China Economic Review 31 (2014) 441–458
Contents lists available at ScienceDirect
China Economic Review
The “growth-first strategy” and the imbalance between
consumption and investment in China
Julan DU a, Hongsheng FANG b,c,⁎, Xiangrong JIN b
a
b
c
Department of Economics, Chinese University of Hong Kong, Shatin, N.T., Hong Kong
College of Economics, Zhejiang University, 38 Zheda Rd., Hangzhou 310027, China
Academy of Financial Research, Zhejiang University, 38 Zheda Rd., Hangzhou, Zhejiang Province 310027, China
a r t i c l e
i n f o
Article history:
Received 6 September 2013
Received in revised form 5 September 2014
Accepted 6 September 2014
Available online 16 September 2014
Keywords:
Overtaking strategy
Real estate development strategy
Biased income distribution structure
Consumption–investment imbalances
a b s t r a c t
The Chinese government has been pursuing economic growth under the guidance of “growth is a
hard principle”. Regional governments have employed the overtaking strategy (placing primary
emphasis on the development of capital and technology-intensive industries) and the real estate
development strategy to push for economic growth and fiscal revenue growth. This led to a primary and secondary income distribution structure biased toward capital and government and against
labor and a government expenditure structure biased toward infrastructure and against social
welfare. Using the empirical strategy of Acemoglu et al. (2003), we confirm that the overtaking
strategy and the real estate development strategy have contributed to the internal imbalances
of overinvestment and underconsumption. The biased primary and secondary income distribution structures as well as the biased government expenditure structure serve as important mediating channels through which the development strategies translate into an imbalanced
consumption–investment structure. It suggests that the Chinese government will be able to accomplish China's transition from an investment-led growth model to a consumption–investment
balanced growth model only if it modifies its development strategies.
© 2014 Elsevier Inc. All rights reserved.
1. Introduction
The rapid economic growth in China in the past few decades has been accompanied by serious internal imbalances,
i.e., overinvestment and underconsumption. Fig. 1 compares the consumption–investment ratio of China and those of some other
East Asian economies, namely, Japan, Korea, Hong Kong, Singapore, and Taiwan, when their per capita real GDP and hence development stages were at comparable levels with China. Two striking observations can be made. First, China has an extremely low consumption–investment ratio, which is considerably lower than the ratios of Hong Kong, Japan, Korea, and Taiwan over all the per
capita income categories. Compared with Singapore, China's ratio has also remained below except that it edges marginally above in
the per capita GDP interval of US$5000–6000. Second, China has experienced a speedy decline in its consumption–investment ratio
in the past few decades, with the ratio dropping substantially from approximately 2.3 in the per capita GDP interval of US$2000–
3000 to around 0.5 in the per capita GDP interval of US$8000. It is therefore widely believed that China has faced serious problems
of internal imbalances and how well China responds to resultant challenges will determine whether the country can continue to
progress rapidly beyond middle income status and become a high-income country (Perkins, 2012; Yang, 2012).
In this study, we argue that to tackle these challenges requires an understanding of what are the fundamental reasons for China's
macroeconomic imbalances. In our view, China's internal imbalances, to a large extent, stem from the “growth-first strategy” of the
⁎ Corresponding author at: College of Economics, Zhejiang University, 38 Zheda Rd., Hangzhou 310027, China. Tel.: +86 571 8795 1986.
E-mail address: [email protected] (H. Fang).
http://dx.doi.org/10.1016/j.chieco.2014.09.002
1043-951X/© 2014 Elsevier Inc. All rights reserved.
442
J. Du et al. / China Economic Review 31 (2014) 441–458
4.50
Consumption-investment ratio
4.00
3.50
China
Hong Kong
Japan
Korea
Singapore
Taiwan
3.00
2.50
2.00
1.50
1.00
0.50
2000-3000 3000-4000 4000-5000 5000-6000 6000-7000 7000-8000
8000
Per capita real GDP˄US$
Fig. 1. Consumption–investment ratio (average).
Source: Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2013), “The next generation of the Penn world table” available for download at www.ggdc.net/pwt.
Chinese government. To achieve the “growth-first strategy”, the Chinese Communist Party (CCP) has established a regionally
decentralized authoritarian regime characterized by political centralization and economic decentralization. The central leadership
employs promotion tournament and tax sharing system to motivate regional bureaucrats to pursue rapid local economic growth.
Naturally, regional bureaucrats adopt the overtaking strategy, i.e., to place primary emphasis on the development of capital and
technology-intensive industries, and the real estate development strategy. As a consequence, these strategies led to an income distribution structure favoring capital and government and disfavoring labor and a government expenditure structure biased toward infrastructure and against social welfare provision. They would discourage consumption and encourage savings and investment,
constituting a salient feature of internal macroeconomic imbalances. Hence, the phenomena of the overinvestment and
underconsumption, high return on capital (Bai, Hsieh, & Qian, 2006), high enterprise and government savings, and vulnerable social
safety net in China are endogenous to the overtaking strategy and the real estate development strategy generated by the “growth-first
strategy”.
Using province-level panel data set over the period 1996–2007, we show that the proxy indicators of the overtaking strategy and
the real estate development strategy have resulted in an imbalanced consumption–investment structure. Two important channels
mediating this effect are the biased primary and secondary income distribution structures that favor capital and government and
disfavor labor and the biased government expenditure structure that gives much higher priority to infrastructure than social welfare
provision. In addition, it is also found that the two growth strategies affect the consumption–investment structure through other
possible channels.
Our study points out that the “growth-first strategy” has contributed tremendously to China's economic miracle in the past three
decades, but has also generated serious distortions in primary and secondary income distribution structures and government expenditure composition, which are a major source of income distribution inequality and social tensions. This directly contributes to
underconsumption and overinvestment, and generates enormous difficulties for China's transition from an investment-led growth
model to a consumption–investment balanced growth model in the current China.
In our analysis, we also take into consideration how the consumption–investment structure evolves with the level of economic
development and demographic structure. Consistent with our expectation, we observe that the consumption–investment ratio
exhibits a U-shaped relationship with both GDP per capita and the aged dependency ratio.
This paper makes the following contributions to the literature. First, we provide a first systematic study of how the “growth-first
strategy” shapes the internal macroeconomic imbalances in China. We emphasize that the “growth-first strategy” reflected in the
overtaking strategy and the real estate development strategy has its positive side (i.e., rapid growth) and negative side (i.e., the unbalanced investment–consumption structure). We conduct a detailed analysis of this negative side and argue that the “growth-first
strategy” is one of the most important fundamental forces in shaping internal imbalances in China. In this sense, our work adds to
the existing studies (e.g., Blanchard & Giavazzi, 2005; Fang & Jin, 2010; Huang, 2010a; Huang, 2010b; Huang & Tao, 2010; Huang &
Wang, 2010; Lin, 2011; Lin, Dinh, & Im, 2010; Perkins, 2012; Wang & Fan, 2009; Woo, 2006; Yang, 2012). Second, following the empirical strategy of Acemoglu, Robinson, Thaicharoen, and Johnson (2003), we provide some evidence that the “growth-first strategy”
has shaped an imbalanced consumption–investment structure through the biased income distribution structure, the biased government expenditure structure as well as other channels. It suggests that getting the bureaucratic incentives in the governance system
right and removing the distortions in government development strategies are preconditions for achieving a consumption–investment
balanced growth model in China.
The rest of this paper is organized as follows. Section 2 develops our hypotheses in detail. Section 3 introduces data and discusses
empirical strategy. Section 4 presents and discusses empirical findings. The final section concludes the paper by offering some important policy implications.
J. Du et al. / China Economic Review 31 (2014) 441–458
443
2. Hypothesis development
2.1. The overtaking strategy and consumption–investment imbalances
The Chinese leadership realized that the CCP did not have procedural legitimacy in keeping the monopoly of political power so that
it must deliver superior performance in economic growth to win performance legitimacy (Jefferson & Zhang, 2008). This gives rise to
the “growth-first” development strategy. Promotion tournament and tax sharing system have laid institutional foundation for the fulfillment of the “growth-first strategy”. On the one hand, China is characterized with highly centralized political and personnel control
at the national level, and a decentralized administrative and economic system at the provincial and local levels (see, e.g., Blanchard &
Shleifer, 2001; Clarke, Murrell, & Whiting, 2008; Xu, 2011). This allows the central government to motivate local bureaucrats to
achieve high growth targets through promotion tournament (Chen, Li, & Zhou, 2005; Li & Zhou, 2005; Tsui & Wang, 2004). On the
other hand, the tax sharing system reform allows the central government to centralize tax revenues to a large extent and leave
more responsibilities for local public expenditure with local governments. This generated mounting pressures on the fiscal capacity
of local governments, which forced local bureaucrats to explore new avenues for raising tax revenues.
To accelerate GDP growth and boost local fiscal revenues, local governments pursued an overtaking development strategy by promoting capital and technology-intensive industries and real estate development strategy, which constitute two major building blocks
of the “growth-first” development strategy.
Firstly, one important method of accelerating local GDP growth is to adopt an industrial policy that places primary emphasis on the
development of capital and technology-intensive industries instead of labor-intensive ones (Hausmann, Hwang, & Rodrik, 2007; Lin &
Chang, 2009; Peneder, 2003; Rodrik, 2006, 2010). In the traditional neo-classical growth model, the accumulation of capital and
capital deepening plays a vital role in promoting output growth, which is largely verified by subsequent empirical studies (see,
e.g., Mankiw, Romer, & Weil, 1992). Recently, in examining the world growth experience, Jorgenson and Vu (2009, 2010) show
that approximately 40–50% of world growth can be attributed to the accumulation and deployment of capital. Surprisingly, the use
of labor input and productivity growth contribute only 25–33% and 20–35% respectively. Hence, the growth experience of the
world economy has testified to the importance of capital and technology-intensive industries in raising GDP growth.
Secondly, the development of capital and technology-intensive industries could swiftly raise local GDP growth and in turn tax revenue growth. Moreover, the production-based value added taxes (VAT) under the tax sharing scheme are divided between the central
and local governments at a ratio of 75:25 as a basis rate. If the local government collects an amount of VAT more than the contracted
target, the division between the central and local governments will follow the ratio of 52.5:47.5, which is much more favorable to the
local government due to the tax rebate scheme. These taxation schemes further motivate local governments to develop capital and
technology-intensive industries because these industries typically require substantial fixed asset investments that could forcefully
generate VAT revenues. As shown in Fig. 2, heavy industry generated a proportion of 65%–75% of the VAT payable in the period
1994–2009. Similarly, the proportion of total tax payable of heavy industry in that of all industries has remained above 56% in the
period 1994–2009, and has exhibited a rising trend since 1998, reaching 68% in 2009.
