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Economic Change and Restructuring

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01.04.2011

International integration and the determinants of regional development in China

verfasst von: T. Gries, M. Redlin

Erschienen in: Economic Change and Restructuring | Ausgabe 1-2/2011

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Abstract

Concerns about the duration of China’s growth and hence the question of a permanent significant contribution of China to world economic growth relate, amongst other things, to the problem of reducing regional disparity in China. While China’s high average growth is driven by a small number of rapidly developing provinces, the majority of provinces have experienced a more moderate development. To obtain broad continuos growth it is important to identify the determinants of provincial growth. Therefore, we introduce a stylized model of regional development which is characterized by two pillars: (1) International integration indicated by FDI and/or trade lead to imitation of international technologies, technology spill overs and temporary dynamic scale economies, and (2) domestic factors indicated by human and real capital available through interregional factor mobility. Using panel data analysis and GMM estimates our empirical analysis supports the predictions from our theoretical model of regional development. Positive and significant coefficients for FDI and trade support the importance of international integration and technology imitation. A negative and significant lagged GDP per capita indicates a catching up, non steady state process across China’s provinces. Highly significant human and real capital identifies the importance of these domestic growth restricting factors. However, other potentially important factors like labor or government expenditures are (surprisingly) insignificant or even negative. Extending the model using an unbalanced panel leads to a positive effect of the quality of governance and institutions on development.

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Average growth of the last 10 years is 8.01%, Source: Penn World Table 6.2.

With a GDP per capita (PPP) of $6,300 the People’s Republic of China was ranked 118th of 232 countries in 2005; Source: The World Factbook 2006.

See e.g. Fujita/Thisse (2002 Chap. 11), or Kelly/Hageman (1999).

See Findlay (1973, 1984).

The interest factor is one + interest rate.

The firm has to determine optimal factor inputs by maximizing profits. Since all capital services have to be paid in terms of exports, the full capital costs include several components like government taxes on output γ _i or transaction costs for exports.

$Y=yA^{-{\frac{1} {1-\beta }}}.$ see also Appendix 1e.

There is a broad literature on international technology diffusion that has suggested various channels. Eaton/Kortum (1999) discuss trade as a channel of diffusion in a multi-country setting. See also Coe/Helpman (1995) who link the direction of technology diffusion to exports. Keller (1998) however has some doubts about the link between trade and diffusion.

E.g. Martin (1999) has analyzed the effects of public policies and infrastructure to the growth performance of a regional economy.

This idea draws back to the well-known Veblen-Gerschenkron hypothesis (Veblen (1915) and Gerschenkron (1962)). Later Nelson/Phelps (1966), Gries/Wigger (1993), Gries/Jungblut (1997) and Gries (2002) further developed these ideas in the context of catching-up economies. The catching-up hypothesis has been tested successfully and robustly by Benhabib/Spiegel (1994), de la Fuente (2002), and Engelbrecht (2003).

Note that ${\mathcal{F}}(t)$ and G(t) are normalized values transformed by an international technology index factor $A(t)^{{\frac{1} {1-\beta }}},$ and A is growing at a given constant rate n. See also Appendix 1e.

Finally, examining the determinants of growth several studies present empirical evidence that there is also an effect of institutions and the quality of governance on growth Basu (2006), Barro (1997), Knack and Keefer (1997a,b), Mauro (1995), Svensson (1998), Chong and Calderon (2000), Hall and Jones (1999), Gradstein (2004) and the series of papers “Governance Matters” (Kaufmann et al. 1999, 2002, 2004, 2005, 2006, 2007).

For the dynamic catching-up-spill-over equation we assume that G and $ {\mathcal{F}}$ are sufficiently large for positive upgrading.

See Appendix 1f.

The dynamic catching-up-spill-over equation contains a scaling problem if H and K are taken as absolute values. As the region is assumed to remain backward, the values of γ, φ, H and K are assumed to be sufficiently small. See Appendix 2 for the derivatives.

