Demographic structure and economic growth: Evidence from China

Journal of Comparative Economics 38 (2010) 472–491

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Journal of Comparative Economics journal homepage: www.elsevier.com/locate/jce

Demographic structure and economic growth: Evidence from China Zheng Wei a,⇑, Rui Hao b a b

Nottingham University Business School China, The University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, China Institute for Advanced Study (IAS), Shenzhen University, Shenzhen 518060, China

a r t i c l e

i n f o

Article history: Received 12 April 2008 Revised 31 August 2010 Available online 6 September 2010 JEL classification: J10 O47 P21 Keywords: Demographic structure Dependency ratio Economic growth Marketization

a b s t r a c t Wei, Zheng, and Hao, Rui—Demographic structure and economic growth: Evidence from China This paper examines the economic implications of demographic change in the Chinese context. We extend the growth equation by incorporating age structure dynamics and apply it to China’s provincial-level data during the period 1989–2004. We find that changes in demographic structure, especially the contribution of fertility decline to lower youth dependency, have helped fuel China’s economic growth since 1989. The effect of demographic change on income growth operates mainly through its impact on steady state income levels and the effect of age structure is more pronounced in provinces that are more open to market forces. We also find a significant feedback effect of economic growth on demographic behaviors through the mechanisms of birth rates, marriage age and life expectancy. Journal of Comparative Economics 38 (4) (2010) 472–491. Nottingham University Business School China, The University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo 315100, China; Institute for Advanced Study (IAS), Shenzhen University, Shenzhen 518060, China. Ó 2010 Association for Comparative Economic Studies Published by Elsevier Inc. All rights reserved.

1. Introduction In the past three decades, few countries in the world have experienced more pronounced economic growth than China. According to the World Bank (2009), China’s per capita GDP growth rate was approximately 8.69% on average per annum during 1978–2008. It was significantly higher than 1.90% for the United States, 1.84% for Europe, 2.07% for Japan and 3.82% for India. The reasons for China’s remarkable economic growth have been extensively studied. Various factors are argued to be attributable to the growth ‘miracle’ in China, including institutional reforms (Woo, 1999; Qian, 2003), rapid accumulation of capital (Chow and Li, 2002; Wang and Yao, 2003) and substantial improvement in total factor productivity (Bosworth and Collins, 2008; Perkins and Rawski, 2008). However, the contribution of a fundamental factor has rarely been investigated in the existing literature on China’s economic growth. This factor is demographic structure of the Chinese population which has experienced substantial changes in the past few decades. In this paper, we systematically examine the dynamics in China’s demographic structure and their implications on economic growth. Demographic structure describes the age distribution of a population and thereby is also called population age structure. It is usually measured by the total dependency ratio, which is the ratio of the total number of the dependent population, aged below 15 and above 65 years, to that of the working-age population. The ratio of the number of the dependent

⇑ Corresponding author. E-mail addresses: [email protected] (Z. Wei), [email protected] (R. Hao). 0147-5967/$ - see front matter Ó 2010 Association for Comparative Economic Studies Published by Elsevier Inc. All rights reserved. doi:10.1016/j.jce.2010.08.002

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population under 15 year-old to that of the working-age population is referred to as the youth dependency ratio, while that of the dependent population above 65 year-old is referred to as the elderly dependency ratio. The lower the dependency ratio, the less is the dependency burden to an economy. Age structure evolves during the demographic transition of a population. When fertility falls in sequence of a mortality decline, the population enters the demographic window. During this period of time, the total dependency ratio considerably falls due mainly to lower youth dependency. Correspondingly, the proportion of the working-age group becomes particularly prominent in the total population. These may result in an ample labor supply to the economy and accumulated savings for investment as well. Given an appropriate economic and social policy environment, the changes in demographic structure during the demographic window may boost economic growth. The contribution is usually referred to as the demographic dividend to economic growth (Bloom and Williamson, 1998). Studies on the demographic dividend have become very popular since the late 1990s. For example, Bloom, Canning and Malaney (2000) claim the demographic dividend accounts for nearly a third of the extraordinary growth in East Asia during 1965–1990. Bloom et al. (2006) find that economic takeoffs in China and India have substantially benefited from changes in their demographic structures during the demographic transition. Bloom et al. (2007) analyze the potential for Sub-Saharan African countries to achieve their demographic dividend in the future provided the policy and institutional context is compatible. However, it is obvious that most of the existing demographic studies focus on the analysis using cross-country data or time-series data of a single country. Few studies have exploited the inter-jurisdictional variation within a country, which leaves a niche to be filled in. In this paper, we examine the presence of the demographic dividend to China’s economic growth using provincial-level data during a particular window of 1989–2004. Inspired by Bloom and Canning (2004), we first modify the standard convergence equation by including the initial level and growth rate of the total dependency ratio. This modification allows us to observe the demographic impact on economic growth in the long-run as well as the short run. The results show that even after controlling for other growth determinants and the endogeneity of demographic variables, changes in demographic structure have a significant impact on China’s economic growth. This impact appears to be more important in the longrun than in the short run.1 The finding is robust to different estimation methods applied. Second, we distinguish the effect on economic growth by youth dependency with that by elderly dependency. Our results indicate that the significant contribution of demographic structure to economic growth is largely attributable to the lower youth dependency ratio, resulting from dramatic declines in fertility. Third, given the fact that changes in the age structure merely create the growth potential (Bloom, Canning and Malaney, 2000), we explore the pre-condition for China to capture its demographic dividend to growth. In doing so, we introduce an interaction term between demographic structure and policy variables into the regression. Our results show that the effect of demographic structure is more pronounced in provinces that are more open to market forces. In these provinces, market reforms, particularly in developing non-state-owned enterprises, may help turn the expanded working-age population to employment and translate accumulated savings into productive investment. This indicates that marketization provides China a favorable policy environment for achieving the demographic dividend. Furthermore, we examine the reverse causality of income change on demographic structure through the channels of life expectancy, birth rates and marriage age. Our results suggest that rapid economic growth has significantly affected China’s demographic situation. Growth has helped to lower birth rates, delay women’s mean age at the first marriage and extend life expectancy. The paper is organized as follows. Section 2 briefly reviews the related literature. Section 3 provides an overview of the demographic changes and economic growth in China. Section 4 describes methodology, data and variables. Section 5 presents estimation results. Concluding remarks and discussion follow in Section 6. 2. Literature review A population enters the demographic transition, when it evolves from a stage of high fertility and mortality to that of low fertility and mortality. At the early stage of the demographic transition, the total fertility rate substantially falls in sequence of a decline in infant mortality. During this period, the prime working-age population grows more rapidly than the dependent population. More laborers, particularly women, are released to the labor market and more resources are accumulated for investment in economic development. This presents an opportunity for more rapid economic growth. Given an appropriate economic and social policy environment, the demographic change may affect economic growth and yield the demographic dividend (Bloom, Canning and Sevilla, 2003). The demographic dividend to growth has been assessed in different empirical frameworks. The convergence (technologygap) model is highlighted as the empirical framework under which the demographic effect on economic growth can be fully demonstrated.2 As Kelley and Schmidt (2001, 2005) point, the convergence model is better in helping identify the channels 1 Indeed, when using fixed-effects estimations, there are larger variances in the estimates, while the results regarding dependency ratios remain qualitatively unchanged. 2 The convergence model, rooted in the neoclassical growth theory by Solow (1956), was empirically enriched in the 1990s. Barro (1991) incorporated demographic variables, such as fertility, mortality and population growth into the convergence model. Barro and Sala-i-Martin (1992) phrased it in per capita terms, while Radelet, Sachs and Lee (1997) re-wrote it in per worker terms. See detailed discussion about evolutions of economic-demographic modeling in Kelly and Schmidt (2001, 2005).

