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«Abstract We examine the effects of precolonial and colonial legacies on the current economic growth rates of ex-colonies. We find that precolonial ...»

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4 Table 1 provides the descriptive statistics of the key variables, along with the average economic growth rate of each ex-colony from 1960 to 2000. The average of the 64 economic growth rates is about 1.5%, although the values vary considerably, from −1.53 to 5.78%.1 Korea ranks first, as shown in columns 5 and 6 of Table 1. For this reason, the Korean economic growth rate has been a popular research topic in the field of economic growth.

The other key variables are classified into three subsets: initial condition, precolonial legacy, and colonial legacy variables. The initial condition variables are those that describe the economic circumstances of each ex-colony in the early 1960s and that affect the economic growth rate. The first initial condition variable is the per capita GDP of each country in 1960. The per capita GDP is well known to be related to the economic growth rate (e.g., Barro, 1991). The per capita GDP of Korea in 1960 is about USD 1,570 using the purchasing power parity (PPP) exchange rate of 1996. This figure is notably lower than the average of the 64 ex-colonies, namely USD 2,814, and Korea ranks 40th among the 64 countries. This shows that Korea was in a position to follow the other ex-colonies. The second initial condition variable is human capital, which is also known to be closely related to the economic growth rate (e.g., Barro, 1991; Barro and Lee, 2000). Two measures are used in our study: the average schooling years per capita and the primary school enrollment rate. Both were measured in 1960. The data on average schooling years are obtained from the adult population aged 25 and over, and there are 53 observations in the sample. The average schooling years in Korea is 3.23 years, which is higher than the average of 2.98 years, and ranks 13th in the 53 ex-colonies.

The primary school enrollment rate of Korea is about 94%, which is far higher than the average of 67%.

Here, Korea ranks 30th in the 96 observations, which also include countries without colonial experiences.

This enrollment rate is also high when compared with other countries in terms of income standard. By regressing the enrollment rate against the per capital GDP in 1960, the projected enrollment rate is about 30 percentage points lower than the observed enrollment rate.2 Based on these observations, the human capital level condition of Korea in 1960 was in a favorable condition for economic growth. Third, the Adelman and Morris index is considered as our fourth initial condition variable. Adelman and Morris (1967) measure the socioeconomic development status of 74 developing countries, and Temple and Johnson (1998) empirically find that the index has high explanatory power for the economic growth rate between 1960 and 1968. The index is measured by weighing 22 socioeconomic variables, including enrollment rate, income distribution, 1 The list of the 64 ex-colonial countries is provided in the appendix to this paper. Note that although the growth rate of Taiwan from 1960 to 2000 is higher than that of Korea, Taiwan is omitted from our dataset. This is because some precolonial and colonial legacy variables are not available for Taiwan, and are essential to our empirical analysis.

2 The estimation result is calculated as follows: enrollment rate = −115.71 + 24.58 log(per capita GDP in 1960). The sample size is 85 with R2 = 0.48, and the t-statistic of the coefficient (24.58) is 8.73, computed using the robust standard error. Here, we did not restrict the data scope to the ex-colonies only.

5 literacy, and so on. The descriptive statistics of the index are reported in Table 1, and show that Korea ranks 12th among the 64 countries. The final initial condition variable measures the distributional status of each country. Perotti (1996) and Deininger and Olinto (1999) empirically show that equal income or wealth distribution leads to higher economic growth. The land ownership Gini coefficient and the share of middle income class are used for this purpose, and are measured between 1960 and 1970 and in the early 1960s, respectively. Here, the middle income class is defined as the share of the third and fourth income quintiles.

As Table 1 shows, Korea exhibits a superior status to other ex-colonies. For both variables, Korea ranks first among the ex-colonies based on the available data observations. The superior status of Korea is mainly the result of the land reforms implemented by the Korean government in the 1950s (e.g., Alesina and Rodrik, 1994).

According to the conditional convergence hypothesis, Korea was in a favorable condition for high economic growth in terms of these initial condition variables. The education and income levels were favorable in Korea in 1960 relative to the other ex-colonies, which means that Barro’s (1991) conditional convergence hypothesis predicts the high economic growth of Korea well. In addition to these variables, the empirical findings of Rodrik (1995) predicted high economic growth for Korea. The distributional and socioeconomic status of Korea was also in a favorable condition for economic growth.

The initial condition variables are not the only variables that affect the economic growth of ex-colonies.

We classify the other variables related to historic legacies into precolonial and colonial legacy variables.

The precolonial legacy variable is a proxy that describes the social or economic standard of each ex-colony before being colonized. In the same vein, if a variable was formed by colonial experiences and potentially affects current economic activities, we refer to it as a colonial legacy variable. The descriptive statistics of these variables are also shown in Table 1.





According to Diamond (1997), the cause of current variations in economic performance in different countries can be traced to the legacies from the Neolithic period. Countries with earlier experiences of the Neolithic revolution could have larger populations, more advanced technologies, and more sophisticated political systems than other countries. By exploiting this hypothesis, we regress the current economic growths against the variables that capture the features of the Neolithic revolution. The selected precolonial legacy variables are the date of agriculture, technology level in AD 1500, the population density in AD 1500, the state antiquity indices in AD 50 and AD 1950, and the urbanization rate in AD 1500. These variables were all formed before the 64 ex-colonies were colonized, except for the state antiquity index in AD 1950.

