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I am grateful to Albert Jaeger for extensive discussions and for generously sharing some of the aggregate data used in this paper. This paper has benefited greatly from the comments of Orazio Attanasio, Jennifer Hunt, Axel Schimmelpfennig, numerous colleagues, and participants at various seminars.
See van der Willigen (1995) for a description of the wage bargaining structure and Jaeger (1999) for a discussion of how it may have been well suited to the Wirtschaftswunder era of the 1960s and 1970s.
Part-time workers and apprentices account for a relatively small fraction of the sample and including them did not have much affect on any of the results discussed below. Results for the sample including part-time workers and apprentices are available from the author. An analysis of wage growth in East Germany following unification constitutes an interesting topic in its own right (see Hunt, 1999b).
As noted by Hunt (1999a), using the sum of the contracted weekly hours and overtime hours variables is problematic. This sum would not capture “under-time” since only positive overtime hours are reported in the survey.
Results using the years of education variable were reported in an earlier version of this paper and are available from the author. The classification used here is similar to that adopted by other authors who have used this data, including Hunt (1999a). Haisken-DeNew (1996, pp. 110-111) has an extensive discussion of the mapping between educational attainment and years of schooling for this dataset and notes that, regardless of the mapping used, when estimating wage equations “…the differences are typically minor, and the results for education and experience remain very robust.”
Other summary measures of inequality such as Gini coefficients revealed a similar pattern.
The top and bottom 5 percentiles have been trimmed out in the figures. A fitted regression line for the cumulative wage changes across percentile points is shown in each panel.
Log hourly real wages were regressed separately for each year on a constant, education dummies, experience and its squared, tenure, a dummy for German citizenship and a fiill set of interactions of this dummy with the other independent variables. The choice of the specification for these regressions is discussed further below.
I experimented with the inclusion of higher order polynomials of experience; the results were essentially unchanged. Coefficients on polynomials of the tenure variable were also small and statistically insignificant. I do not include industry or occupation dummies in these regressions. Since individuals typically self select into industries and occupational groups, inclusion of these dummies could induce substantial bias in estimated skill premia.
As noted earlier, the levels of these premia must be interpreted with caution since the education variable might have different connotations in different countries.
Another reason for limiting subsequent analysis to the West German sample is that some of the variables required for the analysis (e.g., years of education) were not available for East German and other migrants and had to be imputed. Further, it is unclear if human capital variables such as education and experience have similar connotations across these samples. Preliminary analysis indicated that there were some differences in the coefficients on the education and experience dummies across these samples. This problem is also apparent in the jump in standard errors on the estimated returns to education in Table 3A.
Using CPS data for the United States, Buchinsky (1994) also finds a large amount of year-to-year variation in the returns to education and experience at different quantile points. It is also worth noting that, as in Buchinsky’s results, the standard errors for the estimated coefficients in Tables 4-6 are much larger at the extreme quantiles than at the middle quantiles.
In addition, the dispersion of annual earnings could differ from that of monthly earnings. However, the GSOEP data set does not contain a variable indicating the number of months that a worker is employed during the survey year.
Katz and Murphy (1992) construct proxies for relative demand shifts using shifts in the mix of industry-occupation classifications and the relative proportions of skilled and unskilled workers within these industry-occupation cells. Unfortunately, preliminary calculations indicated that the GSOEP does not have enough data available (as reflected in the cell sizes) for such an exercise to yield reliable results.
Source: OECD Education Statistics, 1985-92, Table IV. 3. These ratios can also be calculated (1985,1992) for certain other countries including Canada (0.623, 0.702), Italy (0.191, 0.182), Japan (0.349, 0.348) and the Netherlands (0.280, 0.260). Unfortunately, the relevant data are not available for France and the United Kingdom.
The data for both panels of this figure, which are limited to West Germany, are taken from Reinberg and Rauch (1998) and are based on the Mikrozensus, a more comprehensive survey of the German labor force than the GSOEP. Skill levels are defined on the basis of a number of observed characteristics including education levels and occupational categories. The raw data from this survey are not publicly available. GSOEP data revealed very similar patterns.
To examine the evolution of group-specific employment rates, I used the GSOEP data to estimate annual probit employment equations for men (extending the sample to include men without a job). The estimated coefficients (not shown here) confirm the sharp increase in the employment probabilities of workers with higher levels of education during the 1990s.
These results are based on a classification that corresponds 1-digit SITC sectoral classification. The ten sectors are agriculture, forestry and fishing; utilities; manufacturing; construction; trade; transport and communications; finance and insurance; business and personal services; other basic services; and public administration. Using the full set of GSOEP industry codes, which would be similar to using a 2-digit classification, revealed quite similar results.
Note that the numbers in the table are multiplied by 100; the absolute increase in variance over the full sample is actually quite small.
Cohorts defined on the basis of birth year yielded similar results.
The results reported in this paragraph, including the comparisons of skill premia with and without supplementary earnings, are limited to those observations for which the data needed for constructing the adjustment factor are available. This amounts to about 96 percent of the sample for the years 1990-97. Data on average gross monthly pay in the year prior to the survey were not available for 1984-89. For these years, I constructed the adjustment factors using current year gross income (assuming full-year employment) in the denominator. I do not show the results for 1984-89 here since the adjusted data would not strictly be comparable with those for 1990-97, but those results were also very similar in each year to the corresponding results for wages excluding supplementary income.
Keane and Prasad (1996) provide an example of the importance of accounting for selection bias in estimating skill differentials.
The selection model involves two equations: (i) the basic OLS wage equation and (ii) a probit employment choice equation. The employment equation includes the right hand side variables in equation (i) (except tenure) and a set of additional variables that could influence self-selection into employment but would not be expected to affect the wage. This set of additional variables included dummies for marital status and presence of kids. Additional dummies for status as head of household and home ownership were also tried, but did not add much. The sample for equation (i) conforms to that of the results reported in earlier sections (full-time employed males, excluding self-employed etc.); all other male labor force participants were included in the estimation of the second equation. The parameters of equations (i) and (ii) were jointly estimated by maximum likelihood techniques.
These data are taken from Statistiches Taschenbuch 1998: Arbeits und Sozialstatistik (Bundesministerium fur Arbeit und Sozialordnung). Note that the numbers refer to United Germany starting in 1991 and to West Germany before that. Assuming plausible elasticities of substitution between capital and labor, the labor-supply shift caused by unification can not by itself explain anything close to the observed trend decline in the wage share.
These data were obtained from the German Ministry of Finance. Since aggregate employment has grown by much less than output growth in the 1980s and 1990s, the increase in the capital-output ratio is probably a downward-biased measure of the increase in the capital-labor ratio. Recent developments in investment and output suggest that the capital-output ratio shown through 1995 in Figure 12 has risen further since then.
See Griliches (1969) and Goldin and Katz (1998) for some evidence on capital-skill complementarity. Krusell et al. (2000) argue that capital-skill complementarity is important for understanding changes in wage inequality in the United States.
This is, again, a dynamic phenomenon but, for expositional convenience, is discussed here as a static concept. Steiner and Wagner (1997) present some evidence of relative labor demand shifts in West German manufacturing in response to skill-biased technological change as well as intensified international competition.