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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.

In the evaluation of most interventions in criminal justice settings, evaluators have no control over assignment to treatment and control/comparison conditions, which means that the treated and comparison groups may have differences that lead to biased conclusions regarding treatment effectiveness. Propensity score analysis can be used to balance the differences in the groups, which can be used in a number of ways to reduce biased conclusions regarding effectiveness. A review of propensity scoring studies was conducted for this chapter, where the limited number of evaluations of criminal justice interventions using these methods was identified. Due to the small number of these studies, research was also reviewed if propensity scoring had been employed to evaluate interventions that are similar to those in criminal justice systems. These studies are used as examples to demonstrate how the methods can be used to evaluate criminal justice interventions, the different ways propensity scores can be used to analyse treatment and comparison group differences, and the strengths and limitations of this approach. It is concluded that, while not appropriate for all interventions/settings, propensity score analysis can be useful in criminal justice arenas, at least to investigate the comparability of treatment and comparison groups, with suspected non-comparability being a common weakness of traditional quasi-experimental studies and frequently cited limitation in terms of drawing efficacy conclusions from such evaluations.

This paper aims to introduce matching in propensity score analysis (PSA) as an alternative statistical approach for researchers looking to make causal inferences using intact groups.

An illustrative example demonstrated the varying results of analysis of variance, analysis of covariance and PSA on a heuristic data set. The three approaches were compared by results and violations of statistical assumptions.

Through the illustrative example, it is demonstrated how different statistical approaches can produce varied results. Only PSA mitigated pre-existing group differences without violating the assumption of independence.

This paper attempts to answer calls in the literature for more robust statistical methodologies to better inform human resource development practice and theory.

In this paper, the authors explore the relationship that slack resources and technology-mediated human capital investments can have on individuals’ entrepreneurial intentions. Focusing on human capital investments that individuals make through education and work, the authors analyze the relationship among formal online learning opportunities, informal skill development in the gig economy and entrepreneurial intentions.

Leveraging a novel dataset that merges administrative tax data with a survey of over 8,528 low- and moderate income (LMI) households, this study uses machine learning and propensity score weighting to examine the likelihood that individuals who make these technology-mediated human capital investments will have increased odds of entrepreneurial intentions when compared to similar individuals who do not make these investments.

The authors find that both partaking in online learning and working in the gig economy are significantly associated with increased odds of entrepreneurial intentions. Furthermore, through a variety of robustness and mechanism checks, the authors find that technology-mediation is an important factor in these relationships and that informal skill development and career preparation is one way in which gig employment influences entrepreneurial intentions.

As the study’s data come from a cross-sectional survey, the authors cannot make causal inferences about the relationship between online learning, gig employment and entrepreneurial intentions. Thus, future research should explore sources of longitudinal data.

This study has practical implication for individuals and policymakers that seek to increase entrepreneurship among LMI households.

Despite a wealth of research on the relationships among slack resources, technology and innovation at the firm level, there is little of this research at the individual level – especially among LMI individuals. The authors begin to fill this important gap.

The purpose of this paper is to illustrate how to examine the effectiveness of a pilot summer bridge program for elementary algebra using propensity scores. Typically, selection into treatment programs, such as summer bridge programs, is based on self-selection. Self-selection makes it very difficult to estimate the true treatment effect because the selection process itself often introduces a source of bias.

By using propensity scores, the authors can match students who participated in the summer bridge program with equivalent students who did not participate in the summer bridge program. By matching students in the treatment group to equivalent students who do not participate in the treatment, the authors can obtain an unbiased estimate of the treatment effect. The authors also describe a method to conduct a sensitivity analysis to estimate the amount of hidden bias generated from unobserved factors that would be needed to alter the inferences made from a propensity score matching analysis.

Findings suggest there is no significant difference in the pass rates of the subsequent intermediate algebra course for students who participated in the summer bridge program when compared to matched students who did not participate in the summer bridge program. Thus, students who participate in the summer bridge program fared no better or worse when compared to similar students who do not participate in the program. These findings also appear to be robust to hidden bias.

This study describes a unique way to estimate the causal effect of participating in a treatment program when there is self-selection into the treatment program.

To combat poverty in China's rural areas, Chinese government has established an unconditional cash transfer program known as the Rural Minimum Living Standard Guarantee (Rural Dibao) Program. Interestingly, despite the importance of education in breaking cycles of poverty, little is known about Rural Dibao's impact on rural children's education. This study investigates Rural Dibao's impact on rural children's learning outcomes by first examining targeting issues within the program, exploring a causal relationship between Rural Dibao and learning outcomes, and then exploring potential mechanisms and heterogeneous effects.

Fixed effects model and propensity score weighting method and data from China Family Panel Studies (CFPS) from the years 2010 and 2014 were used.

The results suggest that the Rural Dibao program suffers from high levels of targeting error, yet is still effective (i.e., program transfers generally still go to people in need). The fixed effects and propensity score weighting models find that program participation raises rural children's standardized test scores in CFPS Chinese-language and math tests. In investigating mechanisms, increased education expenditure seems to connect Rural Dibao participation to increased learning results. The heterogeneity analysis shows that poorer, non-eastern, not left behind, younger or male children benefit from the program (while others have no effect).

These findings suggest that Rural Dibao participation boosts rural children's learning, which could indicate a long-term anti-poverty effect, and that if the program can resolve targeting problems, this effect could be even greater.

The purpose of this study is to evaluate the performance of mutual funds during the COVID-19 pandemic with environmental, social and governance (ESG) criteria. The main research question is whether mutual fund performance differs with respect to the level of the mutual fund’s ESG score.

