The Impacts of In Situ Urbanization on Housing, Mobility and Employment of Local Residents in China

Yanxu Li; Zhenfa Xie; Bo Li; Muhammad Mohiuddin

doi:10.3390/su14159058

The Impacts of In Situ Urbanization on Housing, Mobility and Employment of Local Residents in China

Li, Yanxu;Xie, Zhenfa;Li, Bo;Mohiuddin, Muhammad 2022-07-24 00:00:00 sustainability Article The Impacts of In Situ Urbanization on Housing, Mobility and Employment of Local Residents in China 1 1 2 , 3 , Yanxu Li , Zhenfa Xie , Bo Li * and Muhammad Mohiuddin * School of Economics, Xiamen University, Xiamen 361005, China; liyx1995@163.com (Y.L.); xzfxmu2022@163.com (Z.X.) School of Management, Tianjin University of Technology, Tianjin 300384, China Faculty of Business Administration, Laval University, Quebec, QC G1V 0A6, Canada * Correspondence: great2011818@126.com (B.L.); muhammad.mohiuddin@fsa.ulaval.ca (M.M.) Abstract: Rapid economic growth and infrastructure development force in situ urbanization in locations where people from rural areas gain urban residency without experiencing long- distance geographical relocation. However, the impacts of in situ urbanization on farmers’ and other residents’ well-being remains unclear, and there are some arguments about the idea that “urbanization of people lags behind urbanization of land” in China. Therefore, this study firstly finds a reasonable way to measure in situ urbanization: the transfer of rural-urban division codes. On this basis, by applying the PSM-DID method, we use national census data to explore the impacts of in situ urbanization on farmers from the perspective of housing, mobility and employment. The research results show that after the in situ urbanization, the possibility of farmers moving into non-self-built high-rising buildings increases, while the possibility of farmers leaving the county for employment decreases. Besides, the employment structure in the county where in situ urbanization takes place has shifted from primary industry to secondary and tertiary industry. Moreover, this paper also discusses the spillover effects of in situ urbanization on other residents in the county. Our study shows that in situ urbanization can improve residents’ well-being and Citation: Li, Y.; Xie, Z.; Li, B.; Mohiuddin, M. The Impacts of In Situ offers sustainable land-people integrated urbanization. Urbanization on Housing, Mobility and Employment of Local Residents Keywords: housing; mobility; employment; spillover effects; in situ urbanization in China. Sustainability 2022, 14, 9058. https://doi.org/10.3390/su14159058 Academic Editors: Enrico Ivaldi, 1. Introduction Leonardo Salvatore Alaimo and Alfonso Piscitelli Urbanization in China varies from developed countries [1]. Classic urbanization theories usually present the large-scale migration of rural residents to urban areas to seek Received: 26 May 2022 job opportunities in the non-agricultural sector and become urban residents gradually [2–4]. Accepted: 20 July 2022 However, urbanization in China has not only involved the above process, but also in Published: 24 July 2022 situ urbanization, which means the local rural residents gain urban residency without Publisher’s Note: MDPI stays neutral experiencing geographical relocation (i.e., rural areas become urbanized). with regard to jurisdictional claims in The positive impact of urbanization on economic development and people’s welfare published maps and institutional afﬁl- has been shown in many developed economies [5–7]. However, China’s in situ urbanization iations. is not the same as the urbanization of the developed countries. It is usually initiated by the government to implement “urbanization of land” in China, which in turn drives “urbanization of people”. “Urbanization of land” means the land is planned to use for urban development while “urbanization of people” means that farmers are gradually introduced Copyright: © 2022 by the authors. to living the same lifestyles as urban residents. There are some arguments about the idea Licensee MDPI, Basel, Switzerland. that “urbanization of people lags behind urbanization of land” in China, which shows This article is an open access article concerns about Chinese farmers’ well-being after in situ urbanization. Existing research distributed under the terms and places emphasis on empirical case studies and draws different conclusions about the conditions of the Creative Commons impacts of in situ urbanization on Chinese farmers’ well-being by using different samples. Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ So it is worth studying the well-being of Chinese residents after in situ urbanization by 4.0/). using a convincing method. Sustainability 2022, 14, 9058. https://doi.org/10.3390/su14159058 https://www.mdpi.com/journal/sustainability Sustainability 2022, 14, 9058 2 of 21 To explore the changes of well-being after in situ urbanization, we study the impacts of in situ urbanization on Chinese residents from the perspective of housing, mobility and employment. The premise of studying the impacts of in situ urbanization on farmers is how to measure in situ urbanization. According to Gan et al., the transfer of rural-urban division codes is used to measure whether a community has undergone in situ urbanization [8]. On this basis, combined with the data of the two national consensuses in 2010 and 2015, we could identify whether the individual in different communities is affected by in situ urbanization. We found that in situ urbanization will improve the housing conditions of farmers and make farmers less likely to go out for employment and more likely to stay in the county to engage in the tertiary industry. In addition, in situ urbanization will also have an impact on farmers in other non-rural-to-urban communities in the county. In situ urbanization increases the probability of other farmers leaving the county to work, attracts labors ﬂowing into urban communities in the county, and shifts the employment structure in the county from primary industry to secondary and tertiary industry, with tertiary industry as the main transfer direction. The main contributions of this study are as follows. Firstly, this paper uses an appro- priate measure of in situ urbanization, that is, the transfer of rural-urban division codes. Secondly, unlike the existing analysis that place emphasis on empirical case study and draw various conclusions due to different research samples, this paper uses national census data and the conclusions are more informative and universal. Finally, this paper not only studies the impacts of in situ urbanization on rural-to-urban residents, but also studies the spillover effects of in situ urbanization on other residents in the county, thus we could more comprehensively assess the impacts of in situ urbanization. 2. Literature Review Existing research have shown that urbanization promotes economic development in many ways. Bertinelli & Black ﬁnd that urbanization can promote human capital accumu- lation, which is the engine of growth [9]. Iyer ﬁnds that the impacts of urbanization on the productivity of manufacturing industries in India vary both within and across indus- tries [10]. Rosero et al. speciﬁes that the agglomeration externalities during urbanization play an important role in the local productivity of sectors [11]. Tran points out that the productivity of informal household businesses in Vietnam beneﬁts from urbanization [12]. With its unique characteristics, China’s urbanization is still worth studying. At this stage, China’s urbanization mainly includes two ways. The first is the urbanization of migrant workers, that is, farmers move to distant cities to obtain jobs and then become city residents. This is usually accompanied by the process of “urbanization of people” and is similar to the urbanization format of developed countries. The relevant research mainly focuses on the reform of the household registration system (hukou) [13–15], the provision of equal public services [16–19], and the reform of rural land systems [20–22]. There are also some studies focusing on the subjective well-being of farmers after completing the rural-urban migration [23–26]. The second is the in situ urbanization, that is, the local rural residents change to urban residents without experiencing geographical relocation [27–29]. In the early days of reform and opening up, in situ urbanization was mainly driven by the development of township and village enterprises (TVEs) and foreign investment in rural areas [28,30]. But since the late 1990s, with the slowdown of development of TVEs, in situ urbanization has been increasingly dominated by the government, because expropriating rural land at a low price and selling it at a high price has become a chief means for local governments to obtain revenue [31,32]. After expropriating rural land, farmers usually passively become urban residents. Therefore, in situ urbanization is often initiated by “urbanization of land” in China, which in turn drives “urbanization of people”. However, there are some worries about “urbanization of people” lagging behind “urbanization of land” during the in situ urbanization, so scholars hold different attitudes towards in situ urbanization. Sustainability 2022, 14, 9058 3 of 21 Some studies show the negative effects of in situ urbanization. Firstly, due to the dual structure of urban and rural areas in China, rural areas and farmers could not be well incorporated into the urban social, administrative and welfare system in China after in situ urbanization [33]. Even when farmers obtain urban status after in situ urbanization, they may not be able to receive the same beneﬁts as urban residents which leads to signiﬁcant social, economic, and environmental challenges for farmers after in situ urbanization. Many studies show adverse effects of in situ urbanization on farmers in terms of identity, mental health, and satisfaction [34]. Chen et al. ﬁnd that in situ urbanized rural residents have fewer years of education and less skilled employment and compared with the both urban hukou residents and rural-to-urban migrants [32]. Liu et al. point out that residents after in situ urbanization face decreasing income and increasing living cost, and in situ urbanization may lead environmental challenge [35]. Xie & Chen ﬁnd that in situ urbanized rural residents have a low level of urban identity due to the challenges in the course of integration into urban life in terms of income, occupation, social insurance, and housing [27]. Other studies show the positive effects of in situ urbanization, especially from the perspective of housing, mobility and employment. The most direct impacts of in situ urbanization on farmers are at the housing aspect [4,36]. In situ urbanization usually forces farmers to move to urban or concentrated areas to live, and their living conditions are signiﬁcantly improved. At the same time, farmers have more opportunities to engage in non-agricultural work in hometown, and their income also have increased, which in result to improve living conditions in education, medical care, health and so on [29,37]. Therefore, farmers are more likely to stay in their hometown and are less likely to become migrant workers [29]. This