Abstract
This paper examines the effect of accumulated human capital, and particularly occupational human capital, on the workers’ wages. Unlike previous studies that apply occupational tenure as a proxy for occupational human capital, this paper applies the concept of Shaw’s (1984) occupational human capital to capture the transferability of occupational skills and estimates a new measure of occupational human capital, so-called occupational investment. Using the National Longitudinal Survey of Youth (NLSY) from 1979 to 2000, the key findings of this paper suggest that occupational skills from the previous jobs can also affect the workers’ wages at the current job and that occupational investment is one of the important sources of wages supporting the Shaw’s original work on wage determination. Specifically, 5 years of (3-digit) occupational investment relative to current occupational tenure could lead to a wage increase of 7.7 to 18.4 %. I also find that the general labor market experience accounts for a large share of workers’ wages.
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Notes
In addition to human capital theory, an alternative aspect that could also explain how workers’ wages increase with firm tenure even if a worker does not accumulate firm-specific skills is the internal labor market (e.g., deferred compensation, promotions, and transfers within a company). However, it is difficult to expand this aspect to account for wage increases within occupations or industries because it is not clear how implicit contracts could apply between a worker and occupation or industry (Sullivan 2010).
To develop the measure of occupational skills transferability, Shaw (1984) examined the occupational movement across years using the Current Population Survey (CPS). However, this method introduces a significant amount of noise due to the occupational coding errors of the CPS (Kambourov and Manovskii 2004). In addition, it assumes skills transferability to be symmetric, in which the skills transferability from occupation i to j is equal to the skills transferability from occupation j to i. In other words, the direction of the skills transfer is irrelevant. Ormiston’s skills transferability, on the other hand, is a non-symmetric method, in which the skills transferability from occupation i to j does not have to be equal to that from occupation j to i. Thus, Ormiston’s method of skills transferability is preferable in this analysis.
The skills transferability across occupations, developed in this paper, is first based on the SOC and then is matched to the 3-digit occupational categories of the 1980 occupational classification system provided in the CPS. While there are some differences between these two systems, we carefully matched these categories with guidance from the CPS codebook supplement in order to prevent the recoding problems.
A complete occupational skills transferability matrix is available from the author.
One might have concerns regarding whether averaging KSA categories gives the appropriate weights in cross-occupational comparisons given the presence of a vital KSA in the estimation of occupational skills transferability measure. For example, when thinking about the “medicine and dentistry” knowledge category, this is of utmost importance to surgeons and physicians; without it, no amount of similarity from other KSA categories (e.g., interpersonal communication) means anything. In other words, even if transferability is high, it is relatively useless without high transferability in one specific category. Although it might look suspicious in some particular pairs of occupations, since we still do not know exactly how to appropriately weight each KSA category for each occupational pairing, the overall transferability using the averaging over KSA categories is still applicable.
The choice of the occupation may also be relevant in wage determination because workers in occupations that pay higher wages are more likely to obtain higher occupational skills (i.e., higher occupational tenure) and to move to occupations with higher degrees of skills transferability. Thus, to capture this aspect, I control for the 1-digit occupation dummies in the estimation. The same logic also applies to choice of industry.
The reason for including this variable is that the first year of work with an employer typically requires more investment in job-related skills (Parent 2000). In particular, this variable would be expected to be positive if investment in firm-specific skill rapidly increases at the beginning of a job.
This study uses the data only through 2000 to reduce the inconsistency of coding formats. After 2000, both occupation and industry codes were changed from 1980 U.S. census codes to 2000 U.S. census codes.
Some individuals report changes in industry and/or occupation without changing jobs (firm). While these changes are more likely to happen for occupation changes within firms, it is almost impossible to change industries within firms (Parent 2000). Also, there is no way to differentiate between real occupation or industry changes and coding errors in the data. Parent (2000) solves this problem by restricting the industry codes to be the same within a firm.
Industry mobility patterns are quite similar to Fig. 1 even though a higher percentage of workers tend to stay in the same industry. Results are available upon request.
Note that although the squared terms of occupational investment are not statistically significant, the F-test for the significance of both terms of occupational investment, however, indicates that they are jointly significant (for example, the F-test for Occt_3 and Occt_3_square is as follows: F(2,2301) = 14.64, p < 0.0001).
