period 1985-1996 using micro data from the Polish Household Budget Surveys . ..... The means of the education dummies indicate a small increase in avera ge ...
CHANGES IN THE STRUCTURE O F EARNINGS DURING A PERIOD OF RAPI D TECHNOLOGICAL CHANGE : EVIDENCE FROM THE POLISH TRANSITIO N Michael P . Keane Yale University Eswar S . Prasad International Monetary Fund
The National Council for Eurasian and East European Researc h 910 17th Street, N .W. Suite 300 Washington, D .C. 2000 6 TITLE VIII PROGRAM
Project Information* Sponsoring Institution :
University of Minnesot a
Principal Investigator:
Michael P . Kean e
Council Contract Number :
814-08g
Date :
September 3, 200 1
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The work leading to this report was supported in part by contract or grant funds provided by the National Council fo r Eurasian and East European Research, funds which were made available by the U .S . Department of State under Title VIII (The Soviet-East European Research and Training Act of 1983, as amended) . The analysis and interpretations contained herein are those of the author .
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Abstract Poland experienced a dramatic structural change in 1989-90 that has become known as the "bi g bang ." The subsequent economic transition was a period of massive changes in production structures an d technologies, providing an interesting testing ground for theories of the relationship between technica l change and wage inequality . Using micro data on individual workers' earnings over the period 1985-96 , this paper documents various aspects of changes in cross-sectional earnings inequality during the Polis h transition . While education premia rose sharply during the transition . experience premia declined . especially in the early years of transition . These findings are consistent with the notion of technologica l obsolescence driving down the returns to specific human capital and increasing the returns to genera l human capital . As in the case of industrial countries such as the U .S . and the U .K . over the last two decades, changes in within-group inequality account for about two-thirds of the increase in overall wag e inequality . However, in contrast to those countries, increases in within-group inequality in Poland ar e very different across skill groups . In particular, increases in within-group inequality appear to have bee n larger for more highly-educated workers . Refinements to existing theoretical models are proposed t o explain these findings . We present a simple model under which the limited information conveyed by employment histories can keep the returns to unobserved skill attributes relatively compressed in a transition economy .
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Introduction Poland has experienced a dramatic change in its political and economic structures over the las t decade . This transition was launched with a radical set of reforms in 1989-90 that has become known a s the "big bang ." The private sector's contribution to GDP rose sharply after the transition commenced an d state enterprises. which were progressively cut off from subsidies, were forced to change their productio n processes and technologies . Enterprises that were unable to attract new capital to finance suc h modernization rapidly became uncompetitive and were forced to shut down . Thus, the process o f transition to a market economy has been accompanied by marked changes in the structure of productio n and also by massive technological change . The sharp rise in wage inequality m countries such as the United States and the United Kingdo m in the 1980s and early 1990s has produced a spate of theoretical models that have attempted to explai n this phenomenon as the outcome of skill-biased technical change, coupled with capital-skill complementarity . Since it involved a period of rapid technical change, the transition in Poland provide s an interesting testing ground for such theories of the relationship between technological change and wag e inequality . In this paper, we examine the evolution of the structure of labor earnings in Poland over th e period 1985-1996 using micro data from the Polish Household Budget Surveys . The relatively long span of the dataset allows us to trace out changes in the wage structure for an extended period both leading u p to and following the "big bang." We find that overall wage inequality rose significantly during th e transition period of 1989-1996 . For instance, we estimate that the 90-10 percentile ratio for individua l labor earnings increased steadily from 2 .64 in 1988 to 3 .07 in 1996 (using a sample of individuals aged 18-60 for whom labor earnings is the primary source of income) . The increase in male wage inequality was even greater, with the 90-10 percentile ratio rising from 2 .56 in 1988 to 3 .16 in 1996 . We also conduct a more detailed examination of the sources of the increase in wage inequality . Prior to the transition, the wage structure in Poland was highly compacted_ with wages of college educated white-collar workers little different from those of manual workers . We find that income 1
differentials by education level increased rapidly during the transition, reflecting sharp increases i n education premia. But the premium for labor market experience declined in the early years of transitio n and the position of older workers deteriorated relative to younger workers, consistent with the notion o f rapid obsolescence of industry- and firm-specific skills of older workers during a period of industria l restructurin g We find that a substantial fraction of the increase in overall earnings inequality is attributable t o changes in within-group inequality . Interestingly, however. increases in within-group inequality are mor e concentrated among workers with higher levels of formal education . This is quite different from th e patterns documented in the U .S . and the U .K . of sharp increases over the last two decades in between group inequality as well as within-group inequality for virtually all broadly-defined groups ( see . e .g. , Juhn. Murphy and Pierce, 1993 . Machin . 1996, Machin and van Reenen, 1998) . In these countries, in contrast to our observations for Poland, both education and experience premia increased, and inequalit y within all education categories and groups with different levels of experience increased as well , particularly during the 1980s . A popular explanation for the increase in education and experience premia is skill-biase d technical progress . But this cannot explain increases in inequality within skill groups . To rationalize th e U .S . and U .K . evidence, Violante (1996) proposes a model in which technology is embodied in capital o f different vintages . If there are frictions in the matching of workers with the new technology, an acceleration of the rate of technical progress then leads to increased wage dispersion among identica l workers . Aghion and Commander (1999) have argued that transition can be viewed as generating a technology gap between the old state sector and the new entrepreneurial sectors . Growth in within-group inequality can then be explained by frictions in the matching of workers with the new technology, as i n Violante's (1996) framework . Clearly, however, this framework needs to be modified to account for th e fact that increases in within-group inequality in Poland are correlated with education levels. We develop a simple model that allows for capital-skill complementarity but where employmen t histories play an important role in signaling the quality of unobserved worker attributes . Given the
limited length of workers` relevant employment histories in the new market economy, the returns t o unobserved attributes are . therefore. relatively compressed in a transition economy . The correlation between observed and unobserved skill attributes leads to greater increases in within-group inequalit y among groups with better observed skill attributes, such as higher education levels . The model is very similar in spirit and structure to Galor and Moav's (2000) model of ability-biased technological change , which those authors use to explain the concomitant rise in between- and within-group inequality in the U .S . The next section of the paper provides a brief overview of existing literature on wage inequalit y in Poland and other transition economies . Section III describes the dataset and Section IV contains th e main empirical results . Section V presents the theoretical model and concluding remarks are in Sectio n VI .