is in consistent with urbanization process in western countries. Sato and Zenou ﬁnd that workers in regions with a lower urbanization rate experience less social interaction and a higher unemployment rate [38]. Evers points out that one of the effects of urbanization is a high rate of both vertical and horizontal mobility [39]. According to the above literatures, we could ﬁnd that the negative effects of in situ urbanization focus on the farmers’ subjective perceptions or relative situations compared with urban residents. However, research holds almost the same opinion that in situ urbanization leads to better housing, mobility and employment for residents compared with their past. Therefore, we propose the following hypotheses. Hypothesis 1 (H1). In situ urbanization will improve farmers’ housing conditions. Hypothesis 2 (H2). In situ urbanization will make farmers more likely to stay in their native county. Hypothesis 3 (H3). In situ urbanization will enhance possibility of farmers to engage in non- agricultural work. 3. Data and Methods 3.1. Data Source This paper uses the transfer of rural-urban division codes to measure in situ urban- ization, which are collected from the website of the National Bureau of Statistics of China (http://www.stats.gov.cn/tjsj/tjbz/tjyqhdmhcxhfdm (accessed on 7 December 2020)). As what we have mentioned in the literature review, most of the research about in situ urban- ization are case studies, so they seldom use any indicator to identify whether the area takes place in situ urbanization. But there are many studies using the proportion of new urban construction land to measure urbanization. It is not an accurate indicator to measure in situ urbanization. On the one hand, urbanization is different form in situ urbanization. Urbanization includes both classic urbanization (rural residents move to urban areas to seek job opportunities in the non-agricultural sector and become urban residents gradually) and in situ urbanization (the local rural residents gain urban residency without experiencing geographical relocation due to the planned schedule by government). It is not proper to measure in situ urbanization by using the method of measuring urbanization. On the Sustainability 2022, 14, 9058 4 of 21 other hand, there are some arguments to measure the in situ urbanization by using the proportion of new urban construction land. The new urban construction land area will include areas that were originally wasteland and are now newly developed, so it is not an accurate indicator for measuring the level of urbanization. The transfer of rural-urban division codes will not have the above problems. The rural-urban division codes are accompanied by the community codes, which represent the residential area. There is no worry that we will include the wasteland as an urbanization area. Moreover, Gan et al. found that areas with division code changing from rural to urban, are far less likely urbanized than those in urban areas, which is consistent with the characteristics of in situ urbanization [8]. So, we use the change in the rural-urban division code to determine whether a community has undergone in situ urbanization. Since 2009, the National Bureau of Statistics of China has published the 15 digit statistical administrative and rural-urban division codes every year. The ﬁrst 12 digits are the community codes, representing the ﬁve administrative levels in China: the ﬁrst two are province-level (including provinces, autonomous regions and centrally administered municipalities), the third and fourth are city-level (including prefectures and prefecture- level cities), the ﬁfth and sixth are county-level (including districts, counties and county- level cities), the seventh, eighth and ninth are township-level (including towns, townships and sub-districts), and the last three are community ID. The ﬁnal 3 digits are the rural- urban division codes. In this 3 digit code, the ﬁrst digit represents the rural-urban division (1 means urban community while 2 means rural community) and the last two represent a detailed sub-classiﬁcation, which could be found in Table 1. Table 1. Number of Different Types of Communities in 2009–2020. 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Downtown area 58,264 54,635 56,837 58,218 57,742 58,371 60,009 64,238 66,352 67,940 69,412 66,737 (111) Urban-rural 19,926 25,859 25,734 26,047 25,523 25,292 25,950 27,043 27,695 28,023 28,421 28,042 fringe area (112) Town center (121) 46,416 36,554 40,102 42,024 40,800 44,222 45,126 46,644 49,416 49,920 49,852 48,767 Town-township 48,374 62,801 65,450 67,219 64,401 66,406 65,068 62,918 63,912 60,603 58,507 54,852 fringe area (122) Special zone (123) 6522 6582 7217 7357 5445 1340 1377 5789 5935 6007 6065 5426 Urban total 179,502 186,431 195,340 200,865 193,911 195,631 197,530 206,632 213,310 212,493 212,257 203,824 Rural center (210) 23,198 22,429 21,590 21,346 17,803 19,391 18,385 16,750 16,129 15,454 14,583 12,445 Village(220) 496,507 487,206 477,556 472,459 439,290 455,457 451,604 443,273 443,144 436,983 429,941 399,431 Rural total 519,705 509,635 499,146 493,805 457,093 474,848 469,989 460,023 459,273 452,437 444,524 411,876 Total 699,207 696,066 694,486 694,670 651,004 670,479 667,519 666,655 672,583 664,930 656,781 615,700 Notes: The 3 digit number in brackets in the ﬁrst column represents the urban and rural division codes. This paper uses the change in the rural-urban division code to determine whether a community has undergone in situ urbanization. It is worth noting that if the administrative level or its superior authority changes, a region’s community code will also change. For example, in 2016, Chongming County in Shanghai was changed to Chongming District, and the ﬁrst 6 digits of its community code were changed from 310230 to 310151. This paper only considers those communities whose 12 digit community code has not changed between 2009 and 2020. This is different from Gan et al., who track all community ID even its administrative and upper-level administrative units have changed from 2009 to 2017 [8]. Unlike them, we exclude those communities whose administrative or upper-level adminis- trative units have changed, mainly because the change in (upper-level) administrative level Sustainability 2022, 14, 9058 5 of 21 is also a shock to the region, and taking this into account is not conducive to identifying the effect of in situ urbanization. We collect all community codes that have appeared from 2009 to 2020. The number of various types of communities is shown in Table 1. There are 374,789 communities that have survived from 2009 to 2020, that is, the 12 digit community code has not changed during this period. Excluding the communities that have changed from urban to rural and communities in special zones, the sample size we use is 362,899. We recognize those communities transferred from rural to urban are areas that take place in in situ urbanization. There were 16,082 in situ urbanization communities from 2009 to 2020, of which 10,199 occurred between 2011 and 2015. We also use data from the 2010 and 2015 national consensuses to study well-being. The Chinese national census takes place every 10 years, among which a one-percent national sample census is conducted every 5 years. In this paper, we call both as national census. These data are all sampled and contain 2,105,260 and 2,003,563 samples, respectively. The data collect all kinds of information about the surveyed individual, such as housing, work, location, family and so on; and especially, each person has a speciﬁc address code, so it can clearly identify the community where the individual is located. Therefore, it is possible to inspect whether an individual has undergone in situ urbanization by combining the community code. In addition, we also use county-level variables in the process of PSM later: the number of students in secondary and primary schools, and the number of beds in medical and health institutions to control public service; the per capita GDP, the balance of savings deposits of urban and rural residents, the total investment in ﬁxed assets, and the general budget revenue and expenditure of local ﬁnance to control economic development; the area of the administrative district, the number of registered population at the end of year, and the number of townships to control endowment. The county-level data came from the Chinese Research Data Service Platform (CNRDS), and nominal variables involved are adjusted using the GDP deﬂator. 3.2. Variable Selection Based on the national census data, this paper mainly analyzes the impacts of in situ urbanization on residents’ well-being from three aspects: housing, mobility and employment. The first focuses on the household while the latter two focus on the individual labor. In the 2010 and 2015 national census questionnaires, the common questions about the housing conditions include: building area, number of rooms, number of ﬂoors, whether there is a private kitchen and a private toilet, and the source of housing. Based on this, we sort out three types of characteristics to reﬂect housing conditions. The ﬁrst is the physical properties of housing, including building area, number of rooms, and whether it is a high- rise building. The second is the living conditions of the housing. The dummy variables, whether there is a private kitchen or a private toilet, will be used as indicators. The third is the source of housing. In the census questionnaires, housing sources are divided into four categories: purchase, self-build, lease and others. In situ urbanization often induces farmers to move out of self-built housing to commercial building, so we choose whether live in purchased or self-built housing as dependent variables. Regarding the control variables in housing condition analysis, this paper considers the family size due to the availability of data. When discussing individuals’ employment and mobility, we ﬁrstly limit the sample range from 16 to 60 years old, which is usually seen as the working-age in China. At the same time, we excluded unemployed individuals whose reasons are as follows: studying at school, incapacity for work, retirement, doing household chores, and others. When examin- ing in situ urbanization’s impacts on mobility, the dependent variables include whether the individual stays in the household registered (hukou) county, whether the individual left household registered county more than half a year, and whether the individual comes from other communities. When examining in situ urbanization’s impacts on employment, the Sustainability 2022, 14, 9058 6 of 21 sample includes all the population staying in the county at the time of ﬁlling in the national census questionnaires, that is, the population who stayed in the county with local hukou and the migrant population who ﬂowed into the county without local hukou. We inspect the change in employment structure in the county after in situ urbanization. Individual control variables included gender, age, ethnicity, education, marital status, literacy, and family size. Data description of the main variables is shown in Table 2. Table 2. The deﬁnition and data description of variables. Variables Deﬁnition