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Empirical Economics

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26.07.2019

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

verfasst von: Jia Hou

Erschienen in: Empirical Economics | Ausgabe 6/2020

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Abstract

New evidence in the literature on trade effects of the euro often reports different estimates. In this paper, I investigate the impact of trade data, instead of methodology, on the estimation of the key coefficient. In particular, I apply both the log-linearized least squares (OLS) estimator and the Poisson pseudo-maximum likelihood (PPML) estimator to the structural gravity model and compare these estimates by using trade data from two of the most widely used sources (IMF DOTS and UN Comtrade) and by varying samples. One surprising result is that the OLS estimator yields coefficients of the euro with opposite signs for the two data sources, when a sample covering all countries is applied. It is as expected that the PPML estimator is much less sensitive to sample size than the OLS estimator, taking a data source as given. However, the variation in estimates caused by data sources and sampling is consistent for both estimators. It indicates that both estimators are not free from the measurement error issue. More findings include: (1) the discrepancy in OLS estimates derived for the two datasets persists across samples, but the magnitude varies; (2) the magnitude of the discrepancy in PPML estimates from the two datasets is more stable to sampling; (3) both OLS and PPML estimators are sensitive to sample compositions for a given sample size.

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To be more specific, I investigate the trade effects of the adoption of the euro by European union countries. For convenience, I still use “European Monetary Union” (EMU) to describe such an adoption, even though the EMU has a broader scope beyond only adopting the common currency: https://ec.europa.eu/info/business-economy-euro/economic-and-fiscal-policy-coordination/economic-and-monetary-union/what-economic-and-monetary-union-emu_en.

Many studies (Larch et al. 2018; Mika and Zymek 2018, among others) have analyzed the robustness of coefficients across samples. However, most of their focus is to study the performance of a given estimator, taking trade data being perfect as given. Furthermore, they do not vary samples in more extended ways as in this paper.

Regarding trade data imperfection, there are more papers which investigate the trade data asymmetry issue. That is, exporters and importers report different trade data for the same trade flow. For example, Shaar (2017) constructs an index to measure the consistency of bilateral trade statistics of both trade partners using the 1962–2013 Comtrade data. She points out that trade data improve over time for most countries, and that countries are generally more aware of the origin of their imports than they are aware of the destination of their exports. Ferrantino et al. (2012) make use of the asymmetry to analyze tax evasion behavior of Chinese exporters.

There are other data sources on international trade which employ different methodologies to compile data, such as the BACI developed by the CEPII and the Atlas of Economic Complexity (AEC) maintained by the Center for International Development at Harvard University. However, those compiled datasets often use DOTS or Comtrade as their sources and vary by methodologies. If we compare discrepancies in estimates for CEPII and AEC, we might introduce additional bias caused by data compilation methodologies. More importantly, a comparison of estimates from DOTS and Comtrade is adequate for the purpose of this paper, to highlight the impact of measurement errors in trade data on empirical results.

The investigation is not exhaustive. For example, there are unexpected inconsistency in trade data between DOTS and Comtrade, which can be cross-checked with national data more easily. As expressed by Markhonko (2014), “...The blame should be not only on those users who jump to the conclusions before making sufficient effort to understand the nature of the data, but on the producers of official trade statistics as well who, apparently, do not do enough to explain the meaning of the data, their advantages as well as their limitations” (p. 5). My investigation of the two datasets is aimed to be feedback from the user’s side of those datasets.

Please see Rose and Stanley (2005) for a meta-analysis of earlier studies on the trade effects of currency unions. Havránek (2010) reviews more recent studies. Looking at the number of studies which quantify trade effects of currency unions, it might not be exaggerated to say that the trade effects of currency unions or of the European Monetary Union are an “over researched” topic.

In a recent study, Bergin and Lin (2012) employ UN Comtrade data to study the dynamic trade effects of currency unions. However, the results from Bergin and Lin (2012) are not comparable with those from other studies, since the former focuses on the two trade margins, instead of the overall trade value.

