Evidence on Occupational Wage Distribution
3.5. POLARIZATION OF WORK 53 In a next step, I also include occupation dummies and industry dummies to
control whether the results are driven by particular occupations or industries.
I run the estimation approach separately for each task group (see Table 3.5 for a detailed description of the task groups). The results are presented in Tables 3.11 and 3.12. The high r-squared in columns (2)-(4) of both tables indicates that the inclusion of occupation dummies in equation (3.4) might cause a bias due to multicollinearity, whereas including industry dummies does not affect the results.
But, as I am interested in the effect of technological change on the wage of workers performing different tasks, I need to absorb the wage effects driven by particular occupations. Therefore, I modify equation (3.4) in the following way:
lnWot=β1+β2Pt+β3COM Po∗Pt+β5X+ηo+ot, (3.5) where ηo are occupation fixed effects which substitute for the dummy variable COM Po. It is not possible to run the regression including both, the dummy variable COM Po and fixed effects ηo. Again, the interaction term COM Po ∗Pt is of main interest as it represents the treatment effect. The estimation results are given in Tables 3.11 and 3.12 in columns (5) and (6). Neither the sign nor the size of the estimated coefficients of the treatment effect COM Po∗Pt changes, but the signifi-cance of the results varies (the same applies to the other control variables). Thus, the fixed effects estimation based on equation (3.5) seems to be the most suitable approach.
The results presented in columns (5) and (6) at the upper part of Table 3.11 support the hypothesis of increasing wages of workers who perform non-routine an-alytic tasks. Compared to the control group their wages increase about five percent after the introduction of computer technologies. In contrast, workers who perform routine cognitive tasks (like calculating or bookkeeping) experience a wage loss of more than six percent compared to the control group where no computers were introduced in the relevant time period (see lower part of Table 3.11). Thus, also the second hypothesis is supported. Both results are significant at the 5%-level and are robust to the inclusion or exclusion of control variables as well as the in- and
effect of computer introduction on the wage of workers who perform non-routine interactive, routine manual, or non-routine manual tasks.
To support the theory that the task content of work and not the skill level is the channel for the wage effect of technological change, I re-estimate equations (3.4) and (3.5) separately for each skill level.6 The results given in Table 3.13 show no significant effect of computer introduction on the wage level. These findings support the hypothesis that it is not the skill level that affects the degree of an increase or decrease of wages due to technological change.
Based on these results, I generate 15 interaction groups of the skill level and the task groups to analyze if the observed results are driven by one special group (for example low skilled workers that perform routine cognitive tasks). As data coverage is quite low in some cases, it is not possible to run the regression based on equation (3.5) for each of the 15 interaction groups. There are, for example, no low skilled workers that perform non-routine analytic tasks. Therefore, Table 3.14 only contains results for four combinations: Low skilled workers performing non-routine manual tasks, and high skilled workers that perform routine analytic, non-routine interactive, or non-routine manual tasks. The interesting result is that workers performing manual tasks experience a five percent wage loss compared to the control group – no matter if they are low skilled performing non-routine manual tasks or if they are high skilled performing routine manual tasks. In contrast, wages of high skilled workers performing non-routine analytic, or non-routine interactive tasks are increasing after the introduction of computers. Results are significant at the 1%-, 5%-, and 10%-level and are robust to the in- and exclusion of control variables.
However, data coverage is still quite low.
Bringing these results together with theoretical model presented in section 3.2, it becomes evident that each occupation requires a special bundle of skills to perform the series of tasks required for the particular job. I can show that the task content
6Based on the estimation results presented in Tables 3.11 and 3.12, it would be sufficient to run only regressions based on equation (3.5). However, to show that the estimated coefficients of COM Po∗Pt are also robust to the different estimation approaches in this case, I present the results based on equation (3.4) for each skill level, too.
3.6. SUMMARY 55 of work is the channel through which technological change (measured by the intro-duction of computer technologies) affects wages. My results support the hypotheses that computer technologies are a substitute for (non-)routine manual and routine cognitive activities, and are complementary to analytic and interactive occupational tasks. Hence, wages of workers performing manual tasks decrease after the intro-duction of computer technologies no matter if workers are low skilled or high skilled.
In contrast, wages of workers in occupations that are characterized by non-routine analytic or non-routine interactive tasks increase. However, I do not find evidence for the hypothesis that primarily medium skilled workers lose (see e.g. Autor et al., 2006, Michaels et al., 2010).
3.6 Summary
A large amount of research has dealt with the determinants of wages, wage setting and the distribution of wages. Due to a lack of comparable international wage data, there are still many questions about international wage structures and occupational wage distributions which have not yet been analyzed. Making use of the newly stan-dardized and imputedOctober Inquiry database provided by the International Labor Organization (see Freeman & Oostendorp, 2000, 2001; Harsch & Kleinert, 2011) al-lows to analyze international wage structures and occupational wage distribution in a comprehensive way.
To give a brief introduction to the theoretical mechanism of wage setting in occupations, I introduce a intuitive theoretical model of wage setting following Firpo et al. (2011) which captures occupational wage differences. Moreover, the model allows to analyze the channels through which technological change affects wages.
There are several assumptions in the theoretical model which I test empirically for the member states of the OECD, the EU, the United States, and Germany. First, the model assumes that wages do not differ within the same occupation in general because skill requirements do not differ within the same occupation. However, the model makes no clear assumptions concerning wage differences within the same
which are larger in the OECD than in the EU. In the OECD as well as in the EU, wage spreads are increasing since the beginning of the 1980s. This leads to the conclusion that either returns to skill or base payments vary between countries.
Second, I test the very intuitive assumption that the required skills differ between occupations as well as the occupational returns to skills, respectively. Therefore, wages are supposed to differ with regard to different occupational skill requirements.
I find evidence that low skilled workers earn on average about 15 percent less than medium skilled workers, whereas high skilled workers earn about 60 percent more than medium skilled workers. The results are consistent with the assumptions of the theoretical model, as there is a large wage heterogeneity between the different skill levels, which can be explained by different returns to the bundle of skills that is required to carry out an occupation. However, several authors argue that it is not only the skill level that affects wage distribution and wage inequality, but the task content of jobs. I follow Spitz-Oener (2006) and classify each of the 161 occupations reported in the October Inquiry into five task groups. I can show that the spread between workers occupied in jobs with different task requirements is considerable larger than the spread between different skill levels. For example, workers in jobs that require routine manual tasks earn on average about 50% of the wage of workers in jobs with nonroutine analytic tasks requirements. The largest spreads can be found in the United States.
Third, the theoretical model assumes that the returns to skill are equal in the same occupation within a country. Therefore, there should be no wage differences within the same occupation. To test whether this assumption is supported by the data, I choose two occupations that are reported for several industries and determine whether there are differences in payment within occupations across industries. I find significant differences in the wage level for the different industries. A stenograph-typist inWholesale trade sector is on average paid worst in the OECD, the EU, and Germany. He or she earns at least 15% less than a stenograph-typist working for a bank. The differences in payment for a laborer are not as large as for a typist, however, there are also significant differences.
3.6. SUMMARY 57