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through which demographic changes affect economic growth, than other frameworks such as simple-correlations or production-functions.3 In addition, it allows us to explore both the long-run and short-run demographic effects on growth. Various demographic variables have been involved in the convergence model. Amongst them, demographic structure, represented by the dependency ratio or the proportion of the working-age population to the total population, is generally regarded as the key variable for exploring economic implications of demographic change on growth. According to Bloom and Canning (2001), this variable can measure demographic effects on the labor market more directly than birth and death rates. More importantly, changes in demographic structure may reflect changes in people’s ages of dying and thereby to some extent indicate improvements in the health status of a population. Hence, the inclusion of demographic structure in the model does not only allow us to account for the effect4 resulting from the growth in the number of workers relative to that of the total population, but also account for the effect made by people’s behavior changes. Many recent demographic studies are couched on the convergence model with demographic-structure variables included. For example, Bloom and Williamson (1998) disentangle growth in the working-age population with that in the total population from the standard convergence equation. They find that rapid growth in the working-age population has a significantly positive impact on economic growth. In contrast, the effect of the total population growth is insignificant and negative. After controlling for the reverse causality from economic growth to demography, Bloom, Canning and Malaney (2000) find that the effect suggested by changes in demographic structure is much larger than the accounting effect resulting from the relative growth in the number of workers. They argue that the demographic dividend in East Asia accounts for a third to a half of its sustained high economic growth during 1965–1990. Bloom and Finlay (2009) apply the similar approach to the data in a longer time period of 1965–2005. Their results reconfirm the role of demographic change in East Asian economic growth but call for appropriate policies to trade-off the potential adverse impact of population ageing in the coming future. However, as indicated in many studies, the demographic dividend is not unconditional to achieve in an economy. The Sub-Saharan African countries are found to have the potential to achieve the demographic dividend so long as the policy and institutional context is compatible and conducive (Bloom et al., 2007). Latin American countries are found to have experienced similar demographic changes as Asian countries but are unable to realize the demographic dividend due to their unstable political and economic environment (Bloom and Canning, 2004). Demographic changes may not only affect economic growth through the mechanism of the labor market but also savings and capital accumulation. Lee, Mason and Miller (2001), Mason and Lee (2004) and Lee and Mason (2006) argue that a longer life expectancy and a smaller family size may lead to a strong incentive for people to save for their extended period of retirement. Increased savings, no matter investing domestically or abroad, may contribute to economic growth given effective policies in support systems for the elderly. Relying on the life-cycle model and calibrations, Mason and Lee (2004) explain most of the rapid increase in Taiwanese savings by changes in the age structure of the population. The dramatic demographic transition that China has experienced in recent decades has drawn great attention. Various studies have documented the economic implication of China’s demographic changes but the evidence is inconclusive. Bloom et al. (2006) investigate the determinants of economic growth in China and India using cross-country data during 1960– 2000. They find that economic takeoffs in both countries have substantially benefited from a higher labor force per capita ratio induced by dramatic fertility declines, and rising health and life expectancy of the population. Feng and Mason (2008) use the ratio of effective consumers to producers to capture changes in demographic structure of China over time. They show that 15% of China’s economic growth in 1982–2000 is attributable to changes in demographic structure. Lu (2009) examines demographic effects on China’s economic growth and regional disparity using provincial-level data in the production function during 1995–2007. He shows a significant negative impact of the total dependency ratio on income per capita. More interestingly, his results indicate that the coastal provinces that host large amounts of inter-regional migrant workers appear to be less constrained by the demographic effect. This finding raises an interesting question regarding the pre-condition under which the demographic dividend is achievable in the Chinese economy. Cai (2004) and Cai and Wang (2005, 2006) examine the demographic effect on economic growth in the convergence model using pooled data for 28 Chinese provinces in 1982–2000. They find that 1% increase in the total dependency ratio lowers 0.115% of China’s per capita GDP growth rate. In 1982–2000, the total dependency ratio has declined by 20.1%, which thereby contributes one-quarter of China’s economic growth. In their studies, however, the feedback effect of income on demography has not been taken into account in the estimations. As Bloom, Canning and Malaney (2000) argue, the ignorance of the bidirectional causality between income and demography may result in seriously biased estimates. In addition, estimations in these studies are mainly conducted on data with yearly time span. This type of data setup is often considered to be too short to avoid short-term disturbances, business cycle fluctuations and serial correlations (Islam, 1995). Moreover, though having been noted in many cross-country studies, the importance of the policy environment for realizing the demographic dividend has not been highlighted in the Chinese context. Nor has it been formally explored. These indicate that there is much to be desired in studying the economic implication of demographic changes in the Chinese context.

3 According to Kelley and Schmidt (2001), the simple-correlations framework fails to explicit the channels of demographic effects on the economy, while the framework of production functions faces formidable difficulties in data compiling and variable assessment. 4 This effect is thereby also named as the accounting effect of demographic change on economic growth in the literature (Bloom and Williamson, 1998).

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Crude birth rate %

4

Crude death rate %

-1

0

1

2

3

Population growth rate %

1949

1954

1959

1964

1969

1974

1979

1984

1989

1994

1999

2004

Fig. 1. The demographic transition of the Chinese population 1949–2006. Source: Comprehensive Statistical Data and Materials on 55 Years of New China (National Bureau of Statistics of China, 2005); World Development Indicators (World Bank, 2009).

3. Demographic changes and economic growth in China The Chinese population has experienced a dramatic demographic transition since 1949. As shown in Fig. 1, during 1949– 1970, death rates substantially fell to far below 1% except for the famine years (1959–1961). Nevertheless, birth rates remained high and even peaked to 4.34% in 1963.5 As a result, the total population expanded and grew at 1.95% per annum on average. In 1971–1979, China called for ‘‘later marriage, longer birth intervals, and fewer births”, which is also known as the family planning program, to control for over rapid growth of the population. Birth rates thereby declined to 1.78%. Later, this decline was reinforced by the ‘‘one-child” policy implemented since the 1980s.6 In 2005, birth rates fell to 1.21%, while with a low death rate of 0.64%, the population growth rate declined to 0.59% (National Bureau of Statistics of China, 2005). Such a pronounced transition of China’s population has yielded salient changes in demography. Over the past three decades, fertility fell from 7.5 births per woman to far below the replacement rate. In 2007, the rate of fertility even recorded 1.8 births per woman (World Bank, 2009). Mortality, particularly infant mortality, declined dramatically from 2% in the 1960s to 0.66% in the 1990s, a level similar to that of developed countries. Life expectancy increased from 36 years to 73 years, which is well above the average level for less developed countries but approaching that for developed countries (Wang and Mason, 2008). The mean age of women’s first marriage rose from less than 20 year-old to more than 25 year-old. This is particularly true in large municipalities of China. For example, in 2001, the mean marriage age was 25.3 year-old in Beijing and 25.2 year-old in Shanghai, compared to 18.2 year-old on average in 1940. More importantly, the age structure of the Chinese population has substantially changed. As illustrated in Fig. 2, the total dependency ratio declined by 38% during the past 30 years. This seemed largely attributable to changes in the youth dependency ratio, which fell from 72.5% to 30.2% during 1965–2005. Nevertheless, the elderly dependency ratio maintained relatively stable around 7.8%. The substantial decline in the total dependency ratio also makes China outstanding in terms of the degree and the pace of the demographic transition. This is clearly illustrated in Fig. 3. Moreover, the evolution of the total dependency ratio implies the opening of the demographic window in China. According to the United Nations Population Division (2004), the demographic window opens when the total dependency ratio in a country falls to approximately 40–60%. The window opens for China since 1990 when the total dependency ratio declined to 49.8%. A lower dependency ratio indicates a higher ratio of workers per capita and thereby a greater supply of labor to the labor market. It also implies fewer mouths to feed and therefore more savings accumulated for productive investment in the economy. Additionally, a smaller family size also allows more investment in education and health of children. It results in more productive workers with valuable human capital. In short, the demographic window presents an opportunity for China to seize the demographic dividend. However, the realization of the demographic dividend requires an appropriate economic and social policy environment. It is generally agreed that the Chinese economy has grown very rapidly since economic reforms in 1978. As illustrated in Fig. 4, the annual growth rate of China’s GDP per capita increased from 2.65% in 1960–1977 to 8.19% in 1978–1989. In the 1990s, the average growth rate rose to 8.70 per annum (World Bank, 2009). This period of rapid economic growth is coincident with the opening of the demographic window in China. It indicates that China’s economic growth might to some extent be correlated with the demographic transition. More interestingly, since the late 1980s, China has implemented marketization reforms in order to 5 This was supported by the two generations in which the number of children per family was high. One generation was born in the aftermath of the civil war in 1949–1954 and the other was born after the famine in 1962–1965 (Lu, 2009). 6 During 1982–1987, birth rates recorded an unexpected rebound. This can be explained by the incidental overlapping of the marriage–childbirth periods that the two generations have experienced. Most of the second generation born in 1962–1965 generally experienced marriage and childbirth in their late twenties thanks to the call of the family planning program. However, the first generation born after the civil war also had their children in this period. The reason is that the 10-year Cultural Revolution (1966–1976), especially the state-administrated internal migration campaign, had seriously delayed the age of marriage and childbirth. Two periods of marriage–childbirth overlapped, leading to the rebound along the declining tendency of birth dates in the 1980s.

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Youth dependency ratio %

75

Elderly dependency ratio %

5

15

25

35

45

55

65

Total dependency ratio %

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

50

60

70

80

90

Fig. 2. China’s dependency ratios, 1960–2005. Source: World Development Indicators (World Bank, 2009).

High income countries

40

Low income countries East aisan and pacific countries

30

China

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

Fig. 3. Total dependency ratios, 1960–2006. Source: World Development Indicators (World Bank, 2009).

transit the economy towards a market system. The reform aims at optimizing the allocation of economic resources, particularly removing the impediment to the development of the private sector (Chen and Feng, 2000; Laurenceson and Chai, 2000). As a result, the marketization reform has greatly improved the flexibility of the labor market and the capital market, which in turn have helped absorb the increased labor supply released during the demographic window, and translate accumulated savings into productive investment in the economy. This indicates that economic reforms, particularly marketization reforms, might have provided China with the essential policy environment for achieving the demographic dividend. In this paper, we investigate the presence of the demographic dividend in China as well as the compatible policy environment. 4. Empirical methodology and data 4.1. Extended convergence equation Following recent demographic studies, we examine the effect of demographic transition on economic growth in the convergence framework. We extend the convergence equation by introducing demographic structure, represented by the dependency ratio. In doing so, the extended convergence equation allows us to relax the assumption of demographic stability. This assumption is implicitly presumed in previous empirical studies on growth but in fact it is invalidated particularly during the demographic transition.7 We firstly decompose the income per capita as follows: 7 Theoretically, the convergence equation is formulated in terms of income per worker. However, empirical studies usually examined it in income per capita terms given the assumption of a stable demographic structure and full employment. As pointed by many recent demographic studies (Bloom and Williamson, 1998; Bloom, Canning and Malaney, 2000), the assumption is not validated during the demographic transition which results in changes in population age structure. Consequently, income per worker is not equivalent to income per capita. In this sense, the result of the previous empirical studies might have been undermined.

