Economic Reforms, Human Capital, and Economic ...

Economic Reforms, Human Capital, and Economic Growth in India and South Korea: A Cointegration Analysis

This version: August 2008

Svitlana Maksymenko Department of Economics University of Pittsburgh 4703 Posvar Hall 230 S. Bouquet Street Pittsburgh, PA 15260 Tel. 1 412-383-8155 Fax 1 412-648-1793 E-mail: [email protected] Mahbub Rabbani Economic Consulting Milliman Inc. One Pennsylvania Plaza 38th Floor New York, NY 10119 Tel 1 646-473-3431 Fax 1 646-473-3481 E-mail: [email protected]

Corresponding author: [email protected]

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Abstract By employing a multivariate time series model, this study advances theoretical and empirical research on the role of economic reforms and human capital accumulation in the postreform economic growth. We construct two indexes – a human capital index and a composite economic reform index – and perform a cointegration analysis of a long-run equilibrium growth path in India and South Korea twelve years after the implementation of reform. The significant positive effect of human capital accumulation is revealed in both India and South Korea. The impact of economic reforms is found to be heterogeneous across countries: the effect is positive, significant, and sizable in South Korea, while it is negative and relatively small in India. This result is suggestive of different degrees of efficiency of reform measures implementation in two countries.

Keywords: economic growth, human capital, economic reforms, India, South Korea JEL classification: O10, O15, O47, O53

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1. Introduction It is widely believed that both a technological change and capital accumulation play a key role in economic growth. At the same time, growth economists recognize that the development process usually decelerates without organized markets, and as a result, the society gets deprived of a substantial part of growth benefits. Taking into account the importance of economic reforms for market organization, we would like to investigate both the role of economic reforms and the role of human capital on economic development of two Asian economies – South Korea and India – in their respective post reform period. In the 1950s, South Korea was a very poor developing country. Its GDP per capita at the end of Korean War was less than $800. In less than forty years, South Korea’s GDP per capita increased more then ten times, to $7235. India is a good study in contrast. In the middle of twentieth century, India’s GDP per capita was only slightly lower than South Korea’s. India, like South Korea, prior to reform implementation, was also a labor-abundant economy, closed to international trade. Its capital stock per worker accounted to $786, and a degree of openness was only 10.4%. Today, India is still a poor, labor-abundant economy, with a deprived state of demographic and social developments. Considering these two Asian economies, it is extremely interesting to explore the factors that have worked behind their divergent pathpaths of economic growth and explain the considerable differences between them. Experts generally credit South Korean economic success to pragmatic market reforms (World Bank, 1993). However, Rodrik (1996), who attributes South Korean economic growth to market-oriented policies and the reduced role of government intervention, argues that the success of reforms in South Korea could also be explained by a better-educated labor force that might have simplified the establishment of a competent bureaucracy and have enhanced the 2

productivity of interventions aimed at increasing private investment. Furthermore, Edwards (1992), and Levin and Renelt (1992) point out that market reforms are associated with growth only in those economies that have appropriate human capital to absorb new developments efficiently. According to Nehru et al (1995), South Korea has the highest average education stock and the highest growth rate of education stock among developing countries, while India is at a relatively low level in both categories. To this end, Harvie and Pahlavani (2000) indicate that it is the impressive investment in human capital (education, in particular) that has boosted South Korea’s economic growth far beyond the level of other South and East Asian economies. So, which factor – policy reform or human capital – has been more important for growth? This issue has not been resolved yet. We will investigate it in deeper detail in this paper. In this paper, we will comparatively evaluate the impact of two factors - market reforms and human capital - along with their complementary effects on economic growth in India and South Korea in the post-reform period during the second half of the twentieth century. Our choice of countries is justified by the fact that India and South Korea are the two largest economies in Asia. They were at a somewhat similar economic level in per capita GDP when the reforms were initiated. And yet, the impact of reforms has differed significantly between these two economies. In particular, we have the following objectives for this paper. We perform a comparative analysis of the economic reforms and the movement of major economic variables before and after the reform implementation in India and South Korea. As no explicit data are available for market reforms and human capital, we develop a new methodology for determination and construction of two composite indexes - a reform index and a human capital index - to measure these variables implicitly. We use a multi-variable time series model to test the hypotheses of the

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impact of economic reforms and human capital on economic growth in India and South Korea in the long run. Our model is based on a modified production function, which along with conventional factors of production, incorporates composite reform and human capital indexes. We perform a cointegration analysis of the potential long-run growth functions of each country. By finding elasticities of countries’ GDP to economic reforms and human capital augmented labor, we trace the effects of different reforms and human capital accumulation on balanced growth path of each economy. Based on the conclusions of our empirical analysis, we discuss policy implications and assess the soundness of economic reform policies. The rest of the paper is structured as follows. Section 2 reviews the literature. Section 3 develops a simple model to quantify empirically the impact of economic reforms and human capital accumulation on economic growth. Section 4 briefly introduces readers to the recent economic developments in India and South Korea with an assessment of market reforms outcomes, describes data, and performs empirical analysis based on a multi-variable time series model. Section 5 provides policy recommendations, explores avenues for further research, and offers conclusions. 2. Literature Review What is the key to the economic success of South Korea? Among professional economists, the view is that the East Asian miracle could be attributed to market-oriented policies and the reduced role of government intervention 1 . In particular, Krueger (1993), argues that in contrast to the overcontrolled, overregulated, and highly distorted other East Asian economies, the Korean economy has been characterized by diminishing intervention in most spheres of economic activity, and 1

much smaller degree of distortion. Rodrik (1996) also

For additional discussion, see Ranis and Mahmood (1992)

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confirms that the success of South Korea and Taiwan at the end of the twentieth century could actually be explained by an extensive set of reforms during the late 1950s and early 1960s. Harvie and Pahlavani (2000) examine the major determinants of GDP growth in South Korea during 1980-2005, allowing for the presence of potential structural breaks , which coincide in time with the effects of the Asian crisis on the Korean economy. The authors argue that there is a cointegration relationship between the Korean GDP growth, investment, trade, and human capital. Employing an error correction procedure, Harvie and Pahlavani conclude that, in the long run, policies aimed at promoting various types of physical and human capital, and trade openness, has improved Korea’s economic growth in 1980-2005. The impact of total imports is found to be insignificant, primarily due to capital vs consumer goods compositional changes.

