CREFS Working Paper No: 99-06

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manufacturing sector is positively related to China's GDP, GDP growth, wages, and trade ... tour, actual FDI surged by 150 percent to US$11 billion in 1992.
CREFS Working Paper No: 99-06 Determinants of Foreign Direct Investment across China

Qian Sun Division of Banking & Finance Nanyang Technological University Eamil: [email protected]

Wilson Tong Department of Finance School of Business and Management Hongkong University of Science &Technology Eamil: [email protected]

Qiao Yu Department of Economics National University of Singapore Email: [email protected]

August 1999

Abstract Studies of foreign direct investment (FDI) distribution in the US is voluminous but such studies on a developing country are rare. We fill the void by analyzing the spatial and temporal variation in FDI among China’s 30 provinces. We believe this is the first study of its kind. Other than some “standard” results, our pooled regression finds that the cumulative FDI has a negative impact on the new FDI. This raises the question of sustainability of China in attracting FDI. Provincial officials have to improve the investment environment. On the other hand, multinational corporations may want to invest in provinces with fewer FDI competitors. Our analysis is robust across different fixed effect specifications. However, it explains the FDI distribution in the coastal provinces better than it does for Central and Western provinces.

JEL Classification: F21, F30 Keywords: Foreign Direct Investment, A and B shares, Location Theory, Agglomeration, China

1. Introduction Interest in the study of foreign direct investment (FDI) has grown rapidly. FDI is expected to be an important issue in the coming World Trade Organization (WTO) meetings. There have been studies of FDI in the U.S. that help us to understand factors that are important to attracting foreign investments across different states.1 In this paper, we examine if these factors are also important for FDI distribution in a developing country, China. Such an analysis is important for several reasons. First, most of the studies of FDI on developing countries are cross-country analyses.2 As such, the interwoven relationships between social, cultural, economic, and political factors are difficult to delineate. By focusing on only one country, we can make a cleaner study on the economic determining factors that attract FDI. Second, an analysis of FDI in China is in itself valuable. From a popular international finance text, “(l)eading multinational companies are moving aggressively in China to build up a dominant market share to pre-empt the entry of big rivals, according to a survey by management consulting firm McKinsey and Co.”3 No wonder FDI in China has received considerable attention from political and economic analysts since China begins its economic reform. In fact, China has been the second largest host of FDI in the world since 1994, next only to the US. Third, the present analysis focuses upon the spatial and temporal variation in FDI among China’s all 30 provinces and we are not aware of any such study so far.4 This is especially timely as China is expected to enter WTO soon. It would surely sparkle another round of FDI projects. Our study provides implications to both policy-makers in China and foreign investors interested in the China market. There are only a few empirical studies on the overall FDI situation in China. Wang and Swain (1995) examine the host country determinants of FDI in China. They find that the FDI in manufacturing sector is positively related to China’s GDP, GDP growth, wages, and trade barriers, but negatively related to interest rate and exchange rate for the period of 1978-92. Chen, Chang, and 1

See Bartik (1985), Coughlin et al. (1991), Graham and Krugman (1991), Lipsey (1993), Klein and Rosengren (1994), and Hines (1996) among others. 2 See Kravis and Lipsey (1982), Edwards (1990), Lipsey (1999) among others. 3 Eiteman, Stonehill, and Moffett, “Multinational Business Finance”, p.479.

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Zhang (1995) study the effect of FDI on China’s output and find that the FDI has a positive impact on the output growth between 1978 and 1990. Using cross-country data, Wei (1995) finds that despite of the large amount of FDI China has received in recent years, the country still appears to host too little FDI compared to an “average” host country. The analysis is organized as follows. Section 2 gives an overview of FDI development in China. Section 3 discusses the conceptual framework. Data and empirical methodology are described in Section 4. Section 5 presents the empirical results, and Section 6 concludes the paper.

2. Foreign Direct Investment in China FDI is conventionally defined as a form of international inter-firm cooperation that involves a significant equity stake in or effective management control of host country enterprises. However, in China, FDI is considered to also encompass other, non-equity cooperations such as contractual joint ventures, compensation trade, and joint exploration. China has attracted a spectacular amount of FDI since its opening to the outside world in 1978. FDI jumps from virtually zero in 1979 to an amount of US$ 41.7 billion in 1996. As pointed out by Kamath (1990), attracting FDI is an integral and critical part of China’s economic reform and Open Door policy. The “law of the People’s Republic of China on Joint Ventures Using Chinese and Foreign Investment”, the first of its kind, was enacted in July 1979. A state foreign investment commission was established to direct and oversee the investment process. Four special economic zones (SEZs) were quickly set up at Shenzhen, Zhuhai, Xiamen, and Shantou in the early 1980s. In 1984, 14 more coastal cities and Hainan Island were opened to foreign investment. Furthermore, three zones were opened to FDI in early 1985: the Yangtze River delta, the Pearl River delta, and the Zhangzhou-Quanzhou-Xiamen region. In April 1986, the government promulgated the PRC Law on Foreign Enterprises, formally granting legal rights to wholly-owned foreign enterprises in China. In October 1986, the State Council further issued an administrative order to encourage foreign investment, permitting more freedom of independent operations for foreign-invested enterprises and 4

Branstetter and Feenstra (1999) have an interesting working paper which does look at FDI in China at the provincial level. However, their focus is on the welfare implication of possible tax reduction in China after

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granting more tax incentives for foreign investment. Local governments were also given more authority in reviewing the applications of foreign investment. In 1988, Hainan was incorporated as another SEZ and the Chinese government further amended the joint venture laws, which included a legal ban on expropriation, relaxed restrictions regarding expatriation of profits and dividends, and allowed foreign nationals to be the chairman of the board of directors in foreign-invested enterprises. Starting from a very low base, China experienced double- or triple-digit annual growth of FDI from 1979 to 1988 and received a total of US$12.05 actual FDI during this period. The Tiananmen Square Incident slowed down the FDI growth rate to a single digit in 1989 and 1990. However, it resumed double-digit growth in 1991. Following Deng Xiaoping’s South China tour, actual FDI surged by 150 percent to US$11 billion in 1992. It surged another 150 percent in 1993 and maintained the double-digit growth thereafter. In the 1980s, FDI mainly takes the form of contractual or equity joint ventures. However, the wholly-owned foreign firm is the fastest growing form of FDI in the 1990s. It accounts for 40% of the FDI value in 1996. The FDI brings to China not only much needed physical capital but also human capital that are scarce in China. In addition, it has technological spill over and demonstration effects to other domestic firms, which helps to accelerate the urban economic reform, especially the reform of stateowned enterprises in China. Sit (1985) points out that FDI has provided a substantial impetus in modernizing China’s existing industries, including the transfer of technological know-how, managerial expertise, and international marketing skill. According to Chen, Chang, and Zhang (1995) and Tso (1998), FDI has had a positive impact on China’s output growth and foreign trade. The average GDP growth was 5.8 percent in the 1970s but more than 10 percent in the 1990s. China’s openness to international trade increased from less than 10 percent in 1979 to about 50 percent in 1996 making it the 10th largest trading nation in the world5. The share of exports of foreign-invested enterprises in the total exports of China grew from 0.3 percent in 1984 to 45 percent in 1996. FDI also contributes to macroeconomic stability during the economic downturns because it is less affected by

China enters into WTO. 5 The openness is measured as the summation of export and import value divided by GDP.

