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Prepared by Eswar Prasad, Research Department. A substantially revised and extended version of this chapter will be published as an IMF Working Paper.
See van der Willigen (1995) for a description of the wage bargaining structure and Chapter I of this paper for a discussion of how it may have been well suited to the Wirtschaftswunder era of the 1960s and 1970s.
See Chapter I for a description of the possible mechanics of this interaction.
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.
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.”
Restricting the sample to German citizens made no difference to these results.
Log hourly real wages were regressed separately for each year on a constant, education, experience and its squared, a dummy for German citizenship and interactions of this dummy with education, experience and squared experience.
The inclusion of higher order polynomials of experience did not change any of the results.
As noted earlier, the levels of these premia must be interpreted with caution since the education variable might have different connotations in different countries.
Trade includes retail and wholesale trade. Miscellaneous services comprise what are typically thought of as low-end services, including restaurants and hotels, trash removal and other basic services.
A cautionary note is in order here. Even in the United States, as shown by Krueger and Summers (1988), inter-industry wage differentials tend to be significant despite controlling for observed (and even some unobserved) worker attributes. This could account for some but probably not much of the differential between median wages in the miscellaneous services sector and most other sectors.
Further, 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.
Source: OECD Education Statistics, 1985-92, Table IV-3.
These data, which are limited to west Germany, are taken from Reinberg and Rauch (1999) and are based on the Mikrozensus, a comprehensive survey of the German labor force. The raw data from this survey are not publicly available.
Using the GSOEP data, annual probit employment equations were estimated for men (extending the sample to include men without a job). The estimated coefficients 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 roughly to the 1-digit SITC sectoral classification; this coarse classification is intended to capture the effects of shifts in employment from manufacturing to services. Using the full set of GSOEP industry codes, which would be similar to using a 2-digit classification, revealed quite 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 90 percent of the sample for the years 1990-97.
For an explanation of the selection bias problem, see Heckman’s (1979) classic paper. 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 choice equation includes the right hand side variables in equation (i) 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, presence of kids, status as head of household and geographic regions. The parameters of equations (i) and (ii) were jointly estimated by full information maximum likelihood techniques.
The Netherlands, for instance, appears to have attained much better labor market outcomes, with little increase in wage inequality. This appears to be attributable to recent labor market reforms that have greatly increased the relative supply of skilled workers, particularly by fostering higher labor force participation rates among educated women.
The sample in this part of the analysis includes both men and women, A gender dummy and its interactions with other independent variables was included in the wage regressions used to generate the predicted offer wage. The coefficient estimate on this dummy variable was then used to adjust the offer wage for gender effects.
The estimated wage equations explain only about 35 percent of the variation in wages across workers. Thus, unobserved attributes (not observed by the econometrician) clearly play a significant role in wage determination. However, since unobserved attributes would be expected to be lower among the unemployed than among the employed, this bias would probably actually strengthen the result described here. That is, unemployed workers, particularly the less-skilled ones, would be likely to have inferior unobserved attributes compared to employed workers and should therefore expect to earn even less than would be indicated by the wage equation estimates.