Abstract
This chapter contains a study that investigates the role of regional industry mix in explaining China’s interregional disparities in labor productivity. One of our findings is that during 1988–2004, about one half of the total interregional variation of labor productivity can be attributed to the structural and allocative effects, both of which are related to the regional industry mix. We also find that regional openness, interpreted as a proxy variable for regional social infrastructure, has a very significantly positive effect on the relative regional labor productivity over the period 1985–2008. Our empirical results also suggest that in the Chinese regions during 1985–2008, a substantial part of the impact of the regional social infrastructure on the regional labor productivity is manifested through the “structural channel” and “allocative channel” of the regional industry mix. In addition, our findings also suggest that regional openness facilitates structural change in terms of labor moving from the agricultural to the manufacturing sector, and that poorer regions tend to experience a faster process of such structural change, which, in turn, contributes to convergence across different regions in China.
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Notes
- 1.
- 2.
These province-level regions include municipalities and autonomous regions. For convenience, we hereinafter call all these regions “provinces”.
- 3.
The coefficient of variation, by definition, is calculated as the ratio of the standard deviation to the absolute value of the mean.
- 4.
The primary sector refers to agriculture, forestry, animal husbandry and fishery and services in support of these industries. The secondary sector refers to mining and quarrying, manufacturing, production and supply of electricity, water and gas, and construction. The tertiary sector refers to all other economic activities not included in the primary or secondary industries.
- 5.
The upward climb of this coefficient of variation over 1989–2008 experiences four slight drops in the years 1991, 1996, 2002, and 2005.
- 6.
As mentioned earlier, cross-sector reallocation of resources is an important source of economic growth. Dekle and Vandenbroucke (2006), for example, show that over one third of the total growth of labor productivity during 1978–2003 in China can be accounted for by the reallocation of labor from the agricultural sector to the non-agricultural sectors. For related recent discussions on this topic, see, for example, Brandt and Zhu (2010), Yang and Lahr (2010), and Wang and Szirmai (2008).
- 7.
To avoid cluttering the notation, we do not write out the province and year subscripts in the variables in Table 9.2.
- 8.
- 9.
We hereinafter drop the subscript i wherever needed to avoid unnecessary cluttering in the notation.
- 10.
Most of the regressions also have a high R-squared.
- 11.
According to Hall and Jones (1999), a favorable social infrastructure gets the prices right so that individuals capture the social returns to their actions as private returns (North and Thomas 1973). It then follows that an ideal measure of the social infrastructure would be able to quantify the wedge between the private return to productive activities and the social return to such activities. However, in most cases it is difficult to obtain feasible quantifications of wedges between private and social returns.
- 12.
In this chapter, mainly because of missing data, we have relied only on foreign trade, but not foreign direct investment, to construct the regional openness variable. We argue that regional foreign trade is closely related to regional inflows of foreign direct investment. See also Madariaga and Poncet (2007), Ouyang (2009), Whalley and Xin (2010) and Ljungwall and Tingvall (2010).
- 13.
Needless to say, factors such as resource endowment and culture may not really be time-constant. What we mean here is that in case regional resource endowment or culture indeed remains unchanged over time, its effect is then absorbed into the zone dummy variables, and hence is netted out from the effect of openness.
- 14.
In this analysis, whenever we mention the effect of the social infrastructure, it should be understood that econometrically it refers to the effect of openness, where openness is taken as a proxy variable for social infrastructure.
- 15.
The analyses in this and the next subsection incorporate relevant parts of the author’s previous works Jiang (2010, 2011), which were published respectively as Jiang, Yanqing (2010), “An Empirical Study of Structural Factors and Regional Growth in China,” Journal of Chinese Economic and Business Studies, 8(4), 335–352, and Jiang, Yanqing (2011), “Structural Change and Growth in China under Economic Reforms: Patterns, Causes and Implications,” Review of Urban and Regional Development Studies, 23(1), 48–65.
- 16.
In the regressions in this table, the usual standard errors are calculated and used for drawing statistical inferences. For all the regressions in this chapter, it can be shown that the alternative use of the heteroskedasticity-robust standard errors (not reported in the tables) does not alter any of our important results.
- 17.
Needless to say, this schooling rate is a rather coarse measure of human capital formation. The general idea behind this measure is that the variation in the fraction of the population devoted to formal education reflects the variation in investment in human capital. Our choice of a feasible flow or stock measure of human capital formation is severely restricted by data unavailability.
- 18.
All at the 5 % significance level if not otherwise stated.
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Jiang, Y. (2014). Industry Mix and Interregional Disparities in China. In: Openness, Economic Growth and Regional Disparities. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40666-9_9
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