The Chinese Dream: Hukou, Social Mobility and Regime Support in China

Xian Huang1

Abstract Social mobility is important for political stability. Does increased social mobility contribute to authoritarian resilience? I leverage the hukou (household registration) reform in China to study the effect of social mobility on regime support. First, I argue that hukou is an important factor to define social mobility in China. Using the China General Social Survey data of 2010, I find that citizens believed they have achieved upward mobility through changing hukou from rural to urban status; however, changing hukou from non-local to local status has a dampening effect on citizens’ perceived prospect of upward mobility. Second, I argue that social mobility, defined by hukou change, has a significant yet heterogeneous effect on regime support. Specifically, the rural-to-urban hukou change increased one’s trust in the central government, while the nonlocal- to-local hukou change increased one’s trust in the local government. This study highlights the importance of social mobility for the Chinese authoritarian regime. It also sheds new light on the sources of political support.

1 Assistant Professor, Department of Political Science, Rutgers University. Email address: [email protected]. Phone number: 1-848-932-9380. Fax: 1-732-932-7170. Mail address: 89 George St, New Brunswick, NJ 08901, USA.

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1. Introduction

The Chinese government enjoys a remarkably high level of public support. This is surprising for three reasons. First, it is an authoritarian government; the public has neither full information about, nor genuine participation in the selection of government and party leaders.

Second, this government is well-known for regularly using repression, censorship, and coercion to maintain political and social order (Huang and Yang 2004; King, Pan and Roberts 2013; Deng and O’Brien 2013). Third, there is empirical evidence that the high level of political support in

China cannot be completely attributed to political fear or preference falsification (Tang 2015;

Jiang and Yang 2016). Given this puzzle, many scholars have carefully examined political support (including political trust and government satisfaction) in China in order to explain the sources thereof (Chen 2004; Chen 2017; Yang and Tang 2010; Tang 2016; Dickson 2016).

Political values and government performance (e.g., economic development, expanding social benefit and public goods provisions) have been the most discussed sources of political support in

China. Public survey data reveals that Chinese citizens who hold strong national pride and collectivism, who have rising individual or household income, and/or who have social health insurance or pension benefits tend to have significantly higher trust in, support of, or satisfaction with the government (Chen 2017; Huang and Gao 2018; Dickson 2016; Li and Wu 2018).

The extant explanation of the high level of regime support in China is not satisfactory.

First, the ideological or cultural explanation carries the risk of endogeneity: subjective or attitudinal factors such as collectivism, nationalism, and political trust are often interwoven and inseparable. Second, the material- or performance- based explanation can’t fully explain the constantly high level of political support in China. Since the 2008 global financial crisis, China entered the “new normal” (slowing down) phase of economic development, and its social

2 insurance coverage has reached saturation. The sources of political support that were built on the base of material benefits seem to be gradually drying up. However, the public support of government remains high in China according to the survey data collected in 2010 and after (Tang

2016; Dickson 2016; Wang and Dickson 2019). Hence, there must be sources other than ideology and material benefits that have been driving and continue to boost the public support for the Chinese government. In this paper, I demonstrate that social mobility is an important yet understudied determinant of political support in China.

Upward social mobility, or the experience and prospect of individuals moving up the social ladder (class) to achieve success through hard work and talent, plays an important role in stabilizing the political order in democracies (De Tocqueville 1835; Lipset 1992; Turner 1990).

It is believed that in an ideal society with perfect social mobility (e.g., equality of opportunity), individuals are less prone to resort to destructive political behaviors against the government (e.g., protests, demonstrations, and violence) to address grievances due to poverty and bankruptcy, because they tend to attribute these misfortunes to personal incapability rather than structural or institutional hurdles to personal success. This has been found particularly salient in American society during the post-war era, which embraced the idea of “the American Dream” (Lipset and

Bendix 1991; Blau and Duncan 1967). Since the 1980s, despite rising social inequality, stagnant income growth, and proliferating economic risk and insecurity, Americans tend to blame themselves first, rather than the government, for failing to take responsibility for their lives

(Hacker 2008). In a way, social mobility can play a similar political role in authoritarian countries.

In an authoritarian country like China, where the political legitimacy that the Chinese

Communist Party (CCP) derived from the revolution has gradually diminished, and which

3 increasingly relies on the performance of governance and economic development, there is more room and need for social mobility to stabilize political order and maintain social stability. The

Chinese political leaders in the 2000s have well understood this, and in fact have mastered the tactic of engineering social mobility for political stability. From Hu Jintao’s narrative of the

“prosperous society” (xiaokang shehui) to Xi Jinping’s conception of the “Chinese Dream”

(zhongguo meng) in their ruling programs, upward mobility lies at the center of the rose-tinted picture of the future that the CCP has promised the people. More importantly, “the Party wants its people to believe that only under the leadership of the CCP can the dream of a better life be realized” (Wang 2014, p.7). Despite the pervasiveness of the “upward mobility” promise in propaganda and government programs, the relationship between social mobility and regime support has been less established empirically in the Chinese context.2

Social mobility is one of the most popular and long-lasting themes in social science research. Most extant studies of social mobility in contemporary China have focused on its socioeconomic impacts (Wu and Treiman 2004, 2007; Zhang and Treiman 2013; Wu and Zheng

2018) rather than its political causes and consequences. One may question if social mobility is effective for the Chinese government to garner public support, given that a middle class enlarged

2 To my knowledge, there are only two articles specifically studying the relationship between social mobility and political trust in China (Su et al. 2015; Sheng 2013). The biggest difference between this paper and the other two is that I use hukou status to conceptualize and measure social mobility while Sheng (2013) uses occupation and Su et al. (2015) uses self-assessed opportunity in life. The data and the construction of the political trust variables are also different in these three papers.

4 by economic and social development is usually more critical for and informative of government performance. Moreover, extant studies often define and measure social mobility using income, education, occupation or even subjective factors (Bian 2002; Su et al. 2015; Sheng 2013). In the

Chinese context, another important dimension of social inequality and stratification—hukou

(household registration) is not well integrated into the conceptualization and measurement of social mobility. Although some studies have highlighted the importance of the hukou system in greatly shaping the contour of social stratification in China (Wang 2004, 2005; Wu and Treiman

2004; Wu and Zheng 2018), it is not clear to what extent the change of one’s hukou status contributes to the individual-level experience and prospect of social mobility independent of income, education and occupation. Furthermore, in the framework of social mobility and authoritarian resilience, there emerge studies on the stabilization role played by education

(Robinson 2018) and civil servant recruitment (Liu 2018). Nonetheless, few studies have specifically examined whether social mobility, defined by individuals’ hukou status change, can change political attitudes and thus affect the resilience of the Chinese authoritarian regime. This paper attempts to provide some answer to these questions and contribute to the literature on social mobility, political support, and social policy reform.

In this paper, I leverage the hukou reform in China since the early 1980s to study the effect of social mobility on political support. First, I argue that hukou is important for defining social mobility in the Chinese context. Taking advantage of the China General Social Survey

(CGSS) 2010 data, I find that, through changing hukou from rural to urban status, ordinary

Chinese citizens believe that they have achieved upward mobility; however, changing hukou from migrant (non-local) to resident (local) status has a dampening effect on citizens’ perceived prospect of upward mobility. Second, I argue that there is a significant yet heterogeneous effect

5 of social mobility, defined by hukou status changes, on citizens’ trust in government. Using the

CGSS data, I specifically find that the rural-to-urban hukou status change increased one’s trust in the central government, while the migrant-to-resident hukou status change increased one’s trust in the local government. I further find that citizens with the rural-to-urban hukou status change are more likely to tolerate or support government intervention in migration, suggesting a mechanism to explain why Chinese citizens who experienced upward mobility had higher trust in the government.

