The Mechanisms of the Transportation Land Transfer Impact on Economic Growth: Evidence from China
Abstract
:1. Introduction
2. Literature Review and Research Hypotheses
2.1. Effects of TLT on Employment Promotion, Industrial Interaction, and Efficiency Improvement
2.2. Time–Space Lag Conduction Effect of TLT
2.3. Phase Heterogeneity Effect of TLT
3. Data Resources and Empirical Design
3.1. Variable Selection
3.1.1. Explained Variable
3.1.2. Core Explanatory Variable
3.1.3. Other Variables
3.2. Data Resources
3.3. Model Specifications
3.3.1. Spatial Econometric Model
3.3.2. Mediation Effect Model
4. Results
4.1. Analysis of the Spatial Spillover Effects and Time-Lag Effects of TLT on Economic Growth
4.2. Analysis of the Mediation Effects of TLT on Economic Growth
4.3. Analysis of the Effect of Stage Heterogeneity of TLT on Economic Growth
4.4. Analysis of the Threshold Effect of TLT Promoting Economic Growth
4.4.1. Threshold Effect Analysis of Urbanization Rate on TLT to Promote Economic Growth
4.4.2. Threshold Effect Analysis of the Industrial Structure on TLT to Promote Economic Growth
4.4.3. Threshold Effect Analysis of the Advanced Industrial Structure on TLT to Promote Economic Growth
5. Discussion
5.1. Spatial Spillover and Time-Lag Effects of TLT Jointly Promote Economic Growth
5.2. Stage of Economic Development, Industrial Structure, and Urbanization Level Have Significant Threshold Effects on TLT to Promote Economic Growth
5.3. Limitations and Future Directions
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Pradhan, R.P.; Arvin, M.B.; Nair, M. Urbanization, transportation infrastructure, ICT, and economic growth: A temporal causal analysis. Cities 2021, 115, 103213. [Google Scholar] [CrossRef]
- Pan, Y.; Ma, L.; Tang, H.; Wu, Y.; Yang, Z. Land Use Transitions under Rapid Urbanization in Chengdu-Chongqing Region: A Perspective of Coupling Water and Land Resources. Land 2021, 10, 812. [Google Scholar] [CrossRef]
- Zhang, S.; Li, C.; Ma, Z.; Li, X. Influences of Different Transport Routes and Road Nodes on Industrial Land Conversion: A Case Study of Changchun City of Jilin Province, China. Chin. Geogr. Sci. 2020, 30, 544–556. [Google Scholar] [CrossRef]
- Song, M.; Wang, S.; Fisher, R. Transportation, iceberg costs and the adjustment of industrial structure in China. Transp. Res. D Transp. Environ. 2014, 32, 278–286. [Google Scholar] [CrossRef]
- Aljoufie, M.; Brussel, M.; Zuidgeest, M.; van Maarseveen, M. Urban growth and transport infrastructure interaction in Jeddah between 1980 and 2007. Int. J. Appl. Earth. Obs. Geoinf. 2013, 21, 493–505. [Google Scholar] [CrossRef]
- Maparu, T.S.; Mazumder, T.N. Transport infrastructure, economic development and urbanization in India (1990–2011): Is there any causal relationship? Transp. Res. Part A-Policy Pract. 2017, 100, 319–336. [Google Scholar] [CrossRef]
- Sun, Y.; Cui, Y. Evaluating the coordinated development of economic, social and environmental benefits of urban public transportation infrastructure: Case study of four Chinese autonomous municipalities. Transp. Policy. 2018, 66, 116–126. [Google Scholar] [CrossRef]
- Xu, J.; Zhang, M.; Zhang, X.; Wang, D.; Zhang, Y. How does city-cluster high-speed rail facilitate regional integration? Evidence from Shanghai-Nanjing corridor. Cities 2019, 85, 83–97. [Google Scholar] [CrossRef]
- Zhou, Z.; Zhang, A. High-speed rail and industrial developments: Evidence from house prices and city-level GDP in China. Transp. Res. Part A-Policy Pract. 2021, 149, 98–113. [Google Scholar] [CrossRef]
- Kasraian, D.; Maat, K.; Stead, D.; van Wee, B. Long-term impacts of transport infrastructure networks on land-use change: An international review of empirical studies. Transp. Rev. 2016, 36, 772–792. [Google Scholar] [CrossRef]