However, studies about Indian growth are controversial with regard to reforms. Rodrik and Subramanian (2004) explore the causes of India’s productivity surge since 1980, more than a decade before serious economic reforms were initiated. They find that traditional reforms - trade liberalization, expansionary demand, a favorable external environment, and improved agricultural performance - did not play a role. Surprisingly, they find evidence that the trigger might have been an attitudinal shift by the government that unlike the reforms of the 1990s, favored the interests of existing businesses rather than new entrants or consumers. Rodrik and Subramanian (2004) attribute the growth to earlier pre-reform environment that played an important role in determining which states took advantage of further policy changes. Both studies – of Indian and South Korean developemnt - have some shortcommings. Harvie and Pahlavani (2000) explain the growth of Korean GDP employing a very unusual

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specification of production function 2 , which makes the results questionable. Investigation of the Indian growth in contrary overlooks the labor and human capital component of developemnt. In this paper, we will address the gap in the litarature and comparatively evaluate the impact of two factors – combined market reforms measures and human capital - along with their complementary effects on each other on economic growth in India and South Korea in the postreform period based on a cointegration analysis. 3. The Model In this section, we develop an empirical model to ascertain the impact of three major types of market reforms and human capital development on economic growth in India and South Korea in the post-reform periods.

The three major market reforms considered are trade

liberalization, financial reform, and enterprise restructuring. We make a conventional assumption that economic reforms, if properly implemented, boost total factor productivity (TFP). Institutional changes that occur in the process are considered exogenous. We assume a continuous time infinite horizon economy with identical, rational agents. At each time t, production of a single homogenous good is represented by: Q = A( R, O ) F ( K , hL )

(1)

where Q is the quantity of output produced per period of time, A is the total factor productivity, R is the reform measure, O is a catch-all factor for all other effects not explained by R, F is a general constant elasticity of substitution production function, K is a capital stock, L is the number of workers, h is the measure of human capital per worker, and hL is the total labor input in the economy.

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i.e., in Harvie and Pahlavani (2000) framework, the Cobb-Douglas production function incorporates along with traditional determinants - physical and human capital – two additional factors (exports and imports) that come from expenditure approach to measurment of GDP rather than production process.

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According to Weil (2004), productivity A can be determined by two factors: technology, that represents the knowledge about how factors of production are combined to produce output, and efficiency, that measures how effectively given technology and factors of production are employed. The positive impact of reform measures such as trade liberalization, financial reform, and enterprise restructuring on both technology and efficiency components of the total factor productivity A is well supported by Edwards (1998). To this end, we establish the following property of a productivity term in our model AR > 0 . A shock to the economy from market reforms will have a positive effect on total factor productivity A . Moreover, a higher productivity will accelerate economic growth, and total production Q will reach a new higher level at the end of transition process. The effects of trade liberalization, openness to foreign investments, and enterprise restructuring on economic growth are easily justified. Liberalization of trade, with reduction of tariffs, subsidies, and quotas on imports and exports increases competition in the domestic market. In order to compete with imported products, domestic producers would have to increase their productivity. Similarly, exporters would need to increase quality and productivity of their output in order to compete in the world market.

Thus, by engendering efficiency, trade

liberalization will boost economic growth and overall production in the economy. When the economy reduces restrictions on foreign investment, the level of capital inflows into the economy is likely to increase. In the absence of distortions, and with a rational maximizers’ behavior, the hitherto inefficient economy should become more efficient 3 . Increased investment and enterprise restructuring will boost production directly by increasing the capital level, and indirectly by improving efficiencies in the production processes. New 3

However, if trade distortions are present in the economy, foreign investments may further increase production of a “wrong” good and make the economy even more inefficient.

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technologies and more efficient production methods associated with foreign investment will speed up the economic growth. In a similar way, the effect of human capital on economic growth can be assessed. Improvement in human capital will make labor more efficient. According to equation (1), this will have a direct positive impact on the country’s production. We specify the production function similar to Kushnirsky (2001), and use this production function as a benchmark function in our estimations: β

Q = OK α (hL) Rγ

( 2)

where a constant term O and all the exponents α, β, and γ are independent of a country choice. The function above will be log-estimated with cointegration approach to evaluate how different paths of economic reforms and human capital accumulation affect the growth of a country. 4. Empirical Study The main objective of this section is to comparatively evaluate economic reforms in India and South Korea, and to explore the long-run relationship among economic reforms, human capital accumulation, and economic growth in these two countries in the post-reform period by employing a cointegration approach. 4.1. Data Description For empirical study, we confine our dataset to the periods when the major economic reforms were implemented in both countries. Arguably, the defining initial year of a major economic reform in South Korea is 1965, with a second wave of changes initiated in the late 1970s. As for India, Rodrik and Subramanyam (2004) point out that even though the first

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hesitant market-oriented reform took place in the mid 1980s, the more decisive reform occurred only in 1991. Driven by a relatively shorter post-reform period in India, we consider a twelveyear post-reform period. To maintain symmetry in data, we confine our dataset to 1966-1977 in case of South Korea, and to 1992-2003 in case of India. The variables in our empirical analysis for both India and Korea are as follows: GDP (Q): GDP in 2000 constant US dollars comes from the World Development Indicators (WDI) 2005. The World Bank defines this as the sum of gross value added of all resident producers in the economy adjusted for taxes and subsidies. Labor (L): Total labor force comes from the WDI 2005. The World Bank uses International Labor Organization (ILO) definition of economically active population to determine total labor force. It includes both employed and unemployed, but excludes homemakers, other unpaid caregivers, and workers in the informal sector. Taking into account a thriving informal sector in India, and a relatively undeveloped data collection procedures in Korea in the 1960s, we acknowledge that labor force data in both countries might be biased and should be treated with caution. Capital Stock (K): As no explicit data available for the capital stock, we convert Larson et al. (2000) 1967-1992 capital stock database in 1990 US dollars to 2000 constant US dollars. We further extend the dataset to the remaining years of the 1960-2003 period. Specifically, we use the Larson et al. (2000) fixed capital deflator to convert total capital stock in 1990 constant US dollars to current US dollars. We then employ the GDP deflator from the WDI 2005 to convert the total capital stock in current US dollars to constant 2000 US dollars. We expand the capital stock data over the 1960-2003 period by applying a perpetual inventory method (PIM): K t = K t −1 + I t − Dt

(3)

