Openness, economic growth, and human development: The Asian experience

Ghulam Mustafa, Marian Rizov and David Kernohan Department of Economics and International Development Middlesex University Business School

Abstract While policy makers and international development organisations emphasise the role of openness to trade in achieving sustained economic growth, the interdependence of openness, economic growth, and human development is not well studied. We empirically examine this interdependence through a simultaneous equations system which we estimate by three-stage least squares. The results suggest that in Asia (i) openness has a strong positive impact on both economic growth and human development; (ii) human capital and FDI have a strong positive effect on both economic growth and human development; (iii) while human development contributes positively to growth, growth has a negative and significant influence on human development. Our findings confirm the success of trade liberalisation policies in the region in achieving higher growth but also suggest that this has had negative impact on human development. Consequently there may be a role for distributional policies that would improve income distribution and ultimately human development. Key words: openness, growth, human development, Asia JEL classification: F63, O10, O15, O19, O53 Corresponding address: Marian Rizov Middlesex University Business School The Burroughs London NW4 4BT

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Openness, economic growth, and human development: The Asian experience

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Introduction

Trade liberalisation has been one of the main policy reforms recommended in the Washington consensus (Rodrik, 2006) motivating the policies of development organisations such as World Bank, IMF and WTO (Wang et al., 2004). The conventional wisdom has been that trade openness increases economic growth (Dollar, 1992; Falvey et al., 2012; Frankel and Romer, 1999; Greenaway et al., 2002; Sachs and Warner, 1995; Wang et al., 2004). The main arguments in favour of trade liberalisation have been linked to the export-led growth hypothesis (Foster, 2005), as exporting firms become more productive due to their foreign exposure (e.g., Feder, 1983) leading to enhanced competitiveness (Dixton and Thirwall, 1975; UNCTAD, 1996). Dollar and Kraay‟s thesis suggests that growth benefits the poorest quintile in developing countries and thus growth is good for the poor. The authors concluded that standard World Bank and IMF policy packages stimulate development (Dollar and Kraay, 2002). This approach has been severely criticised by, for example, Lubker et al. (2002). The authors argue that the empirical work of Dollar and Kraay is based on theoretically unsound equations, with serious flaws in data casting doubt on any policy conclusion. In addition, there appears to be a trade off between growth and distribution (White and Anderson, 2001), an argument of particular salience in the present world economy with rising economic giants such as China, India, and Brazil all suffering the effects of pernicious income inequality. Here trickle down effects have not yet been realised, with little immediate sign that this will happen. In this view, the Washington Consensus is seriously flawed and the emphasis on

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growth alone inappropriate. In sum, the approach has failed to stimulate pro-poor growth in developing countries.1 There may not be a simple association between openness and poverty alleviation (e.g., Dollar and Kraay, 2004) and improvements in human development may be a pre-requisite to sustain growth (Ranis and Stewart, 2000; Suri et al., 2011). We focus on the example of human development in Asia, and explore the role of economic growth and trade openness as the major instruments for achieving it (Ranis and Stewart, 2000). Our objective is to estimate the three-way relationship between openness, economic growth and human development. Our contribution lies in studying the interdependence between openness, economic growth and human development to better understand their feedback effects in promoting and sustaining development in Asian economies. We focus on a sample of developing Asian countries for two reasons. Asian economies are expected to provide future source of world output growth in the aftermath of the financial crisis in advanced countries. Second, it is interesting to study Asian economies which are at different level of human development and economic growth. Blonigen and Wang (2005) show that the impact of openness to trade is different among developing and advanced economies, and, thus it is possible that the association between openness and human development would also be different. China and India are highlighted as countries which adopted trade liberalisation policies after achieving higher rates of economic growth, while East Asian economies are referred to as successful examples of export lead growth. Is trade liberalisation a pre-requisite or the result of sustained output growth? What is the systematic link between trade openness and economic growth? What are the welfare consequences of trade liberalisation, as reflected in the level of human development?

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http://www.leftbusinessobserver.com/WorldBankNews.html

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The rest of the paper is organized as follows. In Section 2 we provide overview of the related literature. In Section 3 we set the framework of our empirical analysis and introduce our econometric methodology. Section 4 presents the data. Section 5 reports estimation results and discusses our findings. Section 6 concludes with policy recommendations.

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Related literature

In this section, we present a brief review of the key contributions on the relationship between openness to trade and economic growth. We also review existing literature on the link between economic growth and human development, and openness and human development to provide a context to our analysis.

2.1

Trade openness and economic growth

The rapid industrialisation and development in the four East Asian “tigers”, Taiwan, South Korea, Singapore, and Hong Kong is often quoted as an example of beneficiaries of successful trade liberalisation policies since early 1960s (Krueger, 1997). Similarly, the growth experience of post-1980 globalising developing economies including China and India has been much superior to the developed economies. These countries have liberalised by cutting the tariffs by 20% which resulted in increase in trade volumes as a share of GDP from 16% to 32% and a subsequent annual growth in GDP per capita of over 5% between 1980s and 2000 (Dollar and Kraay, 2004). The idea behind trade liberalisation in developing countries is to boost growth through the static and dynamic gains from trade, leading to rapid capital accumulation and faster productivity growth (Khan and Zahler, 1985). However, the empirical literature on the relationship between openness and economic growth has reported mixed results, although a majority conclude that openness fosters economic growth in developing countries (Begum

