We study technology sophistication using a novel approach that measures the sophistication of the most advanced (MAX) and the most widely used (MOST) technologies in each of the key business functions within establishments. Using data from over 21,000 establishments across 15 countries, we find that establishments generally underutilize the most sophisticated technologies available within a business function. These MAX-MOST gaps are persistent and strongly associated with productivity both across establishments and countries. At the establishment level, there is substantial variation in both MAX and MOST, with MOST showing a more skewed distribution. MAX and MOST follow different lifecycle patterns in low-income countries and among small establishments, and they exhibit different associations with several establishment characteristics and performance indicators. This evidence underscores the different nature of the technology upgrading processes that drive MAX and MOST.

Why do managers matter for firm performance? This paper provides evidence of the critical role of managers in matching workers to jobs within the firm using the universe of personnel records from a large multinational firm. The data covers 200,000 white-collar workers and 30,000 managers over 11 years in 100 countries. I identify good managers by their speed of promotion and leverage exogenous variation induced by the rotation of managers across teams. I find that good managers cause workers to reallocate within the firm through lateral and vertical transfers and generate large and persistent gains in workers’ career progression and productivity. My results imply that the visible hands of managers match workers’ specific skills to specialized jobs, leading to an improvement in the productivity of existing workers that outlasts the managers’ time at the firm.

Standard estimators in information provision experiments place more weight on individuals who update their beliefs more in response to new information. This paper shows that, in practice, these individuals who update the most have the weakest causal e!ects of beliefs on outcomes. Standard estimators therefore understate these causal e!ects. I propose an alternative local least squares (LLS) estimator that recovers a representative unweighted average e!ect in a broad class of learning rate models that generalize Bayesian updating. In five of six recent studies, estimates of the e!ects of beliefs on outcomes increase. In two, they more than double.

This paper exploits the exogenous variation of Mexico’s Oportunidades conditional cash transfer program on urban households’ time and consumption allocations to identify and structurally estimate a collective labor supply model with home production. I use my structural estimates to show that participation in Oportunidades increased maternal intrahousehold bargaining power by almost 13%, which is associated with an increase of approximately 14% in the production of a child-related public good in dual-earner beneficiary households. Counterfactual exercises show that Oportunidades is as effective as alternative cash transfer programs and wage subsidies at increasing mothers’ bargaining power, control over household monetary resources, and domestic output.

We analyze the labor market impact of CVS Health’s acquisition of Target’s pharmacy business in December 2015 using Lightcast job postings data. Using differencein- differences and triple-difference designs, we find meaningful negative effects of the merger on posted pay. In our preferred specification, we estimate that the acquisition reduced posted pay in affected labor markets by 2.9%. We test for heterogeneous merger effects by occupational characteristics, finding that the merger caused pay to fall by more in lower-paid occupations than in higher-paid occupations.

Can workplace contact with outgroup members affect attitudes towards that group? If so, can this spread to others within a social network? We randomly assigned Hindu job applicants in India either a Hindu or Muslim partner for training and placement in call center jobs. One year later, those applicants assigned Muslim partners expressed more positive attitudes towards Muslims, were more likely to say Muslims faced discrimination and had greater knowledge of Islamic practices. Close friends and family members of those workers also experience similar, though smaller, changes, despite reporting no additional direct contact with Muslims.

This paper revisits the relationship between federal debt and interest rates in the U.S. A common approach is to regress long-term forward interest rates on long-term projections of federal debt. We show that issues regarding nonstationarity have become more pronounced over the last 20 years, significantly biasing recent estimates. Estimating the model in first differences rather than in levels addresses these concerns. We find that a 1 percentage point increase in the debt-to-GDP ratio raises the 5-year-ahead, 5-year Treasury rate by about 3 basis points. Roughly half of the interest rate response is driven by a higher nominal term premium.

How do trade shocks affect welfare and inequality when human capital is endogenous? Using India’s IT boom and internal migration data, I document that IT employment and engineering enrollment increased with exports, especially in regions with larger college-age populations. I develop a spatial model featuring higher education choice and differential migration costs for college versus work. The IT boom increased welfare by 2.39%, but without educational mobility, gains would be 25% lower and regional inequality 1.5 times larger. Removing endogenous education further reduces gains by over one-third. Education policies like national scholarships could substantially reduce regional inequality from trade shocks.