- Farhadi, M. Transport infrastructure and long-run economic growth in OECD countries. Transp. Res. Part A-Policy Pract. 2015, 74, 73–90. [Google Scholar] [CrossRef]
- Lu, X.; Wang, M.; Tang, Y. The spatial changes of transportation infrastructure and its threshold effects on urban land use efficiency: Evidence from China. Land 2021, 10, 346. [Google Scholar] [CrossRef]
- Timms, P. Urban transport policy transfer: “bottom-up” and “top-down” perspectives. Transp. Policy 2011, 18, 513–521. [Google Scholar] [CrossRef]
- Mendez, V.M.; Monje, C.A.; White, V. Beyond Traffic: Trends and Choices 2045—A National Dialogue about Future Transportation Opportunities and Challenges; Springer International Publishing: Berlin/Heidelberg, Germany, 2017. [Google Scholar]
- Solé-Ollé, A.; Viladecans-Marsal, E. Lobbying, political competition, and local land supply: Recent evidence from Spain. J. Public Econ. 2012, 96, 10–19. [Google Scholar] [CrossRef] [Green Version]
- Ye, L.; Wu, A.M. Urbanization, land development, and land financing: Evidence from Chinese cities. J. Urban Aff. 2014, 36, 354–368. [Google Scholar] [CrossRef]
- Ding, C.; Zhao, X. Land market, land development and urban spatial structure in Beijing. Land Use Policy 2014, 40, 83–90. [Google Scholar] [CrossRef]
- Song, M.L.; Ma, X.W.; Shang, Y.P.; Zhao, X. Influences of land resource assets on economic growth and fluctuation in China. Resour. Policy 2020, 68, 101779. [Google Scholar] [CrossRef]
- Zhou, L.; Tian, L.; Cao, Y.D.; Yang, L.C. Industrial land supply at different technological intensities and its contribution to economic growth in China: A case study of the Beijing-Tianjin-Hebei region. Land Use Policy 2021, 101, 105087. [Google Scholar] [CrossRef]
- Wang, L.; Wang, K.; Zhang, J.J.; Zhang, D.; Wu, X.; Zhang, L.J. Multiple objective-oriented land supply for sustainable transportation: A perspective from industrial dependence, dominance and restrictions of 127 cities in the Yangtze RiverEconomic Belt of China. Land Use Policy 2020, 99, 105069. [Google Scholar] [CrossRef]
- Kasraian, D.; Raghav, S.; Miller, E.J. A multi-decade longitudinal analysis of transportation and land use co-evolution in the Greater Toronto-Hamilton Area. J. Transp. Geogr. 2020, 84, 102696. [Google Scholar] [CrossRef]
- Millard-Ball, A. From mobility to accessibility: Transforming urban transportation and land-use planning. J. Am. Plan. Assoc. 2021, 87, 141–142. [Google Scholar] [CrossRef]
- Lv, T.G.; Wang, L.; Xie, H.L.; Zhang, X.M.; Zhang, Y.W. Exploring the global research trends of land use planning based on a bibliometric analysis: Current status and future prospects. Land 2021, 10, 304. [Google Scholar] [CrossRef]
- Khadaroo, A.J.; Seetanah, B. Transport infrastructure and foreign direct investment. J. Int. Dev. 2010, 22, 103–123. [Google Scholar] [CrossRef]
- Li, J.T.; Ji, J.Y.; Guo, H.W.; Chen, L. Research on the Influence of Real Estate Development on Private Investment: A Case Study of China. Sustainability 2018, 10, 2659. [Google Scholar] [CrossRef] [Green Version]
- Wang, N.; Zhu, Y.; Yang, T. The impact of transportation infrastructure and industrial agglomeration on energy efficiency: Evidence from China’s industrial sectors. J. Clean Prod. 2020, 244, 118708. [Google Scholar] [CrossRef]
- Liu, C.; Wang, W.; Wu, Q. Transportation infrastructure, competition and productivity: Theory and evidence from China. Econ. Lett. 2019, 174, 74–77. [Google Scholar] [CrossRef]
- Chen, Q.; Kamran, S.M.; Fan, H. Real estate investment and energy efficiency: Evidence from China’s policy experiment. J. Clean Prod. 2019, 217, 440–447. [Google Scholar] [CrossRef]