Review of prior researc h Most earlier work on the Polish earnings distribution has relied on aggregate statistics that ar e released annually by the Polish Central Statistical Office (CSO) . These aggregate statistics are described in detail in Atkinson and Micklewright (1992) . Each September, starting in 1981, the CSO conducted a census of enterprises, and "the information requested of the enterprise was the total persons in a numbe r of discrete earnings bands ." The CSO then published aggregate statistics such as total numbers o f employees in various earnings bands . and deciles of the earnings distribution . From this data, it i s possible to construct approximate measures on earnings inequality, such as approximate Gini coefficients . A number of other countries, such as Hungary, the former Czechoslovakia, and the former U .S .S .R ., had similar data collection and reporting procedures for earnings . Using such data, Atkinson and Micklewright (1992) compare the degree of earnings inequalit y across several communist countries in 1986 . They obtain Gini values for Czechoslovakia (1987) , Hungary, Poland and the U .S .S .R . of 0 .197, 0 .221, 0 .242 and 0276, respectively . And they report 9- 1
3
decile ratios of 2 .45, 2 .64, 2 .77 and 3 .28, respectively . ' Thus, there was a clear ranking of inequalit y (consistent across both measures), with Czechoslovakia being the most equal . the U .S .S .R . the least equal . and Poland in the middle . As a point of comparison . these authors report a Gini coefficient and 9-1 decil e ratio of 0 .267 and 3 .23 . respectively, for the U .K . in 1986 . Thus, the earnings distribution in Poland prio r to the transition was noticeably more compact than in the U .K . Based on the same data Atkinson and Micklewright (1992) also calculate that earnings inequalit y in Poland fell significantly over the period 1986-89 . They report Gini values of 0 .242 . 0 .230, 0 .212 an d 0 .207 for these years . and decile ratios of 2 .77 . 2 .76 . 2 .60 and 2 .43 . Rutkowski (1996a) uses the same September earnings distribution survey to examine changes i n the Polish earnings distribution during the transition . His calculations indicate that earnings inequalit y jumped dramatically in the early phase of the transition, with the Gini and decile ratio rising to 0 .242 an d 2 .86 in 1991 . By 1993 . the last year of his study, these had risen further, to 0 .257 and 3 .03 . respectively . Rutkowski also reports that earnings inequality was much greater in the private sector than the publi c sector in 1993, that the ratio of white collar to blue collar wages rose substantially in the transition, an d that this ratio was much higher in the private sector . Rutkowski (1998) extends this analysis to 1995, by which time the Gini for earnings increased to 0 .288 and the decile ratio to 3 .38, a large increase i n inequality over 1993 . Recall that. for 1986 . Atkinson and Micklewright (1992) reported a Gini of 0 .24 2 and a decile ratio of 2 .77 . So the increase in (gross) earnings inequality for Poland from 1986-95 implie d by these data is a bit greater than what they report for the U .K . in the 1980s . 2 Rutkowski (1996b) presents a cross-country analysis of changes in earnings inequality usin g similar data sources for several transition economies . These results indicate that, by 1993, the earning s distribution for Czechoslovakia had become very similar to that for Poland, while the Gini and decil e ratio for Hungary had risen to 0 .315 and 3 .67 (more unequal than Poland) . Thus, given the baselin e
The 9-1 decile ratio is based on the average earnings of individuals in the top and bottom deciles of the earning s distribution . 2
Note that the data are gross income for both Poland and the U .K . 4
figures from Atkinson and Micklewright (1992), it appears that both Czechoslovakia and Hungar y experienced larger increases in earnings inequality from 1986 to 1993 than did Poland . As both Atkinson and Micklewright (1992) and Rutkowski (1996) describe, there are a number o f limitations of the September earnings survey data . First, the aggregate nature of the data may lead t o approximation errors in inequality measures, and limits the type of analysis that can be performed . Second. the coverage of establishments is incomplete because small firms (i .e ., less than 6 employees) are not sampled . This is especially a problem for the transition in Poland because, according to OECD (1998 . p . 107), " Polan d ' s recent growth performance rests on a strong entrepreneurial basis, with many dynami c small and medium-sized enterprises (SMEs) and creations of new firms . . . SMEs make up the bulk o f Poland's 2 .2 million registered non-agricultural enterprises . . . almost 90 percent [are] micro-enterprise s (employing 1 to 5 persons) ." Third, this data does not account for in-kind payments, which have been important in Poland. Fourth, this survey reports gross earnings . This creates comparability problem s over time, because a progressive income tax (with rates up to 45 percent) was introduced in 1992 . Failur e to account for this will tend to exaggerate the measured increase in inequality of net earnings . Another survey that has been used by some authors to examine recent changes in the Polish wag e structure is the Polish Labor Force Survey that was introduced in 1992 (see, e .g ., Newell and Socha, 1998) . However, this survey is clearly not useful for understanding changes in wage inequality in th e crucial early years of transition or changes relative to the pre-transition wage structure . Existing work on other transition economies has mostly focused on the early years of transition . For instance, Orazem and Vodopivec (1995) analyze micro data from Slovenia and report that, from 198 7 to 1991, wage inequality increased significantly, with returns to both education and experience rising ove r this period. Using grouped data, Flanagan (1993) finds that the returns to education rose while the return s to experience declined in the Czech Republic in the initial phase of transition . Based on her analysis o f Russian data, Brainerd (1998) reports that, from J991 to 1994, the marginal return to a year of educatio n rose from 3 .1% to 6 .7% for men and from 5 .4% to 9 .6% for women . Using data from the ILO's seldom-
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used Occupational Wages around the World file . Freeman and Schettkat (2000) also find that overal l earnings inequality and skill differentials increased in transition economies during the 1980s and 1990s .
The datase t The CSO has been collecting detailed micro data on household income and consumption at leas t since 1978 . using fairly sophisticated sampling techniques . In the HBS, the primary sampling unit is the household. A two-stage geographically stratified sampling scheme is used, where the first-stage samplin g units are the area survey units and the second-stage units are individual households . The typical sample size is about 25,000 households per year . The CSO uses the data obtained from these household survey s to create aggregate tabulations that are then presented in their annual Statistical Bulletins . or Surveys . The HBS contains detailed information on sources and amounts of income both for households and individuals within each household . Total income is broken down into four main categories : labo r income (including wages, salaries and nonwage compensation) ; pensions ; social benefits and other transfers ; and other income . Social benefits include income from unemployment benefits, which wer e introduced in late 1989 . A key point is that the data include measures of the value of in-kind payment s from employers to workers . which have been an important part of workers' compensation in Poland an d other transition economies . There were no taxes on personal income until 1992 . 3 After that year, we us e net incomes in the analysis . The HBS also contains information on demographic characteristics of al l household members and on labor earnings of all employed individuals in each household . Unfortunately, data on individual workers' earnings were not collected in 1993 . Hence, the dataset in fact goes fro m 1985-92 and then from 1994-96 . The HBS does include a panel element – a part of the dataset contains households that ar e surveyed for four successive years before being rotated out of the sample . However, attrition bias in th e panel is significant and, in addition, the panel was changed completely a couple of times over the perio d
3 Until 1992, some firms were levied an "excess wage tax," essentially a payroll tax imposed on part of a firm's tota l wage bill . The actual incidence of this tax is, of course, a complicated matter. 6
1985-96 . To maintain the representativeness of the sample and to use all of the information in the dataset , we treat the data as a repeated set of cross-sections . The structure of the survey instrument and the sampling scheme were both kept essentially unchanged after the transition commenced. However, one major change was introduced in 1993 that ha s important implications for analyzing cross-sectional inequality . In order to improve survey respons e rates, in 1993 the CSO switched from quarterly to monthly data collection for the HBS . Since earning s are more variable at the monthly than the quarterly frequency, this shift alone would have created a substantial increase in measures of cross-sectional earnings inequality . Indeed as we demonstrate in th e next section, failure to account for this change in survey frequency has quantitatively important effects o n measures of earnings inequality . In the Appendix, we develop a technique for adjusting the 1994-1996 earnings data for th e increased variability that may be attributable to the shift from quarterly to monthly reporting . Our approach models earnings as the sum of a permanent or predictable component (determined by workers ' education, age and other observable characteristics) and a mean zero idiosyncratic component . We the n assume that the variance of the idiosyncratic component would not have jumped abruptly after the fourt h quarter of 1992 . Rather, we assume that the variance of idiosyncratic earnings varied smoothly over tim e (measured in months) according to a polynomial time trend . We estimate this polynomial trend alon g with a dummy for post-I992 that captures the discrete jump in variance that occurred with the change to monthly earnings reporting . Then, at the individual level, we scale down the idiosyncratic component o f the post-1992 earnings statistics to eliminate this jump in variance . Our procedure for adjusting for the spurious increase in inequality stemming for the switch to th e monthly reporting interval relies on access to the HBS micro data. In particular, the variance correctio n requires access to the data for an extended period of time . Our study is unique in that it is based on th e HBS micro data for a long sample period extending from 4 years prior to the "big bang" to 7 years after.