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where K t is a capital stock at time t, K t −1 is a capital stock at time (t-1), I t is investment at time t, and Dt is a depreciation at time t. We use gross capital formation data from WDI 2005 as a proxy for investment (I) and assume that the depreciation rate is 5 percent, which according to the recent economic literature is close to reality. Reform Index (R): We construct two composite reform indices R1 and R2 in the style of De Melo et al (1996) 4 . Taking into account the specifics of economic reforms in India and South Korea, our indices are weighed average of three reform indicators - trade reform, financial reform, and enterprise restructuring. The distinction between R1 and R2 is based on different approaches to measure effectiveness of trade reform, as explained below. Trade Reform: As both India and South Korea followed an import-substitution development policy prior to reform implementation, trade liberalization did not play a significant role in their economies. In order to increase competitiveness of exports, India implemented a major exchange rate reform in the early 1990s. Imports were also liberalised by removing quantitative restrictions and by gradually decreasing tariff rates. Yet, tariff rates in India were relatively high compared to other East Asian countries until 2002. One of the notable features of Indian exports dynamics was a sharp increase in its service exports. A less repressive regulation and an inflow of foreign investment were the two key factors that stood behind the success of service sector in India. In contrast, the Korean government implemented a major exchange rate reform much earlier, in the mid 1960s. Reforms in the Korean imports focused mainly on the removal of quantitative barriers rather than decreasing tariffs. A trade reform in South Korea was characterized by a consistent government support in expanding export market and broad

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The De Melo et al (1996) reform index is a weighted average of three reform indicators: 1) price liberalization and competition, 2) trade and foreign exchange regime, and 3) privatization and banking reform.

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incentives in the form of tax reduction for export income. As a result, in South Korea, the share of the manufacturing sector in GDP increased significantly in the post-reform period. To measure trade reform, we use two indices: 1) volume of trade, or sum of imports and exports as a percentage of GDP in 2000 constant US dollars, and 2) value of imports in 2000 US constant dollars as a percentage of aggregate consumption. Figures 1 and 2 show these indices for India and South Korea in the period of interest.

(Exports+Imports)/GDP

Figure 1.Volume of Trade as a Percentage of GDP in India and South Korea, 1960-2003 100 80 60 40 20

India

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Figure 2. Share of Imports in Aggregate Consumption in India and South Korea, 1960-2003

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Imports/Consumption

70 60 50 40 30 20 10 -

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The volume of trade as a percentage of GDP in 2000 constant US dollars is available from the WDI 2005. Assuming that changes in this index are driven by policy changes 5 , we use outcome-based measures to evaluate effectiveness of trade reforms. Specifically, the effectiveness of trade reforms is judged meaningfully by an increase in the trade volume it generates. In addition, we employ the share of imports in aggregate consumption to be an alternative indicator of trade reform effectiveness. As imports of consumption goods are heavily restricted in the developing world, the second indicator perhaps is a more reliable estimate for a repressive trade policy 6 . Financial Reform: The major purpose of financial reforms is to assign greater flexibility in determining interest rates and allocating credit to market forces. It is generally expected that reforms in financial sector would lead to this sector’s faster development and further expansion. India implemented financial reforms in early 1990s. These reforms included interest rate deregulation, opening up the banking sector to private and foreign banks, and reduction of government interventions in credit allocation. Prudential regulations following Basel Committee recommendations significantly improved bank supervision. New rules were enacted to manage the securities market. Similarly, South Korea introduced the first step of financial sector reforms by deregulating interest rates. Control over bank credits was reduced, and the banking sector was partially opened to foreign and private banks in the late 1960s - early 1970s. Similar to trade reform measures, we justify variables measuring financial reform effectiveness by the outcome-based approach. Our financial reform index consists of the following two variables, with equal weights: 1) a ratio of broad money to GDP (M2/GDP) that 5

Loayza and Palacios (1997) Following Edwards (1993), we also performed evaluation of the Structure Adjusted Trade Intensity (SATI) to control for country’s size, GDP, transport cost and other relevant variables. This alternative measure of trade reform effectiveness produced no significant results that might have changed our choice. 6

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measures a level of financial development in the economy 7 ; 2) a ratio of domestic credit to private sector to GDP that approximates a reduction of government intervention in bank lending. All variables employed in the construction of this index come from the WDI 2005. Figure 3 shows dynamics of the financial reform indices in India and South Korea in 1960-2003.

100 80 60 40 20 2002

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Year 1972

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-

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Financial Reform Index

Figure 3. Finansial Reform Index in India and South Korea, 19602003

Enterprise Restructuring: As an economy pursues reform measures to restructure enterprises from public to private ownership, or enacts new laws encouraging private sector participation in various economic activities, the private sector share of employment and value added are most likely to increase. Since independence, India’s economy was dominated by huge public sector enterprises. In the 1990s, the government removed subsidies and preferential access to bank loans for these enterprises. A sharp reduction in the number of areas reserved for the public sector enterprises improved incentives for private participation and foreign direct investment (FDI). Reduced government regulation and bureaucratic red tape along with a larger amount of FDI contributed to a surprising growth of the Indian service sector. Unlike India, the South Korean economy was not dominated by public sector enterprises at the beginning of economic reforms. The Korean 7

M2/GDP is assumed to move upward with reforms in financial sector.

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government restructured the public sector in the late 1960s to the early 1970s by mainly selling unprofitable public enterprises, and reorganizing other enterprises geared towards economic development. Important steps were taken in order to remove favorable treatment of large conglomerates and to enact legislation regulating monopolies. Considering data availability, we employ two variables that capture enterprise restructuring effect on the economy: 1) private sector share of total employment (in India), and 2) private sector share of value added (in South Korea). The private sector share of employment data were obtained from the Reserve Bank of India; the data on private sector share of total value added in South Korea come from Kang (1989) and the Bank of Korea. These two measures of enterprise restructuring are highly correlated, and thus reduce the probability of errors due to an apparent asymmetry in reform index composition. We construct an overall reform index as a weighted average of the three reform measures by assigning equal weights to trade reform index, financial reform index, and enterprise restructuring index. Appendix A shows the construction of overall composite reform indices R1 and R2 for India and South Korea in the post-reform years. Figures 4 and 5 depict dynamics of these composite reform indices in two countries in 1960-2003.