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60 50

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Total dependency ratio (left scale)

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Growth rate of GDP per capita (right scale)

0

-30

Predicted tendency of growth rate 1960

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Fig. 4. China’s dependency ratio and economic growth 1960–2006. Source: World Development Indicators (World Bank, 2009).

Y=P ¼ ðY=LÞðL=WAÞðWA=PÞ ¼ ðY=WAÞðWA=PÞ

ð1Þ

where Y represents income or GDP in this case, P is the total population, WA is the working-age population, L is the labor force. Assuming full employment in an economy, the labor force is approximated by the working-age population. Therefore, income per capita (Y/P) can be expressed as the product of the income per working-age population (Y/WA) and the share of the working-age population in the total population (WA/P). The total dependency ratio (D) is defined as the ratio of the number of the dependents (aged below 15 and above 65) to that of the working-age population (aged at 15–64), namely, D = (P  WA)/WA. Eq. (1) can then be re-written as:

Y=P ¼ ðY=WAÞ½1=ð1 þ DÞ

ð2Þ

Taking logarithms on both sides of Eq. (2) and adding time subscripts, we have:

lnðY=PÞt ¼ lnðY=WAÞt þ ln½1=ð1 þ DÞt ¼ lnðY=WAÞt  lnð1 þ DÞt

ð3Þ

Denoting yt = (Y/P)t and zt = (Y/WA)t, we can rewrite Eq. (3) as follows:

ln yt ¼ ln zt  lnð1 þ DÞt

ð4Þ

Obviously, the initial value of the log of income per capita, the log of income per working-age population and the dependency ratio obey the following form:

ln y0 ¼ ln z0  lnð1 þ DÞ0

ð5Þ

Differentiating Eq. (4) with respect to time, we have:

g y ¼ g z  g ð1þDÞ

ð6Þ

where gy stands for the growth rate of income per capita, gz is the growth rate of income per working-age population (or income per worker), and g(1+D) is the growth rate of (1 + D). According to the theoretical framework of the neoclassical growth model (Barro and Sala-i-Martin, 1992; Mankiw, Romer and Weil, 1992), the growth rate of income per working-age population, gz, is a function of the gap between the initial level and the steady-state level of income per working-age population. If the relative steady states are allowed to be affected by shocks, this relation can be expressed as:

g z ¼ kðln z  ln z0 Þ

ð7Þ 

where k is the speed of convergence toward the steady states, ln z . The conditional convergence hypothesis refers to the case when economies converge to different steady states, determined by a set of variables (X), namely,

ln z ¼ Xb0

ð8Þ

Substituting Eqs. (5)–(8) into Eq. (6), we obtain the extended convergence equation:

g y ¼ k½Xb0  ln y0  lnð1 þ DÞ0   g ð1þDÞ

ð9Þ

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Thus, both the initial level and growth rate of the dependency ratio are formally included in the convergence equation. Such a specification of the convergence equation is distinguishable from that in the existing demographic studies (e.g., Kelley and Schmidt, 2005; Cai and Wang, 2005; Cai and Wang, 2006), in which only the level of the dependency ratio is included. As is well-known, the standard convergence equation can be viewed as a dynamic specification with the initial level of income as the explanatory variable (Caselli, Esquivel and Lefort, 1996). From the perspective of methodology, our extended convergence equation appears to be an error-correction model: the level of the dependency ratio, coupled with levels of other control variables, describes the convergence process toward the long-run equilibrium; while the growth rate of the dependency ratio captures the short-run effect of demographic structure on economic growth. 4.2. Specification, data and variables We employ balanced panel data for 30 Chinese provinces over the period 1989–2004.8 Compared to the cross-section regressions, the panel data approach can provide a richer model specification. First, with panel data, we can multiply the within-variation by the number of time-series to alleviate the small sample bias. This is important in our case given that there are only 30 provinces in the cross-section profile. Second, we can involve the variables that vary mostly across provinces (e.g., geographic variables) as well as those that vary in the time and province dimensions (e.g., openness, marketization, investment rates and government expenditure). Third, we can control for the possible endogeneity in the variables of interest using their lagged values. Finally, panel data allow us to reduce the omitted variable bias by controlling for unobserved province-specific and/or time-specific effects. Our empirical analysis involves two steps. We start by examining the effect of demographic structure on economic growth in the extended convergence equation. Eq. (9) is re-written in the panel data specification as follows:

g y ¼ a1 ln yi;t1 þ a2 ln Di;t1 þ a3 ln urbani;t1 þ a4 ln popdeni;t1 þ a5 ln popi;t1 þ a6 g D þ bX i;t1 þ cgeog i þ gi þ dt þ eit

ð10Þ

The dependent variable is the provincial growth rate of GDP per capita (gy). The explanatory variables fall into five categories: (a) the initial level (lnDi,t1) and growth rate (gD) of the dependency ratio,9 which are employed to account for the demographic-structure effect on growth; the urban population share (lnurbani,t1), population density (lnpopdeni,t1) and population size (lnpopi,t1), which are included to capture the effects of urbanization, population density and economies of scale on growth; (b) the initial level of log GDP per capita (lnyi,t1), which is used to capture the income convergence effect; (c) control variables widely used in the Chinese growth literature (Xi,t1), which are considered as determinants of steady state income levels, including openness, marketization, government expenditure, investment rates; (d) geographic variables (geogi) including geographic proximity to sea-based international trade and pure geographic topography10; (e) region dummies (gi) and period dummies (dt), which are used to account for unobserved heterogeneities across regions and over time. In the second step, we examine the feedback effect of economic growth on demographic behaviors. As argued by Bloom, Canning and Malaney (2000, p. 276), the impact of income change on demographic behaviors is likely to be felt only in the long-run. Estimating changes in demographic behaviors on changes in income merely captures the short-run feedback effect, which may mislead our understanding of the long-run impact. We thereby estimate the relationship between demographic behaviors and the level of income, rather than the changes in each. The estimation equation is displayed as follows:

ln demoi;t ¼ a1 ln zi;t þ a2 geog i þ gi þ dt þ eit

ð11Þ

where demoi,t represents demographic behaviors in, here in this paper, birth rates, women’s mean age at the first marriage and life expectancy, respectively; zi,t is real GDP per worker. As suggested by Pritchett and Summers (1996), we exclude other controls that affect demographic behaviors but may be predetermined by income levels. We include period dummies to capture technological improvements in health, and also include region dummies to account for unobserved regional heterogeneities. All variables are expressed in natural logarithms except for dummies. Considering that business cycle fluctuations may not be fully disentangled from long-term economic growth in an annual data setup (Islam, 1995; Beck, Levine and Loayza, 2000), we re-construct our dataset to an overlapping panel with 5-year 8 The data we use cover all of the Chinese provinces except for Macao, Hong Kong Special Administrative Regions and Taiwan province. Specifically, they include the 22 provinces, five autonomous regions and three municipalities. Note that the data for Chongqing, which has not been a municipality until 1997, are combined into those for Sichuan province. 9 We write ln(1 + D) and g(1+D) in Eq. (9) but specify ln D and gD in Eq. (10). Since these two types of variable notation essentially contain the same information about demographic structure, the estimations with ln(1 + D) and g(1+D) derive similar conclusions. Moreover, the estimate of ln D gives more intuitive implications of the estimated demographic effect on growth. 10 We are grateful to Gene H. Chang and Sylvie Démurger for kindly providing us with the data on geography.

