Knowledge that Transforms

In this paper, we propose a statistical model to characterize the null hypothesis of the random product size distribution for multi-product exporters. It serves as a benchmark to test the empirical facts and model predictions. We ask which stylized facts that characterize multi-product exporters and motivate such theories as core competencies, productivity being a driver of both scope and scale, and economies of scope among others, are spurious, and which are informative. The benchmark model also provides alternative moments to test the validity of these theories.

We study an agency model in which the principal knows the agent-optimal actions in response to K “known” contracts but is unaware of other actions available or their costs, and seeks a contract to maximize worst-case profits. The optimal contract is a mixture of the known contracts and output. Moreover, when K = 1, the single known contract maximizes the principal's profit guarantee, whereas with two known contracts, the optimal mixture puts positive weight on one of the known contracts. Our methodology is straightforward to implement, a point that we demonstrate using data from an experimental study of different incentive schemes.

Researchers using instrumental variables to investigate ordered treatments often recode treatment into an indicator for any exposure. We investigate this estimand under the assumption that the instruments shift compliers from no treatment to some but not from some treatment to more. We show that when there are extensive margin compliers only (EMCO) this estimand captures a weighted average of treatment effects that can be partially unbundled into each complier group's potential outcome means. We also establish an equivalence between EMCO and a two-factor selection model and apply our results to study treatment heterogeneity in the Oregon Health Insurance Experiment.

The Lorenz curve and Gini index are standard tools for the evaluation of inequality in one dimension. However, inequality is inherently multi-dimensional. Extending the Lorenz curve and Gini index to a multidimensional context has proved controversial. This paper proposes a new multivariate extension based on multivariate rearrangements of optimal transport theory, which shares many of the desirable properties of their univariate counterparts. In particular, the corresponding multivariate inequality ordering is equivalent to preference by any social planner with inequality averse multivariate rank dependent social evaluation functional.

We augment Henderson et al. (2012)'s two-signal model of true GDP growth with a third signal to overcome its underidentification problem. The additional moment conditions from the third signal help fully identify all model parameters without ad-hoc calibrations of the GDP's signal-to-noise ratio. We characterize the necessary properties of the third signal. Using the model, we recover the optimal weight of official GDP in the composite true GDP growth estimates, which varies with the quality of the national statistics. The model improves on existing methodologies that use signals to measure true income.

This paper shows that the bunching of wages at round numbers is partly driven by firm coarse wage-setting. Using data from over 200 million new hires in Brazil, I first establish that contracted salaries tend to cluster at round numbers. Then, I show that firms that tend to hire workers at round-numbered salaries have worse market outcomes. Next, I develop a wage-posting model in which optimization costs lead to the adoption of coarse rounded wages and provide evidence supporting two model predictions using two research designs. Finally, I examine some consequences of coarse wage-setting for relevant economic outcomes.

This paper presents an alternative perspective to a longstanding empirical puzzle: that most entrepreneurs persevere despite persistently low earnings. Because entrepreneurial earnings are notoriously difficult to measure, I approach the question from an expenditure angle. I look at how switching into self-employment corresponds to changes in reported earnings versus expenditure. Using 45 years of longitudinal data, I find that individuals report earning 27.7% less in self-employment, while spending 3.8% more. This household expenditure premium accrues with entrepreneurial experience and is not offset by lower savings or longer work hours. The results hold in highly educated and incorporated business owner subsamples.

I estimate impulse responses to a 100 basis point US monetary policy shock using setidentified structural vector autoregressions. Identified sets for these responses may be unbounded when the identifying restrictions admit zero impact response of the federal funds rate following a standard-deviation shock. Such a zero response is always admissible when there are fewer sign and zero restrictions than endogenous variables. This is the case under existing restrictions on the systematic component of monetary policy, which consequently yield uninformative identified sets. Additional sign and narrative restrictions yield informative identified sets and imply smaller output responses than some previous estimates.

Parole was eliminated for many US offenders by Truth-in-Sentencing (TIS) laws in the 1990s. I exploit the introduction of TIS in Arizona to explore its impact on offenders before, during, and after incarceration. TIS Offenders were assigned significantly shorter sentences, largely eliminating the intended increase in punishment. These offenders reduced their rehabilitative effort while incarcerated, with rule infractions increasing by 22% and education enrollment falling by 24%. Finally, TIS offenders became 23% more likely to return to prison for a new conviction. I argue these effects were driven by TIS removing parole incentives, given that time served remained largely unchanged.

We use variational inference (VI), a technique from the machine learning literature, to estimate a mortality-based Bayesian model of nursing home quality accounting for selection. We demonstrate how one can use VI to quickly and flexibly estimate a high-dimensional economic model with large datasets. Using our facility quality estimates, we examine the correlates of quality and find that public report cards have near-zero correlation. We then show that in contrast to prior literature, higher quality nursing homes fared better during the pandemic: a one standard deviation increase in quality corresponds to 2.5% fewer Covid-19 cases.