- Carbo, J.M.; Graham, D.J.; Anupriya; Casas, D.; Melo, P.C. Evaluating the causal economic impacts of transport investments: Evidence from the Madrid–Barcelona high speed rail corridor. J. Appl. Stat. 2019, 46, 1714–1723. [Google Scholar] [CrossRef]
- Cervero, R.; Kang, C.D. Bus rapid transit impacts on land uses and land values in Seoul, Korea. Transp. Policy 2011, 18, 102–116. [Google Scholar] [CrossRef] [Green Version]
- Di Berardino, C.; Onesti, G. The two-way integration between manufacturing and services. Serv. Ind. J. 2020, 40, 337–357. [Google Scholar] [CrossRef]
- Mejia-Dorantes, L.; Paez, A.; Vassallo, J.M. Transportation infrastructure impacts on firm location: The effect of a new metro line in the suburbs of Madrid. J. Transp. Geogr. 2012, 22, 236–250. [Google Scholar] [CrossRef] [Green Version]
- Holl, A. Highways and productivity in manufacturing firms. J. Urban Econ. 2016, 93, 131–151. [Google Scholar] [CrossRef]
- Shao, S.; Tian, Z.; Yang, L. High speed rail and urban service industry agglomeration: Evidence from China’s Yangtze River Delta region. J. Transp. Geogr. 2017, 64, 174–183. [Google Scholar] [CrossRef]
- Liang, X.; Li, P. Empirical study of the spatial spillover effect of transportation infrastructure on green total factor productivity. Sustainability 2021, 13, 326. [Google Scholar] [CrossRef]
- Farla, K.; de Crombrugghe, D.; Verspagen, B. Institutions, Foreign Direct Investment, and Domestic Investment: Crowding Out or Crowding In? World Dev. 2016, 88, 1–9. [Google Scholar] [CrossRef] [Green Version]
- Kennedy, C.; Miller, E.; Shalaby, A.; Maclean, H.; Coleman, J. The Four Pillars of Sustainable Urban Transportation. Transp. Rev. 2005, 25, 393–414. [Google Scholar] [CrossRef]
- Lee, D.B.; Averous, C.P. Land Use and Transportation: Basic Theory. Environ. Plan. A 1973, 5, 491–502. [Google Scholar] [CrossRef]
- Zeng, C.; Zhao, Z.; Wen, C.; Yang, J.; Lv, T.Y. Effect of Complex Road Networks on Intensive Land Use in China’s Beijing-Tianjin-Hebei Urban Agglomeration. Land 2020, 9, 532. [Google Scholar] [CrossRef]
- Yao, Z.F.; Ye, K.H.; Xiao, L.; Wang, X.W. Radiation Effect of Urban Agglomeration’s Transportation Network: Evidencefrom Chengdu–Chongqing Urban Agglomeration, China. Land 2021, 10, 520. [Google Scholar] [CrossRef]
- Wang, Y.; Zhu, X.; Li, L.; Wu, B. Reasons and countermeasures of traffic congestion under urban land redevelopment. Procedia Soc. Behav. Sci. 2013, 96, 2164–2172. [Google Scholar] [CrossRef] [Green Version]
- Zheng, S.; Zhang, X.; Sun, W.; Wang, J. The effect of a new subway line on local air quality: A case study in Changsha. Transp. Res. Part D-Transport. Environ. 2019, 68, 26–38. [Google Scholar] [CrossRef]
- Wang, F.; Wei, X.J.; Liu, J.; He, L.Y.; Gao, M.N. Impact of high-speed rail on population mobility and urbanisation: A case study on Yangtze River Delta urban agglomeration, China. Transp. Res. Part A-Policy Pract. 2019, 127, 99–114. [Google Scholar] [CrossRef]
- Yu, Z.; Zhao, P. The factors in residents’ mobility in rural towns of China: Car ownership, road infrastructure and public transport services. J. Transp. Geogr. 2021, 91, 102950. [Google Scholar] [CrossRef]
- Zhang, X.; Hu, Y.; Lin, Y. The influence of highway on local economy: Evidence from China’s Yangtze River Delta region. J. Transp. Geogr. 2020, 82, 102600. [Google Scholar] [CrossRef]
- Yu, N.; de Jong, M.; Storm, S.; Mi, J. Spatial spillover effects of transport infrastructure: Evidence from Chinese regions. J. Transp. Geogr. 2013, 28, 56–66. [Google Scholar] [CrossRef]
