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Panel data provide the possibilities of estimating individual treatment effects for multiple individuals. Two issues are considered: (1) differences in the estimated individual treatment effects are due to heterogeneity or a chance mechanism? (2) what is the best way to estimate the average treatment effects? Testing and aggregation methods are suggested. Monte Carlo simulations are also conducted to shed light on these two issues. An empirical analysis on the involvement of underground organization in China’s Peer-to-Peer (P2P) activities through the “anti-gang” campaign is also provided.

The authors generalize the standard linear-in-means model to allow for multiple types with between and within-type interactions. The authors provide a set of identification conditions of peer effects and consider a two-stage least squares estimation approach. Large sample properties of the proposed estimators are derived. Their performance in finite samples is investigated using Monte Carlo simulations.

Recent empirical studies have improved methodologies for identifying the causal effects of policies especially on a minimum wage hike. This study identifies causal effects of minimum wage hikes across 47 prefectures in Japan from 2008 to 2010 on employment, average hourly wage, work hours, full-time equivalent employment (FTE), total wage costs, average tenure, separation and new hiring in establishments using a micro dataset of business establishments in restaurant, accommodation, and food takeout and delivery industry. Various regression specifications including controls for time-varying regional heterogeneity are implemented by using the bite of the minimum wage in each establishment. First, this study finds that the effects of a revision of minimum wage on employment and FTE in the establishment are statistically insignificant, but the effects on hourly wages and total wage costs are statistically significant. Subsequently, it considers how the establishments react to the increase in total wage costs caused by the revised minimum wage, and finds that separation from the establishment may decrease, and average tenure of workers may increase.

This paper uses two complementary approaches to estimate the effect of right-to-work (RTW) laws on wages and unionization rates. The first approach uses an event study design to analyze the impact of the adoption of RTW laws in five US states since 2011. The second approach relies on a differential exposure design that exploits the differential impact of RTW laws on industries with high unionization rates relative to industries with low unionization rates. Both approaches indicate that RTW laws lower wages and unionization rates. Under the assumption that RTW laws only affect wages by lowering the unionization rate, RTW can be used as an instrumental variable (IV) to estimate the causal effect of unions on wages. In our preferred specification based on the differential exposure design, the IV estimate of the effect of unions on log wages is 0.35, which substantially exceeds the corresponding OLS estimate of 0.16. This large wage effect suggests that RTW may also directly affect wages due to a reduced union threat effect.

This chapter examines the impact of education on income inequality in 18 Latin American countries between 2000 and 2010. This period has raised interest in the academic community because inequality has fallen across the region, after several years of consistent high levels. Employing the novel technique proposed by Firpo, Fortin, and Lemieux (2007), the author’s research provides a detailed decomposition of inequality. Three main findings emerge from the author’s results: First, the expansion of education increases inequality in six countries but reduces inequality in four countries. Second, the changes in returns to education are the driving component of the effects of education on inequality. Those countries where education contributes to a fall in inequality are those where the returns to education fell at the top of the income distribution. Third, the rise in the average years of education, considered alone, had an inequality-increasing effect in most of the countries under analysis.

