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Analysing the determinants of China's aggregate investment in the reform period
China Economic Review 12 (2001) 227 – 242
Analysing the determinants of China’s aggregate investment in the reform period Haiyan SONGa,*, Zinan LIUb, JIANG Pingc a
School of Management Studies, University of Surrey, Guildford GU2 7XH, UK Department of Economics, London Guildhall University, London EC2M 6SQ, UK c Department of Statistics, Dongbei University of Finance and Economics, Dalian, China b
Abstract Although investment has played an important role in China’s phenomenal economic growth over the last two decades, there has been little research on investment determination at the macro level. This study sets out to explore and explain the factors that influence China’s aggregate investment measured by the fixed capital formation during the reform period and to draw useful conclusions from analysing these determinants. The investigation is based on an assumption that aggregate investment in China is determined by both the cost of investment and the increasing aggregate demand created by the reforms. A dynamic investment function is developed to simultaneously capture the long-run and short-run properties of the investment behaviour. The empirical results based on a panel data set of 28 Chinese provinces and autonomous regions suggest that a homogenous equilibrium correction mechanism exists in China’s aggregate investment process. D 2001 Elsevier Science Inc. All rights reserved. JEL classification: E22; E62; C20 Keywords: China; Aggregate investment and error correction model
1. Introduction Aggregate investment measured by fixed capital formation in China has increased exponentially since the economic reform programme first started in 1978. Although there * Corresponding author. Tel.: +44-1483-876353; fax: +44-1483-876301. E-mail address: [email protected] (H. Song). 1043-951X/01/$ – see front matter D 2001 Elsevier Science Inc. All rights reserved. PII: S 1 0 4 3 - 9 5 1 X ( 0 1 ) 0 0 0 5 2 - 9
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are disagreements about whether the aggregate investment is a driving force for China’s recent economic growth, the importance of investment in the creation of aggregate demand and the improvement of economic infrastructures has been widely acknowledged (Chow, 1993; Sun 1998). Since 30% of China’s Gross Domestic Product (GDP) during the 1990s consisted of aggregate investment, its expansion provides an important source for growth in the aggregate demand for construction products, machinery, equipment, and other durable goods. This, in turn, leads to increases in the demand for consumer goods and services. Firms’ investment behaviour and their investment funding allocations differ significantly between the pre- and postreform periods. According to Sun (1998), a system generated ‘‘investment hunger’’ existed before 1978 because firms’ (especially the state-owned enterprises (SOEs)) investment decisions did not depend on profit maximisation or cost minimisation behaviour. Although it was desirable for SOEs to make profits once the new investment projects were completed, any losses were always borne by the central and local governments. This investment expansion drive, coupled with the soft-budget constraint, led to the inefficient allocation of scarce capital resources. The Chinese economy began its transition from a planned system to a market economy in 1978, and the shares of collective-owned enterprises (COEs), township and village enterprises (TVEs), and joint venture enterprises (JVEs) in terms of output and employment in the national economy have increased substantially. This, together with the introduction of various profit-incentive reform programmes, has changed firms’ investment decision behaviour. Profitability and cost of investment have become the most important factors to be considered by firms in their shortrun and long-run production plans. The key sources for investment in the prereform period were mainly from firms’ internal funds and the central and local governments’ budgetary funds. The process of firms’ investment decision-making was, therefore, to compete for the acquisition and use of these funds. Since these investment funds were limited, the distribution of the funds to enterprises was unbalanced, only those enterprises (mostly SOEs) in the key industries, such as defence, public utility, and manufacturing industries, were able to share the limited financial resources. This situation has changed since 1978, and firms have gained more investment autonomy through the increased availability of both internal and external funds. The rise in internal funds was due to the adjustment of the depreciation rate from 3.6% before 1978 to 5.7% in the reform period. In addition, the amount of internal funds was also enlarged by the introduction of the profit-retention scheme and contract responsibility system (CRS). The increase in external funds was also made possible by a significant rise in rural and urban household savings during this period. The reforms in the banking system, such as decentralisation of credit control and the development of other financial institutions, have also helped to channel the financial resources towards a broad range of sectors in the economy. Although many researchers believed that aggregate investment has played an important role in China’s phenomenal economic growth over the last two decades (see Chai, 1998; Chow, 1985, 1993; Li, Liu, & Rebelo, 1998; Sun, 1998), there has been little research on investment determination at the macro level. This study sets out to explore and explain the factors that influence China’s aggregate investment measured by the fixed capital formation during the reform period and to draw useful conclusions from analysing these determinants. This is particularly important on the eve of China’s accession to the WTO, as an under-
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standing of aggregate investment behaviour would help the government to formulate appropriate investment strategies in an environment where financial resources from abroad are widely available. The rest of this paper is organised as follows. Section 2 reviews the various aggregate investment models in the literature and discusses their relevance to the Chinese economy. Section 3 presents the methodology used in the study. Section 4 contains the empirical results and Section 5 concludes the paper.
2. Review of aggregate investment models A number of aggregate investment models have been developed in the literature, and these include the accelerator, cash flow, q, and neoclassical models. Other variants of investment models can also be derived from these specifications with additional extensions/restrictions. In this section, these models are reviewed and their applicability to China discussed. The accelerator model was first proposed by Clark (1917) and subsequently developed by de Leeuw (1962), Evans (1967), and Koyck (1954). This model postulates that a firm’s investment decision is determined by changes in demand for its product. An important implication of the accelerator model is that the size of a firm’s investment is proportional to its output. At the macro level, this investment model is normally specified by relating the aggregate investment to the current and lagged values of total output and the lagged values of capital stock. The lag length is determined by the nature of the investment projects (longer lags for investment in construction and shorter lags for investment in equipment). Two problems are associated with this model: (1) the unrestricted lag structure tends to cause the problem of multicollinearity and generate misleading results; (2) prices of capital goods, wage rates, taxes, and interest rates are not included in the determination of investment, which is difficult to justify on both theoretical and practical grounds. In his study of the Chinese national income determination, Chow (1985) estimated an aggregate investment model based on the acceleration hypothesis and concluded that although the estimated accelerator model explains the data reasonably well over the period of 1952–1983, the estimation results of the model should be interpreted with caution because of the data restrictions and the influences of political forces during the sample period. The cash flow model is associated with Duesenberry (1958) and Meyer and Kuh (1957). The model assumes that a firm’s investment expenditure is determined by its internal cash flow. The cash flow is measured by firm’s profit less taxes plus depreciation allowance. This model is similar to the accelerator model with the current and lagged output variables being replaced by the cash flow variables. Therefore, the problems of this model are the same as those in the accelerator model. Although China started its transition from a centrally controlled economy to a market economy in the late 1970s and early 1980s, the process has been slow and gradual due to the difficulties in transforming the SOEs. Loss making was still a major problem for many of the SOEs during the transition period and the investment expenditures by these loss-making firms were normally financed by external sources, such as government subsidies and debt financing. Even the profit-making firms, both state-owned and
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collective-owned firms, tended to finance their investment by seeking bank loans and other financial sources. Therefore, the cash flow model is not useful in explaining the investment behaviour of the Chinese firms, especially that of the SOEs. The q model is developed by Tobin (1969). This model is a generalisation of the cash flow model in which investment expenditure is related to the ratio of market value of business capital assets to the replacement value of those assets. This ratio is known as Tobin’s q. According to Tobin (1969), a value of q that is close to 1, or greater, encourages investment, while a lower value of q discourages investment. The market value of business capital is also known as the demand price for capital assets, and the replacement value of the business assets is the supply price. In equilibrium, the demand and supply prices for business capital should be the same. However, if market forces create profitable investment opportunities, the demand price will be higher than the supply price and firms will increase investment to expand their businesses. In a competitive market, the demand and supply prices will eventually return to the equilibrium level once the investment profits are competed away. The demand price of capital assets is determined by the value of corporate capital assets in the stock market. Although the q model has a sound theoretical framework, it is unlikely to be a useful tool for modelling China’s investment for the following reasons. First, the q model requires firms’ capital assets to be valued by financial markets. As noted by Song, Liu, and Romilly (1998), the two Chinese stock markets (Shenzhen and Shanghai) only started to operate in 1992, and the number of companies listed in these two markets has been very small. Therefore, it is difficult to evaluate firms’ capital assets through these two financial markets. Second, the q model postulates that a firm’s decision-making behaviour is based on a perfect competitive market in which all firms compete on an equal footing for investment opportunities. Clearly, this assumption does not hold for China. The neoclassical investment model was developed by Hall and Jorgenson (1967) based on an explicit model of optimising behaviour that relates the desired capital stock to the user cost of capital, which, in turn, relates to interest rates, tax policies, and capital prices. The model assumes that the optimal stock of capital for a firm is proportional to its output divided by the user cost of capital. This means that an increase in the demand for final production would encourage the expansion of the fixed capital formation, and increases in interest rates, investment taxes, and capital depreciation would discourage investment. The user cost of capital and expected future demand are regarded as the main determinants of investment decisions in the neoclassical investment model. In this study, we incorporate these two factors in modelling China’s aggregate investment and the rationale for this is discussed in the following section. The difference between our investment model and the neoclassical investment model is that our study utilises the error correction modelling approach to capture both the long-run and short-run properties of investment behaviour in China. Researchers have also used simple time series techniques in forecasting aggregate investment expenditure (see, for example, Kopcke, 1982, 1985). Although time series models are simple in structure, they often outperform the more sophisticated econometric models in forecasting investment expenditures. This is because most econometric models rely heavily on the various assumptions being correctly imposed. However, time series models have been criticised for their lack of theoretical underpinnings. Policymakers cannot use this type of model
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to assess the impact of policy changes on investment. In particular, the time series methods are not applicable to the investment process in China since the models have a strict requirement on the quality of the time series data. Frequent regime changes in the Chinese economy suggest that reliable time series investment data over a long time span are difficult to obtain. Based on the standard investment cycle theory of the socialist economy, Sun (1998) developed an investment model for China using the cointegration and error correction techniques. Investment in real assets was found to be cointegrated with grain output and the effective energy supply. The underlying assumption of this model is that the investment hunger generated by the state investment system is restricted by supply constraints. Hence, there exists an error correction mechanism that prevents investment departing from its longrun path. An interesting finding of this study is that both the long-run and short-run investment functions maintain parameter constancy over the sample period of 1952–1995, suggesting that China’s state investment system has not responded to the rapid changes in demand caused by economic reforms. The aim of this study is to examine the investment behaviour related to all sectors in the Chinese economy including SOEs, COEs, TVEs, and JVEs. The structural stability of the investment model exhibited in Sun’s model is, therefore, unlikely to be featured in the investment model when all types of enterprises are considered over the period of 1952–1995. To overcome the likely problem of structural instability, our study only focuses on the investment behaviour during the reform period, specifically, during the period of 1983–1995.
