Macro_Sonal

Empirical Evidence on Economic Growth

By Sonal Garg M.A. Economics First Year (S163DEC38) Email: [email protected] Ambedhkar University Delhi

Introduction Economic growth is an important component of economic development. Economic growth means a rise in real per capita GDP and is measured as percentage increase in real per capita GDP. Even though economic development is a multidimensional concept and depends on various factors, per capita GDP growth is often considered as one of the best proxies for measuring development performance. Thus it is futile to study economic development without studying economic growth (Debraj Ray, 1998). A good and reliable source of data on real per capita GDP and economic growth is therefore needed. Empirical evidence and first hand data on economic growth can help policy-makers and economists better understand economic situations and implement relevant policies. Empirical evidence is also important to check the consistency and credibility of various growth models. Cross country trends in growth rates are important for comparative analysis. Cross country trends give us a clear picture of income disparity. Lastly, it is incomplete to study empirical evidence on growth without looking at determinants of growth like initial level of income, educational attainment, life expectancy, fertility rate, investment ratio etc. These determinants help us answer crucial questions like- why some countries are richer than others, why do some countries grow faster than others, is there any tendency towards convergence. Empirical research on growth is therefore an indispensable part of modern growth study. The scope of this paper is to look at broad cross-country trends in growth rates and to answer some of the above stated questions. This paper is based on the empirical work done by Daron Acemoglu (2009) and Robert Barro and Xavier Sala-i-Martin (2004) on the determinants of growth and convergence.

Difficulties in measuring variables needed to study economic growth It is not easy to measure economic variables like GDP, investment share of GDP, consumption share of GDP, government share of GDP etc. One of the major difficulties faced while measuring economic variables is unavailability of data or even if the data is available it is not reliable. Such problems are more severe in developing and poor countries. For example according to Barro and Sala-i-Martin (2004) the data that is available on physical capital is unreliable especially for developing countries because they depend on “arbitrary assumptions about depreciation” and “rely on inadequate measures of investment flows.”(Barro and Sala-i-Martin, 2004) There are many problems faced while measuring GDP. Underreporting of income is common in developing countries. People do so to evade taxes. Similarly GDP does not include the underground economy – black economy, illegal activities. Another problem faced in developing countries while measuring output is that output produced by farmers for self-consumption may not be reported adequately. (Debraj Ray, 1998) Another problem could be related to the methodology used for computing a variable. This problem is discussed in the next section.

Main sources of data on economic growth There are many sources of data on economic growth. But not all of them are reliable. Listed below are some of the sources of data on economic growth. World Bank used the simple exchange rate method to compute per capita real GDP. According to Debraj Ray (1998) each country’s income in local currency is converted into a common currency (U.S. dollars) and divided by that country’s population to arrive at a measure of per capita income. This conversion scheme is called the “exchange rate method” because it uses the rates of exchange to express incomes in a common unit. (Debraj Ray; 1998) GDP exchange rate estimates have some shortcomings. Exchange rates are prices that depend on goods and services that are traded in the international market and therefore prices of non-traded goods like infrastructure in all countries are not reflected in exchange rates. However these non-traded prices are related to the economic development of a country and hence these prices need to be corrected for as well. Poor countries have relatively low prices of non-traded good. But if all incomes are converted to common currency using exchange rate method then it will lead to underestimation of real incomes of poor countries (Debraj Ray; 1998). In order to overcome this problem, two economists at the University of Pennsylvania, Alan Heston and Robert Summers created a data set called the Penn World Tables. Their work was aimed at converting national measures of GDP and income into internationally comparable estimates. Adjustments needed to be made to the valuation of goods and services calculated using exchange rate method so that they could be made internationally comparable. These adjustments were made by calculating common international price called as purchasing power parity (PPP) prices for all goods and services. PPP adjustments ensure measures of income per capita are comparable across countries. PWT consists of national accounts of large number of countries going back to 1950. PWT data used International Comparison Program data (“ICP, estimated international prices by carrying out price comparisons for a set of benchmark countries every fifth year between 1970 and 1985” (Debraj Ray, 1998)) (Debraj Ray, 1998). I have used Penn world table version 6.2 (2006) in this paper to produce some of the graphs. Besides GDP, PWT are also source of consumption and investment data. Now, World Bank's official estimates of the size of economies are based on GNI (Gross National Income) converted to current U.S. dollars using the World Bank Atlas method. “The Atlas method smoothes exchange rate fluctuations by using a three year moving average, price-adjusted conversion factor” (www.worldbank.org). Besides this, World Bank is also a source of data on various determinants of growth. For example, Life expectancy data can be drawn from World Bank’s World Development Indicators. (Acemoglu,2009)

One historical source is the data compiled by Angus Maddison for GDP per capita differences across nations going back to 1820. However according to Acemoglu (2009) these data are less reliable since they do not come from standardized national accounts and the sample is very limited and does not include observations of all countries. Nonetheless, it is still considered as an important source of historical data (Acemoglu, 2009).

Cross country trends over the 20th century 1.Cross country income differences (1960-2000) (in 2000 U.S. $). 1960 From a sample of 98 countries (PWT version 6.2), the highest per capita GDP is of Switzerland - $15253 (2000 U.S dollars) followed by the United States ($12892) whereas Ethiopia has the lowest per capita GDP-$400 (2000 US dollars). The mean value corresponds to per capita GDP of $3811.

Variable Mean median max min

GDP per capita 1960 (in 2000 U.S.$)

3811.588 2462.892 15253.38 400.0812

Data from PWT version 6.2, Heston, Summers and Aten (2006) at 2000 U.S$(refer table 1)

Figure 1 plots the distribution of PPP adjusted GDP per capita across 98 countries in 1960

Fig1.Data for 98 countries are PPP adjusted values from PWT version 6.2, Heston, Summers and Aten (2006) (refer table 1)

It is observable that most countries had income per capita less than $1500 as also observed by Acemoglu (2009). The distribution is rightly skewed implying most of the countries are centered on low per capita gdp but there are few countries with exceptionally high level of incomes. Also the richest countries included the OECD countries. Most of Latin America and some Asian countries were in the middle range whereas the poorer countries were mostly African and Asian countries (Barro and Sala-i-Martin, 2004). 2000 Figure 2 plots the distribution of PPP adjusted GDP per capita across 188 countries in 2000.

Fig2.Data for 188 countries are PPP adjusted values from PWT version 6.2 , Heston, Summers and Aten (2006) (refer table 1)

variable mean median max min

GDP per capita,2000(in 2000 U.S.$)

9133.065 5239.831 48217.27 359.1472

Mean in 2000 is $9133 which is approximately 2.5 times the 1960 mean. Richest country (Luxembourg) is 134 times richer than the poorest country (Democratic Republic of Congo). The OECD countries remained at the top. The United States still occupied the second position with a per capita GDP rising to $34300. Some East Asian Countries also joined the top category like Taiwan, Singapore, and South Korea etc. Middle range was dominated by Asian and Latin American countries. African countries still dominated the lower range (Barro and Sala-i-Martin, 2004). Right tale is thicker in 2000 than in 1960 suggesting more outliers in the year 2000 with exceptionally high incomes. The density estimate shows considerable inequality in income per capita in 2000 because it is more spread out than 1960 distribution. (Acemoglu, 2009) Logarithmic Distributions (1960 and 2000)

Spreading out of the 2000 year distribution is because of the increase in average incomes. According to Acemoglu (2009) it is better to look at the logarithm of income per capita. Because even though the absolute gap between rich and poor countries has increased between 1960 and 2000, the proportional gap has not increased much. It is better to compare the proportional gap rather than comparing the absolute gap because growth is the proportional increase in per capita GDP. So when “x(t) grows at a proportional rate log x(t) grows linearly and if x1(t) and x2(t) both grow by the same proportional amount then log x1(t)-log x2(t) remains constant whereas x1-x2 increases”. [Daron Acemoglu, Introduction to Modern Economic Growth 2009] Figure 3 shows a similar pattern, but now the spreading-out is more limited. It is evident from the figure that there has been an increase in number of rich countries from 1960 to 2000. This is because some of the middle income countries of 1960s have joined the high income range in 2000 while others remained in the middle range –“stratification phenomenon” (Acemoglu, 2009). It is also evident from the figure that income disparity has increased in 2000.