The rest of this paper is organized as follows. Section 2 introduces China’s hukou system, providing the background to understand the importance of hukou in shaping the social mobility and political attitudes of Chinese people. Section 3 discusses and formulates hypotheses regarding the relationships between hukou status change and social mobility and between hukou status change and regime support in China. Section 4 describes the data, identification strategy, and model specifications for empirical analysis. Section 5 reports results of the main analysis and the robustness tests. Section 6 concludes with implications of this study.

2. China’s Hukou System

China’s hukou system was officially promulgated by CCP in 1958 to control the movement of people between urban and rural areas (Chan 2009; Solinger 1999; Wang 2005).

Since then, all Chinese citizens have been registered as holding either urban or rural hukou in a particular location. Specifically, the hukou registration has two components. One is hukou type, commonly referred to as “agricultural” (rural) and “non-agricultural” (urban); the other is hukou location, referring to the place of hukou registration, which is usually one’s permanent or official residence. When one lives away from his/her hukou location (especially the registered county or city) for more than six months, he/she is considered as a migrant and having a non-local hukou in

6 the destination area, while people living in their hukou location (registered county or city) are considered as residents and having local hukou. The hukou type and location thus classify

Chinese citizens into four social statuses in a particular administrative unit (e.g., city, county): (a) urban residents: holding urban and local hukou; (b) urban migrants: holding urban yet non-local hukou; (c) rural residents: holding rural and local hukou; (d) rural migrants: holding rural and non-local hukou. Both hukou type and location in one’s hukou registration are determined by his/her place of birth and parents’ hukou status. Only through proper authorization of the government can one change his/her hukou location and especially his/her hukou type from the rural to the urban status.

The hukou system is not merely for population registration and management; more importantly, it plays a crucial role in resource allocation and social welfare provision. In China, it is local governments’ responsibility, rather than the central government’s, to provide public services (e.g., public transportation, public education, public health), welfare benefits (e.g., social health insurance, pensions, unemployment insurance) and social assistance (e.g., poverty relief).

Given fiscal and administrative decentralization, the type and level of social benefits and services individual citizens can enjoy in their residential areas depend on their hukou status (Song 2014;

Gersovitz 2016). Urban residents often receive the most and best benefits from local governments (e.g., pension, health care, education, housing); rural residents have fewer benefits than urban residents do, but still get more than migrants (whether holding urban or rural non- local hukou) who are usually not eligible for the welfare benefits and social programs in the destination areas (Huang 2014; Song 2014). Moreover, rural hukou holders, especially rural migrants, are commonly discriminated against in the labor market, having lower wages, fewer

7 employment opportunities, lower-status occupations, and lower job security than their urban counterparts (Lu and Wang 2013; Zhang and Wu 2017).

Hukou registration is well integrated into CCP’s political control and monitoring system.

Hukou-based social control assists the government in putting targeted people, such as criminals, dissidents, unwelcomed activists, and people suspected of separatist or terrorist activities, onto the watch list to be monitored and “educated” closely by the police (Wang 2004). Prisoners who previously held urban hukou in cities are transferred to small towns near the prisons and settled with rural hukou after being released (Wang 2005). Core or center geographic units (e.g., capital cities, large cities, county seats) impose stricter hukou control in their administrative jurisdictions to reduce migration inflow and population concentration that the Chinese authoritarian regime considers threatening to political stability (Wallace 2014).

3. Hukou Change, Social Mobility and Regime Support in China

As hukou status carries so much social, economic, and political importance for individuals’ material well-being and prosperity in Chinese society, a change of hukou from the lower status (e.g., rural, non-local) to the higher one (urban, local) is sometimes extremely selective and thus considered “upward mobility” for the hukou converters (Wu and Treiman

2004, 2007; Wang 2004). Moreover, the tremendous cross-regional socioeconomic disparities in

China further make urban and local hukou status desirable for individuals. Despite the Hu-Wen administration’s efforts in raising rural revenues and increasing transfers to rural households in the first decade of the 2000s, the provision of public goods and services in rural areas significantly lags behind that of urban areas. For example, despite the general expansion of health insurance coverage in rural areas since 2004, the urban-rural disparity in healthcare provisions remains wide. In 2007, the average number of hospital beds was 5 per 1,000 people in

8 urban areas, but this number was only 2 in rural areas. In this context, people who successfully obtain urban hukou not only can receive better public services (e.g., public transportation, education, healthcare) and more welfare benefits (e.g., pension, minimum living allowance, housing) than their rural counterparts, but also can gain opportunities to secure better jobs, wages, and occupations in cities, advances that constitute upward mobility in conventional views.

Migrants are rational, choosing the destination of migration that can provide better public services, welfare benefits and socioeconomic opportunities for themselves and their families than their original hukou locations can. For these reasons, they want to permanently settle in the destination area. Chinese population censuses since the 1980s reveal that hundred millions of internal migrants move to coastal areas (especially the cities in Guangdong, Zhejiang and Jiangsu provinces, and metropolises such as Shanghai, Beijing, and Tianjin) for better jobs, income or education opportunities (Yu 2008). Given the common discrimination against migrants in local labor markets (Lu and Wang 2013; Zhang and Wu 2017), migrants who manage to obtain local hukou—and thus transform their social status into residents of the destination area—should get better jobs, income or education, in addition to better social services and welfare benefits, than they would have as migrants without local hukou. I thus formulate the following hypotheses regarding the relationships between hukou status changes and social mobility.

Hypothesis 1: The hukou change from rural to urban status will increase one’s perception of social mobility.

Hypothesis 2: The hukou change from non-local (migrant) to local (resident) status will increase one’s perception of social mobility.

China’s hukou system is anachronistic: it was created to serve the plan economy in

Mao’s era, but it is “alive and well, albeit adapted and adjusted” in the post-Mao reform era

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(Wang 2004, pg.129). Consequently, the role of government is as important, if not more important, than that of the market in determining the level of social mobility one can achieve in post-Mao China. It is government initiatives and policies that define the size, boundary, and qualification of social status (e.g., urban/rural, residents/migrants) and hence the opportunities for social mobility. The process of mobility influences the ways in which people think about themselves, their society, and the government. Extant studies have found that people with upward mobility are more likely to feel happier in life (Zhang and Treiman 2013) and say that people can generally be trusted (Sehested-Larsen 1990).

In the Chinese context, the upward hukou status change is expected to increase the hukou converters’ support for the government for at least two reasons. First, the government’s hukou policy, such as rural-to-urban hukou conversion or hukou relocation, brings about tangible benefits to the eligible or targeted population. The rational choice perspective in the political support literature suggests that citizens are in a quasi-exchange relationship with the government: their support for the government is a function of the benefits that the government provides to them (Riker 1990). Accordingly, the increase of social benefits or entitlements brought about by the upward hukou status change will boost the beneficiaries’ support for the government.

Second, the process of upward hukou status change brings the qualified individuals closer to the state’s power, allowing the regime to co-opt them with upward mobility and to increase their reliance on the regime. The relative positioning of social status (e.g., urban resident, rural migrant) are state-constructed through the hukou system, and what they entail politically and economically is determined by the regime’s interest. Individuals going through the hukou conversion or relocation process will learn and experience how omnipresent and powerful the

Chinese government is.

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I thus posit that the upward hukou status change will increase the hukou converters’ political support in general. Nonetheless, the policy design for the two hukou status changes is different. Specifically, the criteria for rural-to-urban hukou status change was well established by the central government while that of migrant-to-resident hukou change was decentralized to the local governments. Hence, the policy effects of hukou status changes on political support might differ between the central and the local governments.

