A Structural Model of Human Capital and Leverage - Duke ...

In the multiple regression all variables remain significant, owing largely to the very large sample size. As
is the case in Table 1, the coefficients on the market to book ratio, profitability, and cash flow volatility are
all attenuated when all independent variables are included. Size becomes more negatively related to leverage
in the simulated multiple regressions, a pattern that is not consistent with the data. It is worth noting
that the coefficient on labor intensity is unchanged in the multiple regression. This is because most of the
variation in labor intensity is due to variations in the exogenous parameter αn, whereas the variation in the
other variables is due to endogenous responses to technology shocks. Thus there are correlations among the
other determinants of capital structure, whereas labor intensity is largely uncorrelated with each of them.
This could be addressed by specifying a model where labor intensity responds endogenously to some sort of
technology shock. It is not clear that this approach is preferable, however, since one of the criticisms of this
class of models is that they do not allow for enough heterogeneity across firms.
6 Conclusion
I write a dynamic quantitative model of corporate finance that incorporates labor market frictions. Labor
market frictions induce match-specific rents associated with the employment relationship. Termination of this
relationship is costly to both firms and employees. The loss of human capital that accompanies unemployment
represents a significant cost of financial distress, large enough to have a quantitatively important affect on
capital structure. I calibrate the model and find that it is able to match informative moments from the data.
Average leverage in the data is 21% compared to 18% in the model, despite using default costs that are only
5% of the capital stock. The model is also able to match the cross-sectional pattern of leverage and labor
intensity. The model predicts a monotonically decreasing relationship between leverage and labor intensity,
with a move from the lowest to the highest decile of labor intensity resulting in a drop in leverage of 21
percentage points, compared to a drop of 27 percentage points in the data. Furthermore, the model is able
to match the cross-sectional relationships between leverage and the market to book ratio, profitability, and
cash flow volatility. Overall the model provides the first quantitative evidence on the importance of human
capital for the firm’s capital structure decision.
27