Best of Times, Worst of Times:Why East Asia Grew Economically and Latin America

Did Not in the Second Half of the 20 th Century

Kevin HellestadStudent ID #4666994

12/14/2016

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Section 1: Introduction

The variations in economic growth paths an entire region can take are almost always unique to the situation or area. Throughout the course of history, the world has seen its share of substantial economic growth, along with tragic depression, and the effects they have had on a singular country, global region, or the entire world. Economists have studied economic growth, and have discovered factors commonly related to it in many cases. These factors come in wide variety, such as human capital investment, accumulation of physical capital, depreciation of capital, domestic savings, population growth rates, and increased productivity.

One way to discover the importance these common factors can have on economic growth is to compare two countries or two regions, along with comparing these factors to discover which ones best explain the difference in growth paths. There are two regions of the world in which one has recently experienced extensive economic growth, while the other experienced stagnated and declining growth during the same time period. Therefore, the question will be asked of, beginning in similar economic conditions, why did East Asia grow extensively economically, while Latin America experienced stagnated growth and even decline in the second half of the 20th century.

This paper will examine what were the major factors behind the difference in the extensive growth that East Asia experienced and the economic stagnation that Latin America experienced in the second half of the 20th century. I am supporting the notion that while all of these common economic factors all have affected the diversion of growth in some way, none was more important than the difference in Total Factor Productivity (TFP).

Section 2 will bring historical context into this analysis, outlining the decisions that the countries in each region made and the overall consequences of those decisions. Section 3 analyzes the Solow Growth Model with respect to TFP that will be used, and how it will be used in order to fit the regression. Section 4 analyzes the data collected and how it will be used. Section 5 provides the regression analysis. The final section will bring the paper to a conclusion and test the predictions against the regression outcome.

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Section 2: Contextual Analysis

In order to understand why the comparison of these two regions is valid, we must look at the history of each region in second half of the 20th century. Following World War II, Latin America was seen as a region with developing countries focused heavily on the exportation of commodities. The Economic Commission of Latin America (ECLA), which was founded by the United Nations in 1948, aimed to change the region’s perception of consisting heavily of developing countries. Raúl Prebish, director of the ECLA at the time, published “The Economic Development of Latin America and its Principal Problems” in 1950, in an attempt to provide Latin America with a way to become a region full of developed countries. He argued that trade was making the developing countries in Latin America worse off because the price of commodities was falling relative to the price of manufactured goods, and would continue over the long run. With this mindset, he concluded that economies structured toward the exportation of commodities and the importation of manufactured goods must be restructured to focus more on exporting manufactured goods. He believed that economic structure had important effects on the overall economic outcome, and this would further develop Latin American countries. Because of Prebish’s ideas, the countries of Latin America adopted the economic structure of Import Substitution Industrialization (ISI) in the 1950’s.

ISI is “a set of economic policies designed to replace the imports of industrial products with domestic production.” (Reyes & Sawyer, 19). Under these sets of economic policies, Latin American countries would create government owned and subsidized industrial firms in order to produce industrial products domestically. These policies placed high tariffs on imports to incentivize domestic purchasing by consumers. Artificially low exchange rates were also created to make it easier for Latin American countries to import capital goods needed to produce domestic industrial products. The countries also created state-owned and operated banks to keep the rates low.

The results showed, but, “GDP per capita in the region increased from 1950-1980 but at a relatively slow rate.” (Reyes & Sawyer, 157). This was because many of the state-owned enterprises (SOE) were producing industrial goods at a high price but the goods were low quality. Many people urbanized and were driven away from their agricultural roots because the government favored industry over agriculture, by subsidizing industrial firms but refusing to financially help the agricultural sector. Because the SOEs were operating at a loss, the governments of Latin America ran massive deficits every year to subsidize these firms. Tax rates were high so the government could have an income, but many evaded taxes by working in the informal sector of the economy. This caused the countries to borrow money and print more of their own currency to finance their massive deficits.