- Boonekamp, T.; Burghouwt, G. Measuring connectivity in the air freight industry. J. Air Transp. Manag. 2017, 61, 81–94. [Google Scholar] [CrossRef]
- Shu, C.; Xie, H.L.; Jiang, J.F.; Chen, Q.R. Is urban land development driven by economic development or fiscal revenue stimuli in China? Land Use Policy 2018, 77, 107–115. [Google Scholar] [CrossRef]
- Li, B.C.; Cao, X.S.; Xu, J.B.; Wang, W.L.; Ouyang, S.S.; Liu, D. Spatial-Temporal Pattern and Influence Factors of Land Used for Transportation at the County Level since the Implementation of the Reform and Opening-Up Policy in China. Land 2021, 10, 833. [Google Scholar] [CrossRef]
- Liu, T.Y.; Su, C.W. Is transportation improving urbanization in China? Socio-Econ. Plan. Sci. 2021, 7, 101034. [Google Scholar] [CrossRef]
- Chi, J.; Baek, J. Dynamic relationship between air transport demand and economic growth in the United States: A newlook. Transp. Policy 2013, 29, 257–260. [Google Scholar] [CrossRef]
- Wang, H.; Han, J.Y.; Su, M.; Wan, S.L.; Zhang, Z.C. The relationship between freighttransport and economic development: A case study of China. Res. Transp. Econ. 2021, 85, 100885. [Google Scholar] [CrossRef]
- Surya, B.; Salim, A.; Hernita, H.; Suriani, S.; Menne, F.; Rasyidi, E.S. Land use change, urban agglomeration, and urban sprawl: A sustainable development perspective of makassar city, Indonesia. Land 2021, 10, 556. [Google Scholar] [CrossRef]
- Jin, W.; Zhou, C.; Zhang, G. Characteristics of state-owned construction land supply in Chinese cities by development stage and industry. Land Use Policy 2020, 96, 104630. [Google Scholar] [CrossRef]
- Henderson, J.V. Cities and development. J. Reg. Sci. 2010, 50, 515–540. [Google Scholar] [CrossRef] [PubMed]
- Shi, G.; Shan, J.; Ding, L.; Ye, P.; Li, Y.; Jiang, N. Urban road network expansion and its driving variables: A case study of Nanjing city. Int. J. Environ. 2019, 16, 2318. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Xiang, W.; Wang, Y.; Li, N.; Zhu, Q. Grey-relation analysis of traffic system and urbanization in Jilin Province of China. Chin. Geogr. Sci. 2007, 17, 216–221. [Google Scholar] [CrossRef]
- Li, G.; Toda, C. Discussions on the local rail transit system in the urbanization. Procedia Soc. Behav. Sci. 2014, 138, 193–198. [Google Scholar] [CrossRef] [Green Version]
- China Land Market Network. Available online: https://www.landchina.com (accessed on 15 August 2021).
- Gao, H. Public land leasing, public productive spending and economic growth in Chinese cities. Land Use Policy 2019, 88, 104076. [Google Scholar] [CrossRef]
- Tong, T.; Yu, T.E. Transportation and economic growth in China: A heterogeneous panel cointegration and causality analysis. J. Transp. Geogr. 2018, 73, 120–130. [Google Scholar] [CrossRef]
- Niu, F.; Xin, Z.; Sun, D. Urban land use effects of high-speed railway network in China: A spatial spillover perspective. Land Use Policy 2021, 105, 105417. [Google Scholar] [CrossRef]
- Chen, W.; Shen, Y.; Wang, Y.; Wu, Q. The effect of industrial relocation on industrial land use efficiency in China: A spatial econometrics approach. J. Clean. Prod. 2018, 205, 525–535. [Google Scholar] [CrossRef]
- Liu, J.M.; Hou, X.H.; Wang, Z.Q.; Shen, Y. Study the effect of industrial structure optimization on urban land-use efficiency in China. Land Use Policy 2021, 105, 105390. [Google Scholar] [CrossRef]
- Wang, D.; Ren, C.; Zhou, T. Understanding the impact of land finance on industrial structure change in China: Insights from a spatial econometric analysis. Land Use Policy 2021, 103, 105323. [Google Scholar] [CrossRef]
- Elhorst, J.P. Matlab software for spatial panels. Int. Reg. Sci. Rev. 2014, 3, 389–405. [Google Scholar] [CrossRef] [Green Version]
