This study assesses the impact of South Carolina’s Temporary Assistance for Needy Families (TANF) program, Family Independence (FI), on the longitudinal earnings of three cohorts of new entrants who entered the study before, at the beginning of, and at the height of the 2007-2009 recession. Applicants who began the application process but did not enroll in TANF were propensity-score matched to entrants by background characteristics including pre-intervention earnings history, and served as the comparison group. We constructed a latent growth curve model to test whether earnings histories were similar for the program and comparison groups up until FI intake, to estimate program impact by comparing post-intake earnings of program participants to those of the comparison group, and to determine the statistical significance of cohort differences in program impact. The findings showed FI had a positive impact on the earnings of participants before the recession. The effect became weaker during the state’s period of rising unemployment, and disappeared during the worst economic recession in decades. This study demonstrates the usefulness of longitudinal administrative data, propensity score matching, and latent growth modeling techniques for evaluating the impact of program interventions.

Our research investigates the effects of residential energy efficiency audit programs on subsequent household electricity consumption. Here there is a one-time interaction between households, which participate voluntarily, and the surveyors. Our research objective is to determine whether and to what extent the surveys lead to behavioral changes. We then examine how persistent the intervention is over time and whether the effects decay or intensify. The main evaluation problem here is survey participants’ self-selection, which we address econometrically via several non-parametric estimators involving kernel-based propensity-score matching. In the first method we use difference-in-differences (DID) estimation. Our second estimator is quantile DID, which produces estimates on distributions. The comparison group consists of households who were not yet participating in the survey but participated later. Our evidence is that the customers who participated in the survey reduced their electricity consumption by about 7%, on average compared to customers who had not yet participated in the survey. Considering the total number of high-usage households participating in the survey in 2009, we estimate that electricity consumption was reduced by an aggregate of 2 million kWh per year, which is approximately equal to the monthly consumption of 3500 typical households in California with an estimated 1527 metric tons less of carbon dioxide emissions. Because the energy audit program is inexpensive ($10–$20 per household) a key issue is that while the program is cost-effective, is it regressive? We find that as the quantiles of the outcome distribution increase, high-use households save proportionally less electricity than do low-use customers. Overall, our results imply that program designers can better target low-use and low-income households, because they are more likely to benefit from the programs through energy savings.