Observation Mean S.D. Minimum Maximum Area House area 354,730 103.011 72.384 1 360 Rooms The number of rooms 354,730 3.239 2.205 1 12 1 if family live in the building with more High-rise 348,069 0.436 0.496 0 1 than 3 ﬂoors, 0 otherwise 1 if the house has private kitchen, Kitchen 348,069 0.865 0.342 0 1 0 otherwise Toilet 1 if the house has private toilet, 0 otherwise 348,069 0.530 0.499 0 1 Self-built 1 if the family built the house, 0 otherwise 348,069 0.469 0.499 0 1 1 if the family bought the house, Bought 348,069 0.156 0.363 0 1 0 otherwise 1 if individual stays in the household In-county 682,119 0.904 0.294 0 1 registered county, 0 otherwise 1 if the individual left household registered Migrant 682,119 0.054 0.226 0 1 county more than half a year, 0 otherwise 1 if individual comes from other Inﬂow 682,119 0.303 0.459 0 1 communities in the county, 0 otherwise Unemployment 1 if individual loses job, 0 otherwise 682,119 0.050 0.217 0 1 1 if individual works in primary industry, Ind1 648,168 0.287 0.452 0 1 0 otherwise 1 if individual works in secondary industry, Ind2 648,168 0.290 0.454 0 1 0 otherwise 1 if individual works in tertiary industry, Ind3 648,168 0.403 0.490 0 1 0 otherwise Male 1 if individual is male, 0 otherwise 682,119 0.562 0.496 0 1 Minority 1 if individual is a minority, 0 otherwise 682,119 0.048 0.214 0 1 1 if individual holds junior college degrees Education 682,119 0.174 0.379 0 1 or above, 0 otherwise Married 1 if individual is married, 0 otherwise 682,119 0.762 0.426 0 1 Literacy 1 if individual is literate, 0 otherwise 682,119 0.989 0.102 0 1 Age Individual’s age 682,119 37.251 10.997 16 60 Familysize Number of family members 682,119 4.047 1.790 1 10 3.3. Methods Our research procedure consists of two parts. First, we use Propensity Score Matching (PSM) to construct data that could be used to compare among counties. Second, we apply Differences-in-Difference (DID) to estimate the impacts of in situ urbanization. 3.3.1. Propensity Score Matching (PSM) To protect individual privacy, the census data does not provide a code that can uniquely identify each person, so we could not match them year by year. Instead, we can only use pooled cross-sectional data for research. Additionally, the rural-urban division code has been published and implemented since 2009, so only the 2010 and 2015 census data can Sustainability 2022, 14, 9058 7 of 21 be used for empirical analysis, and parallel trend tests also cannot be carried out with two-period data. In order to make up for the above deﬁciencies, this paper refers to Xu et al. and Wan & Li, applying the Propensity Score Matching (PSM) and Differences- in-Difference (DID) method to evaluate [22,40]. Note that counties that implement in situ urbanization are selective in our study. For example, counties with higher economic development level are more likely to implement in situ urbanization because of stronger public service affordability. Hence, we match data based on the county-level characteristics when using PSM. Based on the above reasons, this paper, taking 2010 as the base period, matches the counties with in situ urbanization (treatment group) and without (control group) during 2011–2015 by using one-to-one nearest-neighbor logistic model. We also use other matching methods and obtain robust results. The matching results and balancing test are shown in Table 3. We take logarithms on all variables in Table 3 when we run PSM model. As can be seen in Table 3, the p value of each variable after matching is greater than 0.1, indicating that there is no signiﬁcant difference between the variables in the treatment group and the control group after matching; and the standardized bias is signiﬁcantly reduced, much less than the threshold value (20%). This indicates that we pass the balancing test, that is, the treatment group and the control group have very close characteristics in all aspects after matching. Finally, the treatment group of this paper consists of 139,245 households and 298,387 individuals in 253 counties, and the control group consists of 205,485 households and 383,732 individuals in 358 counties, as shown in Table 4. Table 3. PSM results and balancing test. Mean t Test Unmatched % Reduct V(T)/ Variable % Bias Matched |Bias| V(C) Treated Control t p > |t| Number of students in U 10.732 10.777 5.100 0.790 0.432 0.62 * 81.600 secondary schools M 10.732 10.740 0.900 0.150 0.879 0.92 Number of students in U 10.743 10.783 4.100 0.640 0.523 0.59 * 56.000 primary schools M 10.742 10.760 1.800 0.300 0.762 0.92 U 7.774 7.929 14.800 2.310 0.021 0.70 * Number of beds in medical and 98.900 M 7.772 7.774 0.200 0.030 0.980 0.93 health institutions U 5.115 5.224 7.800 1.210 0.225 0.75 * Total investment in ﬁxed assets 69.500 M 5.114 5.148 2.400 0.370 0.713 0.91 U 12.685 12.988 26.800 4.220 0.000 0.85 General budget expenditure of 99.600 local ﬁnance M 12.686 12.687 0.100 0.010 0.989 0.97 U 11.959 12.299 19.600 3.070 0.002 0.80 * General budget revenue of 86.800 local ﬁnance M 11.959 11.914 2.600 0.400 0.690 0.98 U 8.136 9.025 52.100 8.170 0.000 0.80 * Area of the administrative district 97.800 M 8.137 8.118 1.100 0.170 0.865 0.87 Number of registered population U 4.383 4.673 21.700 3.360 0.001 0.54 * 70.200 at the end of the year M 4.383 4.296 6.500 1.120 0.262 0.95 U 3.035 3.177 24.600 3.860 0.000 0.84 Number of townships 98.600 M 3.036 3.038 0.300 0.050 0.959 0.96 U 14.272 14.518 16.600 2.590 0.010 0.71 * Balance of savings deposits of 99.200 urban and rural residents M 14.271 14.269 0.100 0.020 0.983 0.92 U 1.814 2.130 34.500 5.420 0.000 0.88 Per capita GDP 97.600 M 1.815 1.822 0.800 0.120 0.902 0.85 Notes: * indicates p < 0.1. Sustainability 2022, 14, 9058 8 of 21 Table 4. Description of treatment and control group after PSM. County A: 253 B: 358 A1 A2 A3 B1 B2 Household 86,685 58,064 4496 153,110 52,375 Total: 139,245 Total: 205,485 A1 A2 A3 B1 B2 Individual 159,188 129,242 9957 261,292 122,440 Total: 298,387 Total: 383,732 In addition to studying the direct impacts of in situ urbanization on residents who live in the communities transferred from rural to non-agricultural, we also study the spillover effects, that is, the impacts of in situ urbanization on other residents who live in the urban communities or rural communities that has not transferred. For the convenience of explanation, the counties containing the rural-urban conversion community are referred to as A, and A is divided into urban community A1, rural community A2 without in situ urbanization, and A3, the original rural community that has been transferred into urban community. The counties that do not contain rural-to-urban communities are referred to as B, and B includes urban communities B1 and rural communities B2. Table 4 shows the sample size of each group. The direct and spillover effects of in situ urbanization are explored by using different combinations of treatment and control group. 3.3.2. Differences-in-Difference (DID) After the PSM, we use the DID model to identify the impacts of in situ urbanization on family housing, individual mobility and employment. The DID model is constructed as follows: Y = a + bD + gT + l(D T ) + dX + j + # (1) it it it it it it it In Formula (1), the subscripts (c, i and t) represent counties, families or individuals, and years, respectively. Y is the outcome variable affected by in situ urbanization, including family housing, individual mobility and employment. Due to the existence of continuous variables in the outcome variables, we use linear probability models for convenience. D represents the binary variable indicating whether the individual or family has undergone in situ urbanization. T is the binary variable of whether in situ urbanization has occurred at the current. The coefﬁcient l before D T is what we pay attention to, which shows the exactly change after in situ urbanization. According to our hypotheses, housing, mobility and employment conditions will be improved after in situ urbanization, so we expect those regression model with positive independent variable would result l > 0, vice versa. X represents the control variable. When we analyze the impacts of in situ urbanization on housing, X consists of family size due to the data availability. When we analyze the impacts of in situ urbanization on individual mobility and employment, X consists of gender, age, ethnicity, education, marital status, literacy, and family size. The deﬁnition and data description of X could be seen in Table 2. The county ﬁxed effects are controlled by j, while time ﬁxed effects have been controlled by T because there are only two-year cross-sectional data. It should be noted that the characteristics of cross-sectional data lead to the inability to precisely control the individual ﬁxed effects in the model, and we can only control the ﬁxed effects at a higher level. We choose to control the county ﬁxed effects because this paper focuses on county. And we adopt robust standard errors by clustering to individual or family level to solve the problem of heteroscedasticity in all regressions. Sustainability 2022, 14, 9058 9 of 21 4. Results 4.1. Impacts of In Situ Urbanization on Housing This section explores the impacts of in situ urbanization on household housing con- ditions by combining different treatment and control groups, and full regression results are shown in Appendix A, Tables A1–A6. For convenience to compare, we organize the results in Table 5. In the table, the characters in front of “&” represent the treatment group, while in the back represents the control group. For instance, A3&A1A2, means that A3 is the treatment group and A1 and A2 are control group in Formula (1). The following “&” in the table has the same meaning. Table 5. Impacts of In situ Urbanization on Housing. (1) (2) (3) (4) (5) (6) A3&A1A2 A3&A2 A3&B2 A2&B2 A1& B1 A&B 3.032 0.095 0.085 1.812 * 4.913 *** 3.654 *** Area (2.693) (2.729) (2.943) (0.933) (0.669) (0.542) 0.064 0.179 ** 0.215 *** 0.073 ** 0.159 *** 0.123 *** Room number (0.072) (0.074) (0.078) (0.030) (0.020) (0.017) 0.022 ** 0.055 *** 0.029 *** 0.002 0.008 ** 0.002 High-rise (0.011) (0.009) (0.010) (0.002) (0.004) (0.003) 0.049 *** 0.042 *** 0.049 *** 0.007 0.011 *** 0.009 *** Kitchen (0.011) (0.012) (0.012) (0.005) (0.002) (0.002) 0.095 *** 0.079 *** 0.086 *** 0.009* 0.008 ** 0.012 *** Toilet (0.014) (0.014) (0.015) (0.005) (0.004) (0.003) 0.047 *** 0.053 *** 0.040 *** 0.003 0.012 *** 0.011 *** Self-built (0.012) (0.011) (0.012) (0.003) (0.003) (0.003) 0.005 0.013 ** 0.005 0.001 0.011 *** 0.006 ** Bought (0.006) (0.006) (0.006) (0.001) (0.004) (0.003) Notes: ***, **, *, respectively, indicate p < 0.01, p < 0.05, p < 0.1. The standard errors are given in parentheses. Control variables include family size. The results of column (1) in Table 5 show that, compared with A1 and A2, households in A3 are more likely to move into high-rise buildings after in situ urbanization, and those buildings are less likely built by themselves. In addition, the possibility of households with private kitchens and toilets increases. Changing the control group to A2, we will ﬁnd that the number of rooms occupied by A3 families has decreased, but the probability of A3 staying in purchased high-rise buildings