Fig3.PPP adjusted values from PWT version 6.2, Heston, Summers and Aten (2006) (refer table 1)

Cross country trends in growth rates For 98 countries, the average growth rate of real per capita GDP between 1960 and 2000 was approximately 1.9%. Figure 4 plots the density graph of average per capita GDP between 1960 and 2000. (Refer table 2). It can be seen from the figure that there is considerable variability in the growth rates. The distribution ranges from negative rates to rates as high as 6%.

0.1

.2.3

.4

6 8 10 12Log GDP per capita

1960 2000

Fig4.Data for 98 countries, PWT version 6.2, Heston, Summers and Aten (2006) (refer table 2)

The highest growth rate of per capita GDP between 1960 and 2000 among these 98 countries was 6.4% per year for Taiwan. Whereas countries like Democratic Republic of Congo, Madagascar, Tanzania grew at very low growth rates. In this sample of 98 countries the lowest growth rate is that of Madagascar at -2 percent per year (approx.). According to Barro and Sala-i-Martin (2004) differences in growth rates of this magnitude have an important effect on standard of living. Taiwan improved its per capita GDP from $1443.614 to

05

10

15

20

25

De

nsity

-.02 0 .02 .04 .06 .08Average growth rate of GDP,1960-2000

kernel = epanechnikov, bandwidth = .01

Kernel density estimate

avg .0194025 .0193118 .0646731 -.0108 variable mean p50 max min

$19183.93 (2000 U.S. $) i.e. by a factor of 13, while Madagascar lowered its per capita GDP from 1267.512 to 822.8821 i.e. by a factor of 1.5. There were other countries apart from Taiwan who had very high growth rates in this period. South Korea had a growth rate of 5.9%. Singapore had a growth rate of 5%. Hong Kong had a growth rate of 5.2%.Botswana grew at an average growth rate of 6.4% from 1971 to 2000. China grew at 5.4%. Japan grew at 4.2%. Other Asian countries had moderate rate of growth. For example India grew at 2.7%. OECD countries grew moderately or better. For e.g. The United States grew at a rate of 2.45% and was still the second richest country in 2000. At the lower end of distribution were mostly Sub-Saharan countries for e.g. Democratic republic of Congo (-4.7 percent per year from 1970-2000). Other countries with low or negative growth rates are Comoros, Togo, Mali, Rwanda etc. According to Barro and Sala-i-Martin (2004) “the typical country in sub-Saharan Africa increased its per capita GDP by a factor of only 1.3 over 40 years”. Some Latin American countries like Peru, Venezuela, and Argentina have also grown at very low rates (Barro and Sala-i-Martin,2004). Barro and Sala-i-Martin (2004) have summarized these trends as follows. Sub-Saharan Africa started relatively poor in 1960 and grew at low rates and therefore remained poorest in 2000. Asia (particularly East Asia) started slightly above Africa but grew rapidly and therefore ended up in the middle income range. Latin America started in the mid-high range, grew moderately and therefore ended up in the middle range. The OECD countries were at the top in 1960, grew moderately or better and ended up still the richest in 2000. Thus this shows that small differences in growth rates when cumulated over large number of years have drastic impacts on standards of living. Therefore it is important to understand about policies that have even slightest of effects on growth rates (Barro and Sala-i-Martin, 2004). Figure 5 shows the path of log GDP per capita for several countries between 1960 and 2000.

Evolution of GDP per capita, 1960-2000; PWT version 6.2, Heston, Summers and Aten (2006)

At the top were U.S. and U.K. with more or less steady increase in per capita GDP. However the (proportional) gap between the two was slightly larger in 2000 than it was in 1960 indicating that U.S. grew slightly faster than U.K. Spain was poorer than U.S and U.K in 1960 but grew rapidly between 1960 and 1970 and almost closed the large proportional gap. Singapore started poorer than even Spain but grew rapidly through out and by mid-1990s became richer than not only Spain but U.K. as well. The steeper slope also indicates that it grew at a higher rate than US, UK and Spain. South Korea is another country that had shown progress in this period. Starting much poorer than Singapore, it grew rapidly to close the gap with the top countries. Botswana is another country that had shown outstanding progress in this period. It was among the poorest nations in 1970 but grew rapidly to join the middle income group by 2000. What makes Botswana interesting is the fact that it did not join the rest of African countries which started poor in 1960 and remained poor in 2000. That is why it has been called as the “African success story” by Acemoglu. Latin American country Brazil started richer than Botswana and South Korea and grew at a rapid rate till 1980 but experienced stagnation from 1980 and by 2000 became poorer than these two countries. India started out at similar levels as Botswana but experienced little growth till 1980s and rapid growth afterwards. However it was not enough for India to catch up its income level with other countries (Acemoglu, 2009).

Tracing the origin of divergence In order to understand why some countries were richer in 1960 and were able to grow at steady pace it is important to look at the origin of these income differences and economic growth. This

78

910

11

1960 1970 1980 1990 2000var1

USA UK

Spain Singapore

South Korea India

Botswana Brazil

is because the growth rates we see today are not responsible for the large differences in income level that are observed in present times (Acemoglu, 2009). Fig 6 shows that relative ranking of countries in terms of level of per capita income has not changed much and thus Acemoglu (2009) concludes that large income differences across nations did not arise in postwar era.

Fig6 Source; Figure 1.9, Introduction to modern economic growth, Acemoglu (2009)

Acemoglu (2009) used data compiled by Angus Maddison for GDP per capita differences across nations going back to 1820 to track the origin of these income differences and came to the conclusion that much of the divergence took place during the 19th and early 20th centuries. Acemoglu (2009) has shown this through two graphs given below (figure 7 and 8). Figure 7 shows the divergence of average income per capita among five groups of countries (1820-2000): Africa, Asia, Latin America, Western Europe, and Western offshoots of Europe (Australia, Canada, New Zealand, the United States). Western offshoots and West European grew relatively rapidly, Latin America showed little growth and Asian and African countries remained more or less stagnant. Small (proportional) income gap in 1820 got by enlarged by 1960. A sharp decline is observed in the income paths of Western offshoots and Western Europe in 1929 due to Great Depression (Acemoglu, 2009).

Fig7 Source; Figure 1.10, Introduction to modern economic growth, Acemoglu (2009)

Acemoglu (2009) has also collected evidence to show that income differences were even smaller before 1820. He has again used Maddison’s data dating back to 1000 A.D. to illustrate this. Figure 8 shows that income gap decreases as we go back in time. The divergence took place over the past 200 years. Following observation have been made by Acemoglu (2009) to understand the origin of divergence

There was limited economic growth before the eighteenth century. Even the growth of certain civilizations like ancient Greece, China, and Rome was slow placed and not sustained (Acemoglu, 2009).