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To our knowledge, no prior study of earnings inequality in Poland has adjusted for the change in surve y design in 1993.4 We restrict our wage analysis sample to individuals between the ages of 18 and 60 who repor t that labor income is their principal income source . Nominal monthly earnings are deflated by th e aggregate CPI (1992Q4=100) . The results we report in the paper are mostly annual . But . to avoid any distortions arising from the sharp changes in price levels that occurred in the early years of transition, w e deflate nominal wages using CPI data for the survey quarter until the end of 1992 and for the survey month thereafter . Prior to 1993, there were seven education categories reported for individuals in the survey. Beginning in 1993, two of these categories (basic vocational training and some high school) wer e combined into a single category ; for consistency, we combine these categories in a similar manner for th e 1985-92 period. We also combined the two categories primary school and less than primary school into a single base category (since the less than primary school group among workers is quite small), thereb y yielding a total of five educational categories over the full sample . Table 1 (at the end of this paper) reports sample means for some of the variables used extensivel y in our analysis of wage inequality . 5 The demographic characteristics of the cross-sectional sample s remain quite stable during and after the transition . The means of the education dummies indicate a small increase in avera g e levels of educational attainment in the 1990s . The distribution of employment amon g men and women is relatively stable, although there is a slight increase in the share of women in tota l
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At the time we began our study, the Polish CSO had never before released the HBS micro data. Subsequently, th e micro data for the first half of 1993 was released to the World Bank, and this data is used in World Bank (1995) an d Milanovic (1998) . More recently, data for 1993-96 have been obtained by researchers at the World Bank . A subsample of the HBS is also now available in the Luxembourg Income Survey (LIS) for 1987, 1990 and 1992 . Thus, no prior researchers have had access to the micro data for the entirety of the extended period that we examine . Note that the sample size falls in 1992 . In that year, half of the total sample was used to test the new monthl y survey ; these data were considered unreliable and not made available to us . In addition, a number of observations i n that year had to be left out of the analysis due to incomplete data . Cross-sectional sampling weights (at th e household level) are used in all of the analysis to ensure representativeness of the sample in each year, including 1992 .
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employment after 1994 . Private enterprises accounted for less than 10 percent of total employment befor e the transition but this proportion had grown to about 40 percent by 1996 .
Earnings Inequality In this section, we examine the evolution of earnings inequality for individual workers . For th e years 1994-1996 . we use earnings measures that are adjusted (using the procedure described in th e Appendix) for the increase in idiosyncratic variance that occurred with the shift to a monthly reportin g period .
.Measures of overall inequalit v The first panel of Table 2 reports 90-10 and 75-25 percentile differentials of log earnings for all workers . Wage inequality is quite stable in the pre-transition years 1985-88 . Between 1988 and 1989, the 90-10 differential increases sharply, going from 0 .97 to J .04 . A further significant increase occurs from 1991 to 1992, followed by a moderate increase through 1996 . The total increase in inequality from 198 8 to 1996, as measured by the 90-10 differential, is about 15 percent_ a sizeable increase over an 8-year period . The increase in the 75-25 percentile differential is also quite significant_ from 0 .50 in 1988 t o 0 .59 in 1996 . Even by 1996, however . the wag e structure is considerably more compressed than in the United States . For instance, in 1991, the 90-10 differential for full-time workers in the U .S . was close to 1 .7 5 (Gottschalk and Smeeding, 1997) . Interestingly, however, wage differentials are greater in Poland than i n certain continental European countries such as Germany in the 1990s (see Prasad, 2000) . In Table Al, we present an alternative measure of inequality – the Gini coefficient . We prefer not to focus on this coefficient, despite its usefulness as a summary measure of inequality, since it tend s to be more sensitive to outliers than the percentile differentials and is also sensitive to changes around the median/mode of the distribution . Nevertheless, since this measure is used by numerous authors, we sho w the estimated Gini coefficients to enable comparisons with other studies . It will be evident that th e 9
patterns of changes in inequality revealed by the evolutions of the percentile differentials are, in general , quite similar to those indicated by the
coefficients . For instance, the Gini coefficient for the ful l
sample rises sharply from 0 .221 in 1988 to 0 .259 in 1994
and
then remains flat through 1996 .
As discussed earlier, we adjusted the earnings data for 1994-96 to account for the change i n survey frequency . In the second panel of Table 1, we show the percentile differentials for 1994-96 wit h unadjusted data . Clearly, the adjustment makes a significant difference to the absolute level of inequality , although the profile of little change in inequality over the period 1994-96 is not affected by whether or not the data are adjusted .° In terms of understanding changes in inequality over the full sample, however , adjusting for the change in survey frequency is clearly quantitatively important . The first four columns in the lower panels of Table 2 show percentile differentials separately for workers employed in the public and private sectors . In absolute terms, inequality is higher in the privat e sector than in the public sector . The change in inequality is also much greater in the private sector. Fo r instance, from 1988 to 1996, the 90-10 differential rises from 0 .96 to l .05 in the public sector and fro m 1 .04 to l .19 in the private sector . However, the change in the 75-25 differential is similar across the tw o sectors, suggesting that increased inequality at the upper and lower ends of the distribution, rather than i n the middle of the distribution, is responsible for the greater increase in inequality in the private sector. To obtain a visual representation of changes in the shape and features of the entire distribution , we also examine kernel density estimates of the overall earnings distribution as well as the wa g e distributions in the public and private sectors . Figure 1 presents kernel density estimates for real earnings for the years 1988, 1992, 1994 and 1996 .7 Adopting an approach used by Fortin and Lemieux (2000), w e construct weighted kernel density estimates for the two sectors, where the weights are the respectiv e sectoral shares in total employment for each year .
