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Articles 1 - 30 of 2930
Full-Text Articles in Social and Behavioral Sciences
Teen Suicide And The Outcomes Of Surviving Peers, Emily Cuddy, Janet Currie, Elisa Jácome, Lucy Manly
Teen Suicide And The Outcomes Of Surviving Peers, Emily Cuddy, Janet Currie, Elisa Jácome, Lucy Manly
Cowles Foundation Discussion Papers
By age 17, a quarter of U.S. students have experienced a peer suicide. Using linked administrative data from South Carolina and a matched difference-in-differences design, we find that exposure to a peer’s self-harm death increases the probability of a self-harm diagnosis by nearly 50% and both the incidence and frequency of mental health visits. Effects on care use and criminal behavior are concentrated among white boys, and responses diverge by prior mental health history: students without a prior diagnosis increase felony offending rather than care-seeking. Deaths from assault and transportation accidents produce no comparable rise in self-harm, consistent with contagion.
The Near-Optimality Of Two-Part Tariffs For Nonlinear Pricing, Dirk Bergemann, Yang Cai, Jinzhao Wu, Konstantin Zabarnyi
The Near-Optimality Of Two-Part Tariffs For Nonlinear Pricing, Dirk Bergemann, Yang Cai, Jinzhao Wu, Konstantin Zabarnyi
Cowles Foundation Discussion Papers
The optimal mechanism for selling a divisible good under convex production costs can be arbitrarily complex, yet firms overwhelmingly use two-part tariffs: a fixed fee plus a constant markup over production cost. We quantify the profit this simplicity sacrifices. For regular value distributions, a two-part tariff guarantees a fraction of the optimal profit that depends only on a lower bound m on the elasticity of the marginal cost, independent of the value distribution. The guarantee approaches 1 as m grows, and is impossible without such a bound. Beyond regularity, no two-part tariff guarantees a constant fraction, but a menu …
Efficient Dynamic Mechanism Design Without A Common Prior, Dirk Bergemann, Marek Bojko
Efficient Dynamic Mechanism Design Without A Common Prior, Dirk Bergemann, Marek Bojko
Cowles Foundation Discussion Papers
We study efficient dynamic mechanism design with independent private values when agents do not share a common prior over the stochastic environment. Each agent privately observes the stochastic kernel governing the evolution of her own type and may hold arbitrary beliefs about the kernels of others. We extend the agents’ type space to include the kernel itself and show that the dynamic team mechanism of Athey and Segal (2013) and the dynamic pivot mechanism of Bergemann and Välimäki (2010) implement the socially efficient allocation in periodic ex-post equilibrium. We further show that kernels can be elicited only once, at the …
Fast Online Inference On Semiparametric Models, Xiaohong Chen, Elie Tamer, Qingsong Yao
Fast Online Inference On Semiparametric Models, Xiaohong Chen, Elie Tamer, Qingsong Yao
Cowles Foundation Discussion Papers
This paper develops a framework for fast online inference on semiparametric models with large sample sizes and possibly many covariates. The computational algorithm itself is the object of statistical study: after a globally consistent warm start in the first phase, the path of averaged online iterates generated in the second phase automatically delivers estimators with optimal convergence rates and valid confidence sets. Both phases require only a single pass over the data stream and are well suited to streaming data or to settings with storage/privacy constraints. For semiparametric monotone index models, the averaged trajectory of the second phase lead to …
The Risk Protection Value Of “Moral Hazard”, Angie Acquatella, Victoria Marone
The Risk Protection Value Of “Moral Hazard”, Angie Acquatella, Victoria Marone
Cowles Foundation Discussion Papers
Health insurance lowers the out-of-pocket price of healthcare, and it is well-established that this leads to higher utilization of care. This manifestation of “moral hazard” is typ ically viewed as a social cost of insurance. Within a standard model, this paper shows that a consumer’s ability to change her behavior in response to insurance can also play a central role in the ability of insurance to protect her from risk. We provide a theoretical characterization of this channel and quantify its importance empirically. Under stan dard parameterizations and estimates in the literature, we find that insurance-induced healthcare utilization can account …
Explanations For Robust Decisions, Kai Hao Yang, Nathan Yoder, Alexander K. Zentefis
Explanations For Robust Decisions, Kai Hao Yang, Nathan Yoder, Alexander K. Zentefis
Cowles Foundation Discussion Papers
We consider the problem of explaining a data generating process (DGP) to a decision maker (DM) who cannot understand it. Explanations are information, not approximations: they are useful because they rule out possible DGPs. If the DM maximizes her average payoff, explanations using OLS are robustly useful, and augmenting them with summary statistics makes them more so. Sampling error can destroy these guarantees, but theoretical assumptions linking that error to the DGP can restore them. If the DM is sufficiently ambiguity averse, explanations that are affine in outcomes are not robustly useful, but certificates reporting lower bounds on outcomes are.