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domestic credit tightening, and thus, constitutes a rather stable element of overall capital formation compared to the more volatile Chinese domestic investment.

[Insert Table 1 here]

However, FDI is unevenly distributed across provinces within China. The provinces in China are officially classified into three regions: the Eastern or Coastal, the Central, and the Western. Table 1 shows that from 1989 to 1996, the Eastern region received a lion share of the total FDI amount, more than 85 percent, while the Central and the Western regions together only received less than 15 percent. Cheng and Zhang (1998) consider this as one of the reasons that have led to the fast development of the coastal provinces in the east and the widening of the gap in economic development between coastal and inland provinces since 1979. The increasing regional differences have created social and political problems. In order to narrow or slow down the widening of the gap, China’s central government has adopted a series of measures which includes encouraging FDI in the Central and Western regions. The provinces in these regions also try to jump onto the bandwagon to attract FDI. As a result, the share of FDI in the Central and Western regions have been slowly increasing since 1989. The concentration of FDI in the coastal region can be explained by many factors (see Cheng and Zhang, 1998). However, the fact that the coastal region has high population density but poor natural resources, while inland provinces have low population density but rich natural resources seems to suggest that the purpose of FDI in China is mainly for the potential market and labor abundance but not natural resources.

3. Previous Empirical Work and Determining Factors As pointed out by Braunerhjelm and Svensson (1996), the theoretical foundation of FDI is rather fragmented, comprising bits and pieces from different fields of economics to elucidate the locational pattern of firms. Several theories have been put forward to explain the FDI. Hymer (1960) views the multinational corporation (MNC) as an oligopolist. FDI is considered to be the outcome of

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broad corporate strategies and investment decisions of profit-maximizing firms facing worldwide competition. Dunning (1977) and Rugman (1981) invoke transaction costs to explain firms’ internationalization, putting emphasis on the intangible assets firms have acquired. Bhagwati and Srinavasan (1983) and Grossman and Helpman (1991) use international trade theory to explain the allocative aspects of FDI. However, more relevant to our study is the location theory, which is often used to explain why a multinational corporation would choose to invest in a particular host country. It can also be used to explain why foreign investors would choose to invest in a specific location within a particular host country. Previous researchers have identified quite a few determinants for the location of FDI. In their study on state characteristics and the location of FDI within the US, Coughlin, Terza, and Arromdee (1991) assume that a foreign firm will choose to invest in a particular state if and only if doing so will maximize profit. The FDI in a particular state depends on the levels of its characteristics that affect profits relative to the levels of these characteristics in the other states. They identify state land area, per capita income, agglomeration, labor market conditions (wage rates, the degree of unionization, the unemployment rate), transportation network, taxes, and the state expenditures to attract FDI as the determinants of FDI across the states within the US. Per capita income and densities of manufacturing activities affect market demand that, in turn, affects the revenue. State land area, labor market conditions, transportation network, taxes and expenditures to attract FDI affect the cost. Their results indicate that states with higher per capita incomes and higher densities of manufacturing activities attract relatively more FDI. In addition, higher wages deter foreign direct investment, while higher unemployment rates attract it. Overall, higher taxes deter FDI; more extensive transportation infrastructures and larger promotional expenditures are associated with higher FDI. Similarly, Bagchi-Sen and Wheeler (1989) find that population size, population growth, and per capita retail sales are important determinants of the spatial distribution of FDI among metropolitan areas in the US. Friedman, Gerlowski, and Silberman (1996) find that market potential, wage, skilled labor measured by per capita number of scientists and engineers, construction cost, major port, and funds spent on attracting FDI have significant impact on the location of foreign branch plants in the

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US. Braunerhjelm and Svensson (1996) further show that agglomeration, exports, and R&D are important factors affecting Swedish MNCs’ FDI location. Xin and Ni (1995) conducted a survey to rank provinces of China with the best investment environment. They identified eight variables with following weightings: market scale 30%, wage level 20%, education level 10%, extent of industrialization 10%, transport facilities 10%, communication facilities 10%, living environment 5%, and the level of scientific research 5%. Built on the above findings, we identify eight potentially important determinants of FDI distribution across provinces within China, as summarized in Table 2.

[Insert Table 2 here]

First of all, the market demand and market size has positive impact on the FDI because it directly affects the expected revenue of the investment. In fact, one major motivation for FDI is to look for new markets.6 The larger the market size of a particular province is, other things being constant, the more FDI the province should attract. Kravis and Lipsey (1982) and many other empirical studies find such positive relationship. Bloomstrom and Lipsey (1986) show a significant size threshold effect for firms’ decision to invest abroad. We use GDP, GDP per capita, retail sales, and retail sales per capita to capture demand and size effect. Agglomeration refers to the concentration and co-location of economic activities that give rise to the economies of scale and positive externalities. The level of agglomeration of a particular province should be positively related to the FDI. Following Wheeler and Mody (1992), we use infrastructure quality, the degree of industrialization, and level of foreign investment to capture the agglomeration benefits. The GDP per square kilometer is proxied for the quality of infrastructure. Related to infrastructure is the transportation network. More highway and railway mileages per square kilometer are expected to be related positively to FDI. The amount of domestic investment and domestic investment per worker reflects the degree of industrialization. The cumulative FDI amount

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See Shapiro (1998) for a detailed discussion on FDI motivations.

