Abstract
This chapter contains an empirical study of output growth and convergence across the Chinese regions. The study is based on the Ramsey growth model and is motivated by two basic facts. One fact is that the Solow growth model, which was applied in earlier chapters, has the shortcoming of not considering the saving behavior as endogenously determined inside the economy. The other fact is that the Solow model assumes closed economies and therefore does not allow for capital mobility across the borders of economies. Dealing with the issues of endogenously determined saving rates and interregional capital mobility within a unified theoretical framework, we build our empirical models. Our regression results show the existence of a faster speed of convergence among the Chinese provinces over the period 1981–2005—faster than the Solow model predicts. Nevertheless, this finding accords with the basic prediction of the Ramsey model, especially when the model is modified to allow for capital mobility.
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- 1.
The optimizing behavior of the firms can be incorporated into the decisions of the optimizing households. In other words, the model can be as well developed in alternative environments. The separation of functions between households and firms is not central to the analysis.
- 2.
- 3.
The reason why we have the requirement ρ − n − (1 − θ)g > 0 is that it ensures the lifetime utility does not diverge. If this condition does not hold, the household can attain infinite lifetime utility and its maximization problem does not have a well-defined solution.
- 4.
- 5.
- 6.
A dot over a variable represents its time derivative. Recall the firms’ profits are zero and thus do not appear in the equation. Equation 7.18 assumes physical capital and human capital depreciates at the same rate δ. It is also assumed that none of the taxes collected are remitted to households, though the results would not change if these revenues showed up as lump-sum transfers or as government services that did not affect productivity or interact with choices of consumption. See Barro, Mankiw, and Sala-i-Martin (1995).
- 7.
If initially \( \widehat{k}/\widehat{h}\ne \alpha /\phi, \) the households “jump” to the desired ratio. Here for simplicity we assume no adjustment costs or irreversibility constraints. If instead, physical or human capital is (realistically) assumed to be irreversible, then the model will involve transitional dynamics.
- 8.
See, for example, Appendix 2B, Barro and Sala-i-Martin (1995).
- 9.
Additionally, the transversality condition implies that s* < α + ϕ.
- 10.
- 11.
We assume that n = 0.02, and (g + δ) = 0.08.
- 12.
The infinite speed of convergence can be eliminated by introducing adjustment costs and irreversibility conditions for physical and human capital. However, such modifications do not eliminate the counterfactual prediction that convergence rates would be rapid in an open economy with perfect capital mobility. See Barro, Mankiw, and Sala-i-Martin (1995).
- 13.
Again, we assume n = 0.02, and (g + δ) = 0.08.
- 14.
Since our panel data method involves time periods of a 4-year span, the values of these variables are averages over the years in each time period.
- 15.
As an alternative, instead of including the three control variables, we could include in the regression a “preferential policy index” variable. We use the provincial preferential policy index variables calculated in Démurger et al. (2002) (see Table 11 of Démurger et al. (2002)) for our regression. The results are summarized in Table 7.9. It is clear from a comparison between Table 7.9 and the few earlier tables (Tables 7.3, 7.4, 7.5, and 7.6) that the substitution of the preferential policy index variable for the three proxy variables does not change the results of the regression in any important ways.
- 16.
Therefore, (g + δ) = 0.08, as we assumed earlier.
- 17.
By replacing \( \ln \left(1-\theta \cdot \tilde{r}\right) \) with \( \theta \ln \left(1-\tilde{r}\right) \) in the regression equation, we are effectively creating a “measurement error” that we have to include in the error term of the regression equation. Thus the correlation between \( \theta \ln \left(1-\tilde{r}\right) \) and the “measurement error” causes a bias in the estimation of the coefficient on \( \ln \left(1-\tilde{r}\right) \) (and may also cause biases in the estimation of coefficients on other explanatory variables).
- 18.
This is based on our regression results from some of our earlier studies.
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Jiang, Y. (2014). Endogenous Saving, Interregional Capital Mobility, and Convergence across China. In: Openness, Economic Growth and Regional Disparities. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40666-9_7
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DOI: https://doi.org/10.1007/978-3-642-40666-9_7
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