This paper models decisions to apply to and attend charter schools in Boston using a generalized Roy selection framework linking preferences to the achievement gains generated by charter attendance. The model is estimated with instruments based on randomized admission lotteries and distance to charter schools. Charter schools generate larger gains for disadvantaged students, but demand for charters is stronger among more advantaged students. Similarly, gains are inversely related to unobserved preferences for charters. As a result, counterfactual simulations indicate that charter expansion is likely to be most effective when accompanied by efforts to target students who are unlikely to apply.

We show how data from online social networking services can help researchers better understand the effects of social interactions on economic decision making. We combine anonymized data from Facebook, the largest online social network, with housing transaction data and explore both the structure and the effects of social networks. Individuals whose geographically distant friends experienced larger recent house price increases are more likely to transition from renting to owning. They also buy larger houses and pay more for a given house. Survey data show that these relationships are driven by the effects of social interactions on individuals’ housing market expectations.

This paper studies how interest groups allocate campaign contributions when congressmen are connected by social ties. We establish conditions for the existence of a unique Nash equilibrium in pure strategies for the contribution game and characterize the associated allocation of the interest groups’ moneys. While the allocations are generally complex functions of the environment (the voting function, the legislators’ preferences, and the social network topology), they are simple, monotonically increasing functions of the respective legislators’ Katz-Bonacich centralities. Using data on the 109th–113th Congresses and on congressmen’s alumni connections, we estimate the model and find evidence supporting its predictions.

A nudge is a paternalistic government intervention that attempts to improve choices by changing the framing of a decision problem. We propose a welfare-theoretic foundation for nudging similar in spirit to the classical revealed preference approach, by investigating a framework in which preferences and mistakes of an agent can be elicited from her choices under different frames. We provide characterizations of the classes of behavioral models in which the information required for nudging can or cannot be deduced from choice data.

We examine heterogeneous treatment effects of a universal child care program in Germany by exploiting variation in attendance caused by a reform that led to a large expansion staggered across municipalities. Drawing on novel administrative data from the full population of compulsory school entry examinations, we find that children with lower (observed and unobserved) gains are more likely to select into child care than children with higher gains. Children from disadvantaged backgrounds are less likely to attend child care than children from advantaged backgrounds but have larger treatment effects because of their worse outcome when not enrolled in child care.

We analyze how academic experts and nonexperts forecast the results of 15 piece-rate and behavioral treatments in a real-effort task. The average forecast of experts closely predicts the experimental results, with a strong wisdom-of-crowds effect: the average forecast outperforms 96 percent of individual forecasts. Citations, academic rank, field, and contextual experience do not correlate with accuracy. Experts as a group do better than nonexperts, but not if accuracy is defined as rank-ordering treatments. Measures of effort, confidence, and revealed ability are predictive of forecast accuracy to some extent and allow us to identify “superforecasters” among the nonexperts.

We show that in the presence of admissions reserves, the effect of the precedence order (i.e., the order in which different types of seats are filled) is comparable to the effect of adjusting reserve sizes. Either lowering the precedence of reserve seats at a school or increasing the school’s reserve size weakly increases reserve-group assignment at that school. Using data from Boston Public Schools, we show that reserve and precedence adjustments have similar quantitative effects. Transparency about these issues—in particular, how precedence unintentionally undermined intended policy—led to the elimination of walk zone reserves in Boston’s public school match.

We examine how a 16-week cut in potential unemployment insurance (UI) duration in Missouri affected search behavior of UI recipients and the aggregate labor market. Using a regression discontinuity design (RDD), we estimate marginal effects of maximum duration on UI and nonemployment spells of 0.45 and 0.25, respectively. We simulate the unemployment rate implied by the RDD estimates assuming no market-level externalities. The implied response closely approximates the decline in the unemployment rate following the benefit cut, suggesting that, even in a period of high unemployment, the labor market absorbed the influx of workers without crowding out other job seekers.

This paper proposes a new methodology for analyzing nonlinear pricing data. We establish identification of the model primitives with a known tariff and characterize the model restrictions on observables. We propose a quantile-based nonparametric estimator that achieves consistency at the parametric rate. We introduce unobserved product heterogeneity with an unknown tariff and show how our identification and estimation results extend. A Monte Carlo study analyzes the robustness of our methodology to menus of two-part tariffs. Analysis of cellular service data assesses the performance of various pricing strategies. We discuss extensions to network effects, multiproduct firms, bundling, differentiated products, and oligopolies.

It is conventional wisdom that transparency in cartels—monitoring of competitors’ prices, sales, and profits—facilitates collusion. However, in several recent cases cartels have instead worked to preserve the privacy of their participants’ actions and outcomes. Toward explaining this behavior, we show that cartels can sometimes sustain higher profits when actions and outcomes are observed only privately, because better information can hinder collusion by helping firms devise more profitable deviations from the collusive agreement. We provide conditions under which maintaining privacy is optimal for cartels that follow a market-segmentation strategy.

This paper estimates a model of the effort decisions of students and teachers in a classroom setting to understand the performance of Mexican high school students on curriculum-based examinations. The model allows for student heterogeneity in initial mathematics preparation and knowledge preference and for teacher heterogeneity in instructional ability and preferences for student knowledge. Survey data include multiple measurements of student and teacher effort, student and teacher preferences, student initial knowledge, and teacher ability. The most important factor accounting for poor performance, the lack of sufficient prior preparation, suggests a mismatch between the curriculum content and entering grade-level mathematics knowledge.