The overtaking strategy consists of a series of industrial policies to support the development of capital and technology-intensive
industries. This strategy is expected to lead to a primary income distribution structure biased toward capital and government and
against labor. On the one hand, the overtaking strategy raises the amount of capital employed and the expected return on capital
so as to increase the share of capital income in GDP. According to the neo-classical economic theory, the share of labor income is
affected by the capital–output ratio and the elasticity of substitution between labor and capital in the production function. When
75
70
65
60
55
50
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Ratio of tax payable of heavy industry
Ratio of value added tax payable of heavy industry
Fig. 2. The evolution of ratio of tax payable of heavy industry to total tax payable of industry, and ratio of value added tax payable of heavy industry to total value added
tax payable of industry in China. Notes: (1) Manufacturing industries are classified into heavy industry and light industry; (2) tax payable equals the sum of value added
tax payable and taxes and other charges on principal business revenues; (3) this figure shows why Chinese local governments developed capital and technologyintensive heavy industry rather than labor-intensive light industry. If taking enterprise income tax in the secondary income distribution into account, our result will
be reinforced; (4) data sources: China Industrial Economic Statistical Yearbook (Various years) and China Statistical Yearbook (Various years).
444
J. Du et al. / China Economic Review 31 (2014) 441–458
the substitution elasticity between capital and labor is larger than one, an increase in the capital–output ratio is accompanied by a decrease in labor's share in income (Bentolina & Saint-Paul, 2003). It is widely documented in the literature that capital and unskilled
labor are substitutes whereas capital and skilled labor are typically complements (Schneider, 2011). Under China's overtaking strategy, the industrial development biased toward capital and technology-intensive industries typically means a large degree of substitution of capital for unskilled labor. Recent empirical studies find that the elasticity of substitution between labor and capital was greater
than 1 in 28 industries in the period 1996–99 and in 20 industries in the period 2000–5 (Yuan & Li, 2008). Therefore, the overtaking
strategy might well result in a declining labor share and a rising capital share in income by encouraging resources to flow to capital
and technology-intensive industries.
In addition to increasing capital employment, the overtaking strategy also raises the return on capital. (1) Local governments took
a wide range of measures to lower the costs of doing business. They provide preferential tax treatment, repress land use fees, offer
fiscal subsidies, and overlook environmental protections to provide favorable treatment for investors (Cai, 2009; Su, Tao, Lu, & Li,
2012). One striking aspect is that local governments were enthusiastic with establishing economic and/or high-tech industry development zones, and granted the enterprises engaged in capital and technology-intensive industries therein with many favorable treatments. (2) Local governments spared no effort to improve infrastructure facilities. This could attract foreign direct investment (FDI)
(Zhang, Gao, Fu, & Zhang, 2007), generate positive externality effects on capital investment, and could directly create demand for the
products of capital and technology-intensive industries and boost local GDP growth. (3) China's state-dominated financial system
with financial repression helped lower the cost of capital. The tight restriction on capital outflow and the lack of alternative investment instruments enable household savings deposited in banks to keep rising, leading to an increasing supply of capital. The interest
rate regulations kept costs of loans at a low level. These factors pushed down costs of capital (Huang, 2010a), and facilitated local governments to provide low-cost financing from the state-controlled financial system to investors. (4) In addition, local bureaucrats often
helped maintain monopoly positions for favored firms in local markets. By imposing local market entry barriers, local governments
practiced local protectionism to allow local enterprises to retain market monopoly power so as to achieve a high rate of return on investment (Lin & Liu, 2004b). These actions, when combined together, have raised return on capital and capital share in total output
and lowered labor share in total output in capital and technology-intensive industries.1
On the other hand, in the context of the overtaking strategy, medium and small-sized enterprises engaged in labor-intensive
industries typically find it hard to obtain loans from the state-dominated financial system. To enlarge retained earnings on which
their investment relies, those enterprises typically squeezed labor compensation and lengthened working hours. At the same time,
the fierce competition in labor-intensive industries has further forced enterprises to keep a low wage rate and fringe benefits for
labor. When conflicts between capital and labor arise, local governments give priority to protecting the interests of capital while largely overlooking workers' rights (Lu & Gao, 2011). These factors slowed down the expansion of labor employment and the rise in labor
compensation, resulting in an increasing capital share and a diminishing labor share in labor-intensive industries. Therefore, we can
infer that the overtaking strategy will increase capital share and reduce labor share in GDP.
The overtaking strategy is also expected to raise the share of government income. Firstly, capital and technology-intensive industries often have higher value added from which governments can collect a larger amount of value added taxes. Secondly, taxation
capacity variation across industries also matters. Value added taxes are the major source of government income from primary income
distribution. It is easier for tax authority to collect taxes from firms operating in capital- and technology-intensive industries than
those in labor-intensive industries. Capital- and technology-intensive industries often have a higher proportion of large firms than
do labor-intensive industries. It is widely documented in the literature that in developing countries large firms are typically more
visible and are subject to more intensive oversight of governments than do small firms. When the governments have relatively
weak taxation capacity and limited resources for tax enforcement, taxes and regulations are often enforced only among those large
firms (Tybout, 2000). Hence, firms in capital- and technology-intensive industries are less likely to evade tax payment, and the bias
of industrial development toward these firms would result in a larger government share in primary income distribution. Thirdly,
the design of the VAT system creates distortions. By 2008, the production-based VAT does not give deduction to the previous tax
payment for fixed asset purchases so that there exists double taxation of fixed asset depreciations (Lv & Guo, 2012a). For capital
and technology-intensive industries, this double taxation is much more serious because of the larger purchase value of fixed assets,
and consequently government income share is also higher. Hence, preferential development of capital and technology-intensive
industries is expected to raise the government share of income.
It is noteworthy that the overtaking strategy would raise capital and government shares and lower labor share not only in the
primary income distribution structure but also in the secondary income distribution structure. The secondary income distribution
structure results from the redistribution efforts of governments through tax collection and fiscal transfers. According to Lv and Guo
(2012b), the secondary income distribution structure differs from the primary one in China mainly because of the corporate income
taxes levied on capital, the social insurance payments collected from labor, and government transfers to labor. In the context of our
framework, the overtaking strategy would raise income accruing to capital, which in turn increases the share of corporate income
tax and that of government income in GDP in the secondary income distribution structure. At the same time, it is documented that
local government expenditure under the overtaking strategy puts a much larger weight on productive investment such as infrastructure investment than on fiscal transfers for the purpose of social welfare improvement, which has weakened the social safety net for
labor and failed to raise much labor income share after redistribution (Fu & Zhang, 2007; Nitikin, Shen, Wang, & Zhou, 2011). Moreover, the demographic dividends, i.e., the decline in fertility rate coexists with a low dependency rate, enable social insurance
1
Rodrik(2010) expressed similar viewpoints.
J. Du et al. / China Economic Review 31 (2014) 441–458
445
payments collected from labor to exceed that being withdrawn. As a result, government transfers to labor and social welfare expenditure share in GDP remain small (Lv & Guo, 2012b). Thus, we expect that, compared with the primary income distribution structure,
the overtaking strategy will increase the government share and reduce the labor share in national income after redistribution. Across
provinces, those pursuing the overtaking strategy more intensively would have a particularly larger government share and lower
labor share in the secondary income distribution structure. On the other hand, relative to the primary income distribution structure,
corporate income tax might reduce the share of capital in national income in the secondary income distribution structure, which is
confirmed by Lv and Guo (2012b). Nonetheless, because corporate income tax is a type of proportional tax, it would not change
the relative size of capital income share between different provinces after redistributions. Those provinces pursuing the overtaking
strategy more intensively are expected to have a higher capital share in the secondary income distribution structure than those
doing less intensively.
The repressed labor share and the elevated capital and government shares in the primary and secondary income distribution structures are expected to contribute to an imbalanced consumption–investment structure. On the one hand, a declining labor share in
GDP would reduce the share of household disposable income in GDP because labor income constitutes the most basic form of household disposable income for most households. Recent studies (see, e.g., Aziz & Li, 2007; Guo & N'Diaye, 2010) have shown that low disposable household income is a principal culprit of low consumption in the Chinese economy. On the other hand, capital income
typically has a lower propensity to consume than does labor income. In the context of the overtaking strategy, governments in
China prefer productive investment to government consumption, especially social welfare provision.2 Hence, a higher share of
government and capital income in GDP is expected to result in an imbalanced consumption–investment structure.
Hypothesis 1. A more intensive pursuit of the overtaking strategy leads to a primary and secondary income distribution structure
more biased toward capital and government and against labor, and a government expenditure structure favoring infrastructure
investment and disfavoring social welfare provision, which result in a lower ratio of household consumption to investment.
2.2. The real estate development strategy and consumption–investment imbalances
Besides the overtaking strategy, the real estate development scheme is another strategy for local governments to achieve growth
in GDP and fiscal revenues. Under the tax-sharing reform scheme, local governments are given land lease revenues as extra-budgetary
funds to alleviate their fiscal burden (Liu & Sun, 2009; Nitikin et al., 2011; Su et al., 2012). As a natural result, local governments have
vehemently pursued the development of the real estate sector. Property market development and the increase in housing market
prices can help land lease generate maximum fiscal revenues (Liu & Sun, 2009), which provides funds for local governments to pursue
various political and economic objectives, especially government-initiated investment in infrastructure, productive activities, etc.
Property market development can also stimulate the growth of the related upstream and downstream capital-intensive industries,
which gives further impetus to local GDP growth and fiscal revenue growth. Consequently, the real estate development strategy directly contributes to the boom in investment, which reinforces the imbalanced consumption–investment structure. Furthermore,
like the overtaking strategy, real estate development strategy is also expected to generate primary and secondary income distribution
structures biased toward capital and government and against labor, which would contribute to a distorted consumption–investment
structure characterized by underconsumption and overinvestment.
It is noteworthy that local governments' land lease system provides an important institutional support to real estate development
strategy. Under socialism in China, land belongs to the state and only local governments have the authority to conduct requisition of
land from farmers and to lease it to land users (Lin, 2009; Su et al., 2012; World Bank, 2005). To acquire land, local governments
typically exercise administrative power to appropriate land use rights at an extremely low acquisition price from farmers. Afterwards,
by going through the land consolidation and reorganization process and keeping monopoly power over the primary market for land,
local governments have strong incentives and discretion to sell the land use rights at a much higher price by controlling the size of
land supply through the land reserve system, from which local governments reap tremendous land lease revenues (Lin & Yi, 2011;
Nie, 2013; Su et al., 2012; Wu, 2010). At the same time, because limited land supply weakens competition, real estate developers
can easily pass higher costs of acquiring land to house purchasers (Su et al., 2012).
Local governments have adopted various measures to boost real estate market development and raise housing prices. In addition
to fine-tuning land supply, local governments stimulated the household demand for housing by phasing out public housing provision
scheme, implementing policies that the entitlements to public services such as education, medical care and social insurance are
bundled with the purchase of an apartment or house (Nie, 2013),overemphasizing investment in infrastructure facilities and
downplaying the importance of social welfare provision (Su et al., 2012), adjusting taxation strategies (Nie, 2013), forming collusion
with property developers and banks in pushing up housing prices (Liu & Sun, 2009; Nie, 2013), etc. The dramatic rise in housing prices
has encouraged households to form expectations for future increases (Liu & Sun, 2009), which induced large amounts of capital to
flow into the property market, leading to a spiral increase in housing prices and the expansion of capital-intensive industries. This
kind of expectation not only stimulated speculative investment demand for property, but also strengthened the propensity of average
households to save for the purpose of property purchase, which further caused consumption to shrink and investment to expand.
