Using Conjoint Experiments to Analyze Election Outcomes: The Essential Role of the Average Marginal Component Effect (AMCE)

59 Pages Posted: 12 Jun 2020 Last revised: 22 Jan 2022

See all articles by Kirk Bansak

Kirk Bansak

University of California, San Diego

Jens Hainmueller

Stanford University - Department of Political Science; Stanford Graduate School of Business; Stanford Immigration Policy Lab

Daniel J. Hopkins

University of Pennsylvania

Teppei Yamamoto

Massachusetts Institute of Technology (MIT) - Department of Political Science

Date Written: January 20, 2022

Abstract

Political scientists have increasingly deployed conjoint survey experiments to understand multi-dimensional choices in various settings. In this paper, we show that the Average Marginal Component Effect (AMCE) constitutes an aggregation of individual-level preferences that is meaningful both theoretically and empirically. First, extending previous results to allow for arbitrary randomization distributions, we show how the AMCE represents a summary of voters' multidimensional preferences that combines directionality and intensity according to a probabilistic generalization of the Borda rule. We demonstrate why incorporating both the directionality and intensity of multi-attribute preferences is essential for analyzing real-world elections, in which ceteris paribus comparisons almost never occur. Second, and in further empirical support of this point, we show how this aggregation translates directly into a primary quantity of interest to election scholars: the effect of a change in an attribute on a candidate or party's expected vote share. These properties hold irrespective of the heterogeneity, strength, or interactivity of voters' preferences and regardless of how votes are aggregated into seats. Finally, we propose, formalize, and evaluate the feasibility of using conjoint data to estimate alternative quantities of interest to electoral studies, including the effect of an attribute on the probability of winning.

Keywords: conjoint, elections, causal inference, AMCE, survey experiments

JEL Classification: C25

Suggested Citation

Bansak, Kirk and Hainmueller, Jens and Hopkins, Daniel J. and Yamamoto, Teppei, Using Conjoint Experiments to Analyze Election Outcomes: The Essential Role of the Average Marginal Component Effect (AMCE) (January 20, 2022). Available at SSRN: https://ssrn.com/abstract=3588941 or http://dx.doi.org/10.2139/ssrn.3588941

Kirk Bansak

University of California, San Diego ( email )

9500 Gilman Drive
La Jolla, CA 92093
United States

Jens Hainmueller (Contact Author)

Stanford University - Department of Political Science ( email )

Stanford, CA 94305
United States

HOME PAGE: http://www.stanford.edu/~jhain/

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

Stanford Immigration Policy Lab

30 Alta Road
Stanford, CA 94305
United States

Daniel J. Hopkins

University of Pennsylvania ( email )

Stiteler Hall
Philadelphia, PA 19104
United States

HOME PAGE: http://www.danhopkins.org

Teppei Yamamoto

Massachusetts Institute of Technology (MIT) - Department of Political Science ( email )

77 Massachusetts Avenue
Cambridge, MA 02139
United States

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