The ‘skill premium’ and how to measure it
Rising wage inequality has garnered significant attention in the media and among policy circles. Economists have argued that rising inequality is a consequence of increasing demand for skills combined with shifts in the supply of skills (Autor 2014, Katz and Murphy 1992, Autor et al. 1998, Card and Lemieux 2001, Acemoglu 2002, Goldin and Katz 2007, Autor, et al. 2008, and Acemoglu and Autor 2011). The ‘skill premium’ or wage difference between high and low skill workers is increasing and is one of the factors driving income inequality.
In recent research, we explore the determinants of the skill premium (Mueller et al. 2015). We find skill premia are larger at larger firms, providing a firm-based explanation of rising wage inequality.
To understand the determinants of the skill premia, we first have to agree on how best to measure them. Existing measures of skill premia, such as education, experience, or even occupations, are not adequate as they do not reflect a one-to-one mapping between job tasks and skill requirements. In Mueller et al. (2015), we address these issues using a unique, proprietary dataset of UK firms.
In our data, provided by Income Data Services (IDS), we observe how much a firm pays workers employed in different occupations and, crucially, how these occupations map into broader ‘job level’ categories which are comparable across firms. Since job levels are determined based on the skills required for the job, comparing wages for a worker classified at a high job level to a worker classified at a low job level allows us to more directly measure the skill premium. Moreover, since we have these data for a broad cross-section of firms measured at multiple points in time, we can observe within-firm and across-time patterns in the skill premium.
To provide further detail, consider a cleaner and a finance director. The cleaner corresponds to job level 1, work that “requires basic literacy and numeracy skills and the ability to perform a few straightforward and short-term tasks to instructions under immediate supervision”. The finance director corresponds to our highest skill category – job level 9 and involves “very senior executive roles with substantial experience in, and leadership of, a specialist function, including some input to the organisation’s overall strategy”. We measure skill premium using a ratio of a high-skill to low-skill job, at the same firm, in the same year.
‘Upper-tail’, ‘lower-tail’ wage inequality and firm size
When examining ‘top-bottom’ wage ratios in our sample (e.g., the wage associated with job level 8 divided by the wage associated with job level 1 within the same firm and year), we find they increase with firm size. A similar, albeit weaker, relationship arises when we look at ‘top-middle’ wage ratios (e.g. the wage associated with job level 8 divided by the wage associated with job level 4 within the same firm and year). In contrast, ‘middle-bottom’ wage ratios (e.g. the wage associated with job level 4 divided by the wage associated with job level 1 within the same firm and year) stay flat, or if anything slightly decrease with firm size.
- What is interesting is that when low job levels (1 to 5) are compared to one another, an increase in firm size has no effect on within-firm skill premia.
- In contrast, when high job levels (6 to 9) are compared to either one another or low job levels, an increase in firm size widens the wage gap between higher and lower skill categories.
These patterns resemble trends in aggregate data. Specifically, both in the UK and the US, there has been a ‘polarisation’ of wage trends: while overall (e.g., 90th percentile/10th percentile) and ‘upper-tail’ (e.g., 90th percentile/50th percentile) wage inequality has risen steadily, ‘lower-tail’ (e.g., 50th percentile/10th percentile) wage inequality has remained flat or, if anything, contracted slightly. We find the exact same pattern in our data, except that we find it with regard to increases in firm size. Overall, this suggests that rising wage inequality–even nuanced patterns, such as divergent trends in upper and lower-tail inequality – may be related to firm growth.
Automation and managerial career opportunities
Why do wages in high-skill job categories increase with firm size but not wages in low- and medium-skill job categories? We provide two possible explanations.
- First, larger firms invest more in automation which allows them to replace labour with technology in certain routine jobs (Autor et al. 2003).
Consistent with this hypothesis, we find that wages associated with routine jobs decline relative to those associated with non-routine jobs as firms become larger, especially in medium-skill job categories.
- Second, larger firms may pay relatively lower entry-level managerial wages in return for providing better career opportunities (Lazear and Rosen 1981).
Consistent with this hypothesis, we find that managerial wages in low- to medium-skill job categories are relatively lower in larger firms, while those in high-skill job categories are relatively higher in larger firms.
What do our results say about overall wage inequality?
An increasing skill premium at larger firms will lead to greater wage inequality inside those firms. But how has the size of the median employer changed over the last two decades? US firms with 500 or more employees accounted for 51.5% of all employment in 2011. As such, we measure firm size by focusing on the largest firms and find evidence of strong firm growth among larger firms in practically all of the developed countries in our sample. These results suggest that part of what may be perceived as a global trend toward more wage inequality may be driven by an increase in employment by the largest firms in the economy.
Overall, we provide novel evidence that contributes to the heated debate about the income inequality. Our results suggest that growth of larger firms in the economy may partially explain the rise in wage inequality over the last decades.
Acemoglu, D (2002), “Technical Change, Inequality, and the Labor Market”, Journal of Economic Literature 40, 7-72.
Acemoglu, D, and D Autor (2011), “Skills, Tasks and Technologies: Implications for Employment and Earnings”, in: Handbook of Labor Economics, Volume 4, O Ashenfelter and D Card (eds.), 1043-1171, Amsterdam: North-Holland.
Autor, D (2014), “Skills, Education, and the Rise of Earnings Inequality Among the Other 99 Percent”, Science 344, 843-851.
Autor, D, L Katz, and M Kearney (2006), “The Polarization of the U.S. Labor Market”, American Economic Review Papers and Proceedings 96, 189-194.
Autor, D, L Katz, and A Krueger (1998), “Computing Inequality: Have Computers Changed the Labor Market?”, Quarterly Journal of Economics 113, 1169-1213.
Autor, D, F Levy, and R Murnane (2003), “The Skill Content of Recent Technological Change: An Empirical Exploration”, Quarterly Journal of Economics 118, 1279-1333.
Card, D, and T Lemieux (2001), “Can Falling Supply Explain the Rising Return to College for Younger Men? A Cohort-Based Analysis”, Quarterly Journal of Economics 116, 705-746.
Goldin, C, and L Katz (2007), “Long-Run Changes in the Wage Structure: Narrowing, Widening, Polarizing”, Brookings Papers on Economic Activity, 135-165.
Katz, L, and K Murphy (1992), “Changes in Relative Wages, 1963-1987: Supply and Demand Factors”, Quarterly Journal of Economics 107, 35-78.
Lazear, E, and S Rosen (1981), “Rank-Order Tournaments as Optimum Labor Contracts”, Journal of Political Economy 89, 841-864.
Mueller, H, P Ouimet, and E Simintzi (2015), “Wage Inequality and Firm Growth”, CEPR Discussion Paper 10365.