Knowledge that Transforms

AS IMMIGRATION TO OECD COUNTRIES has risen, the question of how to ensure the success of immigrant students has grown more critical. In Europe, the question is complicated by the common practice of tracking students at an early age into different types of schools. One concern is that immigrants may enroll in college preparatory tracks at lower rates than natives with similar achievement. The present study documents gaps in track choice among high-achieving immigrant students in Italy, and evaluates an intervention aimed at aligning the choices of these students with their academic potential. It thereby provides important evidence both on the education of immigrants and on educational tracking. In the United States, there are related debates about programs in which Black and Hispanic students are under-represented: for example, gifted education, advanced tracks within schools, and college prep coursework. These debates pit advocates of tracking and other policies, who extol the benefits of matching curriculum and instruction to levels of student ability, against critics who argue that tracking reinforces segregation and inequality. But the goals of the two sides are not always at odds. Segregation in tracking is often driven by factors unrelated to student ability—such as information barriers, implicit biases, or gaps in parental support and advocacy. Policies that remove these barriers and push capable students from disadvantaged groups to complete higher levels of education can improve match quality, while also promoting social integration and economic opportunity. Further, such policies may have important spillover effects by helping to change perceptions and raise expectations among parents, teachers, and future generations of students. There is mounting evidence that socioeconomic gaps in choice and representation can be reduced through policies that automate or simplify steps in the decision process. Among high-ability elementary school students, gaps in gifted program participation can be reduced by replacing a system of parent and teacher nominations with test-based, universal screening (Card and Giuliano (2016a)). Among high school students, decisions about whether and where to attend college can be influenced by polices that expand college admissions testing (Bulman (2015), Goodman (2016)), facilitate applications for financial aid (Bettinger et al. (2012)), or provide information through targeted mailings (Hoxby and Turner (2013)). One concern about such procedural and informational interventions is that they are temporary fixes and may not address underlying sources of under-representation, such as gaps in information gathering skills, parental support, and expectations. Theses gaps could make under-represented groups more likely to fail in challenging programs. For example, evidence on college mentoring suggests that light-touch “nudges” may be insufficient for producing lasting effects among disadvantaged students, especially when students lack parental support (Carrell and Sacerdote (2017), Cunha, Miller, and Weisburst (2018)). But there is also evidence that discounts such “mismatch” concerns. In a

This paper presents finite‐sample efficiency bounds for the core econometric problem of estimation of linear regression coefficients. We show that the classical Gauss–Markov theorem can be restated omitting the unnatural restriction to linear estimators, without adding any extra conditions. Our results are lower bounds on the variances of unbiased estimators. These lower bounds correspond to the variances of the the least squares estimator and the generalized least squares estimator, depending on the assumption on the error covariances. These results show that we can drop the label “linear estimator” from the pedagogy of the Gauss–Markov theorem. Instead of referring to these estimators as BLUE, they can legitimately be called BUE (best unbiased estimators).

This comment includes a solution to a problem in Section 8 in Andrews (1991) and points out a method to generalize the mean‐squared error (MSE) bounds appearing in Andrews (1988) and Andrews (1991).

We construct robust empirical Bayes confidence intervals (EBCIs) in a normal means problem. The intervals are centered at the usual linear empirical Bayes estimator, but use a critical value accounting for shrinkage. Parametric EBCIs that assume a normal distribution for the means (Morris (1983b)) may substantially undercover when this assumption is violated. In contrast, our EBCIs control coverage regardless of the means distribution, while remaining close in length to the parametric EBCIs when the means are indeed Gaussian. If the means are treated as fixed, our EBCIs have an average coverage guarantee: the coverage probability is at least 1 − α on average across the n EBCIs for each of the means. Our empirical application considers the effects of U.S. neighborhoods on intergenerational mobility.

WE CORRECT A BOUND in the definition of approximate truthfulness used in the body of the paper of Jackson and Sonnenschein (2007). The proof of their main theorem uses a different permutation-based definition, implicitly claiming that the permutation-version implies the bound-based version. We show that this claim holds only if the bound is loosened. The new bound is still strong enough to guarantee that the fraction of lies vanishes as the number of problems grows, so the theorem is correct as stated once the bound is loosened.

How large economic stimuli generate individual and aggregate responses is a central question in economics, but has not been studied experimentally. We provided one‐time cash transfers of about USD 1000 to over 10,500 poor households across 653 randomized villages in rural Kenya. The implied fiscal shock was over 15 percent of local GDP. We find large impacts on consumption and assets for recipients. Importantly, we document large positive spillovers on non‐recipient households and firms, and minimal price inflation. We estimate a local transfer multiplier of 2.5. We interpret welfare implications through the lens of a simple household optimization framework.

We demonstrate that characterizing the minimal dimension of the term structure of interest rates is more challenging than currently appreciated. The highly structured polynomial patterns of the factor loadings, which are widely reported and discussed in the literature, reflect local correlations of smooth curves across maturities. We derive analytical expressions for the loadings of cross‐sectionally dependent processes that tend to favor a much lower dimension than the true dimension of the underlying factor space. Numerical examples illustrate the significant economic costs of erroneously committing to a parsimoniously parameterized factor space that is informed by standard metrics of goodness‐of‐fit. Our results apply to other assets with a finite maturity structure.

This paper develops a model of Bayesian learning from online reviews and investigates the conditions for learning the quality of a product and the speed of learning under different rating systems. A rating system provides information about reviews left by previous customers. observe the ratings of a product and decide whether to purchase and review it. We study learning dynamics under two classes of rating systems: full history , where customers see the full history of reviews, and summary statistics , where the platform reports some summary statistics of past reviews. In both cases, learning dynamics are complicated by a selection effect —the types of users who purchase the good, and thus their overall satisfaction and reviews depend on the information available at the time of purchase. We provide conditions for complete learning and characterize and compare its speed under full history and summary statistics. We also show that providing more information does not always lead to faster learning, but strictly finer rating systems do.

We show that demanding team incentives to be robust to nonquantifiable uncertainty about the game played by the agents leads to contracts that align the agents' interests. Such contracts have a natural interpretation as team‐based compensation. Under budget balance they reduce to linear contracts, thus identifying profit‐sharing, or equity, as an optimal contract absent a sink or a source of funds. A linear contract also gives the best profit guarantee to an outside residual claimant. These contracts still suffer from the free‐rider problem, but a positive guarantee obtains if and only if the technology known to the contract designer is sufficiently productive.

We study the educational choices of children of immigrants in a tracked school system. We first show that immigrants in Italy enroll disproportionately into vocational high schools, as opposed to technical and academically‐oriented ones, compared to natives of similar ability. The gap is greater for male students and it mirrors an analogous differential in grade retention. We then estimate the impact of a large‐scale, randomized intervention providing tutoring and career counseling to high‐ability immigrant students. Male treated students increase their probability of enrolling into the high track to the same level of natives, also closing the gap in grade retention. There are no significant effects on immigrant girls, who exhibit similar choices and performance as native ones in absence of the intervention. Increases in academic motivation and changes in teachers' recommendation regarding high school choice explain a sizable portion of the effect. Finally, we find positive spillovers on immigrant classmates of treated students, while there is no effect on native classmates.