ISI ultimately failed because it tied fiscal and monetary policy with a vice grip-like tightness. The massive borrowing, printing of money, and budget deficits left Latin American countries vulnerable to external shocks. When the world’s fixed exchange

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rate system collapsed in 1971, a floating exchange rate system was implemented worldwide. This caused many of the industries in Latin America that depended upon low, artificial exchange rates to fail. Two oil shocks in the 1970s caused deficits and borrowing to grow to unprecedented levels. With one last exchange rate shock in 1979, many Latin American currencies were depreciated and import prices rose substantially. Because of the high levels of inflation, countries had to borrow money from the International Monetary Fund (IMF) to finance their deficits, but soon had to abandon it, beginning when Mexico couldn’t pay back its debts to private lendors, other countries, and the IMF in 1982. Latin America removed and abandoned ISI by the end of the 1980s. This time in Latin Americais known as the Lost Decade, “a period of low growth in Latin America in the 1980s.” (Reyes & Sawyer, 121). According to Javier A. Reyes and W. Charles Sawyer, they believe the biggest reason ISI failed and Latin America failed to grow in the second half of the 20th century was a low level of overall Total Factor Productivity.

As Reyes and Sawyer define TFP, it is defined, “an increase in GDP not accounted for by changes in the labor force or the stock of capital.” (Reyes & Sawyer, 61). They point out that, following the multiple exchange rate shocks in the 1970s, many inefficiencies created by ISI were exposed. Once the shocks happened, the overall cost of imports rose greatly. Then, “the ISI industries of the region that had been dependent on cheap imports for decades were unable to continue business as usual. The firms were frequently inefficient and simply could not cope with the increase in costs.” (Reyes & Sawyer, 164). These inefficient firms had two options; either operate at a loss and borrow more from the government, or shut down. Ultimately, this caused the real GDP to drop. Because the inefficiencies weren’t changing the GDP due to changes in the labor force or capital stock, a TFP decrease must have been the most important factor in Latin America’s economic demise in the 1980’s following ISI.

East Asia, on the other hand, experienced the opposite in the second half of the 20th century. Instead of experiencing slow to declining growth like Latin America, East Asia grew at a record pace from 1965-1990, called The East Asian Growth Miracle. The region of The East Asian Growth Miracle contains 8 countries that had high performing economies and high GDP growth between the years 1965-1990. The countries were Japan; the four tigers of Hong Kong, Republic of Korea (South Korea), Singapore, and Taiwan; and the three newly industrialized countries of Indonesia, Malaysia, and Thailand. Before the 1960s, these East Asian countries were reliant on the exportation of commodities, much like Latin America. Japan was the only outlier being more industrialized, though still recovering from extensive damage caused by its involvement in WWII.

So what changed? Similar to what Latin America did with the region adopting relatively uniform policies like ISI, the East Asia region adopted new, relatively uniform economic policies. The East Asian governments decided to limit the amount of government intervention in their economic policy, creating a free market feel in the region. These countries also limited their levels of inflation and kept real

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exchange rates from appreciating. This also led to the region running low to no budget deficits whatsoever. These East Asian countries did seek to become more industrialized, and aid its growth, without ostracizing the agricultural sector by focusing the economy more on exports than domestic production of industrial goods. The governments also invested heavily in continuing education and vocational training. This not only increased the level of human capital, but also encouraged domestic savings, promoting bank solvency.

The effects of these policies are rather substantial. Between 1960-1990, the investment rates in the region exceeded 20% of GDP every single year. (The East Asian Miracle, 8). Also, because every country was focused heavily on sustainability, there was an increased amount of private savings, investment, and exports for each country. This led to the region having the fastest growth in GDP in history.

The results are consistent because growth was the shared goal across the region. All of the policies caused increases in domestic savings, human capital, efficiency, and growth of the industrial sector without the agricultural sector taking a large hit. Because of this, the overall productivity in agriculture was increased. From Stanley Fischer and Julio J. Rotemberg, this and export push strategies caused an increase in the overall level of TFP. According to a WorldBank report on the topic, along with Fischer and Rotemberg, many factors contributed to the rapid growth of GDP, but none more important than the increase in TFP.

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Section 3: Growth Model Analysis

In order to discover the major factor or major factors why Latin America and East Asia grew differently in the second half of the 20th century, we will be using the Solow Growth Model to achieve this objective. The base Solow Model with respect to TFP is as follows:

Y=A K ∝L1−∝

Because our base Solow Model produces GDP as its output, we will have to make a change to it. This is because when studying the growth differential between the two regions, overall GDP may improperly skew our data. For example, there are countries like Mexico who have large populations and larger GDPs, but a low GDP per capita due to this fact. Reciprocally, there are countries like Singapore who have smaller GDPs in comparison and small populations, but this gives them a larger GDP per capita. In order to properly study the difference in growth rates between Latin America and East Asia, we must produce a Solow equation that has GDP per capita as its output. Therefore, our base GDP equation is divided by labor (L) to yield:

y=A k∝

Reviewing this equation, it will only allow us to measure capital stock per capita and TFP to come up with GDP per capita. As seen in the contextual analysis, there were multiple other variables that had a hand in influencing the difference in the growth rates of the two regions. For the purpose of this analysis, I have chosen to measure four variables and their influence on the difference in GDP per capita for Latin America and East Asia. The variables that we will be testing for are Total Factor Productivity (A), savings rate (s), capital stock (K), and population growth rate (n).The equation that will be used is the steady state level of GDP per capita given these variables is given by the following:

y¿= sAK 1−∝−(n−δ )

Where: y*=Steady state GDP per capita s=Savings rate A=Total Factor Productivity K=Capital stock n=Population growth rate δ=Depreciation rate α=Elasticity of output with respect to capital

For this analysis, we will be assuming that the depreciation rate is exogenous across all countries that will be analyzed in the Latin American and East Asian regions. We will be doing this so that we can understand the relationship that differences in savings rate, Total Factor Productivity, capital stock, and population growth rate have on the difference in GDP per capita. Because the analysis is to show the

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difference between East Asia and Latin America in regards to GDP per capita, we will be studying the difference in the four variables chosen to be tested of the two regions. Therefore, this turns our equation into the following:

Δ y tEA , LA=( Δst

EA , LA Δ A tEA , LA

ΔK tE A , LA1−α )−(Δnt

EA , LA−δ )

In the Section 5 regression analysis, the test will be conducted to discover how the difference between savings rate, TFP, capital stock, and population growth rate affect the difference in GDP per capita for the East Asian and Latin American regions. If we revert back to the steady state of GDP per capita equation, we are able to see how each variable will affect the difference in GDP per capita, if all other variables are held constant:

With a focus on the savings rate and, holding all other variables constant, we see that there is a direct relationship with savings rate related to GDP per capita. We can see that an increase in the savings rate, or a larger savings rate in general, will result in a larger overall GDP per capita. This is because the savings rate is in the numerator of the equation.

With a focus on TFP and, holding all other variables constant, we can see that like savings rate, it has a direct relationship related to GDP per capita. This is because in the equation, it is in the same spot as the savings rate.

With a focus on population growth rate and, holding all other variables constant, it will impact the GDP per capita in a negative way. This is because an increase in the population growth rate leads to a decrease in the overall GDP per capita. Because GDP per capita is GDP divided by population, an increase in the population growth rate leads to a larger population, which would also lead to a lower overall level of GDP per capita.

With a focus on capital stock and, holding all other variables constant, we can see that an increase in the capital stock would cause a decrease in the overall steady state level of GDP per capita because it is in the denominator of our equation. However, that only holds true if the alpha in the equation is less than 1. If the alpha is greater than 1, however, then an increase in the capital stock would cause an increase in the steady state level of GDP per capita. This is because a negative exponent will yield the inverse and increase GDP per capita.

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Section 4: Data Analysis

For this analysis, I will be collecting data from a sample size of 6 East Asian and 6 Latin America countries. The data I have collected from WorldBank Group includes GDP per capita, total population, and savings rate of all 12 countries. The data for TFP and capital stock of all 12 countries was collected from the Penn World Tables 9.0 compiled by the Federal Reserve Bank of St. Louis.

The reason an equal number of countries will be used in East Asia as well as Latin America for my sample size is for symmetry with the data from both regions. We are only able to use 6 of the 8 high performing Asian economies in this due to data restrictions. The countries that will be used in the East Asia sample size are Japan, Hong Kong, Republic of Korea, Singapore, Thailand, and Malaysia. The reason Taiwan was omitted is due to the WorldBank Group and the Penn World Tables 9.0 seeing Taiwan as a part of China and not its own entity. The reason Indonesia was omitted is due to data necessary for the country was not collected until 1970 by WorldBank. Because there are only 6 countries in the East Asia sample size, the Latin America sample size will reciprocate and have 6 countries that have all the necessary data from 1965 through 2000. The 6 Latin American countries in the sample size are Mexico, Brazil, Chile, Ecuador, Columbia, and Peru.