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captures the possible herding effect among foreign investors, which is directly related to the agglomeration. Labor quality is proxied by the number of research scientists, engineers and technicians per 1000 of the employees (RSET) which has been used by Braunnerhjelm and Svensson (1996). This variable measures the relative endowment of skilled labor in each province and should have a positive impact on FDI. Wage, as a measure of labor cost, should be an important factor for FDI consideration. However, in recent years of fast economic development, China attracts foreign investment not purely through cheap labor. As reflected in the model of Branstetter and Feenstra (1999), multinational firms in China tend to pay a wage premium to their workers. This may be because multinational firms want to hire quality workers. Higher wage may well reflect higher labor quality. Hence, it is conceivable that wages in those provinces that can attract relatively more FDIs can be higher, too. Furthermore, as pointed out by Lipsey (1999), most studies show no evidence that low wages, associated with low per capital real income, were the main attraction for FDI. We will go back to this variable later. The level of scientific research indicates the level of human capital and the level of general development. Measured by R&D expenditures and the number of patents, the higher level of scientific research should promote FDI in a province. Education is another variable measuring human capital. It is commonly proxied by the percentage of population (or employee) who have received the secondary or above education. Since such data are not available, we use the number of universities as a rough proxy for the level of education. Of course, the level of education is expected to have positive impact on the inflow of FDI. The degree of openness has mixed blessings on FDI. On the one hand, a more open economy attracts FDI because it welcomes foreign capital and foreign investors are more familiar with the host economy. Edwards (1990) finds supporting evidence on that. But on the other hand, openness can have a negative impact on FDI due to keen competition. Wheeler and Mody (1992) find that Brazil and Mexico attracted major US investment in their sample period despite these two countries have very low ratings in openness. Hence, the exact relationship between the two is an empirical question. We use the ratio of total trade (export and import) over GDP of a province to measure its degree of openness.

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Political risk is an important factor to consider, especially in developing countries. However, as we are dealing with a single country, the difference in political risk among different provinces should not be much. We use it only as a macro variable and see how it affects the FDI through time. The variable is constructed using the risk ranking provided by Political Risk Services. The last factor to consider is the FDI substitutes. FDI brings in a lot of benefits and one of those is the inflow of foreign capital. A province may not need much FDI if it can tap the foreign capital market and use the money to invest in the local industry. In this sense, other means of foreign capital inflow can partly substitute the need for FDI. The two major sources we consider are foreign equity capital and foreign loans. We look at the number of firms in each province that have B-share, H-share, and/or N-share issues. These are means to raise foreign equity capital. The total number becomes our variable of foreign portfolio investment, FPI. The amount of foreign loans raised in each province constitutes the second proxy variable to capture this factor. We expect both variables to be negatively related to the amount of FDI within the province. Certainly, there are other commonly used variables like number of tourists, number of telephone sets, promotion expenditures for attracting FDI, tax structure, and the special treatment offered to foreign investors that may have impacts, too. However, such data are either not available or hard to measure. We use fixed effect panel data analysis to control for that.

4. Data and Methodology Panel data analysis is adopted because we examine the determinants of FDI distribution across provinces and over time. Most of the data used in this study are obtained from various issues of China’s Statistical Yearbook. The data on the number of firms that issue foreign equity shares are from Datastream. Political risk data are from Political Risk Yearbook published by Political Risk Services. They give 18-month forecast on the risk level of a country on several aspects. We look at their forecasts on the risk level of Financial Transfer, Direct Investment, and the Currency Market in China. During our sample period, their forecasts range from “A-” (lower risk) to “B-” (higher risk). Our risk variable is constructed by assigning “1” to “A-”, “2” to “B+”, 3 to “B”, so on and so forth. As such, when the risk level goes up, the variable becomes larger in value.

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Due to the fact that the breakdown data for provinces are generally not available before 1989, our sample only covers the period from 1989 to1996. Since GDP, retail sales, domestic investment, R&D expenditures, and wage are denominated in RMB (Chinese currency) and the FDI in US dollars, we convert the FDI into RMB using yearly average dollar/RMB exchange rate. The swap market rate is used for the period from 1989 to 1993 while China was still under the dual exchange rate system. Then all monetary data are converted to 1990 constant RMB using the relevant deflater7. The Cumulative FDI (CFDI) starts from 1988 because the earlier provincial FDI data are not available. Therefore, the 1989 CFDI is just the 1988 FDI; the 1990 CFDI is the sum of FDI in 1988 and 1989; so on and so forth. There are a total of 240 pooled observations (pooling 8-time series observations across 30 provinces). A problem with this data set is the possible high correlation between the various proxies. It is quite obvious that the proxies listed in Table 2 may overlap with one another. This may lead to serious multicollinearity. In order to ascertain the degree of multicollinearity, we calculate the correlation matrix between all the potential determinants. We first transform all variables into the natural logarithm form and stack these transformed variables up across the 30 provinces, then calculate the correlation coefficients between them.8 The results are presented in Panel A of Table 3.

(Insert Table 3 Here)

As expected, high degree of correlation (correlation coefficient of 0.7 or above, as highlighted) exists in many pairs of proxies. To avoid multicollinearity in the subsequent analysis, we select only eight proxies in our panel regression model. For Market Demand factor, we use only the GDP series. For Agglomeration factor, we use annual domestic investment per worker (PERWI) and CFDI. For Labor Quality, we use REST. Retail sales, retail sale per capita, GDP per capita, GDP per km2, total domestic investment, railway mileage per km2 and highway mileage per km2 are dropped because they are highly correlated with these variables.

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Retail sales and wage are adjusted by CPI and the others are deflated by the GDP deflator. Since log 0 is undefined, 10-4 is used to replace the zero whenever it occurs in our data set.

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Average wage rate (Wage) may be an important determinant for FDI but its correlation coefficient with PERWI is quite high, equal to 0.72. PERWI is a proxy for agglomeration. As mentioned before, multinational firms tend to pay higher wages. It is conceivable that a province that agglomerates many of these multinational firms may have higher average wage too. To nullify such effect, we run a simple OLS regression of Wage on PERWI (log of the variables). The regression residual, being orthogonal to PERWI, will be our proxy for Wage. R&D Expenditures, Number of Patents, and Number of Universities, which measure the Level of Scientific Research, are highly correlated. We divide the Number of Patents by the R&D Expenditures, i.e., the number of patents per million RMB R&D expenditures. On the other hand, we drop the variable of Number of Universities. The last two variables we use are Foreign Loans and Risk, which do not show high correlation with the other variables. Panel B of Table 3 gives the new correlation matrix for the eight selected proxies and confirms that none of the variables are highly correlated now. Since we will add two more variables, the foreign portfolio investment FPI and the degree of openness that have a shorter data span from 1992 to 1996 into our second set of tests, we also want to make sure they are not highly correlated with the eight chosen variables. The correlation matrix of the 10 variables shown in Table 3 C confirms that it is the case. A general pooled regression model is used on these variables and is specified as

ln(FDIit) = αit + β1ln(GDPit) + β2ln(PERWIit) + β3ln(Patent/RDit) + β4ln(Wit) + β5ln(RESTit) + β6ln(CFDIit) + β7ln(FLit) + β8Riskt + εit , (i = 1, 2, ... 30 and t = 1, 2, ... 8)

(1)

where subscript i refers to individual provinces, t refers to years from 1989 to 1990, and αit is the intercept. This model allows for fixed effects in the cross-section by not requiring that intercepts are identical across different provinces. The log linear specification allows us to interpret the coefficient estimates as elasticities.

