Structural Change and Economic Dynamics最新文献

We analyze the evolution of comparative advantage in 1,240 products from 49 low- and middle-income countries between 1995 and 2015. We show that countries with high education levels were more successful in developing comparative advantage in products unrelated to those they already exported. This effect is strongest for non-core products. In contrast, these countries did not develop comparative advantage in products that were intrinsically complex or education-intensive. These results are robust to corrections for specification errors, for institutional, infrastructure, and FDI-related factors, for regional specialization patterns, for key shifts in global trade rules, and for each economy's degree of industrial dynamism prior to 1995. These findings suggest that the key role of education when seeking to develop new industries is to help a country learn to manage unfamiliar challenges, and so overcome path dependence.

This paper aims to construct a synthetic index of workers’ bargaining power and investigate the relationship between it and inflation in the U.S. economy. As a first step, we identify the factors affecting the bargaining power of workers, referring to different groups of variables: labour market indicators; institutional indicators (e.g., collective bargaining coverage, union density); characteristics of the economy (e.g., degree of freedom for capital mobility, share of employment by sector). We then implement Principal Component Analysis (PCA) to assess the adequacy of the indicators and calculate the weights to aggregate the single indicators into a composite index. As a second step, we estimate the impact of our Bargaining Index on inflation by estimating an equation of the determinants of inflation. The composite index thus has a twofold use: it sheds light on the extent to which changes in the labour market in recent decades have weakened workers’ bargaining power, and it can be used to test how the evolution of the wage bargaining system affects inflation.

Growing concerns surrounding urban investment bond (UIB) defaults in China necessitate a thorough examination of risk mitigation strategies. This study investigates the impact of local government implicit debt governance on UIB credit risk using a propensity score matching difference-in-differences (PSM-DID) approach. Our findings reveal that effective debt governance significantly reduces UIB credit risk, particularly for bonds issued by local government financing vehicles (LGFVs) with “high bargaining power”. However, financial technology development may obscure credit risks and dampen the positive effects of governance. Further analysis, grounded in the “financial potential energy” framework, highlights the mediating roles of asset extension and risk warranty. Comparing the 2014 "Document No. 43" with the 2018 debt governance policy, we find the latter to be more effective in mitigating UIB credit risk. This study offers valuable insights into the micro-level effects of local government debt governance and provides guidance for policymakers in managing credit risks.

Liabilities denominated in foreign currency have established a permanent role in emerging market firms’ balance sheet, which implies that changes in both global liquidity conditions and in the value of the currency may have a long- lasting effect for them. In order to consider the financial conditions that may encourage (discourage) structural change in a small, open economy, we adopt the framework put forward by the “Monetary theory of distribution” (MTD). More specifically, we follow the formulation adopted by Dvoskin and Feldman (2019), whereby the financial system is intended as a basic sector that promotes innovation (Schumpeter, 1911). In accordance to this, financial conditions are binding only for the innovative entrepreneurs, whose methods of production are not dominant and hence they need to borrow from banks to kick- start their production. Through this device, our model offers an explanation to the technological lock-in experienced by a small, open economy which takes international prices as given.

This study investigates the macroeconomic interactions between productive structure and income distribution in the context of Global Value Chains (GVC). Our research goes beyond the traditional scope by presenting a detailed typology that examines how globalisation has amplified uneven development processes on a global scale. This in-depth exploration provides valuable insights into the intricate interplay between global economic integration and regional development disparities, thereby contributing to the broader understanding of growth and distribution in the context of globalisation. We propose a theoretical framework inspired by the Structuralist literature to identify three distinct regimes in globalised production chains: (1) a structure/diversification regime based on the Balance of Payments Constrained Model (BPCM) literature, (2) an integration regime derived from the GVC literature, and (3) a functional income distribution regime in open economies. We use Principal Component Analysis (PCA) to create indices (proxies) for each regime to then identify country patterns within a structured typology. We then analyse growth trajectories and estimate the causal relationship between these regimes and per-capita growth using multiple regression models – fixed effects (FE), instrumental variables (IV), and generalised method of moments (GMM). Our dataset includes data from 60 countries between 1995 and 2018, sourced from the World Development Indicators (WDI), World Input-Output Database (WIOD), Trade in Value Added (TiVA), and the Penn World Tables (PWT). This study significantly enhances the understanding of structuralist growth models by offering a comprehensive and unified narrative on the variety of economic growth regimes within Global Value Chains (GVCs).