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intervals between 1989 and 2004. The intervals are 1989–1994, 1994–1999 and 1999–2004. Namely, we are essentially running three cross-sectional regressions, one for each 5-year period, and assuming common coefficients across these time periods. Following Bloom et al. (2000), we use initial values for all explanatory variables at the start of each 5-year period. The exception is that the growth rates of the dependency ratio are measured over each period. The use of initial values of variables can mitigate, if not eliminate, the problem of reverse causality, as they are measured before economic growth has occurred (Caselli, Esquivel and Lefort, 1996). In addition, choosing to use initial values as steady state determinants appears to be in line with the theoretical framework (Romer, 2001). We take into account regional heterogeneity by using geographic variables rather than province-specific effects. This is due to the fact that the within-group estimator is often biased in panels with a short time dimension (Nickell, 1981). It is particularly true for our sample that covers only 15 years in 1989–2004. More importantly, it is generally agreed that heterogeneity across Chinese provinces is largely attributable to geographical factors (e.g., Démurger et al., 2002; Bao et al., 2002). Hence we introduce two types of geographic variables, namely pop100cr and slavge. The variable, pop100cr, measures the ability of a province to participate in sea-based international trade. The more geographic convenience a province has, the higher proximity is it to international trade, and the more likely is its economy having rapid growth. The geographic variable, slavge, represents geographic ruggedness. The higher is the slope of a province, the more difficult it is for agriculture production and transportation of industrial products. In addition, as highlighted in voluminous studies (e.g., Yao and Zhang, 2001; Hao and Wei, 2010), heterogeneity among the three regions of China (i.e., the East, the Central and the West) becomes increasingly important relative to that among provinces. We then use region dummies to capture the remaining heterogeneity across three regions.11 We also include period dummies to capture common shocks to growth in each 5-year period relative to the base period, 1989–1994. Our data are largely traced from the Comprehensive Statistical Data and Materials on 55 Years of New China (2005), China’s Statistical Yearbooks (1998–2005), and China Population Statistical Yearbooks (1998–2005). Alternative sources of some variables are specified in the variable definitions. All current values are expressed in the local currency (yuan) at 1995 constant prices. The sample period, 1989–2004, is principally limited by data availability of demographic variables. Data for dependency ratios (including the total dependency ratio, the youth dependency ratio and the elderly dependency ratio) are only available in 1982, 1987 and 1989–2004 at the provincial level.12 Therefore, the data during 1982–1987 are used as instruments in the instrumental variable regressions over the sample period (1989–2004). Data for life expectancy are only available for 1981, 1990 and 2000. They are therefore used in estimations over the three 5-year periods respectively. Variable definitions and descriptive statistics are displayed in Appendix A. In Figs. 5 and 6, provincial per capita GDP growth is plotted against the total, youth, and elderly dependency ratios in the initial year, and also against the change in each of the three dependency ratios over 1989–2004.13 The line among the scattered points represents the fitted values obtained from the simple linear regressions. As shown in Fig. 5, the dependency ratios are in general negatively associated with economic growth. The only exception is the elderly dependency ratio whose slope estimate appears to be somewhat positive and significant. Fig. 6 demonstrates that none of the slope estimates of changes in the dependency ratios are significantly different from zero. We also plot provincial GDP per worker against the birth rate, marriage age and life expectancy in the initial year in Fig. 7. Birth rates are negatively and significantly associated with income per worker, whereas the slope estimates for marriage age and life expectancy are both positive and significant.

5. Empirical results 5.1. The contribution of demographic structure to China’s economic growth Table 1 reports the OLS estimates of the extended convergence equation, Eq. (10). In Columns (1)–(4), the level of the total dependency ratio (ln Di,t1) and other controls are included in the regressions. The results show that the initial level of the total dependency ratio is negatively associated with economic growth at the 5% significance level. A 1% decline in the logarithm of the total dependency ratio, which is equivalent to a reduction in the total dependency ratio by 63.2% points, leads to an increase in the growth rate of GDP per capita by 4.1%. This indicates that a 1% decrease in the total dependency ratio contributes to an increase in economic growth by 0.065%. The significantly negative demographic-structure effect captured in our estimations is consistent with the results reported in the existing demographic studies (e.g., Kelley and Schmidt, 2005; Cai and Wang, 2005; Cai and Wang, 2006). Yet in terms of the magnitude of the effect, our finding appears to be smaller than that by Cai and Wang (2005, 2006).14 11 Indeed, when using fixed-effects estimations, there are larger variances in the estimates, while the results regarding dependency ratios remain qualitatively unchanged. 12 Our data on the demographic estimates are largely consistent with the aggregate demographic data reported in the World Bank’s World Development Indicators (2009) and the United Nations’ World Population Prospects (2009). We thank Dewen Wang and Fang Cai for kindly providing us the data for dependency ratios in 1982, 1987, and 1989–1993. 13 We have also plotted histogram graphs of the total, youth and elderly dependency ratios to demonstrate their distributions. These graphs are available upon request. 14 See detailed discussion about the results of Cai and Wang (2005, 2006) in Section 2.

.05

.1

.15

Z. Wei, R. Hao / Journal of Comparative Economics 38 (2010) 472–491

Growth rate of provincial GDP per capita

480

3.4

3.6

3.8

4

4.2

.15 .1 .05

Growth rate of provincial GDP per capita

Total dependency ratio in the initial year (in log)

3

3.2

3.4

3.6

3.8

4

.15 .1 .05

Growth rate of provincial GDP per capita

Youth dependency ratio in the initial year (in log)

1.5

2

2.5

3

Elderly dependency ratio in the initial year (in log) Fig. 5. Dependency ratios and economic growth. Note: The points denote the growth rates of Chinese provincial GDP per capita. The respective line shows the fitted values by the simple linear regression. Source: Authors’ calculations based on the data from the Comprehensive Statistical Data and Materials on 55 Years of New China (2005), China Statistical Yearbooks (1998–2005), and China Population Statistical Yearbooks (1998–2005) by the National Bureau of Statistics of China.

The variation in the estimated demographic-structure effect is mainly attributable to our desirable data setup which can avoid short-term disturbance and business cycles, and also attributable to the control of endogeneity of demographic structure which has been ignored in their estimations. Note that the dependency ratio does not only capture the effect on the labor market but also the effect of people’s behavioral changes on economic growth (Bloom and Canning, 2001). Thus, the demographic-structure effect measured here is not limited to the effect on the labor market but is rather ‘‘accompanied by strong behavioral elements, although it is not fully clear through what channels these behavioral effects operate” (Prskawetz et al., 2007). In Columns (5)–(8), we further add growth rates of the total dependency ratio (gD) to the regressions. We find that the initial level of the total dependency ratio remains statistically significant and negative, while its average growth rate in a 5-year span is always insignificant albeit negative. It indicates that the demographic-structure effect on China’s economic growth appears to be more important in the long-run, rather than the short run. This result is distinct from the evidence derived from a panel of countries at the global level by Bloom and Canning (2004), who find both long-run and short-term effects of demographic structure on economic growth. However, it is not implausible particularly in the Chinese context. It is important to realize that the potential impetus generated by demographic changes may take a period of time to be translated into economic growth in a country. For instance, a lower dependency burden implies a larger labor supply and more

.1

.15

481

.05

Growth rate of provincial GDP per capita

Z. Wei, R. Hao / Journal of Comparative Economics 38 (2010) 472–491

-.08

-.06

-.04

-.02

0

.02

.15 .1 .05

Growth rate of provincial GDP per capita

5-year averaged change in total dependency ratio

-.1

-.05

0

.05

.15 .1 .05

Growth rate of provincial GDP per capita

5-year averaged change in youth dependency ratio

-.1

-.05

0

.05

.1

5-year averaged change in elderly dependency ratio Fig. 6. Dependency ratio growth and economic growth. Note: The points denote the growth rates of Chinese provincial GDP per capita. The respective line shows the fitted values by the simple linear regression. Source: Authors’ calculations based on the data from the Comprehensive Statistical Data and Materials on 55 Years of New China (2005), China Statistical Yearbooks (1998–2005), and China Population Statistical Yearbooks (1998–2005) by the National Bureau of Statistics of China.

domestic savings for investment. However, it may take a period of time for an economy to absorb the increased labor supply in production and translate accumulated savings into productive investment, and eventually turn to higher output. In other words, an economy may need a period of time to adjust to the consequence of demographic changes. The length of the adjustment period essentially relies on how flexible the economy is. In the developing countries like China, the economy is far from flexible due to the segmented labor market and inefficient capital market. It is thereby reasonable to have an insignificant impact of changes in demographic structure on China’s economic growth in the short run. We also include the urban population share, population density and size to account for the impact of economies of scale and also density and scale effects on economic growth. The share of the urban population in the total population describes the degree of urbanization in an economy. It rose substantially from 25.5% in 1978 to 32.0% in 1984, and peaked to 43.1% in 1999. The increased share of urban population is a natural result of the expanded labor supply released during the demographic transition. On the one hand, the urban areas accommodate many large manufactures and industries that need a large amount of workers to achieve economies of scale. On the other hand, too many people gathered in the urban areas may result in congestion in traffic, housing and deterioration in the environment. Therefore, the ultimate effect of urbanization on

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.5 0 -.5 -1

Birth rate in the initial year (in log)

1

482

7

8

9

10

11

3.25 3.2 3.15 3.1 3.05 3

Marriage age in the initial year (in log)

Provincial GDP per worker (in log)

7

8

9

10

11

4.35 4.25 4.3 4.15 4.2 4.1

Life expectancy in the initial year (in log)

Provincial GDP per worker (in log)

7

8

9

10

11

Provincial GDP per worker (in log) Fig. 7. Feedback effects of income on demographic behaviors. Note: The points in the above sub-figures denote birth rates, marriage ages and life expectancy in the initial year respectively. The respective line shows the fitted values by the simple linear regression. Source: Authors’ calculations based on the data from the Comprehensive Statistical Data and Materials on 55 Years of New China (2005), China Statistical Yearbooks (1998–2005), and China Population Statistical Yearbooks (1998–2005) by the National Bureau of Statistics of China.

