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How does wartime rebel governance shape post-conflict institutions? We study this in Nepal, where the Maoist People's War (1996–2006) dismantled a 240-year caste-based monarchy and ended with Maoists entering democratic politics. During the conflict, Maoists established sub-national “People’s Governments” that administered justice, collected taxes, and delivered local services. Using a spatial regression-discontinuity design, we show that exposure to People's Governments increased political knowledge and participation especially among historically marginalized indigenous groups (Janajatis). Exposure also reshaped party institutions and inter-party competition: candidate-selection committees in more exposed areas have 26 percent more Janajati members who, drawing on novel implicit-attitude data, exhibit less pro-upper caste bias. Non-Maoist parties' Janajati nomination rates nearly double in fully exposed areas, consistent with competition for newly mobilized voters. Nearly two decades on, local governments in exposed areas score 0.2–0.3 standard deviations higher on state capacity indices and receive 13% more in conditional federal grants. These findings show that when rebel groups enter competitive democratic politics, wartime governance institutions can — through citizen mobilization, party gatekeeping, and cross-party competition — enable a more inclusive and capable post-war state.

We compare how well agents aggregate information in two repeated social learning environments. In the first setting agents have access to a public data set. In the second they have access to the same data, and also to the past actions of others. Despite the fact that actions contain no additional payoff-relevant information, and despite potential herd behavior, free riding and information overload issues, observing and imitating the actions of others leads agents to take the optimal action more often in the second setting. We also investigate the effect of group size, as well as a setting in which agents observe private data and others’ actions.

We develop a quantitative macroeconomic theory of child mental health. The theory is grounded in child psychiatry, formalized in a life-cycle heterogeneous agent model of child development, and disciplined using micro data on mental health of children and parents. Intergenerational transmission of mental illness arises due to both biological factors and parental behavior. Parents experiencing mental illness have negative expectations and lose time due to rumination. As a result, they invest less in their child’s mental health. We use the model to evaluate policies designed to improve child mental health. We show that subsidizing mental health treatment for children generates sizable welfare gains.

This paper examines the theoretical and empirical consequences of rank-based reward systems in schools in which students’ performance and effort are evaluated relative to their peers. In such environments, classmates act simultaneously as competitors—due to rank-determined rewards—and as educators through peer learning and assistance. Using nationally representative panel survey data from U.S. high schools, combined with administrative information on the location assignments of new refugee student cohorts, we exploit variation in school competition policies and class ability compositions to identify empirically their dual effects on student effort and peer learning. We develop a theoretical tournament model with heterogeneous students who adjust their effort in response to the effort of similar peers and in which students learn from peers. The model predicts that when rewards depend on relative standing, adding higher-ability students to a cohort will reduce both incumbent academic effort and peer assistance, particularly in schools emphasizing rank-based awards, while adding lower-ability students has the opposite effects. Empirical tests of the model confirm these predictions. In schools with strong rank-based reward policies, the addition of stronger peers reduces high-performing incumbent students’ homework time and eliminates the positive spillovers from peer learning observed in less competitive settings. The adverse effects are concentrated among high-ability incumbents, while lower-ability students—who are less likely to win competitive awards—are largely unaffected. The results indicate that performance-based competition undermines cooperative peer learning and reduces student effort and overall academic performance, especially in institutions with high-ability students that explicitly emphasize relative ranking in determining academic recognition.

In GMM estimation it is well known that if the number of moment conditions grows with the sample size, GMM asymptotics differ from the standard case with moment size fixed as the sample size tends to infinity. The present work explores infinite dimensional GMM estimation under various conditions on the moment conditions and the weight matrix. Our approach employs a partial sum process formed by the moment conditions to represent high dimensional moments and an invariance principle to capture the infinite dimensional asymptotics as the moment size grows. Next, the GMM weight matrix is assumed to converge to one of two kernels at the limit: a continuous kernel or the Dirac delta function. Combining these different conditions enables development of a large sample theory for most efficient GMM estimation. The effects of permuting the moment conditions on GMM efficiency are also explored. The resulting theory is applied to weak instrumental variable estimation and the Angrist and Krueger (1991) data are re-analyzed in an empirical application of the new methods.

Solar Radiation Modification (SRM) has been proposed as a potential tool to limit increases in global or regional temperatures caused by anthropogenic greenhouse gas emissions. While previous research has extensively examined the climate system’s response to various SRM strategies, as well as their aggregate economic consequences, the regional distribution of economic impacts has received less attention. In this study, we use NorESM2–DIAM—an Earth System Model coupled to a high-resolution integrated assessment model—to assess the economic impacts, measured in GDP per capita, in an idealised SRM scenario where incoming solar radiation is reduced by 1%. Our results suggest that, relative to a baseline without SRM, most countries experience economic gains under SRM, with only a few countries facing negative impacts. Low-income countries tend to see the largest benefits, reducing global economic inequality relative to the baseline. However, reduced damages and lower inequality are accompanied by higher emissions under SRM, potentially leading to additional adverse effects not captured here. These findings highlight potential trade-offs between economic benefits, reduced inequality, and increased emissions relevant for SRM governance.

Signaling is wasteful. But how wasteful? We study the fraction of surplus dissipated in a separating equilibrium. For isoelastic environments, this waste ratio has a simple formula: β/(β + σ), where β is the benefit elasticity (reward to higher perception) and σ is the elasticity of higher types’ relative cost advantage. The ratio is constant across types and is independent of other parameters, including convexity of cost in the signal. We show that the directional effects of β and σ on waste extend to non-isoelastic environments.

AI/ML methods are increasingly used in economics to generate binary variables (or labels) via classification algorithms. When these generated variables are included as covariates in regressions, even small misclassification errors can induce large biases in OLS estimators and invalidate standard inference. We study whether the bootstrap can correct this bias and deliver valid inference. We first show that a seemingly natural fixed-label bootstrap, which generates data using estimated labels but relies on a corrupted version in estimation, is generally invalid unless a strong independence condition between the latent true labels and other covariates holds. We then propose a coupled-label bootstrap that jointly resamples the true and imputed labels, and show it is valid without this condition. Two finite-sample adjustments further improve coverage: a variance correction for uncertainty in estimated misclassification rates and a Hessian rotation for near-singular designs. We illustrate the methods in simulations and apply them to investigate the relationship between wages and remote work status.