with better facilities has increased signiﬁcantly. This is inconsistent with existing studies which indicate that in situ urbanization will reduce the living area but improve living conditions, and veriﬁes out Hypothesis 1. This also indirectly shows that our method of using the transfer of rural-urban division codes to measure in situ urbanization is reasonable. Then we consider spillover effects. The ﬁrst is to consider whether other rural commu- nities in the county will be affected by in situ urbanization. The control group was replaced by B2, and the regression results are shown in column (3). We can ﬁnd that column (3) is basically the same as that of column (2) in coefﬁcient sign and signiﬁcance, but the coefﬁ- cients vary, indicating that A2 may have been affected by in situ urbanization. Therefore, we directly compare A2 with B2, and the results are shown in column (4). The housing area and the number of rooms of A2 families have decreased, but there is no signiﬁcant change in housing ﬂoors and housing sources, and their living conditions of housing is hardly improved. This shows that in situ urbanization may compress the living area of other rural communities in the county, but the housing sources and living conditions of other rural Sustainability 2022, 14, 9058 10 of 21 communities will not be signiﬁcantly affected. The second is to consider spillover effects on urban communities. The results of column (5) show that the housing area and the number of rooms of the A1 families have decreased signiﬁcantly, but the possibility of living in purchased high-rise buildings with better living facilities has increased signiﬁcantly. The improvement in living conditions and housing source of A1 is not surprising, but the decline in housing area and number of rooms is somewhat odd. We think the reason behind that is the mobility of the labor. The following analysis of mobility shows that A1 will have more inﬂows after in situ urbanization, so the demand for housing in A1 will increase, accompanied with a decline in the housing area and the number of rooms. Finally, we study the spillover effects on the entire county. Column (6) shows that after conducting in situ urbanization, the non-self-built housing in counties and the living conditions will improve comparing to B, but the area and number of rooms will decrease. 4.2. Impacts of In Situ Urbanization on Mobility This section studies the impacts of in situ urbanization on the mobility of residents, full regression results are shown in Appendix A, Tables A7 and A8. It can be found that an individual who is female, unmarried, younger, and well-educated is more likely to leave the hometown and become a migrant worker. We rearrange the results in Table 6. The results in column (1) show that after in situ urbanization, the probability of A3 farmers staying in the county increases signiﬁcantly, while the probability of becoming an outﬂow population decreases signiﬁcantly. Hypoth- esis 2 is veriﬁed. And similar results are shown in column (2). We need to explain why there are signiﬁcant differences between A2 and A3 even if they are both farmers with similar qualities. Three reasons lie behind; ﬁrst is the wealth effect. In situ urbanization is usually accompanied by land expropriation, so A3 residents may reduce motivation to go out to look for jobs after receiving monetary or property compensation. The second is the information advantage. Compared with other labors in the county, A3 residents have more information on the development of counties (such as information about building factories or commercial districts on their occupied land), and they are more likely to return to or stay in their hometowns for employment. The third is the protection for land-losing farmers by local governments. For the sake of farmers’ interests and social stability, local governments may require factories or commercial companies occupying A3 land to hire more A3 residents to solve the employment problem of landless farmers. Table 6. Impacts of In situ Urbanization on Mobility. (1) (2) (3) (4) (5) (6) A3&A1A2 A3&A2 A3&B2 A2&B2 A1& B1 A&B 0.035 *** 0.054 *** 0.036 *** 0.027 *** 0.003 0.008 *** In-county (0.009) (0.009) (0.010) (0.004) (0.002) (0.002) 0.028 *** 0.037 *** 0.033 *** 0.011 *** 0.002 0.003 Migrant (0.007) (0.007) (0.008) (0.004) (0.002) (0.002) 0.014 0.005 0.001 0.001 0.023 *** 0.022 *** Inﬂow (0.013) (0.013) (0.013) (0.003) (0.005) (0.003) Notes: *** indicates p < 0.01. The standard errors are given in parentheses. Control variables include gender, age, ethnicity, education, marital status, literacy, and family size. Next the control group of A3 was replaced by B2, and the same conclusion was obtained in column (2) and column (3), but the absolute value of the coefﬁcient in column (2) was larger than that in column (3), so it is reasonable to study spillover effects of in situ urbanization. By comparing A2 and B2 directly, column (4) shows that the probability of A2 residents staying in the county is signiﬁcantly lower than that of B2, and the probability of going out to become migrant workers is signiﬁcantly higher. A2 and A3 are both farmers Sustainability 2022, 14, 9058 11 of 21 in essence, which means they may seek similar work in the county. However, there are limited job opportunities for A3 and A2 in the county. When A3 labor increases, both A2 and A3 would face ﬁercer competition in the job market. And from the reasons we have mentioned above, A3 residents would be more likely to stay in their hometown county with land expropriation compensation even if they are out of work, so A2 farmers would be crowded out and are more likely to become migrant workers compared with B2. Besides spillover effects on the mobility of other agricultural communities, in situ urbanization can also affect the mobility of residents in non-agricultural communities. The results in column (5) show that after the in situ urbanization, the probability of A1 citizens leaving the county did not change signiﬁcantly, but the population ﬂowing into A1 from other communities increased signiﬁcantly. Moreover, the results in column (6) show that the total population remaining in the county A decreases, and this is resulted from the population outﬂow in A2 exceeds the population return in A3. 4.3. Impacts of In Situ Urbanization on Employment This section studies the impacts of in situ urbanization on the employment of residents in the county. The sample in this section consists of all the labor, including local residents and migrants in the county. Appendix A, Tables A9–A11 show the regression results. We can ﬁnd that ethnic minorities are more engaged in primary production because of differences in language and culture, and younger men are more likely to work in non-agricultural industries, especially in the secondary industry compared with women. Moreover, well- educated laborers appeal more to jobs in the tertiary industry. Table 7 shows the abbreviated regression results. Column (1) in Table 7 shows that compared with other labors in the county, the probability of A3 residents engaging in the primary industry has decreased signiﬁcantly, while the probability of engaging in the tertiary industry has increased signiﬁcantly. Hypothesis 3 is veriﬁed. The results of column (2) show that the A2 and A3 populations who stay in the county have no signiﬁcant difference in their working industries, which is in line with the above point of view, that is, A2 and A3 are farmers with similar qualities and compete for employment. When the control group is replaced by B2, A3 labors are less likely to engage in the primary industry, while are more likely to engage in the tertiary industry. Table 7. Impacts of in situ Urbanization on Employment. (1) (2) (3) (4) (5) (6) A3&A1A2 A3&A2 A3&B2 A2&B2 A1& B1 A&B 0.008 0.001 0.004 0.004 * 0.004 * 0.001 Unemployment (0.006) (0.006) (0.007) (0.002) (0.002) (0.002) 0.051 *** 0.013 0.031 ** 0.050 *** 0.025 *** 0.053 *** Ind1 (0.013) (0.014) (0.015) (0.006) (0.003) (0.003) 0.014 0.015 0.022 0.003 0.002 0.007 ** Ind2 (0.014) (0.014) (0.015) (0.005) (0.004) (0.003) 0.030 ** 0.011 0.041 *** 0.038 *** 0.010 ** 0.027 *** Ind3 (0.013) (0.013) (0.014) (0.005) (0.005) (0.004) Notes: ***, **, *, respectively, indicate p < 0.01, p < 0.05, p < 0.1. The standard errors are given in parentheses. Control variables include gender, age, ethnicity, education, marital status, literacy, and family size. Then we examine the spillover effects. Taking A2 as the treatment group and B2 as the control group, the results in column (4) show that the unemployment probability of A2 farmers has increased compared with that of B2 farmers. This comes from the crowding out effect which has not yet been fully “digested”, that is, the unemployed A2 workers do not have enough time to leave the county for work. Moreover, the probability of A2 engaging in the primary industry has relatively decreased, while the probability of engaging in the tertiary industry has relatively increased. For non-agricultural communities, the unemployment rate of A1 will drop slightly compared to B1, and the probability of Sustainability 2022, 14, 9058 12 of 21 engaging in the tertiary industry will increase signiﬁcantly. This implicates the development of non-agricultural industries in the county, especially in A1. On the whole, column (6) shows that the employment structure of the county has shifted from the primary industry to the secondary and tertiary industries. This paper argues that such a shift consists of the following two parts: one is the increase in the population ﬂowing into the A1 community, and the other is the mobility of A2 and A3 farmers. Speciﬁcally, the results of column (5) show that there is no signiﬁcant difference between A1 and B1 in the probability to engage in the secondary industry, but the results in the previous part show that labor will ﬂow into A1. Although the probability remains the same, the total of labor in A1 increases, so the number of people engaged in the secondary industry will also increase after the in situ urbanization. It is obvious that the tertiary industry attracts more laborers because the results of A1&B1 show that the coefﬁcient of Ind3 is signiﬁcant while Ind2 is not. In addition to that, the shift also consists of internal change among rural labors, that is, due to A3 labors are more likely to stay in the county so the secondary and tertiary industry in the county will attract more A3 and less A2 relatively. 