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For the shorter sample period of 1992-1996, we add two more variables and the model becomes ln(FDIit) = αit + β1ln(GDPit) + β2ln(PERWIit) + β3ln(Patent/RDit) + β4ln(Wit) + β5ln(RESTit) + β6ln(CFDIit) + β7ln(FLit) + β8Riskt + β9ln(FPIit) + β10ln(Opennessit) + εit

,

(i = 1, 2, ... 30 and t = 1, 2, ... 8) .

(2)

A major advantage of using panel data method, as pointed out by Hsiao (1989), is to resolve or reduce the magnitude of a key econometric problem that often arises in empirical studies, namely, the omitted (mismeasured, not observed) variables that are correlated with explanatory variables. By using panel data analysis, one is better able to control for the effects of missing or unobserved variables. The effects of omitted variables are driven by either individual time-invariant variables or period individual-invariant variables. The individual time-invariant variables are variables that are the same for a given crosssectional unit through time but vary across cross-sectional units. Examples of omitted provincial specific variables in our study are the geographical proximity and/or historical connection to the source countries or regions, special economic policies granted by the central government, the degree of openness to the outside world, good sea and air linkages. The period individual-invariant variables are variables that are the same for all cross-sectional units at a given point in time but vary through time. Examples of these are changes of political and macroeconomic policy, widespread optimism or pessimism. All these omitted variables may correlate with the independent variables in equation (2). Risk in the model is used to control for such omitted period individual-invariant variables. However, the provincial specific omitted variables are more of our concern because our main focus is FDI distribution across provinces. The provincial specific characteristics may also give rise to cross-sectional heteroskedasticity. Therefore, GLS is further used in addition to OLS to address such possibility. Finally, the time series data may be autocorrelated. However, in view of the short time series, it does not make much sense to use Praise-Winsten correction (see Greene, 1993). We also do a GLS regression on the first difference

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data. Since our data are transformed into natural logarithm, the first difference gives growth rate for the respective variables. This allows us to test whether FDI growth is determined by the growth rate of GDP and/or other factors. Note that the first difference also “sweeps out” the provincial specific intercept, αi.

5. Results Table 4 reports the estimation results of Equation (1). Model (1) is the OLS fixed effect regression with a different intercept for each province. Model (2) is the GLS fixed effect regression; and Model (3) is the pooled regression on the differencing data.

[Insert Table 4 here]

The coefficient estimates for Model (1) indicate that GDP is statistically significant and with the expected positive sign. A 1% increase in GDP leads to a 3.17% increase in FDI. This supports the hypothesis that the market size and general development level of a province have a positive impact on attracting FDI. This is also consistent with previous findings in the US and other countries. However, as a proxy of the level of industrialization, PERWI does not enter significantly into the equation. Since this variable is used to capture the agglomeration effect, the result is not supportive to the agglomeration argument. The second variable to capture the effect is the level of cumulative FDI amount, CFDI. The result is surprising. A 1% increase in CFDI leads to a 0.15% decrease in FDI. Although a t-value of -1.35 indicates no statistical significance, this suggests that FDI cannot create a herding effect. The more FDI accumulated, the less FDI to come. We will get back to this point later. The level of scientific research, as captured by Patent/RD, does show great importance in attracting FDI. A 1% increase in Patent/RD results in a 0.22% increase in FDI and a t-value of 2.12 shows statistical significance at the 5% level. Surprisingly, the relative endowment of skilled labor (REST) and the wage effect (W) have no statistically significant effect on FDI distribution. This is inconsistent with the findings by Coughlin, Terza, and Arromdee (1991) and Friedman, Gerlowski, 12

and Silberman (1996). Foreign Loans also shows no statistical significance in the regression. This may be due to the fact that foreign borrowing is never an important source for foreign capital to individual provinces. We will go back to this point when we examine foreign portfolio investment (FPI) in Equation (2). The time-series variable Risk enters significantly negative in the regression, as expected. The risk level is negatively related to the FDI. The t-value is -3.50, which is significant at any conventional statistical level. The DW statistic of 1.87 indicates that the first-order serial correlation is not serious. Model (2) addresses the possible heteroskedasticity across provinces by applying GLS to the fixed effect regression and gives even stronger results. Other than the estimates of GDP, Patent/RD, and Risk remain significant as in Model (1), the estimates of RSET, Wage, and CFDI become very significant now. A 1% increase in REST leads to a 1.06% increase in FDI, which is statistically significant at the 5% level. Recall that this variable is the number of engineers, scientists, and technicians relative to the total number of employees within a province. Its significance suggests that labor quality is indeed important to FDI consideration. Interestingly, wage variable enters significantly positive into the regression. A 1% increase in Wage leads to a 1.73% increase in FDI. As discussed before, foreign firms investing in China nowadays may not only be looking for cheap labor. Quality labor is important, too. Positive relationship between FDI and Wage may simply reflect the fact that foreign firms are willing to pay higher wages to attract quality workers. In any event, such a result is not quite consistent with Coughlin, Terza, and Arromdee (1991) and Friedman, Gerlowski, and Silberman (1996). The most interesting result is that CFDI is significantly negative at any conventional statistical level. A 1% increase in CFDI leads to a 0.34% decrease in FDI. Opposite to the agglomeration hypothesis that more accumulated FDI attracts more FDI to come, our findings suggests that the more the accumulated FDI, the less the amount of FDI that will come. This has several important implications. First, the FDI growth in China may not be sustainable without creating more special incentives for FDI. Second, foreign investors in general are not satisfied with the results of their investment in China. This is consistent with many anecdotal stories and the survey results of Chen (1993) in which 22 FDI firms in Tianjin and Shenzhen are asked to evaluate the investment

