Job Market Paper
Measuring Misallocation with Experiments (with David Hughes)
Misallocation of inputs across firms has been proposed as a reason for low levels of development in some countries. However, existing work has largely relied on strong assumptions about production functions in order to estimate the cost of misallocation. We show that, for arbitrary production functions, the cost of misallocation can be expressed as a function of the variance of marginal products. Using an RCT that gave grants to microenterprises, we estimate heterogeneous returns to capital by baseline characteristics, and provide a lower bound on the total variance of returns to capital. This lower bound is a nonlinear function of the parameters from a linear IV model, and we show that standard methods (e.g. the delta method or projection) fail in this setting. We provide novel econometric tools that provide uniformly valid confidence intervals for nonlinear functions of parameters. We find evidence for sizable losses from misallocation of inputs across the firms we study, although the magnitude depends critically on which inputs we allow to be reallocated. We estimate that optimally reallocating capital would increase output by 22%, while optimally reallocating all inputs would increase output by 301%.
Misallocation and the Selection Channel
New Draft Coming Soon, Pending Census Disclosure
An important determinant of aggregate productivity is the selection channel: the process by which less efficient firms are driven out of the market by more efficient firms. Conventional wisdom suggests that markets in developing countries are more sclerotic, allowing inefficient firms to survive that would have exited in a developed country. I provide a tractable model to examine the importance of the selection channel, and show how to calibrate it to panel data on firms. I use this model to show that the effect of the selection channel on aggregate productivity is approximately equal to the average difference in log productivity between stayers and exiters, which can be measured easily in firm panel data. Results for Indonesia, Spain, Chile, and Colombia suggest that Indonesia could raise its aggregate productivity by roughly 30% if its firm exit process became as selective as Spain’s. However, cross-country estimates suggest that the selection channel is not an important explanation for cross-country differences in output per capita.
A Q-Theory of Banks (with Juliane Begenau, Saki Bigio, and Matias Vieyra)
Under Revision for Review of Economic Studies
We propose a dynamic bank theory with a delayed loss recognition mechanism and a regulatory capital constraint at its core. The estimated model matches four facts about banks' Tobin's Q that summarize bank leverage dynamics. (1) Book and market equity values diverge, especially during crises; (2) Tobin's Q predicts future bank profitability; (3) neither book nor market leverage constraints are binding for most banks; (4) bank leverage and Tobin's Q are mean reverting but highly persistent. We examine a counterfactual experiment where different accounting rules produce a novel policy tradeoff.
How Much Should We Trust Regional-Exposure Designs? (with Karthik Sastry)
Many prominent studies in macroeconomics, labor, and trade use panel data on regions to identify the local effects of aggregate shocks. These studies construct regional-exposure instruments as an observed aggregate shock times an observed regional exposure to that shock. We argue that the most economically plausible source of identification in these settings is uncorrelatedness of observed and unobserved aggregate shocks. Even when the regression estimator is consistent, we show that inference is complicated by cross-regional residual correlations induced by unobserved aggregate shocks. We suggest two-way clustering, two-way heteroskedasticity- and autocorrelation-consistent standard errors, and randomization inference as options to solve this inference problem. We also develop a feasible optimal instrument to improve efficiency. In an application to the estimation of regional fiscal multipliers, we show that the standard practice of clustering by region generates confidence intervals that are too small. When we construct confidence intervals with robust methods, we can no longer reject multipliers close to zero at the 95% level. The feasible optimal instrument more than doubles statistical power; however, we still cannot reject low multipliers. Our results underscore that the precision promised by regional data may disappear with correct inference.
We study the effects of the average merger in the consumer packaged goods industry, a sector making up over 10% of US GDP. Using an event-study design and linked retail scanner data from hundreds of consummated mergers, we find that mergers raise prices at the target by 0.9%. Under nested CES demand, we provide sufficient statistics to recover average consumer welfare effects as a function of effects on price, store and product availability, and firm exit. Accounting for availability and exit is quantitatively important. The decline in consumer welfare is equivalent to a 1.9-3.7% price increase at the target firm.
Risky Business and the Process of Development (with Paco Buera, Yongseok Shin, and Kuldeep Singh)
Risk is an important factor that affects investment decisions, especially for undiversified entrepreneurs in less developed economies. Yet standard macro models of financial frictions do not incorporate risk: short-term returns are known in advance, and investment is fully reversible. Thus, even if entrepreneurs are risk averse and credit constrained, they will invest all of their assets in the firm, until the marginal product of capital equals the interest rate. As a result, standard models often find that productive entrepreneurs quickly save their way out of credit constraints, limiting the effect of financial frictions on output and aggregate productivity. We incorporate risk into a model of financial frictions, by making investment partially irreversible. Productive entrepreneurs accumulate capital substantially more slowly than in the first-best, leading to a reduction in aggregate productivity. Credit can play a role in undoing these frictions if firms have an option to default. Default creates a state-contingent contract, in which the entrepreneur repays if productivity stays high and defaults if productivity falls; this encourages investment and improves welfare through risk-sharing with the bank.
Childhood Environment and Gender Gaps in Adulthood (with Raj Chetty, Nathan Hendren, Frina Lin, and Ben Scuderi)
American Economic Review Papers and Proceedings 106(5): 282-88, 2016
We show that differences in childhood environments play an important role in shaping gender gaps in adulthood by documenting three facts using population tax records for children born in the 1980s. First, gender gaps in employment rates, earnings, and college attendance vary substantially across the parental income distribution. Notably, the traditional gender gap in employment rates is reversed for children growing up in poor families: boys in families in the bottom quintile of the income distribution are less likely to work than girls. Second, these gender gaps vary substantially across counties and commuting zones in which children grow up. The degree of variation in outcomes across places is largest for boys growing up in poor, single-parent families. Third, the spatial variation in gender gaps is highly correlated with proxies for neighborhood disadvantage. Low-income boys who grow up in high-poverty, high-minority areas work significantly less than girls. These areas also have higher rates of crime, suggesting that boys growing up in concentrated poverty substitute from formal employment to crime. Together, these findings demonstrate that gender gaps in adulthood have roots in childhood, perhaps because childhood disadvantage is especially harmful for boys.
Works in Progress
Both economists and the public are deeply interested in the degree to which a child’s adult income is determined by her parent’s income and race. Recent work (Chetty, Hendren, Jones, and Porter, 2020) has shown, for recent cohorts, that black boys have much lower incomes in adulthood than do white boys, even after controlling for parent income. Moreover, their estimates suggest that the black-white income gap is already at its steady state: the gap will remain at its current level unless upward mobility improves for black children. This begs the question: has the black-white mobility gap been improving or worsening over time? Estimating intergenerational mobility by race for earlier cohorts has been difficult to date because of data limitations: accurate estimates require large, high-quality data sets, and such linked data containing parent income, child income, and race are only currently available for recent cohorts. I provide a solution to this problem, using a GMM approach to combine panel data from the NLS and NLSY with cross-sectional data from the Census. The addition of the Census data imposes restrictions on the coefficients that allows for substantially more precise estimates over time.