5. Conclusions and Implications In situ urbanization can be considered as beneﬁcial to the local community, and thus is an example of sustainable urbanization in China. Existing studies about in situ urbanization place emphasis on empirical cases and ignore the spillover effects on other residents in the county. This paper ﬁrst ﬁnds a suitable measure: the transfer of rural-urban division codes to identify in situ urbanization. On this basis, combined with the 2010 and 2015 census data, we study the impacts of in situ urbanization on farmers’ well-being from the perspective of housing, mobility and employment. Moreover, we explore the spillover effects of in situ urbanization on residents of other rural and urban communities by combining different treatment and control groups. The main conclusions of this paper are as follows: First, farmers are more likely to move into non-self-built high-rise buildings after in situ urbanization, and their living conditions will be improved, while their housing area and number of rooms will decrease. Furthermore, similar results could be observed for the other rural community without in situ urbanization and urban community in the county, with advancing living conditions and decreasing living areas. Second, the probability of farmers becoming migrant workers after in situ urbanization decreases, while the probability of other farmers in the county increases. Due to the wealth effect, information advantages and the protection by local governments, farmers after in situ urbanization would become more “sticky” to their hometowns, and farmers in other communities with similar qualities are more likely to go out for work due to ﬁercer job competition. Moreover, in situ urbanization will attract laborers inﬂowing non-agricultural communities in the county due to the non-agricultural industrial development. Third, in situ urbanization will promote the shift of employment structure in the county from the primary to the secondary and tertiary industries, of which the tertiary industry is the main direction of the shift. Such a shift consists of two major parts. One comes from the urban community with more people inﬂowing and being engaged in secondary and tertiary industries. The other comes from the internal change between farmers with in situ urbanization and the other farmers without in the county. Overall, our study shows that in situ urbanization can improve residents’ well-being from the perspective of housing, mobility and employment. Concluded from this, in situ urbanization in China is an integrated development of land and human urbanization. Although in situ urbanization in China is always initiated by government, which is different from classic urbanization processes, it leads to similar results; i.e., urbanization provides more non-agricultural job opportunities for residents, attracts rural people ﬂowing, and promoting structural transformation [41–44]. Our research has two theoretical contributions. First, we use an appropriate indicator to measure in situ urbanization. Second, we not only study the effects of in situ urbanization on farmers, but also spillover effects of in situ urbanization on other residents in the county. Sustainability 2022, 14, 9058 13 of 21 As for the practical implication, we suggest that moderate promotion for urbaniza- tion by government is beneﬁcial for developing countries. Existing studies show that urbanization in developing countries could shift labor from agricultural sectors to non- agricultural sectors, promoting non-agricultural development and reducing poverty [42,43]. But urbanization needs some conditions to trigger, which may be difﬁcult or need long time to meet for developing countries. Therefore, some promotions should be made by governments, such as the in situ urbanization conducted by the government in China. Of course, we should pay attention to the phenomenon that “urbanization of people lags behind urbanization of land”, that is, the welfare of people was ignored during the planned urbanization. Establishing a sound and fair social security system, including medical care, education, pension and so on, is the responsibility and obligation of the government during urbanization. Author Contributions: Conceptualization, Y.L. and Z.X.; methodology, Y.L.; software, Y.L.; validation, Y.L., and Z.X.; formal analysis, Y.L.; investigation, Z.X.; resources, B.L.; data curation, Z.X. and M.M. writing—original draft preparation, Y.L.; writing—review and editing, B.L., Z.X. and M.M.; visualization, B.L. and M.M.; supervision, B.L. and M.M.; project administration, Z.X.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by National Social Science Foundation of China [grant number 18BJY061]. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The rural-urban division codes could be obtained from http://www. stats.gov.cn/tjsj/tjbz/tjyqhdmhcxhfdm. The national census data is freely accessible to registered users at https://microdata.stats.gov.cn. Conﬂicts of Interest: The authors declare no conﬂict of interest. Appendix A Table A1. Impacts of In situ Urbanization on Housing (A3&A1A2). (1) (2) (3) (4) (5) (6) (7) Area Rooms High-Rise Kitchen Toilet Self-Built Bought 13.335 *** 0.338 *** 0.198 *** 0.034 *** 0.134 *** 0.193 *** 0.090 *** (1.274) (0.035) (0.006) (0.007) (0.008) (0.007) (0.003) 23.876 *** 0.773 *** 0.045 *** 0.059 *** 0.177 *** 0.000 0.012 *** (0.415) (0.013) (0.002) (0.002) (0.002) (0.002) (0.002) 3.032 0.064 0.022 ** 0.049 *** 0.095 *** 0.047 *** 0.005 D T (2.693) (0.072) (0.011) (0.011) (0.014) (0.012) (0.006) 13.351 *** 0.389 *** 0.028 *** 0.022 *** 0.000 0.059 *** 0.008 *** Familysize (0.126) (0.004) (0.001) (0.001) (0.001) (0.001) (0.000) 59.426 *** 1.985 *** 0.414 *** 0.760 *** 0.416 *** 0.378 *** 0.156 *** Constant (0.449) (0.014) (0.003) (0.003) (0.003) (0.003) (0.002) County Fixed YES YES YES YES YES YES YES Effects N 149,245 149,245 146,925 146,925 146,925 146,925 146,925 adj. R 0.237 0.254 0.336 0.144 0.297 0.399 0.082 Notes: ***, ** respectively, indicate p < 0.01, p < 0.05. The standard errors are given in parentheses. Sustainability 2022, 14, 9058 14 of 21 Table A2. Impacts of In situ Urbanization on Housing (A3&A2). (1) (2) (3) (4) (5) (6) (7) Area Rooms High-Rise Kitchen Toilet Self-Built Bought 6.379 *** 0.020 0.030 *** 0.011 0.045 *** 0.070 *** 0.015 *** (1.333) (0.037) (0.004) (0.008) (0.007) (0.006) (0.003) 25.839 *** 0.858 *** 0.006 *** 0.063 *** 0.180 *** 0.002 0.002 *** (0.644) (0.021) (0.001) (0.003) (0.003) (0.002) (0.001) 0.095 0.179 ** 0.055 *** 0.042 *** 0.079 *** 0.053 *** 0.013 ** D T (2.729) (0.074) (0.009) (0.012) (0.014) (0.011) (0.006) 12.055 *** 0.367 *** 0.002 *** 0.020 *** 0.016 *** 0.015 *** 0.001 *** Familysize (0.174) (0.006) (0.000) (0.001) (0.001) (0.001) (0.000) 70.547 *** 2.509 *** 0.025 *** 0.696 *** 0.076 *** 0.888 *** 0.007 *** Constant (0.715) (0.023) (0.002) (0.004) (0.004) (0.003) (0.001) County Fixed YES YES YES YES YES YES YES Effects N 62,558 62,558 61,622 61,622 61,622 61,622 61,622 adj. R 0.282 0.255 0.142 0.206 0.344 0.201 0.049 Notes: ***, ** respectively, indicate p < 0.01, p < 0.05. The standard errors are given in parentheses. Table A3. Impacts of In situ Urbanization on Housing (A3&B2). (1) (2) (3) (4) (5) (6) (7) Area Rooms High-Rise Kitchen Toilet Self-Built Bought - - - - - - - - - - - - - - 27.386 *** 0.924 *** 0.009 *** 0.070 *** 0.171 *** 0.005 ** 0.001 (0.672) (0.022) (0.001) (0.004) (0.003) (0.002) (0.001) 0.085 0.215 *** 0.029 *** 0.049 *** 0.086 *** 0.040 *** 0.005 D T (2.943) (0.078) (0.010) (0.012) (0.015) (0.012) (0.006) 10.521 *** 0.338 *** 0.003 *** 0.020 *** 0.011 *** 0.018 *** 0.001 *** Familysize (0.173) (0.005) (0.000) (0.001) (0.001) (0.001) (0.000) 65.407 *** 2.198 *** 0.032 *** 0.690 *** 0.083 *** 0.865 *** 0.009 *** Constant (0.724) (0.023) (0.002) (0.005) (0.004) (0.003) (0.001) County Fixed YES YES YES YES YES YES YES Effects N 56,857 56,857 55,871 55,871 55,871 55,871 55,871 adj. R 0.268 0.272 0.188 0.191 0.328 0.194 0.101 Notes: ***, ** respectively, indicate p < 0.01, p < 0.05. The standard errors are given in parentheses. Sustainability 2022, 14, 9058 15 of 21 Table A4. Impacts of In situ Urbanization on Housing (A2&B2). (1) (2) (3) (4) (5) (6) (7) Area Rooms High-Rise Kitchen Toilet Self-Built Bought - - - - - - - - - - - - - - 27.440 *** 0.925 *** 0.009 *** 0.070 *** 0.171 *** 0.005 ** 0.001 (0.672) (0.022) (0.001) (0.004) (0.003) (0.002) (0.001) 1.812 * 0.073 ** 0.002 0.007 0.009 * 0.003 0.001 D T (0.933) (0.030) (0.002) (0.005) (0.005) (0.003) (0.001) 10.919 *** 0.346 *** 0.002 *** 0.019 *** 0.013 *** 0.014 *** 0.000 *** Familysize (0.126) (0.004) (0.000) (0.001) (0.001) (0.000) (0.000) 68.715 *** 2.377 *** 0.021 *** 0.697 *** 0.071 *** 0.895 *** 0.005 *** Constant (0.520) (0.017) (0.001) (0.003) (0.003) (0.002) (0.001) County Fixed YES YES YES YES YES YES YES Effects N 110,435 110,435 108,605 108,605 108,605 108,605 108,605 adj. R 0.277 0.264 0.107 0.202 0.328 0.130 0.013 Notes: ***, **, *, respectively, indicate p < 0.01, p < 0.05, p < 0.1. The standard errors are given in parentheses. Table A5. Impacts of In situ Urbanization on Housing (A1&B1). (1) (2) (3) (4) (5) (6) (7) Area Rooms High-Rise Kitchen Toilet Self-Built Bought - - - - - - - - - - - - - - 26.998 *** 0.869 *** 0.061 *** 0.043 *** 0.166 *** 0.012 *** 0.007 *** (0.408) (0.013) (0.002) (0.001) (0.002) (0.002) (0.002) 4.913 *** 0.159 *** 0.008 ** 0.011 *** 0.008 ** 0.012 *** 0.011 *** D T (0.669) (0.020) (0.004) (0.002) (0.004) (0.003) (0.004) 10.900 *** 0.295 *** 0.017 *** 0.027 *** 0.011 *** 0.055 *** 0.004 *** Familysize (0.108) (0.003) (0.001) (0.000) (0.001) (0.001) (0.001) 53.842 *** 1.723 *** 0.666 *** 0.803 *** 0.609 *** 0.074 *** 0.214 *** Constant (0.340) (0.010) (0.002) (0.002) (0.002) (0.002) (0.002) County Fixed YES YES YES YES YES YES YES Effects N 239,795 239,795 235,010 235,010 235,010 235,010 235,010 adj. R 0.196 0.197 0.268 0.098 0.185 0.343 0.067 Notes: ***, **, respectively, indicate p < 0.01, p < 0.05. The standard errors are given in parentheses. Sustainability 2022, 14, 9058 16 of 21 Table A6. Impacts of In situ Urbanization on Housing (A&B). (1) (2) (3) (4) (5) (6) (7) Area Rooms High-Rise Kitchen Toilet Self-Built Bought - - - - - - - - - - - - - - 27.516 *** 0.892 *** 0.048 *** 0.051 *** 0.168 *** 0.010 *** 0.006 *** (0.352) (0.011) (0.002) (0.001) (0.002) (0.002) (0.002) 3.654 *** 0.123 *** 0.002 0.009 *** 0.012 *** 0.011 *** 0.006 ** D T (0.542) (0.017) (0.003) (0.002) (0.003) (0.003) (0.003) 11.383 *** 0.336 *** 0.025 *** 0.021 *** 0.000 0.056 *** 0.005 *** Familysize (0.082) (0.002) (0.000) (0.000) (0.000) (0.000) (0.000) 57.176 *** 1.862 *** 0.504 *** 0.777 *** 0.474 *** 0.284 *** 0.169 *** Constant (0.283) (0.008) (0.002) (0.002) (0.002) (0.002) (0.001) County Fixed YES YES YES YES YES YES YES Effects N 354,730 354,730 348,069 348,069 348,069 348,069 348,069 adj. R 0.223 0.244 0.405 0.140 0.318 0.482 0.090 Notes: ***, **, respectively, indicate p < 0.01, p < 0.05. The standard errors are given in parentheses. Table A7. Impacts of In situ Urbanization on Mobility (A3&A12; A3&A2; A3&B2). A3&A1A2 A3&A2 A3&B2 (1) (2) (3) (1) (2) (3) (1) (2) (3) In-County Migrant Inﬂow In-County Migrant Inﬂow In-County Migrant Inﬂow D 0.005 0.001 0.037 *** 0.038 *** 0.020 *** 0.088 *** - - - (0.005) (0.004) (0.007) (0.006) (0.005) (0.007) - - - T 0.010 *** 0.011 *** 0.008 *** 0.030 *** 0.020 *** 0.002 0.002 0.009 *** 0.000 (0.002) (0.001) (0.003) (0.003) (0.003) (0.002) (0.003) (0.003) (0.002) D T 0.035 *** 0.028 *** 0.014 0.054 *** 0.037 *** 0.005 0.036 *** 0.033 *** 0.001 (0.009) (0.007) (0.013) (0.009) (0.007) (0.013) (0.010) (0.008) (0.013) Male 0.024 *** 0.022 *** 0.007 *** 0.040 *** 0.035 *** 0.005 *** 0.028 *** 0.025 *** 0.004 *** (0.001) (0.001) (0.001) (0.002) (0.001) (0.001) (0.002) (0.001) (0.001) Minority 0.013 *** 0.010 *** 0.081 *** 0.018 ** 0.017 *** 0.072 *** 0.008 0.008 0.009 (0.004) (0.003) (0.007) (0.007) (0.005) (0.008) (0.006) (0.005) (0.007) Education 0.003 * 