David J. Price

Post-Doctoral Research Associate
Princeton University Department of Economics and Industrial Relations Section

About Me
Curriculum Vita
Research

Working Papers


The Long-Term Effects of Cash Assistance (with Jae Song)

We investigate the long-term effects of cash assistance for beneficiaries and their children by following up, after four decades, with participants in the Seattle-Denver Income Maintenance Experiment. Treated families in this randomized experiment received thousands of dollars per year in extra government benefits for three or five years in the 1970s. Using a novel matching algorithm with administrative data from the Social Security Administration and the Washington State Department of Health, we find that treatment decreased average post-experimental annual earnings by $1,800, mostly taken as earlier retirement. Treated adults were also 6.3 percentage points more likely to apply for disability benefits, but were not significantly more likely to receive them, or to have died. Treated workers also switched to occupations requiring less education and abstract reasoning, suggesting that long term outcomes could be driven by time out of work during treatment lowering human capital, leading adults to switch to worse jobs. These effects on parents, however, do not appear to be passed down to their children: children in treated families experienced no significant effects in any of the main variables studied. Taken as a whole, these results suggest that policymakers should consider the long-term effects of cash assistance as they formulate policies to combat poverty and reduce inequality.
 
Online appendix
 
Selected Media: Washington Post
 
 

Firming Up Inequality (Revise and Resubmit, Quarterly Journal of Economics; with Jae Song, Fatih Guvenen, Nicholas Bloom, and Till von Wachter)

We use a massive, matched employer-employee database for the United States to analyze the contribution of firms to the rise in earnings inequality from 1978 to 2013. We find that one-third of the rise in the variance of (log) earnings occurred within firms, whereas two-thirds of the rise occurred between firms. However, this rising between-firm variance is not accounted for by the firms themselves: the firm-related rise in the variance can be decomposed into two roughly equally important forces--a rise in the sorting of high-wage workers to high-wage firms and a rise in the segregation of similar workers between firms. In contrast, we do not find a rise in the variance of firm-specific pay once we control for worker composition. Instead, we see a substantial rise in dispersion of person-specific pay, accounting for 68% of rising inequality, potentially due to rising returns to skill. The rise in between-firm variance, mostly due to worker sorting and segregation, accounted for a particularly large share of the total increase in inequality in smaller and medium firms (explaining 84% for firms with fewer than 10,000 employees). In contrast, in the very largest firms with 10,000+ employees, 42% of the increase in the variance of earnings took place within firms, driven by both declines in earnings for employees below the median and a substantial rise in earnings for the 10% best-paid employees. However, because of their small number, the contribution of the very top 50 or so earners at large firms to the overall increase in within-firm earnings inequality is small.
 
Data
 
Selected Media: Economist, New York Times, Wall Street Journal, Washington Post
 
 

Consistent Estimation and Inference When Data Follows a Power Law

Power laws are common in economic phenomena, such as the size of cities and firms. When they occur, these power laws can cause estimates of economic quantities to have extremely different variances when those quantities observed at an aggregate level (for example, at the city or firm level). I show that in general, estimators based on observations exhibiting this extreme heteroskedasticity may not be consistent or asymptotically normal, and may have unreliable confidence intervals. In fact, these problems can occur even when no heteroskedasticity is present in the original data if estimates are obtained using weighting (such as by city or firm size). I propose new estimators for these contexts that may help in determining the extent of extreme heteroskedasticity in the data, in forming more accurate estimates, and performing more reliable inference. Further work remains to be done in understanding the properties of these new estimators, and in applying them to empirical work.