By Tsen, Wong Hock; Furuoka, Fumitaka

The main aim of the study is to investigate the relationship between population and economic growth in Asian economies. Generally, the results of the Johansen (1988) and Gregory and Hansen (1996) cointegration methods show that there is no long-run relationship between population and economic growth. Nonetheless, the study finds that there is bidirectional Granger causality between population and economic growth for Japan, Korea, and Thailand. For China, Singapore, and the Philippines, population is found to Granger cause economic growth and not vice versa. For Hong Kong and Malaysia, economic growth is found to Granger cause population and not vice versa. For Taiwan and Indonesia, there is no evidence of Granger causality between population and economic growth. On the whole, the relationship between population and economic growth is not straightforward. Population growth could be beneficial or detrimental to economic growth and economic growth could have an impact on population growth.

I. Introduction

The issue of population and economic growth is as old as economics itself. Malthus (1798) claimed that there is a tendency for the population growth rate to surpass the production growth rate because population increases at a geometrical rate while production increases at an arithmetic rate. Thus, the unfettered population growth in a country could plunge it into acute poverty. However, the pessimist view has proven unfounded for developed economies in that they managed to achieve a high level of economic growth and thus, both population and the real gross domestic product (GDP) per capita were able to increase (Meier 1995, p. 276).1 The debate between positive and negative sides of population growth is ongoing. Population growth enlarges labour force and, therefore, increases economic growth. A large population also provides a large domestic market for the economy. Moreover, population growth encourages competition, which induces technological advancements and innovations. Nevertheless, a large population growth is not only associated with food problem but also imposes constraints on the development of savings, foreign exchange and human resources (Meier 1995). Generally, there is no consensus whether population growth is beneficial or detrimental to economic growth in developing economies. Moreover, empirical evidence on the matter for developing economies is relatively limited.

The issue of population and economic growth is also closely related to the issue of minimum wage. Population growth enlarges labour force and, therefore, will push wage down. The standard economic labour demand model predicts that low wage will raise the demand for labour. As a result, the welfare of the economy is likely to increase. Moreover, low wage would encourage industries that are labour intensive. Low wage is said to be an important factor that has contributed to the industrialization of Asian newly industrialized economies (NIEs), namely Korea, Hong Kong, Taiwan, and Singapore. Moreover, it is also argued to be an important factor that contributes to economic growth in China. Conversely, the standard economic labour demand model predicts that the introduction or rising of minimum wage will break the mechanism, i.e., there would be no link between population and economic growth. Nonetheless, a range of monopsony, efficiency wage, and search models shows that in some circumstances minimum wage could indeed raise employment. The empirical evidence on the matter is mixed, with some studies showing negative effects and others showing positive or zero effects of minimum wage. Thus, there is no clear relationship between population and economic growth. Nevertheless, the studies regarding minimum wage and employment are conducted mainly for developed economies (Stewart 2004, p. Cl 10; Rama 2001; Warr 2004).

The relationship between population and economic growth is complex and the historical evidence is ambiguous, particularly concerning the causes and impacts (Thirlwall 1994, p. 143)?2 Becker, Glaeser, and Murphy (1999, p. 149) demonstrated in a theoretical model that a large population growth could have both negative and positive impacts on productivity. A large population may reduce productivity because of diminishing returns to more intensive use of land and other natural resources. Conversely, a large population could encourage greater specialization, and a large market increases returns to human capital and knowledge. Thus, the net relationship between greater population and economic growth depends on whether the inducements to human capital and expansion of knowledge are stronger than diminishing returns to natural resources. Therefore, it is important to examine the population and economic growth nexus.

The main aim of the study is to investigate the relationship between population and economic growth in Asian economies, namely, China, Japan, Asian NIEs (Korea, Hong Kong, Taiwan, and Singapore) and the ASEAN-4 countries (Indonesia, Malaysia, the Philippines, and Thailand), generally for the period 1950-2000. Those countries vary in population size, economic growth, stage of economic development, and openness to international trade (see Table 1). The study employs the Johansen (1988) (J) cointegration method to examine the long- run relationship between population and economic growth. Moreover, the possibility of a structural break in the long-run relationship between the two variables is examined using the Gregory and Hansen (1996) (GH) cointegration method. The advantage of the method is that it does not require information regarding the timing of or indeed the occurrence of a break. In other words, it determines the break point endogenously from the data rather than on the basis of a priori information, which the problem of data mining can be avoided. Furthermore, the Granger causality between population and economic growth is addressed.