Figure 8 shows a pattern of what Acemoglu (2009) calls takeoff into sustained growth. The economic growth of Western offshoots and Western Europe took a big leap about 200 years ago i.e. in the beginning of the 19th century. The reason could be industrial revolution. But whatever may be the reason; this leap took the slow growing economies of Europe to the path of sustained growth. It is this transformation i.e. responsible for the huge income differences that we see today. European and Western offshoots were the first to make this huge jump and thus are still ahead of the rest of the world in terms of level of incomes (Acemoglu, 2009).

Fig8 Source; Figure 1.11, Introduction to modern economic growth, Acemoglu (2009)

Thus on the basis of the available evidence and information Acemoglu (2009) draws the following conclusion “…..origins of the current cross-country differences in income per capita are in the nineteenth and early twentieth centuries (or perhaps even during the late eighteenth century). This cross country divergence took place at the same time as a number of countries in the world “took off” and achieved sustained economic growth. Therefore understanding the origins of modern economic growth are not only interesting and important in their own right, but also holds the key to understanding the causes of cross-country differences in income per capita today” [Introduction to modern economic growth; Acemoglu(2009)] Thus it is important to understand the history in order to answer the questions about today.

Convergence “Neoclassical growth theory establishes a presumption that countries with access to identical technologies should converge to a common income level. Countries that are poorer and have higher marginal productivity of capital should therefore grow faster in the transition to the long-run steady state” [Dani Rodrik on Unconditional Convergence, 2011] However the empirical evidence and available data does not support unconditional convergence i.e. for poor countries to grow faster than rich ones. The above analysis compared the income gap between two countries regardless of these countries’ characteristics (technology, investment, policies etc.). After looking at such unconditional distribution of income per capita Acemoglu (2009) finds out slight divergence in post war era and larger divergence since the early 1800s. Thus Barro and Sala-i-Martin (2004) argue that it is better and more relevant to look at conditional distribution. Convergence that is found is conditional meaning that it depends on

certain characteristics of the economy like policies, institutions etc. Theory of conditional convergence states that income gap between countries that share the same characteristics closes over time (Acemoglu, 2009).

Conditional Convergence Acemoglu (2009) has used typical Barro growth regression to explain how conditional convergence can be captured.

gi, t-1 = α log yi,t-1 + XTi,t-1β + εi,t (Acemoglu,2009) (1)

where gi, t-1 is the annual growth rate between t-1 and t in country i, yi,t-1 is the output per

worker(or income per capita) at t-1, X is a vector of other variables included in the regression

with coefficient vector β and εi,t is the error term (Acemoglu, 2009).

The regression is based on the neoclassical growth model (Solow model). Acemoglu (2009) describes the variables included in X as potential determinants of steady state income and/or growth such as investment rate, level of schooling, policies, fertility rate, government consumption-ratio etc. This is also the reason why this regression is also used to estimate the determinants of economic growth-how different factors are correlated with growth (discussed in the next section) (Acemoglu, 2009). In case of conditional convergence, α which is the convergence coefficient should be negative i.e. lower the initial level of income per capita higher will be growth rate .Now in case of unconditional distribution that is without the covariates the above equation can be written as

Log yi,t= log yi,t-1(1+α) + εi,t ; (gi,t,t−1 ≈ log yi,t − log yi,t−1) (Acemoglu, 2009)

Figure 6 approximates the relationship between log GDP per worker in 2000 and log GDP per in 1960 by the 45 degree line. This means (1+α) equals one. Hence α should be approximately equal to zero implying no unconditional convergence. This is depicted in figure 10 which plots Log GDP per capita, 1960 against the (geometric) average growth rate of GDP, 1960-2000 for 98 countries using PWT (6.2 ) data. The almost horizontal line corresponds to α value of zero. This shows that there is no unconditional convergence for the entire world over the post war period (Acemoglu, 2009). The convergence coefficient is not statistically significant as shown by the regression results below. Hence we do not reject the null hypothesis which claims that coefficient of convergence α is equal to 0.

Average = Average growth rate of GDP 1960-2000 lgdp = log GDP per capita,1960

Fig10.Data from Penn World Tables version (6.2); Alan, Summers and Heston (2006).(refer table 2)

Evidence on conditional convergence Now even though the entire world does not converge, but we see pattern of convergence when we look at a certain group of countries with similar characteristics. OECD (Organization for Economic Co-operation and Development) countries exhibit this pattern of convergence. Figure 11 plots 15 OECD countries and reveals a negative relationship between log GDP per capita, 1960 and average growth rate 1960-2000. Since these countries are similar in certain characteristics like institutions, policies, and initial conditions therefore they have shown some amount of convergence (Acemoglu, 2009).

_cons .0024568 .0127782 0.19 0.848 -.0229077 .0278213 lgdp .0021721 .0016253 1.34 0.185 -.0010541 .0053983 avg Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total .024010964 97 .000247536 Root MSE = .01567 Adj R-squared = 0.0080 Residual .023572404 96 .000245546 R-squared = 0.0183 Model .000438561 1 .000438561 Prob > F = 0.1846 F( 1, 96) = 1.79 Source SS df MS Number of obs = 98

Algeria Argentina

Australia

Austria

Barbados

Belgium

BeninBolivia

Brazil

Burkina Faso

Burundi

Cameroon

Canada

Cape Verde

Chad

Chile

China

Colombia

Comoros

Congo, Republic of

Costa RicaCote d

Denmark

Dominican Republic

Ecuador

Egypt

El Salvador

Equatorial Guinea

Ethiopia

FinlandFrance

Gabon

Gambia, The

Ghana Greece

Guatemala

Guinea

Guinea-Bissau

Honduras

Hong Kong

IcelandIndia

Indonesia

Iran

Ireland

IsraelItaly

Jamaica

Japan

JordanKenya

Korea, Republic of

Lesotho

Luxembourg

Madagascar

Malawi

Malaysia

Mali

Mauritius

Mexico

Morocco

Mozambique

Nepal

Netherlands

New Zealand

NicaraguaNiger

Nigeria

NorwayPakistan Panama

Paraguay

Peru

Philippines

PortugalRomania

RwandaSenegal

Singapore

South Africa

SpainSri Lanka

Sweden

Switzerland

Syria

Taiwan

Tanzania

Thailand

Togo

Trinidad &TobagoTurkey

Uganda

United KingdomUnited States

Uruguay

Venezuela

Zambia

Zimbabwe

-.0

2

0

.02

.04

.06

6 7 8 9 10Log GDP per capita, 1960

Average growth rate of GDP,1960-2000 Fitted values

The results from this regression are statistically significant. R2 is 0.6 indicating a moderate negative correlation.

