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Regression discontinuity (RD) models are commonly used to nonparametrically identify and estimate a local average treatment effect. Dong and Lewbel (2015) show how a derivative of this effect, called treatment effect derivative (TED) can be estimated. We argue here that TED should be employed in most RD applications, as a way to assess the stability and hence external validity of RD estimates. Closely related to TED, we define the complier probability derivative (CPD). Just as TED measures stability of the treatment effect, the CPD measures stability of the complier population in fuzzy designs. TED and CPD are numerically trivial to estimate. We provide relevant Stata code, and apply it to some real datasets.

We review quantitative methods for analysing the equity impacts of health care and public health interventions: who benefits most and who bears the largest burdens (opportunity costs)? Mainstream health services research focuses on effectiveness and efficiency but decision makers also need information about equity.

We review equity-informative methods of quantitative data analysis in three core areas of health services research: effectiveness analysis, cost-effectiveness analysis and performance measurement. An appendix includes further readings and resources.

Researchers seeking to analyse health equity impacts now have a practical and flexible set of methods at their disposal which builds on the standard health services research toolkit. Some of the more advanced methods require specialised skills, but basic equity-informative methods can be used by any health services researcher with appropriate skills in the three core areas.

We hope that this review will raise awareness of equity-informative methods of health services research and facilitate their entry into the mainstream so that health policymakers are routinely presented with information about who gains and who loses from their decisions.

Conventional tests of the regression discontinuity design’s identifying restrictions can perform poorly when the running variable is discrete. This paper proposes a test for manipulation of the running variable that is consistent when the running variable is discrete. The test exploits the fact that if the discrete running variable’s probability mass function satisfies a certain smoothness condition, then the observed frequency at the threshold has a known conditional distribution. The proposed test is applied to vote tally distributions in union representation elections and reveals evidence of manipulation in close elections that is in favor of employers when Republicans control the NLRB and in favor of unions otherwise.

The asymptotic bias and variance of a general class of local polynomial estimators of M-regression functions are studied over the whole compact support of the multivariate covariate under a minimal assumption on the support. The support assumption ensures that the vicinity of the boundary of the support will be visited by the multivariate covariate. The results show that like in the univariate case, multivariate local polynomial estimators have good bias and variance properties near the boundary. For the local polynomial regression estimator, we establish its asymptotic normality near the boundary and the usual optimal uniform convergence rate over the whole support. For local polynomial quantile regression, we establish a uniform linearization result which allows us to obtain similar results to the local polynomial regression. We demonstrate both theoretically and numerically that with our uniform results, the common practice of trimming local polynomial regression or quantile estimators to avoid “the boundary effect” is not needed.

The purpose of this paper is to investigate whether there is a causal impact of party affiliation of state governors operating within a liberal state legislature on labor market activity of transgender individuals. The common perception is that Republicans are more likely to believe a person’s gender is determined by their sex at birth than Democrats. Such beliefs can influence voting, policies and practices.

Survey data on employment outcomes and other individual level characteristics are used from two surveys focusing on transgender individuals for the periods 2008 and 2015. These surveys represent the largest collection of data that examines the experiences of transgender people in the USA. To establish a casual effect, this analysis employs fixed effects Ordinary Least Squares methods and Propensity Score Matching algorithms.

Exploiting variations in the party affiliation of governors and liberal state governments, the study finds mixed employment effects of living in a Democratic-governed liberal state. Specifically, this paper shows that transgender people living in such states have an 18 percentage point (or 26 percent) higher likelihood of being employed in the 2008 survey, but a 16.4 percentage point (or 25 percent) lower likelihood of being employed in the 2015 survey. Despite the lower propensity to be out of the labor market in the 2008 sample, results from both surveys indicate a higher likelihood of being unemployed. These findings are robust to the inclusion of additional covariates (including confounders), alternative specifications and different estimation techniques. Heterogeneous effects are also explored.

To the authors’ current knowledge, this is the first study to systematically investigate the relationship between living in democratically governed liberal states and labor market circumstances of transgender people in the USA. This is the first paper to establish a causal relationship using matching techniques. The paper also provides suggestive evidence of the role of government ideology in influencing the working lives of transgender people.

