Housing demolition and life satisfaction in China: perspectives from household economic behaviors

Housing demolition and life satisfaction in China: perspectives from household economic behaviors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Housing demolition and life satisfaction in China: perspectives from household economic behaviors Xuecun Zhao, Zhichao Ma, Yanrong Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7816859/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Using the China Family Panel Studies (CFPS), this paper estimates the effect of housing demolition on life satisfaction. This paper not only estimates the impact of housing demolition on overall life satisfaction but also explores how housing demolition affects life satisfaction over time. Our results show that housing demolition has a positive impact on overall life satisfaction. We also find that the positive effect on life satisfaction is primarily driven by young people, females, better-educated people, and people living in urban areas. However, the positive impact on life satisfaction weakens and becomes insignificant over time. We explore possible underlying mechanisms and find that housing demolition improves life satisfaction by increasing total income, transfer income, housing-related consumption, and durable-goods consumption. This paper may provide evidence for relevant departments to make polices aimed at improving the quality of life and life satisfaction. Earth and environmental sciences/Environmental social sciences Social science/Environmental studies Social science/Sociology housing demolition life satisfaction China Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction As the largest developing country in the world, China has undergone a rapid economic development and a massive housing demolition in recent years. In China, about 14.43% of households had experienced housing demolition by 2019, according to the data from the China Household Finance Survey (CHFS). With the economic transformation, Chinese people will have a new understanding of subjective well-being, which includes material life satisfaction, happiness, and spiritual experience (Zhang et al., 2022). Different from happiness, life satisfaction is an overall evaluation of life and a better indicator to assess social evaluations on a broader scale (Deaton, 2008; Abbott et al., 2016; Lombardo et al., 2018). Chinese life satisfaction is gradually becoming a more prominent public concern (Zhang et al., 2022), and it is interesting to explore life satisfaction in China (Appleton and Song, 2008). Some studies find that windfall gains have a positive effect on life satisfaction (Ambrey and Fleming, 2014; Oswald and Winkelmann, 2019). In China, housing assets are the most significant component of household wealth (Xie and Jin, 2015), and housing demolition is a form of income and wealth shock (Zhao and Liu, 2022). Therefore, housing demolition may have an impact on people’s life satisfaction. However, few studies have explored how housing demolition affects life satisfaction. We use the China Family Panel Studies (CFPS) to estimate the effect of housing demolition on life satisfaction. First, we estimate the overall impact of housing demolition on life satisfaction. Second, we explore heterogeneous effects of housing demolition on life satisfaction according to people’s age, gender, educational background, and the nature of their living areas. Third, we use the Event Study method to explore how housing demolition affects life satisfaction over time. Third, we explore possible underlying mechanisms from the perspectives of household economic behaviors, such as household income and consumption. Our results are as follows: First, housing demolition improves overall life satisfaction. Second, the positive effect on life satisfaction is primarily driven by young people, females, better-educated people, and people living in urban areas. Third, the positive impact on life satisfaction weakens and becomes insignificant over time. Finally, we find that housing demolition may improve life satisfaction by increasing total income, transfer income, housing-related consumption, and durable-goods consumption. Our contributions are as follows: First, we explore how housing demolition affects life satisfaction over time. Though some studies have analyzed the short-term impact of housing demolition on life satisfaction or the association between them (Li et al., 2019; Hu et al., 2023), they rarely explore how housing demolition affects life satisfaction over time. Compared to the overall effect, exploring how housing demolition affects life satisfaction over time may provide a deeper analysis and is more valuable. Second, we explore possible underlying mechanisms, such as household income and consumption, which contribute to a better and more comprehensive understanding of how housing demolition affects life satisfaction from the perspectives of household economic behaviors. Third, this paper contributes to the literature that focuses on financial constraints. Our research adds to studies by exploring the effect of turning illiquid housing assets into liquid cash on household consumption and life satisfaction, which may provide a reference for the design of housing demolition in other developing countries. The remainder of this paper is organized as follows: Section 2 offers the background of housing demolition in China. Section 3 presents our data and provides summary statistics. Section 4 introduces our empirical strategy and provides empirical results. Section 5 summarizes and concludes. 2 Background China has experienced a rapid economic development and is among the largest economies in the world (Li et al., 2010 ). Economic growth includes some key steps, including infrastructure construction, urbanization, and the provision of public goods (Huang et al., 2024 ). To realize a rapid economic growth, local governments in China often plan some construction projects and demand a large amount of construction land (Zhao et al., 2022 ; Bao and Peng, 2016 ; Cao and Zhang, 2018 ). In addition, rapid urbanization also needs land for the construction of employment placement, infrastructure, and housing (Ding, 2007 ). To satisfy the demand for land, local governments often expropriate land (Huang et al., 2024 ; Wang et al., 2019 ). Before land expropriation, local governments have to demolish houses on the land that is in the planned areas. Housing demolition is a project that removes houses and gives corresponding compensation for relocated households. China’s urbanization process will persist, and more families in old towns or suburbs will experience housing demolition in the future (Qiu and Chen, 2023 ). In China, land is not privately owned by individuals but by the state (Zhao and Liu, 2022 ; Qu et al., 2018 ). Instead, rural collectives own land in rural areas, while the state owns land in urban areas (Wu et al., 2013 ). When demolishers need to acquire land with buildings, they often must also take ownership of the houses situated on that land. They can do this through expropriation or negotiation, as permitted under Chinese law (Han et al., 2018 ). Regardless of the method used, demolishers are required to provide appropriate compensation to residents whose houses will be demolished, as mandated by Chinese law. Home ownership is the basic demand of daily life and is positively associated with life satisfaction (Cattaneo et al., 2009 ; Zhang et al., 2018 ; Huang et al., 2015 ). For Chinese people, owning a house is the most important life goal, and housing wealth is also an important property related to their lives (Liu and Xu, 2018 ). For instance, owning a house may increase the possibility of marriage by increasing one's advantage in the marriage market (Hu and Wang, 2020 ). Housing demolition and residents’ resettlement are complicated and sensitive processes, which involve the vital interests of relocated residents, local governments, and real estate developers (Liu, 2016 ; Wang et al., 2023 ). Therefore, some households called 'nail households' may negotiate for more compensation. However, local governments usually have the authority to set compensation, and nail households are unlikely to resist housing demolition in the end. As shown above, housing demolition is an important form of exogenous wealth and income shock to relocated households in China. 3 Data and summary statistics 3.1 Data We use nationally representative data from the China Family Panel Studies (CFPS). The Institute of Social Science Survey of Peking University conducted the CFPS, surveying 16,000 target households across 25 provinces/municipalities/cities (excluding Inner Mongolia, Ningxia, Qinghai, Tibet, Xinjiang, Hainan, Taiwan, Hong Kong, and Macao). The CFPS contains multi-level data on individuals, families, and communities, reflecting detailed changes in family economic behaviors and individual behaviors (Xie and Hu, 2014 ). The CFPS is conducted every two years and has completed seven formal surveys: in 2010, 2012, 2014, 2016, 2018, 2020, and 2022. This paper explores how housing demolition impacts life satisfaction by analyzing data from the 2012, 2014, 2016, 2018, 2020, and 2022 waves, with the 2012 wave serving as the baseline wave. Since the information on housing demolition is collected at the household level, this paper only includes households that participated in all six surveys. The main reason for using the CFPS data in this study is that it simultaneously contains detailed information on housing demolition and life satisfaction. Moreover, the CFPS includes detailed information on household income and consumption, which is essential for our analysis of underlying mechanisms. Our data from the CFPS covers 10 years, making it convenient for us to explore how housing demolition affects life satisfaction over time. Waves in the years 2014, 2016, 2018, 2020, and 2022 contain accurate information on housing demolition. The corresponding question in the questionnaire is “has your family experienced housing demolition in the past 12 months?”. We also control for the time-varying individual and household characteristic variables. Individual characteristic variables include individuals’ age, gender, marital status, and educational background. Household characteristic variables include family size, the proportion of children, and the ratio of elderly members in the household. The primary outcome variable is life satisfaction. The CFPS includes detailed information regarding life satisfaction. The corresponding question in the questionnaire is “How satisfied are you with your own life?”. The response is from ‘very dissatisfied’ to ‘very satisfied’, corresponding to the numbers 1 to 5. It indicates that the larger the number, the higher the life satisfaction. Other outcome variables for the analysis of underlying mechanisms include household income and consumption. Household income includes total income and transfer income. Household consumption includes total consumption, housing-related consumption, dress and food consumption, durable-goods consumption, and other consumption. 3.2 Summary statistics Table 1 provides descriptive statistics for individual and household characteristics in 2012 according to whether the household experienced housing demolition from 2012 to 2022. As Panel A of Table 1 shows, relocated individuals and other individuals are different in most of their characteristics. As Panel B of Table 1 shows, heads of relocated households and other households differ in half of their characteristics. According to Panel C of Table 1 , relocated households and other households are similar in all of their characteristics. As shown above, housing demolition may not be random without controlling for community fixed effects (Zhao and Liu, 2022 ). In the section of empirical analysis, we will control for individual fixed effects or household fixed effects, taking into account possible endogeneity concerns. Table 1 Summary statistics of individual and household characteristics in 2012 (1) All (2) Relocated (3) Others (2)-(3) Panel A. Individual characteristics Age 45.27 46.25 45.22 1.04*** Male (0–1) 0.49 0.48 0.49 -0.01 Married (0–1) 0.87 0.88 0.87 0.01** Educational background : Primary school or below (0–1) 0.51 0.48 0.52 -0.04*** Middle school (0–1) 0.28 0.30 0.28 0.02* High school or above (0–1) 0.21 0.22 0.20 0.02* N 29,739 1,548 28,191 Panel B. Head characteristics Age 48.75 49.02 48.73 0.29 Male (0–1) 0.53 0.51 0.53 -0.02 Married (0–1) 0.97 0.96 0.97 -0.01** Educational background : Primary school or below (0–1) 0.50 0.43 0.50 -0.07*** Middle school (0–1) 0.31 0.32 0.30 0.02 High school or above (0–1) 0.20 0.24 0.20 0.04*** N 7,157 481 6,676 Panel C. Household characteristics Family size 4.28 4.21 4.28 -0.07 The proportion of children 0.15 0.14 0.15 -0.01 The ratio of elderly members 0.10 0.10 0.10 0.00 N 7,157 481 6,676 Notes : *** Significant at the 1% level, ** Significant at the 5% level. Table 2 provides descriptive statistics for life satisfaction, household income, and household consumption across relocated people/households and others. As Panel A of Table 2 shows, the life satisfaction of relocated people is higher than that of others. Panel B of Table 2 shows that the total income and transfer income of relocated households are higher than those of other households. Panel C of Table 2 shows that relocated households’ total consumption, housing-related consumption, dress and food consumption, durable-goods consumption, and other consumption are higher than those of non-relocated households. As shown above, relocated individuals or households have higher life satisfaction, household income, and household consumption. Table 2 Summary statistics of outcome variables (1) All (2) Relocated (3) Others (2)-(3) Panel A. Life satisfaction Life satisfaction (1–5) 3.73 3.95 3.72 0.23*** N 133,331 4,361 128,970 Panel B. Household income Total income (RMB yuan) 81,225 258,801 75,309 183,492*** Transfer income (RMB yuan) 13,686 175,804 8,286 167,518*** N 40,357 1,301 39,056 Panel C. Household consumption Total consumption (RMB yuan) 56,726 80,646 55,929 24,717*** Housing-related consumption (RMB yuan) 9,148 18,654 8,831 9,823*** Dress and food consumption (RMB yuan) 21,296 28,008 21,072 6,936*** Durable-goods consumption (RMB yuan) 7,640 10,525 7,544 2,981*** Other consumption (RMB yuan) 18,642 23,459 18,482 4,977*** N 40,357 1,301 39,056 Notes : *** Significant at the 1% level. 