For the rural-to-urban hukou conversion, the criteria were stipulated by the central government in the 1958 hukou regulation, which is the only national legislation on migration and residence promulgated by the National People’s Congress, and were designed to serve the needs of the state (Chan 2009). In Mao’s era, the limited paths for rural-to-urban hukou conversion included: (1) recruited as permanent employees by a state-owned enterprise (zhaogong); (2) displaced due to state-initiated land expropriation (zhengdi); (3) recruited for enrollment in an institution of higher education (zhaosheng); (4) promoted to administrative positions (zhaogan);

(5) relocated because of family crises (such as moving to a city to live with and look after a sick parent); (6) joining the army (canjun) and demobilized to cities; and (7) deemed to belong to special categories (either recipients of compensation for past policy mistakes or people who had endured personal sacrifices and hardships because of their work for the state) (Wang 2005).

In the post-Mao era, and especially since the late 1990s, restrictions on the rural-to-urban hukou change have generally relaxed. A 1997 State Council directive allowed provinces to choose a limited number of small cities and towns for experimental hukou reform to make transferring from rural to urban status easier; in 2001, the State Council issued a second directive that in principle extended the possibility of enacting similar reforms to all small and medium- sized cities across the land (Wallace 2014). According to public survey data collected in this

11 period, individuals achieved the rural-to-urban hukou conversion mainly by two channels, which can be traced back to the 1958 hukou regulation: one is merit-based selection, such as admission into an university or formal employment in the state sector (including joining the army); the other is policy-based incorporation, such as compensation for land expropriation or administrative unit change (Wu and Treiman 2004; Zhang and Treiman 2013; Wu and Zheng

2018).3

As for migrant-to-resident hukou status change (i.e., hukou relocation), the reform process and policies are less clearly formulated by the central government and much decentralized to the local governments. Since the early 1980s, the central government’s control of rural-to-urban migration has gradually relaxed. Beginning in 1983, rural residents were permitted to move into market towns, albert without shedding their rural registration and relying not on the state’s grain rations but on food they had brought in themselves (Solinger 1999). By

1985, China had enacted a nationwide system of temporary residence permits for urban areas, which allowed migrants freedom to move independently but included no change of hukou status.

In 2001, the decade-old rural-to-urban migration quota system was finally abolished in all small

3 In recent years, a national wave of “erasing” the rural/urban distinction in hukou registration took place, with some cities starting to register people as either local or temporary residents only

(Chan 2010; Song 2014). By 2010, however, this “revolutionary” reform was mostly limited to the better developed provinces like Fujian, Guangdong, Jiangsu and Zhejiang, where the rural residents were already de facto urbanized and included in the local social welfare system in exchange for their permanent loss of land, an asset the local governments in these areas were relentlessly going after for urban and industrial construction (Chan 2009).

12 cities and towns (defined as county-level cities, county seats and established towns) (Wang

2004). According to the National Bureau of Statistics, the number of migrant workers increased from 114 million in 2003 to 145 million in 2009, 95.6 percent of which migrated to cities and market towns.

This unprecedented internal migration and urbanization in China is incomplete because the hukou statuses of majority of migrant workers were kept intact (i.e., remained non-local status). These migrations are therefore considered temporary and the migrants are called the

“floating population” (liudong renkou), which accounts for about 17% of China’s population

(specifically, there are 221 million members of the floating population according to the Chinese

Census in 2010) (Liang, Li and Ma 2014). Without local hukou, migrants are treated as second- class or non-citizens in the destination areas no matter how many years they have lived there.

This way, the local governments in the destination areas evade the burden of increasing social spending and welfare provision while taking advantage of cheap and young migrant labor for local economic growth (Solinger 1999; Wang 2005; Wallace 2014).

Accompanying this incomplete urbanization is the trend of localization of hukou management, particularly in the criteria for admitting migrants to the local hukou population category in cities (Chan and Buckingham, 2008). Local governments have full discretion to set the “entry conditions” in their localities for some migrants to apply for local hukou. As a result, there are tremendous regional variations in local hukou policies, or various “local citizenship regimes” (Vortherms 2016). Generally speaking, the window of hukou relocation is opened mainly to the people with extraordinary talents or investments. Moreover, the higher the administrative level (e.g., metropolises, capital cities) or the more wealth (e.g., coastal areas) a locality has, the harder it is to get local hukou in that locality (Wang 2005). According to some

13 researchers, the locally set entry requirements in large and prosperous cities such as Beijing,

Shanghai, Guangzhou, and Shenzhen, have no relevance to the great majority of migrants with rural origins (Chan and Buckingham 2008; Chan 2010). Given the decentralization and localization of the migrant-to-resident hukou status change, migrants who manage to obtain local hukou in the destination area and become local residents there will give the local government rather than the central government credit. I thus formulate the following hypotheses regarding the relationships between hukou status changes and political support:

Hypothesis 3: The hukou change from rural to urban status will increase one’s trust in the central government.

Hypothesis 4: The hukou change from non-local (migrant) to local (resident) status will increase one’s trust in the local government.

4. Empirical Analysis

Data and Variables

The data for empirical analysis is drawn from the Chinese General Social Survey (CGSS) of 2010, which successfully surveyed 11,783 respondents from 134 counties in 31 provinces in

China.4 The CGSS is a national representative continuous survey project using multistage stratified random sampling. Respondents are adults (age ≥18) living in the sampled residence for at least seven days. The CGSS 2010 data are suitable for this study because the 2010 survey collected valuable information (specified below) about individuals’ political attitudes, opinions about social inequality and social mobility, as well as individuals’ detailed histories of migration and hukou status change.

4 For more information about CGSS, see http://cnsda.ruc.edu.cn/index.php?r=site/datarecommendation.

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The key independent variables, or the treatments, are constructed from three specific questions regarding respondents’ hukou status: (1) A19: hukou type changed from rural

(agricultural) to urban (non-agricultural); (2) A21: current hukou location is local (surveyed county or city district as the reference point); (3) A24: original hukou location is local (surveyed county or city district as the reference point). For the first treatment variable, hkchange1, respondents who have experienced situation (1) are coded as 1; otherwise, respondents are coded as 0. Combining questions (2) and (3) I am able to divide the whole sample into three groups: (1)

“natives”: both original and current hukou locations are local; (2) “floaters”: both original and current hukou locations are non-local; (3) “settlers”: original hukou location is non-local but current hukou location is local. I define the second treatment variable, hkchange2, as “1” for settlers and “0” for floaters (and assign a missing value for natives). Hence, the first treatment

(hkchange1) is solely about individuals’ hukou type change (from rural to urban status) and the second treatment (hkchange2) is all about individuals’ hukou location change, or hukou status change from migrant to resident.

In the CGSS 2010 data, there are 2,321 respondents (treatment group for treatment 1), or

20% of non-missing respondents, who have had their hukou changed from rural to urban status.

Among these rural-to-urban hukou converters, 25% obtained urban hukou before 1978, 22% between 1978-1989, 27% between 1990-1999, and 26% between 2000-2010. There are 3,385 non-natives in the CGSS sample (accounting for 29% of non-missing respondents). Among the non-natives, 2,253 respondents (67% of non-natives) are settlers (treatment group for treatment

2) and 1,132 (33% of non-natives) are floaters (control group for treatment 2). Among the settlers, roughly 24% obtained local hukou before 1978, 23% between 1978-1989, 23% between

1990-1999, and 30% between 2000-2010.

