Jinzhao Chen
email: jichen@pse.ens.fr

Joachim Jarreau

Iikka Korhonen

Romain Lafarguette

Barry Naughton

Xun Pomponio

Thérèse Quang

Ben Salem

Vincent Bouvatier

Matthieu Bussière

Eve Caroli

Marie-Françoise Calmette

Changsheng Chen

Cécile Couharde

Gilles Dufrénot

Ludovic Gauvin

Eric Girardin

Pierre-Cyrille Hautcoeur

Sylviane Guillaumont Jeanneney

Maria J Herrerias

Jean Mercenier

Ronald I Mckinnon

Valérie Mignon

Julie Subervie

Real exchange rate and economic growth: Evidence from Chinese provincial data (1992-2008) 1

Keywords: JEL classifications: O53, F31 Real exchange rate, economic growth, China, generalized method of moments

This paper studies the role of the real exchange rate in economic growth and in the convergence of growth rates among provinces in China. Using data from 28 Chinese provinces for the period 1992-2008 together with dynamic panel data estimation, I find conditional convergence among coastal provinces and also among inland provinces. The results reported here confirm the positive effect of real exchange rate appreciation on economic growth in the provinces.

Introduction

Even though its breathtaking growth stalled in 2009, China has achieved a rapid recovery (recording a GDP growth rate of 8.7 percent) by its application of effective monetary and fiscal stimulus (ADB, 2010). However, spectacular and continual growth during the last three decades in China cannot hide the increasing disparity and inequality among the Chinese provinces (see [START_REF] Chen | China's regional disparity and its policy responses[END_REF][START_REF] Heshmati | Continental and sub-continental income inequality[END_REF][START_REF] Xu | Explaining the changes of income distribution in China[END_REF]. 2 [START_REF] Wang | The political economy of uneven development: the case of China[END_REF] even warned of possible severe consequences that could shake the political regime.

The increased spread in unconditional income distribution, as estimated by kernel density (see [START_REF] Jones | On the evolution of the world income distribution[END_REF][START_REF] Quah | Galton's fallacy and tests of the convergence hypothesis[END_REF]1997) makes the point directly: there was a greater disparity in 2004 than in 1994 and 1984, when provincial disparity was already obvious (Figure 1a). 3 Even after controlling for the effect of increasing average income (represented by rightward shifts of the distribution), the income distribution in logarithmic form (Figure 1b) reveals a similar pattern but with limited spreading; this implies a lower rate of increase in the proportional gap than in the absolute difference between rich and poor provinces.

[[INSERT Figure 1 about Here ]]

The regional disparity has long been a concern of policy makers and economists [START_REF] Hu | China regional disparity report, Liaoning People's press[END_REF]NDRC, 2010). The country's present economic development strategy generates considerable social tension because, over the past ten years, that strategy has proved largely incapable of reducing extreme poverty further or significantly improving the rural-urban and regional income imbalances [START_REF] Woo | China in the current global economic crisis[END_REF]. Furthermore, greater social and economic instability created by income disparities may obstruct the growth of China [START_REF] Yang | What has caused regional inequality in China?[END_REF] and hence of other, low-income countries [START_REF] Garroway | The renminbi and poor-country growth[END_REF]-given that, in the 21st century, China has contributed to global growth in general and to poor-country growth in particular. In this context, the issue at stake is whether or not managing the real exchange rate helps the Chinese government to rebalance economic growth and facilitate interprovincial convergence (whereby the poorest regions can benefit from development of the richest). In short: Does a lower real exchange rate spur provincial economic growth?

The real exchange rate (i.e., the relative price of tradable to nontradable goods) does not traditionally occupy a central role in growth regressions because it only recently entered theoretical models of economic growth. 4 However, there has been a gradual recognition of the real exchange rate's role in the growth process (see, e.g., [START_REF] Eichengreen | The real exchange rate and economic growth[END_REF][START_REF] Rodrik | The real exchange rate and economic growth[END_REF]. In fact, the disastrous effects of overvaluation on economic growth are widely documented in the empirical literature [START_REF] Acemoglu | Institutional causes, macroeconomic symptoms: volatility, crises and growth[END_REF][START_REF] Benaroya | Measuring exchange rates misalignments with purchasing power parity estimates[END_REF][START_REF] Cottani | Real exchange rate behavior and economic performance in LDCs[END_REF][START_REF] Dollar | Outward-oriented developing economies really do grow more rapidly: evidence from 95 LDCs, 1976-1985[END_REF][START_REF] Gala | Real exchange rate levels and economic development: theoretical analysis and econometric evidence[END_REF][START_REF] Ghura | The real exchange rate and macroeconomic performance in Sub-Saharan Africa[END_REF][START_REF] Loayza | Economic growth in Latin America and the Caribbean: stylized facts, explanations, and forecasts[END_REF][START_REF] Razin | Real exchange rate misalignments and growth[END_REF]. 5 There are fewer consensuses regarding the effects of undervaluation on growth. For [START_REF] Berg | The real exchange rate and growth revisited: the Washington consensus strikes back?[END_REF], undervaluation-when viewed as a misalignment with long-run equilibrium levels-reduces growth. In contrast, [START_REF] Rodrik | The real exchange rate and economic growth[END_REF] used an index of undervaluation that was adjusted for the Balassa-Samuelson effect and argued that undervaluation actually facilitates economic growth; however, this relationship holds only for developing countries. According to Rodrik, the growth-driven channel is the (usually dynamic) tradable sector, which contributes to innovations and productivity increases. For China, then, maintaining an undervalued currency could serve to subsidize its manufacturing industries, the driving force behind that country's growth [START_REF] Rodrik | Making room for China in the world economy[END_REF]. [START_REF] Gala | Real exchange rate levels and economic development: theoretical analysis and econometric evidence[END_REF] considered technological upgrading and capital accumulation as two main transmission channels. Overall, the real exchange rate can serve as a facilitating condition: it cannot by itself sustain economic growth, yet an appropriate exchange rate policy can make it easier for a country to capitalize on its growth opportunities [START_REF] Eichengreen | The real exchange rate and economic growth[END_REF]. Eichengreen also claimed that keeping the real exchange rate at competitive levels and avoiding excessive volatility are both important for growth. 6 Despite a growing number of studies examining the link between the real exchange rate and economic growth across countries (for a comprehensive literature review, see [START_REF] Eichengreen | The real exchange rate and economic growth[END_REF], none considers the impact of real exchange rate of the RMB on economic growth in China at the provincial level. This paper attempts to fill that gap in the empirical growth literature by focusing on the rate's impact on provincial economic growth and on the conditional convergence of growth rates among the provinces.

