Skip to main content

Housing market regulations and strategic divorce propensity in China

Journal of Population Economics volume 35, pages 1103–1131 (2022)Cite this article

Abstract

In China’s regulated housing markets, a married couple may choose strategically to divorce in order to purchase more houses and/or purchase with more favorable financial conditions. Our study examines the propensity for strategic divorce induced by housing market regulations in China. To overcome the difficulty of using conventional divorce data to distinguish between a “true” divorce and a strategic (or a “fake”) divorce, we design an identification strategy using data on internet searches for divorce- and marriage-related keywords in 32 Chinese major cities from 2009 through 2016. Our difference-in-differences estimates provide robust evidence that housing market regulations significantly increase the propensity for strategic divorce. Our results also show that the increase in the propensity for strategic divorce is weaker in cities with higher male–female ratios and with stronger Confucian ideologies. These findings point to the role that housing market regulations play in distorting a family’s choices, as well as to the importance for policymakers to consider unintended impacts of regulations.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1

source: China City Statistical Yearbooks

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Notes

  1. Throughout this study, we use the term “true divorce” to refer to getting divorced due to unhappy matches, and we use “strategic divorce” or “fake divorce” to refer to divorces whose goal is to attain some economic benefits.

  2. Li and Wu (2014) have shown that high housing prices discourage entrepreneurship, as the extremely high returns make talented people forgo innovative activities and dive in the housing market speculation.

  3. This TV series created a historic audience rating, and its ending episode received a rating of 8 percent. It was also popular in Taiwan and Japan at that time. Due to the sensitivity of its story, censorship authorities banned its broadcast. A staff member of Beijing TV station claimed that the ban was due to real estate businesses’ lobbying. All in all, these suggest serious social grievances resulting from high housing prices. (See Ang (2020) pp. 147; Wikipedia https://en.wikipedia.org/wiki/Dwelling_Narrowness (in Chinese), retrieved on January 24, 2021, also available as supplemental materials upon request).

  4. Whether suppressing demand is the best way to cool down the housing market is beyond the scope of this paper. We take these regulations as given, and we evaluate their impacts on divorce propensity.

  5. This expectation is more likely given that strategic divorces enable households to get around regulations.

  6. See CNN report on October 18, 2010 (http://travel.cnn.com/shanghai/life/faking-divorce-buy-home-shanghai-924307/, retrieved on December 3, 2018, also available as supplemental materials upon request).

  7. See BBC report on September 2, 2016 (https://www.bbc.com/news/blogs-trending-37257747, retrieved on December 3, 2018, also available as supplemental materials upon request).

  8. Again, see CNN report on October 18, 2010 (http://travel.cnn.com/shanghai/life/faking-divorce-buy-home-shanghai-924307/, retrieved on December 3, 2018, also available as supplemental materials upon request).

  9. See Section 3.1 for more discussions about these divorce-related keywords. All keywords in Chinese are displayed in Table 7 in Appendix III.

  10. There are four directly controlled municipalities: Beijing, Shanghai, Tianjin, and Chongqing.

  11. There are five self-planning cities: Dalian, Ningbo, Qingdao, Xiamen, and Shenzhen. They are economically prosperous or politically important, and they are given greater autonomy in drafting economic policies independent of provinces they locate in.

  12. See https://en.wikipedia.org/wiki/Baidu retrieved on January 24, 2021.

  13. Note that data from both the Baidu Index and Google Trends are not equal to actual search volumes, but are transformed from actual search volumes through some algorithms. We do not know the statistical procedures behind the Baidu Index, but, as argued by Qin and Zhu (2018), Baidu Index data are approximately proportional (or linear) to actual search volumes. Therefore, the transformation should not lead to any significant distortions of the Baidu Index in representing people’s online behavior.

  14. To the best of our knowledge, there are no particular criteria for the correlation coefficient to determine a good proxy. But compared to existing literature, we deem that it is not too risky to assert a good proxy based on a correlation coefficient of 0.7 (e.g., Stephens-Davidowitz 2014, where the author got correlation coefficients about 0.6–0.66 between Google Trends data and commonly used measurements).

  15. Chen et al. (2020) provide a comprehensive discussion about the possible connections between the strength of Confucian ideologies and their historical origin. See also Kung and Ma (2014).

  16. Step 2 essentially examines the possibility that housing market regulations may be correlated with some factors increasing marriages and so may lead to more divorce-related searches (given the precautionary search story). It is not very likely that housing market regulations per se would increase marriages.

  17. Qin and Zhu (2018) and Zhang and Mu (2018) implement the Poisson regression models with internet searches as the outcome variable. Here we implement PPML estimations, which is more robust to specification errors than the Poisson maximum likelihood estimation (Gourieroux et al. 1984). PPML estimates can be roughly interpreted as those in a log-level linear regression.