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The first set of dependent variables, perceptions of social mobility, are constructed from questions about respondents’ assessment of the changes in their social classes: (1) A43a: “what’s your current social class?”; (2) A43b: “what was your social class ten years ago?”; (3) A43c:

“what will your social class be in ten years?”; (4) A43d: “what was your family’s social class when you were 14 years old?”. From answers to these questions, I construct three outcome variables about individuals’ perceived social mobility: mobility experience (mobility_exp) as the difference between (1) and (2); mobility prospect (mobility_pro) as the difference between (1) and (3); and intergenerational mobility experience (mobility_gen) as the difference between (1) and (4). These dependent variables are continuous because social class is recorded using a 10- point scale, with “1” as the lowest class and “10” as the highest class. Slightly over half of respondents (52% of non-missing respondents) believe that they have experienced upward mobility (mobility_exp >0) in the past ten years; the majority of respondents (62% of non- missing respondents) considered themselves to have climbed up the social ladder

(mobility_gen >0) given their family background at the age of 14; and the same amount of respondents (62% of non-missing respondents) believed that they will reach a higher social class in the next ten years (mobility_pro>0).

The second set of dependent variables represent individuals’ trust in the government. The

CGSS’s 2010 survey directly asks respondents “how much do you trust the central government”

(D302) and “how much do you trust the local government” (D303). Answers to these questions are recorded in a 5-point scale with “1” indicating “complete distrust” and “5” as “complete trust”. I transform these two ordinary variables into binary variables (trust_cen, trust_loc) with

“1” indicating “trust” (4 and 5 in the 5-point scale) and “0” as “distrust”. Similar to what the

16 extant studies have found (Li 2004; Chen 2017; Zhou and Jin 2018), the CGSS 2010 data shows that Chinese people have much higher trust in the central government (89% of non-missing respondents) than in the local government (65% of non-missing respondents).

The third set of dependent variables concerns individuals’ opinions regarding governments’ roles with respect to social inequality and migration. They are drawn from three specific questions: Do you agree or not (1) D606: “The government is able to reduce income inequality through taxation and spending” (red_govcapacity); (2) D610: “The government should reduce or mitigate social inequality” (red_govmoral); (3) A48: “Individuals have freedom to decide where to work and live, and the government should not intervene (migration)”

(mfreedom). Answers to these questions are recorded using a 5-point scale, with “1” indicating completely disagree and “5” completely agree. I transform them into binary indicators

(red_govcapacity, red_govmoral, freemove) with “1” indicating agree (4 or 5 on the 5-point scale) and “0” disagree.

Besides the above outcome variables, I construct another group of attitudinal variables from the survey questions about social equity and inequality as control variables: Do you agree or not (1) “Generally speaking, most people are trustworthy” (trust); (2) “Generally speaking, the current society is fair” (fairness); (3) “social inequality is mainly due to differences in individuals’ talents and capabilities” (inequality1); (4) “social inequality is mainly due to control or manipulation by a small group of elites” (inequality2). These social values or opinions are recorded using a 5-point scale, with “1” indicating completely disagree and “5” indicating completely agree. Hence, the social value factors are ordinal variables.

I also construct a number of individual-level demographic and socioeconomic variables as control variables. They include age (age), gender (male), marital status (married), ethnic

17 minority (ethnic), education (highschool), residence (urban), CCP membership (ccp), household income per capita (hhincome), working status (working, farming, retired), social insurance enrollment (medinsurance, pension), media use (newmedia), and dummies for resident provinces and counties. The definitions and statistical summaries of all variables are presented in Table 1.

Identification Strategy

One main challenge to drawing causal inferences about the effect of hukou status change on regime support using observational data is that the treatment (i.e., hukou status change) is not randomly assigned. It is possible that individuals who have the experiences of hukou status change have more support for the regime to begin with. In other words, the assignment of treatment might be associated with the outcome of interest (i.e., the selection concern). It is also possible that the underlying distribution of personal characteristics that would impact hukou status change and political attitudes, such as demographic and socioeconomic status, are different across the treatment and the control groups. These preexisting differences in personal characteristics might profoundly affect both hukou status changes and the political attitudes of individuals (i.e., the confounding concern). To address these concerns, I adopt econometrics techniques in the program evaluation literature (Heckman, Ichimura, and Todd 1998) to construct a plausible counterfactual group that matches the treatment group (people who experience hukou status change) on the relevant observable personal characteristics (e.g., age, gender, marital status, ethnic minority, education, residence, CCP membership, household income, working status, social insurance enrollment, media use, and resident provinces) that affect their likelihood of changing hukou status (e.g., from rural to urban, from migrant to resident).

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To achieve the goal of constructing a comparable control group with the treatment group to estimate the causal effect of hukou status change, I use weighting to construct a control group that matches the treatment group on relevant personal characteristics. After weighting, the distribution of relevant observable characteristics is approximately equal across treatment and control groups (Rosenbaum, 1995). Although the estimates of the treatment effect using the weighted sample are not strictly causal and need to be interpreted with caution, as we should always do with observational data, they provide a more accurate evaluation of the effects than without weighting. This step of preprocessing data can also reduce model dependency for the subsequent analysis of treatment effects in the preprocessed data using standard methods such as regression analysis (Abadie and Imbens 2011; Ho et al., 2007).

For the weighting, I employ Hainmueller’s (2013) entropy balancing method (EBW). The procedure to employ entropy balancing is that the analyst first specifies a set of moment conditions (e.g., mean, variance, skewness) of the covariate distributions to hold across the treatment and control groups. The entropy balancing scheme then searches for unit weights for different observations in the control group to satisfy these moment conditions. In the analysis with EBW, I set the balance constraints at the first (mean) and second (variance) moments for each of the observed covariates (including age, male, marital status, ethnicity, education, CCP membership, household income, working status, social insurance enrollment, and media use).

After the weighting, the treatment and control groups are fully balanced on these covariates.5

5 Continuous variables such as age and household income are harder to fully balance than the other binary variables. Despite this, the standardized mean differences between the treatment and

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Besides weighting, the matching method to preprocess the data before estimating treatment effects in observational studies also has the potential to yield more convincing estimates than standard approaches (Hainmueller and Xu 2013). Despite attempting to achieve the same ends, weighting and matching methods, each has their own pros and cons. The matching method is more flexible but also more demanding for sample size because it discards unmatched observations from the final sample for analysis, while the weighting method reweights the observations to prevent loss of information. In matching, analysts often need to go back and forth between matching and balance checking to search for a suitable weighting that balances the covariate distributions, while in weighting the covariate balance is directly built into the weight function that is used to adjust the control units. Given the moderate sample size in the

CGSS data, weighting is preferable to matching in preprocessing the data. That said, I redo the preprocessing using the matching method and report the analysis results using matched data as robustness checks in the next section.

Model Specification

Using the CGSS 2010 data preprocessed by weighting or matching, I use the regression model specified below to estimate the effect of hukou status change on the outcomes of interest.

푗 푌푖푐 = 훼 + 훽푇푖 + ∑ 휑푗푍푖 + 휇푐 + 휀푖 1

The subscript i indexes each respondent and c indexes the county where respondent i resides. Yic represents respondent i’s political or social attitudes (e.g., trust in the central or local

control groups in the variables of age and household income are negligible (< 0.01) after weighting, meaning that the data is well balanced by the weighting.

20 government, perceived social mobility, government’s role regarding inequality and migration).

The coefficient of interest is β, which captures the effect of hukou status change on the outcome of interest. Ti is a binary indicator: Ti =1 means that respondent i has a hukou conversion experience (i.e., hukou changes from rural to urban status or from migrant to resident status);

푗 otherwise, Ti =0. ∑1 푍푖 refers to a vector of covariates measuring personal characteristics of the respondent, including age, gender, marital status, ethnicity, education, CCP membership, household income, enrollment in social health insurance and pensions, employment status, social dispositions (e.g. social trust, opinion about sources of social inequality, redistributive preferences) and use of modern communication and information technologies (e.g., mobile

th phone, internet); φj is the coefficient of the j covariate; 휇푐 is a vector of county dummy variables that capture unobserved county fixed effects; εi is the error term and 훼 is the constant.

