Health Policy Analysis from a Potential Outcomes Perspective: Smoking During Pregnancy and Birth Weight

Abstract

Most empirical research in health economics is conducted with the goal of providing scientific evidence that will serve to inform current and future health policy. The use of parametric nonlinear regression (NR) methods for empirical analysis in health economics abounds. Studies that offer clear policy-relevant interpretations of NR results are, however, rare. We offer a comprehensive policy analytic framework within which the applied researcher can: 1) clearly define the policy-relevant estimation objective; 2) consistently estimate that objective using NR methods designed to account for the possible endogeneity of the policy variable of interest; 3) conduct correct asymptotic inference; and 4) offer policy-relevant interpretations of the empirical results. For binary policies, Rubin (1974, 1977) developed the potential outcomes framework (POF). We propose a generally applicable extension of the POF (EPOF) which covers a broad range of policy analytic contexts. In particular, our EPOF accommodates: a) a non-binary policy variable of interest (Xp ); b) policy-relevant counterfactual versions of Xp that are not fixed values; and c) a policy-defining increment to Xp that is not constant. Moreover, our EPOF facilitates the use of extant nonlinear regression (NR) methods that correct for potential bias due to the endogeneity of Xp . As a case in point, we consider the analysis of potential gains in infant birth weight that may result from a prenatal smoking prevention and cessation policy which, if fully effective, would maintain zero levels of smoking for non-smokers (prevention) and convince smokers to quit before becoming pregnant (cessation). In the context of our EPOF, using endogeneity-correcting NR methods, we re-analyze the data examined by Mullahy (1997) and estimate the potential effect of the smoking prevention/cessation policy described above. The EPOF should serve as a useful guide to applied health policy analysts.