Before examining the roles of the precolonial legacy variables in our analysis, we first discuss how they can be properly used as proxies for the consequences of the Neolithic revolution. For this purpose, we 6 first estimate the correlation coefficients between Olsson and Hibbs’s (2005) biogeography index and the precolonial legacy variables. The biogeography index is computed by measuring favorable bio-conditions that accelerate the Neolithic revolution. In particular, prevalent plants and domesticable animals are key variables used to compute the index, and are mostly determined by biogeographic factors that are invariant over time. Diamond (1997) explains that earlier agricultural experiences are expedited by favorable biogeographical conditions, and Olsson and Hibbs (2005) present empirical evidence in support of this argument.

Therefore, we use the biogeography index as a proxy variable for the likelihood that the Neolithic revolution took place in each country. The estimated correlation coefficients are reported in Table 2. The values are strictly positive, implying that the selected variables are closely related to the Neolithic revolution. Table 2 also shows that the estimated correlation coefficients among the precolonial legacy variables are positive.

Thus, there must be close and strong positive interrelationships among the precolonial legacy variables.

Insert Table 2 around here.

We now examine the roles played by the precolonial legacy variables in our analysis. There is a growing literature that uses these variables in the fields of economic growth and development. First, the years from the agricultural transition to the present are provided for each country, as per Putterman and Trainor (2006).

We refer to this as the date of agriculture. By the definition of the Neolithic revolution, we may regard the date of agriculture as the years from the Neolithic revolution. Olsson and Hibbs (2005) and Bleaney and Dimico (2011) empirically find that there is a positive correlation between the current income level and the date of agriculture by examining the worldwide data observations. We employ the date of agriculture as our precolonial variable and exploit this aspect. Second, Comin, Easterly, and Gong (2010) introduce the technology level index. They measure the technology levels of 112 countries in 1000 BC, AD 0, and AD 1500, and empirically show that there is a positive correlation between the current income level and the technology level index. We use this index as one of our precolonial variables. Third, Acemoglu, Johnson, and Robinson (2001) consider the population density in AD 1500 in each country as a pre-modern income variable to explain their so-called reversal-of-fortune hypothesis, as reviewed below. They observe that the variable is positively correlated with other pre-modern indices. Bandyopadhyay and Green (2012) use a population density data set obtained from the Food and Agricultural Organization (FAO) for the same purpose. We use both population densities as our precolonial variables. Fourth, Putterman (2007) provides an index that measures the status of a political system. Note that different Neolithic periods result in variations in the political systems of each country, according to Diamond (1997). For each country, Putterman (2007) classies political systems as government, chiefdom, and tributary systems, and scores the system over every 50 7 years from AD 1 to AD 1950 using pre-specified rules. He then delivers different index values by summing the scores and discounting older scores using several discount rates. This is called the state antiquity index.

Chanda and Putterman (2007) show that these indices are positively correlated with the economic growth of ex-colonies. The state antiquity index, computed as the sum of 5%-discounted scores from AD 1 to AD 1950, is selected as one of our main precolonial variables, along with other state antiquity indices.3 Finally, Bandyopadhyay and Green (2012) use Chandler’s (1987) urbanization index as a supplement to the population density in Acemoglu, Johnson, and Robinson (2002). This index provides the most comprehensive quantity information on historical urbanization, and is used as another index for precolonial legacies.

We distinguish between precolonial legacy variables and colonial legacy variables, which were formed by colonial experiences. Most studies use the precolonial legacy variables to examine the economic growth in samples that include all countries. Thus, the empirical findings from such studies may not be the same as those obtained from samples of ex-colonies only.4 Although Chanda and Putterman (2007) focus on samples of ex-colonies only, they do not control for the colonial legacy variables, which are important to explaining current ex-colonial economic activities (e.g., Acemoglu, Johnson, and Robinson, 2001). This may lead to an omitted variable bias. Therefore, in the present study, we reinvestigate the precolonial legacy variables by focusing on samples from ex-colonial countries only, and by controlling for the other colonial legacy variables.

Table 1 reports the descriptive statistics of the precolonial legacy variables. As before, we explain these statistics by comparing Korea with other ex-colonies. Note that the agriculture date of Korea is 26th of 103 countries, which include the 64 ex-colonies. In terms of the technology level in AD 1500, Korea ranks second, following Hong Kong, among 72 ex-colonies.5 Furthermore, the population level of Korea ranks third among 90 countries and ranks first in terms of Putterman’s (2007) state antiquity index AD for 1950.

We next examine the colonial legacy variables. Colonial legacies may have positive and negative effects on current economic growth. Therefore, we capture the colonial legacies by employing different variables that exhibit these effects. First, we note that Acemoglu, Johnson, and Robinson (2001, 2002) and Easterly and Levine (2012) provide a hypothesis on the positive legacies from immigrants from colonial ruling countries. According to the hypothesis, Europeans considered endemic diseases, such as malaria, and the population density of ex-colonies as the most important factors for immigration. They preferred ex-colonies 3 Refer to the Appendix for more detailed definitions of the other state antiquity indices.

4 Refer to table 1 of Nunn (2014 in section 7.6.1), in which different estimates are obtained for the effects of the population density in AD 1500 for ex-colonial and non-colonial economies.

5 As further examples, the United Kingdom and Spain rank first among 112 countries in terms of the technology level in AD

1500. China ranks 16th and first in the world and among non-western countries, respectively. Korea ranks 17th in the world and first among the ex-colonies. Refer to Comin, Easterly, and Gong (2010) for more details on the index.



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