This paper proposes a new method to estimate the welfare impact of conflicts and remedy common data constraints in conflict-affected environments. The method first estimates how agents regard spatial welfare differentials by voting with their feet, using pre-conflict data. Then, it infers a lower-bound estimate for the conflict-driven welfare shock from partially observed post-conflict migration patterns. Results for the conflict in Eastern Ukraine between 2014 and 2019 show a large lower-bound welfare loss for Donetsk residents equivalent to 8–32 percent of lifetime income depending on agents’ time preference and risk aversion parameters.

This paper examines how politically connected firms shape housing supply in U.S. cities. Using new data on campaign donations to U.S. mayors and a regression discontinuity design, I present three findings. First, developers connected to the mayor sell more new housing units. Second, more sales of new housing by connected developers coincide with higher local housing supply: cities where mayors received more developer donations issue nearly 70 percent more permits for new housing units. Third, differences in mayors’ pre-existing policy stances—rather than connections to developers—is a quantitatively larger determinant of local housing supply.

Regulatory review is often seen as a barrier to innovation, increasing costs and delaying new medicines. Yet approval may also expand markets by certifying quality and reducing uncertainty. We test this by studying FDA approval for follow-on indications—uses that physicians could already prescribe “offlabel” — and find approval raises use in newly approved diseases by 25 percent within a year, with larger increases in smaller off-label markets. Subsequent approvals in the same disease yield smaller gains. Our results suggest regulatory approval of medicines expands market size by increasing demand, rather than easing insurer-imposed restrictions, revealing limits to marketbased learning.

We study a panel data model with heterogeneous effects, allowing slopes to vary across individuals and time. To reduce dimensionality, we assume these slopes follow a factor structure, so slope matrices can be estimated via low-rank regularized regression. We propose a multi-step estimation procedure incorporating sample splitting and partialing-out to enable valid inference after penalized estimation. We establish the asymptotic normality of the resulting estimator, facilitating inference for individualtime- specific effects and their cross-sectional averages. The method’s performance is illustrated through simulations and an empirical application.

We study how temporary subsidies for adoption of modern foreign technology drove South Korea’s industrialization in the 1970s. Leveraging unique historical data, we provide causal evidence consistent with coordination failures: adoption improved adopters’ performance and generated local spillovers, with firms more likely to adopt when other local firmshad already adopted.We incorporate these findings into a quantitative model, where the potential for multiple steady states depends on parameters mapped to the causal estimates. In our calibrated model, South Korea’s temporary subsidies shifted its economy to a more industrialized steady state, increasing heavy manufacturing’s GDP share by 27% and export intensity by 39%. Larger market access and lower idiosyncratic distortions amplified the effects of these subsidies, as the gains from adoption increase with firm scale.

The production approach to markup estimation has recently attracted the attention of scholars and policymakers for its straightforward implementation and limited data requirements. Criticism has also been directed at the approach and validation of the approach is called for. This paper provides a novel empirical assessment of the approach using rich panel data on the complete population of Swedish firms across four sectors (1998-2019). I first estimate the markup of each firm using the production approach. Using two sources of identification, I then show that these estimates match the stylized fact that markups are higher on more concentrated local markets.

Why do some individuals evade taxes while others do not? We study this question using administrative tax records from Uruguay linked to a tailored survey of taxpayers. Using third-party reports, we measure individual income under-reporting as an indicator of evasion. We then examine how three factors predict who evades: social preferences (e.g., honesty measured through incentivized laboratory games), peers (e.g., the behavior of current and former coworkers), and economic factors (e.g., the marginal tax rate). We find that social preferences have little power to predict evasion, while economic factors matter more and peer behavior is the strongest predictor.

We study how various zoning regulations combine to affect housing supply, prices, and rents of single- and multifamily homes using novel lot-level zoning data from Greater Boston and a cross-sectional boundary discontinuity design at regulation boundaries. Looser density restrictions, alone or with other less restrictive regulations, are most effective in increasing supply and reducing per-housing-unit rents and prices. We theoretically and empirically show that restrictive zoning regulations shift housing stock towards larger units, increasing prices per housing unit. Counterfactuals imply that a recent Massachusetts law increasing building density near transit can reduce long-run rents and prices, particularly in suburbs.

We study how economic development affects female labor force participation, focusing on the role of gender norms. Analyzing quasi-random variation in provincial exports in reunified Vietnam from 2002 to 2018, we find that positive economic shocks reduced women’s labor market engagement, particularly among married women from wealthier households and those with husbands in more skilled occupations. This trend is more pronounced in the South (formerly capitalist) than in the North (always socialist), and among native Southerners compared to Northerners relocated to the South after the war. Our findings highlight how gender role attitudes shape women’s responses to rising incomes.

