As labor market data is scarce in Sub-Saharan Africa (SSA), this paper uses household survey data to analyze the determinants of the gender gap in the labor market and its welfare implications for five SSA countries in multinomial logit models with propensity score matching method. The analysis confirms that education opens up opportunities for women to escape agricultural feminization and enga...

Propensity scores are increasingly being used in large sample studies to control for pregroup differences. Because these scores are often used to match cases, they can result in sample attrition. In smaller sample studies, such attrition leaves too few cases for meaningful analysis. Alternatives when working with small samples are to use propensity scores as covariates to control for pre-group ...

Propensity score matching is a tool for causal inference in non-randomized studies that allows for conditioning on large sets of covariates. The use of propensity scores in the social sciences is currently experiencing a tremendous increase; however it is far from a commonly used tool. One impediment towards a more wide-spread use of propensity score methods is the reliance on specialized softw...

At the household level, livestock plays a critical economic and social role in pastoralists and at the household level, livestock plays a critical economic and social role in pastoralists and smallholder farm households. The objectives of this study were to analyze factors affecting participation in cattle fattening and its impacts on household income in Fadis district of Eastern Hararghe. Both...

In health economic studies that use observational data, a key concern is how to adjust for imbalances in baseline covariates due to the non-random assignment of the programs under evaluation. Traditional methods of covariate adjustment such as regression, depend on correct model specification. Alternatives such as propensity score matching depend on covariate balance being achieved. We demonstr...

Propensity score methods are increasingly being used to reduce or minimize the effects of confounding when estimating the effects of treatments, exposures, or interventions when using observational or non-randomized data. Under the assumption of no unmeasured confounders, previous research has shown that propensity score methods allow for unbiased estimation of linear treatment effects (e.g., d...