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and Shamsuddin, 1998; Connolly, 2003; Dollar and Kraay, 2003; Falvey et al., 2012; Foster, 2005; Greenaway et al., 2002). However, the relationship between openness to trade and economic growth is not as straightforward or systematic. On the one hand, the comparative cross-country analysis has established convincing evidence in arguing that trade liberalisation raises economic growth (Bhagwati, 1978; Krueger, 1978; Srinivasan and Bhagwati, 1999). However, the problem with this literature is that comparative cross country studies cannot be replicated (Lee et al., 2004). Several well publicised econometric studies have attempted to prove a two-way link between trade, economic growth and poverty reduction, using instrumental variables (Dollar and Kraay, 2003; Frankel and Romer, 1999). Frankel and Romer (1999) find positive impact of trade volumes on real GDP per capita using a gravity model. The results purport to show that higher degree of openness and efficient institutions raise economic growth. However, Lee et al. (2004) criticize the instrumentation strategy adopted by the two studies, arguing that geographical instruments are highly correlated with income and trade; and thus produce bias. Rodriguez and Rodrik (2000) question the conventional findings of the positive impact of openness on economic growth as they argue that poor indicators of openness and inappropriate econometric techniques have provided invalid empirical evidence. Rodriguez and Rodrik conclude that the impact of openness depends upon relative comparative advantage and a country‟s domestic and external policies. Stiglitz (2006) suggests that the positive effect of trade for a country depends upon whether the country has a comparative advantage in agriculture or manufacturing. Thus, developing countries where agricultural output dominates the exports risk stagnation and constraints on growth. In addition, if countries become more specialized in low-tech sectors in which little or no R&D takes place, then such allocation of resources does not help to promote long-term growth.

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2.2

Economic growth and human development

Given the search for a better measure of economic progress and social welfare, due to recent criticism of GDP as an indicator of economic performance (Fleurbaey, 2009), we favour the HDI index as an indicator of human development.2 Although, the HDI index has received severe criticism (see Klugman et al., 2011; Srinivasan, 1994), after reviewing the available indicators of development beyond GDP, Fleurbaey (op cit) concludes that it is a real improvement and a prominent indicator of human development due to its simplicity and generalisability. The human development approach takes its inspiration from the human capabilities approach proposed by Sen (1985, 1999). This approach was further developed by Nussbaum (2000) and Robeyns (2005). Since the publication of human development reports (HDRs) in 1990s, human development has emerged as the ultimate objective of economic policy by replacing narrowly defined economic growth. Human development is a broad development paradigm which concentrates on enlarging the human capabilities in order to enable individuals to live long and healthy lives (Anand and Sen, 2000a). Generally, economists expect a positive association between economic growth and human development, however, this connection is not automatic.3 Evidently the strength of the impact of economic growth on human development depends upon a variety of factors, such as economic structures, income and asset distribution, institutional quality, and policy choices many or all of which vary across countries (see Acemoglu and Robinson, 2013). Standard World Bank policies and international business community emphasise the need to achieve

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The search for better indicators includes the committee formed by the French government including J. Stiglitz and four other Nobel Prize winners to propose better indicators of “economic performance and social progress” and the efforts at OECD and the European Union. 3 Despite the strong policy implications of the relationship between economic growth and human development, there are only a few studies on the two-way causation between them (Mayer-Foulkes, 2005; Ranis and Stewart, 2000; Sala-i-Martin, 2005; Suri et al., 2011).

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higher growth which they believe is always pro-poor. However, White and Anderson (2001) provide evidence of a trade off between growth and distribution. The authors conclude that poor developing countries should concentrate on distribution rather than growth. Nevertheless, a stronger relation between economic growth and human development does appear to exist in economies with lower levels of poverty, fairer distribution of income, and higher spending on education and social development. Economic growth contributes to human development through household and government expenditures. The consequent improvement in the quality of the labour force in health, nutrition and education enhances their capabilities and productivity, and in turn, contributes to growth (Ranis and Stewart, 2000). In this view, human development contributes to future economic growth rather than being only an end-product. Ranis and Stewart (2000) and Suri et al. (2011) provide empirical evidence of a two-way causation between economic growth and human development with human development being more important to sustain growth.

2.3

Trade openness and human development

Despite the literature on openness to trade and economic growth, reviewed in section 2.1, empirical work on the relationship between openness and human development is relatively sparse and inconclusive. Trade affects households‟ welfare directly and indirectly via three channels, (i) income distribution through changes in prices of goods; (ii) firms profitability through profits, wages, and employment potential; (iii) government spending through changes in taxes and transfers (McCulloch et al., 2001). In addition, access to market, demographic composition and assets of the household play an important role to reap the benefits and cope with opportunities and challenges arising from liberalisation (Higgins and Prowse, 2010).

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A „conventional‟ approach has it that trade liberalisation has had a positive impact on poverty alleviation. Absolute poverty has declined in developing countries with open trade policies since 1980s with cuts in tariff in 1980s and 1990s. With higher income resulting from trade openness, openness is a source for fiscal expenditures on social protection, unemployment benefits, and to help the poorest quintile by saving them from negative effects of shifts in business cycles, environmental hazards and health needs (Dollar and Kraay, 2004). However, there is no guarantee that gains from trade will be equally distributed between developing and developed countries. While the standard trade theories neglect the balance of payment implications of trade for developing countries, developing countries need to fully understand the effect of their pattern and terms of trade with the advanced economies (Thirwall, 2000). A more sceptical view sees globalisation as a channel to exploit developing countries‟ low labour costs, for example through child labour. However, the empirical evidence suggests that more open countries have fewer incidences of child labour (Neumayer and De Soysa, 2005). Greater tariff rates and anti-export bias hampers the potential growth in exports. Import controls adversely affect efficiency, although they protect the balance of payments (Khan and Zahler, 1985). Openness can stimulate human welfare without effecting economic growth. On the other hand if trade barriers are going to have a negative impact on economic growth it does not necessarily imply that they will reduce the welfare of the society too (Rodriguez and Rodrik, 2000; Rodrik, 2000). Eusufzai (1996) provides evidence that there is positive correlation between trade openness and several measures of the level of human development used by United Nations Development Programme (UNDP) including HDI. The results show that more open economies have a higher level of human development measured by HDI, a lower under-fiveyears mortality rates, and a higher proportion of population with access to safe drinking water.