Long-Run Effects Of H-1b Immigration On The U.S. Economy, Ran Abramitzky, Leah Boustan, Ahmet Gulak, Jens Hainmueller
Long-Run Effects Of H-1b Immigration On The U.S. Economy, Ran Abramitzky, Leah Boustan, Ahmet Gulak, Jens Hainmueller
Cowles Foundation Discussion Papers
We study the effects of H-1B immigration on U.S. industries that employ H-1B workers and their trading partners. Using a novel cross-industry design and the 1999–2003 expansion of the H-1B visa cap for identification, we find that H-1B exposure raised incomes for natives and pre-existing immigrants, with gains concentrated in non-STEM occupations. Income gains propagate forward through supply chains to downstream industries but not backward to upstream industries, consistent with a productivity shock rather than a labor supply shock. We find no direct effect on patenting, suggesting that productivity gains arise from better task execution rather than patentable invention.
Doctor Decision Making And Patient Outcomes, Janet Currie, W. Bentley Macleod, Kate Musen
Doctor Decision Making And Patient Outcomes, Janet Currie, W. Bentley Macleod, Kate Musen
Cowles Foundation Discussion Papers
Doctors often treat similar patients differently, which affects health outcomes and medical spending. We assess the recent literature on doctor decision making through the lens of a model that incorporates diagnostic and procedural skills, beliefs, incentives, and differences in patient pools. Decision making is affected by beliefs, training, experience, peer effects, financial incentives, and time constraints. Interventions to improve decision making include providing information, guidelines, and technologies like electronic medical records and algorithmic decision tools. Economists have made progress in understanding doctor decision making, but applications of that knowledge to improving health care are still limited.
How Much Should A Conversational Recommender System Converse?, Akshit Kumar, Vahideh Manshadi, Akhilesh Tumu
How Much Should A Conversational Recommender System Converse?, Akshit Kumar, Vahideh Manshadi, Akhilesh Tumu
Cowles Foundation Discussion Papers
Conversational recommender systems powered by generative AI can enhance personalization by facilitating information elicitation through follow-up questions. However, engaging in these conversations imposes a communication cost on users. As platforms with different objectives and monetization models deploy these systems, a central question is: how does the platform’s objective and sellers’ strategic response shape the design of these systems in terms of their elicitation strategy? We develop a parsimonious model of conversational elicitation in which interaction generates noisy preference information and imposes a communication cost borne by the user. A user-welfare-maximizing platform elicits more information when accurate niche matching yields large …
Life After Divorce: Effects Of Joint Custody On Parents And Children, Stella Canessa, Gordon B. Dahl, Anna Hasselqvist, Costas Meghir, Susan Niknami, Mårten Palme, Helmut Rainer, Olof Rosenqvist, Pengpeng Xiao
Life After Divorce: Effects Of Joint Custody On Parents And Children, Stella Canessa, Gordon B. Dahl, Anna Hasselqvist, Costas Meghir, Susan Niknami, Mårten Palme, Helmut Rainer, Olof Rosenqvist, Pengpeng Xiao