economic growth depends on the trade-off of economies of scale and congestion effects. As shown in our results, the estimated coefficient for urbanization is positive but insignificant. Population density and size exhibit the effects of density and scale on growth. During 1978–2004, population density averaged at the provincial level has increased by 43%. The size of provincial population also rose, though slowly, from 31.9 million to 43.1 million on average. Higher population density, on the one hand, can stimulate technology change, reduce transportation costs, increase efficiency and facilitate specialization in production. On the other hand, it may lead to diminishing returns to land and deleterious effects of congestion. With respect to population size, scale effects contribute to specialization and diversification between firms, while they may also press fixed resources and attenuate the adverse impact of diminishing returns. Overall, the effects of scale and density are ambiguous and the evidence is mixed. For example, Kelley and Schmidt (2001) find positive and significant effects of scale and density, while Bloom, Canning and Malaney (2000) report a positive but insignificant impact of density. Both effects are argued to be insignificant by Kelley and Schmidt (2005). In our estimates, population density is negative and significant, suggesting that an adverse impact dominates the density effect on China’s economic growth. Scale effects are found to be insignificant in all cases but turn to be negative when period dummies are included. This indicates that the positive scale effect might have been overwhelmed by the consequence of

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Z. Wei, R. Hao / Journal of Comparative Economics 38 (2010) 472–491 Table 1 Contribution of demographic structure to economic growth. Dependent variable: average growth rate of GDP per capita in 5-year period (1) OLS Demographic variables ln D 4.075*** (2.43) gD ln urban ln popden ln pop Control variables ln y ln openness ln market ln expendit ln invest pop100cr Slavge

0.380 (0.93) 0.345 (1.61) 0.371 (0.88) 2.499

***

(3.65) 0.904*** (2.98) 2.132*** (3.37) 0.019 (0.02) 0.609 (0.62) 2.154*** (2.75) 0.035 (0.25)

Central West

(2) OLS

(3) OLS

(4) OLS

(5) OLS

(6) OLS

(7) OLS

(8) OLS

4.063** (2.48)

3.801** (2.47)

3.793** (2.47)

0.185 (0.45) 0.477** (2.20) 0.508 (1.22)

0.383 (1.03) 0.465** (2.11) 0.129 (0.30)

0.297 (0.77) 0.484** (2.19) 0.082 (0.19)

4.227** (2.27) 2.747 (0.19) 0.366 (0.88) 0.334 (1.49) 0.362 (0.85)

4.062** (2.22) 0.016 (0.00) 0.185 (0.44) 0.477** (2.10) 0.508 (1.20)

4.112** (2.39) 5.859 (0.41) 0.355 (0.93) 0.459** (2.06) 0.121 (0.28)

3.740** (2.15) 0.995 (0.07) 0.301 (0.77) 0.486** (2.17) 0.083 (0.19)

2.537*** (3.78) 0.576* (1.73) 1.911*** (3.02) 0.394 (0.44) 1.312 (1.23) 1.776** (2.28) 0.039 (0.25) 1.050* (1.71) 2.030** (2.37)

3.264*** (4.11) 1.485*** (4.00) 2.466*** (3.19) 2.330** (2.16) 0.809 (0.79) 0.960 (1.24) 0.006 (0.04)

3.394*** (4.25) 1.389*** (3.41) 2.111** (2.58) 2.060* (1.85) 1.281 (1.17) 0.850 (1.10) 0.089 (0.61) 0.202 (0.31) 1.150 (1.31) 0.098 (0.10) 1.920** (2.50) 44.240** (2.61)

2.590*** (3.11) 0.936*** (2.69) 2.106*** (3.23) 0.025 (0.03) 0.590 (0.59) 2.174*** (2.74) 0.033 (0.23)

2.536*** (3.10) 0.576 (1.55) 1.911*** (2.96) 0.394 (0.44) 1.313 (1.20) 1.776** (2.24) 0.039 (0.25) 1.050* (1.69) 2.031** (2.33)

3.375*** (4.00) 1.518*** (3.98) 2.508*** (3.20) 2.273** (2.08) 0.860 (0.83) 1.001 (1.28) 0.011 (0.09)

27.811* (1.79)

24.661 (1.58)

0.368 (0.35) 1.755** (2.16) 45.219** (2.61)

3.377*** (4.00) 1.382*** (3.28) 2.099** (2.49) 2.066* (1.83) 1.280 (1.16) 0.841 (1.06) 0.089 (0.61) 0.203 (0.31) 1.166 (1.28) 0.133 (0.12) 1.940** (2.35) 44.027** (2.53)

90 0.458

90 0.343

90 0.372

90 0.450

90 0.451

Period2

Constant

26.282* (1.97)

24.670* (1.88)

0.187 (0.20) 1.864** (2.44) 43.963** (2.59)

Observations Adj R2

90 0.351

90 0.381

90 0.456

Period3

Note: Figures in parentheses are heteroskedasticity-robust t-statistics. Variable definitions are displayed in Appendix A. Significance at 10% level. ** Significance at 5% level. *** Significance at 1% level. *

congestion particularly during the late 1990s. The negative estimated effects of population density and scale not only contribute to mixed and sparse evidence on density and scale effects in the literature, but also reflect the characteristic of China’s population and economy, over-crowded relative to limited resources. Other controls in the regressions are of expected signs and appear to be largely in line with most empirical studies on China’s provincial economic growth (e.g., Chen and Feng, 2000; Guariglia and Poncet, 2008).15 However, the estimated coefficient for the investment ratio (ln invest) is statistically insignificant and economically trivial.16 This may be due to the fact that the investment ratio is a poor proxy for physical capital (Bosworth and Collins, 2003). It may also be attributed to the endogeneity of investment. As Bloom and Williamson (1998) argue, investment might be endogenous and thereby its effects on growth might have already been explained by demographic changes especially through the mechanism of savings. Among geographic variables, the pure geographic effect (slavge) is insignificant in all cases. The estimated coefficient for the proximity to 15 We have tried to include human capital variables like schooling in the estimations, but none of the variables estimates were significant. The lack of significance of human capital in growth regressions is quite common and may be attributed to many reasons. For example, it may be due to measurement problems in human capital variables (Krueger and Lindahl, 2001) and may also result from the composition of human capital (Vandenbussche, Aghion and Meghir, 2006). 16 There are also other growth studies reporting insignificant investment ratios. For example, Berthélemy and Démurger (2000) and Jones, Li and Owen (2003) argue that a higher investment ratio is not necessarily related with a higher rate of economic growth. In China, provincial investment funds dominated in stateowned enterprises may have not been efficiently allocated and utilized for production.

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international trade (pop100cr) is positive as expected, but only significant when neither regional dummies nor period dummies are included. The two region dummies (Central and West) have negative estimates, suggesting that the central and west regions have experienced slower growth rates of GDP per capita than the reference region, the East. However, they lose the significance when period dummies are further included. The lack of significance for both geographic variables and region dummies indicates that most of the heterogeneity across provinces or regions might have been explained by demographic variables and other controls in the model. With respect to period dummies, period3 is significantly negative as compared to the reference period, 1989– 1994. This demonstrates China has experienced a significant deceleration of growth in 1999–2004. It should be noted that the inclusion of region and period dummies not only make no change in the estimates of demographic variables and other controls, but also raise the values of adjusted R2 from 35% to 46%. 5.2. Alternative methods of estimation Some econometric problems, such as autocorrelation, heteroskedasticity and endogeneity, may bias the estimates. We apply the feasible generalized least squared (FGLS) and instrumental variable (IV) to check the robustness of our results. As reported at the bottom of Table 2, the Wooldridge test and the Likelihood Ratio (LR) test suggest that the null hypothesis of no first-order autocorrelation and that of homoskedesticity are all rejected at least at the 5% significance level. We apply the FGLS estimator to account for cross-sectional heteroskedasticity and time-series autocorrelation. As shown in Columns (1)–(4) of Table 2, the magnitudes of estimated coefficients are similar to those by OLS, while the significance level has been greatly improved. This is especially true for the demographic-structure variables. The estimated coefficient for the dependency ratio becomes highly significant at the 1% level. The beneficial impact of urbanization on growth turns to be significant and thus is compatible with most of the studies on urbanization. The adverse effect of population size also becomes significant on growth. The exception is the estimated coefficient for the growth rate of the dependency ratio. It merely appears to be significant in the initial specification. Due to limited space, we opt to report the results of the level of the dependency ratio only. Note that the investment ratio is dropped from the estimation due to its insignificance. Most of the explanatory variables in our model are measured at the start of each 5-year period and are therefore prior to the economic growth being explained. The lagged procedure enables us to tackle the problem of endogeneity, at least partially. Even though, the lagged values of demographic variables may still correlate with residuals. We re-estimate the model using the instrumental variable estimator (IV), or the two-stage least-squares, to control for the possible remaining endogeneity. The dependency ratio is instrumented with its one-period lagged values (i.e., the values in 1982, 1989 and 1994),17 and the log of GDP per capita, birth rates, death rates and the mean age at the first marriage, all lagged by one period (i.e., the values in 1984, 1989 and 1994). The IV results are shown in Columns (5)–(8) of Table 2. The estimates are again very similar to those in Table 1 and the significance level has been largely improved for all variables. The values of Shea partial R2 indicate that the instruments selected are highly related to the endogenous variable. The p-values of the Sargan tests are larger than the 5% significance level in each of the four specifications, suggesting that the validity of the instruments cannot be rejected.18 The results of the Wu–Hausman test show that the null hypothesis of the exogenous dependency ratio is rejected at the 5% significance level particularly in the last two specifications (Columns (7) and (8)). This indicates that the influence of economic growth on the total dependency ratio might still exist, even though the use of initial values has partially alleviated the endogeneity problem. Note that growth in the total dependency ratio (gD) is contemporaneous with the rate of economic growth explained and therefore may be endogenously determined.19 We instrument it with its one-period lagged values (i.e., the values in 1982– 1987, 1989–1994 and 1994–1999), and the log of GDP per capita, birth rate, death rate and the mean age of first marriage, all lagged by one period (i.e., the values in 1984, 1989 and 1994). We find that the estimated coefficient for the growth of the dependency ratio still remains insignificant as that in Table 1. This helps us assure that the insignificance of the growth of the dependency ratio is not due to simultaneity. It also confirms our finding that demographic effects on growth are insignificant in the short run. Again, we omit the IV estimates for growth of the dependency ratio in all specifications for the sake of simplicity. In short, the results obtained from different estimation methods are consistent with each other and they are also compatible with those from cross-country studies. According to the results of IV estimation (our preferred rendering), a 1% decline in the logarithm of the total dependency ratio, equivalent to a reduction in the total dependency ratio by 63% points, leads to an increase in the growth rate of GDP per capita by 6.8%. This indicates that a 1% decrease in the total dependency ratio leads to an increase in economic growth by 0.108%. During the period 1989–2004, China’s provincial mean values of the total dependency ratio fell by 13%, contributing to 1.43% of economic growth. Provincial GDP per capita grew at a rate of 8.6% per annum on average. This indicates that changes in demographic structure has accounted for one-sixth or 17% of China’s provincial economic growth. This finding is close to the result by Feng and Mason (2008), who argue that 15% of China’s 17 Note that we only have data for dependency ratios for 1982, 1987, and 1989–2004. Therefore, we take the period 1982–1987 as the one prior to the period under study (1989–2004), when we instrument the dependency ratio. 18 Indeed, the results obtained are tentative in that the instruments used are modestly plausible. However, they are the best that can be done under the circumstances. 19 It is reasonable to expect that strong economic growth may induce a higher ratio of the working age to total population through migration effects and/or an effect on fertility (Bloom and Canning, 2006).

