An R Package for the Panel Approach Method for Program Evaluation: pampe

Abstract

The pampe package for R implements the panel data approach method for program evalua- tion designed to estimate the causal effects of political interventions or treatments. This procedure exploits the dependence among cross-sectional units to construct a counterfactual of the treated unit(s), and it is an appropriate method for research events that occur at an aggregate level like countries or regions and that affect only one or a small number of units. The implementation of the pampe package is illustrated using data from Hong Kong and 24 other units, by examining the economic impact of the political and economic integration of Hong Kong with mainland China in 1997 and 2004 respectively. An introduction to the panel data approach and program evaluation methods Program evaluation methodologies have long been used by social scientists to measure the effect of different economic or political interventions (treatments). The problem is, of course, that you cannot observe the outcome both under the intervention and in the absence of the intervention simultaneously, hence the need for program evaluation methods. Traditionally, comparative case studies have been the preferred method by researchers in order to compare units affected by a treatment or event (dubbed the treatment group) to one or more units not affected by this intervention (the control group). The idea is to use the outcome of the control group to obtain an approximation of what would have been the outcome of the treated group had it not been treated. In more recent years, synthetic control methods (Abadie and Gardeazabal, 2003; Abadie et al., 2010) have addressed these issues by introducing a data-driven procedure for selecting the control group. However, the synthetic control methods are not without shortcomings: since the synthetic control is calculated as a convex combination of the units in the donor pool, and thus it does not allow for extrapolation, it might be that a suitable synthetic control for our treated unit does not exist. Furthermore, the synthetic control is designed to be used with explanatory variables or covariates that help explain the variance in the outcome variable. For the cases when the researcher finds that extrapolation is needed to obtain a suitable comparison for the treated unit, or when the covariates available do not properly explain the outcome on which the effect of the treatment is intended to be measured, he or she might prefer to use the panel data approach for program evaluation by Hsiao et al. (2012). The panel data approach for constructing the counterfactual of the unit subjected to the intervention is to use other units that are not subject to the treatment to predict what would have happened to the treated unit had it not been subject to the policy intervention. The basic idea behind this approach is to rely on the correlations among cross-sectional units. They attribute the cross-sectional dependence to the presence of common factors that drive all the relevant cross-sectional units. As such, the aim of this article is to present the package pampe that implements the panel data approach for program evaluation procedures in R, which is available from the Comprehensive R Archive Network (CRAN) at http://CRAN.R-project.org/package=pampe. The main function in the package is pampe(), which computes the counterfactual for the treated unit using the modeling strategy outlined by Hsiao et al. (2012). The function includes an option to obtain placebo tests. There is an additional function robustness(), which conducts a leave-one-out robustness on the results. The data example is also from Hsiao et al. (2012), which introduced the panel data approach methodology to study the effect of the political and economic integration of Hong Kong with mainland China using other countries geographically and economically close to Hong Kong as possible controls. The article is organized as follows. The following section is a brief overview of the panel data approach as defined by Hsiao et al. (2012). The main section of the paper, titled Implementing pampe in R, demonstrates the implementation of this method and the use of the pampe package with an example, including how to perform inference and robustness checks.