Xian-nan Cheng, Shuhui Wen, Nuobing Fan, Yuwei Liu, Yujing Wang
This article presents a comprehensive framework for assessing the influence of input variation during the period of stagnant expansion of the global value chain (GVC) by incorporating input elasticity. Each import sector is treated as a complete input demand system with a stochastic preference for input in the framework of Ghosh Inverse, and the input elasticity is employed to quantify the input variation based on the Quadratic Almost Ideal Demand System (QUAIDS) model. New empirical evidence covering three major regional value chains (RVCs) and the time span from 2010 to 2020 reveals that increasing input elasticity leads to additional output for most countries, except those within the North American RVC. Additionally, this article also discusses how external determinants have diverse extents influence to input elasticity, while the substitution effect among homogenous input in the specific input demand system may decide their input's competitiveness.
{"title":"What's the influence of input's variation? Estimating the input elasticity in the global value chain","authors":"Xian-nan Cheng, Shuhui Wen, Nuobing Fan, Yuwei Liu, Yujing Wang","doi":"10.1111/twec.13530","DOIUrl":"https://doi.org/10.1111/twec.13530","url":null,"abstract":"This article presents a comprehensive framework for assessing the influence of input variation during the period of stagnant expansion of the global value chain (GVC) by incorporating input elasticity. Each import sector is treated as a complete input demand system with a stochastic preference for input in the framework of Ghosh Inverse, and the input elasticity is employed to quantify the input variation based on the Quadratic Almost Ideal Demand System (QUAIDS) model. New empirical evidence covering three major regional value chains (RVCs) and the time span from 2010 to 2020 reveals that increasing input elasticity leads to additional output for most countries, except those within the North American RVC. Additionally, this article also discusses how external determinants have diverse extents influence to input elasticity, while the substitution effect among homogenous input in the specific input demand system may decide their input's competitiveness.","PeriodicalId":48147,"journal":{"name":"World Economy","volume":"64 10","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138503585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fabio Montobbio, Jacopo Staccioli, Maria Enrica Virgillito, Marco Vivarelli
This article represents one of the first attempts at building a direct measure of occupational exposure to robotic labour-saving technologies. After identifying robotic and labour-saving robotic patents, the underlying 4-digit CPC (Cooperative Patent Classification) code definitions, together with O*NET (Occupational Information Network) task descriptions, are employed to detect functions and operations which are more directed to substituting the labour input and their exposure to labour-saving automation. This measure allows us to obtain fine-grained information on tasks and occupations according to their text similarity ranking. Occupational exposure by wage and employment dynamics in the United States is then studied, and complemented by investigating industry and geographical penetration rates.
{"title":"Labour-saving automation: A direct measure of occupational exposure","authors":"Fabio Montobbio, Jacopo Staccioli, Maria Enrica Virgillito, Marco Vivarelli","doi":"10.1111/twec.13522","DOIUrl":"https://doi.org/10.1111/twec.13522","url":null,"abstract":"This article represents one of the first attempts at building a <i>direct</i> measure of occupational exposure to robotic labour-saving technologies. After identifying robotic and labour-saving robotic patents, the underlying 4-digit CPC (Cooperative Patent Classification) code definitions, together with O*NET (Occupational Information Network) task descriptions, are employed to detect functions and operations which are more directed to substituting the labour input and their exposure to labour-saving automation. This measure allows us to obtain fine-grained information on tasks and occupations according to their text similarity ranking. Occupational exposure by wage and employment dynamics in the United States is then studied, and complemented by investigating industry and geographical penetration rates.","PeriodicalId":48147,"journal":{"name":"World Economy","volume":"64 11","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138503576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The labour market effects of automation have gained significant attention from scholars and policymakers. Concerns about negative effects are important in emerging countries, where a rapid acceleration of robot adoption and an increasing involvement in global value chains have been observed in recent years, with the subsequent increase in exposure to foreign competition. This paper estimates the effect of local and foreign robots on labour market outcomes and labour shares using a panel dataset composed of 16 sectors and ten emerging countries from 2008 to 2014. The endogeneity of robots' adoption is addressed with an instrumental variables approach and using a shift-share index of exposure to foreign robots. The main results for all sectors show that foreign robot adoption has negatively affected employment in emerging countries. When exploring sectoral heterogeneity, we find that the foreign robots' negative effect on employment has occurred in many sectors, being more prominent in those with higher exposure to foreign robots. Moreover, we found small and negative inter-sectoral spillover effects of local robots on employment and wages. Finally, the results also show that the labour share is affected in some sectors of emerging countries by both the use of local and foreign robots.