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100 80 60 40 20 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002

Overall Reform Index R1

Figure 4. Overall Reform Index R1 in India and South Korea, 19602003

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Year

Korea

80 60 40 20 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002

Overall Reform Index R2

Figure 5. Overall Reform Index R2 in India and South Korea, 19602003 100

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Year

Korea

Human Capital (h): It has been established in economic theory that human capital is one of the most significant sources of economic growth. Nevertheless, the empirical research has not yet produced convincing results to ascertain the importance of it for economic growth. The main problem lies in the construction of a human capital variable, which is not directly measurable. In order to proxy a human capital for the Asian region, most empirical studies (Harvie and Pahlavani, 2006, Song, 1990, Guesan, 2004) rely more on data availability rather than on a theoretical definition. The variables commonly used as a proxy for human capital are investment in education, secondary or total school enrollment ratio, literacy rate, and average years of 15

schooling. However, all these ratios have several disadvantages 8 . In addition, the above mentioned studies omit health component of the human capital variable. In our judgment, a plausible variable for human capital should take into account returns on all types of investments that human beings undertake in order to increase their future wellbeing and production potential. Therefore, using conceptual foundations of the term, we construct a composite human capital index, which captures both major components of human capital – education and health. The composite human capital variable is created as a weighted average of the two indices –average years of schooling and life expectancy at birth - based on a principal component analysis. Data on the years of formal schooling received, on average, by adults over age 15, defined as average years of schooling, are available from Barro and Lee (2000) for 1960-2000. We use a linear interpolation method to estimate missing observations. Data on life expectancy at birth, or the number of years a newborn infant would expect to live if prevailing patterns of mortality at the time of her birth were to stay the same throughout her life, come from the WDI 2005. To address the issue of comparability of indices, we set India’s average years of schooling and life expectancy at birth in 1960 to unity, and normalize the rest of schooling and life expectancy data respectively. Appendix B shows construction of the human capital variable for India and South Korea. Figure 6 depicts the dynamics of human capital indices in two countries in 1960-2003.

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See Barro (1991), Nehru et al (1995), Gemmel (1996), Hanushek and Kimko (2000), and Wobmann (2003) 16

5 4 3 2 1

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Figure 6. Human Capital Index in India and South Korea, 19602003

Korea

Table 1 provides a summary of definitions and sources of variables used in this study. Table 1. Variables for the Empirical Analysis and Index Composition Variable Y

Definition Gross Domestic Product, in constant 2000 US dollars

hL

Human capital augmented labor

L

Total labor force, or economically active population

h

Human capital index, assigns equal weight to: a) average years of schooling b) life expectancy at birth Physical capital, in constant 2000 US dollars

K

Source World Development Indicators 2005 Authors calculation World Development Indicators 2005 a) Barro and Lee (2000) b) World Development Indicators 2005 World Development Indicators 2005 and Larson et al (2000)

R

Reform index, assigns equal weights to trade reform Authors calculations index, financial reform index, and enterprise restructuring index Trade Reform Index a) (Exports + Imports)/GDP and World Development Indicators b) Imports/Consumption 2005 Financial Reform Index Enterprise Restructuring Index

Assigns equal weights to the M2/GDP, and the ratio of domestic credit to private sector to GDP a) Private sector share of total employment (India) and b) private sector share of value added (South Korea)

World Development Indicators 2005 a) Handbook of Indian Economy by the Reserve Bank of India b) Bank of Korea, National Accounts 1970-1987; Kang (1989)

4.2. Methodology

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When different sets of economic reforms are imposed on two economies with similar initial conditions, economic forces might drive these economies towards different long run equilibrium conditions. As discussed earlier, South Korea and India implemented economic reforms in a different way: the Korean reform program was much more profound compared to the gradual Indian reform program. To explore the long-run relationship between economic growth and various economic reforms, we propose to use a cointegration analysis. It will enable us to examine the long-run equilibrium conditions for a growth path in each economy, and to trace the effects of different types of reforms on this balanced growth path over time. The basic idea behind cointegration is that each of the components of a ( p × 1) vector time series Z t may follow a non-stationary unit root I(1) process, yet there may exist some linear combination of β T Z t , which ties the individual components together and is stationary (without a unit root) 9 . This linear combination can be interpreted in economic terms as a long-run equilibrium relationship among variables of a vector Z t . We will use a multivariate maximum likelihood cointegration procedure proposed by Johansen (1988), that allows for the estimation and testing for a number of cointegrating relationships in the system 10 . All the hypothesis tests may be conducted in this case to using standard asymptotic chi-square tests. We consider a p dimensional Vector Autoregression (VAR) model: Z t = μ + Φ1 Ζ t −1 + Φ 2 Ζ t −2 + ... + Φ k Ζ t −k + ε t

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(4)

If this is a case, β is called a cointegrating vector

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Philips (1991) showed that the most preferred approach to the estimation of cointegrated systems is the Johansen’s method, as the coefficient estimates obtained through this procedure are symmetrically distributed, median unbiased, and asymptotically efficient.

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where Z t is a ( p × 1) vector of I(1) variables, μ is a ( p × 1) vector of constant terms, Φi (i = 1,…,k) is a ( p × p ) matrix of parameters, and ε t is a ( p × 1) vector of a white noise, which is assumed to be independently and identically distributed with zero mean and may be contemporaneously correlated. We can rewrite model (4) in a vector error-correction form ΔZ t = μ + Φ Ζ t −1 + Ψ 2 ΔΖ t − 2 + ... + Ψ k ΔΖ t −k +1 + ε t , k

k

1

i= j

(5)

where Φ = −I + ∑ Φ i , Ψ j = −∑ Φ i for j = 2,…, k , and I is a (p×p) identity matrix. In (5), matrices Ψ2,…,Ψk capture the short-run dynamics of the system. The coefficient matrix Φ contains information about the long-run equilibrium relationships. Three possible scenarios arise depending on the rank of Φ. First, the vector process Zt is stationary if Φ has full rank. Second, if Φ has a zero rank, then no long-run equilibrium relationships among variables exist. Finally, if Φ has a rank between zero and p, then Φ contains stationary long-run equilibrium information. Johansen (1991) showed that the number of cointegrating relationships r among variables Zt is given by the rank of Φ, where 0 < rank (Φ ) = r < p . The Φ could be re-parameterized as Φ = αβ′ , where α and β are (p×r)

matrices, β′Zt is a stationary cointegrating combination. Then, according to Johansen and Juselius (1990), columns of β can be interpreted as r stationary cointegrating vectors of the system Zt , and α can be interpreted as the speed of adjustment towards the long-run stable equilibrium state. To determine the rank of cointegrating vector, Johansen (1988, 1991) proposed two likelihood ratio test statistics, which can be constructed from the residual vector. They are called