Based on their analysis of data from the Labor Force Survey, Newell and Socha (1998) also find little change i n wage inequality between 1992 and 1996 . 7 An Epanechnikov kernel with a bandwidth of 4000 was used for the kernel density estimation.
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This figure indicates the rising importance of the private sector in determining the shape of th e overall wage distribution . In addition, it appears that the mean of the private sector wage distribution is t o the right of that of the public sector wage distribution in 1992 . This position seems to be reversed b y 1996 . Figure 2 (left panel), which plots the differential between (unconditional) median private an d public sector wages, confirms this point . Interestingly, the private-public sector differential was positive before the transition but falls sharply and turns negative after the early years of transition . s To sum up, although the increase in wage inequality is higher in the private sector, workers i n public sector enterprises have also experienced significant increases in wage inequality during th e transition . In other words, the rapid reallocation of labor from the public sector to the private sector ca n not by itself explain much of the increase in wage inequality during the transition in Poland .
Within-group wage Inequalit y Next, we examine changes in within-group inequality . In the U .S . and the U .K ., within-group inequality has been documented to have risen sharply within virtually all broadly-defined educationa l groups . The relative importance of between- and within-group inequality in accounting for changes i n overall inequality also has important implications for different sets of theoretical models . In Table 3, we report percentile differentials for workers in each of five educational groups . On e feature of these results is that . prior to the transition . both the 90-I0 and 75-25 percentile differential s were not too dissimilar across these groups . Interestingly, there is a significant and sustained increase i n wage inequality from 1988 to 1996 for two groups – those with college degrees and those with hig h school degrees. Workers with college degrees experience by far the greatest increase in inequality durin g the transition, with the 90-10 percentile differential rising from 0 .96 in 1988 to 1 .14 in 1996 and the 75-2 5 differential going from 0 .50 to 0 .62 over the same period . For workers with only a primary school degree
Earnings data from the Labor Force Survey reveal a similar pattern of a sharp decline in this differential during th e transition . Newell and Socha (1998) report a mean private sector wage premium of 5 percent in 1992, falling to a negative differential of 10 percent by 1996 .
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or with basic vocational training, the increases in within-group inequality are much more modest . The changes in the 90-10 differentials for workers with vocational training or a primary school degree are les s than half of the corresponding differential for workers with a college degree . The difference is eve n greater for the 75-25 differential . Thus, increases in within-group inequality seem to be a prominen t feature of the transition mainly for highly-educated workers . A possible explanation for these results is that workers with higher education levels are mor e likely to be employed in the private sector, where inequality is higher. In fact, the private secto r employment shares of all education groups have risen sharply during the transition . Somewhat surprisingly, the group-specific share of employment in the private sector is even higher for workers wit h lower levels of education . Hence, differential patterns of reallocation of labor across the public an d private sectors can not explain observed differences in increases of within-group inequality . Next, we examine the evolution of inequality within broadly-defined experience groups . Table 4 reports percentile differentials for groups of workers with different experience levels . There are fairl y significant increases in inequality for all groups . For instance, from 1988 to 1996, the 90-10 differential rises by about 0 .2 for all experience levels . Over the same period. the 75-25 differential rises by about 0 . l for all experience groups . We also examined cohort effects on the evolution of overall inequality . Typically, inequality tends to rise over the life cycle within a given cohort as employment histories, cumulative effects o f individual productivity shocks and other factors drive up within-cohort wage dispersion over time . Thus, differences in cohort sizes could result in changes in overall inequality even if within-cohort inequalit y were to evolve in an identical pattern across cohorts . The lower panel of Table 4 reports percentile ratios for cohorts defined on the basis of birth year. All cohorts experienced fairly substantial increases in inequality during the transition . The patterns o f
9 The group-specific shares of private sector employment in 1988 and 1996 for each of the education groups are a s follows : college degree (2 .9,19 .4) ; some college (5 .3,20 .3) ; high school (3 .4,33 .9) ; vocational training (6 .6,48 .3) ; and primary school (3 .8,44 .7) .
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changes are, however, quite similar across cohorts, but for one notable exception . The group of younger workers, born between the years of 1966 and 1975 . have lower levels of inequality before the transitio n compared to other cohorts but this cohort experiences a much greater rise in inequality during th e transition, with the 90-10 percentile differential rising from 0.75 in 1988 to I .07 in I996 . One possibility is that this relatively young cohort experienced the greatest dispersion of employment opportunitie s during the transition . Consistent with this notion . we find that the share of private sector employmen t (relative to total employment) within the cohort is much greater for this cohort than for the older ones . 1 0 Since, as noted earlier, wage inequality tends to be greater in the private sector, it is not surprising tha t this cohort experiences a much greater increase in inequality during the transition . The cohort that experiences the next largest rise in inequality in the early years of transition (through 1992) is the 1926-3 5 birth-year cohort, which includes workers who were relatively old by the time the transition began. Thus, inequality increases during the transition seem to have been largest at the upper and lower ends of the ag e and experience distributions . Another approach to examining within-group wage inequality, suggested by Juhn. Murphy an d Pierce (1993), is to regress earnings on observed attributes such as gender, education . and experience and to examine the dispersion of the wage residuals . These wage residuals arguably control for between group differences across many different group characteristics and indicate the evolution of inequalit y within narrowly-defined groups . Percentile differentials for log wage residuals are reported in the last (top right) panel of Tabl e 2 . 1 k These differentials indicate that about two-thirds of the level of overall wage inequality is accounte d for by within-group inequality . Changes in within-group inequality also account for a substantial fractio n
'° The share of total employment in each cohort accounted for by private sector employment in 1994-96 (average) i s as follows . 1926-35 : 0 .21 ; 1936-45 : 0 .24 ; 1946-55 : 0 .30; 1956-65 : 0 .34 ; 1966-75 : 0 .48 ; 1976-85 . 0 .76 . We do no t report results for the1976-85 birth-year cohort before 1992 since the cell sizes are too small (less than 10 0 observations) . ' Wage residuals are from annual OLS regressions of earnings on a constant, four education dummies, experienc e and its square, and dummies for gender, urban residence, and employment in the private sector . These are identical to the specifications examined in greater detail in the next section .
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of the increase in overall inequality . For instance, the 90-10 percentile differential based on the wag e residuals goes from 0 .82 in 1988 to 0 .91 in 1996 . which accounts for about two-thirds of the increase i n total wage inequality over that period . This is similar to the experience of the U .S ., where the change in residual inequality accounts for more than two-thirds of the total increase in wage inequality during th e 1970s and 1980s (Juhn, Murphy and Pierce . 1993) . To summarize . the evidence in this sub-section indicates that similar to the experiences of the U .S . and other industrial economies, within-group inequality accounts for only a small portion of the total change in earnings inequality duri n g the Polish transition . However, there are some important difference s across groups . In particular . within-group inequality did rise significantly among highly-educate d workers but increased far less for workers with lower levels of formal education . Increases in inequality seem to have been quite similar within broadly-defined experience groups .