We assume that the contribution of FDI to production β as well as the externality effect of FDI on technology δ are sufficiently small. This also reflects the already mentioned assumption of a rather limited spill-over effect of FDI on the relative catching up process.

In Appendix 3 we show that the government can maximize the final development position of the economy and the speed of growth by choosing an optimal level of government expenditure for public infrastructure.

Since $\left({\frac{y^{\prime }} {y}}\right) ^{2}$ is rather small we can approximate the process.

To simplify we consider only linear and log linear processes.

See Gujarati (2003) p. 637.

Monte Carlo results on the finite sample properties of the GMM estimator for dynamic panel data models have been reported by Arellano and Bond (1991), Kiviet (1995), Ziliak (1997), Blundell and Bond (1998) and Alonso-Borrego and Arellano (1999), amongst others.

The choice of the period makes sense for two reasons. First, the early 1990s saw the latest wave of international integration policy in China. Also in the early 1990s the Chinese government started to prepare for WTO accession and a further opening up of the economy. Second, with respect to some important indicators some provinces would have had to be excluded if the time period had been expanded to earlier years.

Barro and Lee (2001) argue that enrollment rates not adequately measure the aggregate stock of human capital available contemporaneously as an input to production and alternatively average years of schooling should be used. However, a multiplicity of empirical studies uses enrollment rates with plausible results supporting the validity of this variable. Since data on average years of schooling is available only on country and not on provincial level we decide to use enrollment rates as a proxy for human capital.

The empirical literature on the new economic geography suggests many variables which could function as a proxy for agglomeration. We concentrate on population density and the share of urbanization. Those measures are widely used and valid proxies for agglomeration (see Büttner et al., Sala-i-Martin et al., Brüllhart and Sbergarmi).

We calculate also the OLS estimates of the three models with FDI and trade. The results are consistent with the GMM results concerning the signs and significance values in large part. In contrast to the dynamic estimation technique in the first and second model only the human capital coefficients shows a negative significant effect. Agglomeration and urbanization exhibit a positive significant effect on growth. A possible explanation for these differences are the omitted province specific effects, so we concentrate are interpretation on the GMM results. Additionally, we use the Husmann test to check for unobserved heterogeneity between the provinces. If the null hypothesis is significant a simple OLS estimator is consistent and efficient, whereas the GMM estimator is consistent in all cases. For our model the null hypothesis is rejected so that the GMM estimation should be favored.

We also checked for multicollinearity of all other variables. Additionally we calculated the VIFs for both models. These are: y 3.65, K 1.23, POP 1.05, HC 1.41, FDI 1.19, GOV1 1.75, GOV2 1.79, POPKM2 1.67, URBAN 2.76, mean VIF 1.83 for the model with FDI, y 3.40, K 1.24, POP 1.05, HC 1.43, T 1.09, GOV1 1.73, GOV2 1.74, POPKM2 1.68, URBAN 2.69, mean VIF 1.78 for the model with trade and y 3.67, K 1.17, POP 1.09, HC 1.35, T 1.34, GOV1 1.83, GOV2 2.10, POPKM2 1.76, URBAN 3.10, MARKET 1.24, mean VIF 1.87 for the model with marketisation.

To get a better impression of the relationship between growth and the lagged GDP per capita, we plotted these variables for the whole panel as well as for each province. All scatter plots suggest a negative linear relationship between these variables which is consistent to our results. Additionally, we run a simple regression in logs to investigate a non linear relationship however the results fell off in quality with regard to the significance level of the linear regression which is again an indication for a linear relationship. The results are available upon request.

Using employees instead of population leads also to insignificant results.

Using secondary school enrollment instead of enrollment in higher education leads to the same results.

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Titel: International integration and the determinants of regional development in China
verfasst von: T. Gries
M. Redlin
Publikationsdatum: 01.04.2011
Verlag: Springer US
Erschienen in: Economic Change and Restructuring / Ausgabe 1-2/2011
Print ISSN: 1573-9414
Elektronische ISSN: 1574-0277
DOI: https://doi.org/10.1007/s10644-010-9084-6

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