The empirical study on the relationship between population and economic growth in Asian economies is limited. Thus, the study provides some evidence of the relationship between population and growth in those countries. Moreover, the empirical studies on the relationship between population and economic growth in the literature are mainly conducted using cross-section data (Thornton 2001, p. 464). Nevertheless, some studies are conducted using time series data (Dawson and Tiffin 1998; Thornton 2001). However, these studies do not consider the possibility of a structural break in their long-run analysis, whereas the issue is considered in the study.

TABLE 1

Asian Economies – Some Facts

TABLE 1

Asian Economies – Some Facts

TABLE 1

Asian Economies – Some Facts

The study is organized as follows. section II gives a discussion of population and economic growth. section III explains data and methodology used in the study. section IV provides the empirical results and discussions. section V gives some concluding remarks.

II. Population and Economic Growth

The debate on the relationship between population and economic growth could be traced back to 1798 when Thomas Malthus published the book An Essay on the Principle of Population. According to the Malthusian model, the causation went in both directions. Higher economic growth increased population by stimulating earlier marriages and higher birth rates, and by cutting down mortality from malnutrition and other factors. On the other hand, higher population also depressed economic growth through diminishing returns. This dynamic interaction between population and economic growth is the centre of the Malthusian model, which implies a stationary population in the long-run equilibrium (Becker, Glaeser, and Murphy 1999, p. 145).

Generally, population growth is associated with food problem, i.e., malnutrition and hunger. Nonetheless, the food problem is more a problem of poverty and inadequate income than a matter of inadequate global food supplies. The population and food problem is solved when income is enough to buy adequate food as prices provide adequate incentives to produce. Developing economies are capable of producing surpluses of food for exports. On the other hand, developing economies would have to export more, receive foreign aid or borrow overseas to meet their increased demand for food by increased imports (Meier 1995, p. 277).

Population growth is much more than a food problem. A high rate of population growth not only has an adverse impact on improvement in food supplies, but also intensifies the constraints on development of savings, foreign exchange, and human resources. Rapid population growth tends to depress savings per capita and retards growth of physical capital per worker. The need for social infrastructure is also broadened and public expenditures must be absorbed in providing the need for a larger population rather than in providing directly productive assets (Meier 1995, pp. 276-77).

Population pressure is likely to intensify the foreign exchange constraints by placing more pressure on the balance of payment. The need to import food will require the development of new industries for export expansion and/or import substitution. The rapid increase in school-age popul\ation and the expanding number of labourforce entrants puts ever-greater pressure on educational and training facilities and retards improvement in the quality of education, which is a problem in developing economies as about 33 per cent of the children of primary-school age are not enrolled in school and of those who enter school, 60 per cent will not complete more than three years of primary school (Meier 1995, p. 285). Also, too dense a population aggravates the problem of improving the health of the population. In most developing economies, the working age population had roughly doubled in the past twenty-five years. At expected growth rates, it will double again in the next twenty-five years. This growth clearly intensifies pressure on employment and the amount of investment available per labour market entrant (Meier 1995, p. 277).

Becker, Glaeser, and Murphy (1999, p. 147) demonstrated in a theoretical model that population growth will increase parental utility if it has a sufficiently positive impact on human capital accumulation or if the impact on current production is not too negative. Since human capital is more important at higher levels of development, greater population is likely to raise per capita welfare in more developed economies. On the other hand, an increase in population growth may lower the productivity of farming in poorer agricultural economies, so that output per capita there would be lower initially. However, even in these economies, greater population growth would tend to raise the accumulation of human capital by raising rates of return on investments in schooling and other human capital. Moreover, families would lower their fertilities if population growth raises rates of return on investments in children because that would increase the cost of having large families compared with investing more in each of children. Therefore, the demographic transition towards smaller families in economies with initially high fertility and low income per capita may be stimulated by an initial growth in population. Thus, an increase in population may both reduce fertility and raise the accumulation of human capital.

A larger population may help overcome possibly diminishing returns to this generation’s human capital in the production of the next generation’s human capital because greater population growth induces more specialization and a larger market that raise returns to human capital and knowledge. If human capital per capita were sufficiently large, the economy would move to steady-state growth, whereby in the steady-state growth path, consumption per capita would increase at a slower rate than human capital if the population is growing and if the production of consumer goods has diminishing returns to population. However, consumption per capita can still be increasing, despite these diminishing returns, if the positive impact of the growth in human capital on productivity in the consumption sector more than offsets the negative impact of population growth. Thus, zero population growth is not necessary for sustainable growth in per capita consumption, even with diminishing returns to population in the production of consumer goods (Becker, Glaeser, and Murphy 1999, p. 148).