Cowles Foundation Discussion Papers
Divorce reshapes family life, yet little is known about one of its most consequential features: the allocation of child custody. We study the impact of joint versus sole custody on both parents and children using rich administrative data from Sweden linked to over 25 years of newly-collected court custody rulings. To address selection concerns, we exploit random assignment of custody disputes to judges who differ sharply in their propensity to grant joint custody. For fathers, joint custody substantially raises earnings and improves mental health, consistent with sustained paternal involvement enhancing labor market attachment and psychological well-being. In contrast, there are …
Ai Premium, Nicola Borri, Yukun Liu, Aleh Tsyvinski
Ai Premium, Nicola Borri, Yukun Liu, Aleh Tsyvinski
Cowles Foundation Discussion Papers
Using 380 trillion tokens of realized AI consumption across more than four hundred large language models from the licensed proprietary OpenRouter dataset covering approximately 2 percent of current global monthly AI token consumption, we analyze how AI affects firms, markets, and workers. Leveraging the unprecedented size, scope and granularity data, we construct the AI Factor from growth in tokens, dollars, and users, estimate firm-level AI Betas from stock return comovement, and characterize the AI Premium. First, we build a high-frequency AI factor and decompose it into salient components. Second, we show that firms whose returns covary more positively with the …
Credit Surfaces And Economic Uncertainty, John Geanokopolis, David E. Rappaport
Credit Surfaces And Economic Uncertainty, John Geanokopolis, David E. Rappaport
Cowles Foundation Discussion Papers
The Credit Surface along the leverage dimension gives the bond spread as a function of the loan-to-value ratio. Empirically, we show that uncertainty shocks typically increase spreads and steepen the credit surface, profoundly affecting the supply of credit. Theoretically, we derive necessary and sufficient conditions for the convexity of the credit surface, and for changes in the anticipated distribution of collateral prices that lead to steepening of the credit surface. Finally, we show that the credit surface itself fully reveals the entire distribution of collateral prices, thus providing a new and vivid language with which to describe uncertainty and stochastic …
The Displacement Effects Of Domestic Outsourcing, Mayara Felix, Michael B. Wong
The Displacement Effects Of Domestic Outsourcing, Mayara Felix, Michael B. Wong
Cowles Foundation Discussion Papers
Evidence that domestic outsourcing lowers pay comes largely from on-site transfers, in which workers move to a contractor but keep the same jobs. Displacement is rarely observed: whether workers lose their jobs, where they go, how earnings evolve. In Brazil’s 1993–1994 pro-outsourcing reforms, which differentially affected security guards, such transfers were rare; firms instead used occupational layoffs, shedding their guards while keeping other workers. Displaced guards’ employment recovered within five years, but many changed occupations and wages stayed about 12% lower. Lifetime losses average 1.2 to 1.5 years of pre-layoff earnings, concentrated among workers from high-wage firms, reflecting lost premia.