0.004 *** 0.005 0.086 *** 0.050 *** 0.050 *** 0.075 *** 0.045 *** 0.078 *** (0.002) (0.001) (0.004) (0.007) (0.006) (0.007) (0.008) (0.006) (0.009) Married 0.034 *** 0.025 *** 0.073 *** 0.040 *** 0.030 *** 0.051 *** 0.050 *** 0.039 *** 0.052 *** (0.002) (0.002) (0.003) (0.003) (0.003) (0.002) (0.003) (0.003) (0.002) Literacy 0.058 *** 0.036 *** 0.036 *** 0.065 *** 0.043 *** 0.021 *** 0.051 *** 0.033 *** 0.010 ** (0.004) (0.003) (0.006) (0.005) (0.004) (0.004) (0.005) (0.004) (0.005) Age 0.004 *** 0.003 *** 0.006 *** 0.007 *** 0.004 *** 0.002 *** 0.006 *** 0.003 *** 0.002 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Familysize 0.017 *** 0.010 *** 0.033 *** 0.016 *** 0.010 *** 0.005 *** 0.014 *** 0.009 *** 0.008 *** (0.001) (0.000) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Sustainability 2022, 14, 9058 17 of 21 Table A7. Cont. A3&A1A2 A3&A2 A3&B2 (1) (2) (3) (1) (2) (3) (1) (2) (3) In-County Migrant Inﬂow In-County Migrant Inﬂow In-County Migrant Inﬂow Constant 0.838 *** 0.095 *** 0.597 *** 0.698 *** 0.172 *** 0.160 *** 0.718 *** 0.161 *** 0.167 *** (0.005) (0.004) (0.008) (0.008) (0.006) (0.008) (0.008) (0.006) (0.008) County Fixed YES YES YES YES YES YES YES YES YES Effects N 298,387 298,387 298,387 139,198 139,198 139,198 132,392 132,392 132,392 adj. R 0.150 0.132 0.273 0.176 0.152 0.192 0.152 0.127 0.198 Notes: ***, **, *, respectively, indicate p < 0.01, p < 0.05, p < 0.1. The standard errors are given in parentheses. Table A8. Impacts of In situ Urbanization on Mobility (A2&B2; A1&B1; A&B). A2&B2 A1&B1 A&B (1) (2) (3) (1) (2) (3) (1) (2) (3) In-County Migrant Inﬂow In-County Migrant Inﬂow In-County Migrant Inﬂow D - - - - - - - - - - - - - - - - - - T 0.003 0.009 *** 0.000 0.005 *** 0.003 *** 0.036 *** 0.001 0.006 *** 0.027 *** (0.003) (0.003) (0.002) (0.001) (0.001) (0.003) (0.001) (0.001) (0.002) D T 0.027 *** 0.011 *** 0.001 0.003 0.002 0.023 *** 0.008 *** 0.003 0.022 *** (0.004) (0.004) (0.003) (0.002) (0.002) (0.005) (0.002) (0.002) (0.003) Male 0.035 *** 0.031 *** 0.005 *** 0.003 *** 0.006 *** 0.016 *** 0.018 *** 0.017 *** 0.007 *** (0.001) (0.001) (0.001) (0.001) (0.000) (0.001) (0.001) (0.000) (0.001) Minority 0.001 0.000 0.016 *** 0.003 * 0.001 0.050 *** 0.003 0.003 * 0.016 *** (0.005) (0.004) (0.005) (0.002) (0.001) (0.005) (0.002) (0.002) (0.004) Education 0.087 *** 0.052 *** 0.063 *** 0.013 *** 0.004 *** 0.096 *** 0.004 *** 0.001 0.035 *** (0.006) (0.005) (0.006) (0.001) (0.001) (0.002) (0.001) (0.001) (0.002) Married 0.045 *** 0.035 *** 0.049 *** 0.018 *** 0.014 *** 0.073 *** 0.030 *** 0.023 *** 0.074 *** (0.002) (0.002) (0.002) (0.001) (0.001) (0.002) (0.001) (0.001) (0.002) Literacy 0.061 *** 0.039 *** 0.012 *** 0.029 *** 0.013 *** 0.144 *** 0.060 *** 0.036 *** 0.051 *** (0.004) (0.003) (0.003) (0.003) (0.002) (0.009) (0.003) (0.002) (0.004) Age 0.006 *** 0.004 *** 0.002 *** 0.001 *** 0.001 *** 0.009 *** 0.004 *** 0.002 *** 0.007 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Familysize 0.015 *** 0.010 *** 0.005 *** 0.014 *** 0.006 *** 0.042 *** 0.016 *** 0.008 *** 0.036 *** (0.001) (0.001) (0.001) (0.000) (0.000) (0.001) (0.000) (0.000) (0.001) Constant 0.706 *** 0.167 *** 0.140 *** 0.975 *** 0.026 *** 1.057 *** 0.885 *** 0.072 *** 0.691 *** (0.006) (0.005) (0.006) (0.004) (0.003) (0.011) (0.003) (0.003) (0.006) County Fixed YES YES YES YES YES YES YES YES YES Effects N 251,679 251,679 251,679 420,480 420,480 420,480 682,119 682,119 682,119 adj. R 0.166 0.142 0.140 0.075 0.074 0.198 0.137 0.123 0.266 Notes: ***, *, respectively, indicate p < 0.01, p < 0.1. The standard errors are given in parentheses. Sustainability 2022, 14, 9058 18 of 21 Table A9. Impacts of In situ Urbanization on Employment (A3&A1A2; A3&A2). A3&A1A2 A3&A2 (1) (2) (3) (4) (1) (2) (3) (4) Unemployment Ind1 Ind2 Ind3 Unemployment Ind1 Ind2 Ind3 D 0.014 *** 0.058 *** 0.020 ** 0.075 *** 0.000 0.096 *** 0.058 *** 0.037 *** (0.002) (0.008) (0.008) (0.007) (0.002) (0.008) (0.008) (0.007) T 0.018 *** 0.098 *** 0.030 *** 0.091 *** 0.024 *** 0.157 *** 0.000 0.101 *** (0.001) (0.002) (0.003) (0.003) (0.002) (0.004) (0.003) (0.004) D T 0.008 0.051 *** 0.014 0.030 ** 0.001 0.013 0.015 0.011 (0.006) (0.013) (0.014) (0.013) (0.006) (0.014) (0.014) (0.013) Male 0.002 *** 0.080 *** 0.090 *** 0.028 *** 0.003 *** 0.107 *** 0.080 *** 0.007 *** (0.001) (0.001) (0.002) (0.002) (0.001) (0.002) (0.002) (0.002) Minority 0.009 *** 0.022 *** 0.021 *** 0.042 *** 0.009 *** 0.025 ** 0.001 0.025 *** (0.003) (0.006) (0.006) (0.007) (0.003) (0.010) (0.008) (0.009) Education 0.004 ** 0.171 *** 0.110 *** 0.279 *** 0.038 *** 0.257 *** 0.066 *** 0.331 *** (0.001) (0.002) (0.003) (0.004) (0.005) (0.009) (0.009) (0.011) Married 0.057 *** 0.062 *** 0.021 *** 0.041 *** 0.045 *** 0.027 *** 0.019 *** 0.006 * (0.001) (0.002) (0.003) (0.003) (0.002) (0.004) (0.004) (0.003) Literacy 0.003 0.144 *** 0.043 *** 0.099 *** 0.004 * 0.058 *** 0.020 *** 0.036 *** (0.003) (0.006) (0.006) (0.006) (0.002) (0.007) (0.005) (0.004) Age 0.001 *** 0.008 *** 0.004 *** 0.004 *** 0.001 *** 0.008 *** 0.005 *** 0.004 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Familysize 0.001 *** 0.016 *** 0.001 0.016 *** 0.000 0.004 *** 0.002 * 0.001 (0.000) (0.001) (0.001) (0.001) (0.000) (0.001) (0.001) (0.001) Constant 0.108 *** 0.264 *** 0.366 *** 0.374 *** 0.095 *** 0.500 *** 0.299 *** 0.211 *** (0.004) (0.008) (0.008) (0.008) (0.004) (0.010) (0.009) (0.008) County Fixed YES YES YES YES YES YES YES YES Effects N 264,057 252,677 252,677 252,677 114,047 111,389 111,389 111,389 adj. R 0.034 0.375 0.171 0.195 0.049 0.306 0.202 0.116 Notes: ***, **, *, respectively, indicate p < 0.01, p < 0.05, p < 0.1. The standard errors are given in parentheses. Table A10. Impacts of In situ Urbanization on Employment (A3&B2; A2&B2). A3&B2 A2&B2 (1) (2) (3) (4) (1) (2) (3) (4) Unemployment Ind1 Ind2 Ind3 Unemployment Ind1 Ind2 Ind3 D - - - - - - - - - - - - - - - - T 0.021 *** 0.104 *** 0.001 0.062 *** 0.021 *** 0.105 *** 0.002 0.062 *** (0.001) (0.004) (0.003) (0.003) (0.001) (0.004) (0.003) (0.003) D T 0.004 0.031 ** 0.022 0.041 *** 0.004 * 0.050 *** 0.003 0.038 *** (0.007) (0.015) (0.015) (0.014) (0.002) (0.006) (0.005) (0.005) Male 0.002 ** 0.079 *** 0.060 *** 0.004 ** 0.002 *** 0.095 *** 0.069 *** 0.009 *** (0.001) (0.002) (0.002) (0.002) (0.001) (0.002) (0.001) (0.001) Minority 0.000 0.036 *** 0.018 *** 0.013 ** 0.003 0.034 *** 0.014 *** 0.017 *** (0.002) (0.008) (0.007) (0.006) (0.002) (0.006) (0.005) (0.005) Education 0.042 *** 0.338 *** 0.017 0.361 *** 0.046 *** 0.318 *** 0.027 *** 0.351 *** (0.006) (0.010) (0.011) (0.012) (0.004) (0.007) (0.008) (0.009) Married 0.048 *** 0.001 0.002 0.002 0.046 *** 0.011 *** 0.009 *** 0.003 (0.002) (0.004) (0.003) (0.003) (0.001) (0.003) (0.002) (0.002) Sustainability 2022, 14, 9058 19 of 21 Table A10. Cont. A3&B2 A2&B2 (1) (2) (3) (4) (1) (2) (3) (4) Unemployment Ind1 Ind2 Ind3 Unemployment Ind1 Ind2 Ind3 Literacy 0.000 0.068 *** 0.023 *** 0.042 *** 0.002 0.060 *** 0.021 *** 0.037 *** (0.002) (0.006) (0.004) (0.004) (0.002) (0.005) (0.004) (0.003) Age 0.001 *** 0.006 *** 0.003 *** 0.003 *** 0.001 *** 0.007 *** 0.004 *** 0.003 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Familysize 0.001 *** 0.001 0.002 0.001 0.001 *** 0.003 *** 0.002 ** 0.001 (0.000) (0.001) (0.001) (0.001) (0.000) (0.001) (0.001) (0.001) Constant 0.084 *** 0.605 *** 0.228 *** 0.175*** 0.089 *** 0.572 *** 0.252 *** 0.185 *** (0.004) (0.010) (0.008) (0.008) (0.003) (0.007) (0.006) (0.006) County Fixed YES YES YES YES YES YES YES YES Effects N 111,886 109,303 109,303 109,303 208,344 203,672 203,672 203,672 adj. R 0.058 0.345 0.239 0.132 0.054 0.305 0.209 0.109 Notes: ***, **, *, respectively, indicate p < 0.01, p < 0.05, p < 0.1. The standard errors are given in parentheses. Table A11. Impacts of In situ Urbanization on Employment (A1&B1; A&B). A1&B1 A&B (1) (2) (3) (4) (1) (2) (3) (4) Unemployment Ind1 Ind2 Ind3 Unemployment Ind1 Ind2 Ind3 D - - - - - - - - - - - - - - - - T 0.016 *** 0.019 *** 0.056 *** 0.068 *** 0.018 *** 0.047 *** 0.038 *** 0.067 *** (0.001) (0.002) (0.003) (0.003) (0.001) (0.002) (0.002) (0.002) D T 0.004 * 0.025 *** 0.002 0.010 ** 0.001 0.053 *** 0.007 ** 0.027 *** (0.002) (0.003) (0.004) (0.005) (0.002) (0.003) (0.003) (0.004) Male 0.003 *** 0.031 *** 0.095 *** 0.076 *** 0.002 *** 0.062 *** 0.089 *** 0.040 *** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Minority 0.001 0.003 0.018 *** 0.018 *** 0.004 ** 0.052 *** 0.003 0.045 *** (0.002) (0.003) (0.005) (0.005) (0.002) (0.004) (0.004) (0.004) Education 0.021 *** 0.057 *** 0.109 *** 0.161 *** 0.012 *** 0.130 *** 0.094 *** 0.220 *** (0.001) (0.001) (0.002) (0.002) (0.001) (0.001) (0.002) (0.002) Married 0.079 *** 0.028 *** 0.016 *** 0.015 *** 0.066 *** 0.044 *** 0.020 *** 0.026 *** (0.001) (0.001) (0.002) (0.003) (0.001) (0.001) (0.002) (0.002) Literacy 0.000 0.145 *** 0.041 *** 0.100 *** 0.007 *** 0.155 *** 0.043 *** 0.108 *** (0.005) (0.009) (0.010) (0.011) (0.002) (0.005) (0.004) (0.004) Age 0.000 *** 0.004 *** 0.002 *** 0.002 *** 0.000 *** 0.006 *** 0.003 *** 0.003 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Familysize 0.000 0.012 *** 0.005 *** 0.017 *** 0.001 *** 0.018 *** 0.002 ** 0.019 *** (0.000) (0.000) (0.001) (0.001) (0.000) (0.001) (0.001) (0.001) Constant 0.120 *** 0.124 *** 0.323 *** 0.549 *** 0.108 *** 0.265 *** 0.304 *** 0.431 *** (0.006) (0.010) (0.011) (0.012) (0.003) (0.005) (0.005) (0.006) County Fixed YES YES YES YES YES YES YES YES Effects N 399,762 372,402 372,402 372,402 616,906 584,590 584,590 584,590 adj. R 0.034 0.257 0.130 0.147 0.039 0.436 0.161 0.232 Notes: ***, **, *, respectively, indicate p < 0.01, p < 0.05, p < 0.1. The standard errors are given in parentheses. Sustainability 2022, 14, 9058 20 of 21 References 1. Guo, S.; Zou, J. Study and Enlightenment of the in-Situ Urbanization of Rural Areas in China in the Background of New Pattern Urbanization-Taking Zhanqi Village, Pi County for Instance. Open J. Soc. Sci. 2015, 3, 137–144. [CrossRef] 2. Dociu, M.; Dunarintu, A. The Socio-Economic Impact of Urbanization. Int. J. Acad. Res. Account. Financ. Manag. Sci. 2012, 2, 47–52. 3. Gross, J.; Ouyang, Y. Types of Urbanization and Economic Growth. Int. J. Urban Sci. 2021, 25, 71–85. [CrossRef] 4. Kan, K.; Chen, J. Rural Urbanization in China: Administrative Restructuring and the Livelihoods of Urbanized Rural Residents. J. Contemp. China 2021, 31, 1–18. [CrossRef] 5. Henderson, J.V. Urbanization and Economic Development. Ann. Econ. Financ. 2003, 4, 275–341. 6. Laumas, P.; Williams, M. 1984, “Urbanization and Economic Development”. East. Econ. J. 2008, 10, 325–332. 7. Mayer-Foulkes, D. Efﬁcient Urbanization for Mexican Development. Int. J. Econ. Financ. 2018, 10, 1–27. [CrossRef] 8. Gan, L.; He, Q.; Si, R.; Yi, D. Relocating or Redeﬁned: A New Perspective on Urbanization in China; Working Paper Series; National Bureau of Economic Research: Cambridge, MA, USA, 2019. 9. Bertinelli, L.; Black, D. Urbanization and Growth. J. Urban Econ. 2004, 56, 80–96. [CrossRef] 10. Iyer, C.G. Urbanization in India and Productivity of Manufacturing Industries: An Empirical Study. Indian Econ. Rev. 2013, 48, 297–322. 11. Rosero, G.C.G.; Riou, S.; Autant-Bernard, C. Agglomeration Externalities and Urbanization in Ecuador; European Regional Science Association: Louvain-la-Neuve, Belgium, 2015. 