The data set contains global fund data, and mutual fund performance is analyzed using two types of data envelopment analysis (DEA) models: the DEA portfolio index (DPEI) and the range direction measure (RDM) DEA. Propensity score matching and logistic regression are also applied.

The results reveal that: nonequity mutual funds present significantly higher performance compared to the performance of equity mutual funds; mutual funds with high ESG scores are associated with significantly higher performance compared to those with low to medium ESG scores; funds with high ESG scores experience higher performance irrespective of their type; and efficiency scores derived from the RDM DEA are significantly higher than those derived from the DPEI model.

Investors, fund managers and market participants can benefit from the findings of this study and improve their investment decision-making process, including more sustainable funds in their portfolios. Regulators and policymakers should further promote or even require the inclusion of more sustainable investments in the financial products offered by institutional investors. The main limitation of the study is related to data availability regarding the ESG score of mutual funds.

To the best of the authors’ knowledge, this is the first study that provides robust evidence in support of a positive association between ESG scores and mutual fund performance during the pandemic-induced crisis applying a DEA methodology.

Using the most recent observations (2005‐2011) from a sample of UK listed companies, This paper aims to investigate whether Big 4 audit firms exhibit a “fee premium” and, if this is the case, whether the premium is related to the delivery of a better audit service.

Univariate tests, multivariate regressions and two methodologies that control for self‐selection bias are used to answer the proposed research questions. Data are collected from DataStream.

Findings provide consistent evidence about the existence of an “audit fee premium” charged by Big 4 firms while they do not highlight any significant relationship between audit quality and type of auditor with respect to the audit quality proxies investigated.

Evidence from this paper might signal the need for legislative intervention to improve the competitiveness of the audit market on the basis that its concentrated structure is leading to “excessive” fees for Big 4 clients. Findings might also enhance Big 4 client bargaining power. However, as the paper analyses only one country, generalizability of the results might be a limitation.

This study joins two streams of the extant literature that investigate the existence of a “Big 4 audit fee premium” and different levels of audit quality among Big 4 and non‐Big 4 clients. Evidence supports the concerns raised by the UK House of Lords in 2010 that the concentrated structure of the audit market could be the driver of “excessive” fees for Big 4 clients as it does not find differences in audit quality between Big 4 and non‐Big 4 clients.

The purpose of this paper is to test the popular perception that the storefront location choices of premium brands are positively related to adjacent rents. Focusing on the case of Starbucks, a popular international coffee chain, the authors examine the association between Starbucks locations and rents in Manhattan, New York.

The authors use a multi-year data set for average rent per square foot for office and multifamily residential properties within 1/10th of a mile of several hundred coffee shop locations in Manhattan, controlling for vacancy, job density, overall amenity density (WalkScore), coffee shop density, transit accessibility, neighborhood and the Starbucks brand. The authors take two different methodological approaches to isolate potential statistical evidence for an association between Starbucks locations and adjacent rents: the authors run a pooled-cross-sectional model and apply propensity-score matching.

The authors find a statistically significant positive relationship between the presence of Starbucks and average office rents when applying the authors’ pooled-cross-sectional model and applying propensity-score matching. This finding is consistent with several potential causal hypotheses: Starbucks may be attributed to higher rent office locations; the “Starbucks effect” may cause higher rents in adjacent locations; or there may be a mutual reinforcing of positive feedback between Starbucks locations and office rents. The authors find no strong association between Starbucks and residential rents (one model indicates an effect of 2.3 percent on residential rent at 10 percent level of significance), which challenges the direct linearity of the consumption theory of gentrification popularly called the “Starbucks effect.”

In the literature, the existence, causality and directionality of a relationship between Starbucks locations and neighborhood change have been largely unstudied. In this paper, the authors test the hypothesis that there is a positive correlation between Starbucks locations and rents.

Several approaches have been proposed to evaluate treatment effect, relying on matching methods propensity score, quantile regression, influence function, bootstrap and various combinations of the above. This paper considers two of these approaches to define the quantile double robust (DR) estimator: the inverse propensity score weights, to compare potential output of treated and untreated groups; the Machado and Mata quantile decomposition approach to compute the unconditional quantiles within each group – treated and control. Two Monte Carlo studies and an empirical application for the Italian job labor market conclude the analysis. The paper aims to discuss these issue.

The DR estimator is extended to analyze the tails of the distribution comparing treated and untreated groups, thus defining the quantile based DR estimator. It allows us to measure the treatment effect along the entire outcome distribution. Such a detailed analysis uncovers the presence of heterogeneous impacts of the treatment along the outcome distribution. The computation of the treatment effect at the quantiles, points out variations in the impact of treatment along the outcome distributions. Indeed it is often the case that the impact in the tails sizably differs from the average treatment effect.

Two Monte Carlo studies show that away from average, the quantile DR estimator can be profitably implemented. In the real data example, the nationwide results are compared with the analysis at a regional level. While at the median and at the upper quartile the nationwide impact is similar to the regional impacts, at the first quartile – the lower incomes – the nationwide effect is close to the North-Center impact but undervalues the impact in the South.

The computation of the treatment effect at various quantiles allows to point out discrepancies between treatment and control along the entire outcome distributions. The discrepancy in the tails may differ from the divergence between the average values. Treatment can be more effective at the lower/higher quantiles. The simulations show the performance at the quartiles of quantile DR estimator. In a wage equation comparing long and short term contracts, this estimator shows the presence of an heterogeneous impact of short term contracts. Their impact changes depending on the income level, the outcome quantiles, and on the geographical region.