3. The model The aggregate investment model in this study is specified based on the assumption that firms’ investment decisions are made by assessing the market demand ( Yt*) and the cost of capital used in production (ct) (Eq. (1)): It ¼ f ðYt ; ct Þ
ð1Þ
where It is gross aggregate investment measured by the fixed capital formation at time t. Variable Yt* is expected output, which may be regarded as the demand-pull element, while ct is a cost constraint factor. The two factors are particularly relevant to China because a systemgenerated investment hunger resulting from the release of the suppressed consumer demand, and a resource constraint coexist in the economy. The relationship between investment and its determinants is assumed to follow a nonlinear process of the form: It ¼ BYta cbt ut
ð2Þ
where ut is a disturbance term designed to capture the effects of all other factors, which are not included in the model, and B, a, and b are constant parameters, which need to be estimated. The user cost of capital is normally determined by the following factors: the opportunity cost of using investment funds (rt); the depreciation of the new capital used (d); the changes in asset prices ( PtI); the effective rate of taxation on capital income (TXt); and the cost of
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searching for investment funds (St). This last factor is particularly relevant to the Chinese investment function because of the imperfection of the credit and financial markets in China. Therefore, the user cost of capital may be written as (see Eqs. (3) and (4)): ct ¼ f ðr; d; PtI ; TX ; St Þ
ð3Þ
ct ¼ TXt ðPtI rt1 þ dPtI DPtI Þ þ St
ð4Þ
or I
where DPt represents the change in asset prices. The exact calculation of ct can be very difficult in the case of China due to data limitations. In this study, we try to obtain an estimate of ct from a production function of the form: Qt ¼ f ðKt ; Lt ; Ot Þ
ð5Þ
Eq. (5) differs from the traditional Cobb–Douglas production function used in the neoclassical investment model since additional human capital/R&D input (Ot) is introduced. The Cobb–Douglas production function assumes constant returns to scale and the factors of production are subject to diminishing marginal productivity. The optimal capital stock will be reached at a point where the expected rate of return of the marginal investment equals the marginal cost of capital. This suggests that there is a constant investment–output ratio, and any investment exceeding this optimal level would be inefficient. According to this view, economic growth is independent of investment and growth could only be achieved by exogenous improvements in technology. Song and Fu (2001) found that the investment– output ratio was not constant over both the pre- and postreform periods, and a positive association was identified between the investment–output ratio and the rate of economic growth in China. Therefore, the assumption that economic growth is independent of investment (constant investment–ouptut ratio assumption) made by the neoclassical investment model is unlikely to be valid for China. The postneoclassical endogenous growth models developed by Rebelo (1991), on the other hand, assumes that the production function exhibits constant returns to broad capital accumulation. Broad capital includes not only tangible investment, but also investment in human capital and/or research and development. The endogenous growth model is based on a modified production function of the form: Qt ¼ AKta Lbt Htg Rht
ð6Þ
where Ht is the input of human capital and Rt is R&D expenditure on technological innovation. The derivation of the user costs of capital in this study is based on the profit maximisation assumption. One may argue that this assumption only holds for the non-SOEs but not for the SOEs. Our argument in favour of this assumption is that the profit-retention system introduced in 1979, the profit-contract system introduced in 1981, the tax-for-profit system introduced in 1983, and the CRS since 1987 are all reform measures designed to improve enterprises’ performance through profit-sharing incentives. Some empirical studies suggest that the Chinese SOEs have become increasingly profit oriented due to the introduction of these incentive schemes. For example, two major surveys of the SOEs in China by the
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Chinese Economic System Reform Research Institute (CESRRI) in 1987 and the Institute of Economics of the Chinese Academy of Social Sciences (IECASS) in 1989 found that the profit and profit-related objectives were considered by most managers in the SOEs as the most important enterprise objectives (Chai, 1998, pp. 90–91). In estimating the user cost of capital, only data for the second half of the reform period (1983–1995) is used, therefore, profit maximisation may be seen as a reasonable assumption and the danger of obtaining biased estimates for ct is minimised. Profit (p) is defined as: p ¼ Pt Qt wt Lt ct Kt mt Ht nt Rt or p ¼ Yt wt Lt ct Kt mt Ht nt Rt
ð7Þ
where Pt is the price of output, Qt is the quantity of final products, Yt is output in monetary terms, wt is the wage rate, Lt is labour inputs, ct is the user cost of capital, and Kt is the capital stock, and mt and nt are unit costs of human capital and innovation, respectively. If we assume that the firm maximises (Eq. (7)) subject to the constraint of a production function (Eq. (6)), forming and solving the usual Lagrange multiplier, we could obtain the first-order conditions for a maximum. One of these conditions is: ct ¼ Pt
@Qt @Kt
ð8Þ
Based on Eqs. (6) and (8), we can obtain the user cost of capital (Eq. (9)): ct ¼ Pt
@Qt ¼ Pt ðAaKta1 Lbt Htg Rht Þ ¼ aPt Qt =Kt @Kt
ð9Þ
or ct ¼ aYt =Kt
ð10Þ
By estimating parameter a in Eq. (6), we could then calculate the user cost of capital using Eq. (10). The nonlinear relationship in Eq. (2) can be linearised by the log transformation: lnIt ¼ lnB þ alnYt þ blnct þ lnut
ð11Þ
It is assumed that firms form their expectations based on an adaptive process1, that is: lnYt lnYt1 ¼ lðlnYt lnYt1 Þ
1
The Chinese reform program is a ‘‘learning by doing’’ process. Deng Xiaoping’s famous phrase ‘‘cross the river by feeling the stones’’ may be seen as a good explanation for the use of adaptive expectations here.
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or lnYt ¼ ð1 lÞlnYt1 þ llnYt
ð12Þ
After repeatedly substituting Eq. (12) into itself, we can obtain: lnYt ¼ ð1 lÞN þ1 lnYtN 1 þ l
N X ð1 lÞj lnYtj
ð13Þ
j¼0
As N ! 1, (1 l)N ! 0, therefore, Eq. (13) becomes: lnYt ¼ l
N X ð1 lÞj lnYtj
ð14Þ
j¼0
By substituting lnYt*, expressed by Eq. (14), into Eq. (11) and lagging the dependent and the cost variables, we can write our investment function as an autoregressive distributed lag (ADL) model of the form: lnIt ¼ m þ
N X
fj lnItj þ
j¼1
N X
jj lnYtj þ
j¼0
N X
rj ctj þ et
ð15Þ
j¼0
where m = lnB, jj = al(1 l)j, and et is the error term, which is assumed to be normally distributed with a zero mean and a constant variance. There are several advantages of specifying the investment model as Eq. (15). First, investment responses to changes in its determinants with time lags, and this is known as the effect of ‘‘animal spirits’’ defined by Keynesian. Changes in business confidence cause changes in expected future sales revenue and the likely profitability of investment, hence, leading to changes in the level of investment. Secondly, according to Hendry (1995, pp. 232– 293), the ADL specification actually encompasses all types of econometric models, which allows one to search for the best model specification by conducting hypothesis tests on the parameters of the ADL model. Thirdly, the general-to-specific methodology of Hendry and Richard (1982) and the error correction technique of Engle and Granger (1987) can be directly applied to the estimation of the ADL model. In particular, if the estimated f, j, and r coefficients sum to unity, this implies that a homogeneous equilibrium correction mechanism exists in the data generating process and Eq. (15) can then be written as: DlnIt ¼ g þ
p X i¼1
Ji DlnIti þ
p X
qi DlnYti þ
i¼0
þ kðlnI lnY lncÞti þ ut
p X
gi Dlncti
i¼0
ð16Þ
Fourthly, if the investment function is written as Eq. (16), we can then examine both the long-run and short-run investment elasticities with respect to various factors that influence
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investment. The long-run investment elasticities are homogenous, i.e., the long-run elasticities of investment with respect to output and investment costs are equal to unity, suggesting the same proportional response of investment to changes in its determinants. The short-run investment elasticities are measured by the coefficients qis and gis. Finally, the procedure by Pesaran, Shin, and Smith (1996) can be directly applied to estimate the long-run cointegration and the short-run dynamic relationships based on Eq. (16). This procedure avoids the problems inherent in pretesting for unit roots prior to testing for cointegration relationships, i.e., we do not need to know the integration orders of the variables in Eq. (16) before estimating the model.