Domestic Outsourcing And Young-Worker Entry Into The Formal Sector, Mayara Felix, Michael B. Wong
Domestic Outsourcing And Young-Worker Entry Into The Formal Sector, Mayara Felix, Michael B. Wong
Cowles Foundation Discussion Papers
We provide evidence that domestic outsourcing increases young-worker entry into the formal sector. Leveraging a pair of 1993–1994 Brazilian reforms that reduced the relative cost of outsourcing security guards, a triple-differences design shows that the reforms increased formal guard employment by 4% and hiring from unemployment or informality by 7%, while reallocating formal employment from older to younger workers and leaving demographic-adjusted wages unchanged. Census data corroborate the rise in formality, driven by the youngest cohorts. The compositional shift mirrors a general pattern in Brazil’s matched employer–employee records: conditional on total firm size, employers with greater occupation-specific scale hire younger …
Multidimensional Screening For Quality With An Application To Health Insurance, Hector Chade, Victoria Marone, Amanda Starc, Jeroen Swinkels
Multidimensional Screening For Quality With An Application To Health Insurance, Hector Chade, Victoria Marone, Amanda Starc, Jeroen Swinkels
Cowles Foundation Discussion Papers
We analyze a multidimensional screening model in which a principal offers a menu of quality-price pairs to a consumer with multiple dimensions of private information and a quasilinear utility function. We derive necessary conditions for optimality, and use them to provide insight into optimal exclusion, positive trade, and screening. We then recast the problem in terms of incremental quality levels and prices, the so-called demand-profile approach (DPA). Under DPA, the problem decouples across increments and can be solved one at a time. We provide novel conditions under which DPA recovers the solution to the full problem exactly or approximately, and …
Neural Networks In Economics: A Selective Review, Xiaohong Chen, Luis Hoderlein, Jonas Lieber
Neural Networks In Economics: A Selective Review, Xiaohong Chen, Luis Hoderlein, Jonas Lieber
Cowles Foundation Discussion Papers
The "deep learning revolution" has led to remarkable success of neural networks in applications across a wide range of fields, such as computer vision, speech recognition, natural language processing, code generation, protein structure prediction, image and video generation, and dynamic control. This review introduces neural networks to economists. Recent advances and challenges in approximation theory, neural network architecture, computation, econometric theory and practice are presented. Finally, we survey the rapidly evolving applications of modern neural networks and Large Language Models in economic research.
Immigration, Innovation, And The Geography Of Growth, Costas Arkolakis, Sun Kyoung Lee, Michael Peters
Immigration, Innovation, And The Geography Of Growth, Costas Arkolakis, Sun Kyoung Lee, Michael Peters
Cowles Foundation Discussion Papers
Between 1880 and 1920, more than 20 million immigrants settled in the United States. We study how this migration wave affected innovation and growth. Using a newly constructed dataset linking individual census records to historical immigration records and the universe of US patents, we highlight a new channel through which immigrants contributed to growth: they disproportionately settled in urban innovation hubs. To quantify the aggregate and regional effects of this mass migration episode, we develop a new spatial growth model in which skilled workers have a comparative advantage in innovation and sort endogenously across space. We find that international arrivals …
From Innovation To Speculation: Ai And The Magnificent Seven, Rerotlhe B. Basele, Peter C. B. Phillips, Shuping Shi
From Innovation To Speculation: Ai And The Magnificent Seven, Rerotlhe B. Basele, Peter C. B. Phillips, Shuping Shi
Cowles Foundation Discussion Papers
The AI boom has driven the Nasdaq and the Magnificent Seven tech stocks to record highs. But how much do these new records reflect underlying value, how much is speculation, and how vulnerable are these stocks and the wider market to a major downturn? Our evidence and analyses show clear signs of bubble exuberance in most of these stocks, concentrated in a few names like Nvidia, leading to latent risks for investors who assume their index funds are safely diversified and supported by wider economic fundamentals.
