Land redevelopment and migrant inflows in Chinese cities

ABSTRACT

This paper investigates how land redevelopment is associated with a city’s potential to attract migrants. Based on empirical analysis conducted by using over one million records of land transaction data between 2008 and 2018 in Chinese cities, we observe that cities with a larger ratio of redevelopment land in the total area of land supply on average have attracted more of the migrant population. Moreover, a high proportion of redevelopment area for public services and infrastructure in the total land redevelopment area can further attract migrants. We also find a larger effect of land redevelopment in attracting migrant inflows in less developed regions than developed regions. These findings suggest the advantages of intensive urban strategy (‘compact urban’) compared with extensive urban strategy (‘urban sprawl’) in attracting migrant population. Implications of these findings for urban governancear are discussed.

KEYWORDS:

• Urban renewal
• land redevelopment
• migrant inflow
• China
• urban governance

JEL CLASSIFICATION:

• Q24
• J61
• R11

Acknowledgement

The authors are grateful for financial support from the National Natural Science Foundation of China (No. 72104088, No. 71974125, No. 71661137004)..

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ ${\chi }^2$ = 59.46, p-value = 0.00).

² Our results remain robust to spatial autoregression. We use the Moran’s I test on the residuals of the OLS model to test the spatial autocorrelation. The results, as shown in Table A1, indicate that the spatial autocorrelation in most years is not significant. We also use the spatial autoregression model to control for spatial autocorrelation, and the results are reported in Table A2. We find that the spatial autoregressive coefficient (rho) is insignificant and the results remain almost unchanged compared to the baseline results (as shown in Column (3) of Table 2).

Additional information

Funding

The work was supported by the National Natural Science Foundation of China (No. 72104088, No. 71974125, No.71661137004].