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environment in China. The average score given for all evaluated items by these firms are at the low end of the scale. Through firm interviews, Branstetter and Feenstra (1999) find that the foreign invested enterprises compete with state-owned firms and the Chinese government tries to impede the ability of foreign firms to compete in the Chinese market. No wonder Wei (1995) find that China has received too little FDI compared to “an average host country” although it has attracted large amount in absolute terms. Finally, from the point of view of multinational enterprises, there may be diminishing return for FDI in China and it may be better to invest in provinces that are not flooded with FDIs. Model (3) examines whether FDI growth rate is affected by the growth rate of the various determinants. The results are qualitatively the same except that the estimates for REST and Wage become insignificant. This means that the FDI growth of a province is positively related to its GDP growth and the growth of research output is negatively related to the CFDI growth. The FDI growth rate is unaffected by the growth rate of PERWI, REST, and Wage. The adjusted R2 for Model (3) is 0.58, much lower than the other three models. The DW is 2.1395 indicating no serial correlation. Our fixed effect regression results are quite robust for Model (1) to (3). Overall, Model (2) gives the best results. The GLS fixed effect model can explain 99% of the variation of FDI across 30 provinces over the period from 1989 to 1996 and there is no indication of serial correlation. Taking a short sample that starts from 1992 allows us to consider two more interesting variables, foreign portfolio investment and the degree of openness in each province. As discussed before, we believe foreign portfolio investment would be a substitute to FDI and the degree of openness will attract more FDI. The regression equation is set in Equation (2) and again, we estimate under three different approaches, the OLS fixed effect (Model 1), the GLS fixed effect (Model 2), and the GLS fixed effect on differencing data (Model 3). The results are given in Table 5.

(Insert Table 5 Here)

Model (1) gives worse results than its counterpart in Table 4 in the sense that some variables now become insignificant. However, after being adjusted for heteroskedasticity in Model (2), the

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results are much better. When contrasting with Model (2) in Table 4, it can be seen that the estimates of the six variables, namely GDP, Patent/RD, RSET, Wage, CFDI, and Risk, remain highly significant. Furthermore, the estimate of PERWI, the domestic investment per worker, now becomes significant at all conventional statistical levels. A 1% increase in PERWI leads to a 0.65% increase in FDI. This is consistent with the findings of Edwards (1999) that domestic investment and FDI are complements, and supports the agglomeration argument of FDI. Interestingly, the estimate of Foreign Loans now becomes negative although still statistically insignificant. Unfortunately, the two new variables, FPI and Openness, do not enter significantly into the regression. The signs, however, are as expected. If a province has more firms issuing foreign shares to attract foreign capital, there is a tendency for this province to rely less on FDI. On the other hand, if a province opens more to the outside world in terms of establishing more foreign trade, this province tends to be able to attract more FDI. But the lack of statistical significance does not allow strong assertion in this regard. Model (3) on change in FDI also gives stronger results than that in Table 4. The three variables, change in GDP, change in Patent/RD, and change in CFDI, still enter significantly into the equation, as in Table 4. It is true that the Risk variable is no longer significant but this is probably because Risk is a time-series variable and the shortened sample does not allow the variable to have much variation within the period. On the other hand, two more variables, change in PERWI and change in Wage, now enter significantly into the regression. One gives a t-value of 4.52 and the other gives a t-value of 3.64. These reinforce the previous results that labor productivity and wage are important factors to FDI. Before concluding this section, we would like to examine how well the predicted FDI that is based on our model fits the actual FDI of each province. Specifically, we look at the residuals generated by Model (2) applied to the full sample period of 1989-1996. A standardized residual greater than 1.65 constitutes a significant outlier. A positive outlier indicates that, after accounting for the impact of the model, a province attracted more than its share of FDI in a particular year. A negative outlier indicates that a province attracted less than its predicted amount of FDI. Since the sum of time series residuals are forced to be zero by model construction, we cannot identify, ceteris

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paribus, which provinces received higher or lower than the average amount of FDI for the whole period of 1989-1996. However, we calculate the standard deviation of time series residuals for each province to identify the FDI of which provinces can be better explained by our fixed effect regression model. Table 6 presents the panel of standardized residuals in the first eight columns. The time series standard deviation of residuals, σ, for each province is shown in the last column.

[Insert Table 6 here]

There is no discernible pattern of positive or negative residuals across the 30 provinces overtime. Among 240 standardized residuals, there are only 14 outliers (highlighted). This is consistent with the high goodness of fit of our model. However, 10 out of 14 outliers are concentrated in the Western region, while Eastern provinces only have one and Central provinces have three. The standard deviations shown in the last column also indicate that σ is generally smaller for eastern provinces than for central and western provinces. This means the model explains the FDI distribution better for Eastern provinces than it does for the Central and Western provinces. This may suggest that the FDI in the Eastern provinces is more predictable because these provinces started earlier in receiving FDI and have a more matured FDI receiving mechanism and more stable environment. By contrast, Central and Western provinces did not start to get FDI until the mid-1980s. The less matured FDI mechanism and environment may make the FDI there less predictable.

6. Conclusion and Further Study China’s economic reform has attracted worldwide attention. Its ability to attract foreign capital is viewed as one crucial factor for the ultimate success of this new “Long March”. Our paper provides a timely study in this area. We look at FDI as one way to attract foreign capital, in addition to foreign loans and foreign portfolio investment. As such, the study advances our understanding in the factors affecting the level of FDI across provinces in China. We have some mild evidence that foreign loans and foreign portfolio investments are substitutes of FDI. A surprising but important

16

finding is that cumulative FDI has a negative impact on new FDI. Given the findings by Wei (1995) that China has received less FDI than it should after adjusting for its size and population, this carries an important policy implication that provincial officials have a lot more to do to improve the investment environment. On the other hand, multinational corporations may want to consider investing in provinces not yet flooded with FDI competitors. The study also re-confirms other findings that GDP is the dominant determinant after controlling for the provincial specific fixed effect. Provinces with high GDP offer better infrastructure, better transportation network, better education, higher degree of industrialization, and better access to large markets. Provinces benefiting from higher GDP level and an influx of FDI are coastal provinces. The relative endowment of skilled workers, the research capability, and wage rate are also important determinants of the distribution of FDI. The higher the proportion of skilled workers and the research capability, the more attractive is the place for foreign investors. Interestingly, higher wage is found to associate with higher FDI in a province. Since China is a developing country, high wage may be a signal of better economic standard of the province that attracts foreign investment. Our model is robust across different fixed effect specifications and has a high goodness of fit. However, it explains the FDI distribution in the coastal provinces better than it does for Central and Western provinces. Our study has several limitations that deserve further investigations. First, we have not broken down the nature of the FDIs. For instance, Wheeler and Mody (1992) find that factors important to FDI in the electronics industry may not be important to the manufacturing industry as a whole. There may be a clientele effect in the sense that different provinces may attract different types of FDI industries. Lumping them together may well conceal some important factors. Second, due to data limitation, we are not able to consider the tax effect of FDI on China. Hines (1996) demonstrates that in the US, higher state tax rates have a significantly negative effect on investment. Third, Branstetter and Feenstra (1999) suggest that FDI in China are competing with state-owned enterprises. Hence, opening up the domestic market to foreign importers and investors means sacrificing the benefits

17

gained by state-owned enterprises. It would be quite interesting to examine empirically whether and to what extent such a trade-off relationship exists.