Average = Average growth rate of GDP 1960-2000 lgdp = log GDP per capita,1960

Fig11.Data from Penn World Tables version (6.2); Alan, Summers and Heston (2006).(refer table2)

Thus there is evidence on conditional convergence. This shows that conditional convergence might be possible if we control certain factors affecting growth. Vector X in Barro’s regression captures these factors. Acemoglu (2009) states that “when this vector includes variables such as years of schooling or life expectancy, using cross-sectional regressions Barro and Sala-i-Martin estimate α to be approximately −0.02, indicating that the income gap between countries that have the same human capital endowment has been narrowing over the postwar period on

_cons 17.46386 2.821285 6.19 0.000 11.36884 23.55887 lgdp -1.59779 .3122957 -5.12 0.000 -2.272464 -.9231164 average Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 7.52641212 14 .537600866 Root MSE = .43831 Adj R-squared = 0.6426 Residual 2.49751704 13 .192116695 R-squared = 0.6682 Model 5.02889508 1 5.02889508 Prob > F = 0.0002 F( 1, 13) = 26.18 Source SS df MS Number of obs = 15

Australia

Austria

Belgium

Denmark

Finland

France

Ireland

Italy

Japan

Luxemburg

Norway

Portugese

Sweden

UK

USA

22.5

33.5

44.5

8 8.5 9 9.5Log GDP per capita 1960

Average Fitted values

average at about 2 percent per year. “[Introduction to Modern Economic Growth, Acemoglu 2009]. A rate of convergence of 2% per year implies that it takes 35 years for a country to close half of the gap between the initial level of per capita income and its target level of per capita income (Barro and Sala-i-Martin, 2004). Thus Acemoglu (2009) concludes that there is no evidence of unconditional convergence in the world income from 1960 to 2000. In fact there is some amount of divergence. But there is evidence for conditional convergence implying countries with similar characteristics converge as observed in the case of OECD countries.

Determinants of Economic Growth A large number of variables are significantly related to economic growth. These variables have the capability of affecting the economic growth and in turn getting affected by the economic growth itself. These are called as the determinants of growth. These variables or factors play a crucial rule in explaining the cross country differences we see throughout the history. But what are these determinants? How are these factors or determinants related to economic growth? Do we have any evidence regarding the ultimate determinants of growth or the effects of different policies? Yes, we do! Sala-i-Martin and Barro (2004) have tried to find the evidence on these determinants by running a multiple regression where determinants are the explanatory variables and growth rate of per capita GDP is the dependent variable. Table given below contains the results of the regression performed by Barro and Sala-i-Martin (2004) for three 10 year periods by using separate dummies for different periods. Barro and Sala-i-Martin (2004) have included 72 countries for 1965–75, 86 countries for 1975–85, and 83 countries for 1985–95.

Source: Table 12.3, Economic Growth, Barro and Sala-i-Martin (2004)

They have used the empirical framework that relates real per capita growth rate to two types of variables “Initial levels of state variables, such as the stock of physical capital and the stock of human capital in the forms of educational attainment and health and Control or environmental variables (some of which are chosen by governments and some by private agents), such as the ratio of government consumption to GDP, the ratio of domestic investment to GDP, fertility rates…” [Economic Growth, Barro and Sala-i-Marin, 2004] Barro and Sala-i-Martin (2004) have defined a function for a country’s per capita growth rate in period t, Dyt, as Dyt = F (yt-1, ht-1 . . .) where yt-1 is initial per capita GDP and ht-1 is initial human capital per person. The omitted variables, denoted by . . . , represent all other control and environmental variables (Barro and Sala-i-Martin, 2004). Note: Barro and Sala-i-Martin (2004) have assumed that a higher level of initial per capita GDP reflects a higher stock of physical capital per person, for given values of schooling and health. 1. Initial levels of state variables According to Barro and Sala-i-Martin (2004), the neoclassical models predict that, keeping the environmental and control variables constant, an equiproportionate increase in yt−1 and ht−1 would reduce Dyt in the above equation because of “diminishing returns to reproducible factors”. Also in these models environmental and control variables represent the steady state

level of the economy. A change in any of these variables, given the values of state variables affects the growth rate (Barro and Sala-i-Martin, 2004). According to Barro and Sala-i-Martin (2004), for a given stock of physical capital per person, a higher value of ht-1 in the above equation raises the growth rate. Initial level of GDP per capita: In the regression by Barro and Sala-i-Martin (2004) log of initial per capita GDP has been used so that the partial coefficient represents the rate of convergence (as done in sub topic-convergence).

Source: Figure 12.3, Economic Growth, Barro and Sala-i-Martin (2004)

The above figure shows the partial relation between the growth rate of per capita GDP and the log of per capita GDP. The estimated coefficient on log (GDP), −0.025, shows the conditional convergence i.e. convergence occurs at the rate of about 2.5 percent per year. The convergence is conditional because a lower initial per capita GDP corresponds to a higher per capita growth rate when all the other independent variables are kept constant (Barro and Sala-i-Martin, 2004). Barro and Sala-i-Martin (2004) have represented human capital in the regression by “average years of school attainment” and “life expectancy”. Educational Attainment: The school-attainment variable is the male upper level schooling, observed at the start of each period, 1965, 1975, and 1985.The estimated partial coefficient is 0.0036 (se=0.0016). Figure below depicts the partial relationship between per capita growth rate and the school-attainment variable. There is a positive correlation as predicted (Barro and Sala-i-Martin, 2004).

Source: Figure 12.4, Economic Growth, Barro and Sala-i-Martin (2004)

Life expectancy: Health capital proxied by the reciprocal of life expectancy at age one. The life-expectancy variable applies to 1960, 1970, and 1980 i.e. for 3 growth equations. The estimated coefficient is −5.0 (s.e. = 0.9) and is highly significant. Hence better health predicts higher economic growth. Figure below shows the partial relation between growth and this variable. The relation appears to be strong (Barro and Sala-i-Martin, 2004).

Source: Figure 12.5, Economic Growth, Barro and Sala-i-Martin (2004)

Control and Environmental Variables Barro and Sala-i-Martin (2004) have considered “international openness, the ratio of government consumption to GDP, a subjective indicator of maintenance of the rule of law, a subjective indicator of democracy (electoral rights), and the log of the total fertility rate, the ratio of real gross domestic investment to real GDP, and the inflation rate” as the measures of control and environmental variable. In Solow model these characterize the steady state of an economy (state where output per effective worker remains constant). Any change in these variables tends to change the steady state level of output per effective worker. The growth rate also changes for the given values of the state variables. However according to neoclassical growth models these variables only have level effects i.e. they change the steady state and cause the economy to grow faster for a short period of time, however once the economy reaches its new state the economy starts growing back at its original rate of growth. Thus these variables are assumed to have no long run effects on per capita growth rates. The long run growth rate in these models is influenced by the exogenous technological progress. On the other hand in the endogenous growth models like Schumpeterian growth model variables that affect R&D also affect long run growth rates. However, even in the Solow– Swan and Ramsey models, sometimes adjustment to the new steady-state position takes very long time and hence the growth effect of a control variable extends to the long run also. (Barro and Sala-i-Martin, 2004).

Therefore, because of their impact on both short and long run growth rates it becomes important to look for evidences on their relationship with the growth rates. Barro and Sala-i-Martin (2004) have used their multiple regression system to show the relation between growth rate and various control variables. Some of the results of the regression performed by them are stated below. Fertility rate: Barro and Sala-i-Martin (2004) anticipate a negative effect of the fertility rate on economic growth because it has a negative effect on steady state capital-effective worker ratio in neoclassical models. Since higher fertility means that greater resources are needed for child rearing therefore higher fertility is expected to reduce growth. The regression results and the figure below show a negative correlation. The estimated partial coefficient is negative (−0.012 (s.e. = 0.005)) and statistically significant. Log of fertility rate has been used as the explanatory variable (Barro and Sala-i-Martin, 2004).

Source: Figure 12.6, Economic Growth, Barro and Sala-i-Martin (2004)

Government Consumption ratio: Barro and Sala-i-Martin (2004) have assumed that this variable measures expenditures that affect productivity indirectly by distorting private decisions. Therefore, a higher value of the government consumption ratio leads to both-a lower steady-state level of output per effective worker and lower growth rate, ceteris paribus (Barro and Sala-i-Martin, 2004).

