Heterogeneous Beliefs and School Choice Mechanisms

Kapor, Adam; Neilson, Christopher; Zimmerman, Seth

doi:10.3386/w25096

Cited by 46 publications

(57 citation statements)

References 10 publications

(20 reference statements)

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“…In particular, this test can be used in a data combination context, where individual realizations and subjective beliefs are observed in two different datasets that cannot be matched. Such situations are common in practice (see, e.g., Delavande, 2008;Arcidiacono, Hotz and Kang, 2012;Arcidiacono, Hotz, Maurel and Romano, 2014;Stinebrickner and Stinebrickner, 2014a;Kuchler and Zafar, 2019;Kapor, Neilson and Zimmerman, 2018). Besides, even in surveys for which an explicit aim is to measure subjective expectations, such as the Michigan Survey of Consumers or the Survey of Consumer Expectations of the New York Fed, expectations and realizations can typically only be matched for a subset of the respondents.…”

Section: Introductionmentioning

confidence: 99%

Rationalizing Rational Expectations? Tests and Deviations

Gaillac¹,

Maurel²

2018

SSRN Journal

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In this paper, we build a new test of rational expectations based on the marginal distributions of realizations and subjective beliefs. This test is widely applicable, including in the common situation where realizations and beliefs are observed in two different datasets that cannot be matched. We show that whether one can rationalize rational expectations is equivalent to the distribution of realizations being a mean-preserving spread of the distribution of beliefs. The null hypothesis can then be rewritten as a system of many moment inequality and equality constraints, for which tests have been recently developed in the literature. Next, we go beyond testing by defining the minimal deviations from rational expectations that can be rationalized by the data in the context of structural models. Building on this concept, we propose an easy-to-implement sensitivity analysis on the assumed form of expectations. Finally, we apply our framework to test for rational expectations about future earnings, and examine the consequences of such departures in the context of a life-cycle model of consumption. participants of various seminars and conferences for useful comments and suggestions. † CREST, xavier.dhaultfoeuille@ensae.fr. ‡ CREST and TSE, christophe.gaillac@tse-fr.eu.

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Section: Introductionmentioning

confidence: 99%

Rationalizing Rational Expectations? Tests and Deviations

Gaillac¹,

Maurel²

2018

SSRN Journal

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“…7 The growing literature estimating school demand from similar datasets includes: Abdulkadiroglu, Agarwal, and Pathak (2015), Abdulkadiroglu, Pathak, Schellenberg, and Walters (2017), Agarwal and Somaini (2014), Burgess, Greaves, Vignoles, and Wilson (2015), Calsamiglia, Fu, and Guell (2017), Glazerman and Dotter (2016), Harris and Larsen (2015), Hastings, Kane, and Staiger (2009), He (2012), Hwang (2015), Kapor, Neilson, and Zimmerman (2017), Ruijs and Oosterbeek (2012), and Walters (2014).…”

Section: Introductionmentioning

confidence: 99%

How Well Do Structural Demand Models Work? Counterfactual Predictions in School Choice

Pathak

Shi

2018

SSRN Journal

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Discrete choice demand models are widely used for counterfactual policy simulations, yet their out-of-sample performance is rarely assessed. This paper uses a large-scale policy change in Boston to investigate the performance of discrete choice models of school demand. In 2013, Boston Public Schools considered several new choice plans that differ in where applicants can apply. At the request of the mayor and district, we forecast the alternatives' effects by estimating discrete choice models. This work led to the adoption of a plan which significantly altered choice sets for thousands of applicants. Pathak and Shi (2014) update forecasts prior to the policy change and describe prediction targets involving access, travel, and unassigned students. Here, we assess how well these ex ante counterfactual predictions compare to actual outcome under the new choice sets. We find that a simple ad hoc model performs as well as the more complicated structural choice models for one of the two grades we examine. However, the structural models' inconsistent performance is largely due to prediction errors in applicant characteristics, which are auxiliary inputs. Once we condition on the actual applicant characteristics, the structural choice models outperform the ad hoc alternative in predicting both choice patterns and policy relevant outcomes. Moreover, refitting the models using the new choice data does not significantly improve their prediction accuracy, suggesting that the choice models are indeed "structural." Our findings show that structural demand models can effectively predict counterfactual outcomes, as long there are accurate forecasts about auxiliary input variables.

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“…In particular, this test can be used in a data combination context, where individual realizations and subjective beliefs are observed in two different datasets that cannot be matched. Such situations are common in practice (see, e.g., Delavande, 2008;Arcidiacono et al, 2012Arcidiacono et al, , 2014Stinebrickner and Stinebrickner, 2014a;Kuchler and Zafar, 2017;Kapor et al, 2018). Besides, even in surveys for which an explicit aim is to measure subjective expectations, such as the Michigan Survey of Consumers or the Survey of Consumer Expectations of the New York Fed, expectations and realizations can typically only be matched for a subset of the respondents.…”

Section: Introductionmentioning

confidence: 99%