  18. This magnitude is based on the calculation that 0.824 × 0. 029/0.123 = 0.194.

  19. Confucianism emphasizes the harmony of interpersonal relationships, where family stability is an important content. Hence, destroying a family would be regarded as an ideological betrayal. Also, Confucianism opposes any pursuit of wealth at the expense of harmony. As Confucius said, “It is void for me to seize wealth in an unjust way.”.

  20. Note that 3.22e − 5 × 547 = 0.0176.

  21. For example, see the Tencent News report on November 14, 2017.

    (https://news.house.qq.com/a/20171114/003890.htm, retrieved January 24, 2021, also available as supplemental materials upon request). As reported there, a couple got divorced for better loan conditions. After the divorce, the husband put one old house under his wife’s name, and purchased a second house together in his name together with his wife. They re-married after that. However, the couple now intends to divorce owing to family struggles, and the husband is on the verge of losing his property rights for the old house because it is under his wife’s name and it cannot not be divided by a divorce.

  22. According to Xinhua News (http://sh.xinhuanet.com/2021-01/22/c_139688366.htm, retrieved January 24, 2021, also available as supplemental materials upon request), starting from January 21, 2021, a divorced couple in Shanghai is subject to housing market regulations that consider two spouses still married in the first three years of a divorce, a provision that invalidates playing the strategic divorce game.

References

  • Alm J, Whittington LA (1999) For love or money? the impact of income taxes on marriage. Economica 66(263):297–316, http://www.jstor.org/stable/2555086.

  • Alm J, Whittington LA (2003) Shacking up or shelling out: income taxes, marriage, and cohabitation. Rev Econ Household 1(3):169–186

    Article  Google Scholar 

  • Ang YY (2020) China’s gilded age: the paradox of economic boom and vast corruption. Cambridge University Press, New York

    Book  Google Scholar 

  • Cameron AC, Gelbach JB, Miller DL (2008) Bootstrap-based improvements for inference with clustered errors. Rev Econ Stat 90(3):414–427

    Article  Google Scholar 

  • Chen T, Kung JKs, Ma C (2020) Long live keju! the persistent effects of China’s civil examination system. Econ J 130(631):2030–2064

    Article  Google Scholar 

  • Cáceres-Delpiano J, Giolito E (2012) The impact of unilateral divorce on crime. J Labor Econ 30(1):215–248, https://doi.org/10.1086/662137, http://www.jstor.org/stable/10101086/662137.

  • Dommaraju P, Jones G (2011) Divorce trends in asia. Asian J Soc Sci 39:725–750

  • Du Z, Zhang L (2015) Home-purchase restriction, property tax and housing price in China: a counterfactual analysis. J Econom 188(2):558–568

    Article  Google Scholar 

  • Enke B (2019) Kinship, cooperation, and the evolution of moral systems. Q J Econ 134(2):953–1019

    Article  Google Scholar 

  • Fang H, Gu Q, Xiong W, Zhou LA (2016) Demystifying the chinese housing boom. NBER Macroecon Annu 30(1):105–166

    Article  Google Scholar 

  • Farnham M, Schmidt L, Sevak P (2011) House prices and marital stability. Am Econ Rev 101(3):615–619

    Article  Google Scholar 

  • Gourieroux C, Monfort A, Trognon A (1984) Pseudo maximum likelihood methods: applications to poisson models. Econometrica 52(3):701–720. https://doi.org/10.2307/1913472

  • Gruber J (2004) Is making divorce easier bad for children? the long-run implications of unilateral divorce. J Law Econ 22(4):799–833. https://doi.org/10.1086/423155

    Article  Google Scholar 

  • Guiso L, Sapienza P, Zingales L (2006) Does culture affect economic outcomes? J Econ Perspect 20(2):23–48

    Article  Google Scholar 

  • Hamid A, Heiden M (2015) Forecasting volatility with empirical similarity and google trends. J Econ Behav Organ 117:62–81. https://doi.org/10.1016/j.jebo.2015.06.005

    Article  Google Scholar 

  • Hellerstein JK, Morrill MS, Zou B (2013) Business cycles and divorce: evidence from microdata. Econ Lett 118(1):68–70

    Article  Google Scholar 

  • Kearney MS, Levine PB (2015) Media influences on social outcomes: the impact of MTV’s 16 and pregnant on teen childbearing. Am Eco Rev 105(12):3597–3632

    Article  Google Scholar 

  • Kung JKs, Ma C (2014) Can cultural norms reduce conflicts? Confucianism and peasant rebellions in Qing China. J Dev Econ 111:132–149