The occupational coding procedure in the NLSY (and also in other datasets such as the CPS) uses coders to assign particular occupation codes from the information given by survey participants. The information usually includes a brief description of the job. There is evidence of substantial measurement error in assigning these codes, especially at the more detailed 3-digit level (Kambourov and Manovskii 2009).
Altonji and Shakotko (1987) obtained a similar result about the relative importance of general human capital. They find that 5 years of general experience increases wages by 28 %, while the effect of firm tenure is very small and statistically insignificant.
References
Allison PD (2005) Fixed effects regression methods for longitudinal data using SAS. SAS Institute Inc., Cary, NC
Altonji J, Shakotko R (1987) Do wages rise with Job seniority? Review of Economic Studies 54:473–59
Altonji J, Williams W (2005) Do wages rise with Job seniority? a reassessment. Industrial and Labor Relations Review 58:370–397
Abraham KG, Farber HS (1987) Job duration, seniority and earnings. American Economic Review 77:278–297
Becker GS (1964) Human capital. University of Chicago Press
Belman D, Heywood JS, Lund J (1997) Public sector earnings and the extent of unionization. Industrial and Labor Relations Review 50:610–628
Belman D, Heywood JS (2004) Public sector comparability: the role of earnings dispersion. Public Finance Review 32:567–587
Goldsmith H, Veum JR (2002) Wages and the composition of experience. Southern Economic Journal 69:429–433
Kambourov G, Manovskii I (2004) A cautionary note on using (march) CPS data to study worker mobility. Mimeo, The University of Pennsylvania
Kambourov G, Manovskii I (2009) Occupational specificity of human capital. International Economic Review 50:63–115
Lazear EP (2009) Firm-specific human capital: a skill-weights approach. Journal of Political Economy 117:914–940
Mincer J (1974) Schooling, experience, and earnings. Columbia University Press, New York, NY
Neal D (1995) Industry-specific human capital: evidence from displaced workers. Journal of Labor Economics 13:653–677
Neal D (1999) The complexity of Job mobility among young Men. Journal of Labor Economics 17:237–261
Ormiston RA (2006) The role of occupation-specific human capital in economic analysis. Michigan State University, Dissertation
Parent D (2000) Industry-specific capital and the wage profile: evidence from the national longitudinal survey of youth and the panel study of income dynamics. Journal of Labor Economics 18:306–323
Podgursky M, Swaim P (1987) Job displacement and earnings loss: evidence from the displaced worker survey. Industrial and Labor Relations Review 41:17–29
Schönberg U, Gathmann C (2010) How general is human capital? a task-based approach. Journal of Labor Economics 28:1–50
Shaw K (1984) A formulation of the earnings function using the concept of occupational investment. Journal of Human Resources 19:319–340
Schmieder J (2007) Returns to tenure: is specific human capital acquired on the Job? Working paper
Sullivan P (2010) Empirical evidence on occupation and industry specific human capital. Labour Economics 17:567–580
Topel R (1991) Specific capital, mobility, and wages: wages rise with Job seniority. Journal of Political Economy 99:145–176
Zangelidis A (2008) Occupational and industry specificity of human capital in the British labour market. Scottish Journal of Political Economy 55:420–443
Acknowledgments
I would like to thank Dale Belman, Todd Elder, Peter Berg, Mary Hamman, Russ Ormiston, Liqiu Zhao, Xuan Chen, Naci Mocan, and two anonymous referees for offering helpful comments and suggestions. I also thank all seminar participants at Renmin University of China and Michigan State University. All remaining errors are mine.
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Appendices
Appendix A
Appendix B
Description of the Main Human Capital Variables in the NLSY Dataset
In this paper, the analysis focuses on the so-called “CPS” job (firm), which is defined as the current or most recent employer at the date of interview. In each survey year, an individual can have up to five different jobs (employers), and the one that is the current or most recent would be assigned as a CPS job. The detailed job information for each CPS job, regardless of whether it is a full- or part-time job, has been used to construct the key variables of this analysis.
The following explains in more detail the key human capital measures used in this analysis.