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a trace statistic and a maximal eigenvalue statistic. The trace statistic evaluates the null hypothesis that there are at most r cointegrating vectors against the alternative hypothesis that there are at most p cointegrating vectors, and is defined as p

Q trace = −T ∑ ln(1 − λ i )

(6)

i = r +1

where λi measures how strongly the linear combination of Zt-1 is correlated with the stationary part of the system ΔZt after correcting for short-run dynamics. The maximal eigenvalue test statistic is used for the null hypothesis that there are at most r cointegrating vectors against the alternative hypothesis that there are at most (r+1) cointegrating vectors, and is defined as Q max = −Tln(1 − λ r +1 )

(7)

For both the maximal eigenvalue statistic test and trace statistic test, the testing procedure is sequential in nature. The test starts with the null hypothesis of r=0 and ends when one fails to reject the null hypothesis under test. In both cases, the null hypothesis is rejected if the calculated test statistic exceeds its corresponding critical value. Generally, the trace statistic tends to have a greater power than the maximal eigenvalue statistic when λ’s are evenly distributed. The maximal eigenvalue test has a greater power when λ’s are either too large or too small. 4.3. Applications to India and South Korea In this section, we conduct a cointegration analysis of the variables in the growth model for India and South Korea. Specifically, the functional relationship in growth equation (2) Qt = F ( K t , hLt , Rt ) will be analysed. Every variable in the equation is measured in logarithms. The cointegration analysis for both countries is conducted for post-reform periods of 12 years, namely, 1992-2003 for India and 1966-1977 for South Korea. 20

To apply Johansen’s method for cointegration analysis, every variable should be nonstationary, i.e. integrated of some order greater than zero. We perform the augmented DickeyFuller (ADF) test to determine the integration properties of each variable series. The ADF test constructs a parametric correction for higher-order correlation by adding p lagged difference terms of dependent variable z to the right-hand side of the test regression under assumption that z series follow an AR(p) process:

Δzt = αzt −1 + δxt + β 2 Δzt −2 + ... + β p Δzt − p + ε t

(8)

where z t is a non-stationary series which variance increases with time and approaches infinity, xt are exogenous regressors, which may contain a constant, or a constant and a trend, α and β are parameters to be estimated, and εt is a white noise. The null and alternative hypotheses are as below: H0 : α = 0 H1 : α < 0

(9)

The above hypotheses are tested using a conventional t-ratio for α: tα =

αˆ se(αˆ )

(10)

As indicated, we conduct the ADF test for the 12-year post-reform period for both India and South Korea. The initial results of ADT statistics for ln(Q), ln(hL), ln(K), ln(R1), and ln(R2) are given in Table 2. Table 2. ADF Unit Root Test for Level Data Variables ln (Q) ln (hL) ln (K)

India ADF Test Statistics -1.499 -0.863 0.438

P Value 0.743 0.743 0.995

South Korea ADF Test Statistics -0.383 -0.152 0.008

P Value 0.881 0.917 0.939

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ln (R1) ln (R2)

1.756 0.718

0.998 0.985

-0.941 -2.456

0.909 0.338

Note: the p-values for the ADF test in the table above are based on McKinnon (1996)

At 10% level of significance, all five variables are non-stationary for India and South Korea. In order to determine the order of integration, we run the ADF test for the series in first differences. The results in Table 3 show that all differenced variables are stationary at least at the 8 percent level. Table 3. ADF Unit Root Test for First-Differenced Data Variables D(ln (Q)) D(ln (hL)) D(ln (K)) D(ln (R1)) D(ln (R2))

India ADF Test Statistics -4.394 -3.079 -7.743 -4.473 -4.308

P Value 0.044 0.069 0.001 0.008 0.009

South Korea ADF Test Statistics -3.345 -10.087 -2.926 -7.356 -6.895

P Value 0.041 0.001 0.077 0.005 0.001

Note: the p-values for the ADF test in the table above are based on McKinnon (1996) D( ) refers to the first-differenced variable

Knowing that all the variables are I(1) in the post-reform period, we proceed with cointegration analysis. The initial step in cointegration analysis is to determine the number of lags needed in the VAR(p) model. We use Sims’ (1980) modified lag test to determine the appropriate number of lags. We begin with the maximum number of lags m and test the hypothesis that coefficients of lag m are jointly equal to zero by employing the likelihood ratio (LHR) test: LHR = (T − C )(ln Ω m−1 − ln Ω m )

(11)

where T is the number of observations, C is the number of parameters estimated in each equation, Ω m is the variance/covariance matrix of the residuals from the VAR(p) system. The above LHR statistic has an asymptotic chi-square distribution with degrees of freedom equal to 22

the number of restrictions in the system. In order to choose the appropriate lag length for the system, we sequentially test the significance of lags by comparing the LHR statistic to the 5 percent critical value starting from the maximum lag and decreasing one lag at a time until we first get a rejection. The LHR statistic indicates that one lag would be appropriate to capture the dynamics in the post-reform period for both India and Korea. Table 4 presents the results of Johansen’s cointegration test that determines the cointegrating rank of the model with a reform index R1. Both the trace and the maximum eigenvalue test statistics indicate a cointegrating rank of one for India and South Korea. Table 4. Johansen Cointegration Test Result for Cointegration Rank with Reform Index R1 Rank None At most 1 At most 2 At most 3

India’s Statistics Trace Maximum Eigenvalue 105.693* 72.134* 33.559 19.294 14.264 11.613 2.652 2.652

South Korea’s Statistics Trace Maximum Eigenvalue 70.563* 50.395* 20.168 15.136 5.032 3.217 1.815 1.815

Note: * denotes rejection of the hypothesis at the 5% level

Table 5 reports the results of Johansen’s cointegration test that determines the cointegrating rank of our model with the reform index R2. Both the trace and the maximal eigenvalue test statistics once again support a cointegrating rank of one for India and South Korea. Table 5. Johansen Cointegration Test Result for Cointegration Rank with Reform Index R2 Rank None At most 1 At most 2 At most 3