Wage inequalitv among men and women
Next, we examine the evolution of wage inequality among men and women . This could have important implications for interpreting labor market developments . Much of the literature on wag e inequality has traditionally tended to focus on male wage inequality . This has largely been driven by th e desire to carefully examine a homogeneous sample of workers and to abstract from the analytical complications resulting from the relatively more tenuous labor market attachment of women in industria l countries . But, in a country like Poland, where women have traditionally had very high employment an d participation rates, it is of considerable interest to analyze these two groups separately and also t o examine their respective impacts on the overall earnings distribution . The bottom right panels of Table 2 show percentile differentials separately for men and women . As in many other countries, the increase in male wage inequality is significantly greater than the increas e in inequality among women . For instance, from 1988 to 1996, the 90-10 percentile differential goes fro m 0 .94 to l .15 for men and from 0 .85 to l .00 for women . Similarly, the increase in the 75-25 differential from 1988-96 is 0 .12 for men and 0 .07 for women . It is worth noting that the increase in male wag e 14
inequality is greater than the increase in overall inequality, a phenomenon that has been documented fo r numerous other countries as well during the 1980s and 1990s (see . e .g ., Fortin and Lemieux. 2000, fo r U .S . evidence) . Figure 3 presents employment-share weighted kernel density estimates for the real earnings o f men
and
women for the years 1988, 1992 . 1994 and 1996 . 12 We defer a more formal analysis of th e
gender wage gap until the next section but it is evident from this figure that, throughout the period 1988 96 . the female wage distribution is to the left of the male wage distribution at virtually all percentil e points of the respective distributions . It is also hard to discern any tendency for the two distributions to conver g e over time . Figure 2 (right panel) shows that the median wage differential between men and women fell during the early stage s of transition but stabilized at around 25 percent after 1992 . A similar pattern in true in the private sector . This is in sharp contrast to the experiences of many industrial countries, where the gender wag e differential has declined and the wage distribution for women has converged markedly towards the wag e distribution for men over the last decade or two . Evidence from other transition economies is more in line with that for industrial countries . Fo r instance, Hunt (2001) reports that German unification resulted in a 10 percentage point decline in th e gender wage gap in the former East Germany but notes that this was accompanied by a much greate r decline in female employment than in male employment . She also finds that low earners were mor e likely to leave employment after unification and that such persons were disproportionately female . I n Poland, by contrast, our evidence suggests that the relative share of women in total employment actuall y increased after the transition (see Table I) . It is also worth noting that the relative employment share o f women, compared to many industrial countries, was quite high even before the transition . Thus, there appear to be very different forces at work in Poland in terms of influences on the gender wage gap . Thi s issue is explored further below .
12 An Epanechnikov kernel with a bandwidth of 4000 was used for the kernel density estimation .
15
Effects of changes in structure of employment on wage inequalit y Since resource allocation in a command economy is typically not determined by market forces , the opening up of the transition economies to competitive pressures . both internally and externall y (through the expansion of trade with other market economies), has resulted in marked changes in th e structure of production and employment . An important question at this juncture is whether such change s in the industry shares of employment could be contributing to changes in overall wage inequality . Thi s could occur due to changes in both the within-industry and between-industry components of inequality . Following Juhn. Murphy and Pierce (1993), consider the following decomposition:
Q,`
where 2 j,
6r,
—
EJ S
Jr QJr +
Es ,
(w/r — W,
)z
(1 )
is the cross-sectional variance of log hourly wages, s it is the employment share of sector f ,
is the within-industry variance of wages, w j, is the mean sectoral wage, w, is the mean wage in the
sample and the subscript t is a time index . Using this formula, the change in variance over time can b e decomposed into changes attributable to within- and between-industry variance as well as composition effects within and between industries . Table 5 presents the results of this decomposition for 13 broadly defined sectors of the economy . ' The total increase of 7 .00 in overall wage variance from 1988 to 1996 is the result of shar p increases in wage variance from 1988 to 1992 and from 1992 to 1994, with a subsequent moderat e increase from 1994 to 1996 . 14 The key result from this table is that virtually all of the increase in overal l wage variance is attributable to within-industry changes in wage variance . The between-industry
'j The disaggregation used here is relatively coarse . Unfortunately, a change in industry classification in 199 3 vitiated our attempts to derive a consistent set of codes at a finer level of disaggregation for the full sample . '4 Note that the numbers in the table are multiplied by I00 ; the absolute increase in variance over the full sample i s actually quite small .
16
component of the change in variance is significantly positive in all three sub-periods, but is roughly offse t by within- and between-industry composition effects . Table 5 also presents (in the footnotes) employment shares of each of the 13 sectors in 1988 an d 1996 . Clearly, there have been significant changes in industry employment shares over this period . Nevertheless, these shifts in sectoral employment do not seem to have played much of a role i n influencing patterns of overall wage dispersion . Within-industry wage variation appears to dominat e overall wage variation and both appear to have evolved in a smaller pattern .
Earnings inequality at the household leve l We have characterized various aspects of the evolution of individual earnings inequality thus far . A related question is how earnings inequality changed at the household level . This is a topic of interest in its own right (see, e .g ., Burtless, 1999) . Although a full-fledged analysis of the determinants o f household earnings inequality is beyond the scope of this paper, it is nevertheless interesting to examin e the dispersion of both labor income and total income at the household level for households that repor t labor income as their primary source of income . To make these measures comparable across households, we adjust both income variables by a simple equivalence scale . 1 5 Table 6 reports Gini coefficients and percentile differentials for equivalence-scale adjuste d measures of total household income and household labor earnings . Note that income data at th e household level are available for 1993 and 1997 as well . The level of household labor earnings inequalit y is greater than that of individual earnings (compare with Table 2), which is to be expected given th e cross-sectional variability in number of persons employed per household . Interestingly, however, th e increase in earnings inequality, as measured by the Gini coefficient or percentile differentials, is not tha t much greater at the household level than at the individual level .
15 The household head gets a weight of l .0; each additional adult gets a weight of 0 .5 ; and each child below the ag e of 17 gets a weight of 0 .3 . We experimented with various other equivalence scales described in Keane and Prasa d (2000) . These made little difference to the time profile of changes in inequality, although the levels of inequality were marginally affected . 17
A decomposition of the variance of household earnings into the components attributable to th e variances of employment (number of persons employed) and of mean household earnings (results no t reported) indicated that cross-sectional employment variance was more important before the transition . During the transition, much of the increase in overall household earnings variance is attributed to th e increase in mean earnings rather than employment . with both components accounting for a roughly equa l contribution to total variance by the 1990s . The covariance component is relatively small throughout th e sample . Social transfers (including unemployment insurance benefits) and other income sources have a clearly moderating effect on total income inequality . as shown in the second panel of Table 6 . A n interesting feature of the results is that . from 1988 to 1992 . the increase in the dispersion of tota l household income is much more muted than the increase in household earnings . From 1992 to 1997 , however, the position is exactly reversed . This corresponds to the pattern of changes in social transfer s that we documented in earlier work (Keane and Prasad . 2000) . For political reasons, transfers as a percent of GDP were almost doubled from 1988 to 1992 (from 10 percent to 20 percent of GDP) . But, in light of significant budgetary pressures, cash transfers as a percent of GDP were then gradually reduce d through 1997 . Consequently . the effects of the increases in labor earnings inequality on total incom e inequality, which had been masked during 1988-92 by the surge in transfers. quickly become evident after 1992 . As noted above . we leave a more detailed analysis of household earnings inequality for futur e work .
