Variable description

As argued, trade and FDI are thought to complement each other and therefore driven by the same factors. Due to this the following description of the explanatory variables and their influence on the dependent variables is applicable to all regressions on the entire set of dependent variables considered.

Geographical distance: Geographical distance is used as a proxy to explain the role of transportation costs and capturing international transaction costs. Moreover, distance increases upfront information and transaction costs due to its impendence on flows of information and products (Rauch, 1999). Analysis of distance has found trade to almost be inversely proportionate to distance. A mean elasticity of 0,9 was established by Disdier and Head (2008). The distance between Denmark and the different trading partners is calculated as the distance between the biggest cities of the two countries weighted with the share of the population of city in the overall country population. In accordance with the Gravity model the factor is expected to have a negative effect. The

data is collected from Centre d’Etudes Prospectives et d’Informations Internationales - (CEPII).

GDP: Gross domestic product represents economic masses. A high economic mass implies both a greater potential export market but also an increased possibility of Danish imports from the given country. The values are expressed in constant year 2000 US dollars. In accordance with the Gravity model the factor is expected to have a positive influence – the level of trade between two nations is proportional to the product of their economic size. The data is collected from World Development Indicators database.

Population: The number of inhabitants in the trading partner country is included and collected from World Development Indicators database. The variable is expected to have to have positive influence.

Danish immigration and descendant stock: The variable immigration is the number of

immigrants residing in Denmark from country j during year t collected from Statistics

Denmark. The definition of an immigrant applied for the statistical division is as

follows from Danish Statistics and Ministry of Refugees, Immigrants and Integration:

A person is Danish if at least one the parents both have Danish citizenship and are born in Denmark. Thus, it has no significance if the person is a Danish citizens or born in Denmark. If the person is not a Dane then the person will be classified as follows:

• An immigrant if born abroad • Descendant if born in Denmark

As the definition unveils then an immigrant is a foreigner if born abroad while a descendant is a foreigner born in Denmark. Moreover, it appears that citizenship is without significance pertaining to the statistical definition of immigrants and descendants. This means that immigrants and descendants that obtain Danish citizenship still occur as an immigrants and descendants in the statistics of the immigrant stock.29

29

Immigrants and descendants from Greenland and Faroe Island have been dropped due to their membership of the Danish national community. Persons from those countries are therefore ensured the right of a Danish citizenship.

During the period investigated some of the origin countries included do not exist anymore or do not exist through the whole time span. This concerns Yugoslavia, Serbia

and Montenegro. Yugoslavia only exists in the period 1995-2002 hereafter it is renamed to Serbia & Montenegro. Because this strictly concerns renaming the country immigrants from Serbia and Montenegro are simply added to the stock of Yugoslavian immigrants. Serbia & Montenegro was a joint country up until 2006 hereafter is was further divided up into two countries namely Serbia and Montenegro. The immigrant stocks from the two countries Montenegro and Serbia are added to the stock that consists of immigrants from Yugoslavia and Serbia and Montenegro. The product is an immigrant stock that consists of immigrants from Serbia, Yugoslavia and Montenegro. The stock is named Yugoslavia through the whole period. Another example of a former economy is the Soviet Union.30 This Immigrant stock has been divided by the individual population share of each country as distributional key. The Soviet Union immigrant stock has been allocated and added to the individual countries’ immigrant stock based on their relative population size in 1995.31 The same methodology has been applied in relation to Czechoslovakia. Czechoslovakia only exists up until the year 1993 and hereafter divided into the two countries Czech Republic and Slovakia. This procedure can have caused bias in the results e.g. inaccurate division of immigration stock. This possible inexact specification may result in an imprecise estimated link between Denmark and these origin countries. In total this is not expected to be of any significance due to the number of countries included and thereby does not cause any concern of bias and non-representative results.