Trace 95.984* 29.709 13.766 2.069

India’s Statistics Maximum Eigenvalue 66.275* 15.943 11.697 2.069

South Korea’s Statistics Trace Maximum Eigenvalue 71.253* 49.961* 21.292 16.175 5.117 5.1 0.017 0.017

Note: * denotes rejection of the hypothesis at the 5% level

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The estimated normalized cointegrating vectors β of Ζ t = (Qt , K t , hLt , Rt ) are reported in Tables 6 and 7 for models with reform indices R1 and R2, respectively. Table 6. Cointegrated Vectors from Johansen’s Cointegration Test Model with Reform Index R1 Variable ln(Q) ln(K) ln(hL) ln(R1)

India -1.999 0.489 3.436 -1.014

Unrestricted South Korea 0.923 -0.357 -0.472 -1.738

India 1 -0.245 -1.719 0.507

Normalized South Korea 1 -0.387 -0.511 -1.882

Table 7. Cointegrated Vectors from Johansen’s Cointegration Test Model with Reform Index R2 Variable ln(Q) ln(K) ln(hL) ln(R2)

India 1.123 0.464 -3.049 1.135

Unrestricted South Korea 3.346 0.069 -3.753 -5.773

India 1 0.414 -2.716 1.112

Normalized South Korea 1 0.021 -1.122 -1.726

By rearranging the cointegrating relationship β T Z T to capture the long-run equilibrium conditions, we obtain the following equations: India: ln(Qt ) = 0.245 ln( K t ) + 1.719 ln(hLt ) − 0.507 ln( R1t )

(12)

ln(Qt ) = −0.414 ln( K t ) + 2.716 ln(hLt ) − 1.012 ln( R 2 t )

(13)

South Korea: ln(Qt ) = 0.387 ln( K t ) + 0.511 ln(hLt ) + 1.882 ln( R1t )

(14)

ln(Qt ) = −0.021 ln( K t ) + 1.122 ln(hLt ) + 1.726 ln( R 2 t )

(15)

In the economic sense, we can interpret equations (12) - (15) as potential long-run growth functions. Several very important results can be observed. First, the positive elasticity of GDP with respect to human capital augmented labor in equations (12) – (15) confirms that in both 24

countries - India and South Korea - human capital accumulation played an important role in the post-reform growth, in compliance with the Nehru et al (1995), Rodrik (1996), and Rabbani and Maksymenko (2008) predictions. The impact of human capital augmented labor on Indian growth is found to be much larger in magnitude compared to its South Korean equation counterparts. Not surprisingly, being a labor-abundant economy with relatively low levels of average years of schooling and life expectancy at birth at the beginning of reform implementation, India experienced a much stronger input to its development process from the accumulation of human capital rather than from its scarce factor of production - capital. Second, long-run equilibrium growth functions indicate that the impact of the reform measures is heterogeneous across countries. Namely, the elasticity of GDP with respect to economic reform in South Korea is positive, whereas it is negative in India, for both R1 and R2. This result is suggestive of a different degree of efficiency in reform measures implementation in two countries. Moreover, the effect of reform measures on Korean growth sizably exceeds the magnitude of the reform coefficient in India’s growth functions. Equations (14)-(15) imply that economic reforms as measured by a compound reform index were much more sizable and significant in the South Korean post-reform growth relative to India. In the case of South Korea, our findings agree with the empirical literature, i.e. Harvie and Pahlavani (2006), on the impact of reform measures. The negative long-run effect of the reform factor on economic growth in India found by employing a cointegration technique might be surprising at the first sight. Yet, there are several factors that may explain our results. First, an original hesitant approach to reform implementation in India might partly explain India’s less effective reform outcomes. Second, in South Korea, related outcome-based measures - index of openness, share of imports in

25

consumption, ratio of broad money to GDP, and the overall reform indexes - significantly exceeded and exhibited a much steeper trend in the post-reform period compared to corresponding indexes in India, which might imply their strong positive impact on growth in South Korea. Third,

the negative effect of reform measures in India can be justified by

unfavourable time-invariant country-specific factors (such as initial state of the economy, income distribution, demographic transition, legal system, democracy, culture and traditions etc), and persistent short-term complications in reform measures implementation, which traditional correlation analysis is not able to capture. With this in mind, we can expect that beyond the twelve-year horizon, when the economy moves further towards market, the negative impact of reform measures implementation in India would fade, and slowly change towards the positive effect. Finally, in accordance with the traditional economic literature, which suggests that having appropriate human capital is important for advancing the opportunities opened by reforms, our cointegration analysis confirms a positive interaction between economic reforms and human capital augmented labor in South Korea. Yet, in India, where the human capital dividend was at a relatively low level prior to reform implementation, we do not observe this interaction, leaving a broad avenue for the policy makers to address by expanding economic reforms to labor market, education, and healthcare.

5. Policy Implications By drawing on the findings of this study, the objective of this section is to attract attention of a wide range of international organizations as well as authorities in the developing countries to the implementation of new economic reform programs. Moreover, by focusing on economic implications of three major reforms – trade reform, financial reform, and enterprise restructuring – as well as policies aimed to improve human capital accumulation, we will provide 26

recommendations to policy makers in India and South Korea to improve the impact of reform on these economies. Both countries in our study implemented trade reform measures and removed quantitative trade restrictions at the beginning of their economic reform process. Yet, compared to India, Korea reaped a significantly larger benefit due to a faster removal of restrictions. Therefore, taking the initial country constraints, we recommend removing the quantitative restrictions early in the reform process. This could also generate revenues to cushion any adverse effect from the reduction of tariffs. Moreover, a success in revenue generation might create a positive impression among various stakeholders and pave the way for reforms in other spheres. Quantitative trade restrictions create incentives for rent seeking, and deprive governments of revenues. They may also create monopolistic control, and hinder competition and innovation. One significant advantage of reforming trade by removing quantitative restrictions lies in the absence of adverse effect on the government revenue experience due to this removal. Therefore, even economies with fiscal deficits can afford to implement these reform measures. Unambiguously, trade reforms should precede capital account liberalization. While a trade reform is usually accompanied by a depreciation of the exchange rate, which increases competitiveness of exports, capital account liberalization brings an influx of foreign capital chasing higher returns and appreciates the domestic currency. Therefore, if implemented together, liberalization of capital accounts and trade reforms may decrease the effectiveness of the latter. India is unique among developing economies in having a huge service sector, exceeding half of its GDP. Economic reforms at the beginning of the 1990s played a critical role in the growth of services. Privatization, deregulation, and FDI created a positive shock to the hitherto