Hence, we have the following hypothesis.
2
However, it should be emphasized that although a rising share of tax revenues in national income has spurred their tendency to expand investment, local governments that adopt the overtaking strategy will not be constrained by the amount of budgetary revenue. In order to increase investment, they will seek funds from land
transfer revenue-based extra-budgetary revenue and land-based mortgage loans (Nitikin et al., 2011).
446
J. Du et al. / China Economic Review 31 (2014) 441–458
Hypothesis 2. A more vehement pursuit of the real estate development strategy leads to a primary and secondary income distribution structure more biased toward capital and government and against labor and a government expenditure structure more biased
toward infrastructure and against social welfare, and results in a lower ratio of household consumption to investment.
In summary, Hypotheses 1–2 argue that the overtaking strategy and the real estate development strategy are the fundamental
forces in shaping the imbalanced consumption–investment structure, i.e., under-consumption and over-investment. Meanwhile,
albeit the existence of other mechanisms, the biased primary and secondary income distribution structures as well as the biased
government expenditure structure might serve as a major mediating channel for the two development strategies to shape the
consumption–investment structure.
2.3. Structural transformation, demographic characteristics, and the consumption–investment structure
In addition to the institutional reasons, consumption and investment structure may evolve with the transformation of economic
structure following economic development.3 Generally, labor share in value added is smaller in the secondary industry than in the
primary and tertiary industries (Chenery & Syrquin, 1975). We therefore expect that labor share will decline when an economy transforms from an agriculture-based one to a secondary industry-based one, but will rise when the economy moves toward a tertiary
industry-based one. Thus, labor share in GDP is expected to show a U-shaped relationship with real GDP per capita (Li, Liu, &
Wang, 2009). Similarly, because capital and government shares are higher in the secondary industry than in other industries, it
is not difficult to infer an inverted U-shaped relationship between both capital and government shares and real GDP per capita.
Since labor share is of utmost importance to household consumption share in GDP, we expect that consumption rate will display a
U-shaped relationship while investment rate will have an inverted U-shaped relationship with real GDP per capita (economic development stages).
Hypothesis 3. Ceteris paribus, consumption–investment ratio displays a U-shaped relationship with real GDP per capita.
It is noteworthy that besides Hypotheses 1–2, Hypothesis 3 also implies that the transformation of economic structure might shape
the consumption–investment structure through the primary income distribution structure, that is, economic structure shapes the
relative shares of labor, capital and government in national income so as to affect the consumption–investment structure.
Moreover, the consumption–investment structure is associated with demographic characteristics. When the dependency ratio,
especially the old-age dependency ratio, is small, workers have less need for savings to support the elderly, which would expand
consumption. When the old-age dependency ratio rises, the working population faces a higher burden and tends to consume less
and save more. Since at this stage the working population still accounts for a large share of the whole population, the decreasing propensity to consume would lead to a shrinking consumption rate and a rising savings rate. When the old-age dependency ratio exceeds
some critical level, the society will enter a phase of dissaving, i.e., the consumption (savings) rate of households would increase
(decrease).
Hypothesis 4. Ceteris paribus, the consumption–investment ratio displays a U-shaped relationship with the old-age dependency
ratio.
3. Data and methodology
3.1. Empirical strategy
To test Hypotheses 1–4, we run a basic regression using a province-level panel dataset for the period from 1996 to 2007. We divide
the whole sample period into four three-year sub-periods, i.e., 1996–1998, 1999–2001, 2002–2004, and 2005–2007. The dependent
variable takes the three-year average value in each sub-period, and all the explanatory variables take the value in the beginning year of
each sub-period (i.e., 1996, 1999, 2002, and 2005). The regression model is specified as follows.
log Y i;t−1;t ¼ C þ α log DSi;t−1 þ β log RSi;t−1 þ γX i;t−1 þ υi þ εi;t−1;t
ð1Þ
where Yi,t − 1,t is the three-year average value of the consumption–investment structure or income distribution structure for province i
in each sub-period. C is the constant, DSi,t − 1 is the indicator of the overtaking strategy, RSi,t − 1 is the indicator of the real estate
development strategy, Xi,t − 1 is a set of control variables, υi is the provincial fixed effect, and εi,t − 1,t is an i.i.d error term.
The specification of Eq. (1) is based on three considerations. Firstly, by taking three-year averages, we control for potential cyclical
macroeconomic movements (this choice is dictated by data availability).4 Secondly, we let all the explanatory variables take the value
3
Some earlier studies (Chenery & Syrquin, 1975; Rostow, 1960) argued that at a lower development level, the share of the secondary industry value added in GDP is
smaller than that of primary industry, resulting in a lower social organic constitution of capital and therefore a lower investment rate. Then, the investment rate will rise
when an economy transforms from a primary industry-based one to a secondary industry-based one, but will decline when the economy moves toward a tertiary
industry-based one. Here, we give a different explanation for the mechanisms behind the effects of structural transformation.
4
We can obtain similar results when each sub-period is four years long and we use the 4-year averages.
J. Du et al. / China Economic Review 31 (2014) 441–458
447
in the beginning year of each three-year sub-period in order to reduce the possible simultaneity problem. Thirdly, by taking the threeyear averages, we allow the possibility that the effects of the development strategies might be spread out over several years.
The development strategies consist of the overtaking strategy (DSi,t − 1) and the real estate development strategy (RSi,t − 1). The
measurement of the overtaking strategy is built upon the technology choice index (TCI), which is proposed by Lin (2009) and is defined as TCI = (AVI / LI) / (GDP / L), where AVI is the value added of the high-technology industries, GDP is the total added value, LI is
the number of employees in high-technology industry and L is the total number of employees in the province. Following Lin and Liu
(2004a) and Chen and Lin (2013), we gauge the overtaking strategy by the ratio of the actual technology choice index (TCIi,t − 1) to the
* − 1), i.e., DSi,t − 1 = TCIi,t − 1 / TCIi,t
* − 1, where the optimal technology choice index refers to the
optimal technology choice index (TCIi,t
ideal technology choice scenario when a regional government adopts comparative advantage strategy.
The rationale for this indicator of the overtaking strategy lies in that China's resource endowment structure dictates that its comparative advantage lies in labor-intensive industries and thus the ideal technology choice index will be biased toward labor-intensive
industries. If a regional government deviates more from its comparative advantage and pursues more intensively the overtaking
development strategy, the actual technology choice will lean much more toward capital-intensive and high-technology industries.
If a regional government adopts the ideal comparative advantage strategy, the actual technology choice index equals optimal technology choice index so that DS = 1. If a regional government pursues an overtaking strategy that deviates from the ideal comparative
advantage strategy, we will expect DS N 1. Therefore, we can rewrite Eq. (1) as follows:
log Y i;t−1;t ¼ C þ α log TCIi;t−1 =TCIi;t−1 þ β log RSi;t−1 þ γX i;t−1 þ υi þ εi;t−1;t
¼ C þ α log TCIi;t−1 þ β log RSi;t−1 þ γX i;t−1 −α log TCI i;t−1 þ υi þ εi;t−1;t :
ð2Þ
Nonetheless, because log(TCI*i,t − 1) is unobservable, it will lead to the omitted variable bias problem. In the presence of this
problem, the fixed effect estimation can produce biased and inconsistent parameter estimates, and hypothesis tests can be seriously
misleading. In order to tackle this problem, we shall introduce sequentially three assumptions on the optimal technology choice index
by following the corresponding approaches proposed by Lin and Liu (2004a) and Chen and Lin (2013) to investigate the robustness of
our results.
* − 1) is only a constant and can be combined with C. This assumption basically
The first assumption denoted by A1 is that log(TCIi,t
claims that all the regions in China have the same pattern of comparative advantage, which also remains constant in the sample
period. Thus, Eq. (2) can be rewritten as a one-way fixed effect model as follows:
log Y i;t−1;t ¼ C 1 þ α log TCI i;t−1 þ β log RSi;t−1 þ γX i;t−1 þ υi þ εi;t−1;t :
ð3Þ
The second assumption denoted by A2 is that log(TCI*i,t − 1) is a function of time only, i.e., the pattern of comparative advantage and
thus the optimal technology choice index of different provinces is the same in the same year, but it might change over time. Therefore,
a full set of time fixed effects for every three-year episode Dt − 1,t, can be used to control for the effect of the optimal technology choice
index. Thus, Eq. (2) can be rewritten to be a two-way fixed effects model as follows:
log Y i;t−1;t ¼ C 2 þ α log TCI i;t−1 þ β log RSi;t−1 þ γX i;t−1 þ Dt−1;t þ υi þ εi;t−1;t :
ð4Þ
The third assumption denoted by A3 is that log(TCI*i,t − 1) is a function of time and geographic areas, which is a more realistic
assumption. On the one hand, it allows the geographical variation of comparative advantage among Chinese regions because of the
differences in resource endowment and development stages. On the other hand, it also incorporates the changes in comparative
advantage over time. Following Chen and Lin (2013), on the basis of the similarity in resource endowment, China is divided into
three mega areas, i.e., eastern area, central area and western area, and the optimal technology choice index in different areas is
assumed to be the same at the same time point. Therefore, a full set of interaction terms between time fixed effects for every
three-year episode (i.e., Dt − 1,t) and different mega area dummy variables (i.e., Ds), Dt − 1,t × Ds, can be used to control for the effects
of the optimal technology choice index, and Eq. (2) is rewritten to be a two-way fixed effects model with different time effects for
different areas as follows:
log Y i;t−1;t ¼ C 3 þ α log TCI i;t−1 þ β log RSi;t−1 þ γX i;t−1 þ Dt−1;t Ds þ υi þ εi;t−1;t :
ð5Þ
In short, we can use Eqs. (3)–(5) to estimate the effects of development strategies on the consumption–investment structure and
income distribution structure under the Assumptions A1–A3, respectively.
Moreover, it is noteworthy that we include GDP per capita in our regressions. It can be regarded as a partial proxy for the optimal
technology choice index because real GDP per capita directly measures the stage of development and hence the ideal technology
choice for the region. This adds further justifications to our regression specifications.
Besides, we also test whether the government strategies have shaped the consumption–investment structure through the income
distribution structure. To do so, we follow the strategy of Acemoglu et al. (2003) to conduct a series of regression analyses. We first
put Labor Income / (Capital Income + Gov't Income) into regression Eqs. (3)–(5). Then, to assess whether the overtaking strategy
448
J. Du et al. / China Economic Review 31 (2014) 441–458
and real estate development strategy affect the consumption–investment structure via income distribution structure, we adopt the
following criteria:
1. If the proxy variables for the overtaking strategy and real estate development strategy lose statistical significance or their statistical
significance and/or the magnitude of the estimated coefficients have dropped substantially, while the income distribution structure
variable is statistically significant, we can regard income distribution structure as a primary mediating channel for the impact of the
government strategies on the consumption–investment structure. Since the income distribution structure is the main mediating
channel, these results would suggest that getting income distribution structure right is likely to be an important policy priority.
2. If the variable of income distribution structure is not statistically significant, it is not regarded as one channel linking government
strategies to an imbalanced consumption–investment structure. In this case, the effect of government strategies on the consumption–investment structure is likely to be working through a variety of other channels.