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Z. Wei, R. Hao / Journal of Comparative Economics 38 (2010) 472–491 Table 2 Alternative methods of estimation. Dependent variable: average growth rate of GDP per capita in 5-year period

Demographic variables ln D ln urban ln popden ln pop Control variables ln y ln openness ln market ln expendit Pop100cr Slavge

(1) FGLS

(2) FGLS

(3) FGLS

(4) FGLS

3.391*** (3.07) 0.331 (1.35) 0.363** (2.43) 0.265 (1.12)

2.846*** (2.75) 0.352* (1.76) 0.472*** (3.19) 0.136 (0.58)

3.886*** (4.08) 0.488*** (3.26) 0.426*** (2.68) 0.538** (2.18)

2.947

2.039*** (5.68) 0.823*** (3.78) 2.682*** (6.89) 0.150 (0.25) 1.618*** (3.67) 0.098 (1.08)

1.998*** (7.16) 0.584*** (2.86) 2.372*** (6.35) 0.334 (0.60) 1.410*** (3.50) 0.012 (0.14) 0.989*** (4.06) 1.464*** (3.51)

3.544*** (7.39) 1.568*** (7.42) 2.668*** (5.85) 2.940*** (3.79) 1.037** (2.23) 0.079 (0.89)

Central West Period2

Constant

22.798*** (2.66)

25.872*** (3.30)

0.388 (0.75) 2.250*** (5.27) 56.486*** (5.35)

Observations Wooldridge test

90 33.899 [0.000] 5.06 [0.025]

90 33.899 [0.000] 9.99 [0.002]

90 29.697 [0.000] 4.06 [0.044]

Period3

LR test WuHausman test 2

Shea partial R Sargan test

(5) IV

(6) IV

(7) IV

(8) IV

5.684*** (2.90) 0.554 (1.49) 0.434** (2.23) 0.401 (1.16)

5.785*** (2.93) 0.395 (1.04) 0.525*** (2.65) 0.449 (1.28)

6.795*** (3.66) 0.525 (1.46) 0.566*** (2.81) 0.037 (0.10)

6.846*** (3.61) 0.469 (1.25) 0.577*** (2.78) 0.004 (0.01)

3.487*** (6.37) 1.344*** (4.97) 2.330*** (4.85) 3.284*** (4.09) 0.708 (1.50) 0.174* (1.90) 0.636 (1.52) 1.450** (2.36) 0.821 (1.37) 2.349*** (4.80) 59.157*** (5.25)

2.406*** (3.75) 0.788*** (2.75) 2.575*** (4.38) 0.015 (0.02) 1.877*** (2.77) 0.002 (0.02)

2.394*** (3.72) 0.545* (1.73) 2.510*** (4.20) 0.170 (0.22) 1.588** (2.24) 0.003 (0.02) 1.007* (1.77) 1.215 (1.60)

3.104*** (3.54) 1.259*** (3.32) 3.076*** (4.17) 1.689* (1.67) 1.068 (1.48) 0.051 (0.40)

31.742** (2.26)

33.436** (2.41)

0.459 (0.53) 1.178 (1.57) 51.607*** (2.96)

3.142*** (3.50) 1.164*** (2.77) 2.941*** (3.56) 1.525 (1.43) 1.057 (1.42) 0.048 (0.34) 0.369 (0.59) 0.437 (0.51) 0.286 (0.30) 1.167 (1.48) 52.332*** (2.93)

90 29.697 [0.000] 6.31 [0.012]

89

89

89

89

1.976 [0.164] 0.551 1.447 [0.836]

2.472 [0.120] 0.546 0.408 [0.982]

10.296 [0.002] 0.594 1.867 [0.760]

10.207 [0.002] 0.593 1.757 [0.780]

***

(2.96) 0.403** (2.00) 0.508*** (3.40) 0.688*** (2.66)

Notes: Figures in parentheses are heteroskedasticity-robust t-statistics. Figures in square brackets are p-values. Instruments for ln D include the total dependency ratio, GDP per capita, birth rate, death rate and the mean age of first marriage, all lagged one period. The Wooldridge statistic is a test for autocorrelation in panel data, distributed as F under the null of no first-order autocorrelation. The LR statistics is a test for heteroscedasticity error structure, distributed as chi-square under the null of no heteroscedasticity across panels. The Wu–Hausman statistic is a test for endogeneity, distributed as F under the null of exogenous regressors. The Shea partial R2 is a test of instrument relevancy. The Sargan statistic is a test of overidentifying restrictions, distributed as chi-square under the null of instrument validity. Variable definitions are displayed in Appendix A. * Significance at 10% level. ** Significance at 5% level. *** Significance at 1% level.

economic growth is attributable to changes in demographic structure. In addition, we also find that China’s economic growth has significantly benefited from rises in urbanization, while deterred from increases in population density and size. Moreover, the significant effect of demographic change on income growth appears to operate mainly through its impact on steady state income levels. 5.3. Policy environment Many cross-country studies have highlighted the importance of an appropriate economic and social policy environment for achieving the demographic dividend (Prskawetz et al., 2007). We explore the policy environment in the Chinese context

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Table 3 Policy environment. Dependent variable: average growth rate of GDP per capita in 5-year period (1) OLS Demographic variables ln D 17.982* (1.87) ln D  5.853** ln market (2.29) ln urban 0.405 (1.03) ln popden 0.297 (1.45) ln pop 0.285 (0.78) Observations Adj R2 Wooldridge test LR test

90 0.388

(2) OLS

(3) OLS

(4) OLS

(5) FGLS

(6) FGLS

(7) FGLS

(8) FGLS

20.987** (2.13) 6.557** (2.52) 0.202 (0.51) 0.383* (1.85) 0.273 (0.75)

18.062** (2.06) 5.814** (2.49) 0.404 (1.13) 0.370* (1.85) 0.281 (0.77)

22.975** (2.52) 7.070*** (2.93) 0.301 (0.82) 0.336* (1.67) 0.367 (0.98)

18.444*** (3.07) 5.988*** (3.67) 0.383 (1.62) 0.401*** (2.71) 0.299 (1.33)

25.609*** (4.61) 7.753*** (5.23) 0.294 (1.56) 0.473*** (3.21) 0.119 (0.53)

13.351** (2.12) 4.634*** (2.69) 0.489** (2.34) 0.463*** (2.80) 0.380 (1.38)

19.425*** (3.02) 6.179*** (3.53) 0.457** (2.26) 0.520*** (3.59) 0.525* (1.91)

90 0.417

90 0.493

90 0.505

90

90

90

90

17.200 [0.000] 2.68 [0.102]

17.200 [0.000] 5.26 [0.022]

14.212 [0.001] 1.16 [0.281]

14.212 [0.001] 1.63 [0.201]