18
Regression analysis of the structure of earning s In this section, we conduct a regression analysis of the structure of earnings in order to gain mor e insight into the sources of changing inequality among workers during the transition . In Table 7, we present estimates of standard human capital earnings functions (see Willis, 1986) for a selected set o f years during the 1985-1996 period. Prior to 1993, the survey contains a question that can be used to determine if the responden t worked for a privately-owned firm . Staring in 1993, this question is refined, so we can determine if th e respondent works for one of three types of privately-owned firms : small firms (including the selfemployed) ; large privately-held firms: and large mixed-ownership firms with majority ownership by th e private sector. So, in the later years, we include separate dummies these three categories . The coefficients on the educational category dummies show that the premia for educationa l qualifications have increased substantially during the transition . For instance, the college degre e coefficient increases from 0 .383 in 1988 to 0 .527 in 1992 and further to 0 .683 in 1996 . Since primary school is the omitted group, this implies that the wage premium for a college degree relative to a primar y school education was approximately 47 percent in 1987, 69 percent in 1992 and 98 percent in 1996 . Th e high school premia relative to primary school for the same three years are 23, 30 and 41 percent , respectively . Note that this implies a widening of the college-high school premium as well . These results also imply that, by 1996, the high school and college premia in Poland were toward the high end o f estimates typically obtained using data from Western countries . One interesting question is whether academic qualifications acquired in the communist era hav e the same value in the labor market as more recently acquired qualifications . Unfortunately, it is difficul t to answer this question decisively with our sample, since it ends in 1996, seven years after the big bang . As a first pass, we constructed a dummy variable for individuals who, based on their age and impute d years of education, attained their highest degree on after 1992 . This dummy, and its interactions with the
19
education dummies . did not show any strong evidence that recent degrees command a premium relative t o older degrees . ' ' Since (potential) labor market experience (age-years of education-6) enters the regressions as a quadratic, the returns to experience need to be evaluated at specific levels of experience . We investigate changes in the returns to experience further below . but it is worth noting the significant decline in th e returns to experience implied by the OLS estimates for the early years of the transition . For instance, fo r workers with 25 years of potential labor market experience, the marginal return to an additional year o f experience is 0 .41 percent in 1988, drops to 0 .36 percent in 1990 and then rises to 0 .48 percent by 1996 . For younger workers with five years of potential experience . this return drops from 2 .64 percent in J98 8 to 2 .46 percent in 1990 and then partially recovers to 2 .52 percent by 1996 . Consistent with the evidence from the kernel density plots in the previous section . the wag e premium for men relative to women fluctuates around 30 percent and shows no clear trend, although there is a temporary decline in this premium in the period 1990-92 . The wage premium for workers in urban areas relative to those in rural areas, on the other hand, almost doubles by the mid-1990s relative to pre transition levels . It is also worth noting the differences in values of the private sector dummies across small and large firms for I994-96 . Wages for those who work in small private firms (this includes th e self employed) are actually below those who work for the government or state-owned firms the exclude d dummy covers all workers in the public sector) . The wage premium for workers in lar g e private firm s (including large foreign-owned firms) is approximately 20 percent, while the premium for people wh o work for large firms with mixed but majority private ownership is around 10 percent . A separate dummy for employment in mixed-ownership firms with majority state ownership was small and insignificant . We also ran regressions separately for workers in the public and private sectors . Some of th e main results, and a comparison with the results for the full sample, are summarized in Figure 4 . Two
06 It has been suggested to us by certain Polish observers that major curriculum changes in the post-Communist era have taken place mainly at the university level . There also appears to be some debate about whether the level o f rigor in school and university programs has improved, with some actually arguing the opposite .
20
aspects of the results are worth noting . First, education premia rose much more sharply in the private sector during the transition . In both sectors, the college and high school premia were about 40 percen t and 20 percent, respectively, before the transition . By 1996 . these premia had risen to 60 percent and 3 0 percent in the public sector . For workers in the private sector, these premia had risen by a much greate r amount, to about 90 percent and 40 percent, respectively, by 1996 . The second interesting aspect is that, for older workers (25 years of potential labor marke t experience) in the private sector, the marginal return to an additional year of experience was close to zer o before the transition . After the transition, this return begins to rise and, by 1996, is close to 0 .6 percent, even higher than in the public sector (0 .4 percent) . We conjecture that this reflects selection effects an d the fact that experienced workers who were able to obtain private sector employment after the transitio n have strong unobserved attributes . For younger workers (five years of potential labor market experience) , the returns to an additional year of experience are in the range of 2 .5-3.0 percent in both sectors before the transition. By 1996, this return declines to 2.3 percent in the private sector and rises marginally to 2 .7 percent in the public sector . One possibility is that, given the perceived higher lifetime earnings in th e private sector, the relative supply of younger workers looking for private sector jobs rose sharply durin g the transition, thereby driving down the returns to experience (at low levels of experience) in the privat e sector. This is consistent with the figures presented earlier showing that the most recent birth cohort i n our dataset has a disproportionately lar ge share of private sector employment . We also computed the wage regressions separately for men and women . To conserve space, we do not report those results in detail . One intriguing finding from this set of regressions was that, before the transition, education premia were substantially higher for women than for men . For instance, in 1988 , the college and high school premia (relative to a primary school degree) for women were 58 percent an d 30 percent, respectively, compared to 38 percent and 16 percent, respectively, for men . By 1996, thes e premia for both men and women had converged to about 95 percent and 40 percent. Thus, although both men and women experienced substantial increases in education premia during the transition, thes e increases were much greater, in relative terms, for men than for women . 21
The roles of selection effects and changes in patterns of labor supply for male and female worker s with different skill attributes in explaining these differences remain to be investigated . Another possibility, especially given the sharper increases in education premia in the private sector noted earlier, i s that men have disproportionately benefited from employment growth in the private sector . Interestingly , a cursory examination of the data strongly refutes this hypothesis . Although the share of women and me n in public sector employment has remained roughly equal over our entire sample, the share of women i n private sector employment in fact rose from about 20 percent before the transition to about 40 percent b y 1996 . We leave a more detailed examination of the gender wage gap for future work .