Because we are doing this analysis on the regions, and not the individual countries, the data must be manipulated in the following ways so that we can get an accurate depiction to find the major reason or reasons for the differences in GDP per capita. In coming up with the data points for each region in terms of GDP per capita, savings rate, capital stock, and TFP, the average was taken of our sample sizes for each year in order to get an accurate data point of the factors for that region. Population growth rate was found by taking the total population of each country, adding each regions’ together, and calculating the population growth rate given the total population of the region.

In order to properly use the capital stock data, the alpha must be calculated for each year. This is done because the data points for savings rate, TFP, and population growth rate are concrete in the equation without being manipulated by an exponent. For symmetry, the alpha will be calculated and then inputted so that data point has an accurate representation. Because depreciation rate is assumed to be exogenous in this study, the equation to calculate the alpha is as follows:

α=( ln ( yas + nas )+ln (K ))

ln (K )

(The tables in Appendix A show the difference in each region in terms of GDP per capita, population growth rate, TFP, capital stock and savings rate.1)

1 All difference calculations have been calculated as (East Asia – Latin America).

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One thing to note is that the savings rate data starts in 1975, not in 1965. This is because the sample size did not have savings rate data available for both regions until 1975. When performing the subsequent regression, I will be inputting the regions’ savings rate from 1975 for the years 1965-1975. This is done in order to nullify the effect of not having the data points would have on the regression to the best of our ability.

Taking a look at the data, we have our evidence that the East Asian region grew substantially compared to the Latin American region between 1965-2000. With both of these regions starting out in similar economic conditions and one experiencing a “growth miracle,” it is imperative to find which factors caused this difference. Because a better understanding of how these factors affect economic growth, this will help us discover the catalysts of economic growth.

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Section 5: Regression Analysis

Following the collection and analysis of the data, our regression test will tell us which factor proves to be the most consequential in determining the reason why East Asia experienced a “growth miracle,” and why Latin America experienced stagnation and decline. Connecting back to the steady state level of GDP per capita equation in terms of the difference in savings rate (s), TFP (A), capital stock (K), and population growth rate (N) in Section 3, we will see if the following predictions will yield to be true:

TFP will prove to be the most important factor in explaining the difference in East Asia’s growth and Latin America’s stagnation.

Although the differences in the savings rate, the capital stock, and the population growth rate will have some merit in explaining the difference, they will not have as much of a difference as TFP.

In the regression, the Beta coefficient for the differences in savings rate and TFP will yield positive numbers, while the Beta coefficient for the difference in the capital stock and population growth rate between the two regions will yield a negative number.

Base Regression testing all 4 variables against difference in GDP per capita

The following regression aims to test all 4 variables of difference in capital stock, savings rate, TFP, and population growth rate against the difference in GDP per capita between East Asia and Latin America. This is done in order to discover the power the variables have in determining the difference in GDP per capita.

The base regression yields the following regression equation:2

∆GDPPC=59,730,000∆ K t+230.7 ∆st+30,830∆ At+5,944∆nt+5,183This regression shows that all variables have a positive impact in explaining the difference in GDP per capita between East Asia and Latin America, showing that greater differences favoring East Asia resulted in greater difference in GDP per capita. However, the differences in both population growth rate (n) and capital stock (K) have positive effects on the difference in GDP per capita, with differences in capital stock having the largest effect in this regression equation. The p-value and R2

for this regression are as follows.

p-value= 6.852e-16

R2= 0.9118

The p-value given is well below the 0.05 threshold needed to confirm the validity that these variables are significant in explaining the differences in GDP per capita between the two regions. The 0.9118 R2 statistic allows us to estimate that these 4 variables can account for 91.18% of the explanation in the difference in GDP per capita between East Asia and Latin America. This regression will be used as the base

2 Graph 1 in Appendix B correlates with base regression equation.

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regression, where the subsequent 4 regressions will remove one different variable in order to see the effect each variables have on the regression. The difference in R2 from subsequent regressions and the base regression will show each variable’s significance in determining the difference in GDP per capita.

Regression with difference in Capital Stock retracted from the Base Regression

The following regression removes the difference in capital stock variable from the regression model, and attempts to explain the difference in GDP per capita between the two regions testing against differences in savings rate, TFP, and population growth rate.

This regression is given by the following regression equation, p-value, and R2:3

∆GDPPC=445.6 ∆st+24,102∆ A t+53,951.8∆ nt+1,174.9p-value= 5.033e-16

R2= 0.8991

Using the base regression with difference in capital stock removed, we see that differences in savings rate, TFP, and population growth rate continue to have a positive effect on the difference in GDP per capita, favoring East Asia, in this regression. The p-value is below the 0.05 threshold, so these 3 variables are significant in determining the difference in GDP per capita between East Asia and Latin America. The R2 for this regression comes out to be 89.91%. In comparison to the base regression, the R2 for this regression lowers by 1.27% when capital stock is removed.

