Volume 90, Issue 1 (January 2022) has just been published
The full content of the journal is accessible at https://www.econometricsociety.org/publications/econometrica/browse
Goals and Gaps: Educational Careers of Immigrant Children
Michela Carlana, Eliana La Ferrara, Paolo Pinotti
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.
Reply to: Comments on “Goals and Gaps: Educational Careers of Immigrant Children”
Michela Carlana, Eliana La Ferrara, Paolo Pinotti
Dynamically Aggregating Diverse Information
Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis
An agent has access to multiple information sources, each modeled as a Brownian motion whose drift provides information about a different component of an unknown Gaussian state. Information is acquired continuously—where the agent chooses both which sources to sample from, and also how to allocate attention across them—until an endogenously chosen time, at which point a decision is taken. We demonstrate conditions on the agent's prior belief under which it is possible to exactly characterize the optimal information acquisition strategy. We then apply this characterization to derive new results regarding: (1) endogenous information acquisition for binary choice, (2) the dynamic consequences of attention manipulation, and (3) strategic information provision by biased news sources.
RCTs to Scale: Comprehensive Evidence from Two Nudge Units
Stefano DellaVigna, Elizabeth Linos
Nudge interventions have quickly expanded from academic studies to larger implementation in so‐called Nudge Units in governments. This provides an opportunity to compare interventions in research studies, versus at scale. We assemble a unique data set of 126 RCTs covering 23 million individuals, including all trials run by two of the largest Nudge Units in the United States. We compare these trials to a sample of nudge trials in academic journals from two recent meta‐analyses. In the Academic Journals papers, the average impact of a nudge is very large—an 8.7 percentage point take‐up effect, which is a 33.4% increase over the average control. In the Nudge Units sample, the average impact is still sizable and highly statistically significant, but smaller at 1.4 percentage points, an 8.0% increase. We document three dimensions which can account for the difference between these two estimates: (i) statistical power of the trials; (ii) characteristics of the interventions, such as topic area and behavioral channel; and (iii) selective publication. A meta‐analysis model incorporating these dimensions indicates that selective publication in the Academic Journals sample, exacerbated by low statistical power, explains about 70 percent of the difference in effect sizes between the two samples. Different nudge characteristics account for most of the residual difference.
Breaking Ties: Regression Discontinuity Design Meets Market Design
Atı̇la Abdulkadı̇roğlu, Joshua D. Angrist, Yusuke Narita, Parag Pathak
Many schools in large urban districts have more applicants than seats. Centralized school assignment algorithms ration seats at over‐subscribed schools using randomly assigned lottery numbers, non‐lottery tie‐breakers like test scores, or both. The New York City public high school match illustrates the latter, using test scores and other criteria to rank applicants at the city's screened schools, combined with lottery tie‐breaking at the rest. We show how to identify causal effects of school attendance in such settings. Our approach generalizes regression discontinuity methods to allow for multiple treatments and multiple running variables, some of which are randomly assigned. The key to this generalization is a local propensity score that quantifies the school assignment probabilities induced by lottery and non‐lottery tie‐breakers. The utility of the local propensity score is demonstrated in an assessment of the predictive value of New York City's school report cards. Schools that earn the highest report card grade indeed improve SAT math scores and increase graduation rates, though by much less than OLS estimates suggest. Selection bias in OLS estimates of grade effects is egregious for screened schools.
Patterns of Competitive Interaction
Mark Armstrong, John Vickers
We explore patterns of price competition in an oligopoly where consumers vary in the set of firms they consider for their purchase and buy from the lowest‐priced firm they consider. We study a pattern of consideration, termed “symmetric interactions,” that generalizes models used in existing work (duopoly, symmetric firms, and firms with independent reach). Within this class, equilibrium profits are proportional to a firm's reach, firms with a larger reach set higher average prices, and a reduction in the number of firms (either by exit or by merger) harms consumers. However, increased competition (either by entry or by increased consumer awareness) does not always benefit consumers. We go on to study patterns of consideration with asymmetric interactions. In situations with disjoint reach and with nested reach, we find equilibria in which price competition is “duopolistic”: only two firms compete within each price range. We characterize the contrasting equilibrium patterns of price competition for all patterns of consideration in the three‐firm case.
Low Interest Rates, Market Power, and Productivity Growth
Ernest Liu, Atif Mian, Amir Sufi
This study provides a new theoretical result that a decline in the long‐term interest rate can trigger a stronger investment response by market leaders relative to market followers, thereby leading to more concentrated markets, higher profits, and lower aggregate productivity growth. This strategic effect of lower interest rates on market concentration implies that aggregate productivity growth declines as the interest rate approaches zero. The framework is relevant for antitrust policy in a low interest rate environment, and it provides a unified explanation for rising market concentration and falling productivity growth as interest rates in the economy have fallen to extremely low levels.