Quantile regressions Next, we follow Buchinsky (1994) in noting that a more complete picture of how various group s fared during the transition can be obtained by use of quantile regression . This enables us to characteriz e in a parsimonious way the changes in the entire conditional distribution of income, as opposed to lookin g only at chan ges in the conditional mean . We ran quantile regressions of log real quarterly (monthly fo r 1994-96) labor income on demographic characteristics of workers . These characteristics were dummie s for the same education categories included in the linear regressions, experience and its square . an d dummies for gender. urban residence and private sector employment . The sample included full-tim e workers in the 25-60 age range with no pension income (same sample as for the OLS regressions) . Regressions were run for the 0 .10, 0 .25, 0 .50 . 0 .75 and 0 .90 quantiles. Table 8 reports the college and high school premia (relative to primary school) obtained from th e quantile regressions . Note that the college premium jumps sharply from 1987 to 1996 at all quantil e points . For example, median earnings were approximately 46 percent (exp(0 .38)) higher for colleg e graduates than primary school graduates in 1987, and this premium increases to 95 percent (exp(0 .67)) b y 1996 . It is interesting that, in the transition years, the increases in the college premia are relatively greater at higher quantiles . By 1996 . the college premium is considerably greater at both the 0 .75 and 0 .9 0 quantiles than it is at the median . At the 0 .90 quantile, it is as high as 120 percent .
The high school premium also jumps substantially from 1987 to 1996 at all quantile points . For example, median income was approximately 20 percent higher for high school graduates compared t o primary school graduates in 1987, and this premium increases to 43 percent in 1996 . The levels of th e high school premium. and its behavior during the transition years, are quite similar across quantiles . When we ran the quantile regressions limiting the sample to workers in the private sector, w e found greater increases in the levels of college premia by 1996 at all quantile points . For instance, the college premium was about 150 percent at the median of the distribution and over 170 percent at the 0 .90 quantile in the private sector . The high school premium is also higher in the private sector, although onl y marginally . By 1996, the pattern of distribution across quantiles of college premia (higher at the uppe r quantiles) and high school premia (similar across quantiles) in the private sector is quite similar to that i n the full economy . Overall, the quantile regressions reveal a rather interesting pattern in terms of how educatio n premia changed during the transition . While the changes in the high school premium were similar a t different points of the distribution, the growth of the college premium was markedly greater at highe r quantiles . Our findings of a sharp increase in education premia after the transition are consistent with thos e of Gorecki (1994), based on his examination of aggregate Polish wage data, and of authors who hav e examined the wage structure in other transition economies . For instance . Ham . Svejnar, and Terrel l (1995) examine surveys conducted by the Federal Ministry of Labor in Czechoslovakia in 1988 and 1991 . They find that the wage gap between university and elementary school graduates increased from 58% hi 1988 to 63% in 1991 . Brainerd (1998) examines surveys conducted by the All-Russian Center for Publi c Opinion Research in 1991, 1993, and 1994 . These surveys apparently suffer from a composition bia s towards urban and more educated workers . Nevertheless, it is interesting to note Brainerd's finding that , from 1991 to 1994, the marginal return to a year of education rose from 3 .1% to 6 .7% for men and from 5.4% to 9 .6% for women .
23
Table 9 reports returns to experience, defined as the derivative of log real labor income with respect to labor market experience . Since experience enters the regressions as a quadratic . we report th e derivatives evaluated at 5 . 15 and 25 years of experience . A finding worth noting here is that returns to experience are much smaller than is found in Western data sets . This is true both before and after the bi g bang . The numbers in Table 7 are in percentage terms . So, for example . in 1986, for workers with 5 years of experience, an additional year of experience raises earnings by about 2 .9 percent at the (conditional) median of the wage distribution . For the U .S ., Buchinsky (1994) reports a correspondin g figure of 4 .97 percent for 1985, which is about 70 percent hi gher . For those with 15 years of experience, the return to experience at the median in 1986 is smaller (1 .67 percent) and. for those with 25 years o f experience, it is even smaller (0 .42 percent) . The results are similar at other quantile points . In contrast . Buchinsky (1994) finds returns to experience for U .S . workers with 15 years of experience to be about 2 . 9 to 3 .0 percent at all quantile points in 1985, nearly double the figure for Poland . A striking break is apparent after the big bang . In 1990 . returns to experience dropped sharply relative to their pre-transition levels . This is true at almost all quantile points for workers with 5 or 1 5 years of experience. In the 1992-1996 period. there is some recovery in experience returns . especially a t higher experience levels . In 1996, the returns to experience at the 25-year experience level range fro m 0 .59 percent at the 0 .10 quantile down to 0 .41 percent at the 0 .90 quantile . These figures are much highe r than those for 1990, especially at the lower quantiles . After 1991, experience premia are relatively stabl e for workers with 15 years of experience but continue to decline for younger workers with only 5 years o f experience . Thus, somewhat surprisingly, recent labor market entrants are the only group for who m experience returns are systematically lower in 1996 than in 1987 . In earlier work (Keane and Prasad, 2000), we have noted the marked increase in the relativ e generosity of pensions in 1991-92 and the consequent surge in the pension rolls in those years . Indeed, in our sample, the share of total employment accounted for by workers in the 51-60 age group fell fro m about 13 percent in 1989-90 to I1 percent in 1991-92 and, further, to 9 percent by 1996 . We plan (in th e
24
next update of this paper) to formally explore the hypothesis that self-selection into retirement account s for most of the recovery in experience premia observed in the mid-1990s at higher experience levels . The key finding from this section is that the returns to general human capital, reflected i n education premia, rose markedly after the transition while the returns to experience declined in the earl y years of the transition. This is consistent with the notion of rapid obsolescence of firm- or industryspecific skills during a period of rapid technological change and industrial restructuring (see Svejnar , 1996) . Workers with higher levels of general human capital are better able to adapt to such changes . Our results also reveal important differences between the wage structure in Poland and that in Wester n capitalist countries . By 1996, however, education premia had risen to a level in line with, if no t somewhat greater than. those found in Western countries . But the divergence in returns to experienc e became, in some respects . larger than before the transition .
A brief theoretical fora y In this section, we present a simple theoretical model that is consistent with the stylized fact s reported in the paper thus far . As discussed earlier, existing models of skill-biased technical change an d capital-skill complementarity have been able to account for a significant fraction of observed increases i n between-group inequality in countries such as the U .S . (see, e .g ., Krusell et al., 2000) . More recen t models have also tried to simultaneously explain the concomitant rise in within-group inequality in th e U .S . (see . e .g ., Violante . 1996) . The Polish evidence is consistent with these models . But an important additional feature of the results for Poland is that increases in within-group inequality appear to b e positively related to levels of general human capital, i .e . . education levels. A key element of the model is that wages depend on observed as well as unobserved skil l attributes . Employers use signals such as employment histories as well as observed attributes (such a s education levels) themselves to infer the quality of unobserved attributes . The former effect is similar t o the history-dependence explanation for within-group inequality proposed by Aghion . Howitt, and Violante (2000) . We also assume that employment histories in the public sector are a far more noisy 25
signal of these unobserved attributes than private sector employment . In a transition economy, where fe w workers have significant periods of private sector employment histories. this could lead to a compressio n of within-group wage inequality . Furthermore, given the correlation between observed and unobserve d attributes, within-group inequality would tend to be higher among workers with better observed attributes . The model is quite similar to that of Galor and Moav (2000) who use ability-biased technological chang e to generate predictions of evolutions of technology, educational attainment and between- and within group wage inequality that are consistent with patterns observed in the U .S . in recent decades .
