Research

This paper studies how a firm’s choice of workforce quality differs by firm performance and evolves over a firm’s life-cycle and what the workforce composition implies for firm and aggregate productivity. Using Canadian matched employer-employee data, I measure a firm’s workforce quality as the average worker-specific wage component, AKM worker fixed effects, of its employees, and take a firm’s surviving year as an indicator for firm performance. I uncover two facts: first, conditional on age, longer-lived firms have better workers; second, conditional on longevity, workforce quality declines as firms age. Firms tend to start with a small, high-quality workforce and grow by expanding into lower-quality workers. To explain these findings, I develop a firm dynamics model in which firms could choose to improve firm-level productivity, given their productivity and age, by accumulating organizational capital with high-quality workers. The model is calibrated to match the observed patterns of workforce quality and firm performance. I then use it to quantify the consequences for Canadian firms and aggregate productivity of an increase in the cost of hiring the high-quality workers. I find that a 1% increase in the wages of high-quality workers lowers long-run aggregate productivity by 0.6%. Young firms are hit the hardest: the shock deprives them of the opportunity to accumulate organizational capital, which compounds into permanent losses in firm-level productivity. Reduced accumulation of organizational capital accounts for 80% of the long-run aggregate productivity loss.

A small group of people accounts for a large majority of flows between labor market states and of spells in un- and non-employment. In this paper, we ask whether it is possible to identify those weakly attached to the labor market during their prime working-age years using information available early in their lives. First, we use information on labor force transitions between ages 30 and 50 contained in the long panel provided by the NLSY79 to identify those weakly connected to the labor market during their prime age. To do so, we use k-means clustering on moments describing observed spells in employment, unemployment, and non-employment between 30 and 50. This points to a group of less attached individuals who are disproportionally female, less educated, and in poor health. In a second step we predict, using information collected at various points before age 30—which we do not use in clustering—whether individuals will turn out to belong to the weakly attached type in their prime age. We find that information from ages 22 to 29 allows predicting membership of the low-attachment group with high precision. Particularly influential is information on early labor market experiences and health. The fact that we can predict weak and strong labor market attachment during prime working age using variables observed in individuals’ twenties suggests the presence of persistent heterogeneity that shapes labor market experiences throughout the life cycle.