4 Empirical Strategy and Empirical Results 4.1 Empirical Strategy To estimate the overall effect of housing demolition on life satisfaction, we use the Difference-in-Difference method and construct a two-way fixed effects model as follows: In Eq. ( 1 ), Satisfaction it ( t = 2012, 2014, 2016, 2018, 2020, 2022) indicates the outcome variable life satisfaction. Demolish it is a dummy variable, which equals one if the observation is after housing demolition, otherwise zero. For we take the 2012 wave as the baseline wave, the variable Demolish it in 2012 is equal to zero. X it is a set of time-varying variables, which include household characteristic variables (family size, the proportion of children, and the ratio of elderly members) and individual characteristic variables (individuals’ age, marital status, and educational background). In our analysis, we do not control for the variable gender, which does not change over time. Taking into account that housing demolition may not be random, we control for individual fixed effects. Individual i is the individual fixed effect, which is used to control for the influence of unobservable and time-independent individual characteristic variables. Year t is the time fixed effect, which is used to control for the influence of possible time trends. The coefficient of interest in this paper is β 1 , which indicates the impact of housing demolition on individual life satisfaction. ε it is the random error term. We also use the Event Study method to explore how housing demolition affects individual life satisfaction over time. We use the number of years before or after housing demolition as dummy variables to replace the variable Demolish it in Eq. ( 1 ), and construct an equation as follows: In Eq. ( 2 ), T = -8, -6, -4, -2, 0, 2, 4, 6, 8, which indicate 8 years before housing demolition, 6 years before housing demolition, 4 years before housing demolition, 2 years before housing demolition, the current year, 2 years after housing demolition, 4 years after housing demolition, 6 years after housing demolition and 8 years after housing demolition. α 1.T indicates the extent to which the period before and after housing demolition affects life satisfaction. 4.2 Main results Using the panel data from CFPS, we estimate the overall effect of housing demolition on life satisfaction. First, we estimate the overall impact of housing demolition without controlling for household and individual characteristic variables. As Column (1) of Table 3 shows, housing demolition significantly improves the overall life satisfaction. Second, we examine the overall impact of housing demolition on overall life satisfaction by controlling for household and individual characteristic variables sequentially. According to Column (2) and Column (3) of Table 3 , the influence coefficient does not change significantly. As shown above, housing demolition has a positive impact on overall life satisfaction. Table 3 The overall effect of housing demolition on individuals’ life satisfaction (1) (2) (3) Housing demolition 0.068*** (0.026) 0.067** (0.026) 0.067** (0.026) R 2 0.507 0.507 0.508 N 124,695 12,4695 124,695 Control variables : Household characteristic variables √ Individual characteristic variables √ √ Time fixed effects √ √ √ Individual fixed effects √ √ √ Notes : Robust standard errors clustered at the household level are shown in parentheses. Household characteristics include family size, the proportion of children, and the ratio of elderly members. Individual characteristics include individuals’ age, marital status, and educational background. *** Significant at the 1% level, ** Significant at the 5% level. 4.3 Heterogeneous effects Housing demolition may have varying impacts on the life satisfaction of people depending on their characteristics. In this subsection, we examine the varied effects of housing demolition on life satisfaction according to individuals’ age, gender, educational background, and the nature of their living areas. First, compared to the young, the aged often have a higher level of life satisfaction in China (Zhang et al., 2022 ), and housing demolition may have different influences on people across different age groups. Therefore, we divide all samples into two groups according to the median age (people aged 48 or below VS people aged 49 or above). As Column (1) and Column (2) of Table 4 show, the positive effect on life satisfaction is mainly driven by young people. Compared to older people, younger people often have to take the responsibility of supporting their households, which may lead to lower life satisfaction. However, housing demolition may relieve young people’s financial burden of supporting households and has a greater impact on their life satisfaction. Second, there is a traditional social norm in China that women do housework at home (Li and Xiao, 2020 ), and married women spend more time on housework after China’s housing reform in the 1990s (Chen et al., 2023 ). Therefore, women are better at perceiving the quality of life. It indicates that housing demolition may have different influences on men and women. Then, we divide all samples into two groups according to people’s gender (males VS females). Columns (3) and (4) of Table 4 show that the positive impact on life satisfaction is primarily attributed to females. Housing demolition may lead to improved living conditions for relocated households, which women are more likely to recognize. Therefore, housing demolition has a greater and positive impact on women’s life satisfaction. Third, people with higher education levels tend to have higher income and overall happiness (Cuñado and De Gracia, 2012 ). Therefore, the impact of housing demolition on life satisfaction may vary based on educational background. Then, we divide all samples into two groups according to people’s educational background (primary school or below VS middle school or above). As shown in Column (5) and Column (6) of Table 4 , the positive effect on life satisfaction is mainly driven by better-educated people. Less-educated people may use housing compensation for essential expenses, which might have a weak relationship with life satisfaction. Finally, there is a significant difference in housing compensation between urban and rural areas (Zhao and Liu, 2022 ), and housing demolition may have different impacts on life satisfaction across people living in urban and rural areas. Therefore, we divide all samples into two groups according to the nature of their living areas (people living in urban areas VS people living in rural areas). As shown in Column (7) and Column (8) of Table 4 , the positive impact on life satisfaction is primarily driven by people living in urban areas. Since housing compensation of urban households is usually higher than that of rural households, urban households may have much more household income and household consumption, which contribute to higher life satisfaction. Table 4 Heterogeneous effects of housing demolition on life satisfaction (1) The young (2) The aged (3) Males (4) Females (5) Less-educated people (6) Better-educated people (7) Urban areas (8) Rural areas Housing demolition 0.128*** (0.038) 0.009 (0.036) 0.059* (0.034) 0.076** (0.035) 0.047 (0.040) 0.082** (0.034) 0.083** (0.036) 0.061 (0.039) R 2 0.525 0.517 0.520 0.496 0.500 0.528 0.519 0.500 N 60,895 60,885 61,979 62,631 57,617 65,982 54,157 70,532 Notes : Robust standard errors clustered at the household level are shown in parentheses. All regressions control for household characteristics, individual characteristics, time fixed effects, and individual fixed effects. Household characteristics include family size, the proportion of children, and the ratio of elderly members. Individual characteristics include individuals’ age, marital status, and educational background. *** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level. 4.4 Effects on life satisfaction over time Compared to the overall effect, exploring the change in the positive effect on life satisfaction over time can provide a deeper analysis and may be more valuable. Therefore, we use the Event Study method to analyze how housing demolition affects life satisfaction over time. As shown in Table 5 , housing demolition improves life satisfaction in the current year, and this positive effect continues for 4 years. However, the positive effect becomes insignificant after 6 years of housing demolition. We also explore the parallel trend assumption in Fig. 1. We find that the impact on life satisfaction is relatively flat before housing demolition, and these results support the parallel trend assumption. The above results indicate that the impact of housing demolition on life satisfaction does not last over time, and the impact on life satisfaction 6 years after housing demolition is nearly the same as it was before. Table 5 Effects of housing demolition on life satisfaction over the years (1) Life satisfaction Current year of housing demolition 0.202** (0.098) 2 years after housing demolition 0.175* (0.100) 4 years after housing demolition 0.206** (0.102) 6 years after housing demolition 0.128 (0.108) 8 years after housing demolition 0.122 (0.117) R 2 0.508 N 124,695 Notes : We take the years before the housing demolition as a reference. Robust standard errors clustered at the household level are shown in parentheses. The above regression controls for household characteristics, individual characteristics, individual fixed effects, and time fixed effects. Household characteristics include family size, the proportion of children, and the ratio of elderly members. Individual characteristics include individuals’ age, marital status, and educational background. ** Significant at the 5% level, * Significant at the 10% level. 4.5 Robustness checks To explore the robustness and reliability of our results, we conduct a series of robustness checks by dropping relocated households without compensation, controlling for the number of houses owned by households, and dropping communities without housing demolition experience, respectively. First, housing compensation is an important way to change household wealth and income in China. Whether a household receives compensation may have different impacts on life satisfaction. We estimate the effect of housing demolition with compensation on life satisfaction by dropping relocated households without compensation. As Column (2) of Table 6 shows, the influence coefficient of housing demolition on life satisfaction becomes larger. We also conduct similar robustness checks for the effects of housing demolition on life satisfaction by year. As Column (2) of Table 7 shows, the influence coefficient of housing demolition on life satisfaction in the current year becomes larger. Second, home ownership is positively correlated to housing satisfaction and significantly increases the life satisfaction of people with medium and high incomes (Elsinga and Hoekstra, 2005 ; Ren et al., 2018 ). Similarly, the number of owned houses after housing demolition indicates a household’s wealth structure, and housing demolition may have a different impact on life satisfaction after controlling for the number of owned houses. Therefore, we do the robustness check by controlling for the number of owned houses after housing demolition. As Column (3) of Table 6 shows, the influence coefficient of housing demolition on life satisfaction does not change significantly. In addition, we find that the number of owned houses has a significant and positive impact on life satisfaction. That is, the more houses one owns, the higher life satisfaction will be. We also conduct similar robustness checks for effects on life satisfaction by years in Table 7 . As Column (3) of Table 7 shows, the influence coefficients of housing demolition on life satisfaction by years do not change significantly. Third, absolute consumption and wealth level do not determine happiness, which is only determined by social comparison (Hsee et al., 2009 ). Similarly, life satisfaction depends on social comparison. Therefore, we compare relocated individuals with other individuals from communities experiencing housing demolition. That is, only individuals from communities experiencing housing demolition are considered the control group. As Column (4) of Table 6 shows, the influence coefficient of housing demolition on life satisfaction does not change significantly. We also conduct similar robustness checks for effects on life satisfaction by years. As Column (4) of Table 7 shows, the influence coefficients of housing demolition on life satisfaction by years do not change significantly, except for the influence coefficient of the 4 years after housing demolition. As shown above, our results are generally robust and credible. Table 6 Robustness checks for overall effects (1) Baseline (2) Drop relocated households without compensation (3) Control for the number of owned houses (4) Drop communities without demolition experience Housing demolition 0.067** (0.026) 0.077** (0.030) 0.068** (0.026) 0.069** (0.030) Number of houses 0.015*** (0.006) R 2 0.508 0.509 0.508 0.501 N 124,695 123,318 124,600 39,588 Notes : Robust standard errors clustered at the household level are shown in parentheses. All regressions control for individual characteristics, household characteristics, individual fixed effects, and time fixed effects. Household characteristics include family size, the proportion of children, and the ratio of elderly members. Individual characteristics include individuals’ age, marital status, and educational background. ** Significant at the 5% level. Table 7 Robustness checks for effects on life satisfaction by years (1) Baseline (2) Drop relocated households without compensation (3) Control for the number of owned houses (4) Drop communities without demolition experience Current year of housing demolition 0.202** (0.098) 0.212** (0.098) 0.202** (0.098) 0.204** (0.100) 2 years after housing demolition 0.175* (0.100) 0.173* (0.101) 0.174* (0.100) 0.142 (0.103) 4 years after housing demolition 0.206** (0.102) 0.196* (0.103) 0.201* (0.103) 0.192* (0.106) 6 years after housing demolition 0.128 (0.108) 0.129 (0.109) 0.126 (0.109) 0.124 (0.113) 8 years after housing demolition 0.122 (0.117) 0.083 (0.119) 0.116 (0.118) 0.125 (0.121) R 2 0.508 0.508 0.508 0.501 N 124,695 123,846 124,600 39,588 Notes : We take the years before the housing demolition as a reference. Robust standard errors clustered at the household level are shown in parentheses. All regressions control for household characteristics, individual characteristics, time fixed effects, and individual fixed effects. Household characteristics include family size, the proportion of children, and the ratio of elderly members. Individual characteristics include individuals’ age, marital status, and educational background. ** Significant at the 5% level, * Significant at the 10% level. 