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Nourzad and Powell (2003) suggest that openness can influence both economic growth and human development in a panel of forty-seven developing countries over twenty five year period (1965-90), using five-year averages.

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Empirical specification and estimation methodology

We set up a three simultaneous equations empirical model (equations for openness, economic growth and human development) to study the interrelationships between openness, economic growth and human development for a panel of 12 major Asian countries. We have three equations in our model because given our review of the literature we argue that openness, economic growth and human development are simultaneously determined. Our empirical growth equation (1) is merely a standard growth equation augmented with levels of openness and human development for county i and time period t. ∆𝑌𝑖𝑡 = 𝛼1 ∆𝐿𝑖𝑡 + 𝛼2 ∆𝐾𝑖𝑡 + 𝛼3 ∆𝐻𝑖𝑡 + 𝛼4 𝑂𝑃𝑖𝑡 + 𝛼5 𝐻𝐷𝑖𝑡 + 𝜇𝑖 + 𝑇 + 𝜀𝑖𝑡 ,

(1)

where ∆𝑌 is the growth rate of output, ∆𝐿 is the growth rate of labour, ∆𝐾 is the growth rate of physical capital stock, ∆𝐻 is the growth rate in human capital stock, 𝑂𝑃 is the level of openness, and 𝐻𝐷 is the level of human development. The term 𝜇𝑖 is the individual country effect, T is a time trend, and 𝜀𝑖𝑡 is the error term which varies across countries and time. Human capital plays an important role in stimulating economic growth in both the augmented neoclassical growth model (Mankiw et al., 1992) and the endogenous growth model (Lucas 1988; Lucas, 1990; Romer, 1986). Some empirical growth studies have found it difficult to demonstrate a strong positive impact of human capital on economic growth predicted by theoretical models. 4 The difficulties encountered in linking human capital to economic growth include unresolved methodological issues, inclusion of skills into the 4

In a survey of macroeconomic literature on the link between education and growth Sianesi and van Reenen (2003) conclude that there is compelling evidence on the positive impact of human capital on productivity growth. The empirical evidence on OECD countries in favour of new growth theories is weak. Moreover, there is still no consensus on whether the stock of human capital influences the level of income in long run (augmented neoclassical models) or the long run growth rate suggested by endogenous growth theories.

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measurement of human capital and the identification of channels through which it affects economic growth (Sianesi and Van Reenen, 2003). Furthermore there is no consensus on how human capital should enter the production function framework and different human capital measures have different implications for productivity (Islam, 2003). Mankiw et al., (1992) treat human capital as an additional input along with labour and capital while others use human capital as a quality adjusting factor (e.g., Senhadji, 2000). We follow Mankiw et al., (1992) and include human capital as an additional input as it is expected to produce long run growth even in the absence of technological advancements (Lucas, 1988). Our a priori expectations are that α1, α2, α3, α4, and α5 > 0. In setting up our empirical human development equation (2), we draw from the capabilities approach that freedom to enhance social justice, and thus human development depends upon long and healthy life, access to knowledge, and a decent living standard. This theory recognises that people are the primary end as well as principal source of human development (Anand and Sen, 1992). Anand and Sen (2000b) postulate that the accumulation of human capital and health facilities are important for both economic growth as well as human development as they promote productivity, raise income and thus contribute to our long and fulfilled lives, and save us from preventable miseries and diseases. Although trade liberalisation can raise the growth in exports and imports, the balance of payments consequences depend upon its relative impact and on the shifts in prices of traded commodities (Khan and Zahler, 1985; Thirwall, 2012). We parsimoniously specify the following human development equation based on development theory and recent empirical research (Binder and Georgios, 2011; Alavan and Ghosh 2007). 𝐻𝐷𝑖𝑡 = 𝛽1 ∆𝑌𝑖𝑡 + 𝛽2 𝑂𝑃𝑖𝑡 + 𝛽3 𝑙𝑛𝐻𝑖𝑡 + 𝛽4 𝐼𝑀𝑅𝑖𝑡 + 𝜇𝑖 + 𝑇 + 𝜀𝑖𝑡 ,

(2)

where, IMR stands for infant mortality rate and other variables are defined earlier. Wagstaff (2002) shows that there is a two-way causation between poverty and ill health. Poor countries

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tend to have poor health condition and likewise it is true for poor people within a country. IMR is an important indicator linked closely to individuals‟ health conditions, levels of poverty and human development. Historically, major causes of high IMR have been dehydration, pneumonia, malnutrition and malaria. There are still huge differences between IMR rates of developing and developed countries. Generally, developing countries with low levels of income are expected to have high IMR rates while decline in IMR rates would reflect the improvement in economic development in more than one dimension. Furthermore, the preliminary evidence suggests that more open countries have a high level of human development (Eusufzai, 1996; Nourzad and Powell, 2003) and the empirical evidence on returns to schooling suggests that investment in education is fruitful for the society and contributes to human development too. Our a priori expectations are that β1, β2, β3>0 while β40 and γ4 0.8; „medium human

