Journal of Economic Surveys最新文献

Growing financial needs for a sustainable transition have led to the development of green finance and citizen-led monetary innovations. These initiatives recognized the necessity of developing new forms of money regarding the environmental crisis, referred to as environmental complementary currency (ECCs). ECCs aim to generate environmental benefits by rewarding pro-environmental behaviors, funding green projects, encouraging short supply chains and circular economies, and increasing environmental awareness and knowledge within communities. Despite case studies highlighting ECCs' potential benefits, no comprehensive synthesis of their environmental impact exists. This paper addresses this gap by theoretically identifying ECCs’ environmental impact and inferring their actual magnitude through a systematic review. Searches conducted on Google Scholar, Scopus, and field-specific databases yielded a dataset of 35 empirical studies on ECCs’ environmental impacts, focusing on grassroots and emerging forms of complementary currencies, including digital and cryptocurrencies. Schemes not designed to reduce the footprint of human activities were excluded. Findings suggest that ECCs can reduce their users’ environmental impacts by raising awareness and knowledge and promoting a shift toward environmentally sustainable consumption. Moreover, we cannot conclude that the localization effects associated with ECCs generate benefits. This paper highlights the limitations of existing research and suggests directions for future studies.

Cryptocurrency markets are known for their wide price fluctuations, lack of central control, and fast-paced development. These characteristics present serious challenges to traditional theories about how markets work and how prices reflect available information. Understanding how information is processed in these markets is essential for investors, policy makers, and academic researchers. This paper offers a thorough review on the extent to which cryptocurrency markets reflect information, based on 977 peer-reviewed articles published between 2015 and 2024 and indexed in Scopus. Using a combined method of bibliometric analysis and thematic review, the study identifies key research directions and common methods used to explore how information affects cryptocurrency prices. The review goes beyond the Efficient Market Hypothesis (EMH) and includes related topics such as volatility modelling, behavioral dynamics, spillovers, liquidity, and institutional influences. It presents a detailed overview of the most influential publications and organises the literature into six thematic research clusters, highlighting conceptual tensions and new methodological approaches. Finally, the paper outlines a future research agenda that connects market efficiency with changing regulatory environments, innovations in market structure, and the increasing role of institutional actors in the cryptocurrency space.

This meta-analysis investigates the impact of remittances on labor supply decisions in recipient households. Using 1811 estimates from 64 studies, we find that international remittances reduce labor force participation by 4.4 percentage points while domestic remittances reduce it by 2.2 percentage points. The decline in labor force participation is stronger in South Asia (−5.6 percentage points) than in other regions. In contrast, conditional on remaining in the labor force, the effect on hours worked is negligible. Although remittances lower participation across most employment types, they have no significant effect on the self-employed. These findings highlight the complex economic implications of remittances, emphasizing the need for policies that foster labor market engagement and entrepreneurship. We also outline key directions for future research on this topic.

This survey describes the recent literature on integration and active labor market policies that strive to enhance the employment of immigrants. We searched several databases, and after screening, 63 studies satisfied our predetermined inclusion criteria. The first criterion is that the studies examine policies in Europe. Second, the search was limited to studies published between 2005 and 2024. Third, our primary outcome of interest is employment. Other outcome variables are income, labor market participation, and duration of unemployment. We focus on studies that offer experimental or quasi-experimental evidence on the effects of policies on the employment of immigrants. We divide the policy measures into the following five groups: integration programs, language training, benefits, childcare, and residency policies. Our main finding is that well-executed integration measures can improve the labor market attachment of immigrants, speed up the process of entering employment, and improve the quality of attained jobs.

This paper systematically compares dominant frameworks for modeling decision-making under risk and uncertainty, evaluating their theoretical trade-offs and practical relevance for economic research. We establish key criteria for model selection—including predictive accuracy, descriptive realism, computational tractability, and ecological validity—to guide researchers in matching frameworks to specific contexts. While classical axiomatic models provide normative benchmarks, our analysis highlights the need for context-sensitive models. We propose the following three research frontiers: (1) integrating behavioral axioms with machine learning architectures, (2) neuroeconomic validation of decision-theoretic assumptions, and (3) dynamic models for evolving uncertainty landscapes. The survey provides a structured framework for advancing decision theory while maintaining methodological pluralism in behavioral economics.

This paper investigates the use of artificial intelligence—specifically, model-free reinforcement learning—as a method to simulate bidding behavior in auctions. We survey six algorithms that are particularly suitable for learning and bidding in environments with large strategy sets and incomplete information such as auctions. We provide a comprehensive analysis of the advantages and disadvantages of these algorithms, detailing how they operate and why they are suitable for use in auction environments. We then present an illustrative example, comparing the performance of each algorithm across three common multi-unit auctions. Most of the chosen algorithms perform very well in terms of learning and bidding, although some outperform others. This paper highlights the significance of using artificial intelligence to enhance auction design, particularly the design of multi-unit auctions.