Does High-Speed Railway Influence Convergence of Urban-Rural Income Gap in China?
Abstract
:1. Introduction
2. Literature Review
3. Heterogeneity of Urban-Rural Income Gap in China’s Cities
4. Convergence Club Identification
4.1. Club Convergence Test Method Based on Nonlinear Time-Varying Factor Model
4.1.1. Logt Test
4.1.2. Convergence Club Identification Step Based on Logt Test
4.2. Identification of the Convergence Club
4.2.1. Judgment of Overall Convergence
4.2.2. Convergence Club Judgment
- (1)
- Divide 274 cities into 4 groups. If t > −1.65, it means that the individual in the club has a convergence trend.
- (2)
- Add the data of the remaining cities to the core group in turn. At this time, the t value is increased from −1.65 to 0, and there is still a possibility of convergence at the 20% significance level between the groups.
- (3)
- Integrate the four groups according to the club integration method. The third largest convergence club for urban and rural income gaps in China was obtained (see Table 2 for detailed club convergence). On the surface of the inspection, the three clubs all showed significant convergence, and most of the cities were located in the first two clubs. According to the club rankings, the average urban-rural income gap of the club members showed a downward trend.
4.2.3. Analysis of Empirical Results
5. Causes of Club Convergence
5.1. Variable Selection and Model Construction
5.2. Empirical Results and Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Region | Provinces, Municipalities, Autonomous Regions | t Value | Convergence |
---|---|---|---|
North-east area | Heilongjiang, Jilin, Liaoning | −0.73 | Yes |
East area | Beijing, Tianjin, Hebei, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan | −3.40 | No |
Central Region | Shanxi, Anhui, Jiangxi, Henan, Hubei, Hunan | −6.30 | No |
Western Region | Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang | −1.02 | Yes |
Club | Group | Number of Cities | Coefficient b | t Value | S.E. | Mean of Urban-Rural Income Gap |
---|---|---|---|---|---|---|
Club1 | 1 | 167 | 0.2017 | 1.41 | 0.1427 | 2.8050 |
Club2 | 2, 3 | 85 | 0.3741 | 1.44 | 0.2606 | 2.2011 |
Club3 | 4 | 5 | 0.3972 | 1.68 | 0.3972 | 2.1033 |
Divergence | 5 | 17 | −1.3583 | −14.31 | 0.9494 | 2.5420 |
Club | Number of Cities | Distribution Area |
---|---|---|
Club1 | 167 | western region (67), central region (51), eastern region (34), northeastern region (15) |
Club2 | 85 | eastern region (39), central region (27), northeastern region (11), western region (8) |
Club3 | 5 | eastern region (3), central region (2) |
Model | 1 | 2 | 3 | 4 |
---|---|---|---|---|
devel | −0.0762359*** (0.0046241) | −0.067033*** (0.0045977) | −0.0620697*** (0.0046505) | −0.0631602*** (0.0050142) |
urb | −0.2297582*** (0.0484925) | −0.1480799*** (0.0489607) | −0.1391756*** (0.0487126) | −0.2171518*** (0.0499533) |
gt | −0.0634502*** (0.0196414) | −0.0520909*** (0.0192339) | −0.0686494*** (0.0193361) | −0.0683473*** (0.0191887) |
fdi | −4.840153*** (0.5251888) | −4.290662*** (0.5306807) | −4.351761*** (0.5274679) | |
gebha | 0.8348368*** (0.1429672) | 0.9678393*** (0.1452194) | ||
ind | 0.3343478*** (0.1278829) | |||
_cons | 2.984747*** (0.0206154) | 3.009603 *** (0.0211706) | 2.851538 *** (0.0342922) | 2.569233*** (0.1102339) |
Prob > F | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
Club | Coef. | St.Err. | t-Value | p-Value | [95% Conf | Interval] | Sig |
---|---|---|---|---|---|---|---|
gt | 0.035 | 0.073 | 0.48 | 0.634 | −0.109 | 0.179 | |
fdi | 11.738 | 1.963 | 5.98 | 0.000 | 7.892 | 15.585 | *** |
gdp | 0.021 | 0.019 | 1.11 | 0.268 | −0.016 | 0.058 | |
gebha | −4.500 | 0.685 | −6.57 | 0.000 | −5.842 | −3.158 | *** |
czl | 0.173 | 0.195 | 0.89 | 0.374 | −0.209 | 0.556 | |
ind | −1.275 | 0.502 | −2.54 | 0.011 | −2.259 | −0.292 | ** |
cut1 | −0.793 | 0.440 | .b | .b | −1.655 | 0.068 | |
cut2 | 2.539 | 0.456 | .b | .b | 1.646 | 3.433 |
dy/dx | Std.Err. | z | P > z | [95%Conf. | Interval] | |
---|---|---|---|---|---|---|
fdi | ||||||
_predict | ||||||
1 | −2.582 | 0.422 | −6.12 | 0 | −3.409 | −1.755 |
2 | 2.348 | 0.383 | 6.13 | 0 | 1.597 | 3.098 |
3 | 0.234 | 0.049 | 4.76 | 0 | 0.138 | 0.331 |
gebha | ||||||
_predict | ||||||
1 | 0.99 | 0.147 | 6.73 | 0 | 0.702 | 1.278 |
2 | −0.9 | 0.134 | −6.74 | 0 | −1.162 | −0.638 |
3 | −0.09 | 0.018 | −5.07 | 0 | −0.125 | −0.055 |
ind | ||||||
_predict | ||||||
1 | 0.281 | 0.11 | 2.55 | 0.011 | 0.065 | 0.496 |
2 | −0.255 | 0.1 | −2.55 | 0.011 | −0.451 | −0.059 |
3 | −0.025 | 0.011 | −2.41 | 0.016 | −0.046 | −0.005 |
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Li, W.; Wang, X.; Hilmola, O.-P. Does High-Speed Railway Influence Convergence of Urban-Rural Income Gap in China? Sustainability 2020, 12, 4236. https://doi.org/10.3390/su12104236
Li W, Wang X, Hilmola O-P. Does High-Speed Railway Influence Convergence of Urban-Rural Income Gap in China? Sustainability. 2020; 12(10):4236. https://doi.org/10.3390/su12104236
Chicago/Turabian StyleLi, Weidong, Xuefang Wang, and Olli-Pekka Hilmola. 2020. "Does High-Speed Railway Influence Convergence of Urban-Rural Income Gap in China?" Sustainability 12, no. 10: 4236. https://doi.org/10.3390/su12104236