4.6 Underlying mechanisms In China, people attach more importance to household status and social comparison (Cui, 2018 ), which are important influence factors of Chinese life satisfaction. On one hand, household income and household consumption are important indicators of measuring a household’s status and social comparison in China. On the other hand, income is often taken as a major indicator of subjective well-being (Qiu et al., 2024 ), and household income is significantly and positively correlated with life satisfaction in China (Yuan, 2016 ). Moreover, the rise in most household consumption is positively related to a higher life satisfaction among individuals with low income (Dumludag, 2015 ). In this subsection, we explore underlying mechanisms such as household income and consumption, which contribute to a better understanding of how housing demolition affects life satisfaction. 4.6.1 Household income First, we estimate the overall effects of housing demolition on household total income and transfer income. As Table 8 shows, housing demolition generally increases total income and transfer income. The coefficient of housing demolition on transfer income is larger than that on total income. That is, housing demolition is an important way to achieve windfall gains in China. Second, we estimate the association between household income and life satisfaction in the appendix. As Table A.1 shows, both total income and transfer income are significantly and positively associated with life satisfaction. The correlation coefficient between total income is larger than that between transfer income and life satisfaction, and total income is more important than transfer income. Income is positively associated with subjective well-being among individuals with middle or low income (Diener and Biswas-Diener, 2002 ), and people are much happier if their income is higher than that of people in the reference group (Ferrer-i-Carbonell, 2005 ). Moreover, more income may enable people to lead a happier life than those who are poor (Mahadea, 2013 ), and people with incomes above the average level have a higher life satisfaction relatively (Illusion, 2006 ). Therefore, housing demolition may improve overall life satisfaction by increasing household income. Table 8 The overall effect of housing demolition on household income (1) Log of total income (2) Log of transfer income Housing demolition 0.580*** (0.054) 2.380*** (0.199) R 2 0.601 0.544 N 40,349 40,349 Notes : Robust standard errors clustered at the household level are shown in parentheses. The above regressions control for head characteristics, household characteristics, household fixed effects, and time fixed effects. A household’s head may change over time, and head characteristics include the head’s age, gender, marital status, and educational background. Household characteristics include family size, the proportion of children, and the ratio of elderly members. *** Significant at the 1% level. Finally, we explore how housing demolition affects total income and transfer income over time. As Table 9 shows, housing demolition has the greatest effect on total income and transfer income in the current year. It might be because the majority of families received substantial housing compensation in the current year of housing demolition. However, these positive effects weaken over time. We also explore the parallel trend assumption in Fig. 2–3 and find that the impacts on total income and transfer income remain relatively flat before housing demolition, supporting the parallel trend assumption. These positive effects of housing demolition on total income and transfer income in the current year may have a certain periodical impact on life satisfaction. In other words, housing demolition may only improve life satisfaction within 4 years by leading significant increase in total income and transfer income in the current year. Table 9 Effects of housing demolition on household income over the years (1) Log of total income (2) Log of transfer income The current year of housing demolition 1.224*** (0.093) 5.059*** (0.346) 2 years after housing demolition 0.235*** (0.084) 1.174*** (0.349) 4 years after housing demolition 0.194** (0.082) 0.679* (0.363) 6 years after housing demolition 0.154* (0.085) 0.998** (0.439) 8 years after housing demolition -0.009 (0.112) 0.983* (0.542) R 2 0.606 0.551 N 40,349 40,349 Notes : We take the years before the housing demolition as a reference. Robust standard errors clustered at the household level are shown in parentheses. All regressions control for head characteristics, household characteristics, household fixed effects, and time fixed effects. Head characteristics include the head’s age, gender, marital status, and educational background. Household characteristics include family size, the proportion of children, and the ratio of elderly members. *** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level. 4.6.2 Household consumption Income indicates the short-term inflow of household consumption (Zhang et al., 2025 ). Compared to income, consumption has a greater positive effect on life satisfaction (Brown and Gathergood, 2017 ). Therefore, we explore how housing demolition affects household consumption. First, we estimate the overall effect of housing demolition on household consumption. As Table 10 shows, housing demolition increases total consumption, housing-related consumption, dress and food consumption, and other consumption as a whole. However, no evidence shows that housing demolition increases overall durable-goods consumption. Household income and wealth are determining factors for household consumption (Sousa, 2009 ), and an increase in wealth leads to an improvement in consumption by reducing the marginal effect of wealth (Hedenus, 2011 ; Larsson, 2011 ). Households can increase their borrowing capacity by selling assets, thereby raising their consumption levels (Marquez et al., 2013). Moreover, households that have cash liquidity constraints but own fixed assets have stronger consumption responses to sudden income (Cui and Feng, 2017 ). That is, with housing compensation, relocated households could increase their consumption level (Shi and He, 2022 ). Second, we estimate the relationship between household consumption and life satisfaction in Table A.2 of the appendix. As Column (1) of Table A.2 shows, no evidence supports that total consumption is significantly associated with life satisfaction. As Column (2) of Table A.2 shows, housing-related consumption is significantly and positively associated with life satisfaction. Housing-related consumption includes paid rent, house maintenance, property management, and utility bills in the CFPS, and energy consumption is a typical utility bill. The household energy consumption is positively correlated with life satisfaction in China (Piao and Managi, 2023 ), and housing quality is positively correlated with one’s happiness (Hu et al., 2020 ). More housing-related consumption means higher energy consumption and house maintenance. That is, higher housing-related consumption indicates higher happiness and life satisfaction. As Column (3) of Table A.3 shows, dress and food consumption are not significantly associated with life satisfaction. According to Column (4) of Table A.3, durable-goods consumption is positively associated with life satisfaction. As Column (5) of Table A.3 shows, other consumption is not significantly associated with life satisfaction. As shown above, housing demolition may improve overall life satisfaction by increasing housing-related consumption. Table 10 Overall effects of housing demolition on household consumption (1) Log of total consumption (2) Log of housing-related consumption (3) Log of dress and food consumption (4) Log of durable-goods consumption (5) Log of other consumption housing demolition 0.167*** (0.036) 0.363*** (0.060) 0.095** (0.047) 0.241 (0.171) 0.052 (0.046) R 2 0.616 0.407 0.550 0.396 0.555 N 40,349 40,349 40,349 40,349 40,349 Notes : Robust standard errors clustered at the household level are shown in parentheses. All regressions control for head characteristics, household characteristics, household fixed effects, and time fixed effects. Head characteristics include the head’s age, gender, marital status, and educational background. Household characteristics include family size, the proportion of children, and the ratio of elderly members. *** Significant at the 1% level, ** Significant at the 5% level. Finally, we explore how housing demolition affects household consumption over time. As Column (1) of Table 11 shows, housing demolition significantly increases total consumption only in the current year of housing demolition. The positive effect on total consumption weakens and becomes insignificant over time. As Column (2) of Table 11 shows, housing demolition significantly increases housing-related consumption in the current year of housing demolition. However, the positive effect weakens and becomes insignificant over time. As Column (3) of Table 11 shows, there is no evidence that housing demolition increases dress and food consumption. As Column (4) of Table 11 shows, housing demolition only increases durable-goods consumption in the current year of housing demolition. The positive on durable-goods consumption weakens and becomes insignificant over time. However, the value of durable goods may last for several years. That is, the positive effect of housing demolition on life satisfaction may last for several years due to the increase in durable-goods consumption. As Column (5) of Table 11 shows, there is no evidence that housing demolition affects other consumption. We also explore the parallel trend assumption in Fig. 4–8 and find that impacts on all types of household consumption are relatively flat before housing demolition. The permanent income theory holds that consumption is mainly determined by the expected future income, and the expected long-term income holds a more significant position in the factors influencing consumption (Friedman, 1957 ). Households that have undergone housing demolition are unlikely to receive similar large-scale transfer income in the short term. The life-cycle theory holds that rational individuals will plan their consumption demands in the long term to optimize the utility of consumption (Modigliani and Brumberg, 2013 ). Therefore, relocated households need to make a comprehensive plan for their economic activities, such as consumption. The above results indicate that housing demolition may improve life satisfaction by increasing housing-related consumption and durable-goods consumption. Table 11 Effects of housing demolition on household consumption over the years (1) Log of total consumption (2) Log of housing-related consumption (3) Log of dress and food consumption (4) Log of durable-goods consumption (5) Log of other consumption Current year of housing demolition 0.177*** (0.068) 0.628*** (0.106) 0.026 (0.074) 0.579* (0.316) -0.037 (0.095) 2 years after housing demolition 0.084 (0.071) 0.279** (0.109) 0.058 (0.082) -0.081 (0.333) 0.026 (0.097) 4 years after housing demolition 0.028 (0.074) 0.070 (0.124) 0.025 (0.092) -0.483 (0.373) -0.002 (0.105) 6 years after housing demolition -0.003 (0.089) -0.046 (0.158) 0.046 (0.092) -0.105 (0.450) -0.127 (0.120) 8 years after housing demolition -0.110 (0.094) -0.106 (0.167) -0.075 (0.162) -0.285 (0.473) -0.095 (0.128) R 2 0.617 0.408 0.550 0.397 0.555 N 40,349 40,349 40,349 40,349 40,349 Notes : We take the years before the housing demolition as a reference. Robust standard errors clustered at the household level are shown in parentheses. All regressions control for head characteristics, household characteristics, household fixed effects, and time fixed effects. Head characteristics include the head’s age, gender, marital status, and educational background. Household characteristics include family size, the proportion of children, and the ratio of elderly members. *** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level. 