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development‟ if 0.5 ≤ HDI ≤ 0.8; and „low human development‟ if HDI < 0.5. Most countries of the region fall under medium human development category in 2011 with Cohen and Soto measure of average years of schooling, while Bangladesh, Nepal, and Pakistan fall in the category of low human development. The use of the Barro and Lee measure slightly increases human development. Human capital stock (H) is constructed using Cohen and Soto (2007) methodology employing Barro and Lee (2010). Physical capital stock is calculated using the Perpetual Inventory Method: 𝐾𝑖𝑡 = (1 − 𝛿)𝐾𝑖𝑡−1 + 𝐼𝑖𝑡 −1 such that the capital stock of country i in year t is equal to the depreciated capital stock of that country in year t-1 plus the value of investments (I) in year t. We assume that initial capital stock K0 is equal to I0 / (g+ δ) where I0 is the investment in 1970 and g is the average growth rate between 1970 and 2011; δ is depreciation. We use a depreciation rate of 5% following Nhehru and Dhareshwar (1993) and Wang and Yao (2003). We use an economic globalization index, a sub index from KOF Globalization Index (Dreher, 2006) as a broad measure of trade openness (OP) as our preferred measure of openness. KOF Index of globalisation is a composite index comprising economic globalization index, social globalization index and political globalization index. Economic globalization index is based on actual trade flows and restrictions on trade. This index is based on the information for long distance flows of goods, capital and services as well as information and perceptions that accompany market exchanges. To check the robustness of our results we use trade volumes measures of openness from Penn World Table (OP2) and World Bank (2012) (OP3). Table 1 provides a brief description, summary statistics, and sources of the regression variables. The empirical analysis that follows is based on the balanced panel of 12 Asian countries over the period 1970-2011. Table 1 here

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5

Estimation results

Table 2 provides comparisons of the estimates of our system of equations using OLS, 2SLS, and 3SLS. Institutional factors that remain fixed over time are accounted for by country fixed effects. In addition, a time trend is included in all specifications to control for business cycles. We prefer to use 3SLS over OLS as OLS estimates of GDP per capita on openness measures generally find a positive association between trade shares and income per person which may not be the effect of trade on economic growth because of the endogeneity of the trade share. This is because the economies can have high incomes for other reasons not associated with trade (Frankel and Romer, 1999). Furthermore, the OLS estimates can be doubtful due to reverse causality from per capita incomes to trade and there is always a possibility of the association between omitted variables with both trade and per capita incomes (Dollar and Kraay, 2003). The instrumental variable estimates produced by 2SLS and 3SLS are much closer to a priori expectations. Overall, 3SLS provides more precise estimates with the correct coefficient signs. We also use Breusch-Pagan Lagrange Multiplier (LM) Diagonal Covariance Matrix test for 3SLS; the test statistic for the LM is 384.26 with a p-value of 0.00. Therefore, we reject the null hypothesis of diagonal disturbance covariance matrix in favour of using 3SLS. In the rest of the paper we present and discuss results based on 3SLS. Table 2 here

5.1

Growth equation

Table 3 reports 3SLS estimates for three specifications of the growth equation (1) within our three-equation system. Each is based upon one of the three alternative measures of openness i.e., OP1, OP2 and OP3 discussed earlier. Despite the fact that trade volume is the most

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widely used measure it has a number of limitations. Therefore we use the economic globalization index (OP1), a sub index from KOF Globalization Index (Dreher, 2006) as a broad measure of trade openness in our analysis. The economic globalization index is based on actual trade flows and restrictions on trade. To check the robustness of our results we use trade volumes measures of openness from Penn World Table (OP2) and the World Bank (2012) (OP3). The estimated coefficients of labour and physical capital growth rates are always positive and highly statistically significant in all specifications as predicted by neoclassical growth theory. We find strong evidence that an increase in the growth of human capital (H) accumulation stimulates growth in Asian economies. The estimated coefficient of growth in human capital is positive and significant at conventional levels. A one percent growth in human capital stock is associated with a 0.2 percent increase in income growth. Thus, our empirical results provide support to the human capital theory of Becker (1964) and Schultz (1981) and endogenous growth models of Lucas (1988) and Romer (1986, 1990). Our empirical findings also provide evidence that openness fosters growth in Asian countries consistent with Wacizarg and Welch (2008). The estimated coefficient of openness is positive and significant at the 1% level for OP1 and OP2. However, when we use OP3 the estimated coefficient is not statistically significant although it is still positive. Our findings indicate that a one percent increase in openness is associated with 0.2 percent increase in growth. Interestingly, our results provide evidence that improvements in the level of human development enhance growth in the region. The estimated coefficient of HD is positive and highly significant in all three specifications. A one percent improvement in the level of human development is associated with a 0.26 percent increase in economic growth.