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1. Firm Tenure:
Firm tenure is measured based on the actual start date to stop date at each interview and added together between survey periods. For example, one individual starts working for Firm A on June 1, 1980 and is interviewed on June 31, 1980. He is still working for that firm on August 2, 1981, when he is interviewed again. He continues to work for the same firm until April 5, 1982, when he changes jobs. He is interviewed again on August 10, 1982. A cumulative firm tenure at Firm A in number of weeks could be constructed as follows:
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Tenure 1 = [Tenure from June 1, 1980 to June 31, 1980 at the 1980 interview]
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Tenure 2 = Tenure 1 + [Tenure from July 1, 1980 to August 2, 1981 at the 1981 interview]
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Tenure 3 = Tenure 1 + Tenure 2 + [Tenure from August 3, 1981 to April 5, 1982, the date that he left this firm at the 1982]
where Tenure 1 is the current firm tenure in the year 1980, Tenure 2 is the firm tenure in the year 1981, and Tenure 3 is the firm tenure in the year 1982.
Note that there is a possibility of double-counting employers. Up until 1998, the employers were only tracked between two contiguous interview years; therefore, an individual who works for a particular firm during the first year, leaves that firm next year, and then returns to work at the same firm a year or more later would be determined to work for a new firm during the second tenure since the previous tenure would have been out of the tracking scopes. As a result of double-counting employers, there is a possibility of firm tenure with a single firm being calculated as tenure with two separate employers, and unfortunately there is no systematic way to measure how often this event might have occured. After 1998, however, the computer-based interview system began to keep track of all employers so that it recognized when an individual returned to the same employer that he/she left a number of years earlier. In addition, there is also a gap within the reported start to stop date (a “between-job” gap) for a specific firm in which an individual is not actively working for that employer, but does not consider himself completely disassociated from that firm during these periods. Thus, the total tenure with that firm will not account for these gaps because they occur before the individual has reported an actual stop date for association with that firm, so these weeks of gaps would be considered as part of his tenure for that firm. Also note that according to the structure of the NLSY79, there is no distinction between a “firm”, “company”, and “job” in this analysis. In particular, all references to a “job” in the survey are essentially references to a given firm. Although a worker changes his location within the firm, the total tenure with that firm would continue to count.
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2. Actual Labor Market Experience:
This measures the total years of work experience in a labor market accumulated as a full-time or part-time employee since the first interview up to the time of the NLSY interview. One advantage of the NLSY is that it is a longitudinal dataset that reports the number of weeks worked for each year in the sample and obtains this information retrospectively for the years preceding the sample. This allows us to construct the measure of actual labor market experience, which is the key variable in this analysis. Specifically, I used the question that asks for the “number of weeks worked since the last interview” of each individual. Then, I added them together and divided this number by 52 to obtain the yearly actual labor market experience for each individual.
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3. Occupation and Industry Tenure:
For the 3-digit level of occupation and industry tenure measures, I used the CPS occupation/industry of individual, which is defined as “the current or most recent occupation/industry that the individual has during the survey week.” Given the CPS occupation/industry for each individual across years, I can construct both occupation and industry tenures directly by comparing each individual’s occupation and industry year by year. If the occupation/industry between two consecutive years were reported as being the same, then I would assume that individual worked for that occupation/industry for the whole year. In that case, I can add the number of weeks worked since the last interview (divided by 52) into the occupation/industry tenure. Note that it is possible that an individual holds more than one occupation/industry within the same firm during the time of the interviews, but only the occupation/industry reported at the interview would be the current/most recent occupation/industry. In addition, in some cases, the occupation and industry codes are missing because individuals declined to give information about the type of work they performed. In other cases, the codes are missing because errors were made when processing the descriptions of the individuals’ jobs. These errors would create “invalid skips” in the occupation and industry data. In addition, in many cases, the codes are missing because the individuals have never been asked about certain characteristics of a particular job. This happened in some, but not all, cases where jobs either involved less than 10 h of work per week or less than 9 weeks of actual work. These missing codes would create “valid skips” in the data (Neal 1999).
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Nawakitphaitoon, K. Occupational Human Capital and Wages: The Role of Skills Transferability Across Occupations. J Labor Res 35, 63–87 (2014). https://doi.org/10.1007/s12122-013-9172-2
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DOI: https://doi.org/10.1007/s12122-013-9172-2