- Hui, E.C.M.; Liang, C. Spatial spillover effect of urban landscape views on property price. Appl. Geogr. 2016, 72, 26–35. [Google Scholar] [CrossRef]
- LeSage, J.P.; Pace, R.K. Introduction to Spatial Econometrics; Chapman and Hall, CRC: New York, NY, USA, 2009. [Google Scholar] [CrossRef] [Green Version]
- Huang, L.; Yang, L.; Tian, L.; Yu, R.; Lu, J. Does the location of construction land supply play an very important role on economic growth? The case study of Tianjin Binhai New Area. J. Urban Manag. 2020, 9, 104–114. [Google Scholar] [CrossRef]
- Jiwattanakulpaisarn, P.; Noland, R.B.; Graham, D.J. Causal linkages between highways and sector-level employment. Transp. Res. Part A-Policy Pract. 2010, 44, 265–280. [Google Scholar] [CrossRef]
- Mcgraw, M.J. The role of airports in city employment growth, 1950–2010. J. Urban Econ. 2020, 116, 103240. [Google Scholar] [CrossRef]
- Ke, X.; Chen, H.Q.; Hong, Y.M.; Hsiao, C. Do China’s high-speed-rail projects promote local economy?—New evidence from a panel data approach. China Econ. Rev. 2017, 44, 203–226. [Google Scholar] [CrossRef]
- Umar, M.; Ji, X.F.; Kirikkaleli, D.; Alola, A.A. The imperativeness of environmental quality in the United States transportation sector amidst biomass-fossil energy consumption and growth. J. Clean. Prod. 2021, 285, 124863. [Google Scholar] [CrossRef]
- Liang, W.; Yang, M. Urbanization, economic growth and environmental pollution: Evidence from China. Sust. Comput. 2019, 21, 1–9. [Google Scholar] [CrossRef]
- Cervero, R. Linking urban transport and land use in developing countries. J. Transp. Land Use 2013, 6, 7–24. [Google Scholar] [CrossRef] [Green Version]
- Zolnik, E.; Malik, A.; Riaz, O. Household transportation expenditures in developing countries: Evidence on the effect of urban land use change from Pakistan. Appl. Geogr. 2019, 108, 39–46. [Google Scholar] [CrossRef]
Variable Types | Symbol | Variable Name | Calculation Method |
---|---|---|---|
explained variable | pgdp | regional economic level | per capita gross domestic product (GDP) |
core explanatory variable | land | transportation land transfer (TLT) | transportation land transfer (TLT) area (the newly transferred transportation land per year) |
control variable | industry | industrial structure | ratio of the added value of the tertiary industry to the added value of the secondary industry |
control variable | gover | financial expenditure scale | proportion of local financial general budget expenditures to the total GDP |
control variable | edu | education level | proportion of local financial education expenditure to the total GDP |
control variable | peo | population size | number of permanent residents at the end of the year |
control variable | invest | scale of foreign investment | ratio of total investment from foreign-invested enterprises to the total GDP |
control variable | trade | trade scale | proportion of total import and export of goods to the total GDP |
control variable | elder | degree of population aging | ratio of the population aged 65 and over to the population aged 15–64 |
control variable | tfp | total factor productivity | stochastic frontier approach (SFA) |
intermediate variable | employment | level of employment | number of urban unit employees |
intermediate variable | industry2 | interactive development of industries | added value of secondary and tertiary industries |
intermediate variable | efficiency | overall efficiency of the economy | total factor productivity |
threshold variable | ur | urbanization rate | proportion of urban population to the total population |