“The ratio of real government consumption to real GDP was adjusted by subtracting the estimated ratio to real GDP of real spending on defense and noncapital real expenditures on education.” [Economic Growth by Robert J. Barro and Xavier Sala-i-Martin, 2004] In the regression, the estimated coefficient of the government consumption ratio is negative and statistically significant: -0.062. The partial relation is shown below. Thus, there is a negative correlation as expected (Barro and Sala-i-Martin, 2004).

Source: Figure 12.7, Economic Growth, Barro and Sala-i-Martin (2004)

Investment Ratio: Ratio of real investment to real GDP is used to measure the effect of the saving rate in neoclassical model. In Neoclassical models, increase in saving rate increases the steady state level of output per effective worker and therefore also increases the growth rate. In the regression, the estimated coefficient (0.083) is positive and significant. The figure below shows the partial relation. A positive correlation is observed as predicted by the neoclassical models (Barro and Sala-i-Martin, 2004).

Source: Figure 12.12, Economic Growth, Barro and Sala-i-Martin (2004)

Rule of law: Barro and Sala-i-Martin (2004) in their studies have assumed that an improvement in the rule of law implies “enhanced property rights” and, therefore, an incentive for higher investment and growth. This is because well-functioning political and legal institutions help to sustain growth. In the regression by Barro and Sala-i-Martin (2004) the variable used to measure rule of law is a subjective measure provided in “International Country Risk Guide by the international consulting company Political Risk Services”. The partial coefficient (0.0185 (0.0059)) is positive as predicted and is statistically significant. The figure below depicts the positive correlation (Barro and Sala-i-Martin, 2004).

Source: Figure 12.8, Economic Growth, Barro and Sala-i-Martin (2004)

Case Study : “African Success Story”-Botswana According to Barro and Sala-i-Martin (2004) historical analyses often relate current institutional and political characteristics to policies at the time of colonial rule. “Acemoglu, Johnson, and Robinson (2002) argue that European colonists were more likely to invest in institutions in regions that were previously poor or empty, notably present-day Canada and the United States, because they lacked the potential for exploitation of mineral wealth and indigenous populations. Acemoglu, Johnson, and Robinson (2001) stress that the adverse mortality experience of settlers in parts of Latin America and Africa may have limited institutional investments in those colonies. Woodberry (2002) argues that the establishment of quality schooling by missionaries in some colonies may have had a long-lasting influence on political institutions. These analyses suggest instrumental variables—from the long-term history—that can be used to get more reliable estimates of the effects of current variables, such as the rule-of-law indicator” [Economic Growth, Barro and Sala-i-Martin,2004] Botswana, an African country, has had an outstanding growth experience. Improvement in rule of law has actually made it one of the fastest growing countries in the world. As Acemoglu,

Johnson, and Robinson (2001) have stated that this occurred despite the fact that most of the African countries were colonized and very little investment was made during the colonial period. After the end of colonialism, most of the countries were left with high inequality and poverty, and have still not come out of it. Botswana on the other hand has been successful in breaking free from the shackles of poverty and inequality. In their paper “An African Success Story Botswana” Acemoglu, Johnson, and Robinson (2001) have suggested that good policies adopted in Botswana are responsible for its success.

Data extracted from PWT (6.2); Heston, Summers and Aten (2006). (Real GDP per capita in 2000 US$)

The above figure shows the evolution of Log per capita GDP from 1970 to 2000 (post-colonial period) for five South African countries. It is evident that while Botswana has grown rapidly other countries have remained poor. As per Acemoglu, Johnson, and Robinson (2001) diamonds account for a major percentage of Botswana’s output but still abundance of this resource has not created political instabilities and conflicts for control of this resource as it has created in other African countries with abundant resources. According to Acemoglu, Johnson, and Robinson (2001) Botswana achieved this spectacular performance solely because it managed to adopt good policies. Equality and minimal government interventions are not reasons behind good policies because as proposed by Acemoglu, Johnson and Robinson (2001) there was plenty of inequality of assets and income and the fact that government expenditure accounted for 40% of GDP shows that Botswana had massive government intervention. Therefore Acemoglu, Johnson and Robinson (2001) concluded that plausible cause of the choice of relatively good economic policies is that both political and economic institutions in Botswana

67

89

1970 1980 1990 2000Year(1970-2000)

Botswana Ethiopia

Democratic Republic of Congo Madagascar

Mali

Evolution of Log GDPper capita (1970-2000)

are good.) They have defined a good institution in the following manner “…a social organization which ensures that a broad cross-section of the society have effective property rights. We refer to this cluster as institutions of private property” [Acemoglu, Johnson, and Robinson (2001)]. These institutions are different from extractive institutions where there is high risk of expropriation of assets by the government, rich and wealthy. Thus it was because of the institution of private property which ensured (a)”secured property rights” and (b) “effective constraints on rulers and political elites to limit arbitrary and extractive behavior” that Botswana experienced rapid growth as compared to other African countries. These institutions by protecting the property rights of potential investors ensured political stability as well (Acemoglu, Johnson, and Robinson, 2001).

The example of Botswana shows that good policies and good institutions can change the economic conditions from unfavorable to favorable. This example shows that how a determinant can be strongly correlated with economic growth and how it can play an important role in enhancing the economic growth of a country. I would like to summarize this section by quoting Robert.J.Barro: “For a given starting level of real per capita GDP, the growth rate is enhanced by higher initial schooling and life expectancy, lower fertility, lower government consumption, better maintenance of the rule of law, lower inflation, and improvements in the terms of trade. For given values of these and other variables, growth is negatively related to the initial level of real per capita GDP. Political freedom has only a weak effect on growth but there is some indication of a nonlinear relation. At low levels of political rights, an expansion of these rights stimulates economic growth. However, once a moderate amount of democracy has been attained, a further expansion reduces growth. In contrast to the small effect of democracy on growth, there is a strong positive influence of the standard of living on a country’s propensity to experience democracy.”[DETERMINANTS OF ECONOMIC GROWTH: A CROSS-COUNTRY EMPIRICAL STUDY Robert J. Barro, 1996]

Kaldor’s stylized facts Nicholas Kaldor (1961) in “Capital Accumulation and Economic growth” suggested six stylized facts as a starting point for the construction of theoretical models on growth. Listed below are the six stylized facts by Kaldor (1961) “1. The continued growth in the aggregate volume of production and in the productivity of labour at a steady trend rate; no recorded tendency for a falling rate of growth of productivity. 2. A continued increase in the amount of capital per worker, whatever statistical measure of `capital' is chosen in this connection. 3. A steady rate of profit on capital, at least in the `developed' capitalist societies ; this rate of profit being substantially higher than the ' pure' long-term rate of interest as shown by the yield of gilt-edged bonds. According to Phelps Brown and Weber the rate of profit in the United