    Article  Google Scholar 

  • Li L, Wu X (2014) Housing price and entrepreneurship in China. J Comp Econ 42(2):436–449

    Article  Google Scholar 

  • Li VJ, Cheng AWW, Cheong TS (2017) Home purchase restriction and housing price: a distribution dynamics analysis. Reg Sci Urban Econ 67:1–10

    Article  Google Scholar 

  • Platte E (1988) Divorce trends and patterns in China: past and present. Pacific Affairs 61(3):428–445. https://doi.org/10.2307/2760459

  • Qin Y, Zhu H (2018) Run away? Air pollution and emigration interests in China. J Popul Econ 31(1):235–266. https://doi.org/10.1007/s00148-017-0653-0

  • Rainer H, Smith I (2010) Staying together for the sake of the home? House price shocks and partnership dissolution in the UK. J R Stat Soc Ser A 173:557–574

    Article  Google Scholar 

  • Stephens-Davidowitz S (2014) The cost of racial animus on a black candidate: evidence using google search data. J Public Econ 118:26–40

    Article  Google Scholar 

  • Sun W, Zheng S, Geltner DM, Wang R (2016) The housing market effects of local home purchase restrictions: evidence from Beijing. J. Real Estate Financ Econ 55(3):288–312. https://doi.org/10.1007/s11146-016-9586-8

    Article  Google Scholar 

  • Wei SJ, Zhang X (2011) The competitive saving motive: evidence from rising sex ratios and savings rates in China. J Polit Econ 119(3):511–564

    Article  Google Scholar 

  • Whittington LA, Alm J (1997) ’til death or taxes do us part: the effect of income taxation on divorce. J Hum Resour 32(2):388–412

    Article  Google Scholar 

  • Wrenn DH, Yi J, Zhang B (2019) House prices and marriage entry in China. Reg Sci Urban Econ 74:118–130

    Article  Google Scholar 

  • Wu Y, Li Y (2018) Impact of government intervention in the housing market: evidence from the housing purchase restriction policy in China. Appl Econ 50(6):691–705

    Article  Google Scholar 

  • Yang Z, Fan Y, Zhao L (2018) A reexamination of housing price and household consumption in China: the dual role of housing consumption and housing investment. J Real Estate Financ Econ 56(3):472–499. https://doi.org/10.1007/s11146-017-9648-6

    Article  Google Scholar 

  • Zhang C (2020) Clans, entrepreneurship, and development of the private sector in China. J Comp Econ 48(1):100–123

    Article  Google Scholar 

  • Zhang J, Mu Q (2018) Air pollution and defensive expenditures: evidence from particulate-filtering facemasks. J Environ Econ Manag 92:517–536

    Article  Google Scholar 

  • Zhang JS (2017) The evolution of China’s one-child policy and its effects on family outcomes. J Econ Perspect 31(1):141–160. https://doi.org/10.1257/jep.31.1.141

    Article  Google Scholar 

Download references

Acknowledgements

The authors thank editor Shuaizhang Feng and two anonymous reviewers for helpful comments.

Funding

This research is supported by the Natural Science Foundation of China (No. 71602149) and National Social Science Foundation of China (20&ZD168).

Author information

Authors and Affiliations

  1. Department of Economics, Tulane University, New Orleans, LA, 70118, USA

    James Alm

  2. Department of Economics, University of Maryland, College Park, MD, 20742, USA

    Weizheng Lai

  3. School of Economics and Management, Wuhan University, Wuhan, 430072, Hubei, China

    Xun Li

Authors
  1. James Alm
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weizheng Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xun Li.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Responsible editor: Shuaizhang Feng

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

  1. I.

    Timetable of regulatory policies

Cities are listed in the chronological order of initial regulations.

Table 6 Timetable for housing market regulations and deregulations
Full size table
  1. II.

    Housing market regulations and online searching

    Fig. 10
    figure 10

    Housing market regulations and online searching. Data source: Baidu Index. Note: Three lines sketch time series of search indices on keywords Housing Purchase Quota, Divorce Agreement, and Divorce Process. Shaded areas denote periods when cities listed in Table 6 in Appendix I initiated a regulation, with city names listed above

    Full size image
  1. III.

    Baidu Index keywords

    Table 7 Baidu Index keywords
    Full size table

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Alm, J., Lai, W. & Li, X. Housing market regulations and strategic divorce propensity in China. J Popul Econ 35, 1103–1131 (2022). https://doi.org/10.1007/s00148-021-00853-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00148-021-00853-2

Keywords

  • Housing market regulations
  • Strategic divorce
  • Baidu Index

JEL codes

  • D78
  • J12
  • J18
  • L50
  • R21