4. Empirical results 4.1. The data The empirical study in this section is based on a panel data set of 28 provinces and autonomous regions in China over the period 1983–1995. Tibet and Hainan are excluded because of data unavailability. The 28 provinces and autonomous regions are divided into three broad regions according to their geographical locations. The east region includes Liaoning, Tianjing, Beijing, Shandong, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, and Guangdong. The central region consists of Heilongjiang, Jilin, Inner-Mongolia, Shanxi, Henan, Anhui, Jiangxi, Hubei, Hunan, and Guangxi. The west region relates to Sichuan, Yunnan, Guizhou, Shaanxi, Gansu, Ningxia, Qinghai, and Xinjiang. The data are obtained from various issues of the Chinese Statistical Yearbook (1995, 1996, 1997, and 1998) published by the Statistical Bureau of China and China’s Provincial Statistics compiled by Hsueh et al. (1993). However, since not all variables in our model have existing measures in published data, substantial efforts were made in this study to pool data from different sources and to construct the best possible measures of the variables concerned. Given the deficiency of Chinese official statistics, extra care had to be taken to ensure the comparability and consistency of the data over time. Output is measured by GDP at 1978 constant prices, and calculated by deflating the nominal value of GDP using the general price index at the provincial level. Labour is measured by total employment. Although the State Statistics Bureau conducts employment surveys and censuses, which provide fairly accurate and detailed estimates of total employment by gender, age, industry, and forms of enterprise ownership, there is no such information available at the provincial level that enables us to construct a qualityadjusted measure of the labour input. Nor are there any data that allow us to measure labour in terms of working hours. The measure for the capital stock value of each province as a whole was based on gross investment flows using the standard perpetual inventory method: Kit ¼ Iit þ ðI xt ÞKi;t1
ð17Þ
where i is province i, K measures capital stock at 1978 constant prices, I is investment in fixed
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capital assets at 1978 constant prices, and x is the national rate of depreciation. The investment series obtained from Hsueh et al. (1993) covers investment in residential and nonresidential construction, investment and renovation investment in fixed capital assets (producers’ durable goods) by the state, collective, and private sectors.2 The investment series excludes business inventories, which are usually treated as a component of investment in the literature. Although, inventories become increasingly significant in China as it is transformed from a command system into a market economy, information on inventories is lumped with working capital in the production process (circulating funds), which does not conform to the definition of inventories in the literature. Given that the perpetual inventory method is sensitive to the level of capital stock chosen to initialise the series, it is desirable to have a sufficiently lengthy historical investment series prior to the first data of the analysis (i.e., 1983). Therefore, the investment series is traced back to 1958. The investment series is deflated using the price index of investment in fixed capital assets at the provincial level. However, the price deflator is not available until 1991. Following Hu and Khan (1997), we deflate the investment series during 1958–1977 by the implicit deflator for accumulation estimated by Chow (1993), and the investment series during 1978–1990 by the national price index of building materials. As far as the initial value of capital stock is concerned, we use the value in 1958 (235.2 billion yuan) as estimated by Hu and Khan (1997). This involves an assumption that the initial value of capital stock is the same for all provinces, which can be questioned. However, given a sufficiently lengthy historical series on investment, the resultant measurement error in capital stock is unlikely to be significant. The national rate of depreciation is derived from Hu and Khan (1997). The official rate of depreciation used in China is low, at only 3.6% on average, as compared to that used in developed market economies.3 This is explained by the fact that Chinese official development policies emphasise new capital investment projects and neglect replacement investment in existing equipment and facilities. As a consequence, the official rate bears little relation to the true vintage price functions that characterise the relative efficiency profile of capital assets (see Hu & Khan, 1997). A more serious problem is that the use of an identical rate of depreciation fails to reflect the substantial variations in capital consumption across the regional economies. In the absence of information about depreciation, we use the national rate of depreciation plus the annual growth rate of GDP as the depreciation rate for each province. This is based on the assumption that rates of physical deterioration of capital assets are proportional to growth rates of regional economies. Under this assumption, Eq. (17) can be written as (Eq. (18)): Kit ¼ Iit þ ðI xt git ÞKi;t1
ð18Þ
where git is the growth rate of GDP for province i at time t. 2
The series includes investment by collective units in cities and towns after 1981, rural housing investment after 1983, and private investment in fixed assets after 1984. It is reasonable to assume that private investment is negligible before 1984. 3 For instance, the unweighted geometric average is 13.3% for a broad classof US equipment assets.
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In estimating the investment functions of Eqs. (15) and (16), we first need to estimate the production function (6). Based on the estimated parameters of Eq. (6), we then can calculate the user cost of capital variable. In the production function, there are another two variables that need to be specified: the human capital input (H) and the R&D input (R). The human capital input is measured by the number of scientists and researchers in employment and the R&D variable is measured by the expenditure on research and innovation deflated by the general price index, both of these measures are obtained from the Chinese Statistical Yearbooks (various issues). 4.2. Estimates of investment model The production functions for the three broad regions are estimated as random effect models using the generalised least squares approach, and the estimates are presented in Table 1. The estimated production functions show that the contribution of capital stock to the level of output in the east region is the highest, suggesting that the fast economic growth in this region may be related to a better utilisation of capital. In all four production functions, the labour input is found to be negative and insignificant at the 5% level. One of the explanations for this might be that the excessive input of labour has caused negative marginal productivity in many of the loss-making SOEs and COEs in China. The massive laying-off (Xia Gang) programme over the last two years may be seen as an effort to improve labour productivity by many of the loss-making firms. The human capital and R&D variables are found to be significant determinants of the production functions. This finding supports the endogenous growth model in which both tangible and intangible capital inputs are considered to be important factors of output.
Table 1 Estimates of production functions (dependent variable: lnYt) Variable
East
Central
West
All
Constant
2.670** (0.553) 0.756** (0.077) 0.026* (0.017) 0.146 (0.110) 0.451** (0.051) 0.973 0.141 130
2.358** (0.642) 0.654** (0.111) 0.026 (0.018) 0.531** (0.156) 0.496** (0.047) 0.941 0.174 130
3.106** (0.326) 0.267** (0.064) 0.001 (0.017) 0.091* (0.049) 0.822** (0.033) 0.975 0.170 117
1.581** (0.359) 0.597** (0.053) 0.020* (0.010) 0.205** (0.066) 0.597** (0.030) 0.972 0.171 364
lnKt lnLt lnHt lnRt R2 s Sample (83 – 95)
Standard errors are in parentheses. * Estimates are significant at the 10% level. ** Estimates are significant at the 5% level.
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The user cost of capital is then calculated based on ct = a ˆ Yt =Kt for each of the three broad regions and for the Chinese economy as a whole. The derived cost variables are then used in the estimation of investment functions. Although longer lag length is used in the estimation of the initial ADL investment model of Eq. (15), only three lags for the central and west regions and two lags for the east regions are found to be statistically significant. The estimated models (omitted) are then tested for the homogeneous equilibrium correction mechanism using the Wald statistic, c2(1), to test the null hypothesis: p X i¼1
fˆ ii þ
p X ˆ i¼1 ðjˆ þ rÞ i¼0
The calculated Wald statistics are: c2EAST (1) = 0.887, c2CENTRAL(1) = 3.910, c2WEST (1) = 0.884, and c2ALL(1) = 1.530, respectively. These results suggest that the data generating process of the Chinese aggregate investment may be best represented by the homogeneous equilibrium correction model of Eq. (16). The data are then fitted to this error correction model and the results are given in Table 2. The estimation results show that the investment models for the three broad regions fit the data quite well. Given the homogeneous long-run response of investment to investment cost and output, the short-run cost elasticity ranges from 0.155 for the west provinces to 0.433 for the central provinces. The short-run cost elasticity of the east region is 0.348. The corresponding output elasticities are 0.852, 0.639, and 1.112, respectively. The three error correction terms are significant at least at the 10% level with the feedback parameters ranging from 0.031 to 0.046, suggesting that firms remove decision errors made in the previous period in order to achieve the desirable equilibrium investment level. The relatively small values of the feedback parameters in these investment models, compared with those of demand functions for consumer goods, suggest that the investment adjustment process is slow in speed and small in magnitude. This, perhaps, is due to the nature of fixed capital formation. Once the investment decision has been made and carried out, the reverse of investment decision is difficult. An investment function for all the 28 provinces and autonomous regions is also estimated, and the results are presented in the last column of Table 2. The values of the estimated parameters appear to be in the same order of magnitude as those in the other three regional models. One may also argue that the costs of investment only influence firms’ short-run decisions, i.e., firms may delay their investment temporarily due to financial constraints. In the long run, however, investment is expected to be linked only to expected future demand, and this is particularly true for China since economic reforms have released the suppressed domestic consumer demand and the open door policy has fuelled the import and export expansions, all these have led to a new type of ‘‘investment hunger,’’ which is different from that under the planned system. Therefore, we reestimated our investment models by excluding the capital cost variable from the error correction term. By doing this, we deliberately impose the assumption that firms’ short-run investment decisions are influenced by both the costs of investment and the demand variable, but in the long run, the cost constraint variable does not have any role to play in the determination of investment. The estimated models are presented in Table 3.
