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«IZA DP No. 3067 How General Is Human Capital? A Task-Based Approach Christina Gathmann Uta Schönberg September 2007 Forschungsinstitut zur Zukunft ...»

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The evidence also suggests that task-speci…c human capital is an important source of individual wage growth, in particular for university graduates. For them, at least 40 percent of wage growth due to human capital accumulation can be attributed to task-speci…c human capital, while occupation-speci…c skills and experience account for 14 and 47 percent respectively. For the medium-skilled (low-skilled), at least 25 (35) percent of individual wage growth is due to task-speci…c human capital. We also provide compared to those of the high-skilled. Also note that in the …rst di¤erence regression, the coe¢ cient on (the change in) experience should not be interpreted as returns to general human capital accumulation, as they additionally re‡ ect the change in the …rm and task match quality.

evidence that the costs of displacement and job reallocation depend on the employment opportunities after displacement: Wage losses are lower if individuals are able to …nd employment in an occupation with similar skill requirements.

Our …ndings on both mobility patterns and wage e¤ects are strongest for the high-skilled, suggesting that task-speci…c skills are especially important for this education group. One explanation for this pattern could be that formal education and task-speci…c human capital are complements in production.

Complementarity implies that high-skilled workers accumulate more task human capital on the job which would account for the sharp decline in the distance of moves over the life cycle. It would also explain why wages in the previous occupation are less valuable in the new occupation and why returns to task human capital are higher than for the two other education groups.

The results in this paper are di¢ cult to reconcile with a standard human capital model with either fully general or …rm- (or occupation-) speci…c skills. Our …ndings also contradict undirected search models of turnover where the current occupation has no e¤ect on future occupational choices, and skills are not transferable across occupations. The …ndings however support a task-based approach to modeling labor market skills in which workers can transfer speci…c human capital across occupations.

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A Data Sources A.1 Data on Occupational Tasks (1979-1999) We use four cross-sections of the German Quali…cation and Career Survey conducted in 1979, 1985, 1991/92 and 1998/99 by the Federal Institute of Vocational Training (BIBB) and the Institute for Labor Market Research (IAB). The data with a sample size of 30,000 covers individuals between 16 and 65, who are employed at the time of the survey. We restrict our sample to men employed in West Germany and exclude the self-employed, civil servants and those working in agriculture. We also exclude those without German nationality since they were not included in each wave. We use the same 64 occupations based on a classi…cation system by the Federal Employment O¢ ce, which is standardized over time.

The aggregation at the 2-digit level decreases well-known measurement error problems of occupational classi…cations in survey data and allows us to match the data to our main data set on job histories.

For each respondent, we know whether the worker performs certain tasks in his job and whether this is his main activity on the job. Unlike the Dictionary of Occupational Titles (DOT) in the United States, we do not know how intensively a particular task is used beyond the distinction of main activity, task performed and not performed. Overall, we have information on 19 di¤erent tasks workers perform in their jobs. Following Autor et al. (2003), we group the 19 tasks into three groups of tasks: analytical tasks, manual tasks and interactive tasks. The assignment of tasks is as follows: manual tasks (equip or operate machines, repair, reconstruct or renovate, cultivate, manufacture, cleaning, serve or accommodate, construct or install, pack or ship or transport, secure, nurse or treat others), analytical tasks (research or evaluate or measure, design or plan or sketch, correct texts or data, bookkeeping or calculate, program, execute laws or interpret rules) and interactive tasks (sell or buy or advertise, teach or train others, publish or present or entertain, employ or manage personnel or organize or coordinate).

A.2 Employee Sample (1975-2001) Our main data set is a two percent sample of all German social security records administered by the Institute for Employment Research. By law, employers are required to report the exact beginning and end of any employment relation of new hires and employees leaving the …rm which are subject to social security contributions. In addition, employers provide information about all their employees at the end of each year. We therefore know the exact date of employer changes and movements into and out of paid employment. Another advantage is that the data contain an unusually in-depth set of background information for each individual, including his age, education, gender, nationality, plant of work and occupation. We distinguish three education levels: low-, medium-, and high-skilled. We de…ne a worker to be high-skilled if at least one spell classi…es him as a graduate from a university or technical college.

(Fachhochschule). A worker is medium-skilled if he spent at least 450 days in apprenticeship training, and no spell classi…es him as a college graduate. A worker is low-skilled if he spent less than 450 days in apprenticeship training and did not attend a technical college or university. The occupational categories of employees and apprentices in the social security records are highly accurate as the classi…cation forms the basis of wage agreements between unions and employers’ association. To make the 130 di¤erent occupations we observe in our sample comparable to the BIBB data, we aggregated them into 64 occupations at the 2-digit level using a code provided to us by the Institute for Employment Research.

All experience and tenure variables refer to the beginning of each spell. Time out of the labor force and time in unemployment as well time in apprenticeship training is not counted. If an employee returns to his occupation, we count the time spent in the earlier spell towards his occupational tenure. The same holds in the unlikely event that a worker returns to a …rm he has worked for previously. Our results on occupational movers exclude these return movers, but the estimates are similar if they are included.

In addition to the sample restrictions mentioned in the text, we dropped all spells in vocational training and those job spells that started prior to an apprenticeship or tertiary education. In addition, we excluded observations that were still in vocational training at the end of the sample period in 2001 or pursued more than one apprenticeship, that is were employed as an apprentice for more than 7 years.



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