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Unobserved Heterogeneity in Observational Studies of Political Behavior


Núñez, Lucas (2018) Unobserved Heterogeneity in Observational Studies of Political Behavior. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/XKQP-8429.


This dissertation comprises three chapters dealing with unobserved heterogeneity in observational studies. In Chapters 2 and 3, I develop new estimators that deal with unobserved heterogeneity in the cases in which panel data is not available or the outcome of interest is binary, respectively. In Chapter 4, I analyze the effect of parties' contacting voters on the extent of tactical voting in the 2015 and 2017 United Kingdom General Elections, applying the estimator developed in Chapter 3.

In Chapter 2, I develop a semi-parametric two-step estimator for linear models with unobserved individual level heterogeneity that can be applied on a series of Repeated Cross-Sections, when panel data is unavailable. I show that this estimator provides consistent and asymptotically normal estimates of the parameters of interest. Identification relies on a restriction that requires the conditional expectation of the unobserved individual-level heterogeneity on observed characteristics to be continuous. Using Monte Carlo simulations, I show that this estimator typically outperforms other available alternatives. In particular, it typically has a smaller Root Mean Squared Error, and a relatively small bias that disappears for moderate sample sizes. Furthermore, it is robust to mild violations of the continuity assumption. Finally, I also show that this estimator can recover sensible estimates compared to those from an real panel.

In Chapter 3, I propose a method for estimating binary outcome models with panel data in the presence of unobserved heterogeneity, called the Penalized Flexible Correlated Random Effects (PF-CRE) estimator. I show that this estimator produces consistent and efficient estimates of the model parameters. PF-CRE also provides consistent estimates of partial effects, which cannot be calculated with existing consistent estimators. Using Monte Carlo simulations, I show that PF-CRE performs well in small samples. To demonstrate that accounting for unobserved heterogeneity has important consequences for empirical analysis, I use PF-CRE in three studies of voting behavior: tactical voting during the 2015 British Election, support for the Brexit referendum of 2016, and vote choice in the 2012 U.S. Presidential election. In all three cases, I find that ignoring the unobserved heterogeneity leads to an overestimation of the effects of interest, and that PF-CRE is a valid approach for the analyses.

In Chapter 4, I apply the PF-CRE estimator developed in Chapter 3 to the study of tactical voting in the United Kingdom General Elections of 2015 and 2017. In particular, I study the effect that party contacts during the electoral campaigns has on the probability that voters decide to cast a tactical vote for a less preferred party when their most preferred party is out of the race. I show that these effects are of moderate size, but substantively important. For example, during the 2017 election, contact by the most preferred party discouraged tactical voting by 7.02%, while contact by the most preferred viable party encouraged it by 13.41%. Combining counterfactual simulations with Multilevel Regression and Poststratification I estimate the effect that party contact has on the seat distribution in Westminster through tactical voting. My results show that between 9 and 18 seats change hands, depending on the election. Importantly, the Conservative party would have obtained a majority in 2017 had non-viable parties given up contacting their supporters.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Unobserved heterogeneity, quantitative methods, political behavior, voting, strategic voting, discrete outcome
Degree Grantor:California Institute of Technology
Division:Humanities and Social Sciences
Major Option:Social Science
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Alvarez, R. Michael
Thesis Committee:
  • Alvarez, R. Michael (chair)
  • Katz, Jonathan N.
  • Hoffman, Philip T.
  • Sherman, Robert P.
Defense Date:4 May 2018
Record Number:CaltechTHESIS:05182018-144248304
Persistent URL:
Núñez, Lucas0000-0001-5107-6775
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:10915
Deposited By: Lucas Nunez
Deposited On:21 May 2018 22:45
Last Modified:04 Oct 2019 00:21

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