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Table 2 Estimates of the investment functions (dependent variable: DlnIt) Variable Constant
East 0.362** (0.157)
Central 0.409** (0.117)
Dlnct 1
2.965** (0.348) 2.617** (0.338)
Dlnct 2 DlnYt
R2 s F statistic DW Sample (83 – 95)
3.863** (0.317) 2.329** (0.344) 0.422** (0.169) 0.042* (0.025) 0.699 0.104 47.64 1.867 130
Short-run elasticity Cost Output
0.348 1.112
DlnYt 1 DlnYt 2 ln(I/cY)t 1
All
0.334** (0.129)
0.330** (0.061)
0.046** (0.017) 0.731 0.082 47.35 2.209 130
0.115* (0.066) 4.240** (0.572) 2.433** (0.768) 1.652** (0.554) 4.479** (0.579) 2.300** (0.771) 1.327** (0.543) 0.031** (0.013) 0.520 0.086 14.50 2.270 117
0.081** (0.035) 3.704** (0.228) 2.296** (0.311) 1.116** (0.213) 4.171** (0.231) 2.345** (0.314) 1.107** (0.225) 0.038** (0.009) 0.642 0.094 79.52 2.033 364
0.433 0.639
0.155 0.852
0.292 0.720
DlnIt 1 DlnIt 2 Dlnct
West
4.901** (0.354) 4.696** (0.327) 0.228** (0.095) 5.329** (0.362) 4.690** (0.360)
Standard errors are in parentheses. * Estimates are significant at the 10% level. ** Estimates are significant at the 5% level.
The results show that the short-run output and cost elasticities have increased for the central and west regions, and decreased for the east region. However, the feedback parameters have doubled for the east and central regions and tripled for the west region. This suggests that the adjustment process is faster when the investment cost variable is ignored in firms’ long-run investment decision-making process. The estimates of the above investment functions have important policy implications. First, both financial constraints and the investment hunger caused by increases in demand for consumer goods determine the level of investment in China. To enhance economic growth, further financial reforms are required to increase firms’ access to investment borrowings. This, however, should be coupled with tight control of the bad debt problem. Secondly, the fast expansion of the Chinese economy has implanted a huge demand for future investment. Since the homogenous equilibrium correction mechanism is found to be
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Table 3 Estimates of the investment functions (dependent variable: DlnIt) Variable
East
Central
West
All
Constant
0.053 (0.053)
0.502 (0.063) 0.112** (0.052)
0.102* (0.061)
0.053* (0.031)
0.059* (0.035) 3.628** (0.226) 2.131** (0.311) 1.083** (0.209) 4.216** (0.224) 2.111** (0.315) 1.047** (0.219) 0.092** (0.018) 0.640 0.093 78.94 1.935 364
0.419 1.058
DlnIt 1 DlnIt 2
R2 s F statistic DW Sample (83 – 95)
3.723** (0.307) 2.327** (0.328) 0.451** (0.169) 0.089** (0.036) 0.698 0.102 47.39 1.879 130
0.096** (0.035) 0.722 0.082 45.46 2.104 130
4.308** (0.563) 2.263** (0.769) 1.544** (0.545) 4.599** (0.563) 2.070** (0.775) 1.293** (0.536) 0.115** (0.037) 0.530 0.086 17.61 2.106 117
Short-run elasticity Cost Output
0.210 0.945
0.429 0.759
0.501 1.236
Dlnct Dlnct 1
2.833** (0.343) 2.623** (0.338)
Dlnct 2 DlnYt DlnYt 1 DlnYt 2 ln(I/Y)t 1
4.436** (0.380) 4.273** (0.349) 0.266** (0.096) 5.000** (0.384) 4.243** (0.379)
Standard errors are in parentheses. * Estimates are significant at the 10% level. * * Estimates are significant at the 5% level.
in force in the aggregate investment function, the long-run economic growth will lead to a same level of growth in investment. Thirdly, since the ratio of investment to output is likely to be linked to economic growth, more resources need to be channelled to the west and central regions in order to reduce the growth gaps between these regions. This includes the introduction of financial incentives for domestic and foreign firms to invest in the central and west provinces and to improve the investment environment through government funded investment projects. Although the economic growth over the last three years in China has slowed down due partially to the effect of Asian financial crisis in 1997 and sluggish demand in consumer goods, the demand for both domestic and foreign investment is still forecast to increase over the next 10 years (Stemp & Wong, 1999). In particular, China’s accession to the WTO will give economic reform a much-needed impetus and enhance China’s interaction with the rest
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of the world. This, in turn, will fuel increases in aggregate demand and lead to further rises in demand for aggregate investment. Moreover, China’s WTO membership will improve the efficiency of financial intermediation and provide more opportunities for both SOEs and nonSOEs to obtain access to the funds needed for investment.
5. Concluding remarks The aggregate investment function with a homogenous error-correction process is estimated for each of the three broad regions in China. The model is specified based on the assumption that both the cost-constraint and demand-pull variables are important factors in the determination of investment behaviour. The investment cost variable is derived from the production function, which is then used to capture such influences as the opportunity cost of investment, depreciation of capital goods, changes in asset prices, taxation, and other costs incurred in the search for investment funds by the Chinese firms. The effect of demand on investment is modelled through the inclusion of expected output. Two types of error correction mechanisms are used to simulate firms’ long-run investment decision behaviour. The estimation results of the investment model for all of the 28 provinces show that the short-run output and cost elasticities are 0.72 and 0.292, respectively, when both investment cost and output are included in the error correction process. The output and cost elasticities for the second error correction model that only contains the lagged ratio of investment to output are found to be 1.058 and 0.419, respectively. Both of these investment models are statistically significant with the desired properties. The empirical findings of this study imply that it is important to introduce favourable investment incentives in the central and west regions in order to create balanced economic growth. The homogenous equilibrium correction mechanism suggests that the demand for investment will change equiproportionately to the changes in investment costs and output. Since investment costs determine firms’ investment decisions, a flexible, yet well-regulated financial system that gives firms easy access to various financial resources is considered to be crucial for the continuous investment expansion and economic growth in China, and China’s WTO membership will prepare the economy for such a system.
Acknowledgments The authors wish to thank Mark Schaffer and the seminar participants of the Centre for Economic Reform and Transformation at Heriot-Watt University for their constructive comments on an early draft of this paper. The final version of this paper also benefits from the suggestions and comments made by an anonymous reviewer. The third author wishes to express her appreciation for the excellent research facilities provided by the School of Management Studies, University of Surrey, where she spent 1 year as a Visiting Research Fellow. The usual disclaimer applies.