Notes: Figures in parentheses are heteroskedasticity-robust t-statistics. Figures in square brackets are p-values. The Wooldridge statistic is a test for autocorrelation in panel data, distributed as F under the null of no first-order autocorrelation. The LR statistics is a test for heteroscedasticity error structure, distributed as chi-square under the null of no heteroscedasticity across panels. Variable definitions are displayed in Appendix A. The estimates of ln yi,t1 and other control variables have all been suppressed due to limited space. The specifications of estimation correspond to the columns displayed in Table 2 respectively. Complete table with detailed estimates are available upon request. * Significance at 10% level. ** Significance at 5% level. *** Significance at 1% level.

by introducing an interaction term between the total dependency ratio and the degree of marketization.20 As shown in Table 3, the results estimated from both OLS and FGLS estimators tell the same story: the estimated coefficients of the interaction term are negative and significant, while those for the total dependency ratio become positive and significant. On the one hand, this indicates that marketization reform plays a vital role in facilitating China to realize the demographic dividend to economic growth. The marketization reform launched since the late 1980s has largely improved the flexibility of the labor market and the capital market. Particularly the reform in promoting the development of the private sectors has urged the emerging of many non-state-owned enterprises (non-SOEs). These enterprises work as an important channel for absorbing increased labor supply and digesting accumulated savings during the demographic transition. In 1990–2004, the average share of non-SOEs employment increased from 23.1% to 34.8%. The contribution of the non-SOEs to the total industrial production rose from 35.7% to 69.8% (National Bureau of Statistics of China, 2005). On the other hand, the significant and positive estimates for the total dependency ratio indicate that in the absence of the marketization reform, a lower dependency ratio may even impede economic growth. For example, in the era of China’s planned economy, economic activities were controlled by government orders rather than the market. In that circumstance, an increased labor supply resulting from declines in fertility might lead to implicit unemployment or being overstaffed, while accumulated capital might contribute to inefficient investment. As a result, changes in demographic structure may not be translated into the ‘‘dividend” but ‘‘deficit” to economic growth. 5.4. The youth and the elderly dependency ratios The total dependents are composed of the youth and the elderly dependents. These two groups of dependent population may have different effects on the economy. We examine the respective impact of the youth and the elderly dependency ratios on growth by introducing their levels and growth rates in place of those of the total dependency ratio in the model. The specifications of estimation are the same as those in Table 2. The results, displayed in Table 4, show that the youth dependency ratio has a significantly negative impact on economic growth. However, the effect of the elderly dependent is insignificant, despite having a negative sign. The statistical insignificance of the elderly dependency ratio might be explained by the fact that there is much less variation in elderly dependency than in youth dependency. As illustrated in Fig. 2, falls in China’s total dependency ratio appears to be largely dominated by the dramatic decline in the youth dependency ratio. This 20 In fact, we have tried the estimations with other interaction terms. For example, as argued by Bloom and Canning (2006), openness may also help gain the demographic dividend to economic growth. However, this rule seems not to fit in the Chinese context. The estimated coefficients for the interaction term between the total dependency ratio and openness are insignificant in all specifications. Similar insignificant estimates are obtained for interaction terms with other variables.

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Z. Wei, R. Hao / Journal of Comparative Economics 38 (2010) 472–491 Table 4 Regressions using the youth and elderly dependency ratios. Dependent variable: average growth rate of GDP per capita in 5-year period (1) OLS

(2) OLS

(3) OLS

(4) OLS

(5) OLS

(6) OLS

(7) OLS

(8) OLS

2.854** (2.22) 0.162 (0.10)

3.020** (2.49) 0.525 (0.35)

2.999** (2.47) 0.615 (0.41)

0.383 (0.93) 0.399 (1.52) 0.226 (0.58)

0.177 (0.42) 0.494* (1.88) 0.253 (0.64)

0.408 (1.09) 0.544** (2.16) 0.403 (1.04)

0.315 (0.81) 0.542** (2.15) 0.452 (1.10)

2.835** (1.99) 0.400 (0.21) 2.496 (0.22) 5.950 (0.64) 0.321 (0.75) 0.407 (1.50) 0.266 (0.66)

2.655* (1.88) 0.476 (0.25) 3.016 (0.27) 7.034 (0.76) 0.100 (0.23) 0.505* (1.87) 0.293 (0.73)

2.724** (2.12) 1.605 (0.94) 8.859 (0.80) 11.996 (1.42) 0.251 (0.64) 0.585** (2.31) 0.351 (0.90)

2.526* (1.95) 1.958 (1.11) 4.711 (0.41) 13.467 (1.56) 0.197 (0.50) 0.590** (2.32) 0.427 (1.04)

90 0.346

90 0.368

90 0.458

90 0.457

90 0.333

90 0.357

90 0.463

90 0.462

Demographic variables ln D_youth 2.996** (2.30) ln D_elderly 0.126 (0.08) gD_youth gD_elderly ln urban ln popden ln pop Observations Adj R2

Notes: Figures in parentheses are heteroskedasticity-robust t-statistics. Variable definitions are displayed in Appendix A. The estimates of ln yi,t1 and other control variables have all been suppressed due to limited space. The specifications of estimation correspond to the columns displayed in Table 2 respectively. Complete table with detailed estimates are available upon request. *** Significance at 1% level. * Significance at 10% level. ** Significance at 5% level.

is in line with the theory of the demographic transition as well as the empirical studies, for instance, Kelley and Schmidt (2005). Moreover, like in Table 1, the estimated coefficients for the growth rates of the two dependency ratios are insignificant in all specifications. In short, our results indicate that the significant demographic-structure impact on growth is mainly attributable to the substantial decline in the youth dependency. 5.5. Feedback effects of income on demographic behaviors The endogeneity of demographic structure identified by the Wu–Hausman test in Table 2 indicates the existence of reverse causality running from economic growth to demographic behaviors. We examine these feedback effects on demographic behaviors by regressing on the level of income per worker, as specified in Eq. (11). Birth rates, women’s mean age at the first marriage and life expectancy are used to represent demographic behaviors. The results are displayed in Table 5. As shown in Columns (1) and (2), birth rates are negatively associated with income per worker at the 1% significance level. The large value of F statistics of the Wu–Hausman test indicates the endogeneity of income per worker estimated by OLS. The high Shea partial R2 and large p-value of the Hansen test are also in favor of the estimated results by IV. The IV estimation results suggest that an increase in income by 10% leads to a reduction in birth rates by 5.7%. This finding is higher than 4.9% reported by Bloom, Canning and Malaney (2000) using cross-country data. It indicates that increases in income have a significantly larger impact on birth reduction in China than in other countries. The pronounced income effect on China’s birth rates is, on the one side, attributable to the population policy implemented. Since the 1980s, the stringent family planning program has been formally adopted to control the population growth. During the past three decades, substantial improvements have been made in how the program is implemented, particularly from pure reliance on administrative coercion to greater emphasis on service provision. However, no changes have been made on the policy itself. Nor has China signaled if and when the policy would be phased out. On the other side, the significant income effect on birth rates is closely related to the remarkable economic and social reforms that have been undergoing in China since 1978. The launch of a market economy has gradually shifted the locus of economic decision-making from the state to the family and individuals. In consequence, the financial burden of children-rearing has been increasingly placed on the shoulder of the Chinese family. This is in a sharp contrast to the socialist planned economy in which the state takes into account all the costs, ranging from children-rearing, housing to labor employment. Such a dramatic change in institutions has brought profound impacts on demographic behaviors.21 For example, married couples usually decide to invest more in the quality of children, in terms of education and health, rather than the quantity. The unmarried men and women often choose earning opportunities instead of having marriage and children early in their lives. As a result, deep-rooted economic and social 21

See Wang and Mason (2008) for detailed discussion about the population policy and institutional changes in China.

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Table 5 Reverse effects of income on China’s demography. Dependent variables:

ln z Period2 Period3 Central West Constant Observations R2 WuHausman test Shea partial R2 Hansen test

ln birth

ln marriage

ln life

(1) OLS

(2) IV

(3) OLS

(4) IV

(5) OLS

(6) IV

0.427*** (5.69) 0.057 (0.86) 0.019 (0.31) 0.046 (0.87) 0.033 (0.46) 4.163*** (5.95)

0.574*** (6.36) 0.067 (0.76) 0.047 (0.75) 0.113 (1.50) 0.047 (0.44) 5.556*** (6.40)

0.030*** (2.79) 0.046*** (3.65) 0.020** (2.46) 0.021*** (2.84) 0.037*** (3.73) 2.904*** (28.99)

0.045*** (3.77) 0.032** (2.44) 0.019** (2.28) 0.014 (1.60) 0.029** (2.43) 2.765*** (24.26)

0.030** (3.47) 0.030*** (3.14) 0.034*** (4.40) 0.024*** (3.84) 0.074*** (8.71) 4.024*** (53.16)

0.048*** (4.23) 0.017 (1.48) 0.025*** (3.01) 0.013 (1.46) 0.056*** (3.94) 3.851*** (35.38)

90 0.695

84

90 0.665

84

88 0.777

84

12.718 [0.001] 0.586 5.706 [0.127]

5.692 [0.020] 0.586 2.427 [0.489]

9.973 [0.002] 0.586 2.655 [0.448]