There are few empirical studies on the relationship between population and economic growth. A majority of them uses cross- section regression to analyse the relationship between the two variables (Easterlin 1967; Thirlwall 1972; Simon 1992; Kelley and Schmidt 1996; Ahlburg 1996). Some of them found no statistically significant relationship between population and economic growth while other studies were not able to come to a conclusive opinion as the results tended to be contradictory.

Dawson and Tiffin (1998) used annual time series data over the period 1950-93 to analyse the long-run relationship between population and economic growth in India. The study employed cointegration and Granger causality methods and reported that there is no long-run relationship between the two variables. Moreover, population growth neither Granger causes economic growth nor is caused by it. Thornton (2001) conducted a similar research on the long-run relationship between population and economic growth in seven Latin American countries, namely, Argentina, Brazil, Chile, Colombia, Mexico, Peru, and Venezuela. The study used annual time series data generally over the period 1900-94 and employed the same methods of analysis as Dawson and Tiffin (1998). The study concluded that there is no long-run relationship between the two variables in any of the seven countries. Furthermore, population growth neither Granger causes economic growth nor is caused by it.

III. Data and Methodology

The population and economic growth data are based on annual data. In the study, the economic growth is expressed by the real GDP per capita. The population and economic growth data were obtained from Heston, Summers, and Aten (2002). The data for Japan, the Philippines, and Thailand are over the period 1950-2000, respectively; the data for China are over the period 1952-2000; the data for Korea are over the period 1953-2000; the data for Taiwan are over the period 1951-98; the data for Hong Kong and Indonesia are over the period 1960-2000, respectively; the data for Singapore are 1960-96, and the data for Malaysia are 1955-2000. All the data were transformed into logarithms. The plots of logarithms of population and the real GDP per capita are given in Figure 1. Generally, the figure shows no relationship between population and the real GDP per capita in all economies examined. The trends of population tend to be stationary while the trends of the real GDP per capita show some fluctuations.

FIGURE 1

The Plots of Logarithms of Population and Real GDP per capita against Time

The empirical estimation in the study begins with the unit root tests. The aim of unit root tests is to examine whether a series is stationary or nonstationary, which is important to avoid spurious regression. In the study, the Dickey and Fuller (1979) (DF) and Phillips and Perron (1988) (PP) unit root test statistics are employed. The DF unit root test statistic uses the parametric approach, i.e., to change the estimation regression to solve the heterogeneity and serial correlation in an error term. In contrast with the DF unit root test statistic, the PP unit root test statistic uses the nonparametric approach, i.e., to modify statistic to obtain estimator and statistic. The DF unit root test statistic is a low power test under the null hypothesis of a unit root that posits root close to the unit circle or tends to stationary. On the other hand, the PP unit root test statistic is known to be more robust in an error term process, i.e., an error term is allowed to be weakly heterogeneous.

FIGURE 1

The Plots of Logarithms of Population and Real GDP per capita against Time

According to Engle and Granger (1987), series that are integrated of the same order may cointegrate together. The cointegrated series may drift apart from each other in the short run but the distance between them tends to be constant or in a stationary process in the long run. More formally, a vector of series (n 1), y, is said to be cointegrated if each of the series is integrated of the same order, an existing non-zero cointegrating vector (n 1), a’ such that the linear combination of these series, a’y, are stationary or is said to be integrated of zero and denoted by 1(0).

The procedure is similar to the Engle and Granger (1987) cointegration method that includes a dummy variable in the cointegrating regression to consider a shift in the long-run relationship. The estimation of the above models using the ordinary least squares estimator yields the estimated error terms, following which the unit root tests (ADF*^sub t^, Z*^sub t^) are applied to them. The unit root tests, ADF*^sub t^ and Z*^sub t^ are designed to test the null hypothesis of no cointegration against the alternative hypothesis of cointegration in the presence of a possible regime shift. If there is one unknown point in the sample, the standard tests for cointegration are not appropriate, since they presume that the cointegrating vector is time-invariant under the alternative hypothesis. The advantage of the GH cointegration method is that it does not require information regarding the timing of or indeed the occurrence of a break. In other words, it determines the break point endogenously from the data rather man on the basis of a priori information, which the problem of data mining can be avoided.

