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The Aggregate and Distributional Impacts of Residence Policy Relaxation

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Abstract

Government often designs strict policy to control the conversion rate from temporary to permanent residents. The residence status may directly affect individuals’ migration decisions and housing tenure choices. We present a dynamic spatial equilibrium framework to study the aggregate and distributional impacts of residence policy relaxation with a focus on the housing market. The DID approach treating the recent hukou policy reform in China as a shock reveals hukou policy relaxation causes housing prices in the treatment cities to be 4.9% higher than the unaffected cities. The impacts are stronger in cities where obtaining hukou was harder. The model is calibrated to the Chinese economy and predicts that hukou policy relaxation can bring a positive spillover effect to those unaffected cities’ welfare. If hukou policy reform were implemented in those super-mega Chinese cities, housing prices would grow by 2.3%, but the welfare gain equivalent to 3.1% of their current levels.

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Notes

  1. See the official website of the State Council for more details.

  2. We use 12 months before the implementation of the new hukou policy as the baseline period. The empirical specification is.

    \(ln\left(H{P}_{it}\right)={\beta }_{0}+\sum {\beta }_{1,k} \ast {\text{POST}}_{t} \ast {\text{TREAT}}_{i}+{\delta }_{i}+{\gamma }_{t}+{\epsilon}_{it}.\)

  3. We consider this assumption innocuous. Although individuals may still flow within the “major” region in the data, the overall magnitudes of the population inflow and outflow are relatively comparable in the Chinese data.

  4. In what follows, we omit the time subscript except in cases in which its omission may be misleading.

  5. In China, they are primarily controlled by the government and are thus exogenous.

  6. If the public-provided private goods and non-housing consumption are substitutable, we may expect temporary residents to consume more non-housing consumption than permanent residents of similar characteristics to compensate for the utility loss. However, we do not observe in the data. In the quantitative exercise, we essentially calibrate \(\xi\) to match the ratio of average non-housing consumption between permanent and temporary residents.

  7. In the remainder of the paper, we use “permanent resident” and “hukou holder,“ and “temporary resident” and “non-hukou holder” interchangeably.

  8. See Wu et al. (2016) and Glaeser et al. (2017) for more discussions about the tiers of cities in China.

  9. Please refer to Appendix 3 for a complete list of cities belonging to each type.

  10. The changes in both the population distribution and housing prices seem to be negligible when hukou restrictions are only relaxed in the type-3 city, so we instead focus on the aforementioned two experiments.

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Author information

Authors and Affiliations

  1. Institute for Advanced Research, Shanghai University of Finance and Economics, Shanghai, China

    Rongsheng Tang

  2. Key Laboratory of Mathematical Economics (SUFE), Ministry of Education, Shanghai, 200433, China

    Rongsheng Tang

  3. Division of Economics, Nanyang Technological University, Singapore, Singapore

    Yang Tang

  4. Department of Construct Management, Hang Lung Center for Real Estate, Tsinghua University, Beijing, China

    Rongjie Zhang

Authors
  1. Rongsheng Tang
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  2. Yang Tang
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Corresponding author

Correspondence to Yang Tang.

Ethics declarations

Rongsheng Tang thanks for the financial support from the National Natural Science Foundation of China (No. 71803112). Rongjie Zhang thanks National Natural Science Foundation of China (No. 71874093 and 72174100) for financial support.

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Appendices

Appendix 1

Figs. 2, 3, 4, and 5

Tables 8, 9, 10, 11, 12, and 13

Fig. 2
figure 2

Raw trend of housing price. Notes: The figure plots the raw trend of the constant-quality housing price index for treatment and control cities. The sample period covers all months between January 2015 and April 2019. The solid line denotes the average housing price index of the treatment group cities, including 114 cities with an urban population of 1 million to 5 million. The dashed line denotes the average housing price index of the control group cities, including 136 cities with an urban population either above 5 million or below 1 million. The vertical line denotes the implementation of the new hukou policy

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Fig. 3
figure 3

Parallel trend test. Notes: The figure visualizes the coefficients estimated with the dynamic DID specification, with both the coefficients and 95% confidence intervals reported. The sample period covers all months between January 2015 and April 2019. The treatment group includes 114 cities with an urban population of 1 million to 5 million, whereas the cities with an urban population either above 5 million (14 cities) or below 1 million (122 cities) serve as the control group. The pre-trend period includes 12 months. The vertical line denotes the implementation of the hukou reform