To investigate the impact of the real exchange rate level on provincial growth, I apply the growth regression of [START_REF] Barro | Economic growth in a cross section of countries[END_REF] to a panel data set of 28 Chinese provinces. The real exchange rate (RER) is often defined as the relative price of tradable to nontradable goods (or inversely)-as in the standard, two-sector framework of a small open economy in which each sector produces a different type of goods. The RER so defined, and used here, is also known as the 'internal' real exchange rate [START_REF] Salter | Internal and external balance: the role of price and expenditure effects[END_REF]; an increase corresponds to a depreciation of the real exchange rate. Using dynamic panel estimations (with fixed effects model and the generalized method of moments estimation for system of equations, known as system-GMM estimation), I find a conditional convergence among the coastal provinces and among inland provinces for the period 1992-2008 but no convergence between these two groups of provinces. The results also show that an appreciation of the real exchange rate boosts provincial economic growth, which is not the same effect found by [START_REF] Rodrik | The real exchange rate and economic growth[END_REF]. The positive effect reported here could result from the redistribution of resources between sectors, the wealth effect stemming 5 For example, [START_REF] Ghura | The real exchange rate and macroeconomic performance in Sub-Saharan Africa[END_REF] showed a negative correlation between currency misalignments (overvaluation) and 33 sub-Saharan African countries' economic performances. [START_REF] Goldfajn | The aftermath of appreciations[END_REF] argued that most balance-of-payments crises are related to overvalued currencies. 6 However, the evidence for a link between the exchange rate volatility (or variability) and economic growth is less definitive [START_REF] Aghion | The real exchange rate and economic growth[END_REF][START_REF] Ghura | The real exchange rate and macroeconomic performance in Sub-Saharan Africa[END_REF]. In addition, other narratives focus on the nexus between the exchange rate regime and growth; [START_REF] Levy-Yeyati | To float or to fix: evidence on the impact of exchange rate regimes on growth[END_REF] found that less flexible exchange rate regimes are associated with slower growth in developing countries but that the choice of a particular exchange rate regime has no statistically significant effect on growth in industrial countries.

from enhancement of agricultural and service sectors, and/or the rising real wages and productivity of workers. The paper is organized as follows: in section two, I present the empirical methodology and the specification of the informal growth regression; section three describes the variables chosen as determinants of China's provincial economic growth and the new data to which the model is to be applied; I present in section four the results of regressions using different estimators and, before concluding, discuss in section five the transmission mechanism through which the role of the real exchange rate in growth takes effect.

Empirical methodology

The unconditional income distributions plotted in Figure 1 provide no evidence of economic convergence among the Chinese provinces. However, for a study of convergence it is more informative to examine the conditional distribution of per capita income. If the income gap became narrower over time between provinces sharing similar characteristics, then this would constitute conditional convergence; hence the provinces would converge to their respective steady states.

Despite a large body of literature seeking to explain China's miraculous growth (e.g., [START_REF] Herrerias | The driving forces behind China's growth[END_REF][START_REF] Wang | The political economy of uneven development: the case of China[END_REF], the empirical findings have yielded no consensus on provincial growth convergence.7 Empirical research on China's economic growth include a few studies that use the cross-country approach (with panel and/or cross-sectional data; see [START_REF] Ding | Can the augmented Solow model explain China's remarkable economic growth? A cross-country panel data analysis[END_REF][START_REF] Li | Testing the neoclassical theory of economic growth: evidence from Chinese provinces[END_REF][START_REF] Rodrik | The real exchange rate and economic growth[END_REF]. There is a larger body of empirical studies that focus on the evidence of interprovince growth in China. Two main empirical approaches are in common use. 8 The first amounts to variants of the neoclassical growth model, often the augmented Solow model pioneered by [START_REF] Mankiw | A contribution to the empirics of economic growth[END_REF]. 9 The second widely used approach incorporates informal growth regressions [START_REF] Barro | Economic growth in a cross section of countries[END_REF][START_REF] Barro | Economic Growth[END_REF]; with this approach, which is followed here, various economic theories of growth as used as a guide to choosing the right-hand-side (RHS) variables in the growth regression.

Informal growth regression

In this paper I follow the informal growth regression approach and implement the traditional linear informal growth regression analysis. 10 The growth regression is written as

, , 1 ; , i t i t i t t i i t y y X f f             (1)
for i = 1, …, N and t = 2, …, T. Here Δy i,t is the log difference in real per capita GDP over a five-year period, y i,t-1 is the logarithm of real per capita GDP at the beginning of each period (a proxy for initial income), X is a vector containing the explanatory variables measured either during that period (period average) or at the beginning of it (period initial value), f i denotes unobserved province-specific factors reflecting differences in the initial level of technical efficiency, and f t is a time-specific effect that captures the productivity changes common to all provinces (see [START_REF] Cottani | Real exchange rate behavior and economic performance in LDCs[END_REF][START_REF] Ding | Can the augmented Solow model explain China's remarkable economic growth? A cross-country panel data analysis[END_REF][START_REF] Ghura | The real exchange rate and macroeconomic performance in Sub-Saharan Africa[END_REF]. Equation (1) can be rewritten as , , 1 ; ,

( 1)

i t i t i t t i i t y y X f f             (2)
for i = 1, …, N and t = 2, …, T, which is a dynamic panel data specification with a lagged dependent variable on the RHS. The correlation between the lagged dependent variable and the time-invariant, country-specific effects renders the ordinary least-squares (OLS) estimator of equation ( 2) biased and inconsistent. Even with a fixed-effects estimator, the coefficient for initial income is likely to be seriously biased downward in a small sample with limited time span [START_REF] Nickell | Biases in dynamic models with fixed effects[END_REF]. I therefore follow the recommendation of [START_REF] Bond | GMM estimation of empirical growth models[END_REF] and use instead a system-GMM estimator, one with demonstrated superior performance on finite samples, developed by [START_REF] Arellano | Another look at the instrumental variable estimation of error-components models[END_REF] and [START_REF] Blundell | Initial conditions and moment restrictions in dynamic panel data models[END_REF]. This estimator uses moment conditions for a system, including both the first-differenced and the original equation in levels: lagged levels of the RHS variables are used as instruments in the first-differenced equations, and lagged first-differences are used as instruments in the original equation. Including the equation in levels in the system yields efficiency improvements as well as a significant reduction in the large bias suffered by the first-differenced GMM estimator (which results because the lagged levels of variables are only weak instruments for subsequent first differences).11 Moreover, Bond et al. claimed that the potential for obtaining consistent parameter estimates-even in the presence of measurement error and endogenous RHS variables-is a considerable strength of the GMM approach to empirical growth research.