When the outcome variables are binary (e.g., trust_cen, trust_loc, red_govcapacity, red_govmoral, mfreedom), the model is specified as the Probit regression; when the outcome variables are continuous (mobility_exp, mobility_pro, mobility_gen), the model is specified as the

OLS regression.

5. Empirical Results

The empirical results are threefold. First, I report how hukou status changes—from rural to urban status and from migrant to resident status, respectively—are associated with individuals’ experienced or perceived social mobility. These results are summarized in Table 2.

Second, I present the treatment effects of hukou changes on individuals’ regime support. The results are presented in Table 3. Third, I discuss the results regarding hukou changes and individuals’ other political attitudes, which suggest possible mechanisms to explain why

21 individuals with hukou changes would have higher trust in the government. These results are summarized in Table 4.

Hukou Change and Social Mobility

The OLS regression results in Table 2 show that the hukou change from rural to urban status is an important determinant of individuals’ upward mobility experience. Specifically, having the rural-to-urban hukou change increased individuals’ social class by 0.16 (the average of social class change among all respondents is 0.68); the rural-to-urban hukou change increased individuals’ social class compared to their families’ at the age of 14 by 0.34 (the average of intergenerational social class change among all respondents is 1.09). These results are statistically significant at the 99% confidence level. However, the rural-to-urban hukou change doesn’t have a consistent and significant effect on individuals’ projected social class in the next ten years.

Table 2 also shows that having hukou change from migrant to resident status is an important determinant of individuals’ social mobility prospect but not of their social mobility experience. Specifically, the nonlocal-to-local hukou change decreased one’s projected social class in ten years by 0.13 (the average of projected social class change among all respondents is

1.10). This result is statistically significant at the 95% confidence level. However, people with a nonlocal-to-local hukou change don’t consider the level of their social class having increased in the past year.

The contrast between the results of different hukou changes is notable, reflecting some key features of social stratification and inequality in China during the reform era. First, by 2010 holding an urban hukou was still a privilege for ordinary Chinese, and achieving this status was considered a kind of upward mobility for rural-born or rural hukou holders. Second, and in

22 contrast, holding a local hukou is not considered a social privilege among non-natives. People who were not born locally but managed to attain local hukou afterward (i.e., settlers) did not believe that they had achieved upward mobility, probably because they were still treated as inferior to the natives (people who are registered locally since birth) and they consider the natives as their reference group. This explains why settlers were significantly more pessimistic about their prospects for upward mobility in the near future than floaters were. Further empirical research is needed to uncover the formal and informal hurdles that prevent settlers from being

“naturalized”. Overall, these results highlight the importance of hukou status in defining one’s social class and shaping one’s experience of and prospect for social mobility in China.

Hukou Change and Regime Support

Table 3 reports the Probit regression results using the weighted data with all the control variables. As shown in the table, the rural-to-urban hukou change significantly increased respondents’ trust in the central government but has no significant effect on respondents’ trust in the local government. All else being equal, individuals who have experienced the rural-to-urban hukou change are more likely to trust the central government by 10 percentage points than individuals without such an experience. This effect, nonetheless, is significant only at the 90% confident level when all controls are included. In contrast, the nonlocal-to-local hukou change significantly increased respondents’ trust in the local government. All else being equal, non- native people who have successfully obtained local hukou (i.e., settlers) are more likely to trust the local government by 21 percentage points than non-native people who still hold non-local hukou (i.e., floaters). This effect is statistically significant at the 99% confidence level.

Overall, the results in Table 3 suggest that the treatment effects of hukou changes in individuals’ political support differ between the central and the local governments. The central

23 government seems to gain more public support from the rural-to-urban hukou conversion treatment while the local government earns more public support from the nonlocal-to-local hukou relocation treatment. Moreover, the effect of hukou change is stronger in the trust in the local government (in terms of statistical significance of coefficients) and larger (in terms of magnitude of coefficients) than the trust in the central government.

Among the control variables, age, CCP membership and social trust are consistent and significant predictors of individuals’ trust in the governments: elders, CCP members and people who tend to trust others in the society are more likely to be supporters of the governments (both central and local).

As for the reason why individuals experiencing the rural-to-urban or nonlocal-to-local hukou change have higher trust in the government (either central or local), it is possible that the increase of social benefits or entitlements brought about by the designated status change has strengthened the beneficiaries’ belief in the government’s responsibility or capacity to fight income inequality (i.e., the redistribution mechanism); it is also possible that the change of resident rights associated with the designated hukou change has decreased individuals’ objection to or increased individuals’ support for government intervention in migration (i.e., the intervention mechanism). To test these conjectures, I apply the same model specifications to the third set of dependent variables about the government’s roles with respect to income inequality and migration. The Probit regression results in Table 4 seem to support both redistribution and intervention mechanisms, but only for the rural-to-urban hukou status change. Specifically, people with a rural-to-urban hukou change are more likely to believe that the government should and can reduce income inequality through taxation and spending; moreover, they are less likely to oppose government intervention in migration (i.e., they tend to disagree that “individuals has

24 freedom to decide where to work and live and the government should not intervene”) than people without this hukou change. These results are statistically significant at conventional levels. In contrast, there is no evidence that people with a nonlocal-to-local hukou change have beliefs significantly different from people without such a hukou change regarding the government’s role with respect to income inequality and migration.

Robustness Tests

The main analysis uses entropy balancing weighting to preprocess the data before regression analysis in order to reduce the selection and confounding biases in the estimates. As robustness tests, I use the matching method to preprocess the data. Specifically, I match subsamples of the control group with the treatment group on the distribution of the observable individual-level characteristics. Then I use the matched data to run all the regressions again.

For the matching, I employ several popular matching techniques in social science studies including Mahalanobis-metric nearest neighbor matching (NNM), propensity score matching

(PSM) and coarsened exact matching (CEM). The first two, NNM and PSM, are approximate matching methods specifying a metric (e.g., Mahalanobis distance in NNM, propensity score in

PSM) to find control units that are closest to the treated unit. The third one, CEM, is essentially exact matching on the covariates within strata. These matching methods have their own pros and cons; there is no consensus in the literature which matching method is absolutely superior to the others. Unlike NNM and PSM, CEM can control the amount of imbalance ex ante in the matching solution by coarsening data so that analysts don’t need to check for balance ex post.

However, CEM often drops more observations than PSM and NNM because it does exact matching on distilled information in the covariates (Blackwell, Iacus and King 2009). Both CEM and NNM allow us to avoid making assumptions on the functional form regarding how the

25 covariates contribute to the probability of being in the treatment group (i.e., the propensity score). But matching on the propensity score (PSM) has been recommended for achieving balance on higher-order moments (Diamond and Sekhon 2005; Rosenbaum and Rubin 1985).

Following the suggestion in Morgan and Winship (2007), I apply various matching techniques to preprocessing the data, and then present the treatment effects across different matching techniques to further examine the robustness of the results.

Appendix Table 1 reports the standardized mean differences (SMD) for the covariates before and after matching. It shows that the matching has achieved acceptable balance

(SMD<.01) in most covariates. The regression results after matching are reported in Appendix

Tables 2, 3, and 4 respectively.

The results regarding hukou changes and social mobility using the matched data

(Appendix Table 2) are quite similar to the ones using the weighted data (Table 2). The only exception is that, according to the CEM estimator in Appendix Table 2, people with the rural-to- urban hukou change are significantly more likely to anticipate upward mobility in the next ten years (the coefficient is insignificant and negative in the main analysis); but the NNM and PSM estimators show no such results, which is consistent with the results in the main analysis.