Despite the fact that artificial intelligence (AI) techniques are increasingly influencing economic and societal dynamics, certain aspects of AI's role in structural change, such as enhancing the resilience of supply chains, have not been thoroughly explored in academic research. Thus, we first identify thirteen variables that are influential in shaping the structural transformation of food supply chains. These variables are chosen based on the financial, technological, social, and organizational challenges to building resilience within the food supply chain. Then, we generate an interval-valued T-spherical fuzzy Combined Compromise Solution (CoCoSo)'B model to uncover the role of AI in the supply chain dynamics and structural change of the food sector. After that, we explore the role of AI in alleviating the factors in building resilience within the food supply chain through the generated decision model and expert interview. The result also reveals that the AI advantage$A{d}_2$“Enhancing information sharing among nodes” (1.983) has the most potential to influence organizational adoption behavior. The outcomes of the present study can provide a new decision-support technique for uncovering the effects of AI on supply chain dynamics and structural change in the food sector.

This paper explores whether failures in innovation projects at the firm level contribute to strengthening firms’ innovative activities and the odds of future innovation. Building on the literature on NK fitness landscapes, our paper focuses on technological complexity as a key factor that influences the response to innovation failures, guiding whether to discontinue the current path or, alternatively, to leverage the failure as a foundation for future innovation success. We then test the hypothesis derived from our model using a panel data set constituted by ten waves of the Community Innovation Survey held in the Netherlands from 1996 to 2014. Our findings show the fundamental relevance of the different forms of learning that are available to the firm. In particular, we highlight the positive role of learning after a previous innovation project has been abandoned. Previous failure in innovating produces some forms of learning inside the firms, and this learning positively contributes to subsequent successful innovation. Moreover, by differentiating between radical and incremental innovation, and between complex and less-complex innovation landscapes, we highlight the role of incremental innovation over the whole spectrum of landscape complexity, while the role of radical innovation appears to be more limited to less turbulent technological landscapes.

Growth is liked, but volatility is not. Volatility implies uncertainty, as up and down are often large unpredictable fluctuations. In fact, according to the financial literature, people pay a price to reduce it. Some studies found a trend that macroeconomic volatility has been changing and proposed some structural changes that are responsible for its decline. Many studies have found that financial development helps growth. And relatively few studies have shown that financial development also explains structurally varying macroeconomic volatility. In this panel study, we investigated the relationship between financial development and growth volatility using the Financial Development Index with recent data from eighty-six countries. We also looked at its relationship with consumption and investment fluctuations using its sub-indices. The index is the result of many dimensions of financial development such as access and efficiency, not just the size of credit. We find that financial development reduces growth variability across various horizons, with some signs of heterogeneity and nonlinearity. Institutional development (polity index) and volatility in global economic growth are other important consistent variables.

What role has labor-saving technological change played in the recent past in charting out the trajectory of employment? Have we already transitioned into a new technological regime where production technologies are more invasive upon labor’s terrain? In this study, I provide empirical evidence to answer these questions. Using industry-level data from 12 advanced economies for 1970–2007, I show that capital goods did not become more effective in labor-saving after 1980 or 1990. Similarly, the strength of the relationship between employment and output did not decline after 1980 or 1990. While many recent econometric studies have estimated the number of workers displaced due to industrial robots with which the media and public are highly preoccupied, there is nothing new in the fact that production technologies are labor-saving and displace workers. The importance of demand side factors and structural change (mainly deindustrialization) in determining employment patterns is often neglected, leading to a misleading assessment of the impact of labor-saving technologies on employment.

We study the effects of robot exposure on worker flows in 16 European countries between 2000 and 2017. Overall, we find small negative effects on job separations and no effects on job findings. We detect significant cross-country differences and find that labour costs are a major driver: the effects of robot exposure are generally larger in absolute terms in countries with relatively low or average levels of labour costs than in countries with high levels of labour costs. These effects are particularly pronounced for workers in occupations intensive in routine manual or routine cognitive tasks but are insignificant in occupations intensive in non-routine cognitive tasks. A counterfactual analysis suggests that robot adoption increased employment and reduced unemployment, especially in European countries with relatively low or average levels of labour costs, and that these effects were driven mainly by lower job separations.