Media Competition and Social Disagreement
Jacopo Perego, Sevgi Yuksel
We study the competitive provision and endogenous acquisition of political information. Our main result identifies a natural equilibrium channel through which a more competitive market decreases the efficiency of policy outcomes. A critical insight we put forward is that competition among information providers leads to informational specialization: firms provide relatively less information on issues that are of common interest and relatively more information on issues on which agents' preferences are heterogeneous. This enables agents to acquire information about different aspects of the policy, specifically, those that are particularly important to them. This leads to an increase in social disagreement, which has negative welfare implications. We establish that, in large enough societies, competition makes every agent worse off by decreasing the utility that she derives from the policy outcome. Furthermore, we show that this decline cannot be compensated by the decrease in prices resulting from competition.
Causal Inference Under Approximate Neighborhood Interference
Michael P. Leung
This paper studies causal inference in randomized experiments under network interference. Commonly used models of interference posit that treatments assigned to alters beyond a certain network distance from the ego have no effect on the ego's response. However, this assumption is violated in common models of social interactions. We propose a substantially weaker model of “approximate neighborhood interference” (ANI) under which treatments assigned to alters further from the ego have a smaller, but potentially nonzero, effect on the ego's response. We formally verify that ANI holds for well‐known models of social interactions. Under ANI, restrictions on the network topology, and asymptotics under which the network size increases, we prove that standard inverse‐probability weighting estimators consistently estimate useful exposure effects and are approximately normal. For inference, we consider a network HAC variance estimator. Under a finite population model, we show that the estimator is biased but that the bias can be interpreted as the variance of unit‐level exposure effects. This generalizes Neyman's well‐known result on conservative variance estimation to settings with interference.
On the Factor Structure of Bond Returns
Richard K. Crump, Nikolay Gospodinov
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.
Robust Screens for Noncompetitive Bidding in Procurement Auctions
Sylvain Chassang, Kei Kawai, Jun Nakabayashi, Juan Ortner
We document a novel bidding pattern observed in procurement auctions from Japan: winning bids tend to be isolated, and there is a missing mass of close losing bids. This pattern is suspicious in the following sense: its extreme forms are inconsistent with competitive behavior under arbitrary information structures. Building on this observation, we develop systematic tests of competitive behavior in procurement auctions that allow for general information structures as well as nonstationary unobserved heterogeneity. We provide an empirical exploration of our tests, and show they can help identify other suspicious patterns in the data.
I study a regression model in which one covariate is an unknown function of a latent driver of link formation in a network. Rather than specify and fit a parametric network formation model, I introduce a new method based on matching pairs of agents with similar columns of the squared adjacency matrix, the ijth entry of which contains the number of other agents linked to both agents i and j. The intuition behind this approach is that for a large class of network formation models the columns of the squared adjacency matrix characterize all of the identifiable information about individual linking behavior. In this paper, I describe the model, formalize this intuition, and provide consistent estimators for the parameters of the regression model.
A ReMeDI for Microstructure Noise
Z. Merrick Li, Oliver Linton
We introduce the Realized moMents of Disjoint Increments (ReMeDI) paradigm to measure microstructure noise (the deviation of the observed asset prices from the fundamental values caused by market imperfections). We propose consistent estimators of arbitrary moments of the microstructure noise process based on high‐frequency data, where the noise process could be serially dependent, endogenous, and nonstationary. We characterize the limit distributions of the proposed estimators and construct confidence intervals under infill asymptotics. Our simulation and empirical studies show that the ReMeDI approach is very effective to measure the scale and the serial dependence of microstructure noise. Moreover, the estimators are quite robust to model specifications, sample sizes, and data frequencies.
Banks, Liquidity Management, and Monetary Policy
Javier Bianchi, Saki Bigio
We develop a tractable model of banks' liquidity management with an over‐the‐counter interbank market to study the credit channel of monetary policy. Deposits circulate randomly across banks and must be settled with reserves. We show how monetary policy affects the banking system by altering the trade‐off between profiting from lending and incurring greater liquidity risk. We present two applications of the theory, one involving the connection between the implementation of monetary policy and the pass‐through to lending rates, and another considering a quantitative decomposition behind the collapse in bank lending during the 2008 financial crisis. Our analysis underscores the importance of liquidity frictions and the functioning of interbank markets for the conduct of monetary policy.