The results regarding hukou changes and regime support using the matched data

(Appendix Tables 3 and 4) are mostly consistent with the results using the weighted data (Table

3): the rural-to-urban hukou change significantly increased individuals’ trust in the central government while it has no significant impacts on their trust in the local government; in contrast, the nonlocal-to-local hukou change significantly increased individuals’ trust in the local government rather than the central government. A notable discrepancy between the matched results and the weighted results is that, according to the CEM estimator in Appendix Table 4, the

26 nonlocal-to-local hukou change also significantly increased one’s trust in the central government; but this result loses significance in the NNM and PSM estimators that are consistent with the results in the main analysis.

The results regarding hukou changes and public opinions about the government’s role in income inequality and migration using the matched data (Appendix Table 5) reveal only one consistent and significant pattern: people with the rural-to-urban hukou change are significantly less likely to agree that the government should not intervene in migration. In other words, the results using the matched data seem to support the intervention mechanism rather than the redistribution mechanism in explaining why people with the rural-to-urban hukou change have more trusts in the government, while results using the weighted data support both mechanisms.

Note that although the matching method reduces the sample size dramatically, most regression results after matching are consistent to the ones in the main analysis using weighting.

Moreover, the results using the matched data, with a few exceptions, are quite robust to the different matching techniques.

One may question about the overlap of the rural-to-urban hukou change and the nonlocal- to-local hukou change. This overlap does exist in the CGSS sample: among the 2301 respondents who have experienced rural-to-urban hukou change, majority of them (83%) also have experienced the nonlocal-to-local hukou change; likewise, among the 2234 respondents who have obtained local hukou, almost half of them (49%) also have experienced the rural-to-urban hukou change. In other words, these hukou changes often go hand in hand for individuals. Does the overlap affect the estimation of the treatment effect of hukou change? To address this, I add an interaction term of the two treatment variables to distinguish rural-to-urban hukou converters who obtain local hukou or not, and settlers (nonlocal-to-local hukou changers) who obtain urban

27 hukou or not. According to the analysis results with the interaction term included (Appendix

Table 6), the interaction term of the two treatment variables are not statistically significant, indicating that the effect of hukou change (rural-to-urban or nonlocal-to-local) on individuals’ regime support doesn’t vary across the group who experience both hukou changes and the group who experience only one of the hukou changes (rural-to-urban or nonlocal-to-local); meanwhile, the main effects of the hukou changes remain statistically significant.

6. Conclusion

Political support is a desirable commodity that all political systems wish to acquire. In an authoritarian country like China where political legitimacy increasingly relies on the performance of governance and economic development, the government seems to have a stronger interest in engineering social mobility to maintain political stability. In this paper, I leverage China’s hukou reform since the 1980s to study how upward social mobility, defined by hukou status change, contributes to Chinese citizens’ support for the government. I find that first, hukou status is an important determinant of social mobility in China. According to the CGSS

2010 data, through changing hukou from rural to urban status, ordinary Chinese citizens believe that they have achieved upward mobility; however, changing hukou from migrant (non-local) to resident (local) status has a dampening effect on citizens’ perceived prospect of upward mobility.

Second, I find that there is a significant yet heterogeneous effect of social mobility (defined by hukou status changes) on citizens’ trust in the government. Specifically, the rural-to-urban hukou change increased one’s trust in the central government, while the nonlocal-to-local hukou change increased one’s trust in the local government. I further find that citizens with the rural-to-urban hukou change are more likely to tolerate or support government intervention in migration, which suggests a mechanism to explain why upward mobility increases political support in China.

28

This study contributes to the literature on social mobility, political support, and social policy reform. First, this study uncovers the close relationship between hukou and social mobility in China. Although the importance of hukou in shaping social stratification and inequality in China has been thoroughly discussed in the literature (Wang 2005; Solinger 1999;

Bian 2002; Wu and Treiman 2004), few studies (with an exception of Wu and Treiman 2004) have explicitly connected hukou status change to social mobility. In conventional wisdoms, social mobility is usually measured by income, education or occupation. But in the Chinese context, hukou, which greatly determines one’s opportunities for education and employment, income and prosperity, was not well integrated into the conceptualization and measurement of social mobility. The findings of this paper regarding the relationships between hukou changes and self-assessed intra- and intergenerational social mobility enhance our understanding of the multifaceted phenomenon of social mobility in China.

Second, the findings of this paper regarding hukou changes and citizens’ trust in government shed new light on the sources of political support that is notably high in China. It shows that besides the material-based sources (e.g., economic development and social welfare expansion) often discussed in the extant studies (Huang and Gao 2018; Saich 2008; Dickson

2016; Lü 2014), upward mobility defined by hukou changes also significantly increases citizens’ trust in the government. It implies that the slowdown of China’s economic growth in recent years won’t immediately result in political instability if the paths for upward mobility are kept open. In

2014, the CCP leadership issued a “National New-Type Urbanization Plan” which set a goal of having 100 million rural-to-urban migrants receive urban hukou before 2020. The Chinese premier Li Keqiang has called this plan a “people-centered” approach to urbanization (Liang

2016). This government initiative can be interpreted from the perspective of authoritarianism as

29 boosting regime support during an economic downturn through engineering social mobility. But the Chinese authoritarian regime constantly faces the political trade-off between stimulating economic growth by urbanization and maintaining social stability by preventing population concentration in cities (Wallace 2014). Further research is needed to explore the long-term effect of the “people-centered” urbanization on regime stability in China.

Third, this study complements the extant studies of China’s hukou system by highlighting the political motivations and consequences of hukou reforms since the 1980s. Although some scholars have analyzed individual-level data on hukou changes (from rural to urban and/or from migrant to resident) (Wu and Zheng 2018; Zhang and Treiman 2013, Wu and Treiman 2007), the focus of extant studies is the socioeconomic outcomes or effects of hukou changes (e.g., income inequality, occupation segregation, personal wellbeing). To my knowledge, this paper is the first to specifically investigate the effects of hukou changes on individuals’ political attitudes. The differential effects of hukou changes on citizens’ trust in the central and local governments suggest that the decentralization of hukou reform has benefited the local governments more than the central government in terms of gaining political support. As some local governments have abolished the urban-rural distinction in local hukou registration and most local governments have gained full control of the qualification and selection of candidates for local hukou, local governments, rather than the central government, are empowered in social-policy making; moreover, local/non-local status distinction is becoming a more salient social cleavage in China, compared to the urban/rural status distinction. This study demonstrates that the ongoing hukou reform has far-reaching consequences beyond the labor market.

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Table 1. Definition and Summary Statistics of Variables

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Table 2. Hukou Status Change and Social Mobility in China

Notes 1. Coefficients are from OLS regressions after preprocessing the data using entropy balancing weighting; 2. Standard errors are in parentheses; 3. * p < 0.10, ** p < 0.05, *** p < 0.01; 4. Unreported control variables include age, gender, marital status, ethnic minority indicator, education, household income, CCP membership, working status, having medical insurance/pension or not, urban residence, using Internet/cell-phone as the main information channel, redistributive preference, reason for social inequality (individual differences), and county dummies.

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Table 3. Effects of Hukou Status Change on Regime Support in China

Notes 1. The variable “current hukou” refers to “local hukou” in the case of rural-to-urban hukou change as the treatment and to “urban hukou” in the case of nonlocal-to-local hukou change as the treatment; 2. Coefficients are from Probit regressions after preprocessing the data using entropy balancing weighting; coefficients of county dummies are not reported. 3. Standard errors are in parentheses; 4. * p < 0.10, ** p < 0.05, *** p < 0.01;

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Table 4. Effects of Hukou Status Change on Public Opinion about Government

Notes 1. Coefficients are estimated from Probit regressions after preprocessing the data using entropy balancing weighting; 2. Standard errors are in parentheses; 3. * p < 0.10, ** p < 0.05, *** p < 0.01; 4. Unreported control variables include age, gender, marital status, ethnic minority indicator, education, household income, CCP membership, working status, having medical insurance/pension or not, urban residence, using Internet/cell-phone as the main information channel, reason for social inequality (elite control), and county dummies.