Kingdom was remarkably steady around 10.5 per cent in the period 1870-1914, the annual variations being within 9.5-11.5 per cent. A similar long-period steadiness, according to some authorities, has shown itself in the United States. 4. Steady capital-output ratios over long periods; at least there are no clear long-term trends, either rising or falling, if differences in the degree of utilization of capacity are allowed for. This implies, or reflects, the near-identity in the percentage rates of growth of production and of the capital stock — i.e. that for the economy as a whole, and over longer periods, income and capital tend to grow at the same rate. 5. A high correlation between the share of profits in income and the share of investment in output; a steady share of profits (and of wages) in societies and/or in periods in which the investment coefficient (the share of investment in output) is constant. For example, Phelps Brown and Weber found long-term steadiness in the investment coefficient, the profit share and the share of wages in the U.K., combined with a high degree of correlation in the (appreciable) short period fluctuations of these magnitudes. The steadiness in the share of wages implies, of course, a rate of increase in real wages that is proportionate to the rate of growth of (average) productivity 6. Finally, there are appreciable differences in the rate of growth of labour productivity and of total output in different societies, the range of variation (in the fast-growing economies) being of the order of 2-5 per cent. These are associated with corresponding variations in the investment coefficient, and in the profit share, but the above propositions concerning the constancy of relative shares and of the capital-output ratio are applicable to countries with differing rates of growth.” [Capital Accumulation and Economic Growth, Nicholas Kaldor, 1961] Barro and Sala-i-Martin (2004) have summarized these facts as follows 1. Per capita output grows over time, and its growth rate does not tend to diminish. 2. Physical capital per worker grows overtime. 3. The rate of return to capital is nearly constant. 4. The ratio of physical capital to output is nearly constant 5. The share of physical capital and labor in national income are nearly constant. 6. The growth rate of output per worker differs substantially across countries. Are these facts true? Does the empirical evidence support them? Barro and Sala-i- Martin (2004) have collected evidences to answer these questions. According to Barro and Sala-i-Martin (2004) facts 1, 2, 4 and 5 are supported well by the long term data for currently developed countries. For example: Figure below shows the evolution of log of per capita GDP of some developed countries- US, UK, Japan, and Italy from 1950-2000. The figure is consistent with the first fact. It is clear that the per capita output has grown overtime for these countries.

Data extracted from PWT version (6.2), Heston, Summers and Aten (2006)

Barro and Sala-i-Martin (2004) have gone further back in time and have used Maddison data to show that growth rates do not decline overtime. Table given below shows the growth rates of few currently developed countries taken together from 1830 to 1990 .The average per capita growth rate is 1.9 percent per year over this period. These growth rates are consistent with Kaldor’s fact 1. The reduction in the growth rate from 3.7 percent per year in 1950–70 to 2.2 percent per year in 1970–90 is because of productivity slowdown (Barro and Sala-i-Martin, 2004).

Source: Table I.1, Economic Growth, Barro and Sala-i-Martin (2004)

Barro and Sala-i-Martin have also shown long run growth rate patterns for 15 currently less developed countries in Asia and Latin America. Table below shows the growth rates for the period 1900-87. The average long run growth rate of this period corresponds to a value of 1.4

78

910

11

1950 1960 1970 1980 1990 2000Time

USA Japan

UK Italy

Evolution of Log GDP per capita (1950-2000)

per cent per year. The post war growth rates are higher than the long term average growth rate. In these countries also growth rates have increased overtime except few periods (Barro and Sala-i-Martin, 2004).

Source: Table I.2, Economic Growth, Barro and Sala-i-Martin (2004)

Maddison (1982) has also shown that there is strong evidence of stability of the long-run ratio of physical capital to GDP in Japan, Germany, Italy, the United Kingdom, and the United States. This shows that income and capital tend to grow at same rate in the long run. Denison (1974) and Jorgenson, Gollop, and Fraumeni (1987) have successfully shown indictors of the long-term stability of factor shares in the United States. Young (1995) has reported that factor shares were stable in four East Asian countries—Hong Kong, Singapore, South Korea, and Taiwan from 1960 to 1990. Studies of seven developed countries (Canada, France, Germany, Italy, Japan, the Netherlands, and the United Kingdom) by Christensen, Cummings, and Jorgenson, 1980, and Dougherty, 1991 indicate that factor shares are similar to the factor shares in the United States. Elias (1990), has shown that capital shares in some Latin American countries tend be higher than the shares in the United States. The cross country trends data discussed earlier in this paper clearly show that growth rate of output per worker is different across countries. Table 2 at the end also confirms variability in per capita growth rate across countries. Therefore fact 6 is also consistent with empirical evidence (Barro and Sala-i-Martin, 2004). Thus economists have collected ample of evidences which prove the consistency of facts 2, 4, 5 and 6. According to Barro and Sala-i-Martin (2004) Kaldor’s stylized fact 3 on the stability of real rates of return appears to be in accordance with the case of United Kingdom only because in UK the real interest rate seems to have no long-run trend. I would like to requote Kaldor here to show that his fact number 3 was influenced by the experience of United Kingdom.-“A steady rate of profit on capital, at least in the `developed' capitalist societies ; this rate of profit being substantially higher than the ' pure' long-term rate of interest as shown by the yield of gilt-edged bonds. According to Phelps Brown and Weber the rate of profit in the United Kingdom was remarkably steady around 10.5 per cent in the period 1870-1914, the annual variations being within 9.5-11.5 per cent.“[Capital Accumulation and Economic Growth, Nicholas Kaldor, 1961]. However this trend is not observed for all countries.

“For the United States, however, the long-term data suggest a moderate decline of real interest rates (Barro, 1997, table 11.1). Real rates of return in some fast-growing countries, such as South Korea and Singapore, are much higher than those in the United States but have declined over time (Young, 1995). Thus it seems likely that Kaldor’s hypothesis of a roughly stable real rate of return should be replaced by a tendency for returns to fall over some range as an economy develops.” [Economic growth, Barro and Sala-i-Martin, 2004] Thus, to summarize, according to Barro and Sala-i-Martin (2004) facts 1, 2, 4, and 5 are consistent with the long term data for currently developed countries and fact 6 is also true because cross country trends show that growth rate of output per worker is substantially different across countries (see table 2 at the end). However stylized fact 3 which states that rate of return to capital is constant is not true in case of most countries. Barro and Sala-i-Martin (2004) therefore suggest replacing constant returns to capital with declining returns to capital because the latter is found to be more consistent with the empirical evidence.

Table 1 Serial No. COUNTRY(188) 1960 1960* 2000 2000(*)