Regression with difference in Savings Rate retracted from the Base Regression

For this regression, we will only be removing the difference in savings rate variable from the base regression. This will be done to see how the differences in capital stock, TFP, and population growth rate have on the difference in GDP per capita between the two regions, and how the removal of savings rate affects the R2 of the regression in comparison to the base regression.

This regression is given by the following regression equation, p-value, and R2:4

∆GDPPC=105,209,371∆K t+37,817∆ At−3,831∆nt+9,255p-value= 3.428e-16

R2= 0.9015

Using the base regression with difference in savings rate removed, we see that both differences in capital stock and TFP have a positive impact on the difference in GDP per capita, and differences in population growth rate have a negative impact. The p-

3 Graph 2 in Appendix B correlates with this regression equation.4 Graph 3 in Appendix B correlates with this regression equation.

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value for this regression is below the 0.05 threshold, so these 3 variables are proven to be significant in determining the difference in GDP per capita between East Asia and Latin America.

The R2 yielded from this regression is 90.15%. In comparison to the base regression, there is a difference of 1.03% when difference in savings rate is removed. Referring back to the R2 when difference in capital stock was removed from the base, that regression’s R2 creates a larger difference from the R2 from base regression than the R2 when difference in savings rate is removed. This shows that difference in capital stock is more important in determining the difference in GDP per capita between the two regions than difference in savings rate.

Regression with difference in TFP retracted from the base regression

The following regression removes difference in TFP from the base regression model, and attempts to explain the difference in GDP per capita between East Asia and Latin America using differences in capital stock, savings rate, and population growth rate.

This regression yields the following regression equation, p-value, and R2:5

∆GDPPC=−87,220,000∆ K t+915∆st+200,600 ∆nt−6,276p-value= 2.622e-10

R2= 0.7694

Using the base regression with difference in TFP removed, we can see that differences in savings rate and populating growth rate have a positive effect on the difference on GDP per capita between the two regions, favoring East Asia. We also see that difference in capital stock has a negative effect on GDP per capita between the two regions. The p-value for the regression is below the 0.05 threshold, so these 3 variables are significant in determining the difference in GDP per capita between the two regions.

The R2 for this regression is 76.94% when difference in TFP is removed from the base regression. In comparison to the base regression, it has a difference of 14.24%. Comparing to the regressions when difference in capital stock was removed and when difference in savings rate was removed, the regression when difference in TFP was removed from the base creates the largest difference in R2 from the base. This means that difference in TFP is a more significant variable in determining difference in GDP per capita between the two regions than difference in capital stock and difference in savings rate were.

Regression with difference in population growth rate retracted from the base regression

5 Graph 4 in Appendix B correlates with this regression equation.

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The following regression removes the difference in population growth rate from the base regression. This regression will test how the differences in capital stock, savings rate, and TFP affect the difference in GDP per capita between East Asia and Latin America.

This regression yields the following regression equation, p-value, and R2:6

∆GDPPC=59,860,000∆ K t+230.6 ∆st+30,860∆ At+5,133p-value= less than 2.2e-16

R2= 0.9118

When difference in population growth rate is removed, we see that difference in capital stock, savings rate, and TFP have a positive effect on determining the difference in GDP per capita between the two regions. The p-value again is below the 0.05 threshold, so we know that these variables are valid in determining the outcome.

The R2 for this regression is 91.18%. It is the same R2 as the base regression, so we can see that difference in population growth rate is minimal in significance when it comes to determining the difference in GDP per capita between East Asia and Latin America. Following suit, it still stands that the regression when difference in TFP was removed produced the biggest difference in R2 from the base regression. This shows that differences in TFP are the largest reason as to why there was a large difference in GDP per capita between East Asia and Latin America between the years 1965-1990.7

Section 6: Conclusion

6 Graph 5 in Appendix B correlates with this regression equation.7 See Appendix C for table with breakdown of each regression’s p-value, R2, and difference in R2 from the base regression.