threshold variable | ind-str | industrial structure | added value of the proportion of tertiary industry to total GDP |
threshold variable | ad-ind-str | advanced industrial structure | two ratios were calculated to determine the index of advanced industrial structure. See text for details. |
Column | 1 | 2 | 3 | 4 | 5 | 6 |
---|---|---|---|---|---|---|
Model Form | Spatial Adjacency Weight Matrix | Spatial Distance Weight Matrix | ||||
Type | SDM_FE | SDM_FE | ||||
Direct | Indirect | Total | Direct | Indirect | Total | |
Variables | ln(pgdp) | ln(pgdp) | ln(pgdp) | ln(pgdp) | ln(pgdp) | ln(pgdp) |
ln(land) | 0.0377 *** (3.1795) | 0.1249 ** (2.3882) | 0.1626 *** (3.0230) | 0.0115 ** (2.3595) | 0.0707 * (1.7062) | 0.0823 * (1.9433) |
ln(industry) | 0.1098 (0.8999) | 0.5965 *** (2.7153) | 0.7063 ** (2.4649) | −0.1542 *** (−4.6929) | 0.0628 (0.4023) | −0.0914 (−0.5539) |
ln(govern) | 0.1291 (1.3245) | −0.0057 (−0.0124) | 0.1234 (0.2427) | −0.1596 ** (−2.4706) | −0.4209 (−0.8967) | −0.5805 (−1.2561) |
ln(edu) | −0.0122 (−0.4098) | 0.0314 (0.3163) | 0.0192 (0.1648) | 0.0031 (0.1452) | 0.1139 (0.7022) | 0.1170 (0.6868) |
ln(peo) | 0.8652 * (1.8378) | 2.3345 (1.5823) | 3.1997 ** (2.4319) | 0.0975 (0.4099) | 1.4435 (1.1738) | 1.5410 (1.1428) |
ln(invest) | −0.0998 ** (−2.4948) | −0.0093 (−0.0472) | −0.1090 (−0.4946) | −0.0133 (−0.9195) | −0.0564 (−0.5078) | −0.0698 (−0.5891) |
ln(trade) | 0.0128 (0.3311) | 0.2356 (1.3319) | 0.2484 (1.3841) | 0.0089 (0.2517) | −0.0147 (−0.0878) | −0.0058 (−0.0327) |
ln(elder) | 0.0639 (1.0032) | 0.2327 (0.9382) | 0.2965 (1.1428) | −0.0499 (−1.2053) | −0.0607 (−0.3461) | −0.1105 (−0.6363) |
ln(tfp) | 0.0113 (1.4125) | 0.0066 (0.4480) | 0.0179 (1.1939) | 0.0099 (1.3233) | −0.0129 (−0.7889) | −0.0030 (−0.1709) |
Year FE | yes | yes | yes | yes | yes | yes |
Province FE | yes | yes | yes | yes | yes | yes |
Observations | 390 | 390 | 390 | 390 | 390 | 390 |
R2 | 0.0198 | 0.0198 | 0.0198 | 0.0006 | 0.0006 | 0.0006 |
Numbers of provinces | 30 | 30 | 30 | 30 | 30 | 30 |
Column | 7 | 8 | 9 | 10 |
---|---|---|---|---|
Model Form | Ordinary Panel Models | Time-Lag Panel Model | ||
Type | FE | RE | First-Order Lag | Second-Order Lag |
Variables | ln(pgdp) | ln(pgdp) | ln(pgdp) | ln(pgdp) |
ln(land) | 0.0082 ** (2.3445) | 0.0983 *** (3.8709) | 0.0050 (1.4071) | 0.0018 (0.5141) |
l1.ln(land) | - | - | 0.0076 ** (2.2958) | 0.0063 * (1.9550) |
l2.ln(land) | - | - | - | 0.0027 (0.8782) |
ln(industry) | −0.1731 *** (−7.9752) | 0.6080 *** (5.1773) | −0.1484 *** (−6.8149) | −0.1209 *** (−5.6461) |
ln(gover) | −0.1221 *** (−3.2425) | 0.6511 *** (5.0752) | −0.1486 *** (−4.1630) | −0.2345 *** (−5.9185) |
ln(edu) | 0.0045 (0.2129) | 0.0202 (0.2150) | 0.0089 (0.4501) | 0.1274 *** (3.4602) |
ln(peo) | 0.0729 (1.0032) | 0.3576 *** (3.8231) | 0.1740 ** (2.2536) | 0.2843 *** (3.5893) |
ln(invest) | −0.0154 (−1.5198) | 0.1383 (1.5307) | −0.0161 (−1.5972) | −0.0125 (−1.2429) |
ln(trade) | 0.0386 *** (3.6258) | 0.1084 * (1.8369) | 0.0366 *** (3.4171) | 0.0306 *** (2.9393) |
ln(elder) | −0.0568 * (−1.7216) | 0.1645 (1.1630) | −0.0497 (−1.5780) | −0.0470 (−1.5976) |
ln(tfp) | 0.0098 (1.3669) | 0.0468 *** (3.9373) | 0.0086 (1.2849) | 0.0030 (0.3962) |
constant | 8.8160 *** (13.4765) | 6.2629 *** (7.6362) | 8.0546 *** (11.6846) | 7.6132 *** (10.8635) |
year FE | yes | no | yes | yes |
province FE | yes | no | yes | yes |
observations | 390 | 390 | 390 | 390 |
F-value/chi2 | 1113.1271 | 438.1977 | 1002.8024 | 912.8071 |
R2 | 0.3356 | 0.3856 | 0.2531 | 0.1529 |
number of provinces | 30 | 30 | 30 | 30 |
Column | 11 | 12 | 13 | 14 | 15 | 16 | 17 |
---|---|---|---|---|---|---|---|
Model Form | First | Second | Third | ||||
Type | Employment | Industry | Efficiency | ||||