IV. Empirical Results and Discussions

The results of the DF and PP unit root test statistics are reported in Table 2. The lag length used to compute the DF test statistic is based on Akaike (1973) information criterion (AIC). For the PP unit root test statistic, the results that are reported are based on three truncation lags, which are used to compute the test statistic after considering truncation lags one to three in computing the test statistic. The results of the DF and PP unit root test statistics show that population is integrated of order one for Hong Kong, the Philippines, and Thailand. For China and Taiwan, the unit root test statistics show that it is integrated of order two. For Japan, Korea, and Singapore, the DF unit root test statistic shows that it is integrated of order two while the PP unit root test statistic shows that it is integrated of order one. For Indonesia and Malaysia, the DF unit root test statistic shows that it is integrated of zero, while the PP unit root test statistic shows that it is integrated of order two. On the other hand, the results of the DF and PP unit root test statistics generally show that economic growth is integrated of order one, except Korea and Thailand. For Korea, the DF test statistic shows that it is integrated of order zero, while the PP unit root test statistic shows that it i\s integrated of order one. For Thailand, the DF and PP unit root test statistics show that it is integrated of order zero. On the whole, population and economic growth are treated to be integrated of order one.

TABLE 2

The Results of the Dickey and Fuller (1979) and Phillips and Perron (1988) Unit Root Test Statistics

The results of the J cointegration method are reported in Table 3. The results of the λ^sub Max^ and λ^sub Trace^ test statistics aie computed with unrestricted intercepts and no trends. For Japan and Hong Kong, the results of the λ^sub max^ and λ^sub Trace^ test statistics show that the null hypotheses, i.e., H^sub 0^: r = 0 is rejected at 95 per cent critical value while H^sub 0^: r [left double arrow] 1 is not rejected at 95 per cent critical value, which indicate that population and economic growth are cointegrated. For China, Korea, Taiwan, Singapore, Indonesia, Malaysia, and Thailand, the results of the λ^sub Max^ and λ^sub Trace^ test statistics show that population and economic growth are not cointegrated. Lastly, for the Philippines, the λ^sub Max^ test statistic shows that the null hypotheses are not rejected at 95 per cent critical value. In contrast with the λ^sub Max^ test statistic, the λ^sub Trace^ test statistic shows that there is one cointegrating vector. Johansen and Juselius (1990) suggested that the λ^sub Max^ test might be better than the λ^sub Trace^ test. Thus, it is concluded that there is no cointegration for population and economic growth in the Philippines. On the whole, the study finds no long-run relationship between population and economic growth. The finding of no cointegration between population and economic growth could be because of the existence of a structural break that biases the test results in favour of not rejecting the null hypothesis of no cointegration. Therefore, the GH cointegration method is employed.

TABLE 3

The Results of the Johansen (1988) Likelihood Ratio Test Statistics

The results of the GH cointegration method are reported in Table 4. Generally, the results of ADF*^sub t^ and Z*^sub t^ test statistics show that the null hypothesis of no cointegration against the alternative hypothesis of cointegration in the presence of a possible regime shift are not rejected at 5 per cent level, except Korea and Singapore. For Korea, ADF*^sub t^ test statistic that tests the null hypothesis of the model C/S is rejected at 5 per cent level, thus implying there is a long-run relationship between population and economic growth. On the other hand, Z*^sub t^ test statistic that tests the null hypothesis of the model C/S is not rejected at 5 per cent level. Therefore, the results are inconclusive. For Singapore, ADF*^sub t^ test statistic that tests the null hypothesis of the model C/T is rejected at 5 per cent level, implying there is a long-run relationship between population and economic growth. On the other hand, Z*^sub t^ test statistic that tests the null hypothesis of the model C/T is not rejected at 5 per cent level. Thus, the results are inconclusive.

TABLE 4

The Results of the Gregory and Hansen (1996) Cointegration Test Statistics

Generally, the results of the GH cointegration method show the same conclusion as the J cointegration method, i.e., there is no evidence of a long-run relationship between population and economic growth. The finding is the same as the findings of Dawson and Tiffin (1998), which reported that there is no long-run relationship between population and economic growth in India, and Thornton (2001), which reported that the long-run relationship between population and economic growth in Latin American countries, namely Argentina, Brazil, Chile, Colombia, Mexico, Peru, and Venezuela, does not exist. In the long run, there is no relationship between population and economic growth regardless of the size of population, openness of international trade, trading partner, state of economic development, and minimum wage.

