Revisiting the trade effects of the euro: data sources and various samples

Hou, Jia

doi:10.1007/s00181-019-01742-0

Cited by 4 publications

(6 citation statements)

References 42 publications

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“…Inconsistencies in trade statistics may trigger bias in the results of related studies, and very often such errors are attributed by researchers as being a result of the sample or the methodology, but the data were not given sufficient attention. Typically, the imperfection of trade statistics is widely recognized [38], but the impact of this imperfection on empirical results has not received much attention [39]. Furthermore, although some researchers and organizations have discussed the "bilateral asymmetries" of UN Comtrade data, analyzing the causes and giving some suggestions to reduce their impact [26][27][28][29], few studies have paid attention to the phenomenon of "statistical imbalance" proposed in this study.…”

Section: Discussionmentioning

confidence: 99%

“…Then, the Bregman block average co-clustering algorithm with I-divergence (BBAC_I) was applied to E-STJM-HS2C and I-STJM-HS2C to express the congregation characteristics of the statistical imbalance generated by the interaction of reporter-year-partner groups and commodity categories, as Figures 7 and 8 show. (28)(29), pharmaceutical products (30), chemical products (38), plastics and articles (39), iron or steel articles (72-73), machinery and mechanical appliances (84), and commodities not specified according to kind (99).…”

Section: Spatio-temporal "Statistical Imbalance" Divided By Hs 2-codesmentioning

confidence: 99%

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Spatiotemporal Statistical Imbalance: A Long-Term Neglected Defect in UN Comtrade Dataset

Song

et al. 2022

Sustainability

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The bilateral trade data provided by the United Nations International Trade Statistics Database are some of the most authoritative trade statistics and have been widely used in many research fields. Here, we propose a new form of inconsistency in its records, namely statistical imbalance, which refers to the phenomenon of inequality between the import or export trade value of a commodity category and the total value of all its subcategories. We investigated the frequency and spatial-temporal patterns of the statistical imbalances of 15 reporters (i.e., Australia, Brazil, Canada, China, France, Germany, India, the Netherlands, the Rep. of Korea, the Russian Federation, Switzerland, the United Arab Emirates, the United States of America, and Vietnam) from 1996–2016 and explored their distributional differences in commodity categories with a co-clustering algorithm. The results show that statistical imbalance is widespread with obvious clustering patterns. Trade records related to specific categories such as fossil fuels, pharmaceuticals, machinery, and unspecified commodity categories presented severe statistical imbalances, which may lead to erroneous trade research results. Since statistical imbalance is difficult to detect in studies focusing only on specific commodity categories, we suggested that researchers should prescreen the data for statistical imbalance to ensure the validity of their results.

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Section: Discussionmentioning

confidence: 99%

Section: Spatio-temporal "Statistical Imbalance" Divided By Hs 2-codesmentioning

confidence: 99%

Spatiotemporal Statistical Imbalance: A Long-Term Neglected Defect in UN Comtrade Dataset

Song

et al. 2022

Sustainability

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“…3.2), it is useful to briefly comment on our choice of estimator also in this context. In brief, the PPML estimator should not raise concerns about bias resulting from the presence of neither heteroscedasticity (Martin and Phamb 2020) nor measurement errors (Hou 2020). There are potentially more efficient estimators but these are either developed in the context of bilateral trade analyses (Egger and Pruša 2016), or they require the adoption of uncertain assumptions concerning the specific structure of the possible measurement errors (Manning and Mullahy 2001).…”

Section: Functional Form Reverse Causality and Measurement Errormentioning

confidence: 99%

Green industrial policies and domestic production of biofuels: an econometric analysis of OECD countries

Bryngemark

Söderholm

2021

Environ Econ Policy Stud

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The purpose of this paper is to investigate the relationship between green industrial policies and domestic biofuel production among OECD countries. The analysis builds on a data set including 24 OECD countries over the time period 2000–2016. This panel is estimated using a variant of the so-called Poisson pseudo-maximum-likelihood model and includes the mix of demand-pull (biofuel blending mandates) and technology-push policies (government R&D), as well as the interaction between these two types of instruments. The results suggest a positive relationship between blending mandates and domestic biofuel production. Thus, a more stringent blending mandate does not only increase the use of biofuels, but also domestic production (as a share of total fuel use). Government R&D has not, however, induced domestic biofuel industrialization processes. The results even suggest a negative interaction effect between government R&D and blending mandates, in turn implying that these two polices target different technological fields. The blending mandates tend to primarily favor commercialized first-generation biofuels, while government support to biofuel R&D has instead been focused on advanced biofuel technology.

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“…There appears to have been a lot of research into how the adoption of the euro has affected trade, especially at the country level and the aggregate product group level-starting from the work of Rose (2000). Estimates of the EMU trade effect vary in magnitude going from + 50% (Glick and Rose 2016;Glick and Rose 2016;Larch et al 2017) to a null or even negative effect (Santos Silva and Tenreyro 2010;Baldwin and Taglioni 2007;Berger and Nitsch 2008;Figueiredo et al 2016;Mika and Zymek 2018;Hou 2020). Recent surveys provided in Rose (2016) and Polák (2018) highlight different data sources and different techniques.…”

Section: The Debate About the Trade Effects Of The Euromentioning

confidence: 99%

“…Recent surveys provided in Rose (2016) and Polák (2018) highlight different data sources and different techniques. As far as the data are concerned, Hou (2020) estimates the trade effects of the EMU using trade data retrieved from IMF DOTS and UN Comtrade databases and finds that different data sources can even cause opposite signs for the coefficient. To evaluate the trade effect of currency unions, researchers typically rely on a standard gravity equation framework and insert a simple currency union dummy variable as a right hand side regressor (e.g., Rose 2000).…”

Section: The Debate About the Trade Effects Of The Euromentioning

confidence: 99%

Currency Unions and Global Value Chains: The Impact of the Euro on the Italian Value Added Exports

et al. 2021

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Many estimates of the effect of the common currency on trade have been made, although a clear answer has yet to be given. This work analyses the trade effect of the euro by providing a twofold contribution. First, one of the main stylised facts that has emerged from the recent literature is that trade flows in gross terms can differ substantially from those measured in value added terms. Accordingly, we focus on the structure of global value chains rather than conventional gross trade. To this aim, we provide an estimate of the value added trade flows that would have existed between Italy and its main trading partners if Italy had not joined the monetary union and show how, and to what extent, international production sharing has been affected. Second, we use a methodology that is different from traditional, parametric ones. Specifically, we apply the synthetic control method to construct appropriate counterfactuals and estimate the causal impact of the euro. Our empirical analysis provides a relevant case for considering value added in addition to gross trade since it shows that the euro facilitated the forward integration of Italian exports, whereas it slowed down backward integration. Overall, these results suggest that the euro had an impact on Italian global value chain participation by altering value added flows across member as well as non-member states, with great heterogeneity in the results across value added trade components and sectors.

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Revisiting the trade effects of the euro: data sources and various samples

Cited by 4 publications

References 42 publications

Spatiotemporal Statistical Imbalance: A Long-Term Neglected Defect in UN Comtrade Dataset

Spatiotemporal Statistical Imbalance: A Long-Term Neglected Defect in UN Comtrade Dataset

Green industrial policies and domestic production of biofuels: an econometric analysis of OECD countries

Currency Unions and Global Value Chains: The Impact of the Euro on the Italian Value Added Exports

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