Investment climate in China: province estimates

Abstract

This paper uses a large firm-level survey to assess differences in performance across regions. A panel data analysis is conducted to explain both productivity and profitability across firms in terms of firm and industry characteristics. Regional dummies are included to pick up additional location-specific impacts and the size of these dummies is used to rank provinces. Province performance is then examined in terms of geography, infrastructure, the policy environment and aspects of the investment climate with the conclusion that the latter plays a major part in explaining provincial differences in performance.

Keywords:

• Asia
• China
• enterprises
• productivity
• profitability
• regional development

JEL Classifications:

• 012
• 018
• R11

Acknowledgment

The research on which the paper is based comes from a project on regional development in China funded and organized by the ADB Institute, Tokyo. The author acknowledges the helpful comments of two anonymous referees.

Notes

∗Significant at 10%

∗∗∗Significant at 1%.

∗∗ Significant at 5% level.

1. In this analysis, it is market size that appears to be the key factor in offsetting poor governance (Keefer 2007, p. 217).

2. For a recent survey on regional development that also draws on a new database for empirical analysis on convergence, see Song (2007).

3. These results refer to ‘absolute’ convergence, with no qualification for other controlling factors. For other measures that reflect the same trend, like the Gini coefficient and the Theil index, see Cai et al. (2002). Tests for ‘conditional convergence’ range from support over a long period in Cai et al. (2002) to only ‘hints at’ in Demurger et al. (2002, p. 457) and weak support in Jones et al. (2003) for shorter periods.

4. Positive effects of FDI on growth are reported in Chen & Fleisher (1996) & Demurger (2001) at the provincial level, in Jones et al. (2003) at the city level and in Mody & Wang (1997) for coastal provinces. Demurger et al. (2002) report a negative impact of SOE share on growth. Their FDI variable is correlated with their policy index and is generally insignificant for this reason.

5. Demurger (2001) finds a composite transport density variable and a variable reflecting telephone access to be significant in explaining provincial growth. The impact of the transport variable is non-linear and diminishes with increases in the variable. Earlier work by Mody & Wang (1997) for industry data across the coastal provinces finds a similar result with a road variable significant but with diminishing impact. In their analysis, the telecommunications variable has positive increasing returns.

6. See Poncet (2003), who suggests that, overall, the tariff equivalent of internal barriers to trade was as high as 51% in 1997 and that this had risen from 37% in 1987 and 41% in 1992.

7. The variables are standardized with a zero mean. They are added to form the index and, by implication, have equal weight. It should be noted that although this index is discussed in the text it is not used in the regressions for firm performance, where the components of the index are entered individually as independent variables; see World Bank (2006, Table B-1).

8. Although our productivity indicator is a measure of single factor productivity, since we include change in the capital-labour ratio in our model implicitly, our sector dummies pick up total factor productivity effects.

9. The regional ranking is broadly in line with the results of World Bank (2006) although the latter does not report individual rankings by province. The exception is that the north-east provinces appear relatively more disadvantaged in our analysis.

10. Both regions had a share of industrial sales taken by SOEs of over 70% in 2004 (China Statistical Yearbook, National Statistical Bureau, 2005).

11. Recent theoretical work on fiscal decentralization provides reasons why poorer regions may be especially prone to arbitrary charges imposed by local officials (Tsui & Wang 2007).