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References Chai, J. C. H. (1998). China: transition to a market economy. Oxford: Clarendon Press. Chow, G. C. (1985). A model of Chinese national income determination. Journal of Political Economy, 93, 782 – 791. Chow, G. C. (1993). Capital formation and economic-growth in China. Quarterly Journal of Economics, 108, 809 – 867. Clark, J. M. (1917). Business acceleration and law of demand: a technical factor in economic cycles. Journal of Political Economy, 25, 217 – 235. de Leeuw, F. (1962). The demand for capital goods by manufacturers: a study of quarterly time series. Econometrica, 30, 407 – 423. Duesenberry, J. S. (1958). Business cycles and economic growth. New York: McGraw-Hill. Engle, R .F., & Granger, C. W. J. (1987). Cointegration and error correction: representation, estimation and testing. Econometrica, 55, 251 – 276. Evans, M. K. (1967). A study of industry investment decisions. Review of Economics and Statistics, 49, 151 – 164. Hall, R. E., & Jorgenson, D. W. (1967). Tax policy and investment behaviour. American Economic Review, 57, 391 – 414. Hendry, D. F. (1995). Dynamic econometrics. Oxford: Oxford Univ. Press. Hendry, D. F., & Richard, J. F. (1982). On the formulation of empirical models in dynamic econometrics. Journal of Econometrics, 20, 3 – 33. Hsueh, T., et al (1993). China’s Provincial Statistics 1949 – 1989. Boulder: Westview Press. Hu, Z., & Khan, M. S. (1997). Why is China growing so fast? IMF Staff Papers, 44, 103 – 131. Kopcke, R. (1982). Forecasting investment spending: the performance of statistical models. New England Economic Review, 13 – 32 (November/December). Kopcke, R. (1985). The determinants of investment spending. New England Economic Review, 20 – 35 (July/ August). Koyck, L. M. (1954). Distributed lags and investment analysis. Amsterdam: North-Holland. Li, H., Liu, Z., & Rebelo, I. (1998). Testing the neo-classical theory of economic growth: evidence from Chinese provinces. Economics of Planning, 31, 117 – 132. Meyer, J. R., & Kuh, E. E. (1957). The investment decisions: an empirical study. Cambridge: Harvard Univ. Press. Pesaran, M. H., Shin, Y., & Smith, R. J. (1996). Testing for the existence of a long-run relationship. DAE Working Paper No. 9622. Department of Applied Economics, University of Cambridge. Rebelo, S. (1991). Long-run policy analysis and long-run growth. Journal of Political Economy, 99, 500 – 521. Song, H., & Fu, Y. (2001). Investment and economic growth in China. Paper presented at the 51st International Atlantic Economic Conference, 13 – 16 March 2001, Athens, Greece. Song, H., Liu, X., & Romilly, P. (1998). Modelling volatility of the Chinese stock markets. International Review of Applied Economics, 12, 129 – 139. Stemp, K., & Wong, K. (1999). China and the WTO. Discussion Paper (ECON499) of the Department of Economics. University of Washington, Seattle, WA. Sun, L. X. (1998). Estimating investment functions based on cointegration: the case of China. Journal of Comparative Economics, 26, 175 – 191. Tobin, J. (1969). A general equilibrium approach to monetary theory. Journal of Money, Credit and Banking, 1, 15 – 29.
Analysing the determinants of China’s aggregate investment in the reform period Haiyan SONGa,*, Zinan LIUb, JIANG Pingc a
School of Management Studies, University of Surrey, Guildford GU2 7XH, UK Department of Economics, London Guildhall University, London EC2M 6SQ, UK c Department of Statistics, Dongbei University of Finance and Economics, Dalian, China b
Abstract Although investment has played an important role in China’s phenomenal economic growth over the last two decades, there has been little research on investment determination at the macro level. This study sets out to explore and explain the factors that influence China’s aggregate investment measured by the fixed capital formation during the reform period and to draw useful conclusions from analysing these determinants. The investigation is based on an assumption that aggregate investment in China is determined by both the cost of investment and the increasing aggregate demand created by the reforms. A dynamic investment function is developed to simultaneously capture the long-run and short-run properties of the investment behaviour. The empirical results based on a panel data set of 28 Chinese provinces and autonomous regions suggest that a homogenous equilibrium correction mechanism exists in China’s aggregate investment process. D 2001 Elsevier Science Inc. All rights reserved. JEL classification: E22; E62; C20 Keywords: China; Aggregate investment and error correction model
1. Introduction Aggregate investment measured by fixed capital formation in China has increased exponentially since the economic reform programme first started in 1978. Although there * Corresponding author. Tel.: +44-1483-876353; fax: +44-1483-876301. E-mail address: [email protected] (H. Song). 1043-951X/01/$ – see front matter D 2001 Elsevier Science Inc. All rights reserved. PII: S 1 0 4 3 - 9 5 1 X ( 0 1 ) 0 0 0 5 2 - 9
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are disagreements about whether the aggregate investment is a driving force for China’s recent economic growth, the importance of investment in the creation of aggregate demand and the improvement of economic infrastructures has been widely acknowledged (Chow, 1993; Sun 1998). Since 30% of China’s Gross Domestic Product (GDP) during the 1990s consisted of aggregate investment, its expansion provides an important source for growth in the aggregate demand for construction products, machinery, equipment, and other durable goods. This, in turn, leads to increases in the demand for consumer goods and services. Firms’ investment behaviour and their investment funding allocations differ significantly between the pre- and postreform periods. According to Sun (1998), a system generated ‘‘investment hunger’’ existed before 1978 because firms’ (especially the state-owned enterprises (SOEs)) investment decisions did not depend on profit maximisation or cost minimisation behaviour. Although it was desirable for SOEs to make profits once the new investment projects were completed, any losses were always borne by the central and local governments. This investment expansion drive, coupled with the soft-budget constraint, led to the inefficient allocation of scarce capital resources. The Chinese economy began its transition from a planned system to a market economy in 1978, and the shares of collective-owned enterprises (COEs), township and village enterprises (TVEs), and joint venture enterprises (JVEs) in terms of output and employment in the national economy have increased substantially. This, together with the introduction of various profit-incentive reform programmes, has changed firms’ investment decision behaviour. Profitability and cost of investment have become the most important factors to be considered by firms in their shortrun and long-run production plans. The key sources for investment in the prereform period were mainly from firms’ internal funds and the central and local governments’ budgetary funds. The process of firms’ investment decision-making was, therefore, to compete for the acquisition and use of these funds. Since these investment funds were limited, the distribution of the funds to enterprises was unbalanced, only those enterprises (mostly SOEs) in the key industries, such as defence, public utility, and manufacturing industries, were able to share the limited financial resources. This situation has changed since 1978, and firms have gained more investment autonomy through the increased availability of both internal and external funds. The rise in internal funds was due to the adjustment of the depreciation rate from 3.6% before 1978 to 5.7% in the reform period. In addition, the amount of internal funds was also enlarged by the introduction of the profit-retention scheme and contract responsibility system (CRS). The increase in external funds was also made possible by a significant rise in rural and urban household savings during this period. The reforms in the banking system, such as decentralisation of credit control and the development of other financial institutions, have also helped to channel the financial resources towards a broad range of sectors in the economy. Although many researchers believed that aggregate investment has played an important role in China’s phenomenal economic growth over the last two decades (see Chai, 1998; Chow, 1985, 1993; Li, Liu, & Rebelo, 1998; Sun, 1998), there has been little research on investment determination at the macro level. This study sets out to explore and explain the factors that influence China’s aggregate investment measured by the fixed capital formation during the reform period and to draw useful conclusions from analysing these determinants. This is particularly important on the eve of China’s accession to the WTO, as an under-
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standing of aggregate investment behaviour would help the government to formulate appropriate investment strategies in an environment where financial resources from abroad are widely available. The rest of this paper is organised as follows. Section 2 reviews the various aggregate investment models in the literature and discusses their relevance to the Chinese economy. Section 3 presents the methodology used in the study. Section 4 contains the empirical results and Section 5 concludes the paper.
