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2016 | OriginalPaper | Buchkapitel

4. Determinants of Spatial (Dis)Integration: Analytics

verfasst von : Rongxing Guo

Erschienen in: China’s Regional Development and Tibet

Verlag: Springer Singapore

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Abstract

Using a modified gravity model of trade and China’s interprovincial panel data, this chapter shows that the negative effect of distance-related transactions costs on trade tends to rise from 2000 to 2010. After constructing all the 56 ethnic groups into a single, interprovincial similarity index, we cannot find any evidence that supports the view that ethnic links may serve as a factor promoting bilateral trade. However, our estimated coefficients on 37 major ethnic groups suggest that both positive and negative ethnic influences on trade exist in China. Finally, we find that the Tibetan and 12 other ethnic groups tend to contribute to China’s interprovincial integration and that the Dai, the Han, the Kazak, and the Va ethnic groups tend to be responsible for China’s spatial disintegration.

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Vorheriges Kapitel Tibetan Unrest and the Dalai Lama: Narrative

Nächstes Kapitel Going Back to Tibet: Analytic Narrative

Nur mit Berechtigung zugänglich

Calculated by author based on the data released by the World Trade Organization (http://www.wto.org/english/thewto_e/countries_e/china_e.htm).

Data sources Rumbaugh and Blancher (2004) and WTO (2011).

Note that Beijing and Shanghai’s reductions of domestic trade from 2000 to 2010 are mainly due to their removals of large industrial, pollution-making plants during the above period.

In their analyses of the negative correlation between distance-related costs and the interdependence for sovereign countries, Frankel et al. (1997a) use the data from the 1980s and obtain slightly larger coefficients (around 0.5−0.6) on distance compared with Eichengreen and Irwin’s (1995) interwar estimates (around 0.3−0.6) based on data from the 1930s.

These trade barriers take a number of forms including legal and institutional differences (Anderson and Marcouiller 2002; Linders et al. 2005; Combes et al. 2005; Guiso et al. 2006), ethnic/linguistic networks (Rauch 2001; Rauch and Trindade 2002) and linguistic/religious dissimilarities (Guo 2004).

The earliest application of the gravity model can be traced back to the 1940s (see, e.g., Zipf 1946; Stewart 1948).

The corresponding names of these 37 ethnic groups are shown in Table 4.2.

We have also tried other forms of gravity models. But, after running their regressions, we found that they could more easily result in multicollinearity than Eq. (4.3).

See, for example, Havrylyshyn and Pritchett (1991), Foroutan and Pritchett (1993), Frankel and Wei (1995), and Frankel et al. (1997a).

In addition to China’s 56 ethnic groups, other unknown ethnic groups and foreigners with Chinese citizenship also exist in China. However, since these ethnic identities are still not known and that there are no specific statistical data on the nationalities of these foreigners for each province, these populations will not be included in the measurement of interprovincial ethnic linkages.

Boisso and Ferrantino (1997), for example, use ∑x _k y _k as the construct of similarity index.

Based on Professor James E. Rauch’s (University of California at Santiago) e-mail sent to the author.

For example, since some ethnic minorities are usually found in China’s transprovincial border areas (here ADJACENT = 1), it is natural to believe that interprovincial ethnic linkage scores are large when ADJACENT = 1 and small when ADJACENT = 0. However, since the Pearson correlation coefficients of “ADJACENT” and all the ethnic variables are less than 0.30 (detailed information is not reported here), they do not suggest potential multicollinearity causing imprecise regression results.

Note that since their max values are not available, the mean values of the Li and Tibetan groups should be slightly smaller than those reported in Table 4.5. However, this will not affect the analytical results below. In addition, since we have excluded, for purpose of overcoming the problems with multicollinearity, six ethnic groups (Blang, Buyi, Maonan, Mulao, Naxi, and Salar) from the regressions (shown in Table 4.4), we are not able to identify how these ethnic groups have changed their influences on interprovincial trade during the period from 2000 to 2010.

For example, the threshold value for the Tibetan group is ln(GDPPC_iGDPPC_j)^* = −β_k/α₄ = −13.694/(−0.115) = 119.078, indicating that the positive effect of the Tibetan linkage on interprovincial trade will not reverse until the geometric mean of per capita GDP of two trading provinces reaches 7.20304 × 10²⁵ yuan!.

Source: The Sixth (2010) National Population Census of the PRC for the population of each ethnic group and author for the other indicators.

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Titel: Determinants of Spatial (Dis)Integration: Analytics
verfasst von: Rongxing Guo
Verlag: Springer Singapore
Buch: China’s Regional Development and Tibet
Print ISBN: 978-981-287-956-1

Electronic ISBN: 978-981-287-958-5

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-981-287-958-5_4

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