TABLE 5

The Results of Granger Causality Test

The results of Granger causality test are reported in Table 5. Generally, there is some evidence that population and economic growth are Granger causality to each other, except for Taiwan and Indonesia. For Taiwan and Indonesia, the findings are consistent with those of Dawson and Tiffin (1998) and Thornton (2001), which found population growth neither Granger causes economic growth nor is caused by it. In other words, population growth neither stimulates economic growth nor detracts from it. On the other hand, for Japan, Korea and Thailand, there is bidirectional causality between population and economic growth, which contradicts with the results of Dawson and Tiffin (1998) and Thornton (2001). For China, Singapore, and the Philippines, population growth is found to Granger cause economic growth. For Hong Kong and Malaysia, economic growth is found to Granger cause population growth. Thus, the relationship between population and economic growth is not straightforward. There is no strong evidence that a large population will contribute to economic growth. Moreover, the relationship between population and economic growth is not the same among the countries that have about the same state of economic development. The size of an economy and openness of international trade do not matter. The implementation of minimum wage has no strong impact on the population and economic growth relationship.

There is no straightforward relationship between population and economic growth. Population growth could be beneficial or detrimental to economic growth, and economic growth could have an impact on population growth. Thus, some economies in Asia, which achieve a low level of economic growth, may not be affected by population growth, but are affected by other factors such as political instability and lack of investments. On the other hand, some economies in Asia, which achieve a high level of economic growth, may not have done so because of population growth, but due to other factors. Tan (1995) claimed that political stability, efficiency of public administration, successful implementation of export-oriented industrialization policies, quality of labour force, and macroeconomic stability are among the factors that have contributed to economic growth in Asian NIEs. Lloyd and MacLaren (2000) argued that the fast growth of the East Asian economies were partly due to their early openness to international trade, and less openness of their economies to international trade will slow down their economic growth rates. Wong (2003) examined foreign direct investment (FDI) and economic growth in the ASEAN-4 countries and China and reported that FDI has contributed to economic growth in these countries. In addition, human capital, domestic investment, and openness to international trade are found to have a positive impact on economic growth.

V. Concluding Remarks

The main aim of the study is to investigate the relationship between population and economic growth in Asian economies, namely China, Japan, Asian NIEs, and ASEAN-4. The results of the DF and PP unit root test statistics show that, generally, population and economic growth are nonstationary in level but become stationary after taking the first differences. In other words, those series are considered to be integrated of order one. Moreover, the results of the J cointegration method show that generally population and economic growth are not cointegrated.

The inability of rejecting the hull hypothesis of no cointegration between population and economic growth in most of the cases examined could be because of the existence of a structural break that biases the test results in favour of not rejecting the null hypothesis of no cointegration. Thus, the study employed the GH cointegration method, which can accommodate the existence of a structural break in the cointegrating vector. Nonetheless, the results of the GH cointegration method show the same conclusion as the results of the J cointegration method. Thus, the results of the cointegration methods employed in the study reaffirm each other. There is no straightforward relationship between population and economic growth.

Furthermore, the study estimates the Granger causality between population and economic growth. The results are mixed. For Japan, Korea and Thailand, there is bidirectional Granger causality between population and economic growth. For China, Singapore and the Philippines, population is found to Granger cause economic growth and not vice versa. On the other hand, for Hong Kong and Malaysia, economic growth is found to Granger cause population and not vice versa. For Taiwan and Indonesia, there is no evidence of Granger causality between population and economic growth.

The relationship between population and economic growth is not straightforward. Population growth could be beneficial or detrimental to economic growth and economic growth could have an impact on population growth.

NOTES

The authors would like to thank the referees and co-editors of the bulletin for their comments on the early versions of the article. All remaining errors are ours.

1. Economic growth in the literature of population and economic growth is measured mainly using the real GDP per capita.

2. Thirlwall (1994) discussed the issue of the relationship between population and economic growth mainly for developing economies.

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Wong Hock Tsen is a lecturer at the School of Business and Economics, Universiti Malaysia Sabah.

Fumitaka Furuoka is a lecturer at the School of Business and Economics, Universiti Malaysia Sabah.

Copyright Institute of Southeast Asian Studies Dec 2005