3. If both income distribution structure and the overtaking strategy and real estate development strategy are statistically significant,
and the statistical significance and magnitude of the latter estimated coefficients have not dropped substantially, we can conclude
that the income distribution structure is only one important channel, but it is not the primary mediating channel via which
government strategies translate into an imbalanced consumption–investment structure.
In panel data regression estimation based on Eqs. (3)–(5) above, we use the method of Driscoll and Kraay (1998) to compute standard errors that can effectively deal with the problems of autocorrelation, heteroskedasticity, and cross-sectional correlation. In contrast to the conventional problematic assumption that residuals are correlated within but uncorrelated between region groups,5
Driscoll and Kraay (1998) propose a more natural assumption that residuals are correlated both within and between region groups
(Hoechle, 2007). This allows us to cope with cross-sectional dependence. Compared with the feasible generalized least squares
(FGLS) method, this method could more effectively deal with the problems of autocorrelation, heteroskedasticity, and crosssectional correlation in small samples where the number of cross-section units is larger than that of time periods.
3.2. Data and variables
Our empirical analysis is mainly based on panel data from 27 provinces in China in the period 1996–2007. Because of lack of
consistent data in the period covered, our sample does not include four province-level administrative regions, i.e., Chongqing, Hainan,
Sichuan, and Tibet. Detailed data sources are reported in Appendix A.
As mentioned above, the overtaking strategy is built upon TCI, the technology choice index proposed by Lin (2009). It is defined as
the ratio of the value added per employee in the high-technology industries to the average GDP per employee in the province. If a
government adopts an overtaking strategy to promote capital and technology-intensive industries, the TCI in this province is expected
to be larger than otherwise. Under an overtaking strategy, the local government typically grants some monopoly power in output
market for those enterprises engaged in capital and technology-intensive industries. At the same time, local governments often provide subsidies and cheap loans for them to lower their investment and operational costs. These policies tend to raise the value added
of the high-technology industries. On the contrary, these capital and technology-intensive industries can absorb a relatively small
amount of labor, which, coupled with the above factor, helps raise the value of the TCI indicator. Tables 1–4 present the TCI index
values for each province in the three mega areas in China, i.e., the eastern area, the central area and the western area, and the average
TCI index value for each of the three mega areas. Clearly, the TCI value in the western area is the highest, followed by that in the central
area, and that in the eastern area is the lowest. Nonetheless, the two-sample t tests show that although the TCI value in the eastern
area is the lowest, the TCI value in the western area is not statistically significantly different from that in the central area. There are
two possible reasons for this variation in TCI across mega areas. First, the tax sharing system created a bigger fiscal pressure to inland
provinces than to the coastal ones (Huang & Chen, 2012; Tsui, 2005), which prompted the western and central areas to pursue the
overtaking strategy more intensively to accelerate output growth and enhance tax collection. Second, the inland provinces typically
have a weaker industrial base and weaker foundation for high-technology industries; consequently, in order to achieve superior
growth performance and alleviate their fiscal burden, local governments in these regions are more likely to employ a series of industrial policies to support the development of capital and technology-intensive industries.
Based on Hypothesis 1, we expect that the estimated coefficients of log(TCIi,t − 1) are statistically significant and negative in
Eqs. (3), (4) and (5).
To gauge the real estate development strategy, we use the ratio of land lease (land transfer) revenues to budgetary fiscal revenue
as a proxy for local governments' efforts to develop local real estate market. This ratio can capture quite much local governments'
efforts to pursue property market development strategy. On the one hand, as discussed above, because local governments play a
dominant role in controlling housing prices, the size and pace of land lease and reaping tremendous land lease revenues, they can
effectively control land lease revenues and thus this ratio variable. On the other hand, since we scale land lease revenue by budgetary
revenue and the size of budgetary revenue reflects to a large degree the dynamism of the local economy, this ratio can effectively
control for the potential effects of the robustness of the economy and market to a large extent. Therefore, it can measure closely
the intensity of local government policies in pursuing property market development, and can be a good proxy variable for the real
estate development strategy. Examining the ratio of land lease revenues to budgetary revenues, we can get a sense of how important
land transfer income is to local governments and thus how hard local governments have pushed for property market development.
5
It can be dealt with by clustered standard errors.
J. Du et al. / China Economic Review 31 (2014) 441–458
449
Table 1
TCI and real estate development strategy by area.
Data sources: China Compendium of Statistics 1949–2008, China Statistical Yearbook (Various years), China Statistics Yearbook on High Technology Industry (Various years),
China Land and Resources Statistical Yearbook, China Land and Resources Yearbook, and Financial Yearbook of China (Various years).
TCI
Real Estate Development Strategy
Eastern area
Central area
Western area
2.88
34.44
3.71
21.47
4.24
13.9
Notes: TCI is measured by the ratio of the added value of high-technology industry per capita to GDP per capita. Real Estate Development Strategy (%) is measured by the
ratio of land lease revenues to budgetary fiscal revenue.
Tables 1–4 provide the mean value of this ratio in each province and in the three mega areas. Clearly, the ratio of land lease revenues to
budgetary fiscal income in the eastern area is the biggest, followed by that in the central area, and that in the western area is the lowest, a pattern just opposite to TCI. Two-sample t tests also confirm this pattern. Meanwhile, Table 5 shows that within each mega area,
although it is statistically significant only in the central area, the TCI index and the ratio of land lease revenue to budgetary revenue
exhibit some positive correlations. In our opinion, this interesting pattern of variation across and within mega areas is mainly caused
by the following reasons. (1) Regional governments rely on both the overtaking strategy and the real estate development strategy for
GDP and fiscal revenue growth, but there are differences in the intensity of the implementation of the two strategies across regions.
(2) Regions in the eastern mega area have implemented the real estate development strategy more strongly whereas regions in the
central and western mega areas display a more intensive implementation of the overtaking strategy. This leads to opposite pattern
across mega areas. (3) The strategic implementation of both strategies may be constrained to some degree by the socioeconomic conditions and resource endowments in each region and mega area. The eastern coastal area has a higher level of industrial development
so that the incremental effect of pursuing the overtaking strategy may be smaller than that for the central and western areas. On the
other hand, the higher population density, the better living environment, the better socioeconomic development, etc. in the eastern
mega area all contribute to a higher potential demand for properties. Consequently, the dynamic economic growth and strong potential demand for property in the eastern coastal area facilitate the government's pursuit of real estate development strategy. The eastern area therefore displays a stronger propensity to develop property markets, which is consistent with Liu and Sun (2009), whereas
the western and central areas exhibit a more intensive implementation of the overtaking strategy. (4) Within each mega area, since
the socioeconomic conditions and resource endowments are similar across regions to a certain degree, the differences in the intensity
and the effects of pursuing the two strategies are fairly consistent across regions, and we therefore observe a positive correlation
across regions in the intensity of implementing the two strategies. According to Hypothesis 2, we expect that the estimated coefficients of log(RSi,t − 1) are negative in Eqs. (3)–(5).
Real GDP per capita (GDPPC) captures the stage of economic development of different provinces. To incorporate the potential
U-shaped relationship, we include both real GDP per capita and its square term (GDPPC−sq).
The old-age dependency ratio (Old Age Dependency Ratio) is measured as the ratio of the number of people aged 65 and over to the
number of people aged 15–64 in a province (National Bureau of Statistics, 2009). To allow for the possible U-shaped relationship, we
include both the aged dependency ratio and its squared term.
Besides, we also include two additional control variables in regression analysis, that is, economic openness and private economy
development. The variable of economic openness is defined as the sum of exports and imports divided by GDP. The variable of private
economy development is defined as the ratio of industrial production value of the non-state sector in the total industrial value (Bai
et al., 2004). Economic openness may affect the bargaining power of owners of different types of production factors and is expected
to raise capital share and lower labor share (Guscina, 2006; Harrison, 2002). After examining over 100 countries over the period
1960–1997, Harrison (2002) finds that economic openness is negatively related to labor share in income. She argues that this is
because of the stronger bargaining power of capital than that of labor in a financially-integrated world. Guscina (2006) argues that
this “bargaining power” mechanism also plays an important role in generating the negative impacts of economic openness on
labor share in industrialized countries. However, Diwan (2000, 2001) notes that the impact of economic openness on labor share
varies from country to country, and that results are highly sensitive to the way of modeling. Theoretically, the impacts of private
economy development on income shares are ambiguous. On the one hand, private economy could lower labor income share.
Table 2
TCI and real estate development strategy in the eastern area.
Data sources: China Compendium of Statistics 1949–2008, China Statistical Yearbook (Various years), China Statistics Yearbook on High Technology Industry (Various years),
China Land and Resources Statistical Yearbook, China Land and Resources Yearbook, and Financial Yearbook of China (Various years).
TCI
Real Estate Development Strategy
TCI
Real Estate Development Strategy
Beijing
Tianjin
Hebei
Liaoning
Shanghai
2.45
29.82
3.06
43.83
3.2
26.74
2.2
27.25
1.62
18.98
Jiangsu
Zhejiang
Fujian
Shandong
Guangdong
3.31
48.14
2.19
66.97
4
38.87
4.12
26.94
2.65
16.83
Notes: TCI is measured by the ratio of the added value of high-technology industry per capita to GDP per capita. Real Estate Development Strategy (%) is measured by the
ratio of land lease revenues to budgetary fiscal revenue.
450
J. Du et al. / China Economic Review 31 (2014) 441–458
Table 3
TCI and real estate development strategy in the central area.
Data sources: China Compendium of Statistics 1949–2008, China Statistical Yearbook (Various years), China Statistics Yearbook on High Technology Industry (Various years),
China Land and Resources Statistical Yearbook, China Land and Resources Yearbook, and Financial Yearbook of China (Various years).
TCI
Real Estate Development Strategy
Shanxi
Jilin
Heilongjiang
Anhui
Jiangxi
Henan
Hubei
Hunan
1.98
10.37
3.31
17.03
2.41
8.375
4.48
38.29
3.67
29.46
3.89
14.37
5.57
27.44
4.4
26.39
Notes: TCI is measured by the ratio of the added value of high-technology industry per capita to GDP per capita. Real Estate Development Strategy (%) is measured by the
ratio of land lease revenues to budgetary fiscal revenue.
Azmat, Manning, and Reenen (2011) find that privatization has been an important factor in the fall of labor's share of value added over
the past two decades in the network industries in OECD countries. They argue that this occurs because publicly-owned firms have
more preference for employment over profits than do privatized firms. This could apply to China in economic transition. Similarly, private economy development in China in the absence of labor rights protection might enhance the bargaining power of capital vis-à-vis
labor so as to lower labor income share. On the other hand, the private sector in China is more likely to be engaged in labor-intensive
industries than does the state sector. Largely denied access to the state-dominated financial system and other favorable government
policies, the private sector firms find it difficult to enter capital and technology-intensive industries having a higher threshold requirement for capital. They are often forced to engage in labor-intensive industries. Consequently, private economy development could
boost labor income share.
