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Without controlling for selection bias and the potential endogeneity of the treatment by using proper methods, the estimation of treatment effect could lead to biased or incorrect conclusions. However, these issues are not addressed adequately and properly in higher education research. This study reviews the essence of self-selection bias, treatment assignment endogeneity, and treatment effect estimation. We introduce three treatment effect estimators – propensity score matching analysis, doubly robust estimation (augmented inverse probability weighted approach), and endogenous treatment estimator (control-function approach) – and examine literature that applies these methods to research in higher education. We then use the three methods in a case study that estimates the effects of transfer student pre-enrollment debt on persistence and first year grades. The final discussion provides guidelines and recommendations for causal inference research studies that use such quasi-experimental methods.

This paper aims to analyse the change in performance of parent Indian firms (home effects) who have invested in overseas locations in recent times.

Difference-in-difference (DiD) estimate of home effects using farm level data.

Home effects of Indian outward foreign direct investment (OFDI), in general, are insignificant. However, in the case of OFDI directed only to non-offshore financial centre (OFC), some firms did enjoy beneficial home effects with respect to turnover, current ratio and leverage ratio. In the case of OFDI directed purely to OFC locations, some of the parameters exhibited negative home effects. In the subsample of Indian OFDI directed to combination of OFC and non-OFC locations, the results show positive home effects with respect to export, operating profit margin and forex earnings; however, impact on turnover seems to be negative for all the quartiles.

Estimation of home effects using data over longer horizon may yield robust outcome.

These results make a strong case to draw a distinction among OFDIs to OFC, non-OFC and combination of OFC and non-OFC locations in studying the beneficial home effects of OFDI.

To the authors’ knowledge, this is the first paper which estimates home effects of different groups of Indian firms (based on their investment locations and size class) using difference-in-difference estimate.

In this chapter, we consider the nonparametric estimation of the average treatment effect (ATE) based on direct estimation of the conditional treatment effect. We establish the asymptotic distribution of the proposed ATE estimator. We also consider consistent testing for a parametric functional form for the conditional treatment effect function. A small-scale Monte Carlo simulation study is reported to examine the finite sample performance of the proposed estimator.

We describe a new Bayesian estimation algorithm for fitting a binary treatment, ordered outcome selection model in a potential outcomes framework. We show how recent advances in simulation methods, namely data augmentation, the Gibbs sampler and the Metropolis-Hastings algorithm can be used to fit this model efficiently, and also introduce a reparameterization to help accelerate the convergence of our posterior simulator. Conventional “treatment effects” such as the Average Treatment Effect (ATE), the effect of treatment on the treated (TT) and the Local Average Treatment Effect (LATE) are adapted for this specific model, and Bayesian strategies for calculating these treatment effects are introduced. Finally, we review how one can potentially learn (or at least bound) the non-identified cross-regime correlation parameter and use this learning to calculate (or bound) parameters of interest beyond mean treatment effects.

This paper aims to test the impact of remittances receipt on agricultural productivity. The paper empirically assesses whether heterogeneity in economic activity of farming households affects the effects of remittances on productivity of tradable and nontradable crop farming households in Ghana.

The authors employ propensity score matching (PSM) methods to address potential endogeneity issues that could arise from the estimation due to selection bias. This paper uses the seventh round of Ghana living standard survey dataset for Ghana.

The authors find that, the involvement of farming households in other economic activities alters the impact of remittances on crop yield. This differential impact also varies according whether the crop is tradeable or not.

Policy can reduce the cost of sending remittances and include financial literacy modules in the farmer training modules to increase farmers' knowledge on investment of remittance in agricultural production.

The authors distinguish the paper from others by controlling for crop types (particularly tradeable or otherwise and gestation period), farming of a second or more crops and engagement of smallholder farmers in nonfarm economic activities.

This study examines the relationship between small-firm managers' propensity to participate in a growth-oriented training program and their subsequent program outcome in terms of strategic reorientation. From a policy perspective, this relates to the important question of what benefit would come from recruiting managers who are normally not easily recruitable for training programs.

A control group design including pre- and post-training surveys is used to assess the effects of a large-scale management training program. Accounting for selection bias, the difference-in-difference method, together with propensity score matching, was applied to assess average program effects. The matching-smoothing method was used to assess heterogeneity in program effects associated with participation propensity.

Overall, program participation associated positively with change in strategic orientations. This effect was especially pronounced for managers with either low or medium to high inclinations for program enrollment, while diminishing in the modest to medium range.

