Knowledge that Transforms

Many argue that minimum wages can prevent efficiency losses from monopsony power. We assess this argument in a general equilibrium model of oligopsonistic labor markets with heterogeneous workers and firms. We decompose welfare gains into an efficiency component that captures reductions in monopsony power and a redistributive component that captures the way minimum wages shift resources across people. The minimum wage that maximizes the efficiency component of welfare lies below $8.00 and yields gains worth less than 0.2% of lifetime consumption. When we add back in Utilitarian redistributive motives, the optimal minimum wage is $11 and redistribution accounts for 102.5% of the resulting welfare gains, implying offsetting efficiency losses of −2.5%. The reason a minimum wage struggles to deliver efficiency gains is that with realistic firm productivity dispersion, a minimum wage that eliminates monopsony power at one firm causes severe rationing at another. These results hold under an EITC and progressive labor income taxes calibrated to the U.S. economy.

Two opposed interested parties (IPs) compete to influence citizens with heterogeneous priors which receive news items produced by a variety of sources. The IPs fight to capture the coverage conveyed in these items. We characterize the equilibrium level of capture of item as well as the equilibrium level of information transmission. Capture increases the prevalence of the ex ante most informative messages and can explain the empirical distribution of slant at the news‐item level. Opposite capturing efforts do not cancel each other and instead undermine social learning as rational citizens discount informative messages. Citizen skepticism makes efforts to capture the news strategic substitutes. Because of strategic substitution, competition for influence is compatible with horizontal differentiation between successful media. In equilibrium, rational citizens choose to consume messages from aligned sources despite knowledge of the bias in a manner consistent with recent empirical evidence.

Policymakers often test expensive new programs on relatively small samples. Formally incorporating informative Bayesian priors into impact evaluation offers the promise to learn more from these experiments. We evaluate a Colombian program for 200 firms which aimed to increase exporting. Priors were elicited from academics, policymakers, and firms. Contrary to these priors, frequentist estimation cannot reject null effects in 2019, and finds some negative impacts in 2020. For binary outcomes like whether firms export, frequentist estimates are relatively precise, and Bayesian posterior intervals update to overlap almost completely with standard confidence intervals. For outcomes like increasing export variety, where the priors align with the data, the value of these priors is seen in posterior intervals that are considerably narrower than the confidence intervals. Finally, for noisy outcomes like export value, posterior intervals show almost no updating from priors, highlighting how uninformative the data are about such outcomes. Future policy experiments could use these posteriors as priors in a Bayesian or empirical Bayesian analysis.

India's Industrial Disputes Act (IDA) requires large manufacturing plants to pay substantial costs if they wish to shrink their workforce. Since the early 2000s, these large plants have dramatically increased their use of contract workers who are not subject to these regulatory constraints. Between 2000 and 2015, the contract labor share in non‐managerial employment nearly doubled at establishments with more than 100 workers (from 21 to 40 percentage points), while it only increased from 14 to 17 percentage points at establishments with less than 50 workers. Over the same period, the thickness of the right tail of the establishment size distribution in formal Indian manufacturing plants increased, the average product of labor at large plants declined, the job creation rate for large plants increased, and the probability that large plants introduced new products rose. We argue that these changes were caused by the increased adoption of contract labor. In a model of establishment growth subject to firing costs, we show that easing access to contract labor increased TFP in Indian manufacturing by 7.3% since the early 2000s, occurring all through a one‐time reduction in misallocation between large and small plants with negligible change in the long‐run growth rate.

We develop a stylized model that allows us to estimate a value-added measure for nursing homes ("SNFs") which accounts for patient selection both into and out of a SNF. We use the model, together with detailed data on the physical and mental health of about 6 million Medicare SNF patients between 2011 and 2016, to estimate the value added for about 14,000 distinct SNFs. We document substantial heterogeneity in value added. Nationwide, compared to a 10th percentile SNF, a 90th percentile SNF is able to discharge a patient at the same health level almost a week sooner, or one quarter of the median length of stay. Heterogeneity in value added within a market is almost as large as it is nationwide. Our results point to the potential for substantial gains through policies that encourage reallocation of patients to higher-quality SNFs within their market.

We study the implications of optimal insurance provision for long‐run welfare and inequality in economies with persistent private information. A principal insures an agent whose private type follows an ergodic, finite‐state Markov chain. The optimal contract always induces immiseration : the agent's consumption and utility decrease without bound. Under positive serial correlation, it also backloads high‐powered incentives : the sensitivity of the agent's utility with respect to his reports increases without bound. These results extend—and help elucidate the limits of—the hallmark immiseration results for economies with i.i.d. private information. Numerically, we find that persistence yields faster immiseration, higher inequality, and novel short‐run distortions. Our analysis uses recursive methods for contracting with persistent types and allows for binding global incentive constraints.

We study the effect of incarceration on wages, self-employment, and taxes and transfers in North Carolina and Ohio using two quasi-experimental research designs: discontinuities in sentencing guidelines and random assignment to judges. Across both states, incarceration generates short-term drops in economic activity while individuals remain in prison. As a result, a year-long sentence decreases cumulative earnings over five years by 13%. Beyond five years, however, there is no evidence of lower employment, wage earnings, or self-employment in either state, as well as among defendants with no prior incarceration history. These results suggest that upstream factors, such as other types of criminal justice interactions or pre-existing labor market detachment, are more likely to be the cause of low earnings among the previously incarcerated, who we estimate would earn just $5,000 per year on average if spared a prison sentence.

Based on the GARCH literature, Engle and Russell (1998) established consistency and asymptotic normality of the QMLE for the autoregressive conditional duration (ACD) model, assuming strict stationarity and ergodicity of the durations. Using novel arguments based on renewal process theory, we show that their results hold under the stronger requirement that durations have finite expectation. However, we demonstrate that this is not always the case under the assumption of stationary and ergodic durations. Specifically, we provide a counterexample where the MLE is asymptotically mixed normal and converges at a rate significantly slower than usual. The main difference between ACD and GARCH asymptotics is that the former must account for the number of durations in a given time span being random. As a by‐product, we present a new lemma which can be applied to analyze asymptotic properties of extremum estimators when the number of observations is random.

How do collateral requirements impact consumer borrowing behavior? Using administrative loan application and performance data from the U.S. Federal Disaster Loan Program, we exploit a loan amount threshold above which households must post their residence as collateral. Our bunching estimates suggest that the median borrower is willing to give up 40% of their loan amount to avoid posting collateral. Exploiting time variation in the threshold, we estimate collateral causally reduces default rates by 36%. Finally, we structurally estimate households' attachment to their homes, net of any equity, and find a median value of $11,000. Attachment creates a wedge between lender and borrower valuation of collateral of 15%. Our results explain high perceived default costs in the mortgage market, and document the importance of collateral for reducing moral hazard in consumer credit markets.

There has been a call for caution regarding the standard procedure for Bayesian inference in set‐identified structural vector autoregressions on the grounds that the common practice of using a uniform prior over the set of orthogonal matrices induces a non‐uniform prior for individual impulse responses or other quantities of interest. This paper challenges this call by formally showing that when the focus is on joint inference, the uniform prior over the set of orthogonal matrices is not only sufficient but also necessary for inference based on a uniform joint prior distribution over the identified set for the vector of impulse responses. In addition, we show how to conduct inference based on a uniform joint prior distribution for the vector of impulse responses.