Variables and data

In this section, I discuss the variables entering into the regression just described; I also present the data set and explain how the proxies for those variables are constructed.

Variables

The dependent variable is the growth rate of real income, as represented by the annual growth rate of the real GDP per capita for each Chinese province. With regard to the chosen explanatory variables for economic growth, a broad range of factors are taken into account. Given the relatively small number of observations (N = 28 and T = 4 when a sample averaged over five years is used) and in order to guarantee enough degrees of freedom for the estimation, only a few proximate determinants were chosen.12 These explanatory variables of economic growth rate are widely identified in the literature of economic growth.

First, the conditional convergence hypothesis argues that, ceteris paribus, countries with lower per capita GDP grow more rapidly owing to higher marginal returns on capital stock.

Hence I included the initial level of real per capita GDP to control for such conditional convergence [START_REF] Barro | Economic Growth[END_REF].

Second, the X vector includes the ratio of investment to GDP (based on the neoclassical model of [START_REF] Solow | A contribution to the theory of economic growth[END_REF] and also the population's natural growth rate. Growth is expected to be positively associated with investment and capital accumulation but negatively associated with population growth. 13 Third, it is widely hypothesized that human capital has a positive effect on economic growth (see [START_REF] Lucas | On the mechanics of economic development[END_REF]. [START_REF] Chen | Regional income inequality and economic growth in China[END_REF], Fleisher and[START_REF] Fleisher | The coast-noncoast income gap, productivity, and regional economic policy in China[END_REF][START_REF] Démurger | The relative contributions of location and preferential policies in China's regional development: being in the right place and having the right incentives[END_REF] provide evidence that education at the secondary or collegiate level helps account for observed differences in Chinese provincial growth rates. However, there is no agreement on the indicators for measuring such capital. Early proxies for human capital consisted of measuring the rate of gross secondary school enrollment (see [START_REF] Barro | Economic growth in a cross section of countries[END_REF][START_REF] Bond | GMM estimation of empirical growth models[END_REF][START_REF] Caselli | Reopening the convergence debate: A new look at cross-country growth empirics[END_REF][START_REF] Mankiw | A contribution to the empirics of economic growth[END_REF]. Alternatively, [START_REF] Ding | Can the augmented Solow model explain China's remarkable economic growth? A cross-country panel data analysis[END_REF] relied on the 'average level of human capital' data provided by [START_REF] Barro | International data on educational attainment: updates and implications[END_REF] to calculate the average years of schooling in the population (above age 15) as a direct measure of the stock of human capital. That approach might be preferable because school enrollment rates may conflate human capital stock and accumulation effects and because such rates are often a poor proxy for either factor [START_REF] Gemmell | Evaluating the impacts of human capital stocks and accumulation on economic growth: some new evidence[END_REF][START_REF] Temple | The new growth evidence[END_REF]. 14 So in addition to using the gross secondary enrollment rate, I follow [START_REF] Fleisher | Human capital, economic growth, and regional inequality in China[END_REF] and calculate the fraction of those in the total population who have at least a senior high school education as another proxy for the stock of human capital. 15 A major force pushing the economy toward marketization has been the introduction of foreign ownership through foreign direct investment (FDI) [START_REF] Fleisher | Human capital, economic growth, and regional inequality in China[END_REF]. Through its potential to bring in new production and managerial technologies, FDI has facilitated the transformation of China's state-owned and collective sectors [START_REF] Liu | Foreign direct investment and technology spillovers: theory and evidence[END_REF]. I incorporate this variable to help explain regional economic disparities.

Furthermore, a policy variable-trade openness-is also included. 16 This variable is viewed as having a positive role on economic growth and poverty reduction not only in neoclassical trade models (via comparative advantage) but also in the endogenous growth literature (via diffusion of technology or economies of scale). However, a large number of studies have yielded different empirical findings and various explanations (see [START_REF] Dufrénot | The trade-growth nexus in the developing countries: a quantile regression approach[END_REF]. 13 This variable is different from the one in Solow's growth model, where the population growth was measured by the rate of growth of the working-age population (and taken exogenously), emphasizing the supply of labor. In this paper, population growth is taken to be endogenous-as in endogenous growth theory, where population growth is negatively associated with growth in human capital and hence with economic growth [START_REF] Bond | GMM estimation of empirical growth models[END_REF]. The fertility rate is often used as an alternative measure of population growth [START_REF] Barro | Economic growth in a cross section of countries[END_REF]. 14 Ding and Knight (2009) also adopted the estimates of [START_REF] Yao | On economic growth, FDI and exports in China[END_REF], who treated each year's school graduates as an addition to the human capital stock. 15 Here, the senior high school includes the special technical secondary school (Zhong zhuan in Chinese) but not the skill school (Ji Xiao). The numbers of people with at least some college education and with at least some senior high school education are estimated based on the respective annual flows of enrollments in college and senior high school; these numbers are anchored to periodic population census data and annual population change survey data. The census data (1982, 1990, and 2000) and the annual population change survey data (1993, 1996, 1999, 2002, and 2003) provide the proportions of people sorted by their educational levels. 16 One could also introduce other variables (e.g., inflation) that reflect macroeconomic stabilization policies and institutions.

It could be argued that the regional divergence (or disparity) in incomes noted in previous studies may be due largely to geographic (or location) factors [START_REF] Chen | Regional income inequality and economic growth in China[END_REF][START_REF] Démurger | The relative contributions of location and preferential policies in China's regional development: being in the right place and having the right incentives[END_REF]. Furthermore, the differences in preferential open-door policies at the provincial level might also lead to growth disparity. These two factors may combine to yield a direct effect on the openness of trade as well as an indirect effect on other sources of growth (e.g., technological progress) via attracting foreign investment. By giving each province a weight that reflects the type of economic zone that it hosts, Démurger et al. constructed an annual index of the level of open-door preferential policies; that index ranges from 0 to 3 for each province during the period 1978-1998, where 3 corresponds to the highest level of central government's policy preference. In addition to these indexes, a simple period average for each province is reported (see Table 4 of their paper). 17 [START_REF] Pedroni | Regional income divergence in China[END_REF] divided these provincial average indexes (which range from 0.33 to 2.86) into three roughly equal quantiles: low, medium, and high (see Table A2 in the Data Appendix). In the regressions reported here, I include the variable of policy preference level in the categorical form of Pedroni and Yao. 18 I introduce the variable of interest-the real exchange rate of Chinese provinces-in order to investigate the impact of rate changes on China's economic growth. Measuring the real exchange rate is not straightforward, and the difficulties are both conceptual and empirical. The many and varied definitions of the real exchange rate, which are drawn from different analytical frameworks and suitable for use in different circumstances, have long complicated the analysis of real exchange rate issues [START_REF] Montiel | An overview[END_REF]. 19 I adhere to the standard framework and directly measure an internal real exchange rate for each of the 28 Chinese provinces. This rate is defined as the relative price of tradable to nontradable goods, expressed in logarithmic form:

T NT ln RER ln( / ) PP 
. 20

Data

The empirical literature on growth in developing countries is rife with inaccurate data and measurement error. Sometimes, data falsification results from political influence. 21 Since there is no alternative data set to the official series for China (see Naughton, 2007, p. 141), researchers 17 Various types of economic zones reveal that the provinces benefited disproportionately from this open-door policy, which consisted of attracting FDI and promoting foreign trade in targeted economic zones. A weight of 3 was assigned to the Special Economic Zone (SEZ) and Shanghai Pudong New Area; a weight of 2 was assigned to the Economic and Technological Development Zone (ETDZ) and Border Economic Cooperation Zone (BECZ); and a weight of 1 was assigned to the Coastal Open City (COC), Coastal Open Economic Zone (COEZ), Open Coastal Belt, major city on Yangtze (MC), bonded area (BA), and capital city of inland province or autonomous region (CC).

A weight of 0 was assigned to each province without an open zone. 18 See also [START_REF] Maasoumi | Economic reform, growth and convergence in China[END_REF], who included the policy preference level in this form. 19 [START_REF] Montiel | An overview[END_REF] remark that the empirical measurement of RER in most developing countries involves many practical problems that are seldom encountered in the case of industrial countries. 20 One could instead take the real effective exchange rate (a.k.a. the 'external' real exchange rate) as a proxy for the real exchange rate, as in Guillaumont Jeanneney and Hua (2008). 21 For influential work on the reliability of China's GDP statistics, see (among others) [START_REF] Maddison | Chinese Economic Performance in the Long Run[END_REF], [START_REF] Rawski | What is happening to China's GDP statistics?[END_REF][START_REF] Holz | China's reform period economic growth: how reliable are Angus Maddison's estimates?[END_REF].

have no choice but to use official Chinese data while making, perhaps, some corrections and adjustments.

The empirical analysis in this paper is based on a data set compiled from several sources: the China Compendium of Statistics 1949-2008 (CCS) (CPESY). An important feature of this study is my use of relatively recent official data to construct a new series of real GDP per capita, for each province, that takes into account the second national economic census of 2008. Hence the GDP series employed here are assumed to be better (i.e., less biased) than those used in previous studies. 22 The annual sample includes 28 provinces for the period 1992-2008. 23 For the panel data study, I obtained a data set for each province that was averaged over a nonoverlapping five-year interval to mitigate the influence of temporary factors associated with business cycles. 24 The dependent variables are the five-year average growth rates g of real GDP per capita; the annual rates are plotted in Figure 2. 25 These growth rates are deflated (by provincial CPI) and calculated in terms of constant 2000 RMB. The proxies for the explanatory variables entered into the regression are as follows. The logarithm of real GDP per capita at the beginning of the five-year period (ly_1) as the proxy for the initial level of income; the share of the gross capital formation in GDP (invest2gdp1) and the share of the fixed capital formation in GDP (invest2gdp2) as proxies for investment share; the ratio of gross secondary school enrollment to total population (school1) and the share of those who have at least some senior high school education (school2) as proxies for human capital; the population's natural growth rate (popgr) as the proxy for population growth; the share of FDI in GDP (fdi2gdp) and in gross capital formation (fdi2invest) as proxies for FDI share; and the ratio of total international trade, both export and import, to GDP (trade2gdp) as the proxy for trade openness.

[[INSERT Figure 2 about Here ]]

22 The current-price GDP series for 2005-2008 that are published in 2010 CSA have been revised to reflect the second economic census; the series published in CSY report preliminary figures only (which are nevertheless used in some studies). 23 From the administrative standpoint, mainland China consists of 31 provinces, minority autonomous regions, and municipalities. Because Chongqing became a municipal city in 1997, we combined Chongqing with Sichuan for the period 1997-2006 to preserve consistency with earlier observations (cf. [START_REF] Ding | Why has China grown so fast? The role of structural change[END_REF]. Hainan and Tibet are excluded because so few data are available for their PPI indices. 24 This is a commonly adopted practice in the empirical growth literature; see [START_REF] Islam | Growth empirics: a panel data approach[END_REF], [START_REF] Bond | GMM estimation of empirical growth models[END_REF], [START_REF] Bussière | Financial openness and growth: short-run gain, long-run pain?[END_REF], and [START_REF] Ding | Why has China grown so fast? The role of structural change[END_REF][START_REF] Woo | China in the current global economic crisis[END_REF]. The limited span of the series explains my choice of five-year intervals (1992-1996, 1997-2001, 2002-2006), although the fourth period (2007)(2008) allows only for two-year averages. See [START_REF] Hao | Development of financial intermediation and economic growth: The Chinese experience[END_REF] for a study in which period-averaged data is based on a three-year interval. One should bear in mind that it is an open question whether using five-or ten-year averages for avoiding business cycle effects or annual data is better. Further investigation is needed on the extent to which averages taken over short time spans reduce such effects (cf. [START_REF] Temple | The new growth evidence[END_REF]. 25 The growth rate for Sichuan includes an outlier that is due mainly to the difficulty of calculating a reliable year-end population for this region in 1997, when Chongqing became a municipal city.

For the location variable, I use a simple indicator set equal to 1 for coastal provinces and to 0 otherwise [START_REF] Ding | Why has China grown so fast? The role of structural change[END_REF]. 26 For the level of policy preference, I create two dummy variables (policy_dum2 and policy_dum3) based on the categorical variable (policy_var) reported in [START_REF] Pedroni | Regional income divergence in China[END_REF] and introduce them into the GMM estimation. Finally, I add trade openness-defined as the ratio of total international exports and imports to GDP (trade2gdp)-as well as the real exchange rate, which is measured not only by the logarithm of the relative price of tradable to nontradable goods (lr1, see Figure 3) but also by the logarithm of the inverse of the price of nontradable goods (lr2). 27 The prices of tradable goods (P T ) and nontradable goods (P NT ) are represented, respectively, by the producer price index28 and the consumer price index.29

[[INSERT Figure 3 about Here ]]

All the variables are plotted in Figure 4, and their definitions and sources are summarized in Table A1.