12. Tran, T.B. Informal Sector and Urbanization Externalities in Vietnam. Eurasian J. Econ. Financ. 2018, 6, 56–63. [CrossRef] 13. Bosker, M.; Deichmann, U.; Roberts, M. Hukou and Highways the Impact of China’s Spatial Development Policies on Urbanization and Regional Inequality. Reg. Sci. Urban Econ. 2018, 71, 91–109. [CrossRef] 14. Dreger, C.; Wang, T.; Zhang, Y. Understanding Chinese Consumption: The Impact of Hukou. Dev. Change 2015, 46, 1331–1344. [CrossRef] 15. Hsu, W.; Ma, L. Urbanization Policy and Economic Development: A Quantitative Analysis of China’s Differential Hukou Reforms. Reg. Sci. Urban Econ. 2021, 91, 103639. [CrossRef] 16. Ahlers, A.L. Weaving the Chinese Dream on the Ground? Local Government Approaches to “New-Typed” Rural Urbanization. J. Chin. Political Sci. 2015, 20, 121–142. [CrossRef] 17. Chen, M.; Liu, W.; Lu, D. Challenges and the Way Forward in China’s New-Type Urbanization. Land Use Policy 2016, 55, 334–339. [CrossRef] 18. Guan, X.; Wei, H.; Lu, S.; Dai, Q.; Su, H. Assessment On the Urbanization Strategy in China: Achievements, Challenges and Reﬂections. Habitat Int. 2018, 71, 97–109. [CrossRef] 19. Lu, J.; Li, B.; Li, H. The Inﬂuence of Land Finance and Public Service Supply On Peri-Urbanization: Evidence From the Counties in China. Habitat Int. 2019, 92, 102039. [CrossRef] 20. Gao, J.; Liu, Y.; Chen, J. China’s Initiatives Towards Rural Land System Reform. Land Use Policy 2020, 94, 104567. [CrossRef] 21. Tao, R.; Xu, Z. Urbanization, Rural Land System and Social Security for Migrants in China. J. Dev. Stud. 2007, 43, 1301–1320. [CrossRef] 22. Xu, Z.; Ning, K.; Zhong, F.; Ji, Y. New Rural Pension Insurance and Land Transfer: Can Institutional Pension Replace Land Pension? Based On the Perspectives of Family Demographic Structure and Liquidity Constraint. Manag. World 2018, 34, 86–97. 23. Chen, J.; Chen, S. Mental Health Effects of Perceived Living Environment and Neighborhood Safety in Urbanizing China. Habitat Int. 2015, 46, 101–110. [CrossRef] 24. Chen, Y.; Chen, H.; Liu, J. Household Split, Income, and Migrants’ Life Satisfaction: Social Problems Caused by Rapid Urbanization in China. Sustainability 2019, 11, 3415. [CrossRef] 25. Liang, Y.; Zhu, D. Subjective Well-Being of Chinese Landless Peasants in Relatively Developed Regions: Measurement Using Panas and Swls. Soc. Indic. Res. 2015, 123, 817–835. [CrossRef] 26. Zhou, S.; Yu, X. Regional Heterogeneity of Life Satisfaction in Urban China: Evidence From Hierarchical Ordered Logit Analysis. Soc. Indic. Res. 2017, 132, 25–45. [CrossRef] 27. Xie, S.; Chen, J. Housing and Identity Expression Among in-Situ Urbanized Rural Residents in China. Popul. Space Place 2021, 28, e2548. 28. Zhu, Y. In Situ Urbanization in China: Processes, Contributing Factors, and Policy Implications. China Popul. Dev. Stud. 2017, 1, 45–66. [CrossRef] 29. Zhu, Y.; Wang, W.W.; Lin, L.; Shen, J.; Ren, Q. Return Migration and in Situ Urbanization of Migrant Sending Areas: Insights from a Survey of Seven Provinces in China. Cities 2021, 115, 103242. [CrossRef] 30. Zhu, Y. In Situ Urbanization in Rural China: Case Studies from Fujian Province. Dev. Change 2000, 31, 413–434. [CrossRef] 31. Chen, A. How Has the Abolition of Agricultural Taxes Transformed Village Governance in China? Evidence From Agricultural Regions. China Q. 2014, 219, 715–735. [CrossRef] 32. Chen, J.; Davis, D.S.; Landry, P.F. Beyond Hukou Reform: Enhancing Human-Centered Urbanization in China. Paulson Policy Memo. 2017, 3–5. Available online: https://www.paulsoninstitute.org/wp-content/uploads/2017/02/PPM_Beyond-Hukou_ Chen_Davis_Landry_English.pdf (accessed on 7 December 2020). Sustainability 2022, 14, 9058 21 of 21 33. Chen, J.; Chen, S.; Landry, P.F.; Davis, D.S. How Dynamics of Urbanization Affect Physical and Mental Health in Urban China. China Q. 2014, 220, 988–1011. [CrossRef] 34. Chen, J.; Davis, D.S.; Wu, K.; Dai, H. Life Satisfaction in Urbanizing China: The Effect of City Size and Pathways to Urban Residency. Cities 2015, 49, 88–97. [CrossRef] 35. Liu, S.; Zhang, P.; Lo, K. Urbanization in Remote Areas: A Case Study of the Heilongjiang Reclamation Area, Northeast China. Habitat Int. 2014, 42, 103–110. [CrossRef] 36. Tong, W.; Lo, K.; Zhang, P. Land Consolidation in Rural China: Life Satisfaction Among Resettlers and its Determinants. Land 2020, 9, 118. [CrossRef] 37. Hou, B.; Nazroo, J.; Banks, J.; Marshall, A. Are Cities Good for Health? A Study of the Impacts of Planned Urbanization in China. Int. J. Epidemiol. 2019, 48, 1083–1090. [CrossRef] 38. Sato, Y.; Zenou, Y. How Urbanization Affect Employment and Social Interactions. Eur. Econ. Rev. 2015, 75, 131–155. [CrossRef] 39. Evers, H. The Formation of a Social Class Structure: Urbanization, Bureaucratization and Social Mobility in Thailand. Am. Sociol. Rev. 1966, 31, 480–488. [CrossRef] 40. Wan, H.; Li, S. The Effects of Household Registration System Discrimination on Urban-Rural Income Inequality in China. Econ. Res. J. 2013, 48, 43–55. 41. Sendut, H. Contemporary Urbanization in Malaysia. Asian Surv. 1966, 6, 484–491. [CrossRef] 42. Mellor, J.W.; Khan, M.H.; Qureshi, S.K. Agricultural Links to Nonagricultural Growth: Urbanization, Employment, Poverty. Pak. Dev. Rev. 1991, 30, 439–456. [CrossRef] 43. Khan, H. Challenges for Sustainable Development: Rapid Urbanization, Poverty and Capabilities in Bangladesh. MPRA Paper No. 9290. Available online: https://mpra.ub.uni-muenchen.de/9290/1/MPRA_paper_9290.pdf (accessed on 7 December 2020). 44. Michaels, G.; Rauch, F.; Redding, S.J. Urbanization and Structural Transformation. Q. J. Econ. 2012, 127, 535–586. [CrossRef] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Sustainability Multidisciplinary Digital Publishing Institute http://www.deepdyve.com/lp/multidisciplinary-digital-publishing-institute/the-impacts-of-in-situ-urbanization-on-housing-mobility-and-employment-bGTigAwllH

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References (36)

Yasuhiro Sato, Y. Zenou (2014)
How Urbanization Affect Employment and Social Interactions
CEPR Discussion Paper Series
H. Evers (1966)
The Formation of a Social Class Structure: Urbanization, Bureaucratization and Social Mobility in Thailand
Journal of Southeast Asian History, 7
A. Ahlers (2015)
Weaving the Chinese Dream on the Ground? Local Government Approaches to “New-Typed” Rural Urbanization
Journal of Chinese Political Science, 20
Shenghua Xie, Juan Chen (2021)
Housing and identity expression among in‐situ urbanised rural residents in China
Population, Space and Place
Shiqin Guo, Jie Zou (2015)
Study and Enlightenment of the In-Situ Urbanization of Rural Areas in China in the Background of New Pattern Urbanization—Taking Zhanqi Village, Pi County for Instance
Open Journal of Social Sciences, 03
Guy Michaels, Ferdinand Rauch, S. Redding (2008)
Urbanization and Structural Transformation
ERN: Quality of Life & Environmental Comparisons (Topic)
J. Gross, Ouyang Yu (2020)
Types of urbanization and economic growth
International Journal of Urban Sciences, 25
Mingxing Chen, Weidong Liu, Dadao Lu (2016)
Challenges and the way forward in China’s new-type urbanization
Land Use Policy, 55
Luisito Bertinelli, D. Black (2004)
Urbanization and growth
Journal of Urban Economics, 56
David MAYER-FOULKES (2018)
Efficient Urbanization for Mexican Development
International Journal of Economics and Finance
R. Tao, Zhigang Xu (2007)
Urbanization, rural land system and social security for migrants in China
The Journal of Development Studies, 43
Thi Tran (2018)
Informal Sector And Urbanization Externalities In Vietnam
Eurasian Journal of Economics and Finance, 6
Juan Chen, Shuo Chen (2015)
Mental health effects of perceived living environment and neighborhood safety in urbanizing China
Habitat International, 46
Monique Abud (2017)
Beyond Hukou Reform: Enhancing Human-Centered Urbanization in China
Shaojie Zhou, Xiaohua Yu (2017)
Regional Heterogeneity of Life Satisfaction in Urban China: Evidence from Hierarchical Ordered Logit Analysis
Social Indicators Research, 132
(2018)
New Rural Pension Insurance and Land Transfer: Can Institutional Pension Replace Land Pension? Based On the Perspectives of Family Demographic Structure and Liquidity Constraint
Karita Kan, Juan Chen (2021)
Rural Urbanization in China: Administrative Restructuring and the Livelihoods of Urbanized Rural Residents
Journal of Contemporary China, 31
Yu Zhu, Wenfei Wang, Liyue Lin, Jianfa Shen, Q. Ren (2021)
Return migration and in situ urbanization of migrant sending areas: Insights from a survey of seven provinces in China
Cities, 115
Juan Lu, Bin Li, He Li (2019)
The influence of land finance and public service supply on peri-urbanization: Evidence from the counties in China
Habitat International
Hamzah Sendut (1966)
Contemporary Urbanization in Malaysia
Asian Survey, 6
Wen-Tai Hsu, Lin Ma (2020)
Urbanization policy and economic development: A quantitative analysis of China's differential hukou reforms
Regional Science and Urban Economics
Yu Zhu (2000)
In Situ Urbanization in Rural China: Case Studies from Fujian Province
Development and Change, 31
Juan Chen, Deborah Davis, Kaming Wu, Haijing Dai (2015)
Life satisfaction in urbanizing China: The effect of city size and pathways to urban residency
Cities, 49
Jinlong Gao, Yan-sui Liu, Jianglong Chen (2020)
China’s initiatives towards rural land system reform
Land Use Policy, 94
Shiwei Liu, Ping-yu Zhang, K. Lo (2014)
Urbanization in remote areas: A case study of the Heilongjiang Reclamation Area, Northeast China
Habitat International, 42
Ying Liang, Demi Zhu (2015)
Subjective Well-Being of Chinese Landless Peasants in Relatively Developed Regions: Measurement Using PANAS and SWLS
Social Indicators Research, 123
Chidambaran Iyer (2013)
Urbanization in India and Productivity of Manufacturing Industries: An Empirical Study
M. Dociu, A. Dunarinţu (2012)
The Socio-Economic Impact of Urbanization
International Journal of Academic Research in Accounting, Finance and Management Sciences, 2
Christian Dreger, Tongsan Wang, Yanqun Zhang (2015)
Understanding Chinese Consumption: The Impact of Hukou: Understanding Chinese Consumption: The Impact of Hukou
Development and Change, 46
Xingliang Guan, Houkai Wei, Shasha Lu, Qi Dai, Hongjian Su (2018)
Assessment on the urbanization strategy in China: Achievements, challenges and reflections
Habitat International, 71
Yang Chen, Hongsheng Chen, Jinhua Liu (2019)
Household Split, Income, and Migrants’ Life Satisfaction: Social Problems Caused by Rapid Urbanization in China
Sustainability
E. Bosker, U. Deichmann, M. Roberts (2015)
Hukou and Highways: The Impact of China?S Spatial Development Policies on Urbanization and Regional Inequality
Development Economics: Microeconomic Issues in Developing Economies eJournal
(2013)
The Effects of Household Registration System Discrimination on Urban-Rural Income Inequality in China
G. Tolley (2007)
Urbanization and Economic Development
W. Tong, K. Lo, Ping-yu Zhang (2020)
Land Consolidation in Rural China: Life Satisfaction among Resettlers and Its Determinants
Land
B. Hou, J. Nazroo, J. Banks, A. Marshall (2019)
Are cities good for health? A study of the impacts of planned urbanization in China.