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The country dummies capture unobservable effects such as institutional factors, relative to China, not captured by other explanatory variables. Given that the coefficients of the dummies seem to change a lot with the change in the measure of openness they are likely to also capture the imprecision in the openness measure. The dummy variables for India and Bangladesh have positive and significant coefficients suggesting that our model predicts a higher growth in these countries relative to China had the unobserved country conditions been more favourable in reality. Besides this point, while pre-1978 China experienced an annual real GDP growth of 3.8 percent per year, post-1978 China saw real GDP growth of 8.7 percent per year. This shift in regime could also partially explain the varied performance of the dummy variables, compared to China. Table 3 here

5.2

Human development equation

Now we turn to interpreting the regression estimates from the human development equation (2). As per a priori expectation, higher infant mortality rate (IMR) leads to lower levels of human development as the coefficient of IMR is negative and significant in all specifications. A one percent increase in IMR is associated with a 0.01 percent fall in the levels of human development index. This result shows that poor health environment indicated by high IMR hampers human development in Asian countries even though the magnitude of the effect is small. Our findings in Table 3 show that an increase in the level of human capital (H) has a strong positive impact on the level of human development. The estimated coefficient of human capital stock (H) is positive and highly significant in all specifications. A one percent increase in the level of human capital is associated with a 0.06 percent improvement in the level of human development. The result provides evidence that accumulation of human

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capital stock is an important factor for enhancing the human development in the region. Sianesi and Van Reenen (2003) explain such positive effects as „positive educational externalities‟ as educated labour force increases technological progress, improves the productivity of co-workers, and investment in human capital which also raises productivity. The result highlights the fact that education plays an important role in contributing towards the well being of the society. Our findings in Table 3 provide an important contribution to the existing empirical literature by showing that increase in economic growth has hampered human development in Asian economies. The estimated coefficient of economic growth is negative and significant in all specifications. This empirical evidence indicates that Asian economies focusing on achieving faster economic growth have lost ground on the human development front. Therefore, the region has fallen in a vicious cycle where bad performance in terms of human development deters economic growth. As discussed above, counties prioritising only economic growth can fall in such vicious cycle as distribution of assets and income is very important in translating economic growth into better human development which in turn would lead to better growth. Otherwise, the unfair distribution of income which usually comes with fast growth may lead to political and economic instability and ultimately hamper economic performance (Ranis and Stewart, 2000). Our result suggests that economic growth and human development are substitutes at least in the case of the developing Asian countries. Therefore, there is a trade off between growth and human development. It appears that achieving human development is not automatic and societies have to cautiously distribute income or otherwise sacrifice economic growth. Trade openness is often associated with implications for distribution of income in developing countries. Our results concerning the openness coefficient measured by OP1 provide evidence that trade openness has a positive impact on human development in Asian

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economies. A one percent increase in openness is associated with a 0.4 percent improvement in the levels of human development. Our findings show that OP2 and OP3 fail to capture the trade liberalisation policy more broadly and thus produce coefficients with negative signs and hence OP1 is a better measure. Overall, the coefficients on country dummies are negative and significant suggesting that the large majority of Asian countries are not better off in terms of human development as compared to China.

5.3

Openness equation

Table 3 also presents regression estimates of the openness equation (3). As per a priori expectation, market size (MS) has a negative and significant impact on openness. This finding is consistent with the argument that large economies are less open than small ones. Our empirical findings in Table 4 show that FDI has a positive and significant effect on openness in the Asian economies. This result is in line with trade theories which suggest that FDI and openness are complementary in nature as higher levels of FDI make economies more open. We find that economic growth promotes greater openness to international trade. This result reflects the growth experience of some Asian countries where trade liberalisation policies have been adopted after achieving higher economic growth (i.e., South Korea, China). Human development seems to have a positive effect on openness although the coefficient on HD is not statistically significant. The coefficients of the country dummies are negative and statistically significant suggesting that China has a more open economy than other Asian countries once its size is taken into account.

5.4

Robustness analyses

The sceptics of globalisation argue that globalisation brings in higher costs, i.e., environmental hazards, rising poverty and financial crisis than benefits for developing

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countries. On the other hand, the existing empirical studies provide some support to the argument that the net impact of globalisation has been positive. However, these studies use trade flows or other openness measures to proxy the globalisation and thus, cannot capture the overall impact of globalisation on growth. One reason is that most dimensions of globalisation are correlated and thus, cannot be included simultaneously in a regression due to collinearity issues. However, excluding some important dimensions of globalisation from the analysis would lead to bias estimates (Dreher, 2006). As overall effect of globalisation is what matters, we further investigate the empirical link between an aggregate measure of globalisation, economic growth and human development in Asian economies. In Table 4, we replace the openness (OP1) variable with Globalisation Index (GLOB). We observe that the empirical results remain stable and in line with our results (with OP1) reported in Table 3. The main finding here is that globalisation has had a positive impact on human development. A one percent increase in globalisation is associated with an improvement in the levels of human development by 0.53 percent. However, globalisation has no effect on economic growth as the coefficient of GLOB in the growth equation is negative but insignificant. One explanation could be that although the Asian economies have started integrating in more recent years, historically they have not been well integrated in the world economy. This finding also suggests that globalisation process involves much more than improving economic growth alone. Table 4 here Next we replace the HDI index with its education sub-index. This helps us to test for the robustness of our results as well as in directly examining the links between openness, economic growth and education. The results in Table 5 provide evidence that our major conclusions from Table 3 remain unchanged. The major finding here is that there is a twoway causation between education and economic growth. While greater openness to trade

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provides incentives and opportunities for more education, education seems to have no impact on trade openness. In sum, openness contributes to education which further helps boost the economic growth in the Asian countries. Table 5 here Similarly, we replace HDI index with life expectancy index (LEI) to directly test for the interaction between openness, economic growth and life expectancy. The results in Table 6 provide evidence of bi-directional association between life expectancy and economic growth. This finding suggests that healthy society contributes positively to economic progress and in turn more resources are used for the health sector in the Asian economies. Thus, openness provides resources and opportunities for healthy society which promotes economic growth. Table 6 here Rodriguez and Rodrik (2000) suggest that openness alone cannot foster economic growth and rather requires a set of domestic and external policies to do so. These polices include the ability and capacity of the host country to benefit from greater economic integration. Kohpaiboon (2003) suggests that FDI affect economic growth through productivity but that this effect depends upon trade policy. FDI can increase growth through diffusion

of

advanced

technology

into

less

developed

economies.