18

Reference Bagchi-Sen, Sharmistha and James O. Wheeler, 1989, "A Spatial And Temporal Model Of Foreign Direct Investment In The United States," Economic Geography, v65(2), 113-129. Bajo-Rubio, Oscar and Simon Sosvilla-Rivero, 1994, "An Econometric Analysis Of Foreign Direct Investment In Spain, 1964-89," Southern Economic Journal, v61(1), 104-120. Bhagwati, J.N. and T.N. Srinavasan, Lectures in International Trade, Cambridge, MA: MIT Press, 1983. Branstetter, Lee and Robert Feenstra, 1999, “Trade and Foreign Direct Investment in China: a Political Economy Approach,” NBER Working Paper No. 7100. Braunerhjelm, Pontus and Roger Svensson, 1996, "Host Country Characteristics And Agglomeration In Foreign Direct Investment," Applied Economics, v28(7,Jul), 833-840. Chen, Jinghan, 1993, "The Environment For Foreign Direct Investment And The Characteristics Of Joint Ventures In China," Development Policy Review, v11(2), 167-183. Chen, Chung, Lawrence Chang, and Yimin Zhang, 1995, “The role of Foreign Direct Investment in China’s Post-1978 Economic Development,” World Development, v23(4), 691-703. Cheng, Joseph Y.S. and Zhang Mujin, 1998, “Analysis of Regional Differences in China and the Delayed Development of the Central and Western Regions,” Issues & Studies, v34(2), 3568. Coughlin, Cletus C., Joseph V. Terza and Vachira Arromdee, 1991, "State Characteristics And The Location Of Foreign Direct Investment Within The United States," Review of Economics and Statistics, v73(4), 675-683. Dunning, J. H. “Trade, Location of Economic Activity and the MNE: A Search for an Eclectic Approach,” in the International Allocation of Economic Activity, Bertil Ohlin, Per Ove Hesselborn, and Per Magnus Wijkman, eds. London: The Macmillan Press, 1977. Edwards, Sebastian, 1990, “Capital Flows, Foreign Direct Investment, and Debt-Equity Swaps in Developing Countries,” NBER Working Paper No. 3497. Eiteman, David, Arthur Stonehill and Michael Moffett, Multinational Business Finance, 8th ed. Addison-Wesley Publishing Company, 1998. Friedman, Joseph, Daniel A. Gerlowski and Jonathan Silberman, 1996, "Foreign Direct Investment: The Factors Affecting The Location Of Foreign Branch Plants In The United States," Global Finance Journal, v7(2,Fall-Winter), 209-222. Graham, Edward and Paul Krugman, Foreign direct investment in the United States, 2nd ed. Washington, DC: Institute for International Economics, 1991.

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Greene, William H., Econometric Analysis, New York: MacMillan Publishing Company, 1993. Grossman, G.M. and E. Helpman, Innovation and Growth in the Global Economy, Cambridge, MA: MIT Press, 1991. Helpman, Elhanan, 1984, “A simple Theory of International Trade with Multinational Corporation,” Journal of Political Economy, v3, 451-471. Hines, James, 1996, “Altered States: Taxes and the Location of Foreign Direct Investment in America,” American Economic Review, v.86, 1076-1094. Hsiao, Cheng, Analysis of Panel Data, New York: Cambridge University Press, 1989. Hymer, S., The International Operations of National Firms: A Study of Direct Foreign Investments, MIT Press, Cambridge. MA, 1960. Kamath, Shyam, 1990, "Foreign Direct Investment In A Centrally Planned Developing Economy: The Chinese Case," Economic Development and Cultural Change, v39(1), 107130. Kravis, Irving and Robert Lipsey, 1982, “The Location of Overseas Production and Production for Export by U.S. Multinational Firms,” Journal of International Economics, v12(3/4), 201-223. Lipsey, Robert, “Foreign Direct Investment in the United States: Changes over Three Decades,” in Kenneth Froot, ed., Foreign direct investment. University of Chicago Press, 1993, 113-170. Lipsey, Robert, 1999, “The Location and Characteristics of U.S. Affliliates in Asia,” NBER Working Paper No. 6876. Rugman, Allen, “Inside the Multinationals: The economics of Internal Markets. New York: Columbia University Press, 1981. Shapiro, Alan, Foundations of Multinational Financial Management, New Jersey: PrenticeHall, 1998. Sit, Victor, 1985, “The Special Economic Zones of China: A New Type of Export Processing Zone?” Development Economics, v23(1), 69-86. Tso, Allen, 1998, “Foreign Direct Investment and China Economic Development,” Issues & Studies, v34(2), 1-34. Wang, Z.Q. and N.J. Swain, “The Determinants of Foreign Direct Investment in Transforming Economies: Evidence from Hungary and China,” Weltwirtschaftsliches Archiv, v131, 359-82. Wei, Shang-Jin, 1995, "Attracting Foreign Direct Investment: Has China Reached Its Potential?," China Economic Review, v6(2,Fall), 187-199.

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Wheeler, David and Ashoka Mody, 1992, “International Investment Location Decisions,” Journal of International Economics, v33, 57-76. Xin Xiangyang and Ni Jianzhong, eds., Dongxi Lunheng: Tianping Shang de Zhongguo (A Discussion of the East and West: China on the Scale), Beijing: China Social Press, 1995.

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Table 1 The Percentage Distribution of FDI across the Three Regions

1989

1990

1991

1992

1993

1994

1995

1996

Eastern

92.16

93.87

92.46

91.30

87.38

87.83

87.71

88.04

Central

3.84

3.87

4.48

6.82

8.88

7.85

9.21

9.52

Western

3.99

2.26

3.06

1.89

3.74

4.31

3.08

2.45

Total

100

100

100

100

100

100

100

100

Note: For the provinces included in each region, please see Table 6.