We argue that entrepreneurship research should use meta-analysis to integrate the findings of the field. A meta-analytical approach has several advantages as compared with narrative reviews: First, narrative reviews are likely to bias empirical evidence because they are limited by the information-processing capacities of the reviewers (Tett, Jackson, & Rothstein, 1991). This is often a downward bias leading to the conclusion of little positive knowledge in the field. For example, frequency counts of significant results ignore sampling errors of individual studies, reliability problems of instruments, range restrictions of samples, dichotomization of continuous variables, imperfect construct validity, and extraneous factors (Hunter & Schmidt, 2004). These issues usually result in a higher incidence of Type II errors (i.e., rejecting the hypothesis wrongly). Thus, narrative reviews are more likely to lead to the conclusion that there are no relationships between independent and dependent variables in entrepreneurship when in fact they are (Hunter & Schmidt, 1990; Tett et al., 1991). Second, meta-analysis accumulates studies based on a set of explicit decision rules and, therefore, is less biased by subjective perceptions of the reviewer than narrative reviews. Meta-analyses require judgments as well, e.g., when defining the area of the study or coding moderator variables. However, the decisions are public and open to criticism and replication by other scientists (Johnson & Eagly, 2000). Third, meta-analysis is based on many studies and, thus, avoids the influence of single studies. Fourth, meta-analysis controls for sampling error variance and, thus, controls for power deficits of individual studies (Hunter & Schmidt, 2004). For example, the Brockhaus and Nord (1979) study is frequently cited in the entrepreneurship literature for providing evidence that there is no relationship of personality characteristics with entrepreneurship. However, this study is based on a small sample of 31 business owners and therefore, has serious statistical power problems. Noteworthy, the effect sizes of small samples are less precise in estimating a population value than effect sizes of larger samples. Fifth, meta-analyses can correct many errors of individual studies (Hunter & Schmidt, 2004). Since meta-analyses estimate population correlations between given variables, it is important to correct for errors of studies (e.g., unreliability, range restriction, and sampling error) to achieve unbiased estimates. Sixth, meta-analysis allows an assessment of the magnitude of relationships and, thus, provides more precise and often comparable assessments of the validity of concepts. Thus, meta-analyses support the assessment of the practical significance of findings. Seventh, meta-analysis tests for variations in relationships across studies and, therefore, allows an assessment of the generalizeability of effects. If the size of reported relationships varies considerably between different studies, there will be context conditions that account for these variations. These context conditions are moderators that affect the size of relationships. The moderators may include study characteristics, method moderators, and theoretical moderators. Thus, meta-analyses also help to identify areas for new studies. Finally, meta-analysis techniques allow to test more than one independent and/or moderator variable by using methods based on regression analysis (Lipsey & Wilson, 2001). Using such procedures allows to estimate the independent contribution of variables on results, to control for methodological variables, and to test the interactions between moderator variables.

This chapter presents an evaluation of the literature on the effect of the pandemic on mental health. It draws mainly on the existing economics literature and presents the state of the art of the COVID-19 effect on mental health. While paying particular attention to how the deterioration of mental health evolved over time and across countries, this chapter also considers variation of mental health across individual demographic characteristics as well as different circumstances through which mental health has been affected. Moreover, it provides a general assessment of the methodological aspects of various studies, by discussing the sample and data used, measures of mental health as well as causality issues. Overall, researchers for various countries around the world adopting different measures of mental health, often non-comparable samples and different methodologies document consistently that the level of mental health has been deteriorated during the pandemic, with the negative effect of the lockdown on mental health being evident in the early stage of the pandemic and on the whole population. Findings point out to a high degree of heterogeneity within demographic groups.

In this chapter, the author argues that Austrians are perhaps uniquely placed to be effective practitioners of causal inference techniques on observational data. This is because, while the methods are easy to implement, their validity and value lies in a detailed, “analytical/historical” narrative to accompany the findings. This is true for several reasons. (1) all the models have identifying assumptions (e.g., no spillovers and parallel trends) that are best addressed by an exposition of the institutional/economic/historical milieu in place before and after the treatment under study; (2) determination of external validity also requires detailed institutional and historical knowledge; and (3) researchers often want to know the mechanisms producing the reduced form result that comes out of most causal inference studies. Here again, institutional and historical learning is crucial. My conclusion is that Austrians should add the tools of causal inference with observational data to their arsenal of analysis. This would be good both for their publication prospects and for the profession at large.

Social movements are made up of organized groups and individuals working together to accomplish shared objectives. Under what circumstances do active groups build and break their coalitions? Five conditions have been identified in the literature as influencing coalition formation: common identity, resources, organizational structure, historical connection, and institutional setting. Whereas coalition dynamics within a movement wave are best understood in terms of institutional opportunities and threats, further research is needed to determine how and to what extent these contextual elements influence coalitions. This chapter examines how threats posed by indiscriminate and selective repression affect the shape and structure of interorganizational coalitions during the 2019 Anti-Extradition Law Amendment Bill (Anti-ELAB) protests in Hong Kong. The analysis relies on an original political event dataset and an organization-event network dataset. These datasets were produced utilizing syntactic event coding techniques based on Telegram posts, which Hong Kong protesters used to distribute information, plan future actions, and crowdsource news. Furthermore, Telegram provides detailed information about state activities, event-level coalitions, and violent groups, which is difficult to access from other sources. This study investigates the coalition networks across the movement's four stages, each of which was marked by a particular type and degree of repression. The findings indicate that indiscriminate and selective repression have varied effects on coalition networks. A wide coalition disintegrates as a result of indiscriminate repression. Selective repression, however, leads to the formation of coalitions around activist groups targeted by repression.