

15

Ofer and Vinokur Ž1992. ŽTable 7.2. find considerably higher returns to ‘advanced’ than to ‘ordinary’ university studies.

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estimate monthly wages. Newell and Reilly, unlike the other studies, use a continuous, homogenous, variable for years of schooling. They estimate a parameter of 9% per year of schooling for men and 6% for women, when occupation is not controlled for, in Russia in 1992. The estimates of Gregory and Kohlhase are not made separately for men and women and only within the white- and blue-collar categories. It is therefore better to compare with those of Ofer and Vinokur. Controlling for age, education, job-role and sector Žop. cit., Table 7.2. they find a premium for higher education over general secondary of 29% for men and 32% for women. 16 A roughly similar model for Taganrog shows a difference of 23% for men and 32% for women. Their data refer to the 1970s, when the supply of educated labour was lower, and returns to education could be expected to be higher. Precise comparisons with Western studies are even more precarious, since school-systems, definitions and models differ. The estimates arrived at in Western studies vary considerably, not only between countries, but also between different studies of the same country. As an example, in Sweden, Edin et al. Ž1994. using different data sets find education-premia of roughly 6% per year of study when comparing university educated and those with only 9 years compulsory schooling at different points in the late 1980s–early 1990s. The closest equivalent in this study, is to divide the parameter for full-time university studies by 7 years of study. ŽFrom the eight form to a university degree.. The result is numerically very close to the Swedish. Further, according to the brief survey of Edin et al., the wage-premia for university education in Sweden, in their turn, were very similar to those in West Germany, as well as in the other Scandinavian countries, in the late 1980s, while they were higher than in Britain, 17 and considerably lower than in the US. Thus, the education premium found for higher education in Taganrog, is at the lower end of an international spectrum, but not extreme.

10. Education premia and choice of schooling and occupation Comparatively small, actual wage-differentials do not prove that there was a conscious, political drive toward wage-equalisation. Rimashevskaia Ž1992. Žp. 18. refers to ‘the necessity to attract unskilled workers to work and to stimulate it’. 16

Their measure is years of different types of schooling. To compare incomplete secondary school and university, the coefficient for a year of regular school, multiplied by two, should be added to that for a year of university, multiplied by five. If sector and job-role are not controlled for, the effect for men is 30% and for women, 37%. 17 Their figure for Britain, however, compares university and A-level secondary school. Comparison of university with compulsory education, indicates much larger returns.

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Newell and Reilly Ž1996. Žp. 346. recognise that low returns to education could reflect ‘over-supply of human capital’, but, without much argument, dismiss this explanation in favour of ‘the consequences of egalitarian policies’. Yet, there was tough competition for places in higher education ŽNarodnoe obrazovanie i kultura v SSSR, 1989.. One of the highest ratios of applicants to admissions was in schools of medicine, despite the very low pay of physicians. There are a number of reasons why children, encouraged by parents, wanted education. Free tuition and stipends reduced the private cost of studying. Parents could not let their children inherit capital assets, but they could support them through an education. As Gregory and Kohlhase and Ofer and Vinokur rightly note, the expected market outcome of this is decreased education premia. Many jobs for the well educated had perks: Fringe benefits, opportunities for side incomes, shorter workweeks and more freedom to use working time for private purposes. 18 White-collar jobs were less often physically harmful. The prestige and self-esteem connected with education and with a qualified white collar occupation would also tend to decrease the wage at which they became attractive. Despite the status of particular very skilled and very onerous blue-collar jobs Žsuch as mining., the evidence of Pravda Ž1982. and Aage Ž1984. indicates a more direct relation between education and prestige in the USSR than in Western studies. In surveys of teen-agers, girls tended to assign very high prestige to jobs like doctor and teacher. One thing the USSR did achieve was wide access to education, for women as well as for men. With a large supply of educated and skilled labour, market forces were exerting a downward pressure on returns to schooling. Even so, the wage-differentials according to education that have been found in this study, the first to use micro-data collected inside the USSR, are not extremely low compared to developed market economies. No assumption of ideologically motivated bias against the well educated is needed to explain that they are relatiÕely low.

Acknowledgements The present article draws upon research from a forthcoming book ŽKatz, 1999.. It was written while the author was a Senior Researcher at the Department of Economics, Goteborg University. The work was funded by the Swedish Council ¨

18

There is very little ‘hard’ data on the distribution of non-monetary benefits from work in the USSR. Most writers conclude that they were positively correlated both with wages and education ŽPravda, 1982; Gregory and Kohlhase, 1988; Kolev, 1996; findings with more detailed data are similar but refer to 1994..

