Journal of Economic Surveys最新文献

Collusive practices continue to be a significant threat to competition and consumer welfare. It should be of utmost importance for academic research to provide the theoretical and empirical foundations to antitrust authorities and enable them to develop proper tools to encounter new collusive practices. Utilizing topical natural language machine learning techniques allows me to analyze the evolution of economic research on collusion over the past two decades in a novel way. It enables me to review some 800 publications systematically. I extract the underlying topics from the papers and conduct a large set of uni- and multivariate time series and regression analyses on their individual prevalences. I detect a notable tendency towards monocultures in topics and an endogenous constriction of the topic variety. In contrast, the overall contents and issues addressed by these papers have grown remarkably. This caused a decoupling: Nowadays, more datasets and cartel cases are studied but with a smaller research scope.

The altruistic crowding-out effect is a decrease of prosocial behavior due to monetary incentives or material rewards that intend to increase an extrinsic motivation for the behavior. The decrease in a behavior by increasing a motivation for that behavior, seems irrational, but behavioral economists presented a dozen different models to explain this crowding-out effect. In these models, the decrease in prosocial behavior is rational in the sense that agents maximize their expected utility. All the models assume that people have utility functions that represent their preferences and motivations. This review clarifies different kinds of motivations, rewards, incentives, and crowding-out effects, presents 13 behavioral economics models, classifies them in five types of models, discusses subtle nuances of the models, summarizes the different predictions of the different models, and provides an overview of the empirical support of the models. The main take-away is that the crowding-out effect could not only be explained in terms of rational, utility-maximizing behavior, but could be done so in many (at least 13) different ways. This review can be used to improve empirical validation of the models and to gain insights in the specific contexts in which crowding-out occurs.

This paper is a critical review of the empirical literature resulting from recent years of debate and analysis regarding technology and employment and the future of work as threatened by technology, outlining both lessons learned and challenges ahead. We distinguish three waves of studies and relate their heterogeneous findings to the choice of technological proxies, the level of aggregation, the adopted research methodology and to the relative focus on robots, automation and AI. The challenges ahead include the need for awareness of possible ex-ante biases associated with the adopted proxies for innovation; the recognition of the trade-off between microeconometric precision and a more holistic macroeconomic approach; the need for granular analysis of the reallocation and transformation of occupations and tasks brought about by different types of new technologies; the call for a closer focus on impacts on labor quality, in terms of types of jobs and working conditions.

We assess statistical power and excess statistical significance among 31 leading economics general interest and field journals using 22,281 parameter estimates from 368 distinct areas of economics research. Median statistical power in leading economics journals is very low (only 7%), and excess statistical significance is quite high (19%). Power this low and excess significance this high raise serious doubts about the credibility of economics research. We find that 26% of all reported results have undergone some process of selection for statistical significance and 56% of statistically significant results were selected to be statistically significant. Selection bias is greater at the top five journals, where 66% of statistically significant results were selected to be statistically significant. A large majority of empirical evidence reported in leading economics journals is potentially misleading. Results reported to be statistically significant are about as likely to be misleading as not (falsely positive) and statistically nonsignificant results are much more likely to be misleading (falsely negative). We also compare observational to experimental research and find that the quality of experimental economic evidence is notably higher.

It is widely accepted that environmental and demographic changes will significantly influence the future of our society. In recent years, an increasing number of studies has analyzed the interlinkages among economic growth, environmental factors, and a specific demographic variable, namely life expectancy, applying an overlapping generations framework. The aim of this survey is threefold. First, we review the role of life expectancy and pollution for sustainable growth. Second, we discuss the role of intervening factors like health investment and technological progress as well as institutional settings including government expenditures, tax structures, and inequality. Finally, we summarize policy implications obtained in different models and compare them to each other.

This study examines the recent literature on the expectations, beliefs and perceptions of investors who incorporate Environmental, Social, Governance (ESG) considerations in investment decisions with the aim to generate superior performance or make a societal impact. Through the lens of equilibrium models of agents with heterogeneous tastes for ESG investments, green assets are expected to generate lower returns in the long run compared to their non-ESG counterparts. However, in the short run, ESG investments can outperform non-ESG investments through various channels. Empirically, results for the relative performance to ESG investment are mixed. We find strong empirical evidence in the literature that investors have a preference for ESG and that their actions can generate positive social impact through engagement. The shift towards more sustainable policies in firms is motivated by the increased market values and the lower cost of capital of green firms driven by investors’ choices.

This article provides concise, nontechnical, step-by-step guidelines on how to conduct a modern meta-analysis, especially in social sciences. We treat publication bias, p-hacking, and systematic heterogeneity as phenomena meta-analysts must always confront. To this end, we provide concrete methodological recommendations. Meta-analysis methods have advanced notably over the last few years. Yet many meta-analyses still rely on outdated approaches, some ignoring publication bias and systematic heterogeneity. While limitations persist, recently developed techniques allow robust inference even in the face of formidable problems in the underlying empirical literature. The purpose of this paper is to summarize the state of the art in a way accessible to aspiring meta-analysts in any field. We also discuss how meta-analysts can use advances in artificial intelligence to work more efficiently.