[[INSERT Figure 4 about Here ]] 4 Results

The Im-Pesaran-Shin panel unit root test [START_REF] Im | Testing for Unit Roots in Heterogeneous panel[END_REF] is applied to check the stationarity of the annual series of each variable from 1992 to 2008 (see Table 1). 30 Except for the growth rate, FDI share, and RER, the null hypothesis of nonstationarity cannot be rejected.

Estimations with fixed-effects model

Because some variables are nonstationary, I implement panel estimations on the data set at five-year intervals rather than performing pooled OLS estimation. The results for the baseline model-which includes initial income, the investment ratio, the population's natural growth rate, and the rate of secondary school enrollment-are reported in Table 2. Based on the Hausman specification test [START_REF] Hausman | Specification Tests in Econometrics[END_REF], I choose the fixed-effects model and then estimate equation (1) on three samples (all provinces, the inland ones, and the coastal ones) using two proxies for the ratio of investment and two for human capital. Except for the variable of human capital (see the coefficients for school1), every explanatory variable of economic growth is correctly signed; however, the significance levels are not always sufficient to validate the assumptions. Among these regressions,31 the significance of the initial income per capita (ly_1) indicates that there is conditional convergence for the coastal provinces and the whole sample but not for the group of inland provinces. However, the ratio of investment to GDP (invest2gdp1 and invest2gdp2) and the population's natural growth rate (popgr) are significant for the whole sample and for the inland provinces, too. As for human capital, only the proportion of those with at least a senior high school education (school2) is significant-and only for the inland provinces.

[[INSERT Table 2 about Here ]]

Next I incorporate the explanatory variables (e.g., FDI, trade openness, RER) into the growth regression; the results are reported in Table 3. There are two main differences relative to the baseline model reported in Table 2. First, the results indicate a conditional convergence for all three samples. Second, the effect of human capital on growth is significant for the sample as a whole and also for the inland provinces. In other words, it is not human capital that drives the economic growth of the most dynamic coastal provinces. This finding may be explained by drawbacks of using within estimator under conditions of endogenous explanatory variables and possible measurement error. The latter condition is more prevalent in developing countries, such as China. Similar negative and/or nonsignificant coefficients for human capital are also reported by [START_REF] Romer | Human capital and growth: theory and evidence[END_REF] and by [START_REF] Levine | A sensitivity analysis of cross-country growth regressions[END_REF].

[[INSERT Table 3 about Here ]]

Neither foreign direct investment nor trade openness is significantly associated with the disparity in economic growth among Chinese provinces. As for the trade openness, one possible explanation for its relative nonsignificance is the heterogeneity of the variable's parameters-that is, the dependence of the trade-growth relation on where a province lies in the distribution of per capita growth. 32 Finally, the real exchange rate is negatively associated with economic growth; this impact is significant in nearly all the regressions. In other words: when the real exchange rate appreciates (i.e., when the tradable/nontradable price ratio decreases), the economy grows more quickly.

Regressions with system-GMM estimator

Given the drawbacks of fixed-effects estimators aforementioned in section 2.1, the literature on economic growth has seen the wide application and continued development of GMM estimation.

Here I implement the one-step system-GMM estimator via the 'xtabond2' module of Stata 10 [START_REF] Roodman | XTABOND2: Stata module to extend xtabond dynamic panel data estimator[END_REF]. 33 The p-values of Hansen tests indicate the overall validity of the instruments used in all models presented in Table 4. The Arellano-Bond AR (2) test confirms that the differenced error term is not second-order serially correlated. (By construction, the differenced error term is probably first-order serially correlated; this is suggested by the p-values of the Arellano-Bond AR(1) test.)

[[INSERT Table 4 about Here ]]

Table 4 presents the results of this dynamic panel specification. Model 0 is the baseline model estimated via the system-GMM estimator. Model 1 (resp., 1a and 1b) adds the real exchange rate (resp., lr1 and lr2). Model 2 (resp., 2a and 2b) is the same as Model 0 but adds dummy variables for location and policy preference level (resp., loca_dum, and policy_dum2 and policy_dum3). Model 3 (resp., Model 4) includes the real exchange rate lr1 (resp., lr2) and the location dummy in addition to the control variables of Model 0; similarly, Models 5 and 6 add the real exchange rate and the policy preference level variable.

Three points are worth noting. First, the fundamental determinants of growth-such as investment rate (invest2gdp1) and human capital (school1)-are significant in most of these dynamic models: the former in all models and the latter in several. The location dummy and one of the dummies for policy preference level (policy_dum3) are also significant, which indicates that differences in location and in policy preference level (between the provinces receiving highest policy preference and the ones receiving lowest preference) have significant effects on growth. So other factors being equal, a coastal province will have a higher growth rate than an inland province and obtaining a greater preference from central government for opening SEZs will have a significant and positive effect on growth.

Second, introducing the location dummy and the policy preference level dummies reveals the existence of conditional convergence-among coastal provinces on the one hand and among inland provinces on the other hand; however, there is no convergence between these two groups of provinces. 34 This confirms previous findings that geographical location and the Chinese central government's preferential policies for locating SEZs both figure largely in the increasing disparity [START_REF] Démurger | The relative contributions of location and preferential policies in China's regional development: being in the right place and having the right incentives[END_REF].

Third, the real exchange rate is significant in several models when proxied by lr1 (but not by lr2). Thus, I focus on the relative price of tradable to nontradable goods that is proved be a better proxy of China's real exchange rate. 35 The coefficient for this variable is negative, which implies that an appreciation in the real exchange rate (that is the increase of the relative price of goods in agricultural or/and service sectors) has a positive effect on growth. Moreover, taking into account the real exchange rate (lr1) accelerates the convergence (i.e., decelerates the divergence) among coastal provinces and among inland provinces. 36 Even so, the different level of real exchange rates calculated for these 28 provinces do not fundamentally explain their divergence: after introducing this variable the coefficient for initial income (ly_1) is still not significant (see Models 1a and 1b). 36 The coefficient for ly_1 in Model 3 (resp., Model 5) is smaller than its counterpart in Model 2a (resp., Model 2b). 112 Notes: loca_dum is the dummy variable for location and is set equal to 1 for coastal provinces (and to 0 otherwise). The dummy variables policy_dum2 and policy_dum3 capture the policy preference level; the reference group is provinces with a low level (for which the value is set equal to 1; it is set to 0 otherwise). The value of policy_dum2 (resp., policy_dum3) is set equal to 1 when the preference level is medium (resp., high) and to 0 otherwise. Data are for five-year intervals between 1992 and 2008; a one-step system-GMM estimator is used with a small-sample adjustment for standard error. The reported robust standard errors (in parentheses) are the heteroskedasticity-consistent ones. The variable ly_1 is treated as predetermined; lr1 and lr2 are treated as exogenous, and all other nondummy variables are treated as endogenous. The data reported for Arellano-Bond tests and the Hansen statistic are p-values. ***, *, * Significance at 1, 5, and 10 percent, respectively.