{"title":"The impact of automation on labour market outcomes in emerging countries","authors":"Luis R. Díaz Pavez, Inmaculada Martínez-Zarzoso","doi":"10.1111/twec.13523","DOIUrl":"https://doi.org/10.1111/twec.13523","url":null,"abstract":"The labour market effects of automation have gained significant attention from scholars and policymakers. Concerns about negative effects are important in emerging countries, where a rapid acceleration of robot adoption and an increasing involvement in global value chains have been observed in recent years, with the subsequent increase in exposure to foreign competition. This paper estimates the effect of local and foreign robots on labour market outcomes and labour shares using a panel dataset composed of 16 sectors and ten emerging countries from 2008 to 2014. The endogeneity of robots' adoption is addressed with an instrumental variables approach and using a shift-share index of exposure to foreign robots. The main results for all sectors show that foreign robot adoption has negatively affected employment in emerging countries. When exploring sectoral heterogeneity, we find that the foreign robots' negative effect on employment has occurred in many sectors, being more prominent in those with higher exposure to foreign robots. Moreover, we found small and negative inter-sectoral spillover effects of local robots on employment and wages. Finally, the results also show that the labour share is affected in some sectors of emerging countries by both the use of local and foreign robots.","PeriodicalId":48147,"journal":{"name":"World Economy","volume":"64 12","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138503575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Motivated by the recently experienced systemic shocks (the COVID-19 pandemic and the full-fledged Russia's war of aggression against Ukraine)—that have created new forms of uncertainties to our supplies—this paper explores the supply chain robustness under risk aversion and ambiguity aversion. We aim to understand the potential consequences of deeply uncertain systemic events on the supply chain resilience and how does the information precision affect individual agents' choices and the chain-level preparedness to aggregate shocks. Augmenting a parsimonious supply chain model with uncertainty, we analyse the relationship between the upstream sourcing decisions and the supply chain survival probability. Both risk-averse and ambiguity-averse individually-optimising agents' upstream sourcing paths are efficient but can become vulnerable to aggregate shocks. In contrast, a chain-level coordination of downstream firm sourcing decisions can qualitatively improve the robustness of the entire supply chain compared to the individual decision-making baseline. Such a robust decision making ensures that in the presence of an aggregate shock—independently of its realisation—part of upstream suppliers will survive and the final goods' supply will be ensured even under the most demanding circumstances. Our results also indicate that an input source diversification extracts a cost in foregone efficiency.
{"title":"Uncertainty of supply chains: Risk and ambiguity","authors":"d'Artis Kancs","doi":"10.1111/twec.13534","DOIUrl":"https://doi.org/10.1111/twec.13534","url":null,"abstract":"Motivated by the recently experienced systemic shocks (the COVID-19 pandemic and the full-fledged Russia's war of aggression against Ukraine)—that have created new forms of uncertainties to our supplies—this paper explores the supply chain robustness under risk aversion and ambiguity aversion. We aim to understand the potential consequences of deeply uncertain systemic events on the supply chain resilience and how does the information precision affect individual agents' choices and the chain-level preparedness to aggregate shocks. Augmenting a parsimonious supply chain model with uncertainty, we analyse the relationship between the upstream sourcing decisions and the supply chain survival probability. Both risk-averse and ambiguity-averse individually-optimising agents' upstream sourcing paths are efficient but can become vulnerable to aggregate shocks. In contrast, a chain-level coordination of downstream firm sourcing decisions can qualitatively improve the robustness of the entire supply chain compared to the individual decision-making baseline. Such a robust decision making ensures that in the presence of an aggregate shock—independently of its realisation—part of upstream suppliers will survive and the final goods' supply will be ensured even under the most demanding circumstances. Our results also indicate that an input source diversification extracts a cost in foregone efficiency.","PeriodicalId":48147,"journal":{"name":"World Economy","volume":"24 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138517366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Giorgio Gnecco, Federico Nutarelli, Massimo Riccaboni
Economic complexity and machine learning have recently become popular approaches for analysing international trade. However, for effective use of machine learning in relation to economic complexity and policymaking, it is important to understand what are the key features for predictions. In this framework, this article addresses the issue of the interpretability of results obtained with a machine learning technique—namely, matrix completion—when applied to economic complexity, specifically in predicting revealed comparative advantages (RCAs) of countries in different product categories. Shapley values are used to measure the role each country plays in predicting the RCAs of other countries. Countries relevant for prediction may differ from countries whose RCA values are similar to those of the country of interest when a standard similarity measure such as cosine similarity is used. We demonstrate the usefulness of our approach to identifying comparable countries by focussing our analysis on export diversification into complex goods of selected European countries.
{"title":"Matrix completion of world trade: An analysis of interpretability through Shapley values","authors":"Giorgio Gnecco, Federico Nutarelli, Massimo Riccaboni","doi":"10.1111/twec.13457","DOIUrl":"https://doi.org/10.1111/twec.13457","url":null,"abstract":"Economic complexity and machine learning have recently become popular approaches for analysing international trade. However, for effective use of machine learning in relation to economic complexity and policymaking, it is important to understand what are the key features for predictions. In this framework, this article addresses the issue of the interpretability of results obtained with a machine learning technique—namely, matrix completion—when applied to economic complexity, specifically in predicting revealed comparative advantages (RCAs) of countries in different product categories. Shapley values are used to measure the role each country plays in predicting the RCAs of other countries. Countries relevant for prediction may differ from countries whose RCA values are similar to those of the country of interest when a standard similarity measure such as cosine similarity is used. We demonstrate the usefulness of our approach to identifying comparable countries by focussing our analysis on export diversification into complex goods of selected European countries.","PeriodicalId":48147,"journal":{"name":"World Economy","volume":"2 4","pages":""},"PeriodicalIF":2.5,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138503603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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