However, at the same time we predict negative scal NPVs for a sizable share of the higher educated individuals: slightly above (below) one third of the simulated life cycles of men (women) who pursue higher education imply scal losses when compared to the average counterfactual life cycles (see Table 1.6). This nding is also observ-able in Figure 1.4, which plots the distribution of scal NPVs for women and men.
Again, both density functions are right-skewed, with women's returns somewhat more compressed.
Figure 1.4: Distribution of the scal NPV of higher education
Notes: Density function of the scal NPV of higher education. A discount rate of 2
% is applied. Source: Own simulations.
1.7 Discussion
As explained above, there are other studies quantifying the lifetime returns to higher education. In general, it is dicult to compare our results to studies from other coun-tries. One fundamental reason is the denition of the counterfactual to higher edu-cation. While Germany has a well-established system of vocational training which is a viable alternative for many young adults, this alternative is missing in other coun-tries. Hence, we compare our results to two studies which also estimate the return to higher education for Germany, Pfeier and Stichnoth (2020) (henceforth PS) and
1.7. DISCUSSION 38 OECD (2019) (henceforth OECD). Table 1.8 compares our IRR estimates to the ones presented in these studies. In sum, we nd that our estimates for the private returns are similar to the ones in PS but considerably lower than the ones presented by the OECD. In contrast, for scal returns our estimates are larger than both OECD and PS, but closer to the ones reported in OECD.
Table 1.8: Returns to higher education across studies Private
Pfeier/Stichnoth 14.2 x x 7.4 6.6
OECD x x 14, 16 x 6, 9
Notes: The table lists estimated IRRs to higher education for Germany (in %). When two numbers are shown, the rst refers to women and the second refers to men. When one single number is shown, the return estimates for women and men are pooled. In-come concepts for which no return estimates were computed are labeled with x. Pfeif-fer/Stichnoth=Pfeier and Stichnoth (2020); OECD= OECD (2019).
Source: Own calculations, Pfeier and Stichnoth (2020), OECD (2019).
Various reasons might be responsible for the dierences found between our study and the others. For instance, PS exclude civil servants and self-employed from their estimation while we include both for private returns and only exclude civil servants for scal returns. In addition, PS assume that tax-transfer components of household income are divided equally between both partners, which might be a reason why they nd lower scal returns (see the discussion above).
Net private returns to higher education estimated by the OECD appear surpris-ingly high. One reason for this should be that the OECD uses another comparison group for the group of academics. This comparison group is comprised of individuals with either a higher education entrance degree or with a vocational training degree.
Hence, this approach precisely excludes our comparison group, i.e. those with both degrees. Another reason might be the modeling of the tax-and-transfer system. The OECD uses a very simplied model for estimating taxes and transfers for each individ-ual. Transfers and benets, for instance, are not simulated, which are more important for individuals with a vocational degree than for academics. Lastly, changes in method-ology across OECD publications that are dicult to trace for the reader have produced substantial variation in results across recent publication years.
39 1.7. DISCUSSION Finally, it should be noted that beyond the reasons mentioned above, there are fundamental dierences in the modeling approaches. While PS and OECD use one single or a few recent cross-section(s), implicitly relying on the so-called synthetic cohort assumption, we account for time and cohort eects. An example is female labor force participation where we see considerable changes across birth cohorts.
One striking feature of our results is that the share of life cycles that yield neg-ative private or scal NPVs to higher education is relneg-atively high, for instance when compared to the estimates of Courtioux et al. (2014), who nd an overall share of 3.5%
negative private returns for France.50 Again, the sharp dierence to their results can be explained for the most part by their denition of the counterfactual income stream, which is based on all individuals without a higher education degree. Following this approach in our model would result in shifting the distribution of NPVs to the right, and decreasing the share of negative NPVs. However, as argued above, we think that our approach is more appropriate to estimate returns to higher education for Germany.
Nevertheless, one might still wonder whether the proportion of negative NPVs is plau-sible. Indeed, we argue that these results have to be interpreted with some caution.
First, our simulation strategy rests on the assumption that the wage residuals we im-pute for each individual are the result of a matching process between employee and employer. However, to some extent the empirical distribution of the wage residuals, or, more specically their variance, also reects measurement error in hourly wages. In our data, this can be expected in particular for hourly wages of self-employed individ-uals.51 The measurement error in observed (log) hourly wages inates the variance of residuals, and thereby the variance of simulated wages and of estimated NPVs. This is also reected in the very low hourly wage levels we observe for the lowest NPV deciles (shown in Table 1.5). Second, we assume that all individuals, conditional on gender and migration background, have the same counterfactual. This implies that there is no correlation between the wage residuals drawn under higher education and those under vocational training. Hence, an academic who draws" a high residual has the same counterfactual as an academic with a low residual. Assuming that there is some positive correlation between residuals would compress the distribution and hence imply a lower share of negative NPVs. A similar argument could be made about the correlation of economic sectors under both educational paths, for instance. In general, however, it is dicult to argue how the correct" counterfactual would look like.
50Note that Courtioux et al. (2014) base their measure of negative returns on individual IRRs instead of NPVs. Even though the two measures are related, their results are not fully comparable.
51In the SOEP questionnaire, they are asked to estimate the income in the month before the interview, which can be expected to be uctuating more sharply compared to dependent employees.