27

stagnant service sector. The introduction of new technology also generated efficiency. But the service sector requires a more highly educated labor force compared to the industry sector. Since the majority of the Indian population is not highly educated, it is important for India to undertake greater investment in industrial production, building on the success of its service sector. An increase in industrial production would generate new opportunities for employment for the less educated poor strata in rural India. The Indian financial sector still has numerous barriers against ownership by foreign companies. As India is quickly marching towards becoming a vibrant and highly sophisticated economy, demand for risk management products will also increase, from both individuals and businesses. Allowing foreign insurers will expose the domestic market to advanced risk management techniques, which in turn will foster growth in the economy. Economic reforms can fully utilize their potential in a labor market devoid of excessive regulation. Social safety nets, including reasonable unemployment allowance and retraining, should be provided to laid-off workers during an enterprise restructuring process. But this discretion must be exercised carefully, without creation of any disincentives for workers such as withdrawal from the labor force due to generous unemployment benefits. A flexible labor market will enhance the effectiveness of economic reforms and accelerate economic growth. Our analysis shows that human capital, being a driving force of economic growth, also complements economic reforms. A higher level of human capital in South Korea at the beginning of its reform implementation to a certain extent explains Korea’s economic success. While India is unique in having a very successful tertiary education system, its primary education is in a poor state, which is evidenced by a low literacy rate. Therefore, primary education system is vital for the Indian effort to improve human capital. More funding, adequate

28

teacher training, and improved infrastructure should lead towards this goal. Vocational education can be provided to people who do not intend to pursue higher education. At the beginning of economic reform implementation, India was characterized by a higher level of child mortality and a lower life expectancy at birth, both contributed to a relatively low weighted average human capital index compared to South Korea. The lack of essential nutrients may stunt a child physically and mentally from birth. To this end, the government should ensure intake of essential nutrition for every child. Providing a meal rich in nutrients during school hours could be an option. Larger spending on healthcare infrastructure and training of medical personnel are essential for a better healthcare and human capital development as such. Based on the empirical analyses of our study, we provide the following summary of policy implications, which will be relevant to any developing country exploring ways to pursue economic reforms and accelerate growth. First, economic reforms such as trade liberalization, financial reform, and enterprise restructuring play an important role in economic growth. The more distorted is an economy; the greater is the benefit from economic reforms. Second, initial conditions in an economy prior to reform implementation are important factors of reform effectiveness. Reform measures should take into account the state of institutions and social implications of the income distribution. Third, both active and gradual reform measures benefit the economy. Introduction of widespread reform measures is more suitable for an economy with a strong, determined government. Gradual reforms, which are more successful in an economy with acute distortions, should be primary targeted to areas likely to generate most welfare gains. Fourth, human capital is critical for economic growth. Appropriate human capital increases the

29

efficiency of labor force. Both education and health components of human capital should be taken into account when designing a policy to improve human capital.

6. Conclusions In this paper, we analyzed the impact of economic reforms and human capital on the economic growth of India and South Korea in the post-reform period. We constructed a modified production function which along with a conventional factor of production - physical capitalincorporates a composite reform index and augmented labor in order show the effect of various reform measures and human capital accumulation on economic growth. By constructing two unique indices - a composite economic reform index and a human capital index - we explored the transitional dynamics of a growth in total factor productivity generated by trade reform, financial reform, enterprise restructuring, schooling, and improvements in life expectancy. We employed a cointegration analysis based on two modifications of our baseline production function to assess the effects of economic reforms on the economic growth in India and South Korea. Our analysis suggests that economic reforms and human capital accumulation produce a significant long-run effect on economic growth in the countries under consideration. The significant positive effect of human capital augmented labor is revealed both in India and South Korea in the twelve-year post-reform period. The impact of market reforms is found to be heterogeneous across these economies. The effect is positive, significant, and sizable in South Korea, while it is negative and small in India. This result is suggestive of different degree of efficiency of reform measures implementation in two countries. The negative long-run effect of the reform factor on economic growth in India is justified by several factors, including an original hesitant approach to reform implementation, unfavourable time-invariant countryspecific factors (such as initial state of the economy, income distribution, demographic 30

transition, legal system, democracy, culture and traditions etc), and the absence of a complementary effect of human capital augmented labor to market reforms. In conclusion, our study opens a broad avenue for further research. We recognize that economic reforms and human capital accumulation are important factors for explaining growth in India and South Korea. As such, it will be interesting to employ a multivariate maximum likelihood cointegration technique in order to explore the long-run interaction between economic growth, reform measures, and human capital development in a broader range of Asian countries that recently implemented market reforms and significantly improved their human capital.

References Agenor, P. & Montiel, P. (1999). Development Macroeconomics. Princeton, New Jersey: Princeton University Press Barro, R. (1991) Economic growth in a cross section of countries. Quarterly Journal of Economics, 106, 407-443 Barro, R. (1996). Health and economic growth. Harvard University Department of Economics Working Paper Barro, R. & Lee, J. (2000). International data on educational attainment: updates and implications. National Bureau of Economic Research Working Paper No. 7911 Becker, G. (1964). Human capital: a theoretical and empirical analysis, with special reference to education. Chicago: University of Chicago Press Bloom, D., Canning, D. & Sevilla, J. (2004). The effect of health on economic growth: a production function approach. World Development, 32, 1-13 De Melo, M., Denizer, C. & Gelb, A. (1996). From plan to market: patterns of transition. The World Bank Working Paper No. 1564. Dickey, D. & Fuller, W. (1981). Likelihood ratio test statistics for autoregressive time series with a unit root. Econometrica, 49, 1057-1072. Edwards, S. (1992). Trade orientation, distortions, and growth in developing countries. Journal of Development Economics, 39, 31-57 31