Exchange rate: Measures the change in Denmark and the given country’s exchange rate expressed as foreign currency unit per Danish Kroner in the given year. This factor represents the terms of trade effect (White, 2007b). The data are collected from the

UNCTAD statistical database. An increase in the exchange rate signals that Danish Kroner is increasing (decreasing) in its value and thereby making Danish goods relatively more (less) expensive. The expected sign is therefore dependent on whether import, export or FDI inflow is considered. A positive relative change between the two

30

This concerns Russia, Georgia, Ukraine, Moldova, Belarus, Armenia, Azerbaijan, Kazakhstan, Uzbekistan, Turkmenistan, Kyrgyzstan, Tajikistan, Estonia, Latvia, and Lithuania.

31

Due to the population size of Russia most of the immigrants from the Soviet Union – approx. 50% - have been added to the Russian immigration stock.

years is consequently expected to have a negative impact on export and positive on import.

Financial Integration: To take account of the level of financial integration in the partner country the net stock of foreign direct investments to GDP has been included (White, 2007b). A higher level of financial integration is expected to have a positive influence. The data is collected from the UNCTAD statistical database.

Trade openness: Trade integration for partner countries is measured by import plus export in a given year relative to its GDP. A high level of integration measures a country’s overall propensity for trade, furthermore countries tend to trade more with nations that are well global trade integrated (Head and Ries, 1998). An additional aspect is that it may reflect the presence and level of trading infrastructure. The variable is expected to have positive influence and data is collected from World Development

Indicators database.

Linguistic distance: Studies including language as an explanatory variable in bilateral

trade flows often apply the methodology whether the two countries share the same dominant common language or whether English is an official language, e.g. (Dunlevy and Hutchinson, 1999). This approach is not applicable when examining Danish trade relations because Danish is not spoken anywhere else in the world why the method linguistic distance is adopted. Previous research using linguistic distance in describing bilateral trade flows have shown that the further away a country’s language is from English the lower will the trade be. Hence, linguistic distance is thought to impede bilateral trade due to the increased communication costs (Hutchinson, 2003). Linguistic distance is a quantitative measure of the distance between English and other languages. This measurement explains the differences in the difficulty of learning another language as the “distance” between origin country language and English (Chiswick and Miller, 2004). The language scores are measured on a scale going from 1-3 with one being the most distant. In general the assigned language scores to Denmark (2,25) and the neighbor countries like Norway (3,0) and Sweden (3,0) are very high signaling that these are the least distant from English. Due to these similarities the same scores measuring this difference between English and the foreign country language are applied as a proxy for common language in the empirical estimation. For appropriateness of measuring linguistic distance the language scores have been reversed – 1/language

score. In composing the data countries have been assigned the language scores according to the official and dominant language. If a country has more than one official language and one of them is English, English has been assigned as the official language. If none of the multiple official languages is English, the country has not been assigned any linguistic score. It is anticipated that the more distant the official language is from English has a negative impact (Hutchinson, 2003).32

Corruption: The data for corruption is extracted from Transparency International database. The level of corruption is measured on an index - Corruption Perception Index (CPI) – on a scale from 1-10, with 1 being the highest level corruption and 10 the lowest level of corruption. For interpretational reasons the index been reversed resulting in a scale where 10 represents the highest level of corruption and 1 being the lowest perceived level of corruption. A high score and high level of corruption is expected to have a negative impact (Bandyopadhyay and Roy, 2007).

Business freedom: Business freedom is an index of the ability to start, operate, and close a business. It represents the overall burden of regulation as well as the efficiency of government in the regulatory process in trading partner countries. The data is sourced in the study “Doing Business” from the World Bank. The index is composed on ten different factors, which are equally weighted. E.g. how many days it takes to start a business, costs of starting a business etc. A high score is expected to be positive

correlated with the trade between Denmark and the partner country (Hatzigeorgiou,

2010b).33

Trade policy: The factor business freedom is another of the variables that are supposed to reflect institutional quality of the partner country. The data is sourced in the World

Heritage Foundation, which has constructed an index based on data from the World Bank, WTO, US Department of Commerce and Transparency International. The index is a composite measure of the absence of tariff and non-tariff barriers that affect imports

and exports of goods and services. The numbers calculated are measured on scale going

from zero to 100 with zero being the worst and 100 the best. It is anticipated that the

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See appendix C.2 for a full description.

degree of freedom of trade is positively correlated with the trade between Denmark and the partner country (Hatzigeorgiou, 2010b).34