{"title":"Export Market Size Matters: The Effect of the Market Size of Export Destinations on Manufacturing Growth","authors":"Thomas Goda, Santiago Sánchez González","doi":"10.1080/10168737.2023.2300301","DOIUrl":"https://doi.org/10.1080/10168737.2023.2300301","url":null,"abstract":"","PeriodicalId":35933,"journal":{"name":"INTERNATIONAL ECONOMIC JOURNAL","volume":"16 26","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139445442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dinh Trung Nguyen, Kim Thanh Duong, H. Phung, Mai Quynh Ha
{"title":"Pandemics and Economic Complexity: A Cross-Country Analysis","authors":"Dinh Trung Nguyen, Kim Thanh Duong, H. Phung, Mai Quynh Ha","doi":"10.1080/10168737.2023.2300309","DOIUrl":"https://doi.org/10.1080/10168737.2023.2300309","url":null,"abstract":"","PeriodicalId":35933,"journal":{"name":"INTERNATIONAL ECONOMIC JOURNAL","volume":"44 12","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Non-tariff Measures on the Production Network: Analysis on the Forward and Backward Participation in Global Value Chains","authors":"Kunhyui Kim","doi":"10.1080/10168737.2023.2298952","DOIUrl":"https://doi.org/10.1080/10168737.2023.2298952","url":null,"abstract":"","PeriodicalId":35933,"journal":{"name":"INTERNATIONAL ECONOMIC JOURNAL","volume":"133 24","pages":""},"PeriodicalIF":1.1,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139453528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Macroeconomic Fundamentals and the Volatility of Foreign Investors’ Net Purchase in Korean Stock Market","authors":"Jin Lee, Hangyong Lee","doi":"10.1080/10168737.2023.2286976","DOIUrl":"https://doi.org/10.1080/10168737.2023.2286976","url":null,"abstract":"","PeriodicalId":35933,"journal":{"name":"INTERNATIONAL ECONOMIC JOURNAL","volume":"94 6","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138596021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Tax Cost: Does It Deter Foreign Direct Investment (FDI)?","authors":"Munmi Saikia","doi":"10.1080/10168737.2023.2286946","DOIUrl":"https://doi.org/10.1080/10168737.2023.2286946","url":null,"abstract":"","PeriodicalId":35933,"journal":{"name":"INTERNATIONAL ECONOMIC JOURNAL","volume":"45 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139202561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"How Wide is the Euro?","authors":"Luigi Ventura, Mark David Witte","doi":"10.1080/10168737.2023.2275306","DOIUrl":"https://doi.org/10.1080/10168737.2023.2275306","url":null,"abstract":"AbstractThis paper examines the impact of Euro invoicing on Italian exports to non-EU countries. In addition to examining the role of currency invoicing on the intensive and extensive margin of trade, we introduce the ‘entrenched’ margin of trade. We define the entrenched margin of trade as the number of transactions between two countries of a particular good. With highly disaggregated data, we use a two-stage methodology to predict the probability of Euro dominated Italian exports and then use that predicted probability on the intensive, extensive and entrenched margin of trade. Results show that the probability of Euro dominated trade invoicing reduces all three margins of trade. Specifically, a 10% increase in probability of Euro dominated Italian exports has roughly the same impact as additional 1532 km on the intensive margin of trade, 1096 km on the extensive margin of trade and 1314 km on the entrenched margin of trade. The negative effect of Euro invoicing is most consistent with lower-middle income trading partners and more thinly traded goods. We surmise that these results are due to varying access to financial instrumentation among Italian trade partners and a trade diversion effect of Italian exports to EU countries versus non-EU markets.KEYWORDS: Trade marginscurrency invoicingEuroexportsdistanceJEL Classifications: F10F14 Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 See Ventura and Witte (Citation2016) and Arioldi et al. (Citation2022).2 It would not be impossible for a good forensic analysis to identify major Italian firms and gather a variety of sensitive trade information with this data.3 Region dummy variables are used in place of country dummies for the same reasons that 2-digit good dummies are used instead of 4-digit dummies: overidentification and multicollinearity in predicting a variable that is often zero or one. Overidentification and multicollinearity are major problems with predicting the currency denomination of trade because, as far as the invoicing currency is concerned, there isn’t much statistical difference between many countries (e.g. the Gulf Cooperation Council countries, the Central African Franc countries) or between 4-digit good designations (e.g. 3901-3909 are likely produced by the same two companies: Vinavil and Versalis). This simplification is used in other studies of the same Italian trade data.4 Notable exceptions are the low-middle income countries with either the East African franc or West African franc. These include Senegal, Cote d’Ivoire, Benin, Cameroon and Congo which are not major importers of Italian goods.","PeriodicalId":35933,"journal":{"name":"INTERNATIONAL ECONOMIC JOURNAL","volume":" 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135291308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Institutional Quality and Economic Growth: A Dynamic Panel Data Analysis of MICs and HICs for 2000–2020","authors":"Mirwais Parsa, Soumya Datta","doi":"10.1080/10168737.2023.2261012","DOIUrl":"https://doi.org/10.1080/10168737.2023.2261012","url":null,"abstract":"ABSTRACTWe investigate the dynamic impact of institutions on economic growth using a panel dataset of 77 countries, divided into MICs and HICs for the period 2000-2020. We critically examine the available institutional indices and construct three weighted indices from 20 indicators closely related to the meaning of the term ‘institutions’ as the ‘rules of the game’ defined by Douglas North. Next, we use the Generalized Method of Moments (GMM) to