Furthermore

Balasubramanyam et al., (1996) illustrate that the effect of FDI on economic growth is positive in countries which follow export promotion strategy. Agosin and Machado (2007) document that FDI and openness are positively associated. Therefore we include a FDI variable as a complement to openness. Human development is also included in the openness equation as it provides necessary environment for the foreign investors to explore and benefit from the existing economic conditions. In Table 7, first we include FDI in the growth equation and then also add it in the human development equation. Our findings provide

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evidence that FDI has a strong positive impact on both economic growth and human development in Asian countries. Table 7 here In Table 8, we compare the results between the base specification augmented with GINI coefficient in the growth equation only and specifications with GINI in both growth and human development equations. As per our a priori expectations, all estimated coefficients remain stable in terms of statistical significance, sign, size as well as the country dummies indicate same relationships as before. Income inequality measured by GINI is negatively associated with economic growth but positively with human development although the coefficients are not significant at conventional levels. The positive (even though insignificant) link of inequality and human development suggests that the gains in human development in Asian countries are likely to be driven by the rich minority of the population while poor groups relatively lose. Table 8 here

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Conclusion

This paper sets out to investigate the links between openness, economic growth and human development for 12 Asian economies between 1970 and 2011. Our empirical strategy allows us to test the interrelationships running between these three variables. Our findings suggest that openness, economic growth, and human development are indeed interrelated. We find that trade openness has a positive impact on both economic growth and human development. Our results demonstrate that higher levels of human capital stimulate economic growth and improve human development. Therefore, human capital accumulation is important in enhancing economic growth as well as human development. Asian countries have achieved strong economic growth during past four decades; however, the region still

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lags behind on the human development front. We provide evidence on the negative and significant effect of economic growth on human development and that the latter contributes positively to the former. In keeping up with the critiques of the Washington Consensus, the results appear to suggest that policies which target growth alone have led to poor performance in the distribution of income, raising inequality and ultimately lowering human development. Policy should instead focus on enhancing human development in conjunction with achieving economic growth (e.g., Suri et al., 2011). In view of the economic progress made in China and India, rising inequality is an economic problem urgently needing a solution. Policy makers could focus on inclusive growth policies to enhance the living standards of the masses. Although the theoretical foundations of the association between openness, economic growth and human development are still at a development stage, our work provides some insight into the links although future research is required at both theoretical and empirical level taking into account important implications of the varying institutional quality in the Asian countries.

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WIDER (2008). World Institute

for Development Economics Research. available at

http://www.wider.unu.edu/research/Database/en_GB/wiid/ World Bank (2012). World Development Indicators. Accessed via ESDS International, University of Manchester.

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Table 1: Description and source of regression variables Variables

Definition

Source

Obs.

Mean

S.D.

∆Y (real GDP)

Growth in GDP in millions, in 1990 US$ (Geary Khamis PPPs)

Conference Board (2011), World Bank (2012)

492

0.054

0.036

∆L (labour force)

Growth in employment in thousands

Conference Board (2011)

492

0.024

0.025

∆K (physical capital stock) H (human capital index)

Constructed series using PIM method Cohen and Soto (2007) methodology

World Bank (2012)

492

0.092

0.059

Authors own calculations using Barro and Lee (2010) data Dreher (2006)

504

4.00

1.357

504

0.422

0.225

Penn World Tables 7.0

504

0.807

0.907

World Bank (2012)

504

0.816

0.897

Dreher (2006) Authors own calculations using UNDP (2011), UNDESA (2011), Barro and Lee (2010) A sub index of HDI, based on Cohen and Soto methodology A sub index of HDI World Bank (2012)

504 504

0.431 0.519

0.179 0.134

504

0.403

0.125

504 504

0.713 0.700

0.124 0.586

Ratio of annual net inflows of FDI to GDP

World Bank (2012)

504

0.020

0.035

log (total population)

World Bank (2012)

504

17.977

1.611

Gini coefficient

WIDER (2008) and

504

38.345

6.820

OP1 (Openness) Economic Globalization Index OP2 (Openness) Ratio of exports plus imports to GDP OP3 (Openness) Ratio of exports plus imports to GDP GLOB Globalisation Index HD (human Composite index of development income, health and index) education indices EDU

Education Index

LEI IMR (infant mortality rate): per thousand FDI (foreign direct investment) MS (market size) GINI

Life expectancy index Mortality rate, under-5 (per 1000)

Deininger et al., (1996 Notes: The missing data on Taiwan economy has been obtained from Taiwan Statistical Data Book (CEPD, 2011).