5 Conclusion Using panel data from the CFPS, we estimate the overall effect of housing demolition on life satisfaction and explore how housing demolition affects life satisfaction over time. Our main results show that housing demolition increases overall life satisfaction, and the positive effect on life satisfaction is primarily driven by young people, females, better-educated people, and people living in urban areas. We also find that the positive impact on life satisfaction weakens and becomes insignificant over time. We explore possible underlying mechanisms and find that housing demolition improves individual life satisfaction by increasing transfer income, total income, housing-related consumption, and durable-goods consumption. Our research contributes to a better understanding of how housing demolition affects life satisfaction over time and provides a reference for the design of housing demolition in other developing countries. Poor rural families use the received cash income for investment in production, which increases their agricultural income and long-term consumption levels (Gertler et al., 2012 ). In addition, the lottery winners maintain a clear mind and make rational plans for future expenditures, thus maintaining a happy and fulfilling life (Hedenus, 2011 ). Similarly, to improve life satisfaction, relocated households should expand income sources and smooth their consumption at the same time. Our results provide evidence for relevant departments to guide relocated households to increase household income and keep household consumption, thereby improving life satisfaction. Declarations Competing interests The authors declare that they have no competing interests. Data availability The data that support the findings of this study are openly available in the China Family Panel Studies at https://cfpsdata.pku.edu.cn/#/resource-detail/4 . Ethical approval Ethical approval is not required Informed consent This paper does not contain any studies with human participants performed by any of the authors. Funding statement This study is under the financial support from the Humanities and Social Science Fund of Colleges and Universities of Hebei Province of China (grant no. SQ2024214). 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09:03:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1462161,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7816859/v1/5c7389a9-5b1e-4777-949f-7d1384884132.pdf"},{"id":96211981,"identity":"f74c33e8-cab8-4fbc-84ca-df3ed0e361a8","added_by":"auto","created_at":"2025-11-18 18:54:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22029,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7816859/v1/b5caf9abc2d7dbdc4ad953ea.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Housing demolition and life satisfaction in China: perspectives from household economic behaviors","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAs the largest developing country in the world, China has undergone a rapid economic development and a massive housing demolition in recent years. In China, about 14.43% of households had experienced housing demolition by 2019, according to the data from the China Household Finance Survey (CHFS). With the economic transformation, Chinese people will have a new understanding of subjective well-being, which includes material life satisfaction, happiness, and spiritual experience (Zhang et al., 2022). Different from happiness, life satisfaction is an overall evaluation of life and a better indicator to assess social evaluations on a broader scale (Deaton, 2008; Abbott et al., 2016; Lombardo et al., 2018). Chinese life satisfaction is gradually becoming a more prominent public concern (Zhang et al., 2022), and it is interesting to explore life satisfaction in China (Appleton and Song, 2008). Some studies find that windfall gains have a positive effect on life satisfaction (Ambrey and Fleming, 2014; Oswald and Winkelmann, 2019). In China, housing assets are the most significant component of household wealth (Xie and Jin, 2015), and housing demolition is a form of income and wealth shock (Zhao and Liu, 2022). Therefore, housing demolition may have an impact on people\u0026rsquo;s life satisfaction. However, few studies have explored how housing demolition affects life satisfaction.\u003c/p\u003e\n\u003cp\u003eWe use the China Family Panel Studies (CFPS) to estimate the effect of housing demolition on life satisfaction. First, we estimate the overall impact of housing demolition on life satisfaction. Second, we explore heterogeneous effects of housing demolition on life satisfaction according to people\u0026rsquo;s age, gender, educational background, and the nature of their living areas. Third, we use the Event Study method to explore how housing demolition affects life satisfaction over time. Third, we explore possible underlying mechanisms from the perspectives of household economic behaviors, such as household income and consumption.\u003c/p\u003e\n\u003cp\u003eOur results are as follows: First, housing demolition improves overall life satisfaction. Second, the positive effect on life satisfaction is primarily driven by young people, females, better-educated people, and people living in urban areas. Third, the positive impact on life satisfaction weakens and becomes insignificant over time. Finally, we find that housing demolition may improve life satisfaction by increasing total income, transfer income, housing-related consumption, and durable-goods consumption.\u003c/p\u003e\n\u003cp\u003eOur contributions are as follows: First, we explore how housing demolition affects life satisfaction over time. Though some studies have analyzed the short-term impact of housing demolition on life satisfaction or the association between them (Li et al., 2019; Hu et al., 2023), they rarely explore how housing demolition affects life satisfaction over time. Compared to the overall effect, exploring how housing demolition affects life satisfaction over time may provide a deeper analysis and is more valuable. Second, we explore possible underlying mechanisms, such as household income and consumption, which contribute to a better and more comprehensive understanding of how housing demolition affects life satisfaction from the perspectives of household economic behaviors. Third, this paper contributes to the literature that focuses on financial constraints. Our research adds to studies by exploring the effect of turning illiquid housing assets into liquid cash on household consumption and life satisfaction, which may provide a reference for the design of housing demolition in other developing countries.\u003c/p\u003e\n\u003cp\u003eThe remainder of this paper is organized as follows: Section 2 offers the background of housing demolition in China. Section 3 presents our data and provides summary statistics. Section 4 introduces our empirical strategy and provides empirical results. Section 5 summarizes and concludes.\u003c/p\u003e"},{"header":"2 Background","content":"\u003cp\u003eChina has experienced a rapid economic development and is among the largest economies in the world (Li et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Economic growth includes some key steps, including infrastructure construction, urbanization, and the provision of public goods (Huang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To realize a rapid economic growth, local governments in China often plan some construction projects and demand a large amount of construction land (Zhao et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bao and Peng, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Cao and Zhang, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition, rapid urbanization also needs land for the construction of employment placement, infrastructure, and housing (Ding, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). To satisfy the demand for land, local governments often expropriate land (Huang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Before land expropriation, local governments have to demolish houses on the land that is in the planned areas. Housing demolition is a project that removes houses and gives corresponding compensation for relocated households. China\u0026rsquo;s urbanization process will persist, and more families in old towns or suburbs will experience housing demolition in the future (Qiu and Chen, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn China, land is not privately owned by individuals but by the state (Zhao and Liu, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Qu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Instead, rural collectives own land in rural areas, while the state owns land in urban areas (Wu et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). When demolishers need to acquire land with buildings, they often must also take ownership of the houses situated on that land. They can do this through expropriation or negotiation, as permitted under Chinese law (Han et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Regardless of the method used, demolishers are required to provide appropriate compensation to residents whose houses will be demolished, as mandated by Chinese law.\u003c/p\u003e\u003cp\u003eHome ownership is the basic demand of daily life and is positively associated with life satisfaction (Cattaneo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Huang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For Chinese people, owning a house is the most important life goal, and housing wealth is also an important property related to their lives (Liu and Xu, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For instance, owning a house may increase the possibility of marriage by increasing one's advantage in the marriage market (Hu and Wang, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Housing demolition and residents\u0026rsquo; resettlement are complicated and sensitive processes, which involve the vital interests of relocated residents, local governments, and real estate developers (Liu, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, some households called 'nail households' may negotiate for more compensation. However, local governments usually have the authority to set compensation, and nail households are unlikely to resist housing demolition in the end. As shown above, housing demolition is an important form of exogenous wealth and income shock to relocated households in China.\u003c/p\u003e"},{"header":"3 Data and summary statistics","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data\u003c/h2\u003e\u003cp\u003eWe use nationally representative data from the China Family Panel Studies (CFPS). The Institute of Social Science Survey of Peking University conducted the CFPS, surveying 16,000 target households across 25 provinces/municipalities/cities (excluding Inner Mongolia, Ningxia, Qinghai, Tibet, Xinjiang, Hainan, Taiwan, Hong Kong, and Macao). The CFPS contains multi-level data on individuals, families, and communities, reflecting detailed changes in family economic behaviors and individual behaviors (Xie and Hu, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The CFPS is conducted every two years and has completed seven formal surveys: in 2010, 2012, 2014, 2016, 2018, 2020, and 2022. This paper explores how housing demolition impacts life satisfaction by analyzing data from the 2012, 2014, 2016, 2018, 2020, and 2022 waves, with the 2012 wave serving as the baseline wave. Since the information on housing demolition is collected at the household level, this paper only includes households that participated in all six surveys.\u003c/p\u003e\u003cp\u003eThe main reason for using the CFPS data in this study is that it simultaneously contains detailed information on housing demolition and life satisfaction. Moreover, the CFPS includes detailed information on household income and consumption, which is essential for our analysis of underlying mechanisms. Our data from the CFPS covers 10 years, making it convenient for us to explore how housing demolition affects life satisfaction over time. Waves in the years 2014, 2016, 2018, 2020, and 2022 contain accurate information on housing demolition. The corresponding question in the questionnaire is \u0026ldquo;has your family experienced housing demolition in the past 12 months?\u0026rdquo;. We also control for the time-varying individual and household characteristic variables. Individual characteristic variables include individuals\u0026rsquo; age, gender, marital status, and educational background. Household characteristic variables include family size, the proportion of children, and the ratio of elderly members in the household.\u003c/p\u003e\u003cp\u003eThe primary outcome variable is life satisfaction. The CFPS includes detailed information regarding life satisfaction. The corresponding question in the questionnaire is \u0026ldquo;How satisfied are you with your own life?\u0026rdquo;. The response is from \u0026lsquo;very dissatisfied\u0026rsquo; to \u0026lsquo;very satisfied\u0026rsquo;, corresponding to the numbers 1 to 5. It indicates that the larger the number, the higher the life satisfaction.\u003c/p\u003e\u003cp\u003eOther outcome variables for the analysis of underlying mechanisms include household income and consumption. Household income includes total income and transfer income. Household consumption includes total consumption, housing-related consumption, dress and food consumption, durable-goods consumption, and other consumption.