Besides, economic openness and private economy development are also introduced into Eqs. (3)–(5) since they are likely to affect
the consumption–investment structure directly. A more open economy might create more opportunities for investment and consumption for domestic firms and consumers, and thus affects the consumption–investment structure. The development of the private
economy sector might restrain the investment impulse of local governments, which in turn affects the consumption–investment
structure.
4. Empirical results
4.1. Growth-first strategy and the imbalanced consumption–investment structure in China
In Table 6, we present the regression results of the impacts of two government development strategies on the consumption–
investment structure under the Assumptions A1–A3, and find qualitatively robust results as follows.
Firstly, a more intensive pursuit of the overtaking strategy and the real estate development strategy is associated with a lower ratio
of consumption to investment, i.e., a more imbalanced consumption–investment structure. This is fully consistent with the predictions of Hypotheses 1–2.
Secondly, consistent with Hypothesis 3, the ratio of consumption to investment exhibits a U-shaped relationship with real GDP per
capita. When the annual real GDP per capita is smaller (larger) than RMB 14225 (A3), the ratio of consumption to investment would
decrease (increase) with real GDP per capita. If we look at the data, we find that except Shanghai, all provinces of China still lie in the
left part of the U-shaped curve where the ratio of consumption to investment declines with real GDP per capita.
Thirdly, the ratio of consumption to investment exhibits a U-shaped relationship with the aged dependency ratio, which is consistent with Hypothesis 4. When the aged dependency ratio remains below (above) 11%, the ratio of consumption to investment would
decrease (increase) in the aged dependency ratio. An examination of the data tells us that most provinces of China have gradually
moved to the right part of the U curve where the consumption–investment ratio is increasing in the aged dependency ratio.
Private economy development has robust significant negative effects on the ratio of consumption to investment, but economic
openness does not have robust significant positive effects on the ratio of consumption to investment.
4.2. Growth-first strategy and the biased factor income distribution structure in China
To see how the “growth-first strategy” shapes the consumption–investment structure, we analyze, as an intermediate step, how
the overtaking strategy and the real estate development strategy as well as other factors affect the structure of factor income
distribution.
Table 4
TCI and real estate development strategy in the western area.
Data sources: China Compendium of Statistics 1949–2008, China Statistical Yearbook (Various years), China Statistics Yearbook on High Technology Industry (Various years),
China Land and Resources Statistical Yearbook, China Land and Resources Yearbook, and Financial Yearbook of China (Various years).
TCI
Real Estate Development Strategy
Neimenggu
Guangxi
Guizhou
Yunnan
Shangxi
Gansu
Qinghai
Ningxia
Xinjiang
3.72
9.52
5.42
22.87
6.45
12.97
6.38
13.41
3.74
18.98
3.73
10.02
3.8
5.29
2.99
22.21
1.92
9.86
Notes: TCI is measured by the ratio of the added value of high-technology industry per capita to GDP per capita. Real Estate Development Strategy (%) is measured by the
ratio of land lease revenues to budgetary fiscal revenue.
J. Du et al. / China Economic Review 31 (2014) 441–458
451
Table 5
Correlations between TCI and real estate development strategy within each mega area.
Data sources: China Compendium of Statistics 1949–2008, China Statistical Yearbook (Various years), China Statistics Yearbook on High Technology Industry (Various years),
China Land and Resources Statistical Yearbook, China Land and Resources Yearbook, and Financial Yearbook of China (Various years).
Corr(TCI, REDS)
Eastern area
Central area
Western area
0.08
(0.83)
0.75**
(0.03)
0.16
(0.68)
Notes: TCI is measured by the ratio of the added value of high-technology industry per capita to GDP per capita. REDS (i.e., Real Estate Development Strategy (%)) is measured by the ratio of land lease revenues to budgetary fiscal revenue. P values are in parentheses. *, **, and *** indicate significance at 10%, 5%, and 1% levels, respectively.
Table 7 presents the results on the effects of the overtaking strategy, real estate development strategy, etc. on the ratio of labor
income to the sum of capital and government income in the primary income distribution under the Assumptions A1–A3. Some similar
observations can be made. First, a more intensive pursuit of the overtaking strategy and the real estate development strategy is associated with a lower ratio of labor income to the sum of capital and government income, i.e., a primary income distribution structure
more biased against labor. Most of the estimated coefficients are statistically significant. These findings are fully consistent with
Hypotheses 1–2.
Second, as predicted by Hypothesis 3, the ratio of labor income to capital and government income displays a U-shaped relationship
with real GDP per capita.
Private economy development has significant negative effects on the ratio of labor income to capital and government income (see
A3, which is a more realistic assumption), which is consistent with the results of Luo and Zhang (2010). As pointed out by Li et al.
(2009), economic transition increases capital owners' bargaining power, resulting in a decline in the share of labor income. Besides,
economic reforms force urban redundant workers and encourage rural surplus labor to enter the labor market, thereby increasing
labor supply and exerting downward pressures on wages (Luo & Zhang, 2010). In contrast, economic openness does not have robust
significant positive effects on the ratio of labor income to capital and government income, which is not consistent with the results of
Luo and Zhang (2010). One possible reason is that China's export sector is still concentrated on labor-intensive products or laborintensive parts of the production chain such as assembly, which might help raise the share of labor in national income.
In addition to the primary income distribution structure, we also examine the secondary income distribution structure and obtain
qualitatively equivalent results.
Table 6
Growth-first strategy and the imbalance between consumption and investment in China.
Data sources: China Compendium of Statistics 1949–2004, China Compendium of Statistics 1949–2008, China Statistical Yearbook (Various years), China Statistics Yearbook on
High Technology Industry (Various years), China Land and Resources Statistical Yearbook, China Land and Resources Yearbook and Financial Yearbook of China (Various years).
Dependent
log(TCI)
log(Real Estate Development Strategy)
GDPPC
GDPPC_sq
Old Age Dependency Ratio
Old Age Dependency Ratio−sq
Private economy development
Open
Intercept
Assumptions
Methods
Observations
R2
(1)
(2)
(3)
log(C/I)
log(C/I)
log(C/I)
−0.168***
[0.000]
−0.062**
[0.025]
−0.0001***
[0.000]
5.96e−9***
[0.000]
−0.277***
[0.003]
0.011***
[0.002]
−0.006***
[0.002]
0.001
[0.767]
2.496***
[0.000]
A1
One-way FE
108
0.7786
−0.132***
[0.000]
−0.074*
[0.107]
−0.00007
[0.311]
4.57e−9 ***
[0.000]
−0.220***
[0.000]
0.009***
[0.000]
−0.008***
[0.000]
0.001
[0.852]
2.130***
[0.000]
A2
Two-way FE
108
0.7913
−0.059***
[0.001]
−0.103**
[0.011]
−0.0002***
[0.000]
7.03e−9***
[0.000]
−0.110**
[0.046]
0.005**
[0.029]
−0.010***
[0.000]
0.001
[0.574]
1.815***
[0.000]
A3
Two-way FE
108
0.8454
Notes: C (yuan) is the household consumption, I (yuan) is the total value of fixed capital formation. TCI is measured by the ratio of the added value of high-technology
industry per capita to GDP per capita. Real Estate Development Strategy (%) is measured by the ratio of land lease revenues to budgetary fiscal revenue. GDPPC (yuan) and
GDPPC_sq are the real GDP per capita and its square term. Old Age Dependency Ratio (%) and Old Age Dependency Ratio−sq are measured as the ratio of the number of
people aged 65 and over to the number of people aged 15–64 in a province and its square term. Private economy development (%) is the ratio of production value of
industry produced by non-state-owned enterprises over that of industry (Bai et al., 2004). Open (%) is the sum of exports and imports divided by GDP. P values are
in parentheses. *, **, and *** indicate significance at 10%, 5%, and 1% levels, respectively.
452
J. Du et al. / China Economic Review 31 (2014) 441–458
Table 7
Growth-first strategy and biased income distribution structure in China.
Data sources: China Compendium of Statistics 1949–2004, China Compendium of Statistics 1949–2008, China Statistical Yearbook (Various years), China Statistics
Yearbook on High Technology Industry (Various years), China Land and Resources Statistical Yearbook, China Land and Resources Yearbook, and Financial Yearbook of
China (Various years), Lv and Guo (2012b).
Dependent
log(TCI)
log(Real Estate Development Strategy)
GDPPC
GDPPC_sq
Private economy development
Open
Intercept
Assumptions
Methods
Observations
R2
(1)
(2)
log(income structure)
log(income structure)
(3)
log(income structure)
−0.142***
[0.000]
−0.050***
[0.000]
−0.00008***
[0.000]
2.03e−9***
[0.005]
−0.0003
[0.787]
0.004***
[0.006]
0.365***
[0.000]
A1
One-way FE
108
0.5187
−0.085*
[0.068]
−0.021
[0.162]
−0.00003
[0.257]
1.12e−9***
[0.000]
−0.001
[0.526]
0.003*
[0.098]
0.246**
[0.020]
A2
Two-way FE
108
0.5375
−0.015
[0.819]
−0.021***
[0.005]
−0.0001***
[0.000]
4.46e−9***
[0.000]
−0.003***
[0.000]
0.002
[0.167]
0.593***
[0.000]
A3
Two-way FE
108
0.6133
Notes: Incomestructure is the Labor Income / (Capital Income + Gov't Income). Labor income (yuan) is the labor compensation, Capital income (yuan) is the sum of operating surplus and fixed asset depreciation and Government income (yuan) is the net production tax. TCI is measured by the ratio of the added value of high-technology
industry per capita to GDP per capita. Real Estate Development Strategy (%) is measured by the ratio of land lease revenues to budgetary fiscal revenue. GDPPC (yuan) and
GDPPC_sq are the real GDP per capita and its square term. Private economy development (%) is the ratio of production value of industry produced by non-state-owned
enterprises over that of industry (Bai, Du, Tao, & Tong, 2004). Open (%) is the sum of exports and imports divided by GDP. P values are in parentheses. *, **, and *** indicate
significance at 10%, 5%, and 1% levels, respectively.
4.3. Income structure as one channel
So far we have shown that government development strategies have shaped the primary and secondary income distribution structures and the consumption–investment structure. We now turn to test whether the impacts of development strategies upon the consumption–investment structure work through the channel of the income distribution structure. Table 8 presents the results of channel
tests. We add into regressions the indicator of primary income distribution structure, i.e., the ratio of labor income to the sum of capital
and government income. We find that the primary income distribution structure variable generates a statistically significant and
positive estimated coefficient, whereas the estimated coefficients of the two government strategy proxy variables remain statistically
significant. At the same time, the magnitude of the estimated coefficients of the real estate development strategy and the overtaking
strategy is unchanged. This indicates that the primary income distribution structure serves as one important mediating channel for
the impact of government strategies on the consumption–investment structure, but it is hardly the primary channel. Furthermore,
when we test whether the impacts of development strategies upon the consumption–investment structure work through the channel
of secondary income distribution structure, we obtain similar results.6 It suggests that a significant part of the impacts is mediated by
the distorted primary and secondary income distribution structures.
In addition, Table 8 also shows that the overtaking strategy and real estate development strategy indicators remain statistically
significant after including the primary income distribution structure indicator. This “residual significance” suggests that the
“growth-first” development strategy has also contributed to the biased consumption–investment structure through other channels.