2. Review of aggregate investment models A number of aggregate investment models have been developed in the literature, and these include the accelerator, cash flow, q, and neoclassical models. Other variants of investment models can also be derived from these specifications with additional extensions/restrictions. In this section, these models are reviewed and their applicability to China discussed. The accelerator model was first proposed by Clark (1917) and subsequently developed by de Leeuw (1962), Evans (1967), and Koyck (1954). This model postulates that a firm’s investment decision is determined by changes in demand for its product. An important implication of the accelerator model is that the size of a firm’s investment is proportional to its output. At the macro level, this investment model is normally specified by relating the aggregate investment to the current and lagged values of total output and the lagged values of capital stock. The lag length is determined by the nature of the investment projects (longer lags for investment in construction and shorter lags for investment in equipment). Two problems are associated with this model: (1) the unrestricted lag structure tends to cause the problem of multicollinearity and generate misleading results; (2) prices of capital goods, wage rates, taxes, and interest rates are not included in the determination of investment, which is difficult to justify on both theoretical and practical grounds. In his study of the Chinese national income determination, Chow (1985) estimated an aggregate investment model based on the acceleration hypothesis and concluded that although the estimated accelerator model explains the data reasonably well over the period of 1952–1983, the estimation results of the model should be interpreted with caution because of the data restrictions and the influences of political forces during the sample period. The cash flow model is associated with Duesenberry (1958) and Meyer and Kuh (1957). The model assumes that a firm’s investment expenditure is determined by its internal cash flow. The cash flow is measured by firm’s profit less taxes plus depreciation allowance. This model is similar to the accelerator model with the current and lagged output variables being replaced by the cash flow variables. Therefore, the problems of this model are the same as those in the accelerator model. Although China started its transition from a centrally controlled economy to a market economy in the late 1970s and early 1980s, the process has been slow and gradual due to the difficulties in transforming the SOEs. Loss making was still a major problem for many of the SOEs during the transition period and the investment expenditures by these loss-making firms were normally financed by external sources, such as government subsidies and debt financing. Even the profit-making firms, both state-owned and
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collective-owned firms, tended to finance their investment by seeking bank loans and other financial sources. Therefore, the cash flow model is not useful in explaining the investment behaviour of the Chinese firms, especially that of the SOEs. The q model is developed by Tobin (1969). This model is a generalisation of the cash flow model in which investment expenditure is related to the ratio of market value of business capital assets to the replacement value of those assets. This ratio is known as Tobin’s q. According to Tobin (1969), a value of q that is close to 1, or greater, encourages investment, while a lower value of q discourages investment. The market value of business capital is also known as the demand price for capital assets, and the replacement value of the business assets is the supply price. In equilibrium, the demand and supply prices for business capital should be the same. However, if market forces create profitable investment opportunities, the demand price will be higher than the supply price and firms will increase investment to expand their businesses. In a competitive market, the demand and supply prices will eventually return to the equilibrium level once the investment profits are competed away. The demand price of capital assets is determined by the value of corporate capital assets in the stock market. Although the q model has a sound theoretical framework, it is unlikely to be a useful tool for modelling China’s investment for the following reasons. First, the q model requires firms’ capital assets to be valued by financial markets. As noted by Song, Liu, and Romilly (1998), the two Chinese stock markets (Shenzhen and Shanghai) only started to operate in 1992, and the number of companies listed in these two markets has been very small. Therefore, it is difficult to evaluate firms’ capital assets through these two financial markets. Second, the q model postulates that a firm’s decision-making behaviour is based on a perfect competitive market in which all firms compete on an equal footing for investment opportunities. Clearly, this assumption does not hold for China. The neoclassical investment model was developed by Hall and Jorgenson (1967) based on an explicit model of optimising behaviour that relates the desired capital stock to the user cost of capital, which, in turn, relates to interest rates, tax policies, and capital prices. The model assumes that the optimal stock of capital for a firm is proportional to its output divided by the user cost of capital. This means that an increase in the demand for final production would encourage the expansion of the fixed capital formation, and increases in interest rates, investment taxes, and capital depreciation would discourage investment. The user cost of capital and expected future demand are regarded as the main determinants of investment decisions in the neoclassical investment model. In this study, we incorporate these two factors in modelling China’s aggregate investment and the rationale for this is discussed in the following section. The difference between our investment model and the neoclassical investment model is that our study utilises the error correction modelling approach to capture both the long-run and short-run properties of investment behaviour in China. Researchers have also used simple time series techniques in forecasting aggregate investment expenditure (see, for example, Kopcke, 1982, 1985). Although time series models are simple in structure, they often outperform the more sophisticated econometric models in forecasting investment expenditures. This is because most econometric models rely heavily on the various assumptions being correctly imposed. However, time series models have been criticised for their lack of theoretical underpinnings. Policymakers cannot use this type of model
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to assess the impact of policy changes on investment. In particular, the time series methods are not applicable to the investment process in China since the models have a strict requirement on the quality of the time series data. Frequent regime changes in the Chinese economy suggest that reliable time series investment data over a long time span are difficult to obtain. Based on the standard investment cycle theory of the socialist economy, Sun (1998) developed an investment model for China using the cointegration and error correction techniques. Investment in real assets was found to be cointegrated with grain output and the effective energy supply. The underlying assumption of this model is that the investment hunger generated by the state investment system is restricted by supply constraints. Hence, there exists an error correction mechanism that prevents investment departing from its longrun path. An interesting finding of this study is that both the long-run and short-run investment functions maintain parameter constancy over the sample period of 1952–1995, suggesting that China’s state investment system has not responded to the rapid changes in demand caused by economic reforms. The aim of this study is to examine the investment behaviour related to all sectors in the Chinese economy including SOEs, COEs, TVEs, and JVEs. The structural stability of the investment model exhibited in Sun’s model is, therefore, unlikely to be featured in the investment model when all types of enterprises are considered over the period of 1952–1995. To overcome the likely problem of structural instability, our study only focuses on the investment behaviour during the reform period, specifically, during the period of 1983–1995.
3. The model The aggregate investment model in this study is specified based on the assumption that firms’ investment decisions are made by assessing the market demand ( Yt*) and the cost of capital used in production (ct) (Eq. (1)): It ¼ f ðYt ; ct Þ
ð1Þ
where It is gross aggregate investment measured by the fixed capital formation at time t. Variable Yt* is expected output, which may be regarded as the demand-pull element, while ct is a cost constraint factor. The two factors are particularly relevant to China because a systemgenerated investment hunger resulting from the release of the suppressed consumer demand, and a resource constraint coexist in the economy. The relationship between investment and its determinants is assumed to follow a nonlinear process of the form: It ¼ BYta cbt ut
ð2Þ
where ut is a disturbance term designed to capture the effects of all other factors, which are not included in the model, and B, a, and b are constant parameters, which need to be estimated. The user cost of capital is normally determined by the following factors: the opportunity cost of using investment funds (rt); the depreciation of the new capital used (d); the changes in asset prices ( PtI); the effective rate of taxation on capital income (TXt); and the cost of
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searching for investment funds (St). This last factor is particularly relevant to the Chinese investment function because of the imperfection of the credit and financial markets in China. Therefore, the user cost of capital may be written as (see Eqs. (3) and (4)): ct ¼ f ðr; d; PtI ; TX ; St Þ
ð3Þ
ct ¼ TXt ðPtI rt1 þ dPtI DPtI Þ þ St
ð4Þ
or I
where DPt represents the change in asset prices. The exact calculation of ct can be very difficult in the case of China due to data limitations. In this study, we try to obtain an estimate of ct from a production function of the form: Qt ¼ f ðKt ; Lt ; Ot Þ
ð5Þ
Eq. (5) differs from the traditional Cobb–Douglas production function used in the neoclassical investment model since additional human capital/R&D input (Ot) is introduced. The Cobb–Douglas production function assumes constant returns to scale and the factors of production are subject to diminishing marginal productivity. The optimal capital stock will be reached at a point where the expected rate of return of the marginal investment equals the marginal cost of capital. This suggests that there is a constant investment–output ratio, and any investment exceeding this optimal level would be inefficient. According to this view, economic growth is independent of investment and growth could only be achieved by exogenous improvements in technology. Song and Fu (2001) found that the investment– output ratio was not constant over both the pre- and postreform periods, and a positive association was identified between the investment–output ratio and the rate of economic growth in China. Therefore, the assumption that economic growth is independent of investment (constant investment–ouptut ratio assumption) made by the neoclassical investment model is unlikely to be valid for China. The postneoclassical endogenous growth models developed by Rebelo (1991), on the other hand, assumes that the production function exhibits constant returns to broad capital accumulation. Broad capital includes not only tangible investment, but also investment in human capital and/or research and development. The endogenous growth model is based on a modified production function of the form: Qt ¼ AKta Lbt Htg Rht
ð6Þ
where Ht is the input of human capital and Rt is R&D expenditure on technological innovation. The derivation of the user costs of capital in this study is based on the profit maximisation assumption. One may argue that this assumption only holds for the non-SOEs but not for the SOEs. Our argument in favour of this assumption is that the profit-retention system introduced in 1979, the profit-contract system introduced in 1981, the tax-for-profit system introduced in 1983, and the CRS since 1987 are all reform measures designed to improve enterprises’ performance through profit-sharing incentives. Some empirical studies suggest that the Chinese SOEs have become increasingly profit oriented due to the introduction of these incentive schemes. For example, two major surveys of the SOEs in China by the
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Chinese Economic System Reform Research Institute (CESRRI) in 1987 and the Institute of Economics of the Chinese Academy of Social Sciences (IECASS) in 1989 found that the profit and profit-related objectives were considered by most managers in the SOEs as the most important enterprise objectives (Chai, 1998, pp. 90–91). In estimating the user cost of capital, only data for the second half of the reform period (1983–1995) is used, therefore, profit maximisation may be seen as a reasonable assumption and the danger of obtaining biased estimates for ct is minimised. Profit (p) is defined as: p ¼ Pt Qt wt Lt ct Kt mt Ht nt Rt or p ¼ Yt wt Lt ct Kt mt Ht nt Rt
ð7Þ
where Pt is the price of output, Qt is the quantity of final products, Yt is output in monetary terms, wt is the wage rate, Lt is labour inputs, ct is the user cost of capital, and Kt is the capital stock, and mt and nt are unit costs of human capital and innovation, respectively. If we assume that the firm maximises (Eq. (7)) subject to the constraint of a production function (Eq. (6)), forming and solving the usual Lagrange multiplier, we could obtain the first-order conditions for a maximum. One of these conditions is: ct ¼ Pt
@Qt @Kt
ð8Þ
Based on Eqs. (6) and (8), we can obtain the user cost of capital (Eq. (9)): ct ¼ Pt
@Qt ¼ Pt ðAaKta1 Lbt Htg Rht Þ ¼ aPt Qt =Kt @Kt
ð9Þ
or ct ¼ aYt =Kt
ð10Þ
By estimating parameter a in Eq. (6), we could then calculate the user cost of capital using Eq. (10). The nonlinear relationship in Eq. (2) can be linearised by the log transformation: lnIt ¼ lnB þ alnYt þ blnct þ lnut
ð11Þ
It is assumed that firms form their expectations based on an adaptive process1, that is: lnYt lnYt1 ¼ lðlnYt lnYt1 Þ
1
The Chinese reform program is a ‘‘learning by doing’’ process. Deng Xiaoping’s famous phrase ‘‘cross the river by feeling the stones’’ may be seen as a good explanation for the use of adaptive expectations here.