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Summary statistics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides descriptive statistics for individual and household characteristics in 2012 according to whether the household experienced housing demolition from 2012 to 2022. As Panel A of Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows, relocated individuals and other individuals are different in most of their characteristics. As Panel B of Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows, heads of relocated households and other households differ in half of their characteristics. According to Panel C of Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, relocated households and other households are similar in all of their characteristics. As shown above, housing demolition may not be random without controlling for community fixed effects (Zhao and Liu, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the section of empirical analysis, we will control for individual fixed effects or household fixed effects, taking into account possible endogeneity concerns.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary statistics of individual and household characteristics in 2012\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003cp\u003eAll\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003cp\u003eRelocated\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(2)-(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003ePanel A. Individual characteristics\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.04***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale (0\u0026ndash;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried (0\u0026ndash;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEducational background\u003c/em\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary school or below (0\u0026ndash;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.04***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle school (0\u0026ndash;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school or above (0\u0026ndash;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29,739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28,191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003ePanel B. Head characteristics\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale (0\u0026ndash;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried (0\u0026ndash;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.01**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEducational background\u003c/em\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary school or below (0\u0026ndash;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.07***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle school (0\u0026ndash;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school or above (0\u0026ndash;1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003ePanel C. Household characteristics\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe proportion of children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe ratio of elderly members\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6,676\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNotes\u003c/em\u003e: *** Significant at the 1% level, ** Significant at the 5% level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides descriptive statistics for life satisfaction, household income, and household consumption across relocated people/households and others. As Panel A of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows, the life satisfaction of relocated people is higher than that of others. Panel B of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the total income and transfer income of relocated households are higher than those of other households. Panel C of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that relocated households\u0026rsquo; total consumption, housing-related consumption, dress and food consumption, durable-goods consumption, and other consumption are higher than those of non-relocated households. As shown above, relocated individuals or households have higher life satisfaction, household income, and household consumption.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary statistics of outcome variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003cp\u003eAll\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003cp\u003eRelocated\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003cp\u003eOthers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(2)-(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003ePanel A. Life satisfaction\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife satisfaction (1\u0026ndash;5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.23***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133,331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128,970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003ePanel B. Household income\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal income (RMB yuan)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81,225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e258,801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75,309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e183,492***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransfer income (RMB yuan)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13,686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e175,804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e167,518***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40,357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39,056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003ePanel C. Household consumption\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal consumption (RMB yuan)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56,726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80,646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55,929\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24,717***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousing-related consumption (RMB yuan)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18,654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,831\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9,823***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDress and food consumption (RMB yuan)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21,296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28,008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21,072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6,936***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDurable-goods consumption (RMB yuan)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10,525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7,544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2,981***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther consumption (RMB yuan)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18,642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23,459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18,482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4,977***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40,357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39,056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNotes\u003c/em\u003e: *** Significant at the 1% level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Empirical Strategy and Empirical Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Empirical Strategy\u003c/h2\u003e\u003cp\u003eTo estimate the overall effect of housing demolition on life satisfaction, we use the Difference-in-Difference method and construct a two-way fixed effects model as follows:\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1763491637.png\" style=\"width: 662px;\"\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), \u003cem\u003eSatisfaction\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003et\u0026thinsp;=\u003c/em\u003e\u0026thinsp;2012, 2014, 2016, 2018, 2020, 2022) indicates the outcome variable life satisfaction. \u003cem\u003eDemolish\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is a dummy variable, which equals one if the observation is after housing demolition, otherwise zero. For we take the 2012 wave as the baseline wave, the variable \u003cem\u003eDemolish\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e in 2012 is equal to zero. \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is a set of time-varying variables, which include household characteristic variables (family size, the proportion of children, and the ratio of elderly members) and individual characteristic variables (individuals\u0026rsquo; age, marital status, and educational background). In our analysis, we do not control for the variable gender, which does not change over time. Taking into account that housing demolition may not be random, we control for individual fixed effects. \u003cem\u003eIndividual\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the individual fixed effect, which is used to control for the influence of unobservable and time-independent individual characteristic variables. \u003cem\u003eYear\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e is the time fixed effect, which is used to control for the influence of possible time trends. The coefficient of interest in this paper is \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e, which indicates the impact of housing demolition on individual life satisfaction. ε\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e is the random error term.\u003c/p\u003e\u003cp\u003eWe also use the Event Study method to explore how housing demolition affects individual life satisfaction over time. We use the number of years before or after housing demolition as dummy variables to replace the variable \u003cem\u003eDemolish\u003c/em\u003e\u003csub\u003e\u003cem\u003eit\u003c/em\u003e\u003c/sub\u003e in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and construct an equation as follows: \u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1763491697.png\" style=\"width: 650px;\"\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eIn Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), \u003cem\u003eT\u003c/em\u003e = -8, -6, -4, -2, 0, 2, 4, 6, 8, which indicate 8 years before housing demolition, 6 years before housing demolition, 4 years before housing demolition, 2 years before housing demolition, the current year, 2 years after housing demolition, 4 years after housing demolition, 6 years after housing demolition and 8 years after housing demolition. α\u003csub\u003e\u003cem\u003e1.T\u003c/em\u003e\u003c/sub\u003e indicates the extent to which the period before and after housing demolition affects life satisfaction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Main results\u003c/h2\u003e\u003cp\u003eUsing the panel data from CFPS, we estimate the overall effect of housing demolition on life satisfaction. First, we estimate the overall impact of housing demolition without controlling for household and individual characteristic variables. As Column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows, housing demolition significantly improves the overall life satisfaction. Second, we examine the overall impact of housing demolition on overall life satisfaction by controlling for household and individual characteristic variables sequentially. According to Column (2) and Column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the influence coefficient does not change significantly. As shown above, housing demolition has a positive impact on overall life satisfaction.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe overall effect of housing demolition on individuals\u0026rsquo; life satisfaction\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.068***\u003c/p\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.067**\u003c/p\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.067**\u003c/p\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.507\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124,695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12,4695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e124,695\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eControl variables\u003c/em\u003e:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold characteristic variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual characteristic variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime fixed effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndividual fixed effects\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026radic;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNotes\u003c/em\u003e: Robust standard errors clustered at the household level are shown in parentheses. Household characteristics include family size, the proportion of children, and the ratio of elderly members. Individual characteristics include individuals\u0026rsquo; age, marital status, and educational background. *** Significant at the 1% level, ** Significant at the 5% level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Heterogeneous effects\u003c/h2\u003e\u003cp\u003eHousing demolition may have varying impacts on the life satisfaction of people depending on their characteristics. In this subsection, we examine the varied effects of housing demolition on life satisfaction according to individuals\u0026rsquo; age, gender, educational background, and the nature of their living areas.\u003c/p\u003e\u003cp\u003eFirst, compared to the young, the aged often have a higher level of life satisfaction in China (Zhang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and housing demolition may have different influences on people across different age groups. Therefore, we divide all samples into two groups according to the median age (people aged 48 or below VS people aged 49 or above). As Column (1) and Column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show, the positive effect on life satisfaction is mainly driven by young people. Compared to older people, younger people often have to take the responsibility of supporting their households, which may lead to lower life satisfaction. However, housing demolition may relieve young people\u0026rsquo;s financial burden of supporting households and has a greater impact on their life satisfaction.