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In this paper, I analyzed the differences in overall growth in GDP per capita between East Asia and Latin America between the years 1965-2000. Because both regions faced similar economic conditions following World War II and until the mid 1960’s, I wanted to find out why the two regions’ economies diverged, and what were the major factors that were associated with this difference in growth. Referring back to Section 4, the data tables in Appendix A show just how different these regions fared economically. We saw that East Asia grew nearly six times as much as Latin America did in terms of GDP per capita over those 35 years. We also saw that East Asia had a higher average savings rate and level of TFP growth than Latin America did. In terms of population growth rate, we saw that East Asia had an average rate lower than Latin America did by about 41%. All of these factors favor East Asia in growth over Latin America, but we aim to find out which factor is the most important.

I predicted that differences in TFP would show to be the main cause of the divergence of growth, followed by differences in savings rate, capital stock, and population growth rate. I also predicted that the betas from the regressions would be positive for the differences in savings rate and TFP, but negative for population growth rate.

Testing against the base regression, differences in TFP proved to be the biggest reason for East Asia’s and Latin America’s diverged growth path. This is because when differences in TFP were removed from the base regression, we saw that it generated the greatest difference in R2 from the base regression. Difference in savings rate and capital stock proved to be similarly important, where as differences in population growth rate were barley significant. The betas in the regressions for TFP and savings rate, but the betas for capital stock and population growth rate varied. So my prediction that the betas for TFP and savings rate were accurate, but betas for capital stock and population growth rate were inconsistent and not proven valid.

One thing to remember is that we were unable to collect 10 years of savings rate data, and used the difference in savings rate from 1975 as a placeholder for all years 1965-1974. This may have skewed the regressions to either favor savings rate differences less or more, we may never know. We also had to hold the difference in depreciation rate as exogenous because there was no data to be found on it, so its inclusion could potentially change the results of this regression test.

TFP was proven to be the most important in explaining the varied growth paths in GDP per capita between East Asia and Latin America. East Asia’s ability to better use its resources and shared growth goal amongst its nations to achieve growth aided the economy of the region in growing more effectively than Latin America’s ISI policies. The fact also stands for all other nations and regions going forward that generate political reforms that aim to increase the total level of Total Factor Productivity in the long run will experience economic growth.

Bibliography

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Reyes, J. A., & Sawyer, W. C. (2011). Latin American Economic Development. New York, NY: Routledge.

The East Asian Miracle: Economic Growth and Public Policy. (1993). New York, NY: Oxford University Press.

Fischer, S., & Rotemberg, J. J. (1994, January). The East Asian Miracle: Four Lessons in Economic Development. Retrieved September 22, 2016, from http://nber.org/books/fisc94-1

The World Bank Group. (2016). GDP per Capita, Current Prices. Retrieved October 19, 2016, from http://databank.worldbank.org/data/reports.aspx?Code=NY.GDP.PCAP.CD&id=af3ce82b&report_name=Popular_indicators&populartype=series&ispopular=yData from 1965-2000 for Brazil, Chile, Columbia, Ecuador, Hong Kong, Japan, Korean Republic, Malaysia, Mexico, Peru, Singapore, and Thailand

The World Bank Group. (2016). Population, Total. Retrieved October 19, 2016, from http://databank.worldbank.org/data/reports.aspx?Code=NY.GDP.PCAP.CD&id=af3ce82b&report_name=Popular_indicators&populartype=series&ispopular=yData from 1965-2000 for Brazil, Chile, Columbia, Ecuador, Hong Kong, Japan, Korean Republic, Malaysia, Mexico, Peru, Singapore, and Thailand

The World Bank Group. (2016). Gross Savings (% of GDP). Retrieved October 19, 2016, from http://databank.worldbank.org/data/reports.aspx?Code=NY.GDP.PCAP.CD&id=af3ce82b&report_name=Popular_indicators&populartype=series&ispopular=yThe World Bank Group. (2016). Population, Total. Retrieved October 19, 2016, from http://databank.worldbank.org/data/reports.aspx?Code=NY.GDP.PCAP.CD&id=af3ce82b&report_name=Popular_indicators&populartype=series&ispopular=y Data from 1965-2000 for Brazil, Chile, Columbia, Ecuador, Hong Kong, Japan, Korean Republic, Malaysia, Mexico, Peru, Singapore, and Thailand

Federal Reserve Bank of St. Louis. (2015). Total Factor Productivity Level at Current Purchasing Power Parities. Retrieved October 19, 2016, from https://fred.stlouisfed.org/release?et=&ob=t&od=&pageID=92&rid=285&t=.Data from 1965-2000 for Brazil, Chile, Columbia, Ecuador, Hong Kong, Japan, Korean Republic, Malaysia, Mexico, Peru, Singapore, and Thailand