The paper is structured around three main contributions. First, it takes advantage of a unique survey on Afghan asylum seekers in Germany to provide novel descriptive insights into asylum seekers’ beliefs about their outcomes and the associated intention to overstay. Second, it estimates asylum seekers’ perceived ex ante returns on overstaying and option values of regularisation, deportation, and experimentation. Third, it assesses and rejects the cost-effectiveness argument for assisted voluntary return policies. Instead, it estimates a sizeable willingness-to-pay of asylum seekers for investments that would guarantee their regularisation.

We document political interdependence driven by international migration. To examine whether elections in residence countries impact emigrant turnout in homeland elections, we assemble a novel dataset covering 1,267 elections across 43 origin countries and 217 residence countries. Exploiting the quasi-random timing of elections between countries, we find that emigrant turnout increases by 7 percentage points in homeland elections held after residence country elections, compared to those held before. This is consistent with a model of salience where exposure to competitive residence country elections and expanded media coverage increases interest in the political process and drives emigrants to participate in their homeland elections.

How does the credible announcement of an unconventional monetary policy intervention affect bank lending standards during crises? We use a major central bank announcement, the “whatever it takes” speech of the European Central Bank President that boosted the capital of banks, as a natural experiment. We compare changes in lending standards of subsidiaries of euro area versus other banks in a third country, Mexico. The speech reversed a prior trend of euro area banks augmenting their risk-taking via loan growth, lending rates, and credit risk. Our findings show that policies that amount to capitalization can reduce risk-taking in times of stress, adding a new dimension to the bank capital channel. (JEL: E51; G21; F34)

We generalize the synthetic control (SC) method to a multiple-outcome framework, where the conventional pre-treatment time dimension is supplemented with the extra dimension of related outcomes in computing the SC weights. This generalization reduces the risk of overfitting and improves the reliability of the SC method, particularly when the number of pre-treatment periods is small. To illustrate our method, we provide a new perspective on the classic SC application to the 1990 German reunification.

We introduce a generic class of dynamic nonlinear heterogeneous parameter models that incorporate individual and time fixed effects in both the intercept and slope. These models are subject to the incidental parameter problem, in that the limiting distribution of the point estimator is not centered at zero, and that test statistics do not follow their standard asymptotic distributions as in the absence of the fixed effects. To address the problem, we develop an analytical bias correction procedure to construct a bias-corrected likelihood. The resulting estimator follows an asymptotic normal distribution with mean zero. Moreover, likelihood-based test statistics—including likelihood-ratio, Lagrange-multiplier, and Wald tests—follow the limiting chi-squared distribution under the null hypothesis. Simulations demonstrate the effectiveness of the proposed correction method, and an empirical application on the labor force participation of single mothers underscores its practical importance.

A century of wildfire suppression policies has led to the build-up of combustible fuel loads in forests, increasing the size, severity, and costs of wildfires. This study explores whether fuel-reduction treatments reduce wildfire suppression costs. Focusing on wildfires igniting on U.S. Forest Service lands in the Pacific Northwest, we leverage exogenous variation in protections for the Northern Spotted Owl that unintentionally restrict fuel treatments. Conservative estimates indicate that five to six dollars are saved in suppression costs for every dollar spent on fuel treatments. Our results highlight the potential for reforming environmental protections to achieve economic savings and conservation benefits.

We develop a quantitative, multi-country general equilibrium framework to study the impact of economic growth, dietary restrictions, and food trade policies on global greenhouse-gas emissions (GHG) from agriculture. Motivated by new cross-country relationships between economic development, diet patterns, agricultural technologies, and GHG emissions, our framework features different income elasticities of demand across food products, and multiple agricultural technologies for production across grid cells covering the surface of the Earth, with food products and technologies being heterogeneous in their GHG emissions per calorie. Using our model’s open-economy structure, we propose a simple procedure to estimate the income elasticities without price data. We find that GHG emissions following economic growth are strongly influenced by the general equilibrium effects related to dietary changes, agricultural modernisation, and food supply readjustments. Moreover, compared to food trade policies, dietary restrictions are both substantially more effective in reducing GHG emissions, and more favourable when considering the welfare losses in developing countries.