\u003c/p\u003e\u003cp\u003eSecond, there is a traditional social norm in China that women do housework at home (Li and Xiao, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and married women spend more time on housework after China\u0026rsquo;s housing reform in the 1990s (Chen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, women are better at perceiving the quality of life. It indicates that housing demolition may have different influences on men and women. Then, we divide all samples into two groups according to people\u0026rsquo;s gender (males VS females). Columns (3) and (4) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show that the positive impact on life satisfaction is primarily attributed to females. Housing demolition may lead to improved living conditions for relocated households, which women are more likely to recognize. Therefore, housing demolition has a greater and positive impact on women\u0026rsquo;s life satisfaction.\u003c/p\u003e\u003cp\u003eThird, people with higher education levels tend to have higher income and overall happiness (Cu\u0026ntilde;ado and De Gracia, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, the impact of housing demolition on life satisfaction may vary based on educational background. Then, we divide all samples into two groups according to people\u0026rsquo;s educational background (primary school or below VS middle school or above). As shown in Column (5) and Column (6) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the positive effect on life satisfaction is mainly driven by better-educated people. Less-educated people may use housing compensation for essential expenses, which might have a weak relationship with life satisfaction.\u003c/p\u003e\u003cp\u003eFinally, there is a significant difference in housing compensation between urban and rural areas (Zhao and Liu, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and housing demolition may have different impacts on life satisfaction across people living in urban and rural areas. Therefore, we divide all samples into two groups according to the nature of their living areas (people living in urban areas VS people living in rural areas). As shown in Column (7) and Column (8) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the positive impact on life satisfaction is primarily driven by people living in urban areas. Since housing compensation of urban households is usually higher than that of rural households, urban households may have much more household income and household consumption, which contribute to higher life satisfaction.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHeterogeneous effects of housing demolition on life satisfaction\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003cp\u003eThe young\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003cp\u003eThe aged\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003cp\u003eMales\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003cp\u003eFemales\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003cp\u003eLess-educated people\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003cp\u003eBetter-educated people\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(7) Urban areas\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(8) Rural areas\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.128***\u003c/p\u003e\u003cp\u003e(0.038)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003cp\u003e(0.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.059*\u003c/p\u003e\u003cp\u003e(0.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.076**\u003c/p\u003e\u003cp\u003e(0.035)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003cp\u003e(0.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.082**\u003c/p\u003e\u003cp\u003e(0.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.083**\u003c/p\u003e\u003cp\u003e(0.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003cp\u003e(0.039)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.528\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.519\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60,895\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e60,885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61,979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62,631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e57,617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e65,982\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e54,157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e70,532\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNotes\u003c/em\u003e: Robust standard errors clustered at the household level are shown in parentheses. All regressions control for household characteristics, individual characteristics, time fixed effects, and individual fixed effects. Household characteristics include family size, the proportion of children, and the ratio of elderly members. Individual characteristics include individuals\u0026rsquo; age, marital status, and educational background. *** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Effects on life satisfaction over time\u003c/h2\u003e\u003cp\u003eCompared to the overall effect, exploring the change in the positive effect on life satisfaction over time can provide a deeper analysis and may be more valuable. Therefore, we use the Event Study method to analyze how housing demolition affects life satisfaction over time. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, housing demolition improves life satisfaction in the current year, and this positive effect continues for 4 years. However, the positive effect becomes insignificant after 6 years of housing demolition. We also explore the parallel trend assumption in Fig.\u0026nbsp;1. We find that the impact on life satisfaction is relatively flat before housing demolition, and these results support the parallel trend assumption. The above results indicate that the impact of housing demolition on life satisfaction does not last over time, and the impact on life satisfaction 6 years after housing demolition is nearly the same as it was before.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEffects of housing demolition on life satisfaction over the years\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003cp\u003eLife satisfaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent year of housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.202**\u003c/p\u003e\u003cp\u003e(0.098)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2 years after housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.175*\u003c/p\u003e\u003cp\u003e(0.100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4 years after housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.206**\u003c/p\u003e\u003cp\u003e(0.102)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6 years after housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003cp\u003e(0.108)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8 years after housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003cp\u003e(0.117)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124,695\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eNotes\u003c/em\u003e: We take the years before the housing demolition as a reference. Robust standard errors clustered at the household level are shown in parentheses. The above regression controls for household characteristics, individual characteristics, individual fixed effects, and time fixed effects. Household characteristics include family size, the proportion of children, and the ratio of elderly members. Individual characteristics include individuals\u0026rsquo; age, marital status, and educational background. ** Significant at the 5% level, * Significant at the 10% level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Robustness checks\u003c/h2\u003e\u003cp\u003eTo explore the robustness and reliability of our results, we conduct a series of robustness checks by dropping relocated households without compensation, controlling for the number of houses owned by households, and dropping communities without housing demolition experience, respectively.\u003c/p\u003e\u003cp\u003eFirst, housing compensation is an important way to change household wealth and income in China. Whether a household receives compensation may have different impacts on life satisfaction. We estimate the effect of housing demolition with compensation on life satisfaction by dropping relocated households without compensation. As Column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows, the influence coefficient of housing demolition on life satisfaction becomes larger. We also conduct similar robustness checks for the effects of housing demolition on life satisfaction by year. As Column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows, the influence coefficient of housing demolition on life satisfaction in the current year becomes larger.\u003c/p\u003e\u003cp\u003eSecond, home ownership is positively correlated to housing satisfaction and significantly increases the life satisfaction of people with medium and high incomes (Elsinga and Hoekstra, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Ren et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, the number of owned houses after housing demolition indicates a household\u0026rsquo;s wealth structure, and housing demolition may have a different impact on life satisfaction after controlling for the number of owned houses. Therefore, we do the robustness check by controlling for the number of owned houses after housing demolition. As Column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows, the influence coefficient of housing demolition on life satisfaction does not change significantly. In addition, we find that the number of owned houses has a significant and positive impact on life satisfaction. That is, the more houses one owns, the higher life satisfaction will be. We also conduct similar robustness checks for effects on life satisfaction by years in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. As Column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows, the influence coefficients of housing demolition on life satisfaction by years do not change significantly.\u003c/p\u003e\u003cp\u003eThird, absolute consumption and wealth level do not determine happiness, which is only determined by social comparison (Hsee et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Similarly, life satisfaction depends on social comparison. Therefore, we compare relocated individuals with other individuals from communities experiencing housing demolition. That is, only individuals from communities experiencing housing demolition are considered the control group. As Column (4) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows, the influence coefficient of housing demolition on life satisfaction does not change significantly. We also conduct similar robustness checks for effects on life satisfaction by years. As Column (4) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows, the influence coefficients of housing demolition on life satisfaction by years do not change significantly, except for the influence coefficient of the 4 years after housing demolition. As shown above, our results are generally robust and credible.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobustness checks for overall effects\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003cp\u003eDrop relocated households without compensation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003cp\u003eControl for the number of owned houses\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003cp\u003eDrop communities without demolition experience\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.067**\u003c/p\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.077**\u003c/p\u003e\u003cp\u003e(0.030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.068**\u003c/p\u003e\u003cp\u003e(0.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.069**\u003c/p\u003e\u003cp\u003e(0.030)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of houses\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.015***\u003c/p\u003e\u003cp\u003e(0.