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or lnYt ¼ ð1 lÞlnYt1 þ llnYt
ð12Þ
After repeatedly substituting Eq. (12) into itself, we can obtain: lnYt ¼ ð1 lÞN þ1 lnYtN 1 þ l
N X ð1 lÞj lnYtj
ð13Þ
j¼0
As N ! 1, (1 l)N ! 0, therefore, Eq. (13) becomes: lnYt ¼ l
N X ð1 lÞj lnYtj
ð14Þ
j¼0
By substituting lnYt*, expressed by Eq. (14), into Eq. (11) and lagging the dependent and the cost variables, we can write our investment function as an autoregressive distributed lag (ADL) model of the form: lnIt ¼ m þ
N X
fj lnItj þ
j¼1
N X
jj lnYtj þ
j¼0
N X
rj ctj þ et
ð15Þ
j¼0
where m = lnB, jj = al(1 l)j, and et is the error term, which is assumed to be normally distributed with a zero mean and a constant variance. There are several advantages of specifying the investment model as Eq. (15). First, investment responses to changes in its determinants with time lags, and this is known as the effect of ‘‘animal spirits’’ defined by Keynesian. Changes in business confidence cause changes in expected future sales revenue and the likely profitability of investment, hence, leading to changes in the level of investment. Secondly, according to Hendry (1995, pp. 232– 293), the ADL specification actually encompasses all types of econometric models, which allows one to search for the best model specification by conducting hypothesis tests on the parameters of the ADL model. Thirdly, the general-to-specific methodology of Hendry and Richard (1982) and the error correction technique of Engle and Granger (1987) can be directly applied to the estimation of the ADL model. In particular, if the estimated f, j, and r coefficients sum to unity, this implies that a homogeneous equilibrium correction mechanism exists in the data generating process and Eq. (15) can then be written as: DlnIt ¼ g þ
p X i¼1
Ji DlnIti þ
p X
qi DlnYti þ
i¼0
þ kðlnI lnY lncÞti þ ut
p X
gi Dlncti
i¼0
ð16Þ
Fourthly, if the investment function is written as Eq. (16), we can then examine both the long-run and short-run investment elasticities with respect to various factors that influence
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investment. The long-run investment elasticities are homogenous, i.e., the long-run elasticities of investment with respect to output and investment costs are equal to unity, suggesting the same proportional response of investment to changes in its determinants. The short-run investment elasticities are measured by the coefficients qis and gis. Finally, the procedure by Pesaran, Shin, and Smith (1996) can be directly applied to estimate the long-run cointegration and the short-run dynamic relationships based on Eq. (16). This procedure avoids the problems inherent in pretesting for unit roots prior to testing for cointegration relationships, i.e., we do not need to know the integration orders of the variables in Eq. (16) before estimating the model.
4. Empirical results 4.1. The data The empirical study in this section is based on a panel data set of 28 provinces and autonomous regions in China over the period 1983–1995. Tibet and Hainan are excluded because of data unavailability. The 28 provinces and autonomous regions are divided into three broad regions according to their geographical locations. The east region includes Liaoning, Tianjing, Beijing, Shandong, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, and Guangdong. The central region consists of Heilongjiang, Jilin, Inner-Mongolia, Shanxi, Henan, Anhui, Jiangxi, Hubei, Hunan, and Guangxi. The west region relates to Sichuan, Yunnan, Guizhou, Shaanxi, Gansu, Ningxia, Qinghai, and Xinjiang. The data are obtained from various issues of the Chinese Statistical Yearbook (1995, 1996, 1997, and 1998) published by the Statistical Bureau of China and China’s Provincial Statistics compiled by Hsueh et al. (1993). However, since not all variables in our model have existing measures in published data, substantial efforts were made in this study to pool data from different sources and to construct the best possible measures of the variables concerned. Given the deficiency of Chinese official statistics, extra care had to be taken to ensure the comparability and consistency of the data over time. Output is measured by GDP at 1978 constant prices, and calculated by deflating the nominal value of GDP using the general price index at the provincial level. Labour is measured by total employment. Although the State Statistics Bureau conducts employment surveys and censuses, which provide fairly accurate and detailed estimates of total employment by gender, age, industry, and forms of enterprise ownership, there is no such information available at the provincial level that enables us to construct a qualityadjusted measure of the labour input. Nor are there any data that allow us to measure labour in terms of working hours. The measure for the capital stock value of each province as a whole was based on gross investment flows using the standard perpetual inventory method: Kit ¼ Iit þ ðI xt ÞKi;t1
ð17Þ
where i is province i, K measures capital stock at 1978 constant prices, I is investment in fixed
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capital assets at 1978 constant prices, and x is the national rate of depreciation. The investment series obtained from Hsueh et al. (1993) covers investment in residential and nonresidential construction, investment and renovation investment in fixed capital assets (producers’ durable goods) by the state, collective, and private sectors.2 The investment series excludes business inventories, which are usually treated as a component of investment in the literature. Although, inventories become increasingly significant in China as it is transformed from a command system into a market economy, information on inventories is lumped with working capital in the production process (circulating funds), which does not conform to the definition of inventories in the literature. Given that the perpetual inventory method is sensitive to the level of capital stock chosen to initialise the series, it is desirable to have a sufficiently lengthy historical investment series prior to the first data of the analysis (i.e., 1983). Therefore, the investment series is traced back to 1958. The investment series is deflated using the price index of investment in fixed capital assets at the provincial level. However, the price deflator is not available until 1991. Following Hu and Khan (1997), we deflate the investment series during 1958–1977 by the implicit deflator for accumulation estimated by Chow (1993), and the investment series during 1978–1990 by the national price index of building materials. As far as the initial value of capital stock is concerned, we use the value in 1958 (235.2 billion yuan) as estimated by Hu and Khan (1997). This involves an assumption that the initial value of capital stock is the same for all provinces, which can be questioned. However, given a sufficiently lengthy historical series on investment, the resultant measurement error in capital stock is unlikely to be significant. The national rate of depreciation is derived from Hu and Khan (1997). The official rate of depreciation used in China is low, at only 3.6% on average, as compared to that used in developed market economies.3 This is explained by the fact that Chinese official development policies emphasise new capital investment projects and neglect replacement investment in existing equipment and facilities. As a consequence, the official rate bears little relation to the true vintage price functions that characterise the relative efficiency profile of capital assets (see Hu & Khan, 1997). A more serious problem is that the use of an identical rate of depreciation fails to reflect the substantial variations in capital consumption across the regional economies. In the absence of information about depreciation, we use the national rate of depreciation plus the annual growth rate of GDP as the depreciation rate for each province. This is based on the assumption that rates of physical deterioration of capital assets are proportional to growth rates of regional economies. Under this assumption, Eq. (17) can be written as (Eq. (18)): Kit ¼ Iit þ ðI xt git ÞKi;t1
ð18Þ
where git is the growth rate of GDP for province i at time t. 2
The series includes investment by collective units in cities and towns after 1981, rural housing investment after 1983, and private investment in fixed assets after 1984. It is reasonable to assume that private investment is negligible before 1984. 3 For instance, the unweighted geometric average is 13.3% for a broad classof US equipment assets.
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In estimating the investment functions of Eqs. (15) and (16), we first need to estimate the production function (6). Based on the estimated parameters of Eq. (6), we then can calculate the user cost of capital variable. In the production function, there are another two variables that need to be specified: the human capital input (H) and the R&D input (R). The human capital input is measured by the number of scientists and researchers in employment and the R&D variable is measured by the expenditure on research and innovation deflated by the general price index, both of these measures are obtained from the Chinese Statistical Yearbooks (various issues). 4.2. Estimates of investment model The production functions for the three broad regions are estimated as random effect models using the generalised least squares approach, and the estimates are presented in Table 1. The estimated production functions show that the contribution of capital stock to the level of output in the east region is the highest, suggesting that the fast economic growth in this region may be related to a better utilisation of capital. In all four production functions, the labour input is found to be negative and insignificant at the 5% level. One of the explanations for this might be that the excessive input of labour has caused negative marginal productivity in many of the loss-making SOEs and COEs in China. The massive laying-off (Xia Gang) programme over the last two years may be seen as an effort to improve labour productivity by many of the loss-making firms. The human capital and R&D variables are found to be significant determinants of the production functions. This finding supports the endogenous growth model in which both tangible and intangible capital inputs are considered to be important factors of output.
Table 1 Estimates of production functions (dependent variable: lnYt) Variable
East
Central
West
All
Constant
2.670** (0.553) 0.756** (0.077) 0.026* (0.017) 0.146 (0.110) 0.451** (0.051) 0.973 0.141 130
2.358** (0.642) 0.654** (0.111) 0.026 (0.018) 0.531** (0.156) 0.496** (0.047) 0.941 0.174 130
3.106** (0.326) 0.267** (0.064) 0.001 (0.017) 0.091* (0.049) 0.822** (0.033) 0.975 0.170 117
1.581** (0.359) 0.597** (0.053) 0.020* (0.010) 0.205** (0.066) 0.597** (0.030) 0.972 0.171 364
lnKt lnLt lnHt lnRt R2 s Sample (83 – 95)
Standard errors are in parentheses. * Estimates are significant at the 10% level. ** Estimates are significant at the 5% level.