For instance, the real estate development strategy has distorted government expenditure structure, stimulated speculative investment demand through expectations of surging property prices, strengthened households' motive for precautionary savings, and generated huge land lease revenues, while the overtaking strategy has softened government and SOE budgetary constraint, distorted
government expenditure structure, weakened the social safety net for local residents and intensified households' motive for precautionary savings, all of which raise investment and lower consumption.
4.4. Government expenditure structure as another channel
As discussed above, the biased government expenditure structure could also be an important mediating channel. In Column 1, we
investigate first the relationship between the government development strategies and the government expenditure structure.
Because A3 is the most realistic assumption, Table 9 only presents the results under this assumption. We find that both the overtaking
strategy and the real estate development strategy are statistically significantly and negatively correlated with the ratio of social welfare expenses to infrastructure investment, which suggests that the “growth-first strategy” truly biases the government expenditure
6
The results are available from the authors upon request.
J. Du et al. / China Economic Review 31 (2014) 441–458
453
Table 8
Growth-first strategy and the imbalance between consumption and investment in China: channel test.
Data sources: China Compendium of Statistics 1949–2004, China Compendium of Statistics 1949–2008, China Statistical Yearbook (Various years), China Statistics Yearbook
on High Technology Industry (Various years),China Land and Resources Statistical Yearbook, China Land and Resources Yearbook, and Financial Yearbook of China (Various
years), Lv and Guo(2012b).
Dependent
log(incomestructure)
log(TCI)
log(Real Estate Development Strategy)
GDPPC
GDPPC_sq
Old Age Dependency Ratio
Old Age Dependency Ratio−sq
Private economy development
Open
Intercept
Assumptions
Methods
Observations
R2
(1)
(2)
log(C/I)
log(C/I)
(3)
log(C/I)
0.285***
[0.000]
−0.130***
[0.000]
−0.048**
[0.040]
−0.0001***
[0.001]
5.28e−9***
[0.000]
−0.262***
[0.001]
0.010***
[0.001]
−0.006***
[0.000]
−0.00001
[0.996]
2.309***
[0.000]
A1
One-way FE
108
0.7975
0.293***
[0.000]
−0.108***
[0.000]
−0.068*
[0.098]
−0.00006
[0.339]
4.12e−9***
[0.000]
−0.208***
[0.000]
0.009***
[0.000]
−0.008***
[0.000]
−0.0001
[0.965]
1.987***
[0.000]
A2
Two-way FE
108
0.8106
0.154***
[0.001]
−0.056***
[0.005]
−0.099**
[0.012]
−0.0002***
[0.001]
6.33e−9 ***
[0.000]
−0.115**
[0.034]
0.005**
[0.023]
−0.009***
[0.000]
0.001
[0.633]
1.759***
[0.000]
A3
Two-way FE
108
0.8498
Notes: C (yuan) is the household consumption. I (yuan) is the total value of fixed capital formation. Incomestructure is Labor Income / (Capital Income + Gov't Income).
Labor income (yuan) is the labor compensation, Capital income (yuan) is the sum of operating surplus and fixed asset depreciation and Government income (yuan) is the
net production tax. TCI is measured by the ratio of the added value of high-technology industry per capita to GDP per capita. Real Estate Development Strategy (%) is measured by the ratio of land lease revenues to budgetary fiscal revenue. GDPPC (yuan) and GDPPC_sq are the real GDP per capita and its square term. Old Age Dependency
Ratio (%) and Old Age Dependency Ratio−sq are measured as the ratio of the number of people aged 65 and over to the number of people aged 15–64 in a province and its
square term. Private economy development (%) is the ratio of production value of industry produced by non-state-owned enterprises over that of industry (Bai et al.,
2004). Open (%) is the sum of exports and imports divided by GDP. P values are in parentheses.*, **, and *** indicate significance at 10%, 5%, and 1% levels, respectively.
toward infrastructure. Next, we test whether the government development strategies affect the consumption–investment structure
through the mediating channel of government expenditure structure. As shown in Column 2, we add into regressions the indicator
of government expenditure structure, i.e., the ratio of social welfare expenses to infrastructure investment. Compared with the regression in Column 3, we find that the government expenditure structure variable produces a statistically significant and positive estimated coefficient, whereas the estimated coefficients of the two government strategy proxy variables remain statistically significant. At
the same time, the magnitude of the estimated coefficients of the real estate development strategy and the overtaking strategy is
largely unchanged. This indicates that the government expenditure structure also serves as one important mediating channel for
the impact of the two types of “growth-first strategy” on the consumption–investment structure, but it can also be hardly regarded
as the primary channel.
4.5. Robustness check
In the abovementioned empirical strategy, we take the three-year or four-year average value of the dependent variable. A potential
concern is that in China the political business cycle normally takes five years due to the National Congress of the Chinese Communist
Party held every five years. So it is possible that both the dependent variables and development strategies were driven by omitted
political factors. In order to address the problem of bias from omitted variables, we use the provincial-yearly based panel data and
introduce political cycle indicator variables to control for each five-year political cycle. Our sample period 1996–2007 covers three national congresses (the 15th Congress in 1997, the 16th Congress in 2002, and the 17th Congress in 2007). Thus, we divide the sample
period into four political cycles, i.e., 1996, 1997–2001, 2002–2006 and 2007. Treating 1996 as the comparison group, we set up three
political cycle indicator variables, namely, Political1997–2001 = 1, if year = 1997, 1998, 1999, 2000, 2001; Political2002–2006 = 1, if
year = 2002, 2003, 2004, 2005, 2006; and Political2007 = 1, if year = 2007. Tables 10–11 show that even when political cycle factors
are controlled, our main results remain unchanged. Moreover, we also find that the three political cycle indicator variables produce
positive and statistically significant estimated coefficients. In particular, the increment in the ratio of consumption to investment is
larger under the Hu-Wen administration (2002–6, 2007) than under the Jiang-Zhu administration. This is probably because the
Hu-Wen administration tried to alleviate the distortions in development strategies to a larger degree by advocating the vision of
scientific development and harmonious society.
454
J. Du et al. / China Economic Review 31 (2014) 441–458
Table 9
Growth-first strategy and the imbalance between consumption and investment in China: another channel test (government expenditure structure).
Data sources: China Compendium of Statistics 1949–2004, China Compendium of Statistics 1949–2008, China Statistical Yearbook (Various years), China Statistics Yearbook on
High Technology Industry (Various years),China Land and Resources Statistical Yearbook, China Land and Resources Yearbook, and Financial Yearbook of China (Various years).
Dependent
(1)
(2)
(3)
log(welfare/infra)
log(C/I)
log(C/I)
−0.108*
[0.054]
−0.117**
[0.012]
−0.0001***
[0.000]
5.32e−9**
[0.036]
−0.39***
[0.004]
0.021***
[0.002]
−0.008**
[0.019]
−0.008***
[0.009]
3.221***
[0.000]
A3
Two-way FE
81
0.7178
0.058*
[0.053]
−0.285***
[0.000]
−0.086***
[0.000]
−0.0002***
[0.000]
5.19e−9***
[0.000]
−0.011
[0.623]
0.003***
[0.000]
−0.006***
[0.000]
0.002*
[0.077]
0.953***
[0.000]
A3
Two-way FE
81
0.8166
−0.291***
[0.001]
−0.093***
[0.000]
−0.0002***
[0.000]
5.5e−9***
[0.000]
−0.034***
[0.005]
0.004***
[0.000]
−0.007***
[0.000]
0.001
[0.156]
1.139***
[0.000]
A3
Two-way FE
81
0.8147
log(welfare/infra)
log(TCI)
log(Real Estate Development Strategy)
GDPPC
GDPPC_sq
Old Age Dependency Ratio
Old Age Dependency Ratio−sq
Private economy development
Open
Intercept
Assumption
Methods
Observations
R2
Notes: C (yuan) is the household consumption. I (yuan) is the total value of fixed capital formation. Welfare (yuan) is the pensions and relief funds for social welfare and
social security subsidiary expenses, which are available over the period 1998–2006. Infra (yuan) is the expenditure for capital construction. TCI is measured by the ratio
of the added value of high-technology industry per capita to GDP per capita. Real Estate Development Strategy (%) is measured by the ratio of land lease revenues to budgetary fiscal revenue. GDPPC (yuan) and GDPPC_sq are the real GDP per capita and its square term. Old Age Dependency Ratio (%) and Old Age Dependency Ratio−sq are
measured as the ratio of the number of people aged 65 and over to the number of people aged 15–64 in a province and its square term. Private economy development (%)
is the ratio of production value of industry produced by non-state-owned enterprises over that of industry (Bai et al., 2004). Open (%) is the sum of exports and imports
divided by GDP. P values are in parentheses. *, **, and *** indicate significance at 10%, 5%, and 1% levels, respectively.
Besides, at the risk of substantially reduced sample size due to limited data availability, in order to take into account the effects of
the five-year political cycles, we have also used the five-year averages of dependent variables and conduct regression analysis based
on five-year periods. In unreported results, we find that our main results on the importance of the two development strategies remain
unchanged.7
5. Conclusion
In this study, we conduct an analysis to understand the unbalanced consumption–investment structure in the context of China's
“growth-first strategy”. We argue that the overtaking strategy and the real estate development strategy contributed tremendously to
China's neck breaking economic growth, but at the same time they led to distorted income distribution structure, distorted government expenditure structure, and internal macroeconomic imbalances.
Using the overtaking strategy and the real estate development strategy to characterize the behavior of regional governments, we
document a strong relationship between the two strategies and the imbalanced consumption–investment structure. The impacts of
the two strategies remain on track even after considering the effects of economic structure evolution and demographic structure
transformation. Moreover, we verify that the biased primary and secondary income distribution structures as well as the biased
government expenditure structure only serve as important mediating channels through which the development strategies translate
into an imbalanced consumption investment structure. Our findings imply that the Chinese government will be able to accomplish
China's transition from an investment-led growth model to a consumption–investment balanced growth model only if it removes
the distortions in development strategies.
To our pleasure, we observe in recent years some positive changes in government policies that are conducive to the removal of
these distortions. Some examples include: 1. The implementation of and the rise over years in the minimum wages (Cai, 2010). 2.
The improvement in the labor-related legislation and labor market institutions (Cai, 2010). 3. The improvement in the level of compulsory education attained by migrant workers' children (Cai, 2010). 4. The increasing importance attached to the provision of social
security for rural senior residents and a wide range of medical insurance systems. These safety nets would lessen many concerns of the
7
The results are available from the authors upon request.
J. Du et al. / China Economic Review 31 (2014) 441–458
455
Table 10
Growth-first strategy and the imbalance between consumption and investment in China (income distribution structure): using dummy term to control for political factors.
Data sources: China Compendium of Statistics 1949–2004, China Compendium of Statistics 1949–2008, China Statistical Yearbook (Various years), China Statistics Yearbook on High
Technology Industry (Various years), China Land and Resources Statistical Yearbook, China Land and Resources Yearbook, and Financial Yearbook of China (Various years), Lv and
Guo(2012b).