Offsetting Carbon With Lemons: Adverse Selection And Certification In The Voluntary Carbon Market, Vahideh Manshadi, Faidra Monachou, Ilan Morgenstern
Offsetting Carbon With Lemons: Adverse Selection And Certification In The Voluntary Carbon Market, Vahideh Manshadi, Faidra Monachou, Ilan Morgenstern
Cowles Foundation Discussion Papers
To meet voluntary climate targets, firms often complement internal decarbonization efforts by purchasing carbon credits in the voluntary carbon market (VCM), which finance projects that reduce emissions elsewhere. However, these emissions reductions are difficult to verify, and growing evidence of overcrediting has cast doubt on the VCM's potential to genuinely offset emissions. We investigate how the VCM's defining features shape its climate effectiveness. Our model captures three central elements: adverse selection, as high-quality projects that truly reduce emissions are costlier yet difficult to distinguish from low-quality ones; imperfect third-party certification, as projects are screened based on a noisy signal of …
Cross Section Curve Data Autoregression, Peter C.B. Phillips, Liang Jiang
Cross Section Curve Data Autoregression, Peter C.B. Phillips, Liang Jiang
Cowles Foundation Discussion Papers
This paper develops and applies new asymptotic theory for estimation and inference in parametric autoregression and vector autoregression with function valued cross section curve time series. The study provides a new approach to dynamic panel regression with high dimensional dependent cross section data. We deal with the stationary case and provide a full set of results extending those of standard Euclidean space autoregression, showing how function space curve cross section data raises efficiency and reduces bias in estimation and shortens confidence intervals. Wild-bootstrap methods are also developed to improve testing and inference. The findings reveal that function space models with …
From Unstructured Data To Demand Counterfactuals: Theory And Practice, Timothy Christensen, Giovanni Compiani
From Unstructured Data To Demand Counterfactuals: Theory And Practice, Timothy Christensen, Giovanni Compiani
Cowles Foundation Discussion Papers
Empirical models of multi-product demand rely on low-dimensional product representations to capture substitution patterns, increasingly using proxies built from unstructured data. When proxies are imperfect, standard workflows yield biased counterfactuals and invalid inference. We develop a practical toolkit to address these issues. Our methods apply to market-level and/or individual data, require minimal additional computation, provide simple standard-error formulas, and accommodate proxies from fine-tuned models. Further, we propose diagnostics to assess proxy quality. Our methods yield meaningful improvements in predicting substitution in empirically calibrated simulations and in an application where we assess counterfactual prediction performance against a ground truth.
How Much And How Fast Do Investors Respond To Equity Premium Changes? Evidence From Wealth Taxation, Andreas Fagereng, Luigi Guiso, Marius Ring
How Much And How Fast Do Investors Respond To Equity Premium Changes? Evidence From Wealth Taxation, Andreas Fagereng, Luigi Guiso, Marius Ring
Cowles Foundation Discussion Papers
Using administrative panel data on Norwegian investors’ portfolios, we document strong but slow portfolio allocation responses to a persistent wealth-tax-induced shock to the equity premium. Short-run responses resemble the modest sensitivity documented using surveys. The longer-run responses are much larger and can be rationalized by moderate risk aversion. We document that equity premium shocks affect stock market entry but not exits, suggesting that entry costs dominate participation costs. Our finding of slow responses supports the asset-pricing literature that uses adjustment frictions to explain important asset-pricing puzzles, and has implications for optimal capital taxation when tax rates differ across assets.
How Much Should A Conversational Recommender System Converse?, Akshit Kumar, Vahideh Manshadi, Akhilesh Tumu
How Much Should A Conversational Recommender System Converse?, Akshit Kumar, Vahideh Manshadi, Akhilesh Tumu
Cowles Foundation Discussion Papers
Conversational recommender systems powered by generative AI can enhance personalization by facilitating information elicitation through follow-up questions. However, engaging in these conversations imposes a communication cost on users. As platforms with different objectives and monetization models deploy these systems, a central question is: how does the platform’s objective and sellers’ strategic response shape the design of these systems in terms of their elicitation strategy? We develop a parsimonious model of conversational elicitation in which interaction generates noisy preference information and imposes a communication cost borne by the user. A user-welfare-maximizing platform elicits more information when accurate niche matching yields large …