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Appendix A A.1. Definitions and means of variables

Wage

lw h Age Agesq Stazh Stazhsq Tenure Tensq Pens Mts Unskill Physqual Nonman-lowed Nonman-seced Nonman-highed

Mean for men

Mean for women

Wage from the state sector reported by the respondent for the previous month Žincluding overtime, sickness benefits and second jobs. hourly wage rate imputed from ‘wage’ and ‘usual’ hours per week natural logarithm of ‘w’ usual hours of workrweek, in all jobs in state sector Žincluding overtime. Age in years Age squared Work record Stazh squared Years at present place of employment Tenure squared Self-defined occupation ‘pensioner’r‘working pensioner’ or above normal retirement age Married or cohabiting Unskilled blue-collar worker ŽReference. Skilled physical or partly physical work Non-manual, non-managerial work without full secondary education Non-manual, non-managerial work and secondary education Non-manual, non-managerial work and higher education

240.228

157.673

1.327

0.973

0.214 42.643

y0.106 39.535

41.572

39.778

22.644

19.104

13.114

11.280

0.076

0.089

0.864 0.090 0.615 0.003

0.724 0.178 0.373 0.007

0.063

0.195

0.192

0.216

K. Katz r Labour Economics 6 (1999) 417–434

w

Variable definition

Highqual Highed Highed2

Lowed Heavyind Transp Constr Light Serv Trade Teach Health Art Science Govt Other Heat Heavy Nervous Othercond

0.04

0.028

0.235

0.216

0.061

0.042

0.034 0.288 0.156 0.066 0.066 0.082

0.030 0.303 0.238 0.044 0.009 0.087

0.010 0.543 0.080 0.064 0.043 0.048 0.021 0.053 0.021 0.013 0.056 0.005 0.056 0.084 0.152 0.268 0.291

0.025 0.430 0.039 0.042 0.066 0.061 0.072 0.123 0.059 0.015 0.044 0.017 0.040 0.024 0.076 0.207 0.136

K. Katz r Labour Economics 6 (1999) 417–434

IncHighed Specsec Gensec PTU1 PTU2 Incsec

Managerial or highly qualified and creative non-manual work Higher education, full-time studies, 15 years of schooling 19 Higher education from evening or correspondence courses Incomplete higher education, 13–14 years Specialised secondary education, 12–13 years General secondary education, 10 years PTU with secondary education, 11 years PTU without secondary education, 9 years Incomplete secondary education, 8 years ŽReference category. Less than incomplete secondary education Heavy industry ŽReference. Transport and communications Construction Light industry Žincluding food. Services in utilities, consumption and housing Trade and catering Schools Žnot institutes of higher education. Health care and physical education Art and culture Research institutes and higher education Public administration and social organisations Other sector Hot workplace Physically heavy work Nervous strain Hazards, dust, fumes, noise or vibrations

431

432

K. Katz r Labour Economics 6 (1999) 417–434

for Social Research. Thanks to the Institute of Socio-Economic Studies of the Population, Natalia M. Rimashevskaia and Anastasia I. Posadskaia for making the data available and to them, Anders Klevmarken, Bjorn ¨ Gustafsson and Ludmila I. Nivorozhkina for their advice, to participants at the 1995 EALE Conference, the editors of this journal and two referees for their comments. Views expressed and errors made are the responsibility of the author. A.2. A note on the wage-rate measure This was computed from information on after-tax wage of the preceding month and on ‘usual’ hours of work. To combine wage for a specific month with ‘usual hours’ per week implies some error and there is probably some error also in the reporting of weekly hours. The distribution of reported weekly hours was, however, checked against information on hours worked the previous workday. No systematic discrepancies appeared. Sickness benefits were paid through the employer, together with the wage, and were therefore included by respondents in the wage they reported Žaccording to the senior Russian researcher who designed the survey.. These benefits were from 50 to 100% of the wage, depending on tenure and paid also to care for a sick child. This brings the wage measure into better agreement with the ‘usual hours’ measure, which is not likely to take sick leave into account. The format of the survey may have led respondents who received a quarterly or yearly bonus the month before the interview to include it and therefore to exaggerate their average monthly wage. Comparison with data from another survey however indicates that this made little difference to the wage-equations, except that all sector-coefficients may be a few percentage points too low relative to heavy industry Žtransport and construction less than the others.. For details, imputations and consistency checks, see Katz Ž1994..

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19

The years refer to the school system from 1963 onwards. For details of changes in the Soviet school-system, see McAuley Ž1981. and Marnie Ž1986..

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