Federal Reserve Bank of St. Louis. (2015). Capital stock at Current Purchasing Power Parities. Retrieved October 30, 2016, from https://fred.stlouisfed.org/release?et=&ob=t&od=&pageID=5&rid=285&t=.Federal Reserve Bank of St. Louis. (2015). Total Factor Productivity Level at Current Purchasing Power Parities. Retrieved October 19, 2016, from https://fred.stlouisfed.org/release?et=&ob=t&od=&pageID=92&rid=285&t=. Data

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from 1965-2000 for Brazil, Chile, Columbia, Ecuador, Hong Kong, Japan, Korean Republic, Malaysia, Mexico, Peru, Singapore, and Thailand

Appendix A

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GDP per capita (y) [WorldBank Database]East Asia Latin America

Region GDP per Capita (1965) $444.14 $440.21Difference $3.93Region GDP per Capita (2000) $17,469.52 $3,583.02Difference $13,886.49Average GDP per Capita $6,963.67 $1,776.49% Change 3,833.33% 713.93%

Savings Rate (s) [WorldBank Database]East Asia Latin America

Region Savings Rate (1975) 0.245 0.163Difference .0872Region Savings Rate (2000) 0.341 0.190Difference .151Average Savings Rate 0.315 0.196% Change 39.10% 16.42%

TFP (A) [Penn World Tables 9.0]East Asia Latin America

Region TFP (1965) 0.477 0.714Difference -0.237Region TFP (2000) 0.695 0.509Difference 0.184Average TFP 0.677 0.692% Change 45.72% -28.70%

Capital Stock (K) [Penn World Tables 9.0]East Asia Latin America

Region Capital Stock (1965) 260,864.595 206,907.630Difference 53,956.962Region Capital Stock (2000) 3,182,631.333 1,478,821.091Difference 1,703,810.242Average Capital Stock 1,275,435.083 709,275.912% Change 1,120.03 614.73%

Calculated Alpha

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East Asia Latin AmericaRegion calculated alpha (1965) 1.660851963 1.672729438Difference -0.011877475Region calculated alpha (2000) 1.748499641 1.740285026Difference 0.008214615Average calculated alpha 1.71702766 1.700917286Largest calculated alpha 1.753901024 1.750419458Smallest calculated alpha 1.656987394 1.669461652Range (Largest – smallest) 0.096913629 0.080957806

Capital Stock with Alpha inputtedEast Asia Latin America

Region Capital Stock With Alpha (1965)

0.000263361 0.0002265412

Difference 0.0000368198Region Capital Stock With Alpha (2000)

0.0000135727 0.000027071

Difference -0.0000134983Average Capital Stock with Alpha

0.0000779429 0.000111379

% Change -94.85% -89.80%

Population Growth Rate (n)East Asia Latin America

Region Population (1965) 171,325,129 168,402,140Region Growth Rate (1965) 0.018 (1.8%) 0.029 (2.9%)Difference -0.011 (-1.1%)Region Population (2000) 270,658,071 372,712,848Region Growth Rate (2000) 0.007 (0.7%) 0.015 (1.5%)Difference -0.008 (0.8%)Average Population Growth Rate

0.013 (1.3%) 0.022 (2.2%)

% Change -59.94% -48.89%

Appendix B

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Graph 1: Base regression where all four variables predict the difference in GDP per capita between the two regions.

Graph 2: Regression where difference in capital stock is removed from the base regression.

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Graph 3: Regression where difference in savings rate is removed from the base regression.

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Graph 4: Regression where difference in Total Factor Productivity is removed from the base regression.

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Graph 5: Regression where difference in population growth rate is removed from the base regression.

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Appendix C

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Base Regression

Regression with Captial Stock removed from Base

Regression with Savings Rate removed from Base

Regression with TFP removed from Base

Regression with Population Growth Rate removed from Base

p-value 6.852e-16 5.033e-16 3.482e-16 2.622e-10 Less than 2.2e-16

R2 0.9118(91.18%)

0.8991(89.91%)

0.9015(90.15%)

0.7694(76.94%)

0.9118(91.18%)

R2 difference from Base Regression

0 0.0127(1.27%)

0.0103(1.03%)

0.1424(14.24%)

0

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