Variables | ln(pgdp) | ln(emp) | ln(ind) | ln(tfp) | ln(pgdp) | ln(pgdp) | ln(pgdp) |
ln(land) | 0.0017 (0.4952) | −0.0000 (−0.0059) | 0.0089 (1.0285) | −0.0208 (−0.7631) | 0.0017 (0.4835) | −0.0011 (−0.4646) | 0.0005 (0.1540) |
l1.ln(land) | 0.0063 ** (1.9721) | 0.0065 (0.9366) | 0.0162 ** (1.9836) | 0.0118 (0.4567) | 0.0057 * (1.7023) | 0.0020 (0.8563) | 0.0004 (0.1449) |
l2.ln(land) | 0.0030 (0.9846) | 0.0126 * (1.9494) | 0.0097 (1.2433) | 0.0862 *** (3.5793) | 0.0042 (1.3630) | −0.0001 (−0.0384) | 0.0023 (0.8433) |
ln(emp) | - | - | - | - | 0.1427 *** (5.0480) | - | - |
ln(ind) | - | - | - | - | - | 0.2760 *** (16.1227) | - |
ln(tfp) | - | - | - | - | - | - | 0.0111 * (1.7046) |
control | yes | yes | yes | yes | yes | yes | yes |
year FE | yes | yes | yes | yes | yes | yes | yes |
province FE | yes | yes | yes | yes | yes | yes | yes |
observations | 330 | 330 | 330 | 330 | 330 | 330 | 330 |
F-value | 961.3341 | 46.0773 | 209.1668 | 60.7261 | 1047.1166 | 1860.4953 | 897.2644 |
R2 | 0.9856 | 0.7463 | 0.9373 | 0.8042 | 0.9844 | 0.9929 | 0.9889 |
number of provinces | 30 | 30 | 30 | 30 | 30 | 30 | 30 |
Column | 18 | 19 | 20 | 21 | 22 | 23 |
---|---|---|---|---|---|---|
Type | Employment | Industry | tfp | Employment | Industry | tfp |
Variables | ln(Employment) | ln(Industry) | ln(tfp) | ln(pgdp) | ln(pgdp) | ln(pgdp) |
ln(land) | 0.2351 *** (6.9434) | 0.3777 *** (10.3798) | 0.1377 *** (5.1362) | −0.0661 *** (−2.6848) | −0.0955 *** (−4.5587) | 0.0491 ** (1.9692) |
ln(employment) | - | - | - | 0.3524 *** (9.7867) | - | - |
ln(industry) | - | - | - | - | 0.4388 *** (17.0096) | - |
ln(tfp) | - | - | - | - | - | 0.1540 *** (3.3792) |
cons | 4.1345 *** (15.0130) | 6.4575 *** (22.1022) | −0.8410 *** (−3.9080) | 8.8893 *** (37.0521) | 7.0400 *** (31.5133) | 10.0031 *** (50.6772) |
estimates | type | Delta | Sobel | Monte Carlo | ||
ln(employment) | 0.083 *** (5.663) | 0.083 *** (5.663) | 0.083 *** (5.494) | |||
indirect effect | ln(industry) | 0.166 *** (8.860) | 0.166 *** (8.860) | 0.167 *** (8.538) | ||
ln(tfp) | 0.021 *** (2.823) | 0.021 *** (2.823) | 0.021 *** (2.797) | |||
log(likelihood) | −1201.3942 | −1332.6993 | −1316.0624 | −1201.3942 | −1332.6993 | −1316.0624 |
observations | 360 | 390 | 390 | 360 | 390 | 390 |
number of provinces | 30 | 30 | 30 | 30 | 30 | 30 |
Stage | Stage Name | 1970 | 1990 | 1995 | 2000 | 2005 | 2010 |
---|---|---|---|---|---|---|---|
1st stage | primary product stage Ⅰ | 100–150 | 350–470 | 390–550 | 440–620 | 500–700 | 560–790 |
primary product stage Ⅱ | 150–280 | 470–950 | 550–1100 | 620–1240 | 700–1410 | 790–1580 | |
2nd stage | primary industrialized stage | 280–570 | 950–1890 | 1100–2210 | 1240–2480 | 1410–2830 | 1580–3150 |
middle industrialized stage | 570–1130 | 1890–3780 | 2210–4410 | 2480–4970 | 2830–5650 | 3150–6310 | |
late industrialized stage | 1130–2100 | 3780–7070 | 4410–8250 | 4970–9330 | 5650–10,570 | 6310–11,820 | |
3rd stage | primary developed stage | 2100–3360 | 7070–11,310 | 8250–13,200 | 9330–14,910 | 10,570–16,920 | 11,820–18,900 |
developed stage | 3360–5050 | 11,310–16,980 | 13,200–19,810 | 14,910–22,390 | 16,920–25,390 | 18,900–28,360 |
Stage Name | Province Name |
---|---|
primary industrialized stage | Guizhou, Gansu |
middle industrialized stage | Hebei, Jilin, Heilongjiang, Shanxi, Anhui, Jiangxi, Henan, Hunan, Guangxi, Hainan, Sichuan, Yunnan, Shaanxi, Qinghai, Ningxia, Xinjiang |
late industrialized stage | Neimenggu, Liaoning, Fujian, Shandong, Hubei, Guangdong, Chongqing |
primary developed stage | Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang |
Primary Industrialized Stage | Middle Industrialized Stage | Late Industrialized Stage | Primary Developed Stage | |
