Knowledge that Transforms

Economists have studied the impact of numerous state laws, from welfare rules to voting ID requirements. Yet for all this policy evaluation, what do we know about policy diffusion—how these policies are introduced and spread from state to state? We present a series of facts based on a data set of 602 U.S. state policies spanning the past seven decades. First, proxies of state capacity do not predict a higher likelihood of innovating new policies, but the political leaning of the state does predict a higher likelihood of introducing partisan laws since 1990. Second, the diffusion of policies from 1950 to 2000 is best predicted by proximity—a state is more likely to adopt a policy if nearby states have already done so—as well as similarity in voter policy preferences. Third, since 2000, party alignment has become the strongest predictor of diffusion, and the speed of adoption has increased. Models of learning and correlated preferences can account for the earlier patterns, but the findings for the last two decades indicate a sharply increasing role of party control. We conclude that party polarization has emerged as a key factor recently for policy adoption, plausibly leading to a worse match between state policies and voter preferences.

This article views hiring as a contextual bandit problem: to find the best workers over time, firms must balance “exploitation” (selecting from groups with proven track records) with “exploration” (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on supervised learning approaches, are designed solely for exploitation. Instead, we build a resume screening algorithm that values exploration by evaluating candidates according to their statistical upside potential. Using data from professional services recruiting within a Fortune 500 firm, we show that this approach improves the quality (as measured by eventual hiring rates) of candidates selected for an interview, while also increasing demographic diversity, relative to the firm’s existing practices. The same is not true for traditional supervised learning-based algorithms, which improve hiring rates but select far fewer Black and Hispanic applicants. Together, our results highlight the importance of incorporating exploration in developing decision-making algorithms that are potentially both more efficient and equitable.

This paper estimates the Brazilian Amazon’s carbon-efficient forest cover—i.e. when farmers internalize the social cost of carbon. We propose a dynamic discrete choice land-use model and estimate it using a panel of land use and carbon stock of 5.7 billion pixels between 2008 and 2017. The business-as-usual scenario implies an inefficient release of 42 billion tons of CO2 in the long run, resulting from the deforestation of an area twice the size of France. A carbon tax that makes farmers internalize the social cost of carbon would implement the efficient allocation and generate welfare gains exceeding 1.6 trillion dollars. Responses from a carbon tax are highly convex: a carbon tax of only $10/ton would preserve 95% of the efficient carbon stock. An excise tax on cattle ranching, a second-best policy, achieves at most 87% of the first-best welfare gains.

We discuss how to estimate the interplay between genes (nature) and environments (nurture), with an empirical illustration of the moderating effect of school starting age on one’s genetic predisposition towards educational attainment. We argue that gene–environment (G×E) studies can be instrumental for (i) assessing treatment effect heterogeneity, (ii) testing theoretical predictions, and (iii) uncovering mechanisms, thereby improving understanding of how (policy) interventions affect population subgroups. Empirically, we find that being old-for-grade and having a higher genetic propensity for education benefits children on assessment tests as they progress through school. In this setting, families appear to increase genetic inequalities while schools seem to reduce them.

We develop a model of a Financial Stability Fund (the “Fund” henceforth) for a union of sovereign countries. By design, the contract prevents country defaults, as well as undesired expected losses, which in a union translate into excessive risk mutualizations. A participant country has greater ability to borrow and share risks than using sovereign debt financing. The Fund contract also provides better incentives for the country to reduce endogenous risks. These efficiency gains arise from the ability of the Fund to offer long-term contingent financial contracts, subject to limited enforcement and moral hazard constraints. We develop the theory and quantitatively compare the constrained-efficient Fund economy with an incomplete markets economy with default. We calibrate our economy to the euro area “stressed countries” in the debt crisis (2010–2). Substantial welfare gains are achieved, particularly in times of crisis. The Fund is, in fact, a risk-sharing, crisis prevention and resolution mechanism, which transforms the participant countries’ defaultable sovereign debt into the union’s safe assets. In sum, our theory can help to improve current official lending practices and, for example, to eventually design a European Fiscal Fund.

We explore the power that precise information about consumers’ preferences grants an intermediary in shaping competition. We think of an intermediary as an information designer who chooses what information to reveal to firms, which then compete à la Bertrand in a differentiated product market. We analyse the information designs that maximize consumer and producer surplus and uncover systematic differences in how information can be used to intensify or soften competition. Our analysis demonstrates the power that users’ data can endow intermediaries with and speaks directly to current regulatory debates regarding digital marketplaces.

Labour productivity in manufacturing differs starkly across regions in China. We document that productivity, wages, and start-up rates of non-state firms have nevertheless experienced rapid unconditional regional convergence after 1995. To analyse these patterns, we construct a Hopenhayn model that incorporates location-specific capital wedges, output wedges, and entry barriers. Using Chinese Industry Census data, we estimate these wedges and examine their role in explaining differences in performance and growth across prefectures. Entry barriers explain most of the differences. We investigate the empirical covariates of these entry barriers and find that changes in barriers are causally related to changes in the size of the state sector: a smaller state sector leads to lower entry barriers.

We document dramatic changes in euro area interbank money markets during the financial and sovereign debt crises: unsecured borrowing declined across the euro area, while secured market haircuts on sovereign bonds increased, and bank borrowing from the European Central Bank rose in southern countries. We construct a quantitative general equilibrium model to assess the macroeconomic impact of these developments and the associated policy response. Our model features heterogeneous banks and sovereign bonds, secured and unsecured money markets, and a central bank. We compare a benchmark policy—the central bank providing collateralized lending to banks at haircuts lower than the market—with an alternative policy that maintains a constant central bank balance sheet. We show that the fall in output, investment, and capital would have been twice as high under the alternative policy.

Developing countries face massive urbanization and slum upgrading is a popular policy to improve shelter for many. Yet, preserving slums at the expense of formal developments can raise concerns of misallocation of land. We estimate causal, long-term impacts of the 1969–84 KIP programme, which provided basic upgrades to 5 million residents covering 25% of land in Jakarta, Indonesia. We assemble high-resolution data on programme boundaries and 2015 outcomes and address programme selection bias through localized comparisons. On average, KIP areas today have lower land values, shorter buildings, and are more informal, per a photographs-based slum index. The negative effects are concentrated within 5 km of the CBD. We develop a spatial equilibrium model to characterize the welfare implications of KIP. Counterfactuals suggest that 79% of the welfare effects stem from removing KIP in the centre and highlight how to mitigate losses to displaced residents.

Do periods of persistently loose monetary policy increase financial fragility and the likelihood of a financial crisis? This is a central question for policymakers, yet the literature does not provide systematic empirical evidence about this link at the aggregate level. In this article, we fill this gap by analysing long-run historical data. We find that when the stance of monetary policy is accommodative over an extended period, the likelihood of financial turmoil in the medium term increases considerably. We investigate the causal pathways that lead to this result and argue that credit creation and asset price overheating are important intermediating channels.