To clarify the impact of the real exchange rates on growth, I investigate further whether the effect of RER differs with the province's location (coastal versus inland) or with the policy preference level. In other words: Does the real exchange rate have the same impact on growth in coastal provinces as in inland ones? Does it have the same impact in provinces with 'high' as in those with 'low' policy preference levels? To answer these questions, I introduce three interaction terms into the growth regressions (see Table 5). In Model 7, loca_dum × lr1 is the interaction term of RER and the location dummy. In Model 8, policy_dum2 × lr1 and policy_dum3 × lr1 are the respective interaction terms of RER and the two dummies for policy preference level. In both models, the nonsignificance of the interaction terms indicates that there is no meaningful difference between these two groups of provinces in terms of how RER affects growth. Moreover, introducing these interaction variables does not change the sign of the real exchange rate and has little effect on its significance: the impact of an appreciation of the RER remains positive in both models and is significant in Model 7. 4. policy_dum2 is not included in Model 8 because its coefficients are not significant in previous regressions (see Table 4).

Discussion

Several channels of transmission could explain the impact of the real exchange rate on economic growth. For example, the size of a country's tradable sector was proposed by [START_REF] Rodrik | The real exchange rate and economic growth[END_REF], who argued that the tradable sector suffers more than the nontradable sector from institutional weakness and/or market failure. If this is true then currency undervaluation would have the effect of shifting resources toward industry and would thus constitute a subsidy policy for spurring tradables and generating more rapid growth.

There is no doubt that price distortions exist in China's industries and tradable sectors. Yet the changes observed during its transition from a planned to a market economy cannot be explained solely by the undersized tradable sector that characterizes developing countries. After all, some nontradable sectors in China, including agriculture and service, have suffered from price distortions and have been also undersized. During the post-1978 reforms, the government eliminated many of the distortions that it had previously imposed on the price system. Before China's economic opening, the priority given to industrial development (and the need to ensure a source of budget revenues) led the socialist government to set high prices for industrial production; in relative terms, then, agricultural products and services were undervalued. The gradual opening of the economy to competition and international trade-along with the elimination of government price controls-decreased the price of tradable goods relative to the price of services and agricultural products (Naughton, 2007, pp. 154-155).

While reducing price distortion, the increase of prices in nontradable sectors can facilitate economic growth in three ways. First, an RER appreciation will reallocate resources between the tradable and the nontradable sectors. Therefore, it could not only encourage monopolistic (or collusively oligopolistic) firms to improve their technical efficiency but also spur a more balanced economic growth in the medium and long term. Second, an increasing RER generates a wealth effect by enriching the agricultural and service sectors-or the provinces that specialize in these sectors-and increasing the demand and consumption of tradable and nontradable goods both. Third, but no less important, is that an increase in the price of nontradable goods would induce a rise in the real remuneration of workers and hence improvement in 'X-efficiency' proposed by [START_REF] Leibenstein | Allocative efficiency vs. "X-efficiency[END_REF].

Concluding remarks

This paper uses the robust system-GMM estimator to implement a dynamic panel estimation of an informal growth equation applied to 28 Chinese provinces. In most cases I find conditional convergence among the coastal provinces and also among the inland provinces, even though there is no conditional convergence between the two groups of provinces. Consistently with the literature, I find that geographic location and level of policy preference have an impact on provincial performance and on convergence at the national level. Finally, an appreciation of the real exchange rate has a positive influence on provincial economic growth.

Further studies and evidence are needed to shed more light on the mechanisms-such as domestic savings and investment-through which the real exchange rate affects economic growth (cf. [START_REF] Bresser-Pereira | Exchange rate, fix, float or manage it?[END_REF][START_REF] Gala | Real exchange rate levels and economic development: theoretical analysis and econometric evidence[END_REF]. Such studies would help to clarify the effectiveness of a country's RER policy as a tool for improving the structure of industry specialization and thereby ensuring sustainable growth.

Figure 1 .

 1 Figure 1. Estimates of provincial income distribution in 1984, 1994, and 2004

  , published in 2010; the China Statistical Abstract 2010 (CSA); various issues of the China Statistical Yearbook (CSY) and the China Population Statistical Yearbook (CPSY); and the China Population and Employment Statistics Yearbook 2009

Table 1 . Im-Pesaran-Shin panel unit root test

 1 All variables are demeaned. For g, only the constant is included; for other variables, both the trend and the constant are included. The test is augmented by one lag.

	Variable	t-bar statistics	p-value	Observations
	g	-2.402	0.000	420
	g (no Sichuan)	-2.356	0.000	405
	ly_1	-2.231	0.367	420
	ly_1 (no Sichuan)	-2.213	0.408	405
	invest2gdp1	-2.079	0.705	420
	invest2gdp2	-2.447	0.057	420
	popgr	-2.190	0.458	420
	school1	-1.878	0.954	420
	school2	-2.338	0.183	420
	open	-1.831	0.975	420
	fdi2gdp	-3.704	0.000	420
	fdi2invest	-3.385	0.000	420
	lr1	-2.713	0.001	420
	lr2	-2.541	0.017	420
	Notes:

Table 2 . Panel baseline model (fixed effects)

 2 Tableentriesare p-values for three different specifications; t-statistics are given in parentheses. The variables ip2, ip3, and ip4 are period dummies (for the second, third, and fourth 5-year period) that are used to capture time-specific effects. LM = Lagrange multiplier. ***, **, * Significance at 1, 5, and 10 percent, respectively.