Gemmel, N. (1996). Evaluating the impacts of human capital stocks and accumulation on economic growth: some new evidence. Oxford Bulletin of Economics and Statistics, 58, 9-28 Guesan, M. (2004) Human capital, trade and development in India, China, Japan and other Asian countries, 1960-2002: econometric models and causality tests. Applied Econometrics and International Development, 4, 123-139 Hanushek, E. & Kimko, D. (2000). Schooling, labor-force quality, and growth of nations. American Economic Review, 90(5), 1184-1208 Harvie, C. & Pahlavani, M. (2006) Sources of economic growth in South Korea: an application of the ARDL analysis in the presence of structural breaks – 1980-2005. University of Wollongong Economics Working Paper No.06-17 Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12, 231-254. Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in Gaussian vector autoregressive models. Econometrica, 59, 1551-1580. Johansen S. & Juselius, K. (1990). The full information maximum likelihood procedure for inference on cointegration – with applications to the demand for money. Oxford Bulletin of Economics and Statistics, 52, 169-210. Kang, S. (1989). Korea’s privatization plans and past experiences. Korea Development Institute Working Paper Series 8823 Krueger, A. (1993) Political Economy of Policy Reform in Developing Countries. Cambridge, MA and London: MIT Press Kushnirsky, F. (2001) A modification of the production function for transition economies: reflecting the role of institutional factors. Comparative Economic Studies, 43, 1-30 Levin, R. & Renelt, D. (1992). A sensitivity analysis of cross-country growth regressions. American Economic Review, 82, 942-963 Loayza, N. & Palacios, L. (1997) Economic reform and progress in Latin America and the Caribbean. The World Bank Policy Research Working Paper No.1829 McKinnon, J. (1996). Numerical distribution functions for unit root and cointegration tests. Journal of Applied Econometrics, 11, 601-618 Nehru, V., Swanson, E. & Dubey, A. (1995). A new database on human capital stock in developing and industrial countries: sources, methodology, and results. Journal of Development Economics, 46, 379-401 32

Phillips, P. (1991). Optimal inference in cointegrated systems. Econometrica, 59, 283-306. Rabbani, M. & Maksymenko, S. (2008). Do economic reforms and human capital explain postreform growth? University of Pittsburgh Working Paper Series Ranis, G. and Mahmood, S. (1992) The Political Economy of Development Policy Change. Cambridge, MA and Oxford: Basil Blackwell Rodrik, D. (1996). Understanding economic policy reform. Journal of Economic Literature, 34 (1), 9-41 Rodrik, D. & Subramanian, A. (2004). Hindu growth to productivity surge: the mystery of the Indian growth transition. International Monetary Fund Working Paper No. 04/77 Sims, C. (1980). Macroeconomics and Reality. Econometrica, 48, 1-48. Song, B. (1990), The Rise of the Korean Economy. Oxford University Press: Hong Kong Weil, D. (2004). Economic Growth. Boston, MA: Addison Wesley Wobmann, L. (2003). Specifying human capital. Journal of Economic Surveys, 17( 3), 239-270 World Bank Study (1993). East Asian Miracle. Washington, DC: World Bank World Development Indicators (2005) Washington, DC: World Bank.

Appendix A Construction of Reform Indices Table A1. Construction of Overall Reform Index for India, 1992-2003

Year

Trade Reform Index 1

Trade Reform Index 2

Financial Reform Index

Enterprise Restructuring Index

Overall Reform Index (R1)

Overall Reform Index (R2)

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

18.75 20.04 20.37 23.21 22.36 22.96 24.13 25.47 28.54 27.58 30.82 30.47

10.06 11.46 13.50 16.23 14.74 16.08 18.18 18.15 18.84 18.62 19.42 20.32

33.39 33.37 33.38 32.42 32.91 34.23 35.03 37.34 40.65 41.97 45.64 46.12

28.79 28.98 28.90 29.04 29.57 30.53 30.84 31.03 30.95 30.98 31.09 31.20

26.98 27.46 27.55 28.22 28.28 29.24 30.00 31.28 33.38 33.51 35.85 35.93

24.08 24.60 25.26 25.90 25.74 26.95 28.02 28.84 30.15 30.53 32.05 32.55

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Table A2. Construction of Overall Reform Index for South Korea, 1966-1977

Year

Trade Reform Index 1

Trade Reform Index 2

Financial Reform Index

Enterprise Restructuring Index

Overall Reform Index R1

Overall Reform Index R2

1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977

29.37 32.09 36.59 37.09 36.48 39.38 42.33 59.08 63.68 60.87 60.92 60.58

6.38 7.89 10.39 11.27 11.09 12.22 11.82 14.79 16.50 15.81 18.35 20.94

14.29 19.04 26.18 32.68 34.47 35.20 36.07 36.69 35.89 34.08 31.63 32.38

12.30 16.15 20.99 26.51 28.99 28.99 30.07 31.47 28.52 26.99 25.71 27.45

42.69 45.31 49.32 51.79 52.32 53.66 54.85 60.55 61.73 60.10 59.22 59.19

35.02 37.24 40.59 43.18 43.85 44.61 44.68 45.79 46.00 45.09 45.03 45.97

Appendix B Construction of a Human Capital Variable Table B1. Construction of a Human Capital Variable for India, 1992-2003

Year

Average Years of Schooling

Life Expectancy at Birth

1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

4.27 4.35 4.44 4.52 4.63 4.74 4.84 4.95 5.06 5.17 5.28 5.38

60.15 60.57 60.98 61.40 61.82 62.24 62.43 62.62 62.80 63.09 63.38 63.42

Normalized Normalized Average Life Years of Expectancy Schooling at Birth 2.54 2.59 2.64 2.69 2.75 2.82 2.88 2.95 3.01 3.08 3.14 3.20

1.36 1.37 1.38 1.39 1.39 1.40 1.41 1.41 1.42 1.42 1.43 1.43

Composite Human Capital Variable 1.95 1.98 2.01 2.04 2.07 2.11 2.15 2.18 2.21 2.25 2.29 2.32

Table B2. Construction of a Human Capital Variable for Korea, 1966-1977 Normalized

Normalized

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Year

Average Years of Schooling

Life Expectancy at Birth

Average Years of Schooling

1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977

5.29 5.20 5.10 5.01 4.91 5.25 5.59 5.92 6.26 6.60 6.86 7.12

57.17 57.66 58.41 59.17 59.93 60.68 61.44 62.26 63.07 63.89 64.71 65.52

3.15 3.09 3.04 2.98 2.92 3.12 3.33 3.53 3.73 3.93 4.08 4.24

Life Composite Expectancy Human Capital at Birth Variable 1.29 1.30 1.32 1.33 1.35 1.37 1.39 1.40 1.42 1.44 1.46 1.48

2.22 2.20 2.18 2.16 2.14 2.25 2.36 2.47 2.58 2.68 2.77 2.86

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