1 Afghanistan

478.1043 6.169829

2 Albania

3796.805 8.241916

3 Algeria 3843.16 8.25405 5753.122 8.657498

4 Angola

1974.977 7.588312

5 Antigua

14064.74 9.551426

6 Argentina 7838.333 8.966782 11331.96 9.335382

7 Armenia

3471.409 8.152316

8 Australia 10815.08 9.288696 25834.54 10.15947

9 Austria 8444.026 9.041215 26999.77 10.20358

10 Azerbaijan

3590.968 8.186177

11 Bahamas

19088.04 9.856817

12 Bahrain

18652.15 9.833716

13 Bangladesh

1851.156 7.523566

14 Barbados 7039.401 8.859279 16086 9.685704

15 Belarus

10005.07 9.210847

16 Belgium 8069.946 8.995902 24661.91 10.11302

17 Belize

6014.447 8.70192

18 Benin 955.9087 6.862662 1251.474 7.132077

19 Bermuda

34031.57 10.43504

20 Bhutan

828.1631 6.71921

21 Bolivia 2431.393 7.796219 2929.186 7.98248

22 Bosnia and Herzegovina

3037.182 8.018685

23 Botswana

7256.446 8.889646

24 Brazil 2643.531 7.879871 7193.598 8.880947

25 Brunei

25111.92 10.1311

26 Bulgaria

7257.503 8.889791

27 Burkina Faso 767.6941 6.643391 933.2079 6.838628

28 Burundi 676.6124 6.517098 698.8438 6.549427

29 Cambodia

513.9064 6.242041

30 Cameroon 1946.723 7.573903 2471.727 7.812672

31 Canada 10575.74 9.266318 26820.73 10.19693

32 Cape Verde 1416.964 7.256272 4983.355 8.513859

33 Central African Republic

945.1021 6.851293

34 Chad 1141.747 7.040315 829.5051 6.720829

35 Chile 5086.118 8.53427 11430.19 9.344013

36 China 448.1323 6.105089 4001.824 8.294505

37 Colombia 2818.614 7.944001 6079.678 8.712707

38 Comoros 1353.663 7.210569 1358.78 7.214343

39 Congo, Dem. Rep.

359.1472 5.883732

40 Congo, Republic of 1009.5 6.91721 1286.19 7.15944

41 Costa Rica 4513.028 8.414723 8341.47 9.028995

42 Cote d`Ivoire 1334.004 7.19594 2171.659 7.683247

43 Croatia

8979.602 9.102711

44 Cuba

5698.618 8.647979

45 Cyprus

20456.78 9.926069

46 Czech Republic

13616.58 9.519044

47 Denmark 11438.23 9.344717 27827.28 10.23377

48 Djibouti

4375.841 8.383854

49 Dominica

8196.849 9.011505

50 Dominican Republic 2079.948 7.640099 6497.367 8.779152

51 Ecuador 2396.334 7.781695 4314.442 8.369723

52 Egypt 1468.97 7.292317 4535.832 8.419764

53 El Salvador 2990.639 8.003242 4732.127 8.462131

54 Equatorial Guinea 970.4337 6.877743 6494.539 8.778717

55 Eritrea

555.3912 6.319673

56 Estonia

11080.91 9.312979

57 Ethiopia 400.0812 5.991667 725.3652 6.586675

58 Fiji

4571.948 8.427694

59 Finland 7784.671 8.959911 22740.69 10.03191

60 France 8530.817 9.05144 25044.54 10.12841

61 Gabon 6741.386 8.816021 10438.83 9.253287

62 Gambia, The 721.8858 6.581867 953.8588 6.860516

63 Georgia

3885.835 8.265093

64 Germany

25061.34 10.12908

65 Ghana 411.8636 6.020692 1392.201 7.238641

66 Greece 4177.123 8.337378 13982.39 9.545553

67 Grenada

5896.24 8.68207

68 Guatemala 2494.391 7.8218 3859.467 8.258285

69 Guinea 3072.128 8.030126 2546.122 7.842327

70 Guinea-Bissau 492.6669 6.199833 762.4012 6.636473

71 Guyana

3733.182 8.225017

72 Haiti

2069.288 7.63496

73 Honduras 1714.777 7.447038 2239.656 7.714077

74 Hong Kong 3321.62 8.108208 27236.15 10.2123

75 Hungary

11382.95 9.339872

76 Iceland 8380.452 9.033657 25794.63 10.15792

77 India 891.5249 6.792933 2643.851 7.879992

78 Indonesia 1071.275 6.976605 3771.861 8.235324

79 Iran 3311.077 8.105029 6045.526 8.707074

80 Iraq

2445.349 7.801943

81 Ireland 5293.792 8.57429 24947.55 10.12453

82 Israel 6749.981 8.817295 22236.9 10.00951

83 Italy 7167.139 8.877262 22487.21 10.0207

84 Jamaica 3476.777 8.153861 4520.838 8.416452

85 Japan 4508.687 8.413761 23970.56 10.08458

86 Jordan 4151.359 8.331191 3901.837 8.269203

87 Kazakhstan

6519.556 8.782561

88 Kenya 1179.371 7.072737 1267.716 7.144972

89 Kiribati

1432.161 7.26694

90 Korea, Dem. Rep.

1378.953 7.22908

91 Korea, Republic of 1458.353 7.285063 15702.27 9.661561

92 Kuwait

25135.37 10.13203

93 Kyrgyzstan

3389.278 8.128372

94 Laos

1257.347 7.13676

95 Latvia

8998.11 9.10477

96 Lebanon

6174.898 8.728248

97 Lesotho 575.9489 6.356019 1833.902 7.514201

98 Liberia

472.437 6.157904

99 Libya

10334.85 9.243277

100 Lithuania

9160.769 9.122685

101 Luxembourg 12919.79 9.466516 48217.27 10.78347

102 Macao

24224.41 10.09512

103 Macedonia

5270.727 8.569923

104 Madagascar 1267.512 7.144812 822.8821 6.712813

105 Malawi 460.4585 6.132223 838.9891 6.732198

106 Malaysia 1800.735 7.49595 11405.5 9.341851

107 Maldives

4530.145 8.418509

108 Mali 796.7164 6.680499 1046.719 6.953416

109 Malta

18862.84 9.844949

110 Mauritania

1521.481 7.32744

111 Mauritius 3661.552 8.205643 15121.01 9.62384

112 Mexico 3718.845 8.221169 8082.091 8.997406

113 Micronesia, Fed. Sts.

3781.689 8.237926

114 Moldova

2217.594 7.704178

115 Mongolia

1500.781 7.313741

116 Morocco 1298.768 7.169172 3720.048 8.221492

117 Mozambique 838.3329 6.731415 1093.179 6.996846

118 Namibia

5268.552 8.56951

119 Nepal 800.1591 6.684811 1421.009 7.259122

120 Netherlands 10462.46 9.255549 26293.09 10.17706

121 Netherlands Antilles

14014.4 9.547841

122 New Zealand 12062.57 9.397862 20422.92 9.924413

123 Nicaragua 4427.883 8.395677 3437.851 8.142602

124 Niger 1167.434 7.062563 807.4544 6.693887

125 Nigeria 1105.776 7.008303 1073.93 6.979081

126 Norway 9473.016 9.156202 33092.16 10.40705

127 Oman

16193.18 9.692346

128 Pakistan 800.7665 6.685569 2477.129 7.814856

129 Palau

9357.119 9.143892

130 Panama 2499.052 7.823667 7934.798 8.979013

131 Papua New Guinea

4354.577 8.378983

132 Paraguay 2509.525 7.827849 4965.414 8.510252

133 Peru 3129.175 8.048525 4204.5 8.343911

134 Philippines 2038.721 7.620078 3825.615 8.249475

135 Poland

8611.005 9.060797

136 Portugal 3689.274 8.213185 17323.14 9.759798

137 Puerto Rico

21211.21 9.962285

138 Qatar

32260.65 10.3816

139 Romania 1276.377 7.151781 5211.109 8.558548

140 Russia

9263.46 9.133833

141 Rwanda 1019.894 6.927454 1018.07 6.925663

142 Samoa

3070.917 8.029732

143 Sao Tome and Principe

1300.183 7.17026

144 Saudi Arabia

15826.52 9.669442

145 Senegal 1775.606 7.481897 1571.367 7.359702

146 Serbia and Montenegro

2094.67 7.647151

147 Seychelles

10592.79 9.267928

148 Sierra Leone

683.7297 6.527563

149 Singapore 4219.137 8.347385 29433.77 10.2899

150 Slovak Republic

9696.866 9.179558

151 Slovenia

18205.51 9.80948