For example, Bannister et al. (2017) assess trade data quality for Lao P.D.R. The authors find that trade data between Lao P.D.R. and different trade partners for different products may be more or less misreported. They also notice that different national statistics departments in Lao P.D.R. report very different trade data.

Nevertheless, it is likely that Frankel (2010) uses the same data source, DOTS, as in Micco et al. (2003), since Frankel (2010) starts his analysis by replicating the results from the latter.

For example, Eurostat regularly publishes quality reports on European statistics on international trade in goods, e.g., https://ec.europa.eu/eurostat/web/products-statistical-working-papers/-/KS-TC-15-002. Head et al. (2010) fix some errors in DOTS database. Different international organizations also corporate with each other to improve the quality of trade statistics, e.g., https://wits.worldbank.org/wits/wits/witshelp/content/data_retrieval/T/Intro/B2.Imports_Exports_and_Mirror.htm. Studies such as Ferrantino et al. (2012) investigate the reasons, such as tax evasion, for the difference in trade statistics reported by importers or exporters. Escaith (2015) discusses the birth, past and future of trade statistics.

Please see the User guide on European statistics on international trade in goods, 2016 edition, p. 43: User guide on European statistics on international trade in goods—2016 edition.

For example, a joint note of OECD and WTO, Trade in value-added: concepts, methodologies and challenges (2012), mentions that “conventional trade statistics may give a misleading perspective of the importance of trade to economic growth and income.” (p. 1)

Please see: https://comtrade.un.org/db/help/uReadMeFirst.aspx.

As noted by Marini et al. (2018), “Annual data reported to the UN COMTRADE database are used for those countries that do not report to the IMF.” However, it is not clear how accurate the statement is in the practice of DOTS. In the cleaned dataset for this paper, there are still 42,116 observations missing in DOTS but non-missing in Comtrade.

Those countries are Austria, Belgium, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Netherlands, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden and the United Kingdom. Due to a statistic reason, Belgium and Luxembourg are treated as one unit. The second section of “Appendix B” provides a brief description of those countries’ population, GDP and trade patterns.

In this section, I do not control other gravity variables including common language, common history of colonies, free trade agreements. The main reason is to keep as many observations as possible. The change in coefficients of the euro caused by the omission is negligible. Table 16 presents results when no other gravity variable is included, as compared to benchmark results in Table 4.

Similarly, I conduct the analysis when the percentile of both gap measures are defined at country pair level. That is, the threshold is defined within each country pair. The results are available upon request.

I estimate the effects by using Comtrade data as well. The main conclusion does not change, and the result is available upon request.

It is worth noting that only over 100 out of 1000 PPML coefficients are statistically significant for each sample size. However, it does not affect the argument. Figures of the distribution of the significant coefficients are available upon request.

In addition, I also conducted analysis on the distribution of estimates of the euro across samples which are restricted by the rank of other selected indicators including the average distance from EU countries, the percentile of import share, GDP per capital, GINI index, adult literacy rate, arable land in 2010. The results are available upon request. Ideally, this line of analysis can identify how each individual country affects the estimates.

It would be better to use data from Eurostat for those observation, but Eurostat uses euro as the measurement currency, which is not consistent with dollar used for observations of other countries.

You can bulk download directly from: http://data.imf.org/?sk=9D6028D4-F14A-464C-A2F2-59B2CD424B85 and the World Integrated Trade Solution (WITS), maintained by the World Bank, but the data is compiled by UN Comtrade: https://comtrade.un.org/.

The FOB price is export value reported by exporters, while the CIF price is reported by importers.

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Titel: Revisiting the trade effects of the euro: data sources and various samples
verfasst von: Jia Hou
Publikationsdatum: 26.07.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Empirical Economics / Ausgabe 6/2020
Print ISSN: 0377-7332
Elektronische ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-019-01742-0

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