Dependent
(1)
(2)
(3)
log(C/I)
log(C/I)
log(incomestructure)
Intercept
−0.21***
[0.000]
−0.08***
[0.000]
−0.0001***
[0.001]
4.07e−9***
[0.000]
−0.08**
[0.021]
0.003**
[0.034]
−0.006***
[0.003]
0.004
[0.163]
1.39***
[0.000]
1.42***
[0.000]
1.41***
[0.000]
b
0.13*
[0.075]
−0.19***
[0.000]
−0.08***
[0.000]
−0.0001***
[0.002]
3.86e−9***
[0.000]
−0.08**
[0.012]
0.003**
[0.022]
−0.006***
[0.004]
0.003
[0.202]
1.39***
[0.000]
1.43***
[0.000]
1.42***
[0.000]
b
Year dummy
Methods
Observations
R2
No
FE
297
0.7240
No
FE
297
0.8166
log(incomestructure)
log(TCI)_1
log(Real Estate Development Strategy)_1
GDPPC_1
GDPPC_sq_1
Old Age Dependency Ratio_1
Old Age Dependency Ratio−sq_1
Private Economy Development_1
Open_1
Political1997–2001
Political2002–2006
Political2007
−0.16***
[0.003]
−0.01
[0.447]
−0.0001***
[0.001]
1.5e−9***
[0.003]
−0.0002
[0.817]
0.004**
[0.020]
0.10
[0.128]
0.01
[0.668]
a
0.18
[0.205]
No
FE
297
0.3767
Notes: C (yuan) is the household consumption. I (yuan) is the total value of fixed capital formation. Incomestructure is Labor Income / (Capital Income + Gov't Income).
Labor income (yuan) is the labor compensation, Capital income (yuan) is the sum of operating surplus and fixed asset depreciation and Government income (yuan) is the
net production tax. TCI is measured by the ratio of the added value of high-technology industry per capita to GDP per capita. Real Estate Development Strategy (%) is measured by the ratio of land lease revenues to budgetary fiscal revenue. GDPPC (yuan) and GDPPC_sq are the real GDP per capita and its square term. Old Age Dependency
Ratio (%) and Old Age Dependency Ratio−sq are measured as the ratio of the number of people aged 65 and over to the number of people aged 15–64 in a province and its
square term. Private economy development (%) is the ratio of production value of industry produced by non-state-owned enterprises over that of industry (Bai et al.,
2004). Open (%) is the sum of exports and imports divided by GDP. Political1997–2001 = 1, if year = 1997, 1998, 1999, 2000, and 2001; Political2002–2006 = 1, if
year = 2002, 2003, 2004, 2005, and 2006; Political2007 = 1, if year = 2007. a and b show that Political2007 and intercept are dropped when regressions are carried
out. If Year dummy is controlled, the main results are not unchanged. P values are in parentheses. *, **, and *** indicate significance at 10%, 5%, and 1% levels, respectively.
migrant workers and facilitate migration (Zhang, Yang, & Wang, 2011). 5. Policies to ensure farmers to enjoy a larger share of land
value appreciation in the process of urbanization (Su et al., 2012). 6. The implementation of the macro-control policies over the
property market including property taxes, property purchase quota, loaning limitation, etc.
Considering that these policies are favorable to migrant workers and can mitigate the intensity of the implementation and
marginal effects of the overtaking strategy and real estate development strategy, we believe that they will, to some extent,
reduce the distortive impacts of these development strategies on the primary and secondary income distribution structures
as well as government expenditure structure, and therefore, will alleviate the internal imbalances of overinvestment and
underconsumption.
Furthermore, many economists claimed that China has already reached the Lewis turning point8 (see, e.g., Cai, 2010; Cai & Du,
2011; Cai & Wang, 2010; Zhang et al., 2011). Then, the comparative advantage of the Chinese economy will likely shift from laborintensive industries to capital-intensive industries in the foreseeable future (Zhang et al., 2011). Needless to say, it will greatly weaken
the enthusiasm of local governments to implement the overtaking strategy, and reinforce the bargaining power of workers in the
labor market and the level of social protection and social welfare enjoyed by workers. Thus, we are optimistic that the changes in
government policies and the pattern of comparative advantages will gradually foster the changes in the development strategies
and remove the distortions created by these strategies in the future.
8
It is worth noting that the notion that China is quickly approaching or has already reached the Lewis turning point is not without controversy. Please see the opposing views (Knight, Deng, & Li, 2011; Ge and Dennis, 2011; Golley & Meng, 2011; Minami & Ma, 2010; Yao & Zhang, 2010; the World Bank, 2007) for details.
456
J. Du et al. / China Economic Review 31 (2014) 441–458
Table 11
Growth-first strategy and the imbalance between consumption and investment in China (government expenditure structure): using dummy term to control
for political factors.
Data sources: China Compendium of Statistics 1949–2004, China Compendium of Statistics 1949–2008, China Statistical Yearbook (Various years), China Statistics
Yearbook on High Technology Industry (Various years),China Land and Resources Statistical Yearbook, China Land and Resources Yearbook, and Financial Yearbook of
China (Various years).
Dependent
(1)
(2)
(3)
(4)
log(C/I)
log(C/I)
log(welfare/infra)
log(welfare/infra)
−0.20***
[0.000]
−0.07***
[0.000]
−0.0001***
[0.000]
4.89e−9***
[0.000]
−0.06*
[0.054]
0.002*
[0.081]
−0.006***
[0.005]
0.003
[0.112]
1.30***
[0.000]
1.34***
[0.000]
a
No
FE
243
0.6972
0.05*
[0.059]
−0.20***
[0.000]
−0.08***
[0.000]
−0.0002***
[0.000]
4.85e−9***
[0.000]
−0.07**
[0.021]
0.002**
[0.047]
−0.005***
[0.007]
0.003*
[0.089]
1.36***
[0.000]
1.38***
[0.000]
a
No
FE
243
0.7006
−0.06
[0.380]
0.02
[0.279]
0.0001**
[0.044]
9.6e−10
[0.578]
0.10
[0.235]
−0.002
[0.415]
−0.01***
[0.002]
−0.007***
[0.009]
−1.15*
[0.084]
−0.86
[0.272]
a
No
FE
243
0.4821
−0.32***
[0.000]
−0.03
[0.272]
−0.0001***
[0.003]
6.84e−9***
[0.000]
0.04
[0.635]
−0.001
[0.780]
−0.007*
[0.082]
−0.003*
[0.054]
−0.15
[0.798]
1.02
[0.113]
a
Yes
FE
243
0.5817
log(welfare/infra)
log(TCI)_1
log(Real Estate Development Strategy)_1
GDPPC_1
GDPPC_sq_1
Old Age Dependency Ratio_1
Old Age Dependency Ratio−sq_1
Private Economy Development_1
Open_1
Political1997–2001
Political2002–2006
Intercept
Year dummy
Methods
Observations
R2
Notes: C (yuan) is the household consumption. I (yuan) is the total value of fixed capital formation. Welfare (yuan) is the pensions and relief funds for social welfare and
social security subsidiary expenses, which are available over the period 1998–2006. Infra (yuan) is the expenditure for capital construction. TCI is measured by the ratio
of the added value of high-technology industry per capita to GDP per capita. Real Estate Development Strategy (%) is measured by the ratio of land lease revenues to budgetary fiscal revenue. GDPPC (yuan) and GDPPC_sq are the real GDP per capita and its square term. Old Age Dependency Ratio (%) and Old Age Dependency Ratio−sq are
measured as the ratio of the number of people aged 65 and over to the number of people aged 15–64 in a province and its square term. Private economy development (%)
is the ratio of production value of industry produced by non-state-owned enterprises over that of industry (Bai et al., 2004). Open (%) is the sum of exports and imports
divided by GDP. Political1997–2001 = 1, if year = 1998, 1999, 2000, and 2001; Political2002–2006 = 1, if year = 2002, 2003, 2004, 2005, and 2006. a shows that
intercept is dropped when regressions are carried out. If Year dummy is controlled, the main results are not unchanged. P values are in parentheses. *, **, and *** indicate
significance at 10%, 5%, and 1% levels, respectively.
Acknowledgment
The authors would like to thank three referees and participants at the Beijing Conference on Inequality, Growth and the MiddleIncome Trap in China for their valuable comments and suggestions. We are also grateful for the generous support from the Universities Service Centre for China Studies (USC) and Office of Academic Links (OAL) of Chinese University of Hong Kong, Key Research Project “Development Strategy, Financial Structure and Economic Structural Adjustment: Theory and Evidence” of the Humanities and
Social Sciences of Ministry of Education of the People's Republic of China (grant no. 12JJD790009), Zhejiang Provincial Natural Science
Foundation of China (grant no. LR12G03001), Zhejiang Provincial Social Science Foundation of China (grant no. 11ZJQN043YB), the
Fundamental Research Funds for the Central Universities of China, Center for Research in Regional Economic Opening and Development of Zhejiang University of China, and Real Estate Investment Research Center of Zhejiang University of China. Part of the research
was done when Julan Du was visiting the Hong Kong Institute for Monetary Research in 2012, whose support is generously
acknowledged.
Appendix A. Data sources
The data on the primary and secondary income distribution structures is taken from Lv and Guo(2012b). Household consumption
expenditures and gross fixed capital formation are taken from China Compendium of Statistics 1949–2004 and China Compendium of
Statistics 1949–2008. Besides, the data used to construct the measure TCI (1996–2007), i.e., the data on the value added of hightechnology industry, GDP, employees in high-technology industry, and the total employees, are from China Compendium of Statistics
1949–2008, China Statistical Yearbook (Various years), and China Statistics Yearbook on High Technology Industry (Various years). The
data to measure real estate development strategy are from China Land and Resources Statistical Yearbook, China Land and Resources
J. Du et al. / China Economic Review 31 (2014) 441–458
457
Yearbook and Financial Yearbook of China (Various years). The original data to measure GDPPC9, i.e., GDP per capita and index of GDP
per capita, are from China Compendium of Statistics 1949–2008. The data to measure the old dependency ratio, i.e., the number of people aged 65 and over and the number of people aged 15–64, are from China Statistical Yearbook (Various years). Open is the sum of exports and imports divided by GDP, which are from China Compendium of Statistics 1949–2008. Privatization is the ratio of production
value of industry produced by non-state-owned enterprises over that of industry (Bai et al., 2004), which are from China Statistical
Yearbook (Various years). Welfare expenditure including pensions and relief funds for social welfare and social security subsidiary expenses and government infrastructural investment for the period 1998–2006 are available from Financial Yearbook of China (1999–
2007).
References
Acemoglu, D., Robinson, J. A., Thaicharoen, Y. Y., & Johnson, S. (2003). Institutional causes, macroeconomic symptoms: Volatility, crises and growth. Journal of Monetary
Economics, 50, 49–123.
Aziz, J., & Li, C. (2007). Explaining China's low consumption: The neglected role of household income. IMF working paper, WP/07/181.
Azmat, G., Manning, A., & Reenen, J. V. (2011). Privatization and the decline of labour's share: International evidence from network industries. Working paper.