152 Solomon Islands

2012.588 7.607177

153 Somalia

681.633 6.524491

154 South Africa 4927.117 8.502509 8226.063 9.015062

155 Spain 4880.826 8.49307 19536.38 9.880033

156 Sri Lanka 866.0729 6.763969 4046.63 8.30564

157 St. Kitts & Nevis

14393.29 9.574518

158 St. Lucia

6838.996 8.830397

159 St.Vincent & Grenadines

7671.753 8.9453

160 Sudan

1047.713 6.954365

161 Suriname

4753.42 8.466619

162 Swaziland

8517.029 9.049823

163 Sweden 11065.36 9.311575 25231.77 10.13586

164 Switzerland 15253.38 9.632556 28831.25 10.26922

165 Syria 837.364 6.730259 2000.888 7.601346

166 Taiwan 1443.614 7.274905 19183.93 9.861828

167 Tajikistan

1660.401 7.414814

168 Tanzania 502.0778 6.218755 816.6624 6.705226

169 Thailand 1059.09 6.965166 6473.596 8.775487

170 Togo 832.5754 6.724524 823.1664 6.713159

171 Tonga

3398.268 8.131021

172 Trinidad &Tobago 6273.559 8.744099 14770.03 9.600356

173 Tunisia

6993.312 8.85271

174 Turkey 2250.397 7.718862 5714.591 8.650778

175 Turkmenistan

7624.23 8.939087

176 Uganda 873.1702 6.77213 1057.792 6.96394

177 Ukraine

5002.87 8.517767

178 United Arab Emirates

32181.68 10.37915

179 United Kingdom 10323.29 9.242158 24666.41 10.1132

180 United States 12892.02 9.464364 34364.5 10.44478

181 Uruguay 6142.511 8.722989 10739.74 9.281706

182 Uzbekistan

3543.241 8.172797

183 Vanuatu

3234.729 8.0817

184 Venezuela 6092.064 8.714743 7322.97 8.898771

185 Vietnam

2189.408 7.691386

186 Yemen

1081.908 6.986481

187 Zambia 910.4294 6.813916 865.6494 6.76348

188 Zimbabwe 2298.137 7.739854 3255.93 8.088233 Data on Real GDP per capita (in 2000 U.S.$) for 188 countries for the years 1960 and 2000.

Source: Alan Heston, Robert Summers and Bettina Aten, Penn World Table Version 6.2, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, September 2006. 1960 – Real GDP per capita (Constant Prices: Chain series) (in 2000 U.S. $) in 1960 1960(*) - Log of Real GDP per capita, 1960 2000 - Real GDP per capita (Constant Prices: Chain series) (in 2000 U.S. $) in 2000

2000(*) -Log of Real GDP per capita, 2000.

Table 2

Country Average growth rate (1960-2000)

Afghanistan Albania Algeria 1.008619

Angola Antigua Argentina 0.9215019

Armenia Australia 2.176928

Austria 2.905923

Azerbaijan Bahamas Bahrain Bangladesh Barbados 2.066066

Belarus Belgium 2.792783

Belize Benin 0.6735364

Bermuda Bhutan Bolivia 0.4656512

Bosnia and Herzegovina Botswana Brazil 2.50269

Brunei Bulgaria Burkina Faso 0.4880918

Burundi 0.0808216

Cambodia Cameroon 0.5969234

Canada 2.32653

Cape Verde 3.143967

Central African Republic Chad -0.7987132

Chile 2.024358

China 5.473542

Colombia 1.921766

Comoros 0.0094337

Congo, Dem. Rep. Congo, Republic of 0.6055737

Costa Rica 1.535678

Cote d`Ivoire 1.218265

Croatia Cuba Cyprus Czech Republic Denmark 2.222638

Djibouti Dominica Dominican Republic 2.847635

Ecuador 1.47007

Egypt 2.818617

El Salvador 1.14722

Equatorial Guinea 4.752435

Eritrea Estonia Ethiopia 1.487519

Fiji Finland 2.679998

France 2.692426

Gabon 1.093167

Gambia, The 0.6966216

Georgia Germany Ghana 3.044872

Greece 3.020439

Grenada Guatemala 1.091211

Guinea -0.469498

Guinea-Bissau 1.091599

Guyana Haiti

Honduras 0.6675975

Hong Kong 5.260231

Hungary Iceland 2.810661

India 2.717646

Indonesia 3.146797

Iran 1.505112

Iraq Ireland 3.875602

Israel 2.980533

Italy 2.858601

Jamaica 0.6564797

Japan 4.177052

Jordan -0.1549707

Kazakhstan Kenya 0.180589

Kiribati Korea, Dem. Rep. Korea, Republic of 5.941244

Kuwait Kyrgyzstan Laos Latvia Lebanon Lesotho 2.895456

Liberia Libya Lithuania Luxembourg 3.292393

Macao Macedonia Madagascar -1.079996

Malawi 1.499938

Malaysia 4.614753

Maldives Mali 0.6822929

Malta Mauritania Mauritius 3.545494

Mexico 1.940594

Micronesia, Fed. Sts.

Moldova Mongolia Morocco 2.6308

Mozambique 0.663576

Namibia Nepal 1.435779

Netherlands 2.30378

Netherlands Antilles New Zealand 1.316376

Nicaragua -0.6326877

Niger -0.9216916

Nigeria -0.0730554

Norway 3.127123

Oman Pakistan 2.823215

Palau Panama 2.888366

Papua New Guinea Paraguay 1.706008

Peru 0.7384657

Philippines 1.573492

Poland Portugal 3.866534

Puerto Rico Qatar Romania 3.516918

Russia Rwanda -0.0044753

Samoa Sao Tome and Principe Saudi Arabia Senegal -0.3054894

Serbia and Montenegro Seychelles Sierra Leone Singapore 4.85628

Slovak Republic Slovenia Solomon Islands Somalia South Africa 1.281384

Spain 3.46741

Sri Lanka 3.854177

St. Kitts & Nevis St. Lucia St.Vincent & Grenadines Sudan Suriname Swaziland Sweden 2.060712

Switzerland 1.591647

Syria 2.177719

Taiwan 6.467308

Tajikistan Tanzania 1.216177

Thailand 4.525804

Togo -0.0284135

Tonga Trinidad &Tobago 2.140642

Tunisia Turkey 2.32979

Turkmenistan Uganda 0.4795225

Ukraine United Arab Emirates United Kingdom 2.1776

United States 2.451039

Uruguay 1.396792

Uzbekistan Vanuatu Venezuela 0.4600725

Vietnam Yemen Zambia -0.126091

Zimbabwe 0.8709481 Average (geometric) Growth rate computed using the data on Real GDP per capita (in 2000 U.S.$) for 98 countries for the years 1960 and 2000. Source: Alan Heston, Robert Summers and Bettina Aten, Penn World Table Version 6.2, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, September 2006.

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w5698. National Bureau of Economic Research, 1996. o Acemoglu, Daron and Johnson, Simon and Robinson, James A., An African Success Story:

Botswana (February 2002), CEPR (Centre for Economic Policy Research) Discussion Paper No. 3219.

o Rodrik, Dani. Unconditional convergence. No. w17546. National Bureau of Economic Research, 2011.

o Kaldor, Nicholas. "Capital accumulation and economic growth." The theory of capital. Palgrave Macmillan UK, 1961. 177-222.

o Ray, Debraj, Development Economics, (DE) Princeton University Press, 1998. o http://data.worldbank.org/ o http://datacentre2.chass.utoronto.ca/pwt62/ ; Alan Heston, Robert Summers and Bettina

Aten, Penn World Table Version 6.2, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, September 2006. (188 countries, 1950-2004, 2000 as base year) Note: Graphs, tables and regressions that are based on this data have been generated using Stata 10: Data Analysis and Statistical Software.