In recent years, climate-smart agriculture (CSA) was introduced to Vietnam to enhance farmers’ resilience and adaptation to climate change. Among the climate-smart agricultural technologies (CSATs) introduced were water-saving techniques and improved stress tolerant varieties. This study aims to examine the determinants of farmers’ adoption of these technologies and the effects of their adoption on net rice income (NRI) in three provinces as follows: Thai Binh (North), Ha Tinh (Central) and Bac Lieu (South).

Determinants of adoption of CSATs and the adoption effects on NRI are analyzed by using a multinomial endogenous switching regression framework.

The results showed that gender, age, number of family workers, climate-related factors, farm characteristics, distance to markets, access to climate information, confidence on the know-how of extension workers, membership in social/agricultural groups and attitude toward risk were the major factors affecting the decision to adopt CSATs. However, the effects of these factors on the adoption of CSATs varied across three provinces. These technologies when adopted tend to increase NRI but the increase is much greater when these are combined.

It is important to consider first the appropriateness of the CSA packages to the specific conditions of the target areas before they are promoted. It is also necessary to enhance the technical capacity of local extension workers and provide farmers more training on CSATs.

This study is the first attempt to identify key determinants of adoption of CSATs either singly or in combination and the adoption effects on NRI in Vietnam.

This chapter reviews the recent developments at the intersection of personalization and AI in marketing and related fields. We provide a formal definition of personalized policy and review the methodological approaches available for personalization. We discuss scalability, generalizability, and counterfactual validity issues and briefly touch upon advanced methods for online/interactive/dynamic settings. We then summarize the three evaluation approaches for static policies – the Direct method, the Inverse Propensity Score (IPS) estimator, and the Doubly Robust (DR) method. Next, we present a summary of the evaluation approaches for special cases such as continuous actions and dynamic settings. We then summarize the findings on the returns to personalization across various domains, including content recommendation, advertising, and promotions. Next, we discuss the work on the intersection between personalization and welfare. We focus on four of these welfare notions that have been studied in the literature: (1) search costs, (2) privacy, (3) fairness, and (4) polarization. We conclude with a discussion of the remaining challenges and some directions for future research.

This study aims to uncover the extent to which cultural traits may explain the puzzling international divergence in COVID-19 outcomes, and how those traits interact with state action to produce compliance with pandemic health policy.

A theoretical framework illustrates the surprising possibility that culture and state action may not reinforce each other but rather act as substitutes in eliciting anti-pandemic behavior. This possibility is tested empirically in two specifications: a cross-sectional regression that includes several novel COVID-related measures, and a panel model that controls for contemporaneous disease burden. Across these models, we use the measures of national culture developed by Hofstede (1984) and a newer metric developed by Schwartz (1990).

Individualism and egalitarianism have a positive effect on disease prevalence, while cultural heterogeneity was associated with a more robust public health response. Consistent with our model, we find that culture and state action served as substitutes in motivating compliance with COVID-19 policy.

The results of this study imply that culture and state interact in determining the effectiveness of public health measures aimed at combating COVID-19; these results recommend culturally aware state intervention when combating pandemics.

This study offers several new contributions. First, it proposes a model to help contextualize the empirical analysis. Second, it examines a wider range of traits than previous studies, including cultural homogeneity and the Schwartz variables. Third, it employs a richer econometric specification that explores the interaction between state and culture in a panel context.

Saving energy is an essential issue in the world to attenuate climate change. To achieve the goal, energy-saving appliances such as refrigerators should be promoted. This study aims to analyze the conditions enabling Japanese households to purchase such appliances, focusing on the relation with preferences for renewable energy as one of the non-monetary incentives.

A conjoint analysis is used. A random parameter logit model and nested logit model are used for estimation. Data were collected through an online questionnaire of the Rakuten Insight service.

Households will purchase energy-saving appliances when renewable energy is used for electricity generation. This implies that households will purchase energy-saving appliances with electric power generators by renewable energy such as solar panels and home micro-wind generators.

The response rate and attributes of respondents and non-respondents are not shown to researchers in the web-questionnaire service.

Promoting energy-saving appliances and renewable energy is essential in Japan (as in other countries) to save energy and to attenuate climate change. Based on the results, both energy-saving appliances and renewable energy will be widely used.

Although many studies have analyzed households’ preferences for energy-saving appliances and the effects of non-monetary incentives, studies that mentioned the relation with preferences for renewable energy are few. This study analyzes the relation and proposes policy recommendations to promote both energy-saving appliances and renewable energy.