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Table 2: Base Specification using OLS, 2SLS and 3SLS

∆L ∆H ∆K OP1 HD IMR lnH ∆Y OP1 MS FDI ∆Y HDI

OLS ∆Y 0.202*** (0.062) 0.138 (0.102) 0.253*** (0.040) -0.063* (0.034) 0.101 (0.080) HD -0.008 (0.007) 0.038*** (0.007) -0.040 (0.024) -0.032 (0.020) OP1 -0.333*** (0.023) 0.010 (0.094) -0.115** (0.049) -0.557*** (0.086)

2SLS ∆Y 0.204*** (0.064) 0.209* (0.111) 0.290*** (0.043) 0.075 (0.061) 0.477*** (0.138) HD 0.027** (0.013) 0.100*** (0.015) -0.324*** (0.107) 0.396*** (0.069) OP1 -0.322*** (0.027) -0.130 (0.110) 0.423*** (0.140) -0.327** (0.156)

3SLS ∆Y 0.176*** (0.056) 0.188* (0.097) 0.225*** (0.040) 0.195*** (0.058) 0.260** (0.131) HD -0.020** (0.010) 0.057*** (0.012) -0.514*** (0.101) 0.422*** (0.065) OP1 -0.277*** (0.026) 0.212*** (0.078) 0.455*** (0.131) 0.006 (0.143)

Notes: ***, **, and * denote statistical significant at 1%, 5% and 10 % level respectively; number of observations is 492; figures in parentheses are the standard errors; trend and country dummies are included but not reported; openness is measured by economic globalisation (OP1).

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Table 3: 3SLS estimates of the base specification using alternative measures of openness Growth (∆Y) equation Variable ∆L ∆H ∆K OP HD Trend Bang Inda Nepal Pak Sri Indo Mala Phil Sing Korea Thai

OP1 0.176*** (0.056) 0.188* (0.097) 0.225*** (0.040) 0.195*** (0.058) 0.260** (0.131) -0.002*** (0.001) 0.070* (0.038) 0.059* (0.031) 0.044 (0.038) 0.054 (0.033) 0.003 (0.013) -0.012 (0.022) -0.065*** (0.022) -0.024 (0.018) -0.132*** (0.034) -0.038*** (0.011) -0.013 (0.015)

OP2 0.192*** (0.066) 0.261** (0.115) 0.315*** (0.051) 0.098** (0.044) 0.997*** (0.200) -0.006*** (0.001) 0.251*** (0.059) 0.227*** (0.051) 0.222*** (0.056) 0.238*** (0.053) 0.024* (0.014) 0.097*** (0.026) -0.077* (0.043) 0.0411** (0.017) -0.359** (0.145) -0.064*** (0.020) 0.019 (0.017)

34

OP3 0.194*** (0.064) 0.261** (0.112) 0.305*** (0.046) 0.040 (0.024) 0.774*** (0.166) -0.004*** (0.001) 0.183*** (0.048) 0.168*** (0.041) 0.156*** (0.047) 0.177*** (0.042) 0.025* (0.013) 0.072*** (0.023) -0.024 (0.025) 0.039** (0.016) -0.165** (0.080) -0.039*** (0.013) 0.028** (0.014)

Human development (HD) equation Variable IMR lnH ∆Y OP Trend Bang Inda Nepal Pak Sri Indo Mala Phil Sing Korea Thai

OP1 -0.020** (0.010) 0.057*** (0.012) -0.514*** (0.101) 0.422*** (0.065) 0.001** (0.001) -0.173*** (0.014) -0.160*** (0.009) -0.178*** (0.016) -0.181*** (0.010) -0.089*** (0.007) -0.185*** (0.010) -0.190*** (0.022) -0.168*** (0.013) -0.213*** (0.036) -0.039*** (0.009) -0.134*** (0.010)

OP2 -0.010* (0.006) 0.026*** (0.008) -0.092 (0.095) -0.081*** (0.026) 0.006*** (0.000) -0.267*** (0.009) -0.219*** (0.009) -0.265*** (0.010) -0.224*** (0.009) -0.055*** (0.011) -0.129*** (0.007) 0.0490* (0.029) -0.070*** (0.011) 0.293*** (0.082) 0.047*** (0.011) -0.036*** (0.014)

35

OP3 -0.008 (0.007) 0.037*** (0.007) -0.115* (0.070) -0.022 (0.014) 0.005*** (0.000) -0.257*** (0.007) -0.206*** (0.006) -0.258*** (0.007) -0.212*** (0.006) -0.075*** (0.007) -0.138*** (0.005) -0.014 (0.016) -0.092*** (0.007) 0.106** (0.043) 0.028*** (0.007) -0.064*** (0.008)

Openness (OP) equation Variable MS FDI ∆Y HDI Trend Bang Inda Nepal Pak Sri Indo Mala Phil Sing Korea Thai LM test p-value

OP1 -0.277*** (0.026) 0.212*** (0.078) 0.455*** (0.131) 0.006 (0.143) 0.012*** (0.001) -0.813*** (0.081) -0.143*** (0.036) -1.250*** (0.122) -0.665*** (0.077) -1.163*** (0.110) -0.392*** (0.056) -0.795*** (0.106) -0.659*** (0.079) -1.059*** (0.148) -0.773*** (0.083) -0.708*** (0.080) 384.260 0.000

OP2 0.450*** (0.133) -0.002 (0.461) -0.004 (0.720) -5.262*** (0.706) 0.032*** (0.005) -0.495 (0.444) -1.199*** (0.184) 0.274 (0.663) -0.308 (0.419) 1.817*** (0.584) 0.201 (0.307) 2.747*** (0.560) 1.155*** (0.422) 6.011*** (0.763) 1.933*** (0.432) 1.448*** (0.428) 440.140 0.000

OP3 0.521*** (0.127) 2.381*** (0.519) -1.216* (0.684) -5.682*** (0.726) 0.032*** (0.005) -0.432 (0.423) -1.247*** (0.185) 0.456 (0.628) -0.196 (0.398) 2.104*** (0.553) 0.293 (0.291) 3.002*** (0.531) 1.309*** (0.399) 6.204*** (0.731) 2.215*** (0.411) 1.648*** (0.404) 164.930 0.000

Notes: ***, **, and * denote statistical significant at 1% , 5%, and 10 % level respectively; number of observations is 492; OP1, OP2 or OP3 are defined in the text; figures in parentheses are the standard errors; China is the reference country.