22

Table 2 The Possible Determinants of FDI Distribution

Category 1. Market Demand and Market Size

Proxy GDP GDP Per Capita Retail Sales Retail Sales Per Capita

2. Agglomeration Infrastructure

GDP Per km2 Highway Per km2 Railway Per km2 Domestic Investment Domestic Investment Per Worker (PERWI) Cumulative FDI (CFDI) REST – Number of research engineers, scientists and technicians as a percent of the total employees.

Degree of Industrialization Level of Foreign Investment 3. Labor Quality

4. Labor Cost

Average Wage (Wage)

5. The Level of Scientific Research

6. Degree of Openness

R&D Expenditures Number of Patents Number of Universities Total trade amount

7. Political Risk

Risk ranking by Political Risk Services

8. FDI substitutes

Foreign portfolio investment Foreign Loans

23

Table 3 Correlation Matrix for Potential Determinants Panel A: All Variables GDP PCAPG INV CINV PERWI POP RETAIL PCAPR PATENT UNIV WAGE RD REST HOSBD HIGHW RAILW FL RISK

CFDI 0.69 0.51 0.81 0.82 0.38 0.56 0.74 0.39 0.73 0.64 0.23 0.69 0.18 0.09 0.63 0.57 -0.01 -0.39

GDP 0.35 0.94 0.84 0.15 0.87 0.98 0.30 0.88 0.90 0.03 0.87 0.06 -0.07 0.72 0.63 -0.04 -0.22

PCAPG

0.52 0.58 0.90 -0.16 0.35 0.93 0.46 0.27 0.70 0.48 0.67 0.70 0.38 0.33 -0.02 -0.38

INV

0.90 0.44 0.71 0.92 0.47 0.86 0.79 0.24 0.83 0.14 0.05 0.66 0.55 -0.05 -0.32

CINV

PERWI

0.48 0.58 0.77 0.41 0.77 0.63 0.42 0.73 0.11 0.06 0.53 0.44 -0.07 -0.59

POP

-0.32 0.16 0.84 0.27 0.03 0.72 0.25 0.54 0.65 0.14 0.11 -0.02 -0.39

RETAIL

0.85 -0.18 0.69 0.81 -0.33 0.66 -0.30 -0.45 0.55 0.49 -0.03 -0.03

PCAPR

0.38 0.89 0.93 0.00 0.88 0.12 -0.02 0.74 0.64 -0.04 -0.09

0.45 0.30 0.58 0.48 0.73 0.73 0.41 0.33 -0.02 -0.11

PATENT

0.86 0.07 0.90 0.29 0.14 0.74 0.74 -0.04 -0.19

UNIV

-0.13 0.90 0.24 0.05 0.75 0.69 -0.01 0.00

WAGE

RD

0.11 0.37 0.31 0.09 -0.19 -0.08 -0.54

0.42 0.20 0.77 0.75 -0.04 -0.16

REST

0.77 0.37 0.37 0.02 0.00

HOSBD

HIGHW

0.01 0.22 0.07 -0.02

0.74 -0.08 -0.04

RAILW

FL

-0.03 -0.01

0.07

Panel B: Correlation Matrix for Selected Determinants PERWI PATENT/RD REST W CFDI FL RISK

GDP 0.15 0.27 0.05 -0.10 0.69 -0.03 -0.21

PERWI

PATENT/RD

REST

W

CFDI

FL

0.11 0.53 0.00 0.49 -0.02 -0.39

-0.19 -0.19 0.22 -0.01 -0.12

-0.02 0.29 0.02 -0.01

-0.01 -0.08 -0.36

-0.04 -0.34

0.07

Panel C: Correlation Matrix (1992-1996) GDP 0.12 0.40 0.06 -0.18 0.29 -0.08

Per Worker Inv.

Patent/RD

REST

Wage

FPI

Per Worker Investment Patent/RD REST Wage FPI FL

0.10 0.55 -0.11 0.49 -0.04

-0.19 -0.39 -0.03 0.00

0.06 0.25 0.00

0.29 -0.10

-0.18

Openness

0.14

0.74

0.12

0.42

0.13

0.47

-0.08

CFDI Risk

0.77 -0.04

0.45 -0.06

0.35 -0.04

0.27 0.01

-0.09 -0.15

0.41 -0.05

-0.11 0.06

24

FL

Openness

CFDI

0.56 -0.07

-0.09

Table 4 Pooled Regression Results (1989-1996) The pooled regression model is ln(FDIit) = αit + β1ln(GDPit) + β2ln(PERWIit) + β3ln(Patent/RDit) + β4ln(Wit) + β5ln(RESTit) + β6ln(CFDIit) + β7ln(FLit) + β8Riskt + εit ,

(i = 1, 2, ... 30 and t = 1, 2, ... 8)

where FDI and GDP are in millions RMB, PERWI is defined as domestic investment divided by the employment and is in thousands of RMB per worker, Patent/RD is the number of patents per million of RD expenditure, W is the wage rate in thousands of RMB after purified its correlation with PERWI, REST is the total number of research engineers, scientists and technicians divided by the employment, CFDI is the cumulative foreign direct investment in millions of RMB, FL is the amount of foreign loans in millions of RMB, and Risk takes the value of 1 to 4 according to the ranking in the Political Risk Yearbook for the period of 19891996. All RMB are in 1990 constant price. i refers to individual provinces and t refers to each year in the sample period. (1) OLS Fixed Effect

(2) GLS Fixed-Effect

(3) GLS First Difference

240

240

210

3.1798 (3.704)** 0.2244 (0.878) 0.2219 (2.128)** 0.9406 (1.449) 1.5511 (1.612) 2.1412 ( 1.202) -0.1574 (-1.358) -0.3140 (-3.504)**

4.2236 (7.830)** 0.0744 (0.453) 0.1699 (2.089)** 1.0635 (2.220)** 1.7345 (2.681)** 1.2830 (0.965) -0.3476 (-4.460)** -0.3036 (-5.713)**

5.4598 (11.49)** 0.0999 ( 0.650) 0.1708 ( 3.330)** 0.3115 (1.085) 0.0092 (0.016) 0.0687 (0.221) -0.6825 (-9.291)** -0.1289 (-3.662)**

