Data on other variables

In the previous chapter the normally used variables and other explanatory variables, or control variables, inherent to gravity equation were explained and this section describes the sources of the data for them.

Similarly to the trade data, the data on GDP is obtained from the UNCTAD database for the countries included in the study. The nominal and real GDP data reported in US dollars is available for the years 1970-2014. The data is derived from UNCTAD secretariat calculations, based on UN DESA Statistics Division, National Accounts Main Aggregates Database (UNCTAD 2015). The nominal GDP for each country was selected as an independent variable for the purpose of this study because the rest of the variables are also reported in nominal values. The nominal GDP for Finland as well as all the trading partners is included in the data, although the former is not required in the estimation of the traditional gravity equation of bilateral trade since the pooled OLS with year fixed effects as well as fixed effects model takes into account the trade between country pairs, which makes the Finnish GDP data redundant in the model. Thus the fixed effects regression automatically takes into account the country pair specific fixed effects and the Finnish GDP will always be the same in observations with any country. The GDP data on the aggregate entities (i.e. former SSSR and so on) is combined from the same data source.

Other than the trading partners’ GDPs that are usually included in the gravity equation, the data on distance is one of the explaining factors in describing trade. The data for distance between Finland and its trading partners is obtained from the information provided by the Ge- oDistance database of CEPII, which reports the distance between the capital cities and main cities for 225 countries and their trading partners as well as the population weighted distance between the most populous cities in each country (CEP 2015). Thus the dataset reports the above mentioned bilateral information for all the possible country pairs. In this study, the dis- tance between capitals is used. However, what comes to the distance data, for the aggregate entities, former Yugoslavia, former SSSR and China, the distance to the biggest cities is used so that the biggest city of the biggest country of the aggregate group is considered, i.e. Belgrade, Moscow and Beijing.37

37 _{This assumption might not be realistic since the economic centre of each of the aggregate regions is likely to be} different from the current capitals. In the case of Indonesia and Netherland Antilles the difference is not that large due to Indonesia presenting major part of the trade and Netherland Antilles located very close to each other.

73 The CEPII also reports information on other country specific factors between the coun- try pairs, such as contiguity, several measures for colonial ties and common official languages between the country pairs; these reported as dummies. These dummies get a value of 1 when the countries share common features and 0 otherwise. These variables are also used commonly in the empirical estimation of gravity equation, for example.

Furthermore, in a complete gravity model in which all the bilateral trade relations were considered, country-pair-specific dummy variables could be used to control for a country-pair’s a membership in multilateral organisations or participation in free trade agreements. Using the Finnish data only, data on OECD membership as well as EU membership is collected and used as explanatory variables. The EU membership variable also include the countries in EEA and Switzerland since these countries are also in free trade agreement with the EU and Finland. Dummy variable 1 indicates membership in the organisation and 0 designs non-members. In several earlier empirical studies (e.g. Santos Silva and Tenreyro (2006)), membership in free trade agreements or areas are also used as explanatory variables.

Additionally, since in this study the multilateral resistance is intended to be captured somehow, data on price indices and real exchange rates is needed. Thus the data on price indices is obtained from UNCTAD database and contains information on the changes of the consumer price indices in each country (UNCTAD 2015). Although this is not the best measure for the multilateral resistance since it contains changes in countries’ internal prices as well, the speci- fication is tested with these changes included. Additionally, Bergstrand (1995) suggests the use of real exchange rates depicting multilateral resistance and consequently the data on real ex- change rates are obtained from Bruegel Think Tank’s calculations for the real effective ex- change rate for 178 countries. The data comprises real effective exchange rate for almost all of the countries under study in this investigation and represents the real value of country’s cur- rency against the basket of its trading partners.

The appendix 1 summarizes all the variables used in the study and describes them. In total 3043 observations on Finnish imports, exports, trading partner GDPs and other variables except for the relative real exchange rates and price index changes are observed over the 17 year period and 179 trading partners. The data available forms a balanced panel, although some of the specifications used imply that all of the observations are not taken into account in the estimation.

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