Knowledge that Transforms

We estimate the causal effect of each county in the United States on children's incomes in adulthood. We first estimate a fixed effects model that is identified by analyzing families who move across counties with children of different ages. We then use these fixed effect estimates to (i) quantify how much places matter for intergenerational mobility, (ii) construct forecasts of the causal effect of growing up in each county that can be used to guide families seeking to move to opportunity, and (iii) characterize which types of areas produce better outcomes. For children growing up in low-income families, each year of childhood exposure to a one standard deviation (std. dev.) better county increases income in adulthood by 0.5%. There is substantial variation in counties' causal effects even within metro areas. Counties with less concentrated poverty, less income inequality, better schools, a larger share of two-parent families, and lower crime rates tend to produce better outcomes for children in poor families. Boys' outcomes vary more across areas than girls' outcomes, and boys have especially negative outcomes in highly segregated areas. Areas that generate better outcomes have higher house prices on average, but our approach uncovers many "opportunity bargains"-places that generate good outcomes but are not very expensive.

Please do not cite or circulate without permission This paper examines whether and how firms should adopt job testing technologies. If hiring managers have other sources of information about a worker’s quality (e.g. interviews), then firms may want to allow managers to overrule test recommendations for candidates they believe show promise. Yet if firms are concerned that managers are biased or have misaligned objectives, it may be optimal to impose hiring rules even if this means ignoring potentially valuable soft information. We evaluate the staggered introduction of a job test across 130 locations of 15 firms employing service sector workers. We show that testing improves the match-quality of hired workers, as measured by their completed tenure, by about 14%. These gains largely come from managers who follow test recommendations, as opposed to those who frequently make exceptions to test recommendations. That is, when faced with similar applicant pools, managers who make more exceptions systematically end up with workers with lower tenure. In this setting, our results suggest that firms can improve productivity by taking advantage of the verifiability of test scores to limit managerial discretion. 1 1

This article develops a new test for identifying racial bias in the context of bail decisions—a high-stakes setting with large disparities between white and black defendants. We motivate our analysis using Becker’s model of racial bias, which predicts that rates of pretrial misconduct will be identical for marginal white and marginal black defendants if bail judges are racially unbiased. In contrast, marginal white defendants will have higher rates of misconduct than marginal black defendants if bail judges are racially biased, whether that bias is driven by racial animus, inaccurate racial stereotypes, or any other form of bias. To test the model, we use the release tendencies of quasi-randomly assigned bail judges to identify the relevant race-specific misconduct rates. Estimates from Miami and Philadelphia show that bail judges are racially biased against black defendants, with substantially more racial bias among both inexperienced and part-time judges. We find suggestive evidence that this racial bias is driven by bail judges relying on inaccurate stereotypes that exaggerate the relative danger of releasing black defendants.

AbstractIn the centuries leading up to the Industrial Revolution, Western Europe gradually pulled ahead of other world regions in terms of technological creativity, population growth, and income per capita. We argue that superior institutions for the creation and dissemination of productive knowledge help explain the European advantage. We build a model of technological progress in a preindustrial economy that emphasizes the person-to-person transmission of tacit knowledge. The young learn as apprentices from the old. Institutions such as the family, the clan, the guild, and the market organize who learns from whom. We argue that medieval European institutions such as guilds, and specific features such as journeymanship, can explain the rise of Europe relative to regions that relied on the transmission of knowledge within closed kinship systems (extended families or clans).

We reconsider the role for human capital in accounting for cross-country income differences. Our contribution is to bring to bear new data on the pre- and post- migration labor market experiences of immigrants to the U.S. Immigrants from poor countries experience wage gains that are only 40 percent of the GDP per worker gap, which implies that "country" accounts for 40 percent of income differences, while human capital accounts for 60 percent. Our approach handles selection by comparing the wage of the same individual in two different countries. We also provide evidence on and a correction for skill transfer.