Concluding remarks This paper has analyzed changes in the Polish wage structure over the period 1985-96 . Thi s period covers the last few years of the pre-transition era the big bang in 1989-90, and six subsequen t years of the transition . We find that labor earnin g s inequality increased quite si nificantly over thi s g period . In particular . education premia rose considerably during the transition . In sharp contrast. experience premia declined after the transition began (although they began to recover in the later stages o f transition) . These results are consistent with the notion of general human capital being valued much more than specific human capital in a period of rapid technical change and associated high rates o f technological obsolescence . We also found that changes in within-group wage inequality account for about two-thirds of th e overall increase in wage inequality . These results are similar to those for industrial economies such as the U .S . and the U .K. that have experienced large increases in both between- and within-group inequalit y over the last two decades . However, increases in within-group inequality appear to have been muc h greater for highly-educated workers in Poland . By contrast, in the U .S . and the U .K ., virtually all skill groups experienced large increases in within-group inequality . We argued that these stylized facts fo r Poland can be explained by a model where, in the context of a limited history of a market economy, workers' employment histories convey limited information about unobserved skill attributes . Consequently, observed skill attributes such as education levels serve as a more widely used sortin g 26
mechanism than in a market economy . In our model, this generates a positive correlation between th e average quality of such observed attributes and within-group wage dispersion . We also reported a number of other interesting results . For instance, the increase in wag e inequality was much greater in the burgeoning private sector than in the still-dominant public sector . Furthermore . male wage inequality rose much more sharply than overall wage inequality . However, there does not appear to be a strong tendency towards convergence of wage distributions for men and women .
27
Appendix : Accounting for the change in survey frequency after 199 2 Most aspects of the HBS, including the two stage sampling scheme and the structure of th e survey instrument, were left unchanged during the transition . However, a few refinements wer e introduced in 1993 . While most of these modifications were relatively minor, one important change wa s that, starting in 1993 . households were surveyed for only one month rather than for a full quarter in a n attempt to further improve survey response rates . The change in survey frequency has importan t implications for the purposes of measuring chan g es in cross-sectional inequality . In this appendix, we develop a technique for adjustin g the 1994-1996 earnings data for the increased variability that may b e attributable to the shift from quarterly to monthly reportin g . We begin by assumin g the following statistical model for earnings : ( 1)
Yht –
a lt+ PtXht + o'! c h t
where Y h , is the labor income of person h in period
L
X ht is a vector of individual-specific characteristic s
used to predict labor income, and Eht is the unpredictable or idiosyncratic component of labor incom e scaled to have a standard deviation of unity . The time-specific standard deviation that scales th e idiosyncratic component of labor income is denoted by a t . Our objective is to estimate the increase in a, for the 1994-96 period that is due solely to the switch to a monthly reporting interval . We begin by estimating equation (l) separately for each quarter from 1985-1992 and each mont h from 1994-1996 . " The variables included in X ht are controls for education level, experience and it s square, sex, urban residence, and sector of occupation . A key feature of the results is that the R 2 value s drop sharply after the shift to monthly reporting in 1994 . Presumably. the bulk of this drop is due to greater idiosyncratic variability of income, as well as greater relative importance of measurement error , when income is reported at monthly rather than quarterly frequencies . Next, we assume that the standard deviation of the residuals from estimation of equation (1 ) follow the process :
17 These results are not reported in the paper but are available from the authors .
28
(2)
In a, = tt„ + rt,t + 1t2 t t
+ ;~ j t 3 +
tt4 YBt
+
tSPVTSHRt
+7
6 I[t>96] +
pt
t = 2 . 4, . . ., 95 ; t = 109, 110, . . . . 144 . Here t is a monthly time index . For the years 1985 through 1992, the data are quarterly . so t is assigned as the midpoint of the interval covered by each quarter (that is, t = 2, 4,
95) . The variable I[t>96] is an
indicator for the post-1992 period in which the data is monthly . Thus. 1t(, captures the structural shift i n the error standard deviation attributable to the shift to a monthly data frequency . The time polynomial s capture the evolution of the error standard deviation over time due to changes in within-group incom e inequality, controlling for the group characteristics included in X ht . The term YBt controls for the effec t of changes in mean income on the error standard deviation . PVTSHR, is the share of total employment i n the private sector . This share increased significantly after the transition (see Table 1) and, given tha t earnings inequality tends to be higher in the private sector, could be an important determinant of overal l earnings inequality . Finally, rl t captures purely idiosyncratic period-specific changes in incom e variability . Note that. since we do not have earnings data for 1993, we assume smooth adjustment fro m the end of 1992 through the beginning of 1994, with no discrete breaks in the intervening year . After estimating the first and second stage equations, we adjust labor income data for 1994-199 6 to account for the increase in the idiosyncratic variance that we estimate occurred solely due to the shift t o a monthly reporting period after 1992 . We define adjusted income for the 1994-96 period as : (3) Here
YA ht h,
= alt +
(3 t X h , - { 6t / exp(7t6 ) }
ht
t = 109, HO__ , 144 .
is the estimated residual from equation (l) and 7t6 is our estimate from equation (2) of the
increase in the log of the residual standard deviation due to the switch to monthly income reporting . The scale factor exp(7t6 ) that we estimated was 1 .051 . This implies that the change in survey frequency results in an estimated increase of about 5 percent in the residual error variance . Thus, even though employed workers (a majority of whom are salaried) have reasonably stable labor earning s streams, there is still a significant increase in earnings variability in going from a quarterly to a monthl y frequency .
29
Interestingly, the corresponding adjustment factor that we estimated in Keane and Prasad (2000 ) for total income of worker-headed households was 1 .179 (which, in turn, was much lower than th e adjustment factors for farmer and mixed farmer-worker households) . One would indeed expect th e variability of household income and household labor earnings to be higher than for individual employe d workers . Note that we observe wages for workers only if they are employed . On the other hand, we d o observe zero labor incomes for worker households . This. in addition to variability in employment statu s among household members . suggests that there is likely to be greater cross-sectional variability in labo r earnings of households compared to the sample of employed workers . Further . as the estimated adjustment factors indicate, the increase in cross-sectional variability at higher frequencies i s proportionally greater for households than for individual employed workers .
We thank the staff at the Polish Central Statistical Office, especially Wies`aw Cagodzi=ski and Jan Kordos, fo r assistance with the data . Krzystof Przybylowski and Barbara Kami . ska for translations of the survey instruments; and Kenichiro Kashiwase for research assistance . We received helpful comments from Oded Galor . Susan Collins, numerous other colleagues and seminar participants at the IMF . Research funding was provided by the Nationa l Council for Eurasian and East European Research .
30
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Figure 1 . Weighted Kernel Density Estimates : By Sector
Figure 2 . Wage Differentials
Figure 3 , Weighted Kernel Density Estimates : By Gender
Figure 4 . Education and Experience Premia