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Appendix Tables Appendix Table 1. Standardized Mean Differences (SMD) of Covariates

NNM PSM CEM Rural-to-Urban Nonlocal-to-local Rural-to-Urban Nonlocal-to-local Rural-to-Urban Nonlocal-to-local (1) (2) (1) (2) (1) (2) (1) (2) (1) (2) (1) (2) Age .178 .040 .664 .247 .178 .081 .665 .017 .177 -.007 .649 .001 Male .050 -.040 -.141 -.121 .050 -.021 -.144 -.042 .051 .000 -.149 .000 Married .003 .103 .220 .010 .003 -.005 .211 .017 .004 .000 .198 .000 Ethnicity -.122 -.057 -.027 .015 -.122 -.041 -.027 -.071 -.138 .000 -.029 .000 Highschool .495 .156 -.092 -.011 .496 .111 -.087 -.050 .490 .000 -.105 .000 householdincome .466 .179 -.222 -.107 .466 -.011 -.222 -.001 .439 -.009 -.226 .010 CCP .421 .039 .240 .109 .421 .034 .236 -.005 .475 .000 .244 .000 Working .291 .232 -.465 -.164 .291 -.013 -.461 -.019 .291 .000 -.460 .000 Farming -.777 -.308 .374 .120 -.777 .017 .365 .034 -.645 .000 .258 .000 Retired .412 .027 .340 .062 .412 .055 .338 -.043 .456 .000 .373 .000 Medinsurance -.063 .015 .368 .044 -.063 -.141 .363 .007 -.063 .000 .431 .000 Pension .445 .114 .299 .107 .445 .047 .300 -.050 .434 .000 .287 .000 New media .193 -.008 -.325 -.043 .193 .039 -.321 .009 .194 .000 .134 .000 Urban resident .930 .468 -.196 -.106 .930 -.006 -.192 .024 .780 .000 -.166 .000 Urban hukou .496 .279 .496 -.071 .452 .000 Local hukou .016 .055 .016 -.129 .014 .000 .649 .000 # of treated 1931 1931 1872 1872 1931 1931 1858 1858 1541 780 2031 222 # of control 7640 1307 849 492 7640 1356 849 452 8191 1164 955 177 Notes 1. NNM: nearest neighbor matching; PSM: propensity score matching; CEM: coarsened exact matching; 2. “Rural-to-urban” refers to the hukou change from rural to urban status; “Nonlocal-to-local” refers to hukou change from non- local to local status. 3. Column (1) report SMD in the unmatched data; Column (2) report SMD in the matched data.

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Appendix Table 2. Hukou Status Change and Social Mobility in China

Individual Social Inter-generational Social Individual Social Mobility Experience Mobility Experience Mobility Prospect Rural-to-urban NNM 0.186*** 0.246*** -0.007 hukou change (0.054) (0.066) (0.043) N 3807 3807 3732 PSM 0.257*** 0.391*** -0.053 (0.055) (0.068) (0.044) N 3824 3819 3747 CEM 0.436*** 0.394*** 0.267*** (0.088) (0.110) (0.072) N 1754 1754 1722 Nonlocal-to-local NNM 0.025 0.078 -0.150*** hukou change (0.058) (0.075) (0.046) N 3712 3706 3664 PSM -0.054 0.065 -0.101** (0.056) (0.074) (0.045) N 3686 3685 3645 CEM 0.029 0.088 -0.447*** (0.194) (0.263) (0.165) N 360 361 357 Notes 1. Coefficients are from OLS regressions after preprocessing the data using a respective matching method (NNM—nearest neighbor matching; PSM—propensity score matching; CEM—coarsened exact matching); 2. Standard errors are in parentheses; 3. * p < 0.10, ** p < 0.05, *** p < 0.01; 4. Unreported control variables include age, gender, marital status, ethnic minority indicator, education, household income, CCP membership, working status, having medical insurance/pension or not, urban residence, using Internet/cell-phone as the main information channel, reason of social inequality (individual differences), and county dummies.

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Appendix Table 3. Effect of Rural-to-Urban Hukou Status Change on Regime Support in China

Trust in Central Government Trust in Local Government NNM PSM CEM NNM PSM CEM Rural-to-urban hukou change 0.111* 0.121* 0.230** -0.046 -0.018 0.043 (0.064) (0.063) (0.097) (0.046) (0.046) (0.073) Age 0.009** 0.013*** 0.007 0.002 0.005** 0.007 (0.003) (0.003) (0.006) (0.002) (0.002) (0.005) Male 0.106 0.094 0.079 -0.082* -0.049 -0.154** (0.069) (0.065) (0.098) (0.049) (0.048) (0.074) Married 0.038 0.155* -0.041 -0.036 -0.023 -0.199* (0.090) (0.080) (0.131) (0.065) (0.061) (0.102) Ethnic minority 0.303 0.053 -0.277 -0.020 0.141 -0.413** (0.230) (0.189) (0.242) (0.138) (0.126) (0.194) High school 0.085 0.003 -0.175 0.034 0.036 -0.039 (0.080) (0.076) (0.127) (0.056) (0.055) (0.097) Household income -0.004 -0.045 -0.144 -0.032 -0.040 0.039 (0.033) (0.035) (0.091) (0.025) (0.025) (0.068) CCP 0.115 0.166** 0.313** 0.157** 0.098* 0.116 (0.088) (0.081) (0.139) (0.062) (0.058) (0.107) Working 0.067 0.072 0.225 -0.068 -0.071 0.049 (0.091) (0.087) (0.173) (0.067) (0.065) (0.130) Farming -0.026 0.232 -0.585 0.292** 0.192 -0.175 (0.203) (0.225) (0.447) (0.133) (0.132) (0.290) Retired 0.327*** 0.172 0.425* -0.037 -0.087 -0.124 (0.119) (0.113) (0.218) (0.081) (0.079) (0.161) Medical insurance 0.257** 0.068 0.228 0.159** 0.021 0.002 (0.101) (0.094) (0.233) (0.074) (0.069) (0.179) Pension -0.247*** -0.089 -0.116 -0.075 -0.050 0.104 (0.084) (0.078) (0.159) (0.056) (0.054) (0.109) New media 0.020 -0.053 -0.121 -0.135* -0.091 -0.161 (0.113) (0.109) (0.150) (0.071) (0.069) (0.122) Local hukou -0.083 -0.101 -0.212 0.291*** 0.198** 0.213 (0.140) (0.142) (0.237) (0.084) (0.085) (0.196) Social trust 0.142*** 0.195*** 0.185*** 0.169*** 0.179*** 0.221*** (0.028) (0.027) (0.043) (0.021) (0.021) (0.034)

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Constant 0.299 0.537 2.859** -0.152 -0.050 -0.894 (0.546) (0.636) (1.399) (0.409) (0.425) (0.941) County fixed effect Yes Yes Yes Yes Yes Yes R-squared 0.123 0.130 0.137 0.113 0.107 0.139 N 3207 3249 1415 3849 3833 1672 Notes 1. Coefficients are from Probit regressions after preprocessing the data using a respective matching method (NNM—nearest neighbor matching; PSM—propensity score matching; CEM—coarsened exact matching). 2. Standard errors are in parentheses; 3. * p < 0.10, ** p < 0.05, *** p < 0.01; 4. Coefficients of county dummies are not reported.

