Numerous studies use the gravity model to estimate the effects of a policy

Numerous studies use the gravity model to estimate

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flows between countries. Numerous studies use the gravity model to estimate the effects of a policy change or trade frictions on trade flows. Baier and Bergstrand (2007) investi- gate the effects of free trade agreements on trade. Frankel and Rose (2002) and Baldwin and Taglioni (2007) study the effects of currency unions and the euro respectively. Mc- Callum (1995) looks at the effects of national borders on trade between Canada and the US. Head et al. (2010) look at the effects of colonial links on trade and whether such effects erode overtime. Recently, Bernhofen et al. (2016) use a reduced gravity equation to study the effects of containerization on trade flows. See Bergstrand and Egger (2013) for a comprehensive literature review on the gravity equation and its applications. The paper proceeds as follows. Section 2 discusses that data and the data sources for the study and describes the instrument in detail. Section 3 proposes the main empirical specification to be estimated. Section 4 discusses the main results from bilateral trade regressions. Section 5 estimates product level regressions and presents the results thereof. Section 6 presents an alternative IV that works for European countries and presents the results from product level regressions using the alternative IV. Finally, section 7 concludes. 2 Data Data on tourist flows comes from The United Nations World Tourism Organization (henceforth UNWTO). Countries report one or more types of flows. I use ’Arrivals of non-resident tourists at national borders, by country of residence’ as the preferred reported tourist flow but where unavailable, I use other types of reported tourist flows. I list the type of flows used for each reporter country in Table A2. This data is bilateral in that countries report the countries of origin of the tourists. Most countries do not report zero flows however and only report positive tourist flows. Also, tourist flows from the larger economies are more frequently reported than those from smaller countries. In many cases, tourist origin countries are lumped up in regions or continents such as ’Middle East’ or ’Other Europe’. It is therefore not possible to identify zero flows from positive ones and the sample will therefore only include reported positive tourist flows. In Table A3, I list the frequency of reported tourist flows by origin country. The United States of America is the country with the most frequent tourist flow observations followed closely by Germany, the United Kingdom, and France. That the larger and more developed economies are over-represented in the tourist flow data reduces the incidence of zeros in the trade data after matching however. In order to remove business travelers from the tourism data, I use data on the annual percentage of arrivals for business purposes for each destination country. Because this data is not available by source country, I implicitly make the assumption that this (1998) and Eaton and Kortum (2002) 4
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percentage is the same across all source countries. Where this data is missing, I use
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