Notes: Figures in parentheses are heteroskedasticity-robust t-statistics. Figures in square brackets are p-values. The instruments for ln zi,t include the geographic convenience to trade (pop100cr), average slope of a province (slavge), and the investment ratio and schooling, both lagged one period. The Wu– Hausman statistic is a test for endogeneity, distributed as F under the null of exogenous regressors. The Shea partial R2 is a test of instrument relevancy. The Hansen statistic is a test of the overidentifying restrictions, distributed as chi-square under the null of instrument validity. * Significance at 10% level. ** Significance at 5% level. *** Significance at 1% level.

reforms have not only increased people’s income but also affected their decision-making particularly in marriage and fertility. This is demonstrated by the pronounced feedback effect on birth rates. It is also reflected by the significant and positive estimates of the marriage age. As shown in Columns (3) and (4), a 10% higher in people’s income results in a 0.5% later in their age of marriage. The estimation results in the last two columns indicate that life expectancy is positively and significantly related with the income level. A 10% increase in income results in 0.5% increase in life expectancy. This result is generally in line with the existing literature, though the magnitude of our estimates is relatively smaller than 1.9% derived from cross-country regressions by Bloom, Canning and Malaney (2000). The positive feedback effect on life expectancy partly reveals that increases in income help improve the health status of the population, for example, through easier access to nutritious food, clean water and education. It also reflects the contribution of the implementation of China’s health-care policy. The health-care policy, characterized by preventive treatment in the 1970s, has greatly improved the environmental sanitation and hygiene and has also eradicated certain epidemic diseases, such as cholera, plague, typhoid and scarlet fever. Since the 1980s, substantial investments and efforts have been made to improve and expand medical facilities as well as quantity and quality of the health-care personnel. The number of hospitals increased from 63,000 in 1976 to 296,492 in 2004. The number of hospital beds increased from 1.7 million in 1976 to 3.3 million in 2004 (National Bureau of Statistics of China, 2005). The reforms in the health-care system since the 1990s has introduced cost recovery measures and profit-making incentives, increased the independence of health facilities from political or bureaucratic control, provided people with a wider choice of health services and developed alternative health financing mechanisms (Hsiao, 1995; Wong and Chiu, 1997; Bloom, 1998). Substantial improvements in China’s health policy have resulted in great improvements in the health status of the Chinese population. This is particularly evident in the average life expectancy, which rises from 32 years in 1950 to 73 years in 2004 (National Bureau of Statistics of China, 2005). The estimates of region and period dummies exhibit heterogeneities in feedback effects across regions and over time. They show that people in the west region appear to have shorter life expectancy. This is due mainly to the severe natural environment and it may also be related to the earlier marriage that has been required in some ethnic customaries. Life expectancy declines significantly in the early 2000s, compared to the period of 1989–1999. This may due to environment pollution or worldwide epidemic diseases like SARS. However, in the mid-1990s, women’s marriage age appears to become earlier than that in the reference period. In summary, we find that there are significant feedback effects of income on demographic behaviors. Improvements in income levels may lower birth rates, delay marriage age and increase life expectancy. Compared with other countries, income changes appear to have more pronounced effects on China’s birth rate and significant albeit smaller effects on life expectancy.

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489

6. Conclusion and discussion This paper examines the relationship between demographic change and economic growth in the case of China during the period 1989–2004. It explores variations of the economic implications of demographic change across provinces in an extended convergence equation. Our analysis shows that China’s rapid economic growth has been significantly attributable to changes in demographic structure. The substantial decline in the dependency ratio, particularly the youth dependency ratio resulting from fertility falls, has accounted for about one-sixth of the provincial growth rate of GDP per capita in 1989–2004. The analysis also shows that the effect of demographic structure on growth appears to be more pronounced in the long-run through affecting the steady-state level of income. The launch of the market reform is found to have greatly improved the efficiency of the labor and capital markets, and thereby helped absorb an expanded labor supply released during the demographic transition as well as transform accumulated savings into productive investment. It is thus highlighted as the essential policy environment for China to reap the demographic dividend. Furthermore, the analysis reveals that improvements in income have pronounced feedback effects on demographic behaviors such as birth rates, marriage age and life expectancy. In this paper, the analysis does not address the issue of cross-provincial economic spillovers particularly through internal migration of prime-age workers. Particularly since the 1990s, increasing amounts of prime-age workers have migrated from rural areas to urban cities. They are generally not recorded as living in the place of work and usually send remittances back home. The emergence of a large ‘‘floating” population may bias our estimation results in the way of making an illusion of a lower dependency ratio and higher domestic savings. It may thereby yield a spurious correlation between provincial economic growth rates and the dependency ratio. However, as noted by Cai and Wang (2008) and Cai (2009), the internal migration of labor in China mainly occurs within a province or between inland and coastal provinces. The former does not substantively affect the results of estimation which relies on provincial-level data. The latter might vary our results in the way of increasing rather than attenuating the magnitude of the estimated demographic-structure effect. Yet our conclusion of the significant economic implications of demographic change remains essentially unchanged. By all means, the effects of labor migration on demographic changes and economic growth remain an interesting topic to study in the future research. In addition, the analysis in this paper may be subject to changes in the measurement of the dependency ratio. The measure of the dependency ratio is sensitive to variations in the ages that mark the entry into the workforce and the onset of retirement. As Bongaarts (2001) notes, variations at the beginning as well as the end of the working-ages affect both the

Table A1 Variable definitions and descriptive statistics.

a b

Variable

Obs.

Mean

SD

Min

Max

Definition

gy y z D

90 90 90 90

8.99 5028 7353 49.38

2.10 3935 5421 7.90

4.38 1259 1996 33.73

16.01 25,814 35,464 65.49

D_youth

90

39.94

8.89

18.37

59.04

D_elderly

90

9.44

2.40

4.38

19.01

gD gD_youth gD elderly Urban Popden Pop Birth Death Life Marriage Pop100cr

90 90 90 90 90 90 90 90 88 90 90

0.02 0.03 0.02 38.19 323.54 3.81  107 1.68 0.64 68.95 22.75 0.40

0.02 0.03 0.02 22.21 403.73 2.66  107 0.47 0.08 3.73 1.13 0.41

0.07 0.09 0.10 9.46 1.60 1,966,800 0.50 0.44 59.64 19.98 0.00

0.03 0.02 0.08 88.08 245.67 1.16  108 2.65 0.81 78.14 25.88 1.00

Slavge Openness Market Invest Central West Period2 Period3

90 90 90 90 90 90 90 90

2.75 22.73 50.42 33.20 0.30 0.30 0.33 0.33

1.72 29.45 20.99 9.02 0.46 0.46 0.47 0.47

0.05 3.19 15.65 15.34 0 0 0 0

6.98 186.31 91.52 56.47 1 1 1 1

Average growth rate of real GDP per capita over each 5-year period: % Real GDP per capita: RMB Yuan Real GDP per working-age population: RMB Yuan Ratio of number of total dependents (below 15 and above 65) to that of working-age population (15–64)a: % Ratio of number of young dependents (below 15) to that of working-age population (15–64)a: % Ratio of number of elderly dependents (above 65) to that of working-age population (15–64)a: % Average growth rate of total dependency ratio over each 5-year period Average growth rate of youth dependency ratio over each 5-year period Average growth rate of elderly dependency ratio over each 5-year period Urban share of the total population: % Total population per square kilometre: person Total population in a province: person Birth rate: % Death rate: % Life expectancy: year Average age of first marriage of women at childbearing age: year The proportion of the population distribution of a province in 1994 within 100 km of the coastline or ocean-navigable river, excluding the coastline above the winter extent of sea ice and the rivers that flow to this coastlineb Average slope of a provinceb Sum of imports and exports divided by total real GDP: % Share of non-SOEs in total industrial production: % Share of real investment in real GDP: % Equals 1 if in Central region Equals 1 if in West region Equals 1 if in 1994–1999 Equals 1 if in 1999–2004

The data for the total, young and old dependency ratios (D, D_young and D_old) in 1982, 1987, and 1989–1993 come from Cai and Wang (2005). The data for geographic variables (pop100cr and slavge) come from Démurger et al. (2002).

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numerator and denominator of the dependency ratio. In recent years, the age at the entry into the workforce has gradually risen in China as a whole due to increased educational attainment. Considerable variations also appear in the age at the entry into the labor force across provinces. The age at retirement appears to vary over time and across provinces too. Thus, changes in the age assumption may vary our results. Notwithstanding, note that on the one hand, the age assumption of the workforce adopted in the analysis is conventional, while the measurement of the dependency ratio applied is consistent with those in most of the existing demographic studies. This facilitates the comparison with the literature. On the other hand, the analysis is constrained by the availability of detailed and reliable microeconomic data on variant ages of working and retirement over time and across provinces. The pronounced contribution of demographic change to economic growth suggests that market reforms should continue being implemented in depth in the Chinese economy. In particular, the private sector ought to be encouraged to develop for creating more employment opportunities and translating savings into more productive investment. The significant albeit relatively small effect of income on life expectancy calls for large improvements in the national health-care system particularly in the west region and rural areas. Moreover, the one-child policy, which has helped shape a favorable demographic structure for contemporary China to achieve the demographic dividend, may need to be re-considered for future demographic and economic perspectives. Persisting in implementing the policy will force China’s population to transit to an ageing society at an extraordinarily accelerated pace. In this case, China might become aged before getting rich. 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