The findings have important practical implications for selection of target groups and recruitment strategies in relation to small-firm management training programs. From the results, recruitment strategies may effectively include managers with either high or low participation propensities, rather than aiming to “fill up” with managers with moderately low participation propensity.

Several extant studies have examined average treatment effects from small-firm training programs. Yet there has been a lack of examination of the extent to which participation propensity modifies the effect of training on outcomes. This study brings new knowledge of the direction and magnitude of such heterogeneous training effects.

This paper aims to presents one of the first direct micro-econometric impact of competitive industries (based on revealed comparative advantage [RCA] between Pakistan and China) on household welfare in Pakistan using semi-parametric matching technique.

The study has also measured and identified the industrial competitiveness in both agricultural and non-agricultural (manufacturing) industries using RCA approach. RCA at the four-digit ISIC level are matched to household survey data (Pakistan Social and Living Standard Measurement) for 2013-2014 to represent the competitive industries in which the household’s higher earner is employed.

The findings of the study reveal that the China–Pakistan ex-post treatment effect (industrial competitiveness) provides welfare-improving effects. Furthermore, on this behalf, this study further assesses ex-ante treatment effects of recently signed China–Pakistan Economic Corridor (CPEC) on household welfare and suggests that CPEC would boost further trade liberalization and, therefore, would lead to industrial competitiveness and hence economic growth.

Paper contributes to two streams of literature. First, it measures and identifies the industrial competitiveness in both agricultural and non-agricultural industries using RCA approach; and second, it assesses the welfare of those households associated with these industries using semi-parametric propensity score matching technique.

The protective aspects of relationships for health have been extensively studied. Here, we assess whether different dimensions of partnership status at the time of a child’s birth are associated with better self-assessed health later in mid-life. Data are from three countries with different social welfare policies relating to union status and parenting: the US, the UK, and Norway. Results indicate that women who were partnered at first birth had better health at midlife in all three countries than women who were unpartnered. The analysis indicates no differences in the mid-life health of Norwegian women who were married or cohabiting at birth, whereas for US and UK women, being married at the birth of a first child is more beneficial for mid-life health than bearing the child in a cohabiting union. In the US, women who are least likely to marry do not demonstrate better mid-life health if they had wed relative to cohabiting. In the UK, in contrast, the women least likely to be married at the birth experience better returns if they marry. These findings highlight the importance of paying closer attention to heterogeneous treatment effects as they relate to childbearing, relationship status, and mid-life health.

This work examines the impact of institutional agricultural credit on crop productivity of some major crops such as paddy, cotton, wheat and pulses for small and marginal farmers across various social groups.

The cross-sectional field data on socio economic variables was collected from three Indian states from about 400 small and marginal farmers across various social groups using multi-stage stratified random and purposive sampling through a structured questionnaire by interviewing. The method of propensity score matching (PSM) was employed to calculate average treatment effect (ATE) and average treatment effect on the treated (ATET) by categorising sample farmers as treatment group and control group where crop productivity was considered as outcome variable and access to institutional credit was considered as treatment variable.

The PSM estimates reveal that ATE and ATET for all the selected crops are found to be significantly higher for the treated group vis-à-vis non-treated group suggesting that institutional agricultural credit has a statistically and significant positive impact on the crop productivity.

Similar study can be extended for more crops and across regions in India for a universal coverage.

The agricultural credit policy of India has been to increase the access and availability of institutional farm credit. This has led to in general increase in the flow of formal farm credit to agricultural sector. However, the impact of institutional credit and crop productivity especially for small and marginal farmers across social groups is not well recognized in India using field data. Accordingly, this field data study contributes to the existing research by providing fresh evidence from field across social groups for both kharif and rabi crops using recent survey data from small and marginal farmers which has important policy implications.

This chapter reviews recent developments in the density discontinuity approach. It is well known that agents having perfect control of the forcing variable will invalidate the popular regression discontinuity designs (RDDs). To detect the manipulation of the forcing variable, McCrary (2008) developed a test based on the discontinuity in the density around the threshold. Recent papers have noted that the sorting patterns around the threshold are often either the researcher’s object of interest or may relate to structural parameters such as tax elasticities through known functions. This, in turn, implies that the behavior of the distribution around the threshold is not only informative of the validity of a standard RDD; it can also be used to recover policy-relevant parameters and perform counterfactual exercises.