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The user cost of capital is then calculated based on ct = a ˆ Yt =Kt for each of the three broad regions and for the Chinese economy as a whole. The derived cost variables are then used in the estimation of investment functions. Although longer lag length is used in the estimation of the initial ADL investment model of Eq. (15), only three lags for the central and west regions and two lags for the east regions are found to be statistically significant. The estimated models (omitted) are then tested for the homogeneous equilibrium correction mechanism using the Wald statistic, c2(1), to test the null hypothesis: p X i¼1
fˆ ii þ
p X ˆ i¼1 ðjˆ þ rÞ i¼0
The calculated Wald statistics are: c2EAST (1) = 0.887, c2CENTRAL(1) = 3.910, c2WEST (1) = 0.884, and c2ALL(1) = 1.530, respectively. These results suggest that the data generating process of the Chinese aggregate investment may be best represented by the homogeneous equilibrium correction model of Eq. (16). The data are then fitted to this error correction model and the results are given in Table 2. The estimation results show that the investment models for the three broad regions fit the data quite well. Given the homogeneous long-run response of investment to investment cost and output, the short-run cost elasticity ranges from 0.155 for the west provinces to 0.433 for the central provinces. The short-run cost elasticity of the east region is 0.348. The corresponding output elasticities are 0.852, 0.639, and 1.112, respectively. The three error correction terms are significant at least at the 10% level with the feedback parameters ranging from 0.031 to 0.046, suggesting that firms remove decision errors made in the previous period in order to achieve the desirable equilibrium investment level. The relatively small values of the feedback parameters in these investment models, compared with those of demand functions for consumer goods, suggest that the investment adjustment process is slow in speed and small in magnitude. This, perhaps, is due to the nature of fixed capital formation. Once the investment decision has been made and carried out, the reverse of investment decision is difficult. An investment function for all the 28 provinces and autonomous regions is also estimated, and the results are presented in the last column of Table 2. The values of the estimated parameters appear to be in the same order of magnitude as those in the other three regional models. One may also argue that the costs of investment only influence firms’ short-run decisions, i.e., firms may delay their investment temporarily due to financial constraints. In the long run, however, investment is expected to be linked only to expected future demand, and this is particularly true for China since economic reforms have released the suppressed domestic consumer demand and the open door policy has fuelled the import and export expansions, all these have led to a new type of ‘‘investment hunger,’’ which is different from that under the planned system. Therefore, we reestimated our investment models by excluding the capital cost variable from the error correction term. By doing this, we deliberately impose the assumption that firms’ short-run investment decisions are influenced by both the costs of investment and the demand variable, but in the long run, the cost constraint variable does not have any role to play in the determination of investment. The estimated models are presented in Table 3.
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Table 2 Estimates of the investment functions (dependent variable: DlnIt) Variable Constant
East 0.362** (0.157)
Central 0.409** (0.117)
Dlnct 1
2.965** (0.348) 2.617** (0.338)
Dlnct 2 DlnYt
R2 s F statistic DW Sample (83 – 95)
3.863** (0.317) 2.329** (0.344) 0.422** (0.169) 0.042* (0.025) 0.699 0.104 47.64 1.867 130
Short-run elasticity Cost Output
0.348 1.112
DlnYt 1 DlnYt 2 ln(I/cY)t 1
All
0.334** (0.129)
0.330** (0.061)
0.046** (0.017) 0.731 0.082 47.35 2.209 130
0.115* (0.066) 4.240** (0.572) 2.433** (0.768) 1.652** (0.554) 4.479** (0.579) 2.300** (0.771) 1.327** (0.543) 0.031** (0.013) 0.520 0.086 14.50 2.270 117
0.081** (0.035) 3.704** (0.228) 2.296** (0.311) 1.116** (0.213) 4.171** (0.231) 2.345** (0.314) 1.107** (0.225) 0.038** (0.009) 0.642 0.094 79.52 2.033 364
0.433 0.639
0.155 0.852
0.292 0.720
DlnIt 1 DlnIt 2 Dlnct
West
4.901** (0.354) 4.696** (0.327) 0.228** (0.095) 5.329** (0.362) 4.690** (0.360)
Standard errors are in parentheses. * Estimates are significant at the 10% level. ** Estimates are significant at the 5% level.
The results show that the short-run output and cost elasticities have increased for the central and west regions, and decreased for the east region. However, the feedback parameters have doubled for the east and central regions and tripled for the west region. This suggests that the adjustment process is faster when the investment cost variable is ignored in firms’ long-run investment decision-making process. The estimates of the above investment functions have important policy implications. First, both financial constraints and the investment hunger caused by increases in demand for consumer goods determine the level of investment in China. To enhance economic growth, further financial reforms are required to increase firms’ access to investment borrowings. This, however, should be coupled with tight control of the bad debt problem. Secondly, the fast expansion of the Chinese economy has implanted a huge demand for future investment. Since the homogenous equilibrium correction mechanism is found to be
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Table 3 Estimates of the investment functions (dependent variable: DlnIt) Variable
East
Central
West
All
Constant
0.053 (0.053)
0.502 (0.063) 0.112** (0.052)
0.102* (0.061)
0.053* (0.031)
0.059* (0.035) 3.628** (0.226) 2.131** (0.311) 1.083** (0.209) 4.216** (0.224) 2.111** (0.315) 1.047** (0.219) 0.092** (0.018) 0.640 0.093 78.94 1.935 364
0.419 1.058
DlnIt 1 DlnIt 2
R2 s F statistic DW Sample (83 – 95)
3.723** (0.307) 2.327** (0.328) 0.451** (0.169) 0.089** (0.036) 0.698 0.102 47.39 1.879 130
0.096** (0.035) 0.722 0.082 45.46 2.104 130
4.308** (0.563) 2.263** (0.769) 1.544** (0.545) 4.599** (0.563) 2.070** (0.775) 1.293** (0.536) 0.115** (0.037) 0.530 0.086 17.61 2.106 117
Short-run elasticity Cost Output
0.210 0.945
0.429 0.759
0.501 1.236
Dlnct Dlnct 1
2.833** (0.343) 2.623** (0.338)
Dlnct 2 DlnYt DlnYt 1 DlnYt 2 ln(I/Y)t 1
4.436** (0.380) 4.273** (0.349) 0.266** (0.096) 5.000** (0.384) 4.243** (0.379)
Standard errors are in parentheses. * Estimates are significant at the 10% level. * * Estimates are significant at the 5% level.
in force in the aggregate investment function, the long-run economic growth will lead to a same level of growth in investment. Thirdly, since the ratio of investment to output is likely to be linked to economic growth, more resources need to be channelled to the west and central regions in order to reduce the growth gaps between these regions. This includes the introduction of financial incentives for domestic and foreign firms to invest in the central and west provinces and to improve the investment environment through government funded investment projects. Although the economic growth over the last three years in China has slowed down due partially to the effect of Asian financial crisis in 1997 and sluggish demand in consumer goods, the demand for both domestic and foreign investment is still forecast to increase over the next 10 years (Stemp & Wong, 1999). In particular, China’s accession to the WTO will give economic reform a much-needed impetus and enhance China’s interaction with the rest
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of the world. This, in turn, will fuel increases in aggregate demand and lead to further rises in demand for aggregate investment. Moreover, China’s WTO membership will improve the efficiency of financial intermediation and provide more opportunities for both SOEs and nonSOEs to obtain access to the funds needed for investment.
5. Concluding remarks The aggregate investment function with a homogenous error-correction process is estimated for each of the three broad regions in China. The model is specified based on the assumption that both the cost-constraint and demand-pull variables are important factors in the determination of investment behaviour. The investment cost variable is derived from the production function, which is then used to capture such influences as the opportunity cost of investment, depreciation of capital goods, changes in asset prices, taxation, and other costs incurred in the search for investment funds by the Chinese firms. The effect of demand on investment is modelled through the inclusion of expected output. Two types of error correction mechanisms are used to simulate firms’ long-run investment decision behaviour. The estimation results of the investment model for all of the 28 provinces show that the short-run output and cost elasticities are 0.72 and 0.292, respectively, when both investment cost and output are included in the error correction process. The output and cost elasticities for the second error correction model that only contains the lagged ratio of investment to output are found to be 1.058 and 0.419, respectively. Both of these investment models are statistically significant with the desired properties. The empirical findings of this study imply that it is important to introduce favourable investment incentives in the central and west regions in order to create balanced economic growth. The homogenous equilibrium correction mechanism suggests that the demand for investment will change equiproportionately to the changes in investment costs and output. Since investment costs determine firms’ investment decisions, a flexible, yet well-regulated financial system that gives firms easy access to various financial resources is considered to be crucial for the continuous investment expansion and economic growth in China, and China’s WTO membership will prepare the economy for such a system.
Acknowledgments The authors wish to thank Mark Schaffer and the seminar participants of the Centre for Economic Reform and Transformation at Heriot-Watt University for their constructive comments on an early draft of this paper. The final version of this paper also benefits from the suggestions and comments made by an anonymous reviewer. The third author wishes to express her appreciation for the excellent research facilities provided by the School of Management Studies, University of Surrey, where she spent 1 year as a Visiting Research Fellow. The usual disclaimer applies.
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