Fixed Effect

Yes

Yes

Adjusted R2

0.9163

0.9902

0.5874

DW

1.8726

1.7940

2.2835

Number of Observation GDP Per Worker Investment Patent/RD RSET Wage Foreign Loans Cumulative FDI Risk

*(**) denotes significant at 10(5) percent

25

Table 5 Pooled Regression Results (1992-1996) The pooled regression model is ln(FDIit) = αit + β1ln(GDPit) + β2ln(PERWIit) + β3ln(Patent/RDit) + β4ln(Wit) + β5ln(RESTit) + β6ln(CFDIit) + β7ln(FLit) + β8Riskt + β9ln(FPIit) + β10ln(Opennessit) + εit ,

(i = 1, 2, ... 30 and t = 1, 2, ... 5)

where FDI and GDP are in millions RMB, PERWI is defined as domestic investment divided by the employment and is in thousands of RMB per worker, Patent/RD is the number of patents per million of RD expenditure, W is the wage rate in thousands of RMB after purified its correlation with PERWI, REST is the total number of research engineers, scientists and technicians divided by the employment, CFDI is the cumulative foreign direct investment in millions of RMB, FL is the amount of foreign loans in millions of RMB, Risk takes the value of 1 to 4 according to the ranking in the Political Risk Yearbook for the period of 19891996. FPI is the number of total B-, H- and N-shares available for foreign investors, and Openness is defined as total trading volume as a percentage of GDP. All RMB are in 1990 constant price. i refers to individual provinces and t refers to each year in the sample period. (1) OLS Fixed Effect

(2) GLS Fixed-Effect

(3) GLS First Difference

150

150

120

1.9740 (5.213)** 0.6548 (4.617)** 1.0632 (13.62)** 1.6125 (2.328)** 1.9300 (3.105)** -0.0157 (-1.278) -3.8535 (-0.301) -0.0122 (-0.112) -0.2085 (-3.306)** -0.0976 (-2.748)** Yes

2.0577 (3.952)** 0.6779 (4.527)** 1.1011 (12.17)** 0.6701 ( 0.623) 2.3564 (3.649)** -0.0188 (-1.190) 0.0010 (-1.386) 0.0547 ( 0.413) -0.2553 (-3.140)** -0.0556 (-1.381)

Fixed Effect

1.7225 (1.639)* 0.4450 (1.312) 1.0764 (4.982)** 1.5476 (0.928) 0.6855 (0.555) -0.0098 (-0.436) 4.9134 (0.281) 0.1974 (0.713) -0.0809 (-0.507) -0.1425 (-1.131) Yes

Adjusted R2

0.9216

0.9989

0.8365

DW

1.6631

1.7596

1.8696

Number of Observation GDP Per Worker Investment Patent/RD RSET Wage FPI Foreign Loans Openness Cumulative FDI Risk

*(**) denotes significant at 10(5) percent

26

Table 6 Standardized Difference between Actual and Predicted FDI (Based on GLS Fixed-Effect Model for 1989-1996) 1989 Eastern Beijing Tianjin HeBei Liao Ning Shandong Jiangsu Shanghai Zhejiang Fujian Guangdong Guangxi Hainan

1990

1991

1992

1993

1994

1995

1996

σ

0.94 -0.70 -0.23 0.17 -0.22 -0.51 0.54 0.01 0.46 0.69 0.42 0.88

1.91** -0.63 0.34 1.06 0.06 -0.58 0.12 -0.52 0.19 0.70 -0.50 0.28

-0.03 0.54 0.17 0.83 -0.23 -0.29 -0.71 -0.50 0.37 0.53 -0.88 0.60

-0.60 -1.41 -0.38 -0.22 0.27 0.01 -0.78 -0.53 0.21 -0.29 -0.10 0.09

-0.41 0.56 0.53 -0.02 0.15 0.63 1.12 0.76 0.05 -0.21 1.07 -0.57

-0.25 0.19 0.06 -0.61 0.24 -0.82 -0.25 0.21 -0.29 0.06 0.36 -0.32

-1.20 0.38 -0.46 -0.22 -0.12 0.73 -0.20 0.09 -0.44 -0.91 -0.23 -0.18

-0.37 1.07 -0.03 -0.98 -0.15 0.82 0.16 0.47 -0.54 -0.57 -0.13 -0.78

0.91 0.78 0.33 0.64 0.19 0.61 0.59 0.45 0.35 0.56 0.56 0.53

0.88 -2.65** -1.59 0.28 -1.13 -1.07 1.35 -0.27 -1.55

-0.66 1.67* -0.22 0.16 -0.54 -1.19 -0.83 -0.21 -0.97

-0.69 -1.35 0.20 -0.36 -0.29 -0.39 0.46 0.07 -0.56

1.07 -0.97 -0.22 -0.22 -0.49 0.42 -0.82 0.33 0.18

0.90 2.09** 0.74 0.49 0.53 0.67 0.53 0.42 1.22

-1.05 0.12 -0.14 -0.16 0.36 0.67 -0.09 0.18 0.31

-0.66 0.47 0.58 -0.10 0.80 0.38 -0.30 -0.26 0.58

0.21 0.62 0.64 -0.09 0.77 0.50 -0.29 -0.26 0.79

0.81 1.48 0.70 0.27 0.66 0.72 0.69 0.27 0.88

-1.39 0.65 0.48 1.46 2.40** -0.12 0.85 -0.60 0.46

-0.80 0.13 -1.10 1.57 1.14 -0.81 0.39 0.14 2.26**

0.70 1.06 -0.80 0.53 0.21 1.38 -0.11 -0.95 -3.20**

-0.43 0.17 0.41 0.39 -0.89 -4.13** 0.19 -1.57 -5.65**

1.19 0.40 1.55 -0.55 0.08 -0.03 1.38 2.25** 2.48**

1.03 0.07 -0.10 -0.93 -0.69 1.71* 0.17 0.67 1.10

-0.01 -0.70 0.18 -1.83 -1.01 0.80 -0.98 -0.05 0.95

-0.29 -1.78* -0.62 -0.64 -1.23 1.20 -1.89* 0.11 1.61

0.85 0.82 0.79 1.12 1.17 1.75 0.96 1.08 2.70

0.16 0.47 -0.67 *(**) denotes significant at 10(5) percent.

-2.91**

3.65**

0.33

-0.72

-0.31

Central Shanxi Inner Mongolia JiLin Heilongjiang Anhui Jiangxi Henan Hubei Hunan Western Sichuan Guizhou Yunan Tibet Shaanxi Gansu Qinghai Ningxia Xingjiang Total

27