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.509\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124,695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123,318\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e124,600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39,588\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNotes\u003c/em\u003e: Robust standard errors clustered at the household level are shown in parentheses. All regressions control for individual characteristics, household characteristics, individual fixed effects, and time fixed effects. Household characteristics include family size, the proportion of children, and the ratio of elderly members. Individual characteristics include individuals\u0026rsquo; age, marital status, and educational background. ** Significant at the 5% level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobustness checks for effects on life satisfaction by years\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003cp\u003eBaseline\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003cp\u003eDrop relocated households without compensation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003cp\u003eControl for the number of owned houses\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003cp\u003eDrop communities without demolition experience\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent year of housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.202**\u003c/p\u003e\u003cp\u003e(0.098)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.212**\u003c/p\u003e\u003cp\u003e(0.098)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.202**\u003c/p\u003e\u003cp\u003e(0.098)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.204**\u003c/p\u003e\u003cp\u003e(0.100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2 years after housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.175*\u003c/p\u003e\u003cp\u003e(0.100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.173*\u003c/p\u003e\u003cp\u003e(0.101)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.174*\u003c/p\u003e\u003cp\u003e(0.100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.142\u003c/p\u003e\u003cp\u003e(0.103)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4 years after housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.206**\u003c/p\u003e\u003cp\u003e(0.102)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.196*\u003c/p\u003e\u003cp\u003e(0.103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.201*\u003c/p\u003e\u003cp\u003e(0.103)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.192*\u003c/p\u003e\u003cp\u003e(0.106)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6 years after housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003cp\u003e(0.108)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003cp\u003e(0.109)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003cp\u003e(0.109)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.124\u003c/p\u003e\u003cp\u003e(0.113)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8 years after housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.122\u003c/p\u003e\u003cp\u003e(0.117)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003cp\u003e(0.119)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003cp\u003e(0.118)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003cp\u003e(0.121)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.501\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e124,695\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123,846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e124,600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39,588\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNotes\u003c/em\u003e: We take the years before the housing demolition as a reference. Robust standard errors clustered at the household level are shown in parentheses. All regressions control for household characteristics, individual characteristics, time fixed effects, and individual fixed effects. Household characteristics include family size, the proportion of children, and the ratio of elderly members. Individual characteristics include individuals\u0026rsquo; age, marital status, and educational background. ** Significant at the 5% level, * Significant at the 10% level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.6 Underlying mechanisms\u003c/h2\u003e\u003cp\u003eIn China, people attach more importance to household status and social comparison (Cui, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), which are important influence factors of Chinese life satisfaction. On one hand, household income and household consumption are important indicators of measuring a household\u0026rsquo;s status and social comparison in China. On the other hand, income is often taken as a major indicator of subjective well-being (Qiu et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and household income is significantly and positively correlated with life satisfaction in China (Yuan, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, the rise in most household consumption is positively related to a higher life satisfaction among individuals with low income (Dumludag, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In this subsection, we explore underlying mechanisms such as household income and consumption, which contribute to a better understanding of how housing demolition affects life satisfaction.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e4.6.1 Household income\u003c/h2\u003e\u003cp\u003eFirst, we estimate the overall effects of housing demolition on household total income and transfer income. As Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows, housing demolition generally increases total income and transfer income. The coefficient of housing demolition on transfer income is larger than that on total income. That is, housing demolition is an important way to achieve windfall gains in China.\u003c/p\u003e\u003cp\u003eSecond, we estimate the association between household income and life satisfaction in the appendix. As Table \u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003eA.1\u003c/span\u003e shows, both total income and transfer income are significantly and positively associated with life satisfaction. The correlation coefficient between total income is larger than that between transfer income and life satisfaction, and total income is more important than transfer income. Income is positively associated with subjective well-being among individuals with middle or low income (Diener and Biswas-Diener, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), and people are much happier if their income is higher than that of people in the reference group (Ferrer-i-Carbonell, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Moreover, more income may enable people to lead a happier life than those who are poor (Mahadea, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and people with incomes above the average level have a higher life satisfaction relatively (Illusion, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Therefore, housing demolition may improve overall life satisfaction by increasing household income.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe overall effect of housing demolition on household income\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003cp\u003eLog of total income\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003cp\u003eLog of transfer income\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.580***\u003c/p\u003e\u003cp\u003e(0.054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.380***\u003c/p\u003e\u003cp\u003e(0.199)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.544\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40,349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40,349\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNotes\u003c/em\u003e: Robust standard errors clustered at the household level are shown in parentheses. The above regressions control for head characteristics, household characteristics, household fixed effects, and time fixed effects. A household\u0026rsquo;s head may change over time, and head characteristics include the head\u0026rsquo;s age, gender, marital status, and educational background. Household characteristics include family size, the proportion of children, and the ratio of elderly members. *** Significant at the 1% level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFinally, we explore how housing demolition affects total income and transfer income over time. As Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e shows, housing demolition has the greatest effect on total income and transfer income in the current year. It might be because the majority of families received substantial housing compensation in the current year of housing demolition. However, these positive effects weaken over time. We also explore the parallel trend assumption in Fig.\u0026nbsp;2\u0026ndash;3 and find that the impacts on total income and transfer income remain relatively flat before housing demolition, supporting the parallel trend assumption. These positive effects of housing demolition on total income and transfer income in the current year may have a certain periodical impact on life satisfaction. In other words, housing demolition may only improve life satisfaction within 4 years by leading significant increase in total income and transfer income in the current year.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEffects of housing demolition on household income over the years\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003cp\u003eLog of total income\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003cp\u003eLog of transfer income\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThe current year of housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.224***\u003c/p\u003e\u003cp\u003e(0.093)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.059***\u003c/p\u003e\u003cp\u003e(0.346)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2 years after housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.235***\u003c/p\u003e\u003cp\u003e(0.084)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.174***\u003c/p\u003e\u003cp\u003e(0.349)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4 years after housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.194**\u003c/p\u003e\u003cp\u003e(0.082)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.679*\u003c/p\u003e\u003cp\u003e(0.363)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6 years after housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.154*\u003c/p\u003e\u003cp\u003e(0.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.998**\u003c/p\u003e\u003cp\u003e(0.439)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8 years after housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003cp\u003e(0.112)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.983*\u003c/p\u003e\u003cp\u003e(0.542)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.606\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40,349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40,349\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNotes\u003c/em\u003e: We take the years before the housing demolition as a reference. Robust standard errors clustered at the household level are shown in parentheses. All regressions control for head characteristics, household characteristics, household fixed effects, and time fixed effects. Head characteristics include the head\u0026rsquo;s age, gender, marital status, and educational background. Household characteristics include family size, the proportion of children, and the ratio of elderly members. *** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e4.6.2 Household consumption\u003c/h2\u003e\u003cp\u003eIncome indicates the short-term inflow of household consumption (Zhang et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Compared to income, consumption has a greater positive effect on life satisfaction (Brown and Gathergood, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, we explore how housing demolition affects household consumption.\u003c/p\u003e\u003cp\u003eFirst, we estimate the overall effect of housing demolition on household consumption. As Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e shows, housing demolition increases total consumption, housing-related consumption, dress and food consumption, and other consumption as a whole. However, no evidence shows that housing demolition increases overall durable-goods consumption. Household income and wealth are determining factors for household consumption (Sousa, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and an increase in wealth leads to an improvement in consumption by reducing the marginal effect of wealth (Hedenus, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Larsson, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Households can increase their borrowing capacity by selling assets, thereby raising their consumption levels (Marquez et al., 2013). Moreover, households that have cash liquidity constraints but own fixed assets have stronger consumption responses to sudden income (Cui and Feng, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). That is, with housing compensation, relocated households could increase their consumption level (Shi and He, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecond, we estimate the relationship between household consumption and life satisfaction in Table \u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003eA.2\u003c/span\u003e of the appendix. As Column (1) of Table \u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003eA.2\u003c/span\u003e shows, no evidence supports that total consumption is significantly associated with life satisfaction. As Column (2) of Table \u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003eA.2\u003c/span\u003e shows, housing-related consumption is significantly and positively associated with life satisfaction. Housing-related consumption includes paid rent, house maintenance, property management, and utility bills in the CFPS, and energy consumption is a typical utility bill. The household energy consumption is positively correlated with life satisfaction in China (Piao and Managi, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and housing quality is positively correlated with one\u0026rsquo;s happiness (Hu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). More housing-related consumption means higher energy consumption and house maintenance. That is, higher housing-related consumption indicates higher happiness and life satisfaction. As Column (3) of Table A.3 shows, dress and food consumption are not significantly associated with life satisfaction. According to Column (4) of Table A.3, durable-goods consumption is positively associated with life satisfaction. As Column (5) of Table A.3 shows, other consumption is not significantly associated with life satisfaction. As shown above, housing demolition may improve overall life satisfaction by increasing housing-related consumption.