Is peace a missing value or a zero? On selection models in political science
| Author | Colin Vance,Nolan Ritter |
| Published date | 01 July 2014 |
| Date | 01 July 2014 |
| DOI | http://doi.org/10.1177/0022343314528200 |
| Subject Matter | Research Articles |
Is peace a missing value or a zero?
On selection models in political science
Colin Vance
Rheinisch-Westf¨
alisches Institut fu
¨r Wirtschaftsforschung &
Jacobs University Bremen
Nolan Ritter
Rheinisch-Westf¨
alisches Institut fu
¨r Wirtschaftsforschung
Abstract
Sample selection models, variants of which are the Heckman and Heckit models, are increasingly used by political
scientists to accommodate data in which censoring of the dependent variable raises concerns of sample selectivity
bias. Beyond demonstrating several pitfalls in the calculation of marginal effects and associated levels of statistical
significance derived from these models, we argue that many of the empirical questions addressed by political scientists
would – for both substantive and statistical reasons – be more appropriately addressed using an alternative but closely
related procedure referred to as the two-part model (2 PM). Aside from being simple to estimate, one key advantage
of the 2 PM is its less onerous identification requirements. Specifically, the model does not require the specification
of so-called exclusion restrictions, variables that are included in the selection equation of the Heckit model but
omitted from the outcome equation. Moreover, we argue that the interpretation of the marginal effects from the
2 PM, which are in terms of actual outcomes, are more appropriate for the questions typically addressed by political
scientists than the potential outcomes ascribed to the Heckit results. Drawing on data from the Correlates of War
database, we present an empirical analysis of conflict intensity illustrating that the choice between the sample selec-
tion model and 2 PM can bear fundamentally on the conclusions drawn.
Keywords
actual effects, conflict, Heckit model, identification, potential effects, two-part model
Introduction
Empirical research in political science has increasingly
used Heckman’s sample selection model to accommo-
date datasets in which censoring of the dependent vari-
able raises concerns of biases emerging from sample
selectivity. Recent examples include the study by Lebovic
(2004) of the influence of democracy on the contribu-
tion to peacekeeping operations, the analysis by Drury
et al. (2005) of the amount of US disaster relief assis-
tance, and the analysis by Bo
¨hmelt (2010) of the effec-
tiveness of third-party intervention in conflict
mediation. All of these studies observe the outcome of
interest – in these examples various forms of foreign aid
– only when it is positive, with the remainder of observa-
tions censored at zero. This raises the possibility that the
sample used for estimation is non-random, in turn
causing bias through the correlation of the error term
with the explanatory variables. Heckman (1979) devel-
oped a two-stage estimator, alternatively called the
Heckit or sample selection model, to mitigate this bias.
In stage one, referred to as the selection equation, a
probit model is estimated on the entire dataset to cap-
ture the determinants of censoring. Stage two, referred
to as the outcome equation, involves estimation of a
heteroskedasticity-corrected OLS regression on the
non-censored observations. To control for potential
bias emerging from sample selectivity, this second stage
Corresponding author:
vance@rwi-essen.de
Journal of Peace Research
2014, Vol. 51(4) 528–540
ªThe Author(s) 2014
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DOI: 10.1177/0022343314528200
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