# **Appendix I: Data, Sources and Methodology**

# **Data**

In this paper, we investigate the relationship between the distribution of education, economic reform and economic growth by a panel data set of 12 countries from 1970 to 1994. The data set consists of variables on GDP per capita measured by purchasing power parity (PPP) and by constant dollars, physical capital stock, labor, level of education of the labor force, and the distribution of education in the labor force, terms of trade, a dummy variable on economic reforms, and a few time dummies.<sup>5</sup> Most data was extracted from the World Bank's main database, except for a few.

Output level is measured by GDP per capita in constant US dollar at 1987 prices. Growth rates are calculated using the log-difference method. In order to allow cross country comparison of "unexplained residual" or total factor productivity, we also converted GDP into one measured by Purchasing Power Parity (PPP) or international dollars, by using a single year (1995) PPP conversion ratios. Table 3 shows the average growth rates by decades and for reform versus non-reform periods. See also the charts at the end of the paper.

For physical capital stock, we used the variable estimated by Nehru and Dhareshwar (1993) using the perpetual inventory method. Gross domestic investment as a ratio of GDP was used as a proxy measure in some regressions. Labor input was taken from the labor force information from the World Bank main database. For human capital stock, we used the variable---average years of schooling for the labor force, as estimated by Nehru, Swanson and Dubey 1994.

The distribution of education was characterized by coefficient of variability of education for population aged over 15, and several other dispersion measures. The calculation took several steps. First, we obtain data on educational attainment at various levels for the population over the age 15 from Barro and Lee 1997. Second, school cycling data was obtained from Psacharopoulos & Arriagada 1986, Table B-I. Third, we then calculated the mean year of education, variance of education, standard deviation of education, coefficient of variability of education, and GINI coefficient all for the population aged over 15. Table 2 presents the calculated coefficient of variability of education by five-year intervals. In most countries, this variable shows a decline over 25 year period.

The dummy variables for economic reforrns (DREF) and for political upheavals (DPOL) were constructed based on the chronological description of development and reform for each country (Appendix 11), which summarize both the economic and the political processes occurred in the 12 countries during the historical period from 1970 to 1995. On the terms of trade (TOT), we used the country specific terms of trade for each country whenever it is available in the IMF International Finance Statistics Yearbook (IFS). Some proxy measures were used when the TOT data is not available for a particular country.

Twelve countries were selected into the sample based on the following rationales: First, determined by the objectives ofthis paper, we must select countries which conducted some policy reforms. Second, the effects of policy reforms must have been felt within a reasonable period. This rules out most countries in East Europe and Formal Soviet Union because their market reforms were too recent. This also rules out some low-income countries in Africa where the implementation of reform measures were too uncertain due to weak institutions or lack of legal infrastructure. Third, in order to do a good in depth analysis, one has to be reasonably familiar with the economy and political history in these countries. Data availability, our knowledge regarding the countries and resource constraint put a limit on the number of We are grateful to Jong-Wha Lee and Robert Barro, V. Nehru and S.Patel for kindly allowing us to use the education, and capital variables that they have. Other variables are from the World Bank main database.

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