Journal of Economic Surveys最新文献

The availability of public and social goods is vital for development in emerging economies, yet its effect on new business formation remains ambiguous. This study, grounded in public goods theory, investigates how increased investment in public and social goods influences new business formation, with digitalization and innovation linkages serving as mediators. Using Driscoll-Kraay (DK) standard errors and a two-step system GMM, the model is tested on panel data from 38 Asian countries (2011–2022). The findings support the theoretical framework, demonstrating that investment in public and social goods facilitates new business formation when integrated with digital infrastructure and innovation linkages. Further analysis by income group reveals that low-income countries face resource constraints that impede the development of digital and innovation hubs, thereby limiting support for new business formation. The study suggests that strategic public investment in digital infrastructure and innovation hubs is crucial for fostering entrepreneurship and promoting sustainable economic growth.

Motivated by the recent boom in artificial intelligence (AI) playing a significant role in the economics of individuals, firms, and government bodies, we investigate the role of AI in economics by reviewing the literature (2231 articles) during the last 34 years (1990 to November 2024). We identify five research streams: (1) AI and economic modeling, (2) AI and macroeconomics (eight sub-streams), (3) AI and equity and debt market, (4) AI and prediction modeling (three sub-streams), and (5) AI and economics of innovation. Further, we offer suggestions for future research (20 questions). Additionally, we outline a framework to consider changes in economics before and after AI adoption. Further, the critical AI-based methods are identified and discussed.

The quantitative assessment of budget performance management policies is vital for improving the scientific rigor of policy formulation, enhancing the practicality of policy implementation, optimizing public resource distribution, and advancing the refinement of the national governance system. This research, which analyzes 2501 Chinese budget performance management policies from 2001 to 2024, utilizes latent Dirichlet allocation (LDA) topic model, natural language processing (NLP), text encoding techniques, and the PMC-TE index model to quantitatively evaluate the effectiveness and evolutionary trends of China's budget performance management policies at various developmental stages. The result reveals that China's budget performance management policies can be grouped into nine primary policy tools. Notably, financial budget management, resource allocation, and operation management are the most frequently employed and effective policy tools. These policy tools are further divided into three policy domains: financial strategy and social development, financial supervision and innovative management, and financial governance and reform. Furthermore, the historical evolution of China's budget performance management policy tools displays a four-stage development pattern: foundational and preliminary development, rapid expansion and deepening, peak and adjustment, and optimization and transformation. These findings offer a novel theoretical framework for understanding the progress and patterns of the evolution of China's budget performance management policies.

The notion of spatial spillovers has been widely used in applied spatial econometrics. In this paper, we consider how they can be identified in both structural and causal reduced-form models. First, discussing the various threats to identification in structural models, we point out that the typical estimation framework proposed in the applied spatial econometric literature boils down to considering spatial spillovers as a side-effect of a data-driven chosen specification. We also discuss the limits of blindly relying on interaction matrices purely based on geography to identify the source and content of spillovers. Then, we present reduced forms impact evaluation models for spatial data and show that the current spatial versions of usual impact evaluation models are not fully satisfactory when considering the identification issue. Finally, we propose a set of recommendations for applied articles aimed at identifying spatial spillovers.

This paper undertakes a comprehensive review of some of the less-discussed factors that harbor the potential to explain the wide variations in structural transformation, especially amongst developing economies. We begin by critically reviewing the effects of income, relative price, and international trade, which are generally considered drivers of structural change, with special emphasis on developing economies. Subsequently, we bring to the forefront three comparatively less-discussed factors—income inequality, financial market imperfections, and government policy that can explain a large part of the divergences in the transformation patterns of developing economies. Underscoring the limitation of the conventional discourse that relates cross-country differences in structure in terms of cross-country differences in manufacturing, we emphasize the need for the theoretical discourse to move beyond the industry-centric approach to focus on the recent research that views the surge of services as an additional engine of growth and the possible avenues through which it can affect structural transformation.