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Fig. 4
figure 4

Welfare comparison. Notes: We plot the evolution of the average welfare along age cohorts in different policy scenarios in each city. In the benchmark economy, the probability of obtaining hukou is increasing with the type of the city

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Fig. 5
figure 5

Ownership Rate Comparison. Notes: We plot the evolution of the ownership rate along age cohorts in different policy scenarios in each city. In the benchmark economy, the probability of obtaining hukou is increasing with the type of the city

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Table 8 Ownership rate comparison in the type-1 city
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Table 9 Ownership rate comparison in the type-2 city
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Table 10 Ownership rate comparison in the type-3 city
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Table 11 Welfare comparison in the type-1 city
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Table 12 Welfare comparison in the type-2 city
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Table 13 Welfare comparison in the type-3 city
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Appendix 2 Computation Algorithm

The household value and policy functions are solved by backward induction starting from the final period of life. We discretize the idiosyncratic state by fixing grids on liquid assets B (150 points), mortgages M (11 points), house sizes H (2 points), and income Y (100 points), persistent income shocks (7 points), transitory income shocks (7 points). Households choose liquid assets and house sizes on the grids of B and H respectively. We follow Kaplan et al. (2020) and Chen et al. (2020) by restricting household mortgage choice when purchasing a house to be only on M. However, when computing the next period mortgage balance after the current mortgage is repaid, it can either be exactly M, or follow the amortization schedule, which is computed via linear interpolation between grid points. The following algorithm is used to compute the steady-state equilibrium: (1) Make an initial guess of the market clearing house price and the provision of public goods \(G\). (2) Given the initial guess, solve backward for the individuals’ value and policy functions. Given individuals’ choices, solve forward for the distribution of households over individual states. (3) Calculate the aggregate housing demand, housing supply, and net government expenditure on public goods in the stationary equilibria. (4) Compare the updated housing price with the initial guess. If not the same, replace the initial guess by a weighted average between the two, and return to step 2. (5) Compare the updated government expenditure on public goods with the initial guess. If not the same, replace the initial guess by a weighted average between the two, and return to step 2.

Appendix 3 The List of Cities Within Each City Type

  • Type-1 city: Chengdu, Nanjing, Guangzhou, Shantou, Hangzhou, Shanghai, Tianjin, Shenzhen, Beijing, Zhengzhou, Chongqing, Xi’an, Shenyang, Wuhan.

  • Type-2 city: Shijiazhuang, Dalian, Changchun, Harbin, Suzhou, Ningbo, Hefei, Fuzhou, Xiamen, Nanchang, Ji’nan, Qingdao, Changsha, Nanning, Haikou, Kunming.

  • Type-3 city: Tangshan, Handan, Baoding, Taiyuan, Datong, Jincheng, Hohhot, Baotou, Chifeng, Anshan, Jilin city, Qiqihar, Daqing, Yichun, Xuzhou, Changzhou, Nantong, Lianyungang, Yangzhou, Zhenjiang, Taizhou, Wenzhou, Huzhou, Shaoxing, Taizhou, Wuhu, Bengbu, Huainan, Fuyang, Suzhou, Liuan, Bozhou, Putian, Quanzhou, Ganzhou,Fuzhou, Zibo, Yantai, Weifang, Jining, Weihai, Rizhao, Linyi, Dezhou, Liaocheng, Luoyang, Pingdingshan, Anyang, Xinxiang, Luohe, Nanyang, Xinyang, Yichang, Xiangfan, Jingzhou, Zhuzhou, Hengyang, Changde, Yiyang, Yongzhou, Zhuhai, Foshan, Jiangmen, Zhanjiang, Huizhou, Qingyuan, Dongguan, Zhongshan, Chaozhou, Liuzhou, Qinzhou, Guigang, Yulin, Hezhou, Laibin, Zigong, Luzhou, Mianyang, Suining, Neijiang, Leshan, Nanchong, Meishan, Yibin, Dazhou, Bazhong, Ziyang, Guiyang, Zunyi, Baoji, Ankang, Lanzhou, Tianshui, Wuwei, Xining, Yinchuan, Urumqi.

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Tang, R., Tang, Y. & Zhang, R. The Aggregate and Distributional Impacts of Residence Policy Relaxation. J Real Estate Finan Econ (2022). https://doi.org/10.1007/s11146-022-09912-1

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