36

Table 4: Globalisation, economic growth, and human development Variable

∆Y

Variable

HD

Variable

GLOB

∆L

0.159*** (0.061) 0.317*** (0.104) 0.318*** (0.042) -0.129 (0.089) 0.474*** (0.132)

IMR

-0.014 (0.009) 0.076*** (0.009) -0.354*** (0.101) 0.525*** (0.089)

MS

-0.247*** (0.020) 0.318*** (0.074) -0.105 (0.106) -0.293** (0.115)

∆H ∆K GLOB HD

lnH ∆Y GLOB

FDI ∆Y HDI

Notes: ***, **, and * denote statistical significant at 1%, 5% and 10 % level respectively; number of observations is 492; figures in parentheses are the standard errors; the test statistic for LM test is 183.17 with a p- value of 0.000 in favour of 3SLS (over OLS).

Table 5: Openness, economic growth, and education Variable

∆Y

Variable

EDU

Variable

OP1

∆L

0.147*** (0.053) 0.210** (0.090) 0.215*** (0.039) 0.273*** (0.060) 0.235** (0.102)

IMR

0.013 (0.009) 0.112*** (0.012) -0.518*** (0.089) 0.354*** (0.058)

MS

-0.257*** (0.025) 0.156* (0.087) 0.547*** (0.134) -0.144 (0.111)

∆H ∆K OP1 EDU

lnH ∆Y OP1

FDI ∆Y EDU

Notes: ***, **, and * denote statistical significant at 1%, 5% and 10 % level respectively; number of observations is 492; figures in parentheses are the standard errors; the test statistic for LM test is 382.52 with a p- value of 0.000 in favour of 3SLS (over OLS).

37

Table 6: Openness, economic growth, and life expectancy Variable

∆Y

Variable

LEI

Variable

OP1

∆L

0.167*** (0.056) 0.206** (0.093) 0.229*** (0.038) 0.181*** (0.057) 0.246*** (0.059)

IMR

-0.145*** (0.012) 0.016 (0.014) -0.552*** (0.128) 0.289*** (0.080)

MS

-0.289*** (0.025) -0.018 (0.081) 0.444*** (0.148) 0.032 (0.082)

∆H ∆K OP1 LEI

lnH ∆Y OP1

FDI ∆Y LEI

Notes: ***, **, and * denote statistical significant at 1%, 5% and 10 % level respectively; number of observations is 492; figures in parentheses are the standard errors; the test statistic for LM test is 411.73 with a p- value of 0.000 in favour of 3SLS (over OLS).

Table 7: Base specification with FDI Variable ∆Y

∆Y

Variable HD

HD

Variable

OP1

OP1

∆L

0.134** (0.054) 0.212** (0.093) 0.208*** (0.040) 0.185*** (0.058) 0.291** (0.133) 0.176** (0.086)

IMR

-0.0268*** (0.010) 0.0662*** (0.014) -0.881*** (0.132) 0.524*** (0.076) 0.462*** (0.092)

MS

-0.274*** (0.026) 0.197** (0.084) 0.465*** (0.131) 0.017 (0.144)

-0.282*** (0.026) -0.189* (0.108) 0.623*** ()0.134) -0.171 (0.147)

∆H ∆K OP1 HD FDI

0.174*** (0.056) 0.182* (0.096) 0.221*** (0.040) 0.202*** (0.058) 0.242* (0.133) 0.033 (0.083)

lnH ∆Y OP1

-0.0190** (0.010) 0.0565*** (0.012) -0.513*** (0.102) 0.423*** (0.065)

FDI

FDI ∆Y HD

Notes: ***, **, and * denote statistical significant at 1%, 5% and 10 % level respectively; number of observations is 492; figures in parentheses are the standard errors; the test statistic for LM test is 514.62 and 515.60 with a p- value of 0.000 in favour of 3SLS (over OLS).

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Table 8: Base specification with GINI Variable ∆Y

∆Y

Variable HD

HD

Variable OP1

OP1

∆L

0.177*** (0.0568) 0.195** (0.0981) 0.236*** (0.0410) 0.191*** (0.0596) 0.271** (0.133) -0.0191 (0.0333)

IMR

-0.0206** (0.00951) 0.0558*** (0.0121) -0.512*** (0.0985) 0.405*** (0.0651) 0.0272 (0.0210)

MS

-0.281*** (0.0253) 0.201** (0.0802) 0.427*** (0.130) 0.0115 (0.142)

∆H ∆K OP1 HD GINI

0.178*** (0.057) 0.194** (0.098) 0.237*** (0.041) 0.196*** (0.060) 0.273** (0.133) -0.0215 (0.0333)

lnH ∆Y OP1

-0.0190** (0.009) 0.0575*** (0.012) -0.506*** (0.099) 0.426*** (0.063)

GINI

FDI ∆Y HD

-0.277*** (0.025) 0.220*** (0.079) 0.419*** (0.130) 0.012 (0.142)

Notes: ***, **, and * represent statistical significant at 1%, 5% and 10 % level respectively; number of observations is 492; figures in parentheses are the standard errors; the test statistic for LM test is 778.83 and 760.16 with a p- value of 0.000 in favour of 3SLS (over OLS).

39