Survey Measures of Family Decision Processes for Econometric Analysis of Schooling Decisions

Giustinelli, Pamela; Manski, Charles F.

doi:10.1111/ecin.12322

Cited by 25 publications

(13 citation statements)

References 146 publications

(236 reference statements)

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“…Without a properly specified model, empirical results from choice data will mistake parental preferences for student preferences. Giustinelli and Manski (2016) review the current literature on family decision-making in education, and emphasize how differences in expectations held by parents and students mean that family decision-making should be considered an essential component of models of educational choice. Without a proper collective treatment of choice, we will misunderstand how educational incentives of interest actually translate into choices made by making incorrect inferences about individual choice functions, and will fail entirely to see mechanisms that operate only through a collective structure.…”

Section: Introductionmentioning

confidence: 99%

College Choice as a Collective Decision

Huntington-Klein

2017

Economic Inquiry

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Although the choice between colleges can be thought of as being made collectively by a family, models of educational choice almost universally portray the decision as made by the student alone. Using a novel experimental method for identifying collective decision functions, I find that students have more influence than parents over the decision, but not exclusive control. Students care more than parents about classroom experience and future earnings. Ignoring the dual‐agent nature of the decision can weaken predictions and lead to poorly targeted policy designs. (JEL I21, J24, D13)

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Section: Introductionmentioning

confidence: 99%

College Choice as a Collective Decision

Huntington-Klein

2017

Economic Inquiry

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“…Similar decision making problems with multiple DMs have been been considered in an experimental or practical framework. For example, Giustinelli and Manski [19] investigate how families make practical decisions about the school a child should attend and Beynon et al [20] consider how multiple DMs rank cars on the basis of multiple traits in an experimental setting. However, these decision problems are considered as processes of group decision making, rather than by using a game-theoretic approach.…”

Section: Definitionmentioning

confidence: 99%

A Game Theoretic Model of Choosing a Valuable Good via a Short List Heuristic

Ramsey¹

2020

Mathematics

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The Internet gives access to a huge amount of data at the click of a mouse. This is very helpful when consumers are making decisions about which product to buy. However, the final decision to purchase is still generally made by humans who have limited memory and perception. The short list heuristic is often used when there are many offers on the market. Searchers first find information about offers via the Internet and on this basis choose a relatively small number of offers to view in real life. Although such rules are often used in practice, little research has been carried out on determining, for example, what the size of the short list should be depending on the parameters of the problem or modelling how the short list heuristic can be implemented when there are multiple decision makers. This article presents a game theoretic model of such a search procedure with two players. These two players can be interpreted, for example, as a couple searching for a flat or a second-hand car. The model indicates that under such a search procedure the roles of searchers should only be divided when the preferences of the players are coherent or there is a high level of goodwill between them. In other cases, dividing the roles leads to a high level of conflict.

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“…This endeavor was prompted by earlier empirical evidence and theoretical arguments demonstrating the greater informativeness of elicited probabilities for binary events relative to "yes/no" intention measures (Juster, 1966;Manski, 1990). Manski (2004Manski ( , 2017, Attanasio (2009), Hurd (2009), van der Klaauw (2012), Armantier et al (2013), Delavande (2014), Schotter and Trevino (2014), and Giustinelli and Manski (2018) review the literature from various perspectives.…”

Section: Introductionmentioning

confidence: 99%

Tail and Center Rounding of Probabilistic Expectations in the Health and Retirement Study

Giustinelli

Manski

Molinari

2018

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We thank Maura Coughlin, Adam Karabatakis, and Miriam Larson-Koester for able research assistance. We received useful feedback from seminar participants at the HRS work-in-progress series, Bocconi University, University of Southampton, NYU CUSP, University of Michigan, Purdue University, Laval University, University of Oslo, Statistics Norway, University of Munich, and University of Padova, as well as from participants in the 2016 NYFed and ESRC RCMiSoC Workshop on Subjective Expectations. Giustinelli gratefully acknowledges support from the National Institute on Aging (NIA P01-AG10179 and P30-AG012846), the National Science NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.© 2018 by Pamela Giustinelli, Charles F. Manski, and Francesca Molinari. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source. ABSTRACTA growing number of surveys elicit respondents' expectations for future events on a 0-100 scale of percent chance. These data reveal substantial heaping at multiples of 10 and 5 percent, suggesting that respondents round their reports. This paper studies the nature of rounding by analyzing response patterns across expectations questions and waves of the Health and Retirement Study. We discover a tendency by about half of the respondents to provide more refined responses in the tails of the scale than the center. Only about five percent provide more refined responses in the center than the tails. We find that rounding varies across question domains, which range from personal health to personal finances to macroeconomic events. We develop a two-stage framework to characterize person-specific rounding. The first stage uses observed responses to infer respondents' rounding practice in each question domain and scale segment. The second stage replaces each original point response with an interval, representing the range of possible values of the respondent's true latent belief implied by the degree of rounding inferred in the first stage. We study how the inferred rounding types in the first stage vary with respondent characteristics, including age and cognitive abilities.

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Survey Measures of Family Decision Processes for Econometric Analysis of Schooling Decisions

Cited by 25 publications

References 146 publications

College Choice as a Collective Decision

College Choice as a Collective Decision

A Game Theoretic Model of Choosing a Valuable Good via a Short List Heuristic

Tail and Center Rounding of Probabilistic Expectations in the Health and Retirement Study

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