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOverall effects of housing demolition on household consumption\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003cp\u003eLog of total consumption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003cp\u003eLog of housing-related consumption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003cp\u003eLog of dress and food consumption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003cp\u003eLog of durable-goods consumption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003cp\u003eLog of other consumption\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ehousing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.167***\u003c/p\u003e\u003cp\u003e(0.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.363***\u003c/p\u003e\u003cp\u003e(0.060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.095**\u003c/p\u003e\u003cp\u003e(0.047)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.241\u003c/p\u003e\u003cp\u003e(0.171)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003cp\u003e(0.046)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.555\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40,349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40,349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40,349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40,349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40,349\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNotes\u003c/em\u003e: Robust standard errors clustered at the household level are shown in parentheses. All regressions control for head characteristics, household characteristics, household fixed effects, and time fixed effects. Head characteristics include the head\u0026rsquo;s age, gender, marital status, and educational background. Household characteristics include family size, the proportion of children, and the ratio of elderly members. *** Significant at the 1% level, ** Significant at the 5% level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFinally, we explore how housing demolition affects household consumption over time. As Column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows, housing demolition significantly increases total consumption only in the current year of housing demolition. The positive effect on total consumption weakens and becomes insignificant over time. As Column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows, housing demolition significantly increases housing-related consumption in the current year of housing demolition. However, the positive effect weakens and becomes insignificant over time. As Column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows, there is no evidence that housing demolition increases dress and food consumption. As Column (4) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows, housing demolition only increases durable-goods consumption in the current year of housing demolition. The positive on durable-goods consumption weakens and becomes insignificant over time. However, the value of durable goods may last for several years. That is, the positive effect of housing demolition on life satisfaction may last for several years due to the increase in durable-goods consumption. As Column (5) of Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows, there is no evidence that housing demolition affects other consumption. We also explore the parallel trend assumption in Fig.\u0026nbsp;4\u0026ndash;8 and find that impacts on all types of household consumption are relatively flat before housing demolition. The permanent income theory holds that consumption is mainly determined by the expected future income, and the expected long-term income holds a more significant position in the factors influencing consumption (Friedman, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1957\u003c/span\u003e). Households that have undergone housing demolition are unlikely to receive similar large-scale transfer income in the short term. The life-cycle theory holds that rational individuals will plan their consumption demands in the long term to optimize the utility of consumption (Modigliani and Brumberg, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Therefore, relocated households need to make a comprehensive plan for their economic activities, such as consumption. The above results indicate that housing demolition may improve life satisfaction by increasing housing-related consumption and durable-goods consumption.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEffects of housing demolition on household consumption over the years\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003cp\u003eLog of total consumption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003cp\u003eLog of housing-related consumption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003cp\u003eLog of dress and food consumption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003cp\u003eLog of durable-goods consumption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003cp\u003eLog of other consumption\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent year of housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.177***\u003c/p\u003e\u003cp\u003e(0.068)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.628***\u003c/p\u003e\u003cp\u003e(0.106)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003cp\u003e(0.074)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.579*\u003c/p\u003e\u003cp\u003e(0.316)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.037\u003c/p\u003e\u003cp\u003e(0.095)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2 years after housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003cp\u003e(0.071)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.279**\u003c/p\u003e\u003cp\u003e(0.109)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003cp\u003e(0.082)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.081\u003c/p\u003e\u003cp\u003e(0.333)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003cp\u003e(0.097)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4 years after housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003cp\u003e(0.074)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.070\u003c/p\u003e\u003cp\u003e(0.124)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003cp\u003e(0.092)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.483\u003c/p\u003e\u003cp\u003e(0.373)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003cp\u003e(0.105)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6 years after housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.003\u003c/p\u003e\u003cp\u003e(0.089)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.046\u003c/p\u003e\u003cp\u003e(0.158)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003cp\u003e(0.092)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.105\u003c/p\u003e\u003cp\u003e(0.450)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.127\u003c/p\u003e\u003cp\u003e(0.120)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8 years after housing demolition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.110\u003c/p\u003e\u003cp\u003e(0.094)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.106\u003c/p\u003e\u003cp\u003e(0.167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.075\u003c/p\u003e\u003cp\u003e(0.162)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.285\u003c/p\u003e\u003cp\u003e(0.473)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.095\u003c/p\u003e\u003cp\u003e(0.128)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.555\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40,349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40,349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40,349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40,349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40,349\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNotes\u003c/em\u003e: We take the years before the housing demolition as a reference. Robust standard errors clustered at the household level are shown in parentheses. All regressions control for head characteristics, household characteristics, household fixed effects, and time fixed effects. Head characteristics include the head\u0026rsquo;s age, gender, marital status, and educational background. Household characteristics include family size, the proportion of children, and the ratio of elderly members. *** Significant at the 1% level, ** Significant at the 5% level, * Significant at the 10% level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eUsing panel data from the CFPS, we estimate the overall effect of housing demolition on life satisfaction and explore how housing demolition affects life satisfaction over time. Our main results show that housing demolition increases overall life satisfaction, and the positive effect on life satisfaction is primarily driven by young people, females, better-educated people, and people living in urban areas. We also find that the positive impact on life satisfaction weakens and becomes insignificant over time. We explore possible underlying mechanisms and find that housing demolition improves individual life satisfaction by increasing transfer income, total income, housing-related consumption, and durable-goods consumption. Our research contributes to a better understanding of how housing demolition affects life satisfaction over time and provides a reference for the design of housing demolition in other developing countries.\u003c/p\u003e\u003cp\u003ePoor rural families use the received cash income for investment in production, which increases their agricultural income and long-term consumption levels (Gertler et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In addition, the lottery winners maintain a clear mind and make rational plans for future expenditures, thus maintaining a happy and fulfilling life (Hedenus, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Similarly, to improve life satisfaction, relocated households should expand income sources and smooth their consumption at the same time. Our results provide evidence for relevant departments to guide relocated households to increase household income and keep household consumption, thereby improving life satisfaction.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are openly available in the China Family Panel Studies at \u003cu\u003ehttps://cfpsdata.pku.edu.cn/#/resource-detail/4\u003c/u\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval is not required\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is under the financial support from the Humanities and Social Science Fund of Colleges and Universities of Hebei Province of China (grant no. SQ2024214).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXuecun Zhao put forward the idea and wrote the main manuscript text, Zhichao Ma carried out data analysis, and Yanrong Liu prepared all tables and figures. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbott P, Wallace C, Lin K, Haerpfer C (2016) The quality of society and life satisfaction in China. Soc Indic Res 127(2):653\u0026ndash;670\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAppleton S, Song L (2008) Life satisfaction in urban China: Components and determinants. World Dev 36(11):2325\u0026ndash;2340\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmbrey CL, Fleming CM (2014) The causal effect of income on life satisfaction and the implications for valuing non-market goods. 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Rev Dev Econ 26(3):1663\u0026ndash;1692\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"housing demolition, life satisfaction, China","lastPublishedDoi":"10.21203/rs.3.rs-7816859/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7816859/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUsing the China Family Panel Studies (CFPS), this paper estimates the effect of housing demolition on life satisfaction. This paper not only estimates the impact of housing demolition on overall life satisfaction but also explores how housing demolition affects life satisfaction over time. Our results show that housing demolition has a positive impact on overall life satisfaction. We also find that the positive effect on life satisfaction is primarily driven by young people, females, better-educated people, and people living in urban areas. However, the positive impact on life satisfaction weakens and becomes insignificant over time. We explore possible underlying mechanisms and find that housing demolition improves life satisfaction by increasing total income, transfer income, housing-related consumption, and durable-goods consumption. This paper may provide evidence for relevant departments to make polices aimed at improving the quality of life and life satisfaction.\u003c/p\u003e","manuscriptTitle":"Housing demolition and life satisfaction in China: perspectives from household economic behaviors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 18:54:25","doi":"10.21203/rs.3.rs-7816859/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"84ae3e66-78ab-4995-a0ed-710cdd57fb53","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58102004,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":58102005,"name":"Social science/Environmental studies"},{"id":58102006,"name":"Social science/Sociology"}],"tags":[],"updatedAt":"2026-02-13T09:25:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 18:54:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7816859","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7816859","identity":"rs-7816859","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}