Data Sources, Country Selection and Methodology

The primary sources of data used in this study are the United Nations Commodity Statistics Trade Database (UN Comtrade) and the World Bank’s World Development Indicators 2002.

UN Comtrade provides annual trade data on the value and quantity of imports, exports and re-exports for over 130 reporting countries representing a market share of over 90 percent of the current world trade. Commodities are classified according to the Standard International Trade Classification (SITC) and the Harmonized Systems (HS). The HS classification is based on the nature of the commodity, whereas the SITC classification identifies commodities according to their stage of production (UN 2004: 87-88). By using special correlation tables, data conversion from one classification to the other is in principle possible. I made use of SITC Revision 3 to obtain trade data on fuel imports and exports³³. SITC Revision 3 classifies commodities in a hierarchy of five levels. The first level divides commodities into 10 sections (1-digit level). These sections contain a total of 67 divisions (2-digit level), 261 groups (3-digit level) and 3,118 basic headings and subheadings (4- and 5-(3-digit level). Fuels comprise SITC Section 3 (mineral fuels, lubricants and related materials). This section is subdivided in

33 The SITC was first developed by the United Nations in 1950 and has been revised several times since then.

Currently, trade in commodities is classified according to SITC Revision 3 from 1988.

Costs of net

Chapter 3: Quantitative Analysis of Energy Import Dependency the divisions: coal, coke and briquettes (32), petroleum, petroleum products and related materials (33), gas, natural and manufactured (34) and electric current (35)³⁴. In the following analysis, data on fuel imports and exports relate to the import and export of all commodities falling under SITC Section 3. Thus, I only make use of aggregate fuel data and therefore do not specify on the exact composition of fuel imports and exports. This approach is in line with international trade reporting practices. For instance, the World Development Indicators and the International Trade Statistics of the WTO both use aggregated SITC section 3 data when reporting on fuel trade. The value of fuel imports is generally reported on the basis of “cost, insurance and freight” (c.i.f.), meaning that the cost of delivery to the importing country’s port is included in the value. In contrast, the value of fuel exports is measured on a “free on board” basis (f.o.b.) including the costs associated with loading the commodities on a ship or aircraft but excluding international transportation costs.

All data on the value of fuel imports and exports –if not otherwise stated– has been downloaded from the UN Comtrade web page (www.unstats.un.org/unsd/comtrade)³⁵. The remaining data, like country data on GDP, health spending and poverty incidence, has solely been taken from the World Development Indicators. Further information on the specific data sources and definitions will be given in the relevant sections. For the calculation and statistical analysis of the indicators, I used SPSS 11.5 and Microsoft Excel 2002. All tables, diagrams and calculations which are not included in the text, are found in the appendix. The CD-Rom enclosed in the paperback version contains the utilized UN Comtrade and World Bank raw data in Excel format.

The main criterion for country selection was sufficient data availability to obtain or calculate at least one of the three following energy import dependency indictors: (1) Physical Import Dependency, (2) Ratio of Fuel Imports to Total Merchandise Exports, and (3) Ratio of Fuel Imports to GDP. To ensure timeliness and increase the explanatory power of the calculated indicators, country data older than 1991 was excluded from the analysis. This selection procedure also makes allowance for the fact that the overall data quality considerably improved during the last decade and that most of the data for Eastern European and former Soviet Union countries was not reported until the mid 1990s. In order to smooth out fluctuations in fuel trade values and to deal with missing data points in the data set,

34 A 2-digit overview of the SITC Revision 3 classification is given in Appendix A.

35 Full access to UN Comtrade data is limited to registered users. Guest users cannot download data or save queries. The current “not for profit” subscription rate is US$ 100 for a download capability of up to 100,000 records.

Chapter 3: Quantitative Analysis of Energy Import Dependency I calculated the economic dependency indicators by averaging the annual values for 5 consecutive years from 1998 to 2002. The year 2002 corresponds to the latest year for which comprehensive UN Comtrade data is currently available. With regard to the World Bank data, I encountered the problem that data is usually published with a lag of two years. The World Development Indicators 2002 are therefore based on data from 2000 and earlier. To calculate the third dependency indicator, which is based on UN Comtrade data for fuel imports as well as on World Bank data for the GDP values, I downloaded the missing GDP data for the years 2001 and 2002 from the online accessible free segment of the World Development Indicators 2004³⁶. For all other World Bank indicators used in this paper, data coverage ends with the year 2000. For the same reason as noted above, I calculated 10-year mean values (1991-2000) for most of the indicators used in the opportunity cost and poverty analysis. Mean values based on a period of 5 or 10 years are better suited to make judgements on structural differences between countries and country groups than the comparison of single year data.

To analyse structural differences in import dependency between industrialized countries and developing countries, I grouped all countries according to the World Bank income group classification. The World Bank classifies economies according to their gross national income (GNI)³⁷ per capita into high income (HIC), upper middle income (UMC), lower middle income (LMC) and low income countries (LIC). In the World Development Indicators 2002, economies are divided on the basis of 2000 GNI per capita. In monetary terms, low income countries have per capita incomes of less than 755 US$, lower middle income countries between 756- 2,995 US$, upper middle income countries between 2,996- 9,265 US$ and high income countries possess per capita incomes of $9,266 US$ or more (WDI 2002:

Classification of Economies). Middle income and low income countries are often referred to as developing or transitional economies. Nevertheless, attention should be paid to the fact that classifying countries according to their national income may not always be an appropriate method to fully grasp the development status of a country. For example, the ranking of countries according to their capita income does not necessarily correspond to the country order based on the Human Development Index. In some cases, countries with low per capita

36 The free segment of the WDI 2004 is limited to the last 5 reporting years and 54 indicators. In comparison, the complete data set contains 550 development indicators and covers the period from 1960 to 2003. For online data queries visit www.worldbank.org/data/onlinedatabases.

37 The GNI is a measure of the sum of value added by all national residents of an economy from engaging in various economic activities, irrespective of whether the activities are carried out within the national economic territory or outside. In contrast, GDP is a measure of the sum of value added by all residents (national and foreign) solely from production in the national economic territory. To reduce the impact of exchange rate fluctuations in the cross-country comparison of per capita incomes, the World Bank uses the so-called Atlas method when calculating GNI and GDP values (WDI 2002c: Statistical Methods).

Chapter 3: Quantitative Analysis of Energy Import Dependency incomes outperform “richer” countries when HDI values are compared. For example, in the Human Development Report 2003, Cuba is still considered a “high human development country” (HDI Rank 52), even though many “medium human development countries” like Brazil, Thailand and Saudi Arabia have considerably higher per capita incomes (UNDP 2003:

238). The reason why I decided against using the HDI country classification is the fact that the World Bank income classification allows distinguishing between four analytical groups, whereas the HDI classification only differentiates between three country groups (countries with high, medium and low human development).

Based on the outlined selection criteria and the applied World Bank income classification, my quantitative analysis on energy import dependency comprises a total of 166 countries of which 36 countries belong to the high income, 32 to the middle income, 44 to the lower middle income and 54 to the low income category (See Appendix B, Table B-1). In the following sections special emphasis is put on the analysis of inter-group differences in energy import dependency and reference to individual country data is limited. Therefore, a comprehensive overview of the dependency indicators used and calculated for each individual country is provided in the Country Data Tables C1-C4 in Appendix C.

3.3 Measuring Energy Import Dependency