Concentration of human capital, externalities and the wage gap in US metro areas

ABSTRACT

The effects of the concentration of human capital on wages and productivity have been widely studied, but despite their heterogeneity, little attention has been paid to its effects on the wage gap. This paper assesses the impacts of human capital externalities on wages and on the US wage gap. The main results suggest a positive association between the share of high-educated workers and the wage gap between high- and low-educated workers. Moreover, the effect associated with the concentration of high-educated workers is entirely captured by changes in their wages, as wages of low- and medium-educated workers are statistically unaffected.

KEYWORDS:

• human capital externalities
• knowledge spillovers
• wage gap
• metro areas

JEL:

• I24
• J24
• J31

ACKNOWLEDGEMENTS

The authors thank John Winters, David Blau and Bruce Weinberg for their invaluable contributions to this paper. Gratitude is also extended to the three anonymous referees as well as the reviewers, discussants and attendants at the SRSA 2016 Conference in Washington, DC; the AERUS 2016 Conference at the University of Illinois Urbana-Champaign; the NARSC 2016 Conference in Minneapolis, Minnesota; and the SRSA 2017 Conference in Memphis, Tennessee. All errors remain the authors’ own.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. We broadly use the term ‘knowledge spillovers’ to refer to human capital externalities.

2. Capital is implicitly included in the model as it differentially affects worker productivity through ${\theta }_H$ and ${\theta }_L$, and the productivity of the economy as a whole through $\delta$.

3. These parameters capture any exogenous shifters to productivity and could affect differentially high- and low-educated workers. The theory is then consistent with changes in productivity and the wage gap that are initially driven by skill-biased technological change that is enhanced by knowledge spillovers, increasing demand for skilled workers.

4. For a discussion of these effects, see Rodríguez-Pose and Tselios (2009).

5. The observable characteristics of the city ($Z_{ct}$) and the unobservable characteristics contained in the city fixed-effect ${\pi }_c$ are assumed to have common effects over workers.

6. Autor et al. (2013) and Autor, Dorn, Hanson, and Song (2014) instrumented for growth in US imports from China by using the growth in Chinese imports in other high-income countries. The Hausman et al. (1994) approach generally uses values from adjacent or very close city/regions as the instrument for values of the original city/region. We go further by eliminating demand spillovers (as described below) and more specifically targeting better matches using matching techniques.

7. The Mahalanobis distance for cities i and j is defined as $\mathrm{M}\mathrm{D}=(x_i-x_j{)}^{\mathrm{\prime }}{\hat{C}}^{-1}(x_i-x_j)$, where x is a vector of covariates; and $\hat{C}$ is the estimated covariance matrix. The covariates associated with relative labour supply of skilled and unskilled workers are education, age, mean of wages, the proportion of blacks, women, foreign-born and married workers.

8. Whereas we cannot fully rule out the possibility that at least part of the variation in the instrument is demand driven, the construction of the instrument along with other factors in how we specify the empirical model should greatly mitigate any remaining concerns that our model is misspecified. Moreover, the results are similar when using a purely supply shifter instrument such as the existence of a land-grant university and deep lags of the relative skilled/unskilled labour supply.

9. In particular, we regress the share of high-educated workers in the ‘treated’ city on a set of labour supply covariates including the population size of the city, the age, share of married workers, share of blacks, share of women and the share of foreign in each group of workers (low-, medium- and high-educated workers). For each city, the procedure creates a counterfactual city that resembles the ‘treated’ one in these covariates. The instrument used is then the predicted share of high-educated workers in the counterfactual city.

10. Unfortunately, three are no clear natural experiments to further tease out causality. Of course, appropriate caution in interpreting the results should be exercised.

11. For the first and second IV, the underlying assumption is that they are correlated with the current share skill/unskilled shares, but uncorrelated with current wages (conditional on controlling for other factors such as contemporaneous industry shares and state fixed-effects).

12. For MSAs with geographical boundaries that extend to multiple states, the MSA is assigned to the state in which the MSA’s population is larger. Following this criterion, cities such as Cincinnati (which covers parts of Ohio, Kentucky and Indiana), Chicago (covering Illinois, Indiana and Wisconsin) and Boston (covering areas of Massachusetts and New Hampshire) are assigned to Ohio, Illinois and Massachusetts, respectively.

13. Both the ACS 2010 and 2014 do not provide the actual number of weeks worked, unlike the ACS 2005. Instead, a categorical measure is reported with ranges of 50–52, 48–49, 40–47, 27–39, 14–26 and < 14 weeks worked. To consistently measure the number of weeks, we construct the 2010/14 ranges for 2005 as well. Hence, regressions using the full sample of workers use a categorical variable for all the brackets described as the control for the number of weeks worked. Regressions for full-year workers consider only those working 50–52 weeks a year.

14. For example, when using aggregate data, we only consider the percentage of black workers as a proxy for race. When using individual level data, we use the variable ‘RAC1P’ with dummies for each race in the ACS.

15. Regressions including all groups simultaneously, with a group dummy, yield similar results.

16. We also tried using the NAICS codes 54 and 55 (Management of companies and enterprises) altogether as our proxy for highly skilled workers. The results remain mostly unchanged.

17. This procedure excludes MSAs such as New York, Dallas, Los Angeles, Chicago, Washington, Miami, San Francisco and Phoenix, among others.