Knowledge that Transforms

This paper examines whether high-speed Internet affects students' college applications. Our analysis links the diffusion of residential broadband to the testing and application outcomes of millions of PSAT and SAT takers and reveals that students with broadband in their postal code perform better on the SAT and apply to a higher number and more expansive set of colleges. While the availability of broadband generally improved applications to college, the effects appear to be concentrated among high-SES students, suggesting that the new technology may have increased preexisting inequities.

We conduct a disaggregated empirical analysis of civil conflict at the subnational level in Africa over 1997 to 2011 using a new gridded data set. We construct an original measure of agriculture-relevant weather shocks exploiting within-year variation in weather and in crop growing season and spatial variation in crop cover. Temporal and spatial spillovers in conflict are addressed through spatial econometric techniques. Negative shocks occurring during the growing season of local crops affect conflict incidence persistently, and local conflict spills over to neighboring cells. We use our estimates to trace the dynamic response to shocks and predict how future warming may affect violence.

This paper analyzes "attribute-based regulations," in which regulatory compliance depends upon some secondary attribute that is not the intended target of the regulation. For example, in many countries, fuel-economy standards mandate that vehicles have a certain fuel economy, but heavier or larger vehicles are allowed to meet a lower standard. Such policies create perverse incentives to distort the attribute upon which compliance depends. We develop a theoretical framework to predict how actors will respond to attribute-based regulations and to characterize the welfare implications of these responses. To test our theoretical predictions, we exploit quasi-experimental variation in Japanese fuel economy regulations, under which fuel-economy targets are downward-sloping step functions of vehicle weight. Our bunching analysis reveals large distortions to vehicle weight induced by the policy. We then leverage panel data on vehicle redesigns to empirically investigate the welfare implications of attribute-basing, including both potential benefits and likely costs.

We study the effect of birth order on personality using Swedish population data. Earlier-born men are more emotionally stable, persistent, socially outgoing, willing to assume responsibility, and able to take initiative than later borns. Firstborn children aremore likely to bemanagers and to be in occupations requiring leadership ability, social ability, and Big Five personality traits.We find a significant role for the sex composition within the family. When investigating possible mechanisms, we find that negative effects of birth order are driven by postnatal environmental factors. We also find evidence of lower parental human capital investments in later-born children.

The Beveridge-Nelson decomposition based on autoregressive models produces estimates of the output gap that are strongly at odds with widely held beliefs about transitory movements in economic activity. This is due to parameter estimates implying a high signal-to-noise ratio in terms of the variance of trend shocks as a fraction of the overall forecast error variance. When we impose a lower signal-to-noise ratio, the resulting Beveridge-Nelson filter produces a more intuitive estimate of the output gap that is large in amplitude and highly persistent, and it typically increases in expansions and decreases in recessions. Notably, our approach is also reliable in the sense of being subject to smaller revisions and predicting future output growth and inflation better than other trend-cycle decompositions that impose a low signal-to-noise ratio.

This paper presents a new approach to the measurement of the effects of spatial mismatch that takes advantage of matched employeremployee administrative data integrated with a person-specific job accessibility measure, as well as demographic and neighborhood characteristics. We focus on a group of job searchers for plausibly exogenous reasons: lower-income workers with strong labor force attachment separated during a mass layoff. Our results support the spatial mismatch hypothesis. We find that better job accessibility significantly decreases the duration of joblessness among lower-income displaced workers, especially for blacks, women, and older workers.

In this paper, we empirically measure the impact of natural disasters on firm-level operating performance and examine if such impact can be mitigated by technology diversification. Using major natural disasters specified by Barrot and Sauvagnat (2015) and factory location data from the toxic release inventory (TRI) database, we first find that firms with factories located in states affected by natural disasters are much less profitable. Second, we find that firms with diversified technologies are significantly less subject to the impact of natural disasters, suggesting that technology diversity enhances firms’ sustainability.

This paper develops a multicountry model of international trade that provides a simple microfoundation for buyer-seller relationships in trade. We explore a rich data set that identifies buyers and sellers in trade and establish a set of basic facts that guide the development of the theoretical model. We use predictions of the model to examine the role of buyer heterogeneity in a market for firm-level adjustments to trade shocks, as well as to quantitatively evaluate how firms’ marginal costs depend on access to suppliers in foreign markets.

Policymakers are often interested in estimating how policy interventions affect the outcomes of those most in need of help. This concern has motivated the practice of disaggregating experimental results by groups constructed on the basis of an index of baseline characteristics that predicts the values of individual outcomes without the treatment. This paper shows that substantial biases may arise in practice if the index is estimated by regressing the outcome variable on baseline characteristics for the full sample of experimental controls. We propose alternative methods that correct this bias and show that they behave well in realistic scenarios.

We investigate how the largest use of time—sleep—affects productivity. Time use data from the United States allow us to test a model in which sleep improves productivity. Consistent with theory, we find sleep is more complementary to home production than to leisure for nonemployed individuals. We then show that later sunset time reduces worker sleep and earnings. After ruling out alternative hypotheses, we implement an instrumental variables specification that provides causal estimates of the impact of sleep on earnings. A 1-hour increase in location-average weekly sleep increases earnings by 1.1% in the short run and 5% in the long run.