---|---|---|---|---|
Variables | ln(pgdp) | ln(pgdp) | ln(pgdp) | ln(pgdp) |
ln(land) | 0.1088 * | 0.0841 *** | 0.0686 ** | 0.0320 *** |
(2.0545) | (5.5818) | (2.4600) | (3.7694) | |
ln(industry) | 0.1680 | 0.4966 *** | 0.6273 *** | 0.6382 *** |
(0.4225) | (6.2736) | (4.7797) | (9.3314) | |
ln(govern) | 0.1981 | 0.3755 ** | 1.1071 *** | −0.2324 * |
(0.5057) | (2.4978) | (4.6997) | (−1.7706) | |
ln(edu) | −0.0615 | 0.3491 ** | −0.5329 ** | 0.1241 |
(−0.7882) | (2.5068) | (−2.3322) | (0.8817) | |
ln(peo) | 5.2567 ** | 2.3531 *** | 2.6639 *** | 2.0327 *** |
(2.2779) | (6.6378) | (5.4419) | (6.8360) | |
ln(invest) | 0.2048 | −0.0071 | −0.3917 *** | −0.2538 *** |
(1.2069) | (−0.1683) | (−4.3172) | (−5.1229) | |
ln(trade) | −0.3222 ** | 0.0338 | −0.0039 | −0.2966 *** |
(−2.4755) | (0.7382) | (−0.0673) | (−4.0497) | |
ln(elder) | 0.3467 | 0.5027 *** | 0.4585 ** | 0.1102 * |
(0.3926) | (3.1168) | (2.2456) | (1.9933) | |
ln(tfp) | 0.0003 | 0.0273 | 0.0403 | 0.0105 |
(0.0068) | (1.4357) | (1.4308) | (1.2792) | |
constant | −32.0606 * | −9.3311 *** | −13.0380 *** | −2.4443 |
(−1.7835) | (−3.1114) | (−2.7727) | (−0.8072) | |
F | 36.2347 | 101.3088 | 69.5252 | 270.7611 |
province FE | yes | yes | yes | yes |
observations | 26 | 156 | 91 | 65 |
R2 | 0.9560 | 0.8710 | 0.8930 | 0.9795 |
Threshold Variable | Test Type | Threshold Value | F Value | p Value | 10% Critical Value | 5% Critical Value | 1% Critical Value |
---|---|---|---|---|---|---|---|
urbanization rate | single threshold test | 0.4353 *** | 76.99 | 0.0010 | 34.1310 | 40.4873 | 53.2495 |
double threshold test | 0.3870 *** 0.5350 *** | 46.12 | 0.0100 | 28.2019 | 33.9444 | 46.0744 | |
three threshold test | 0.4469 | 72.59 | 0.5980 | 122.3648 | 137.5176 | 161.5293 | |
industrial structure | single threshold test | 0.4416 * | 27.15 | 0.0665 | 24.9042 | 29.8508 | 42.0312 |
double threshold test | 0.4416 0.7590 | 8.58 | 0.6245 | 21.7127 | 25.8614 | 37.0924 | |
three threshold test | 0.5399 | 4.40 | 0.8260 | 17.6989 | 22.0327 | 31.7115 | |
advanced industrial structure | single threshold test | 0.0880 *** | 43.09 | 0.0125 | 26.0599 | 31.0918 | 44.5134 |
double threshold test | 0.1590 | 22.70 | 0.1300 | 25.4270 | 32.4303 | 44.2599 | |
three threshold test | 0.0310 | 24.80 | 0.5510 | 47.0825 | 54.0334 | 68.2644 |
Threshold Interval | Interactive Items | Coefficient Estimation |
---|---|---|
d1 (advanced industrial structure ≤ 8.80%) | lnland × d1 | 0.0792 *** (7.9485) |
d2 (advanced industrial structure > 8.80%) | lnland × d2 | 0.0975 *** (9.5366) |
d3 (urbanization rate ≤ 38.70%) | lnland × d3 | 0.0050 (0.4397) |
d4 (38.70% < urbanization rate ≤ 53.50%) | lnland × d4 | 0.0495 *** (5.1890) |
d5 (urbanization rate > 53.50%) | lnland × d5 | 0.0690 *** (7.5170) |
d6 (industrial structure ≤ 44.16%) | lnland × d6 | 0.0717 *** (6.9252) |
d7 (industrial structure > 44.16%) | lnland × d7 | 0.0859 *** (8.5094) |
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Zhang, M.; Li, Z.; Wang, X.; Li, J.; Liu, H.; Zhang, Y. The Mechanisms of the Transportation Land Transfer Impact on Economic Growth: Evidence from China. Land 2022, 11, 30. https://doi.org/10.3390/land11010030
Zhang M, Li Z, Wang X, Li J, Liu H, Zhang Y. The Mechanisms of the Transportation Land Transfer Impact on Economic Growth: Evidence from China. Land. 2022; 11(1):30. https://doi.org/10.3390/land11010030
Chicago/Turabian StyleZhang, Mingzhi, Zhaocheng Li, Xinpei Wang, Jiajia Li, Hongyu Liu, and Ying Zhang. 2022. "The Mechanisms of the Transportation Land Transfer Impact on Economic Growth: Evidence from China" Land 11, no. 1: 30. https://doi.org/10.3390/land11010030