Impact Evaluation of Multiple Overlapping Programs using Difference-in-differences with Matching

Difference-in-differences with matching is a popular method in impact evaluation. Traditional impact evaluation methods including difference-in-differences with matching often deal with impact measurement of a single binary program. Imbens (1999) and Lechner (2001) extend the matching method to the case of multiple mutually exclusive programs. Frölich (2002) discusses different impact evaluation methods in the similar context. In reality, one can participate in several programs simultaneously and the programs may be overlapping. This paper discusses the method of difference-in-differences with matching in a general context of multiple overlapping programs. The method is applied to measure impacts of formal and informal credit in Vietnam using panel data from two Vietnam Household Living Standard Surveys in 2002 and 2004

Using State Administrative Data to Measure Program Performance

This paper uses administrative data from Missouri to examine the sensitivity of job training program impact estimates based on alternative nonexperimental methods. In addition to simple regression adjustment, we consider Mahalanobis distance matching and a variety of methods using propensity score matching. In each case, we consider estimates based on levels of post-program earnings as well as difference-in-difference estimates based on comparison of pre and post-program earnings. Specification tests suggest that the difference-in-difference estimator may provide a better measure of program impact. We find that propensity score matching is generally most effective, but the detailed implementation of the method is not of critical importance. Our analyses demonstrate that existing data available at the state level can be used to obtain useful estimates of program impact.Noexperimental Methods, Matching, Difference-in-Difference

Using State Administrative Data to Measure Program Performance

We use administrative data from Missouri to examine the sensitivity of earnings impact estimates for a job training program based on alternative nonexperimental methods. We consider regression adjustment, Mahalanobis distance matching, and various methods using propensity score matching, examining both cross-sectional estimates and difference-in-difference estimates. Specification tests suggest that the difference-in-difference estimator may provide a better measure of program impact. We find that propensity score matching is most effective, but the detailed implementation is not of critical importance. Our analyses demonstrate that existing data can be used to obtain useful estimates of program impact.

Farm level impact of rural development policy: a conditional difference in difference matching approach

We use a conditional difference-in-difference matching estimator and a 2003-2007 balanced panel drawn from the FADN Italian sample to evaluate the impact at the farm level of the implementation of the first Italian Rural Development Programme (RDP). We find that, in average, farms receiving at least a RDP payment increased family labor, while they did not increase total labour employed on farm. In addition, they experienced an increase in labor profitability and added value, even though the estimate significance varies accordingly to the matching method used. Our findings, suggest that the implementation of the first RDP produced a positive direct impact on rural GDP, while it did not prove to be effective in terms of rural employment growth.Common Agricultural Policy, Rural Development Policy, conditional diff-in-diff matching, Agricultural and Food Policy, Q12, Q18, C14,

Employment subsidies - A fast lane from unemployment to work?

The treatment effect of a Swedish employment subsidy is estimated using exact covariate-matching and instrumental variables methods. Our estimates suggest that the programme had a positive treatment effect for the participants. We also show how non-parametric methods can be used to estimate the time profile of treatment effects as well as how to estimate the effect of entering the programme at different points in time in the unemployment spell. Our main results are derived using matching methods. However, as a sensitivity test, we apply instrumental variables difference-in-difference methods. These estimates indicate that our matching results are robustEvaluation; employment subsidies; exact covariate-matching

A Matching Method with Panel Data

Difference-in-differences with matching is a popular method to measure the impact of an intervention in health as well as social sciences. This method requires baseline data, i.e., data before interventions, which are not always available in reality. Instead, panel data with two time periods are often collected after interventions begin. In this paper, a simple matching method is proposed to measure impact of an intervention using two-period panel data after the intervention.Impact evaluation, difference-in-differences, matching, propensity score, panel data

Shortest Reconfiguration of Perfect Matchings via Alternating Cycles

Motivated by adjacency in perfect matching polytopes, we study the shortest reconfiguration problem of perfect matchings via alternating cycles. Namely, we want to find a shortest sequence of perfect matchings which transforms one given perfect matching to another given perfect matching such that the symmetric difference of each pair of consecutive perfect matchings is a single cycle. The problem is equivalent to the combinatorial shortest path problem in perfect matching polytopes. We prove that the problem is NP-hard even when a given graph is planar or bipartite, but it can be solved in polynomial time when the graph is outerplanar

Assessing the Frontiers of Ultra-Poverty Reduction: Evidence from CFPR/TUP, an Innovative Program in Bangladesh

This paper uses household panel data to provide robust evidence on the effects of BRAC’s Targeting the Ultra-poor Program in Bangladesh. Our identification strategy exploits type-1 errors in assignment, comparing households correctly included with those incorrectly excluded, according to program criteria. Evidence from difference-in-difference matching and sensitivity analysis shows that participation had significant positive effects on income, food consumption and security, household durables, and livestock, but no robust impact on health, ownership of homestead land, housing quality and other productive assets. Using quantile difference-in-difference, we find that the income gains from program participation is smaller for the lowest two deciles.Ultra-poor, CFPR/TUP, BRAC, Bangladesh, Microfinance, Bangladesh, Assignment Error, Difference-in-Difference, Matching, Heteroskedasticity-Based Identification