	Variable		All provinces		Inland provinces		Coastal provinces
	ly_1	-0.091*** -0.093*** -0.104*** -0.035	-0.043	-0.053	-0.189*** -0.189*** -0.198***
		(-4.219)	(-4.403)	(-4.808)	(-1.204) (-1.480) (-1.906)	(-6.310)	(-6.564)	(-6.550)
	invest2gdp1	0.097**		0.115**	0.110*		0.124*	0.060		0.058
		(2.727)		(3.174)	(2.077)		(2.668)	(1.358)		(1.346)
	invest2gdp2		0.113**			0.131*			0.082
			(3.362)			(2.599)			(1.922)
	school1	-0.050	-0.042		0.017	0.093		-0.267	-0.379
		(-0.184)	(-0.158)		(0.045) (0.248)		(-0.720)	(-1.033)
	school2			0.223			0.417*			0.089
				(1.817)			(2.462)			(0.746)
	popgr	-4.112*	-3.790*	-5.008**	-6.503* -6.451* -7.667** 0.908	0.934	1.158
		(-2.159)	(-2.033)	(-2.694)	(-2.427) (-2.467) (-2.990)	(0.355)	(0.383)	(0.468)
	ip2	0.033**	0.031**	0.032**	0.011	0.009	0.010	0.090*** 0.091***	0.089***
		(3.033)	(2.933)	(3.031)	(0.849) (0.664) (0.825)	(5.957)	(6.206)	(5.926)
	ip3	0.108*** 0.102*** 0.100***	0.055*	0.048	0.043	0.216*** 0.214***	0.211***
		(5.427)	(5.240)	(5.037)	(2.086) (1.859) (1.741)	(7.951)	(8.160)	(7.762)
	ip4	0.158*** 0.152*** 0.151***	0.072	0.068	0.060	0.315*** 0.310***	0.314***
		(4.801)	(4.693)	(4.645)	(1.673) (1.606) (1.472)	(6.907)	(7.033)	(6.861)
	Constant	0.817*** 0.831*** 0.886***	0.364	0.425	0.461*	1.703*** 1.701***	1.748***
		(4.701)	(4.943)	(5.136)	(1.621) (1.919) (2.159)	(6.455)	(6.782)	(6.633)
	Breusch-Pagan LM test 0.1971	0.1255	0.7158	0.3735	0.2332	0.1924	0.4663	0.7081	0.5225
	Hausman spec. test	0.0094	0.0014	0.0007	0.9990	0.9990	0.6496	0.0000	0.0000	0.0000
	Time fixed effects test	0.0000	0.0000	0.0000	0.0335	0.0588	0.1548	0.0000	0.0000	0.0000
	Observations	112	112	112	72	72	72	40	40	40
	Notes:

Table 3 . Panel evidence for effect of RER on growth (fixed effects)

 3

	Variable		All provinces		Inland provinces	Coastal provinces
	ly_1	-0.092*** -0.108*** -0.072***	-0.071* -0.072**	-0.028	-0.201*** -0.189*** -0.169***
		(-4.241)	(-5.170)	(-3.591)	(-2.515)	(-2.749)	(-1.063)	(-6.037)	(-6.253)	(-5.945)
	invest2gdp1	0.085*	0.104**	0.130***	0.074	0.101*	0.133**	0.042	0.064	0.081
		(2.252)	(2.954)	(3.859)	(1.441)	(2.274)	(3.091)	(0.918)	(1.220)	(1.842)
	popgr	-4.757*	-5.735**	-5.098**	-5.280	-6.426*	-6.043*	-0.118	0.342	-1.089
		(-2.548)	(-3.265)	(-3.117)	(-1.937)	(-2.662)	(-2.512)	(-0.045)	(0.131)	(-0.461)
	school2	0.276*	0.201	0.254*	0.288	0.377*	0.355*	0.096	0.094	0.158
		(2.253)	(1.705)	(2.333)	(1.644)	(2.397)	(2.274)	(0.786)	(0.787)	(1.463)
	fdi2gdp	0.308	0.379*	0.180	0.079	-0.065	-0.389	0.159	0.205	0.174
		(1.923)	(2.435)	(1.242)	(0.234)	(-0.209)	(-1.205)	(1.137)	(1.519)	(1.456)
	open	-0.023			-0.241*			0.008
		(-1.364)			(-2.175)			(0.623)
	ip2	0.029**	0.030**	-0.182***	0.017	0.016	-0.213**	0.089***	0.081*** -0.050
		(2.694)	(2.933)	(-3.860)	(1.317)	(1.352)	(-3.143)	(5.223)	(5.180)	(-0.896)
	ip3	0.095*** 0.099*** -0.125*	0.076*	0.063*	-0.179*	0.211***	0.198*** 0.057
		(4.845)	(5.273)	(-2.445)	(2.680)	(2.630)	(-2.547)	(7.168)	(7.094)	(0.894)
	ip4	0.140*** 0.161*** -0.138*	0.117*	0.107*	-0.225*	0.318***	0.298*** 0.120
		(4.341)	(5.131)	(-2.043)	(2.472)	(2.638)	(-2.413)	(6.394)	(6.453)	(1.405)
	lr1		-0.050**			-0.064**			-0.029
			(-2.977)			(-3.165)			(-0.962)
	lr2			-0.318***			-0.343**			-0.192*
				(-4.590)			(-3.348)			(-2.470)
	Constant	0.790*** 0.922***	-0.661	0.639**	0.622**	-1.106*	1.773***	1.660***	0.719
		(4.534)	(5.494)	(-1.841)	(2.863)	(3.046)	(-2.173)	(6.146)	(6.273)	(1.557)
	Breusch-Pagan LM test	0.6577	0.3971	0.0936	0.3634	0.0598	0.0799	0.4634	0.5485	0.9027
	Hausman spec. test	0.0093	0.0074	0.0000	0.4272	n.a.	0.0116	n.a.	0.0000	0.0000
	Time fixed effects test	0.0000	0.0000	0.0000	0.0222	0.0178	0.0017	0.0000	0.0000	0.0000
	Observations	112	112	112	72	72	72	40	40	40
	Notes: See notes to Table 2.