The Macroeconomics Of Intergenerational Mental Health Dynamics, Boaz Abramson, Job Boerma, Diego Daruich, Aleh Tsyvinski
The Macroeconomics Of Intergenerational Mental Health Dynamics, Boaz Abramson, Job Boerma, Diego Daruich, Aleh Tsyvinski
Cowles Foundation Discussion Papers
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 …
Position: The Pre/Post-Training Boundary Should Govern Ip In Industry–Academia Ml Collaborations, Dirk Bergemann, Soheil Ghili, Nitzan Mekel-Bobrov
Position: The Pre/Post-Training Boundary Should Govern Ip In Industry–Academia Ml Collaborations, Dirk Bergemann, Soheil Ghili, Nitzan Mekel-Bobrov
Cowles Foundation Discussion Papers
Industry–academia ML collaborations routinely fail to launch—not for scientific reasons, but because academics must publish while companies must protect models trained on proprietary data, and no standard contract framework resolves this tension. Because contracts are negotiated by legal departments alone, many apparent legal disputes are incentive misalignment problems that only scientists at the table can correctly diagnose. We propose PBOS (Protect-the-Business / Open-Source-the-Science), a community-adoptable contract template anchored to a single technically-grounded boundary: pre-training artifacts (architectures, training code, benchmarks, untrained weights) are open science; post-training artifacts (weights trained on proprietary data) are business IP. This boundary is technically meaningful, legally …
Class Peers As Competitors And Educators: The Consequences Of Rank-Based Academic Rewards, Mark R. Rosenzweig, Bing Xu
Class Peers As Competitors And Educators: The Consequences Of Rank-Based Academic Rewards, Mark R. Rosenzweig, Bing Xu
Cowles Foundation Discussion Papers
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 …
Regional Economic Impacts And Emission Responses Under Solar Radiation Modification, Jenny Bjordal, Evelien Van Dijk, Henri Cornec, Anthony A. Smith Jr, Trude Storelvmo
Regional Economic Impacts And Emission Responses Under Solar Radiation Modification, Jenny Bjordal, Evelien Van Dijk, Henri Cornec, Anthony A. Smith Jr, Trude Storelvmo
Cowles Foundation Discussion Papers
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 …
Beyond Exposure: Predicting Ai Adoption Based On Comparative Advantage, Ilse Lindenlaub, Ryungha Oh, Maria Alejandra Rodriguez, Laura Veldkamp
Beyond Exposure: Predicting Ai Adoption Based On Comparative Advantage, Ilse Lindenlaub, Ryungha Oh, Maria Alejandra Rodriguez, Laura Veldkamp
Cowles Foundation Discussion Papers
We document and explain the gap between measures of AI exposure and measures of AI adoption in the workplace. This leads us to propose a new AI adoption index based on comparative advantage. Using the representative German DiWaBe employee survey linked to worker and establishment information, we compare worker-reported AI use to prominent exposure measures and find that the relationship is weak. Motivated by this gap, we develop a framework in which adoption depends not only on technical feasibility—AI’s absolute advantage measured by exposure—but on profitability—AI’s comparative (dis)advantage relative to a specific worker—balancing AI productivity against AI user costs and …
Towards A Methodology For Measuring Rental Property Ownership In The United States, Stephanie Kestelman, Rebecca Diamond, John Eric Humphries, Kate Pennington, Winnie Van Dijk, John Voorheis
Towards A Methodology For Measuring Rental Property Ownership In The United States, Stephanie Kestelman, Rebecca Diamond, John Eric Humphries, Kate Pennington, Winnie Van Dijk, John Voorheis
Cowles Foundation Discussion Papers
Roughly one-third of U.S. households rent their homes, yet measuring who owns rental property is difficult: ownership is frequently obscured by LLCs, partnerships, and other intermediary entities that separate legal from economic control. We develop a method that traces ownership through administrative records—combining deeds and property assessments with the Census Bureau’s Business Register, IRS Schedule K-1 filings, and SEC filings on REITs—to identify ultimate owners and construct property portfolios across the full landlord size distribution. Applying the method to 11 large CBSAs, we find that individual landlords own a large majority of rental units, though their share varies meaningfully across …
From People’S War To People’S Rule: Rebel Governance And The Foundations Of Inclusive Democracy, Bhishma Bhusal, Michael Callen, Rohini Pande, Soledad Prillaman, Deepak Singhania, Apurva Subedi
From People’S War To People’S Rule: Rebel Governance And The Foundations Of Inclusive Democracy, Bhishma Bhusal, Michael Callen, Rohini Pande, Soledad Prillaman, Deepak Singhania, Apurva Subedi
Cowles Foundation Discussion Papers
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 …