Bai, C. E., Du, Y. J., Tao, Z. G., & Tong, S. Y. (2004). Local protectionism and regional specialization: Evidence from China's industries. Journal of International Economics,
63(2), 397–417.
Bai, C. E., Hsieh, C. T., & Qian, Y. Y. (2006). The return to capital in China. Brookings Papers on Economic Activity, 2, 1–28.
Bentolina, S., & Saint-Paul, G. (2003). Explaining movements in labor share. Contributions to Macroeconomics, 3(1), 1–31.
Blanchard, O., & Giavazzi, F. (2005). Rebalancing growth in China: A three-handed approach. Working paper 05-32.
Blanchard, O., & Shleifer, A. (2001). Federalism with and without political centralization: China vs Russia. IMF Staff Papers, 48, 171–179.
Cai, F. (2009). Scientific development concept and sustainability of growth. Beijing: Zhonghua Book Company.
Cai, F. (2010). The Lewis turning point and the reorientation of public policies: Some stylized facts of social protection in China. Social Science in China, 6, 125–137.
Cai, F., & Du, Y. (2011). Wage increases, wage convergence, and the Lewis turning point in China. China Economic Review, 22, 601–610.
Cai, F., & Wang, M. (2010). Growth and structural changes in employment in transition China. Journal of Comparative Economics, 38(1), 71–81.
Chen, Y., Li, H. B., & Zhou, L. A. (2005). Relative performance evaluation and the turnover of provincial leaders in China. Economics Letters, 88, 421–425.
Chen, B. K., & Lin, Y. F. (2013). Development strategy, urbanization and the rural–urban income disparity in China. Social Science in China, 4, 81–102.
Chenery, H. B., & Syrquin, M. (1975). Patterns of development, 1950–1970. Oxford University Press.
Clarke, D., Murrell, P., & Whiting, S. (2008). The role of law in China's economic development. In Loren Brandt, & Thomas Rawski (Eds.), The great transformation:
China's economy since reform. New York: Cambridge University Press.
Diwan, Ishac (2000, November). Labor shares and globalization. World Bank working paper. Washington, DC: World Bank.
Diwan, Ishac (2001, July). Debt as sweat: Labor, financial crises, and the globalization of capital. World Bank working paper. Washington, DC: World Bank.
Driscoll, J., & Kraay, A. (1998). Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics, 80(4), 549–560.
Fang, H. S., & Jin, X. R. (2010). The role of economic structural adjustment for long-term economic stability in China: Estimation based on variance decomposition.
Pacific Economic Review, 15(5), 637–652.
Feenstra, Robert C., Inklaar, Robert, & Timmer, Marcel P. (2013). The Next Generation of the Penn World Table, Groningen Growth and Development Centre.
Fu, Y., & Zhang, Y. (2007). Chinese style decentralization and biased fiscal expenditure structure. The Management World, 3, 4–22.
Ge, S. Q., & Dennis, Yang T. (2011). Labor market developments in China: A neoclassical view. China Economic Review, 22, 611–625.
Golley, J., & Meng, X. (2011). Has China run out of surplus labour? China Economic Review, 22, 555–572.
Guo, K., & N'Diaye, Papa (2010). Determinants of China's private consumption: An international perspective. IMF working paper, WP/10/93.
Guscina, A. (2006). Effects of globalization on labor's share in national income. IMF working paper 06/294.
Harrison, Ann E. (2002). Has globalization eroded labor's Share? Some cross-country evidence? Working paper. UC-Berkeley and NBER.
Hausmann, R., Hwang, J., & Rodrik, D. (2007). What you export matters. Journal of Economic Growth, 12, 1–25.
Hoechle, D. (2007). Robust standard errors for panel regressions with cross-sectional dependence. The Stata Journal, 7(3), 281–312.
Huang, Y. P. (2010a). China's great ascendancy and structural risks: Consequences of asymmetric market liberalization. Asian-Pacific Economic Literature, 1(24), 65–85.
Huang, Y. P. (2010b). Dissecting the China puzzle: Asymmetric liberalization and cost distortion. Asian Economic Policy Review, 5, 281–295.
Huang, B. H., & Chen, K. (2012). Are intergovernmental transfers in China equalizing? China Economic Review, 23(3), 534–551.
Huang, Y. P., & Tao, K. Y. (2010). Factor market distortion and the current account surplus in China. Asian Economic Papers, 9(3), 1–36.
Huang, Y. P., & Wang, B. (2010). Cost distortions and structural imbalances in China. China and World Economy, 18(4), 1–17.
Jefferson, G. H., & Zhang, J. (2008). China's political reform: A property rights interpretation. Working paper.
Jorgenson, D., & Vu, K. M. (2009, March). Growth accounting within the international comparison program. The ICP Bulletin.
Jorgenson, D., & Vu, K. M. (2010). Potential growth of the world economy. Working paper. Harvard University and National University of Singapore.
Knight, J., Deng, Q. H., & Li, S. (2011). The puzzle of migrant labor shortage and rural labor surplus in China. China Economic Review, 22, 585–600.
Li, D. K., Liu, L. L., & Wang, H. L. (2009). Changes in the labor share of GDP: A U-shaped curve. Social Science in China, 4, 131–153.
Li, H. B., & Zhou, L. A. (2005). Political turnover and economic performance: The incentive role of personnel control in China. Journal of Public Economics, 89, 1743–1762.
Lin, George C. S. (2009a). Developing China: Land, politics and social conditions. London and New York: Routledge.
Lin, Y. F. (2009b). Economic development and transition: Thought, strategy, and viability. Cambridge: Cambridge University Press.
Lin, Y. F. (2011). China and the global economy. Remarks at the 20th anniversary. Hong Kong: University of Science and Technology.
Lin, Y. F., & Chang, H. J. (2009). Should industrial policy in developing countries conform to comparative advantage or defy it? A debate between Justin Lin and Ha-Joon
Chang. Development Policy Review, 27(5), 483–502.
Lin, Y. F., Dinh, H. T., & Im, F. (2010). US–China external imbalance and the global financial crisis. China Economic Journal, 3(1), 1–24.
Lin, Y. F., & Liu, M. (2004b). Development strategy, transition and challenges of development in lagging regions. In F. Bourguignon, & B. Pleskovic (Eds.), Annual World
Bank conference on development economics 2004: Accelerating development. Bangalore Conference Proceedings. Washington D.C..: World Bank.
Lin, Y. F., & Liu, P. L. (2004a). Local protectionism and market segmentation: A development strategy perspective. No. C2004015.
Lin, George C. S., & Yi, F. (2011). Urbanization of capital or capitalization on urban land? Urban Geography, 32(1), 50–79.
Liu, M. Q., & Sun, B. (2009). Land price, housing price bubbles, and governance. Financial Research, 10, 22–37 (Chinese).
Lu, M., & Gao, H. (2011). Labour market transition, income inequality and economic growth in China. International Labour Review, 150(1–2), 101–126.
Luo, C. Y., & Zhang, J. (2010). Declining labor share: Is China's case different? China and World Economy, 18(6), 1–18.
Lv, B. Y., & Guo, Q. W. (2012a). Why China's tax revenue is likely to maintain its rapid growth: An explanation within the framework of tax capacity and tax effort.
Social Sciences in China, 1, 108–126.
Lv, B. Y., & Guo, Q. W. (2012b). Calculation on China's functional income distribution and redistribution. Economic Research Journal, 10, 27–40.
Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. The Quarterly Journal of Economics, 107(2), 407–437.
Minami, R., & Ma, X. (2010). The Lewis turning point of Chinese economy: Comparison with Japanese experience. China Economic Journal, 3(2), 163–179.
9
1978 is a base year.
458
J. Du et al. / China Economic Review 31 (2014) 441–458
National Bureau of Statistics (2009). China Statistical Yearbook 2009. Beijing: China Statistics Press.
Nie, H. H. (2013). Collusion between local governments and enterprises and economic growth: Rethinking China model. China Renmin University Press, 71–95.
Nitikin, D., Shen, C. L., Wang, Q., & Zhou, H. F. (2011). Land taxation in China: Assessment of prospects for politically and economically sustainable reform. CEMA
working papers 431. China Economics and Management Academy, Central University of Finance and Economics.
Peneder, M. (2003). Industrial structure and aggregate growth. Structural Change and Economic Dynamics, 14, 427–448.
Perkins, D. H. (2012). Rapid growth and changing economic structure: The expenditure side story and its implications for China. China Economic Review, 23(3),
501–511.
Rodrik, D. (2006). What's so special about China's exports? China and World Economy, 14(5), 1–19.
Rodrik, D. (2010). Making room for China in the world economy. American Economic Review Papers & Proceedings, 100, 89–93.
Rostow, W. W. (1960). The stages of economic growth: A non-communist manifesto. Cambridge: Cambridge University Press.
Schneider, D. (2011). The labor share: A review of theory and evidence. Working paper. Berlin, Germany: Humboldt University.
Su, F. B., Tao, R., Lu, X., & Li, M. (2012). Local officials' incentives and China's economic growth: Tournament thesis reexamined and alternative explanatory framework.
China & World Economy, 20(4), 1–18.
Tsui, K. (2005). Local tax system, intergovernmental transfers and China's local fiscal disparities. Journal of Comparative Economics, 33, 173–196.
Tsui, K., & Wang, Y. (2004). Between separate stoves and a single menu: Fiscal decentralization in China. China Quarterly, 177, 71–90.
Tybout, J. (2000). Manufacturing firms in developing countries: How well do they do, and why? Journal of Economic Literature, XXXVIII, 11–44.
Wang, X. L., & Fan, G. (2009). Economic crisis, Keynesianism and structural imbalance in China. In R. Garnaut, L. G. Song, & W. T. Woo (Eds.), China's new place in a world
in crisis: Economic, geopolitical and environmental dimensions (pp. 137–154). The Australian National University Press, Brooking Institution Press, Social Sciences
Academic Press.
Woo, W. (2006). The structural nature of internal and external imbalances in China. Journal of Chinese Economic and Business Studies, 4(1), 1–19.
World Bank (2005). China: Land policy reform for sustainable economic and social development. Washington, DC: World Bank.
World Bank (2007, September). China quarterly update. World Bank Beijing Office.
Wu, W. P. (2010). Urban infrastructure financing and economic performance in China. Urban Geography, 31(5), 648–667.
Xu, C. (2011). The fundamental institutions of China's reforms and development. The Journal of Economic Literature, 49(4), 1076–1151.
Yang, T. (2012). Aggregate savings and external imbalances in China. Journal of Economic Perspectives, 26(4), 125–146.
Yao, Y., & Zhang, K. (2010). Has China passed the Lewis turning point? A structural estimation based on provincial data. China Economic Journal, 3(2), 155–162.
Yuan, F., & Li, Y. (2008, October). Capital deepening and employment adjustment in China's manufacturing sector. China Economic Quarterly, 8(1), 198–210 (Chinese).
Zhang, J., Gao, Y., Fu, Y., & Zhang, H. (2007). Why does China enjoy so much better physical infrastructure? Economic Research Journal, 3, 4–19.
Zhang, X. B., Yang, J., & Wang, S. L. (2011). China has reached the Lewis turning point. China Economic Review, 22, 542–554.