Knowledge that Transforms

This paper reports causal evidence on how the extent of hybrid work—the number of days worked from home relative to days worked from office—affects employee attitudes and performance. Workers who spent around two days in the office each week on average self-reported greater work-life balance, more job satisfaction, and lower isolation from colleagues compared to workers who spent more or fewer days in the office. Employees in the intermediate hybrid condition received no different performance ratings compared to peers who spent more or fewer days in the office.

We study treatment-effect estimation using panel data. The treatment may be nonbinary, nonabsorbing, and the outcome may be affected by treatment lags. We make a parallel-trends assumption and propose event-study estimators of the effect of being exposed to a weakly higher treatment dose for ℓ periods. We also propose normalized estimators that estimate a weighted average of the effects of the current treatment and its lags. We also analyze commonly used two-way, fixed-effects regressions. Unlike our estimators, they can be biased in the presence of heterogeneous treatment effects. A local-projection version of those regressions is biased even with homogeneous effects.

Migrant workers and employers rely on intermediaries to facilitate labor market placements. If information frictions obscure their reputation, however, intermediaries may under-invest in placement quality. Using data on over 1.5 million Sri Lankan migrants to the Gulf region, we examine the effects of a government-run intermediary rating program that publicly revealed ratings two years after it was announced. Prior to the ratings' public release, under-performing intermediaries invest in the rating criteria and place migrants in less abusive and more remunerative employment. Our results suggest the government's threat of quality revelation induced intermediaries to prospectively screen employers and improve placement quality.

Countries with greater commodity export intensity have more concentrated markets for imported goods. Import market concentration is associated with higher domestic prices, suggesting that markups due to greater concentration outweigh any potential cost efficiency. Tariffs, non-tariff measures, and tariff evasion are mechanisms that concentrate import markets. These results suggest a novel channel for the resource curse stemming from the monopolization of imports.

We present a statistical test for the hypothesis of (approximate) utility maximization on the basis of nonparametric revealed preference conditions. We take as null hypothesis that the consumer behaves randomly, and we reject this hypothesis only if the data provides sufficient evidence to support the alternative hypothesis of approximate utility maximization. Our statistical test uses a permutation method to operationalize the principle of random consumption behavior. We show that our test (i) is valid for any sample size under the null and (ii) has an asymptotic power of one. We also provide simulated power results and two empirical applications.

Using administrative labor market data matched to mortality statistics and patient records, we document that negative labor market shocks produce sizable health spillovers in couples. For every 100,000 displaced men, there are 1,100 additional deaths. Of those, 60% accrue to the displaced worker, but 40% are due to excess spousal mortality. We find a stunning gender asymmetry: while male job displacement generates persistent negative health effects, no such dire consequences are observed after a woman’s job loss. We explore several explanations for these patterns: risk sharing through spousal labor supply; earnings losses and public insurance; widowhood; regional mobility and gender roles in the family.

We evaluate health insurance reclassification risk in the Small Group Market before ACA community rating regulations. We use detailed claims and premiums data from a large insurance company, controlling non parametrically for selection. We find a pass-through of 11% from changes in health risk to changes in premiums, with a stronger equilibrium relationship between the two. The pricing patterns are consistent with the insurer offering “guaranteed renewability” contracts with one-sided pricing commitment. The observed pricing policy adds 55% of the consumer welfare gain from community rating relative to experience rating, with welfare gains limited because of switching across insurance companies.

This article presents and analyzes an approach to cluster-based inference for dependent data. The primary setting considered here is with spatially indexed data in which the dependence structure of observed random variables is characterized by a known, observed dissimilarity measure over spatial indices. Observations are partitioned into clusters with the use of an unsupervised clustering algorithm applied to the dissimilarity measure. Once the partition into clusters is learned, a cluster-based inference procedure is applied to a statistical hypothesis testing procedure. The procedure proposed in the article allows the number of clusters to depend on the data, which gives researchers a principled method for choosing an appropriate clustering level. The article gives conditions under which the proposed procedure asymptotically attains correct size. A simulation study shows that the proposed procedure attains near nominal size in finite samples in a variety of statistical testing problems with dependent data.

This paper uses the randomized rollout of a national agricultural extension program in Uganda to study subsistence smallholders' decisions to adopt cash oilseed crops and shift to commercial farming. By eliciting yield and price expectations, we examine how beliefs evolve after the intervention and influence adoption decisions. Our findings indicate that technical and market information significantly raises farmers' expectations, leading to an average 15% increase in oilseed adoption. Results highlight the role of information in shaping beliefs and behavior, and suggest that addressing knowledge gaps and belief misperceptions about crop profitability is crucial for improving technology adoption and agricultural transformation.

Antimicrobial resistance (AMR) increases healthcare costs, hospital stays, and mortality. This study examines how AMR affects antibiotic prescribing for cystitis in France (2002–2019), using data from general practitioners. A decision model is developed to capture prescribing behavior with and without rapid bacterial or susceptibility testing. To address endogeneity, veterinary antibiotic sales are used as instruments. Results show rising resistance prompts drug substitution, and physicians respond to predictable resistance trends. Counterfactual analysis reveals that reducing animal antibiotic use and limiting fluoroquinolones lowers resistance but affects substitution and consumer welfare differently. The study also assesses the value of rapid diagnostic testing.