Firms often sell a basic good as well as ancillary ones. Hold-up concerns have led to ancillary good regulations, such as transparency and price caps. The hold-up narrative, however, runs counter to evidence in many retail settings where ancillary good prices are set below cost (e.g., free shipping or limited card surcharging in countries where the “no-surcharge rule” was lifted). We argue that the key to unifying these conflicting narratives is that the seller may absorb partly or fully the ancillary good’s cost so as not to miss sales on the basic good. A supplier with market power on the ancillary good market then takes advantage of cost absorption and jacks up its wholesale price. Hold-ups occur only when consumers are initially uninformed or naive about the drip price and shopping costs are high. The price of the basic good then acts as a signal of the drip price, since a high markup on the basic good makes the firm more wary of missed sales. Regardless of whether consumers are informed, uninformed but rational, or naive, mandating price transparency and banning loss-making on the ancillary good leads to (i) an efficient consumption of the ancillary good, and (ii) a reduction of its wholesale price, generating strict welfare gains.

We present new evidence on the evolution of black–white earnings differences among all men, including both workers and nonworkers. We study two measures: (i) the level earnings gap—the racial earnings difference at a given quantile; and (ii) the earnings rank gap—the difference between a black man's percentile in the black earnings distribution and the position he would hold in the white earnings distribution. After narrowing from 1940 to the mid-1970s, the median black–white level earnings gap has since grown as large as it was in 1950. At the same time, the median black man's relative position in the earnings distribution has remained essentially constant since 1940, so that the improvement then worsening of median relative earnings have come mainly from the stretching and narrowing of the overall earnings distribution. Black men at higher percentiles have experienced significant advances in relative earnings since 1940, due mainly to strong positional gains among those with college educations. Large relative schooling gains by blacks at the median and below have been more than counteracted by rising return to skill in the labor market, which has increasingly penalized remaining racial differences in schooling at the bottom of the distribution.

Can machine learning improve human decision making? Bail decisions provide a good test case. Millions of times each year, judges make jail-or-release decisions that hinge on a prediction of what a defendant would do if released. The concreteness of the prediction task combined with the volume of data available makes this a promising machine-learning application. Yet comparing the algorithm to judges proves complicated. First, the available data are generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the variable the algorithm predicts; for instance, judges may care specifically about violent crimes or about racial inequities. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: one policy simulation shows crime reductions up to 24.7% with no change in jailing rates, or jailing rate reductions up to 41.9% with no increase in crime rates. Moreover, all categories of crime, including violent crimes, show reductions; these gains can be achieved while simultaneously reducing racial disparities. These results suggest that while machine learning can be valuable, realizing this value requires integrating these tools into an economic framework: being clear about the link between predictions and decisions; specifying the scope of payoff functions; and constructing unbiased decision counterfactuals.

Much of the variation in international trade volume is driven by firms’ extensive margin decisions of whether to participate in export markets. We evaluate how the information potential exporters possess influences their decisions. We estimate a model of export participation in which firms weigh the fixed costs of exporting against the forecasted profits from serving a foreign market. We adopt a moment inequality approach, placing weak assumptions on firms’ expectations. The framework allows us to test whether firms differ in the information they have about foreign markets. We find that larger firms possess better knowledge of market conditions in foreign countries, even when those firms have not exported in the past. Quantifying the value of information, we show that, in a typical destination, total exports rise while the number of exporters falls when firms have access to better information to forecast export revenues.

This article studies how a recommender system may incentivize users to learn about a product collaboratively. To improve the incentives for early exploration, the optimal design trades off fully transparent disclosure by selectively overrecommending the product (or “spamming”) to a fraction of users. Under the optimal scheme, the designer spams very little on a product immediately after its release but gradually increases its frequency; she stops it altogether when she becomes sufficiently pessimistic about the product. The recommender’s product research and intrinsic/naive users “seed” incentives for user exploration and determine the speed and trajectory of social learning. Potential applications for various Internet recommendation platforms and implications for review/ratings inflation are discussed.