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Appendix Table 4. Effects of Non-local to Local Hukou Status Change on Regime Support in China

Trust in Central Government Trust in Local Government NNM PSM CEM NNM PSM CEM Nonlocal-to-local hukou change 0.108 0.027 0.473** 0.304*** 0.178*** 0.498** (0.068) (0.068) (0.201) (0.053) (0.052) (0.204) Age 0.013*** 0.010*** -0.005 0.004 0.005* -0.004 (0.003) (0.003) (0.016) (0.003) (0.003) (0.016) Male -0.029 -0.097 -0.780*** -0.111** -0.265*** -0.793*** (0.073) (0.072) (0.228) (0.056) (0.055) (0.228) Married 0.039 0.090 0.685 -0.123* -0.031 0.713 (0.095) (0.096) (0.477) (0.073) (0.072) (0.480) Ethnic minority -0.249 -0.106 -0.067 -0.124 -0.103 -0.002 (0.153) (0.149) (0.508) (0.122) (0.114) (0.513) High school 0.035 -0.058 -0.082 0.240*** 0.227*** -0.065 (0.084) (0.083) (0.323) (0.065) (0.064) (0.326) Household income -0.022 -0.081** -0.086 -0.060* -0.095*** -0.106 (0.036) (0.033) (0.200) (0.031) (0.025) (0.201) CCP 0.392*** 0.440*** 1.000*** 0.246*** 0.480*** 1.009*** (0.104) (0.099) (0.330) (0.075) (0.074) (0.331) Working 0.219** -0.045 -0.347 -0.051 -0.089 -0.456 (0.098) (0.098) (0.671) (0.076) (0.077) (0.699) Farming 0.624*** 0.520*** -1.897 -0.061 -0.038 -2.807 (0.163) (0.154) (1.481) (0.105) (0.101) (1.743) Retired 0.353*** 0.195 -0.110 0.090 -0.062 -0.224 (0.120) (0.120) (0.739) (0.091) (0.090) (0.761) Medical insurance 0.094 0.162 0.195 0.190** 0.106 0.073 (0.105) (0.101) (0.692) (0.081) (0.080) (0.707) Pension -0.122 -0.095 -0.541 -0.038 0.139** -0.407 (0.087) (0.086) (0.554) (0.063) (0.063) (0.566) New media -0.200** -0.022 -0.344 -0.252*** -0.187** -0.354 (0.098) (0.099) (0.251) (0.078) (0.079) (0.253) Urban hukou -0.157 -0.128 0.720 -0.260*** -0.217** 0.957 (0.109) (0.115) (0.637) (0.084) (0.087) (0.094) Social trust 0.175*** 0.177*** 0.190** 0.163*** 0.184*** 0.198** (0.029) (0.029) (0.094) (0.023) (0.023) (0.094) Constant -1.225* -0.642 1.209 0.949** 0.732 2.342

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(0.666) (0.652) (2.526) (0.445) (0.448) (2.757) County fixed effect Yes Yes Yes Yes Yes Yes R-squared 0.222 0.191 0.266 0.188 0.182 0.269 N 3076 3034 278 3623 3616 278 Notes 1. Coefficients are from Probit regressions after preprocessing the data using a respective matching method (NNM—nearest neighbor matching; PSM—propensity score matching; CEM—coarsened exact matching). 2. Standard errors are in parentheses; 3. * p < 0.10, ** p < 0.05, *** p < 0.01; 4. Coefficients of county dummies are not reported.

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Appendix Table 5. Effects of Hukou Status Change on Public Opinion about Government

Government should Government can Government should reduce inequality reduce inequality not intervene in migration Rural-to-Urban NNM 0.048 -0.009 -0.153*** hukou change (0.054) (0.046) (0.047) N 3787 3800 3839 PSM 0.047 0.052 -0.108** (0.054) (0.046) (0.046) N 3718 3813 3861 CEM -0.143 0.040 -0.330*** (0.098) (0.078) (0.082) N 1606 1743 1722 Nonlocal-to-local NNM -0.111* -0.006 -0.069 hukou change (0.062) (0.052) (0.054) N 3555 3652 3707 PSM -0.133** -0.058 0.005 (0.062) (0.051) (0.052) N 3446 3640 3642 CEM 0.047 0.074 -0.492** (0.239) (0.175) (0.218) N 218 337 288 Notes 1. Coefficients are estimated from Probit regressions after preprocessing the data using a respective matching method (NNM—nearest neighbor matching; PSM—propensity score matching; CEM—coarsened exact matching). 2. Standard errors are in parentheses; 3. * p < 0.10, ** p < 0.05, *** p < 0.01; 4. Unreported control variables include age, gender, marital status, ethnic minority indicator, education, household income, CCP membership, working status, having medical insurance/pension or not, urban residence, using Internet/cell-phone as the main information channel, reason of social inequality (elite control), and county dummies.

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Appendix Table 6. Effects of Hukou Status Change on Regime Support in China

Rural-to-Urban Hukou Change Nonlocal-to-local Hukou Change Central Local Central Local government government government government Hukou change 0.373** -0.046 0.042 0.237** (0.165) (0.127) (0.115) (0.099) Current hukou+ 0.038 0.121 0.237 0.106 (0.123) (0.103) (0.191) (0.147) Hukou status change -0.260 0.010 -0.139 -0.146 * Current hukou+ (0.199) (0.152) (0.213) (0.166) Age 0.010** 0.006* 0.011*** 0.005 (0.004) (0.003) (0.004) (0.003) Male 0.016 -0.107 -0.088 -0.158** (0.088) (0.072) (0.091) (0.077) Married 0.184* 0.021 0.112 -0.041 (0.109) (0.090) (0.109) (0.096) Ethnic minority -0.133 -0.030 0.118 -0.056 (0.203) (0.166) (0.224) (0.177) High school 0.024 0.125 -0.013 0.137 (0.102) (0.082) (0.098) (0.088) Household income -0.107** -0.094*** -0.009 -0.079** (0.048) (0.036) (0.045) (0.036) CCP 0.218* 0.102 0.343*** 0.327*** (0.114) (0.089) (0.126) (0.100) Working 0.042 -0.015 0.025 -0.060 (0.126) (0.100) (0.123) (0.103) Farming 0.313 0.341* 0.505*** 0.081 (0.278) (0.204) (0.191) (0.148) Retired 0.198 0.039 0.146 -0.017 (0.152) (0.120) (0.166) (0.130) Medical insurance 0.131 0.062 0.225* 0.218** (0.122) (0.101) (0.124) (0.105) Pension -0.056 0.039 -0.037 0.096 (0.104) (0.083) (0.112) (0.088) New media -0.097 -0.159* -0.059 -0.145 (0.114) (0.095) (0.120) (0.111) Social trust 0.113*** 0.132*** 0.178*** 0.166*** (0.037) (0.030) (0.037) (0.032) Constant 0.163 0.533 -1.351* 0.755 (0.817) (0.568) (0.740) (0.570) County fixed effect Yes Yes Yes Yes R-squared 0.193 0.181 0.192 0.173 N 2279 2666 2293 2682 Notes 1. The variable “current hukou” refers to “local hukou” in the case of rural-to-urban hukou status change as the treatment and to “urban hukou” in the case of migrant-to-resident hukou status change as the treatment; the “interaction” term refers to the interaction term between the hukou status change and the current hukou variables. 2. Coefficients are from Probit regressions after preprocessing the data using entropy balancing

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weighting; coefficients of county dummies are not reported. 3. Standard errors are in parentheses; 4. * p < 0.10, ** p < 0.05, *** p < 0.01;

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