Table 4 . Panel evidence for effect of RER on growth (system-GMM)

 4

	Variable	Model 0	Model 1a	Model 1b	Model 2a	Model 2b	Model 3	Model 4	Model 5	Model 6
	ly_1	-0.012	-0.015	-0.007	-0.037**	-0.026**	-0.047***	-0.028**	-0.031***	-0.017
		(0.319)	(0.291)	(0.583)	(0.001)	(0.009)	(0.001)	(0.037)	(0.010)	(0.097)
	invest2gdp1	0.143*	0.152**	0.133**	0.150*	0.155*	0.163**	0.138**	0.165**	0.141**
		(0.013)	(0.012)	(0.012)	(0.013)	(0.015)	(0.012)	(0.012)	(0.013)	(0.014)
	popgr	-1.232	-1.438	-1.408	-2.752	-2.292	-3.516	-2.559	-2.840	-2.225
		(0.638)	(0.599)	(0.554)	(0.202)	(0.368)	(0.118)	(0.193)	(0.284)	(0.345)
	school1	0.447	0.600*	0.398	0.361	0.378	0.544**	0.332	0.542*	0.345
		(0.112)	(0.059)	(0.140)	(0.103)	(0.160)	(0.026)	(0.122)	(0.065)	(0.189)
	lr1		-0.031				-0.044**		-0.038*
			(0.192)				(0.046)		(0.069)
	lr2			-0.203				-0.257		-0.222
				(0.199)				(0.115)		(0.169)
	loca_dum				0.025*		0.030***	0.023**
					(0.017)		(0.009)	(0.034)
	policy_dum2					0.001			-0.001	-0.001
						(0.940)			(0.937)	(0.871)
	policy_dum3					0.018*			0.019*	0.013
						(0.044)			(0.058)	(0.139)
	Constant	0.099	0.116	-0.746	0.312**	0.212*	0.387**	-0.782	0.249**	-0.737
		(0.438)	(0.423)	(0.271)	(0.005)	(0.047)	(0.003)	(0.264)	(0.041)	(0.279)
	Time dummies Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
	Time fixed	0.0000	0.0001	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000	0.0000
	effects test
	Arellano-Bond	0.099	0.085	0.016	0.093	0.086	0.073	0.008	0.067	0.010
	AR(1) test
	Arellano-Bond	0.488	0.296	0.253	0.382	0.416	0.154	0.151	0.204	0.199
	AR(2) test
	Hansen	0.451	0.433	0.364	0.530	0.706	0.497	0.455	0.630	0.528
	statistic
	Observations	112	112	112	112	112	112	112	112

Table 5 . Panel evidence for effect of RER on growth (system-GMM with interaction terms)

 5 See notes to Table

	Variable

The geographical location and preferential policies of the Chinese government are identified as two main factors for the increase in this disparity[START_REF] Démurger | The relative contributions of location and preferential policies in China's regional development: being in the right place and having the right incentives[END_REF].

As a proxy for income, the real GDP per capita was calculated using the Chinese renminbi (RMB) at constant 2000 prices but adjusted to reflect purchasing power parity (PPP). The PPP for each province is measured as the ratio of the provincial consumer price index (CPI) to the national one, which is taken as the benchmark (2000 is the base year). In contrast,[START_REF] Fleisher | Human capital, economic growth, and regional inequality in China[END_REF] constructed the PPP in terms of a basket of goods in Beijing at constant 1990 prices.

The only exceptions are the works of Guillaumont[START_REF] Guillaumont Jeanneney | How does real exchange rate influence income inequality between urban and rural areas in China?[END_REF] 2008). However, their research mainly concerned the effect of the real exchange rate on urban-rural income inequalities, not on income itself or on economic growth.

See Maasoumi and Wang (2008) for a recent measure of convergence in China and a comprehensive literature review.

Another empirical approach is the growth accounting method; see[START_REF] Zheng | Potential output in a rapidly developing economy: the case of China and a comparison with the United States and the European Union[END_REF] for an application to potential output in China.

See Chen and Fleisher (1996).[START_REF] Li | Testing the neoclassical theory of economic growth: evidence from Chinese provinces[END_REF] utilized both cross-sectional and panel data on the provinces of China.

[START_REF] Béreau | Currency misalignments and growth: a new look using nonlinear panel data methods[END_REF] used the panel smooth transition regression (PSTR) model introduced by[START_REF] Gonzalez | Panel smooth transition regression models[END_REF] to explore the nonlinear relationship between currency misalignment and economic growth for an extensive panel that included both industrialized and emerging economies.

The downside is the need to add more instruments to the estimation.

Even some potentially explanatory variables of growth are not included in the regression. Note that panel data methods can control for omitted variables.

For a more precise classification of Chinese provinces' locations, see[START_REF] Démurger | The relative contributions of location and preferential policies in China's regional development: being in the right place and having the right incentives[END_REF].

If one assumes that the prices of tradable goods are the same for all provinces.

In China, the PPI is referred to as the Producer Price Index for Manufactured Goods.

[START_REF] Edwards | Real exchange rates in developing countries: concepts and measurement[END_REF] and[START_REF] Devereux | Commercial policy, the terms of trade and the real exchange rate revisited[END_REF] used the CPI as a proxy for the price of nontraded goods.

See Hurlin and Mignon (2005) for a thorough review of the unit root tests for panel data.

For each sample, three regressions are run using alternative proxies for the investment ratio and for human capital.

To be precise, openness has a greater impact on growth among low-growth than among high-growth provinces[START_REF] Dufrénot | The trade-growth nexus in the developing countries: a quantile regression approach[END_REF].

In finite samples, asymptotic standard errors associated with the two-step GMM estimators may be strongly biased downward, which makes such estimators an unreliable guide for inference[START_REF] Bond | GMM estimation of empirical growth models[END_REF].

The coefficients for ly_1 are significant in Models 2a and 2b but are neither significant in Model 0 nor in Models 1a and 1b.

The assumption of the law of one price for tradable goods cross provinces is too strict here in that the tradable goods used in composing the PPI for each province may not be the same as a result of specialization and development strategy.

Figure 4. Growth rate and determinants (continued)

Data Appendix PPI:

The missing values from 1992 to 1996 for certain provinces correspond to Shanxi (1992Shanxi ( -1994)), Jilin (1992Jilin ( -1996)), Fujian (1992-1994), Hubei (1992-1994[START_REF] Gemmell | Evaluating the impacts of human capital stocks and accumulation on economic growth: some new evidence[END_REF], Guangdong (1992Guangdong ( -1996)), Guizhou (1992Guizhou ( -1993)), Gansu (1992), and Ningxia (1992[START_REF] Gemmell | Evaluating the impacts of human capital stocks and accumulation on economic growth: some new evidence[END_REF]. The missing values were replaced with the country PPI for the corresponding year (as extracted from the CSY of 2000, 2001, and 2002).

Trade openness:

For the period 2005-2008, the values of imports (resp., exports) are calculated in terms of their provinces of destination in China where the imported commodities are consumed, used or transported to (resp., their provinces of origin in China where the exported commodities are produced or originally delivered); the only alternative statistics available at the provincial level for this period are based on the location (i.e. province) where import or export corporations are situated, i.e., where they have applied and have registered at the Chinese Customs. The calculation method of foreign trade is not specified for the years prior to 2005. [START_REF] Ding | Why has China grown so fast? The role of structural change[END_REF] for location; [START_REF] Pedroni | Regional income divergence in China[END_REF] for policy preference level.

Table A1. Definition and sources of variables