show that institutions significantly influence economic growth through investment and trade more than the total factor productivity channel. While the quality of the legal system and property rights and regulatory quality all positively and significantly influence output per capita, output gains from each unit of improvement in the quality of legal systems and protection of private property rights are comparatively higher than gains from a unit of improvement in the regulatory environment. An average MIC gains relatively more from improving its quality of legal system and property rights, whereas an average HIC benefits relatively more from each unit of improvement in its regulatory environment. The results from the Granger non-causality test demonstrate and unidirectional causality from institutions to economic growth in MICs but no significant causal relationship between institutions and economic growth in HICsKEYWORDS: Institutionsinstitutional qualityproperty rightsregulationseconomic growthtransmission mechanismGMMJEL CLASSIFICATIONS: O43O47 AcknowledgementThis article is drawn from the first author's Ph.D. thesis, titled ‘Essays in Institutions and Economic Development’, completed at South Asian University, New Delhi, India. The authors gratefully acknowledge the useful comments of the referees on earlier versions of the article. The authors are also grateful to Sunil Kumar and Binoy Goswami for their comments and suggestions. The usual disclaimer applies.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 The extent to which regulations and bureaucratic procedures restrain entry and reduce competition.2 We dropped a couple of indicators from Area 4 of EFW database that were relevant to our perception of institutions but had extensive missing values. We excluded Area 1 because it is all about the size of the government. We have included a direct independent variable in the model that captures the size of the government. Adding this area to the index would have led to identification issues. Similarly, we left out some variables from Area Three and Area Four, like ‘control of the movement of capital and people,’ ‘Freedom of foreigners to visit,’ ‘capital controls,’ etc., as we believe these indicators do not relate closely to our perception of institutions.3 The index is based on years of schooling and returns to education.4 One-unit increase in institutional quality in the sample of high-income countries is a difference between the rating of a country like the U","PeriodicalId":35933,"journal":{"name":"INTERNATIONAL ECONOMIC JOURNAL","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135351373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"World Output and Commodity Price Cycles*","authors":"William Ginn","doi":"10.1080/10168737.2023.2263844","DOIUrl":"https://doi.org/10.1080/10168737.2023.2263844","url":null,"abstract":"AbstractThis study investigates the cyclical patterns of energy, agriculture, and metals and minerals (MetMin) commodity prices. We identify three super cycles since 1960, and a potential fourth arising from the Ukraine crisis and global COVID-19 pandemic. Employing a Structural Vector Autoregression (SVAR) approach, we establish an empirical relationship between output, CPI, and commodity prices. Our analysis reveals that an output shock leads to a general increase in all commodity prices, where the highest impact is on energy inflation. Moreover, we examine the heterogeneous effects of commodity inflation on overall inflation, uncovering ‘second round' effects across all commodities. Notably, agriculture inflation has the most significant impact on aggregate inflation, potentially explaining the destabilizing nature of food inflation in many countries. Our findings enhance understanding of these dynamics, offering important insights for policymakers and informing the public.Highlights We analyze the cyclical patterns of energy, agriculture and MetMin commodity prices.Real output, CPI and commodities exhibit the same cyclical patterns.A shock to output increases all commodities, where the highest response is energy inflation.We find ‘second-round' effects, where agriculture prices have the highest impact on inflation.KEYWORDS: Super cyclesGlobal Commodity PricesGlobal Macroeconometric ModelingJEL Classifications: Q43O13L61E23 Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 We convert nominal global GDP (FRED mnemonic NYGDPMKTPCDWLD), which is denominated in U.S. Dollars (FRED mnemonic CPALTT01USA661S), to real GDP by dividing the former with the U.S. CPI.2 The data is publicly available https://www.worldbank.org/en/research/commodity-markets. The agricultural index is a weighted average of prices of food (e.g., cereals, oils), beverages (e.g., coffee, cocoa and tea), agricultural raw materials (e.g., timber, cotton), and metals and minerals (e.g., aluminum, copper, iron ore, lead, nickel, steel, tin, zinc). The oil price is based on the average of the Brent, Dubai and WTI crude oil price.3 The cycle trend is consistent with Christiano and Fitzgerald (Citation2003) who use the asymmetric CF band pass filter of up to 8 years for output.4 The 25 economies include: Australia (‘AUS’), Austria (‘AUT’), Belgium (‘Belgium’), Canada (‘CAN’), Switzerland (‘CHE’), Germany (‘DEU’), Spain (‘ESP’), Finland (‘FIN’), France (‘FRA’), United Kingdom (‘GBR’), Greece (‘GRC’), India (‘IND’), Iceland (‘ISL’), Italy (‘ITA’), Japan (‘JPN’), South Korea (‘KOR’), Luxembourg (‘LUX’), Netherlands (‘NLD’), Norway (‘NOR’), New Zealand (‘NZL’), Portugal (‘PRT’), Sweden (‘SWE’), Turkey (‘TUR’), United States (‘USA’) and South Africa (‘ZAF’).5 Our results are similar to Ratti and Vespignani (Citation2016), who show that the first principal component captures 89.6% of the variation for prices relating to the G5 countries.6 While the foc","PeriodicalId":35933,"journal":{"name":"INTERNATIONAL ECONOMIC JOURNAL","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134947338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}