International journal of epidemiology

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Abstract

sustainability Article The Impacts of In Situ Urbanization on Housing, Mobility and Employment of Local Residents in China 1 1 2 , 3 , Yanxu Li , Zhenfa Xie , Bo Li * and Muhammad Mohiuddin * School of Economics, Xiamen University, Xiamen 361005, China; liyx1995@163.com (Y.L.); xzfxmu2022@163.com (Z.X.) School of Management, Tianjin University of Technology, Tianjin 300384, China Faculty of Business Administration, Laval University, Quebec, QC G1V 0A6, Canada * Correspondence: great2011818@126.com (B.L.); muhammad.mohiuddin@fsa.ulaval.ca (M.M.) Abstract: Rapid economic growth and infrastructure development force in situ urbanization in locations where people from rural areas gain urban residency without experiencing long- distance geographical relocation. However, the impacts of in situ urbanization on farmers’ and other residents’ well-being remains unclear, and there are some arguments about the idea that “urbanization of people lags behind urbanization of land” in China. Therefore, this study firstly finds a reasonable way to measure in situ urbanization: the transfer of rural-urban division codes. On this basis, by applying the PSM-DID method, we use national census data to explore the impacts of in situ urbanization on farmers from the perspective of housing, mobility and employment. The research results show that after the in situ urbanization, the possibility of farmers moving into non-self-built high-rising buildings increases, while the possibility of farmers leaving the county for employment decreases. Besides, the employment structure in the county where in situ urbanization takes place has shifted from primary industry to secondary and tertiary industry. Moreover, this paper also discusses the spillover effects of in situ urbanization on other residents in the county. Our study shows that in situ urbanization can improve residents’ well-being and Citation: Li, Y.; Xie, Z.; Li, B.; Mohiuddin, M. The Impacts of In Situ offers sustainable land-people integrated urbanization. Urbanization on Housing, Mobility and Employment of Local Residents Keywords: housing; mobility; employment; spillover effects; in situ urbanization in China. Sustainability 2022, 14, 9058. https://doi.org/10.3390/su14159058 Academic Editors: Enrico Ivaldi, 1. Introduction Leonardo Salvatore Alaimo and Alfonso Piscitelli Urbanization in China varies from developed countries [1]. Classic urbanization theories usually present the large-scale migration of rural residents to urban areas to seek Received: 26 May 2022 job opportunities in the non-agricultural sector and become urban residents gradually [2–4]. Accepted: 20 July 2022 However, urbanization in China has not only involved the above process, but also in Published: 24 July 2022 situ urbanization, which means the local rural residents gain urban residency without Publisher’s Note: MDPI stays neutral experiencing geographical relocation (i.e., rural areas become urbanized). with regard to jurisdictional claims in The positive impact of urbanization on economic development and people’s welfare published maps and institutional afﬁl- has been shown in many developed economies [5–7]. However, China’s in situ urbanization iations. is not the same as the urbanization of the developed countries. It is usually initiated by the government to implement “urbanization of land” in China, which in turn drives “urbanization of people”. “Urbanization of land” means the land is planned to use for urban development while “urbanization of people” means that farmers are gradually introduced Copyright: © 2022 by the authors. to living the same lifestyles as urban residents. There are some arguments about the idea Licensee MDPI, Basel, Switzerland. that “urbanization of people lags behind urbanization of land” in China, which shows This article is an open access article concerns about Chinese farmers’ well-being after in situ urbanization. Existing research distributed under the terms and places emphasis on empirical case studies and draws different conclusions about the conditions of the Creative Commons impacts of in situ urbanization on Chinese farmers’ well-being by using different samples. Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ So it is worth studying the well-being of Chinese residents after in situ urbanization by 4.0/). using a convincing method. Sustainability 2022, 14, 9058. https://doi.org/10.3390/su14159058 https://www.mdpi.com/journal/sustainability Sustainability 2022, 14, 9058 2 of 21 To explore the changes of well-being after in situ urbanization, we study the impacts of in situ urbanization on Chinese residents from the perspective of housing, mobility and employment. The premise of studying the impacts of in situ urbanization on farmers is how to measure in situ urbanization. According to Gan et al., the transfer of rural-urban division codes is used to measure whether a community has undergone in situ urbanization [8]. On this basis, combined with the data of the two national consensuses in 2010 and 2015, we could identify whether the individual in different communities is affected by in situ urbanization. We found that in situ urbanization will improve the housing conditions of farmers and make farmers less likely to go out for employment and more likely to stay in the county to engage in the tertiary industry. In addition, in situ urbanization will also have an impact on farmers in other non-rural-to-urban communities in the county. In situ urbanization increases the probability of other farmers leaving the county to work, attracts labors ﬂowing into urban communities in the county, and shifts the employment structure in the county from primary industry to secondary and tertiary industry, with tertiary industry as the main transfer direction. The main contributions of this study are as follows. Firstly, this paper uses an appro- priate measure of in situ urbanization, that is, the transfer of rural-urban division codes. Secondly, unlike the existing analysis that place emphasis on empirical case study and draw various conclusions due to different research samples, this paper uses national census data and the conclusions are more informative and universal. Finally, this paper not only studies the impacts of in situ urbanization on rural-to-urban residents, but also studies the spillover effects of in situ urbanization on other residents in the county, thus we could more comprehensively assess the impacts of in situ urbanization. 2. Literature Review Existing research have shown that urbanization promotes economic development in many ways. Bertinelli & Black ﬁnd that urbanization can promote human capital accumu- lation, which is the engine of growth [9]. Iyer ﬁnds that the impacts of urbanization on the productivity of manufacturing industries in India vary both within and across indus- tries [10]. Rosero et al. speciﬁes that the agglomeration externalities during urbanization play an important role in the local productivity of sectors [11]. Tran points out that the productivity of informal household businesses in Vietnam beneﬁts from urbanization [12]. With its unique characteristics, China’s urbanization is still worth studying. At this stage, China’s urbanization mainly includes two ways. The first is the urbanization of migrant workers, that is, farmers move to distant cities to obtain jobs and then become city residents. This is usually accompanied by the process of “urbanization of people” and is similar to the urbanization format of developed countries. The relevant research mainly focuses on the reform of the household registration system (hukou) [13–15], the provision of equal public services [16–19], and the reform of rural land systems [20–22]. There are also some studies focusing on the subjective well-being of farmers after completing the rural-urban migration [23–26]. The second is the in situ urbanization, that is, the local rural residents change to urban residents without experiencing geographical relocation [27–29]. In the early days of reform and opening up, in situ urbanization was mainly driven by the development of township and village enterprises (TVEs) and foreign investment in rural areas [28,30]. But since the late 1990s, with the slowdown of development of TVEs, in situ urbanization has been increasingly dominated by the government, because expropriating rural land at a low price and selling it at a high price has become a chief means for local governments to obtain revenue [31,32]. After expropriating rural land, farmers usually passively become urban residents. Therefore, in situ urbanization is often initiated by “urbanization of land” in China, which in turn drives “urbanization of people”. However, there are some worries about “urbanization of people” lagging behind “urbanization of land” during the in situ urbanization, so scholars hold different attitudes towards in situ urbanization. Sustainability 2022, 14, 9058 3 of 21 Some studies show the negative effects of in situ urbanization. Firstly, due to the dual structure of urban and rural areas in China, rural areas and farmers could not be well incorporated into the urban social, administrative and welfare system in China after in situ urbanization [33]. Even when farmers obtain urban status after in situ urbanization, they may not be able to receive the same beneﬁts as urban residents which leads to signiﬁcant social, economic, and environmental challenges for farmers after in situ urbanization. Many studies show adverse effects of in situ urbanization on farmers in terms of identity, mental health, and satisfaction [34]. Chen et al. ﬁnd that in situ urbanized rural residents have fewer years of education and less skilled employment and compared with the both urban hukou residents and rural-to-urban migrants [32]. Liu et al. point out that residents after in situ urbanization face decreasing income and increasing living cost, and in situ urbanization may lead environmental challenge [35]. Xie & Chen ﬁnd that in situ urbanized rural residents have a low level of urban identity due to the challenges in the course of integration into urban life in terms of income, occupation, social insurance, and housing [27]. Other studies show the positive effects of in situ urbanization, especially from the perspective of housing, mobility and employment. The most direct impacts of in situ urbanization on farmers are at the housing aspect [4,36]. In situ urbanization usually forces farmers to move to urban or concentrated areas to live, and their living conditions are signiﬁcantly improved. At the same time, farmers have more opportunities to engage in non-agricultural work in hometown, and their income also have increased, which in result to improve living conditions in education, medical care, health and so on [29,37]. Therefore, farmers are more likely to stay in their hometown and are less likely to become migrant workers [29]. This is in consistent with urbanization process in western countries. Sato and Zenou ﬁnd that workers in regions with a lower urbanization rate experience less social interaction and a higher unemployment rate [38]. Evers points out that one of the effects of urbanization is a high rate of both vertical and horizontal mobility [39]. According to the above literatures, we could ﬁnd that the negative effects of in situ urbanization focus on the farmers’ subjective perceptions or relative situations compared with urban residents. However, research holds almost the same opinion that in situ urbanization leads to better housing, mobility and employment for residents compared with their past. Therefore, we propose the following hypotheses. Hypothesis 1 (H1). In situ urbanization will improve farmers’ housing conditions. Hypothesis 2 (H2). In situ urbanization will make farmers more likely to stay in their native county. Hypothesis 3 (H3). In situ urbanization will enhance possibility of farmers to engage in non- agricultural work. 3. Data and Methods 3.1. Data Source This paper uses the transfer of rural-urban division codes to measure in situ urban- ization, which are collected from the website of the National Bureau of Statistics of China (http://www.stats.gov.cn/tjsj/tjbz/tjyqhdmhcxhfdm (accessed on 7 December 2020)). As what we have mentioned in the literature review, most of the research about in situ urban- ization are case studies, so they seldom use any indicator to identify whether the area takes place in situ urbanization. But there are many studies using the proportion of new urban construction land to measure urbanization. It is not an accurate indicator to measure in situ urbanization. On the one hand, urbanization is different form in situ urbanization. Urbanization includes both classic urbanization (rural residents move to urban areas to seek job opportunities in the non-agricultural sector and become urban residents gradually) and in situ urbanization (the local rural residents gain urban residency without experiencing geographical relocation due to the planned schedule by government). It is not proper to measure in situ urbanization by using the method of measuring urbanization. On the Sustainability 2022, 14, 9058 4 of 21 other hand, there are some arguments to measure the in situ urbanization by using the proportion of new urban construction land. The new urban construction land area will include areas that were originally wasteland and are now newly developed, so it is not an accurate indicator for measuring the level of urbanization. The transfer of rural-urban division codes will not have the above problems. The rural-urban division codes are accompanied by the community codes, which represent the residential area. There is no worry that we will include the wasteland as an urbanization area. Moreover, Gan et al. found that areas with division code changing from rural to urban, are far less likely urbanized than those in urban areas, which is consistent with the characteristics of in situ urbanization [8]. So, we use the change in the rural-urban division code to determine whether a community has undergone in situ urbanization. Since 2009, the National Bureau of Statistics of China has published the 15 digit statistical administrative and rural-urban division codes every year. The ﬁrst 12 digits are the community codes, representing the ﬁve administrative levels in China: the ﬁrst two are province-level (including provinces, autonomous regions and centrally administered municipalities), the third and fourth are city-level (including prefectures and prefecture- level cities), the ﬁfth and sixth are county-level (including districts, counties and county- level cities), the seventh, eighth and ninth are township-level (including towns, townships and sub-districts), and the last three are community ID. The ﬁnal 3 digits are the rural- urban division codes. In this 3 digit code, the ﬁrst digit represents the rural-urban division (1 means urban community while 2 means rural community) and the last two represent a detailed sub-classiﬁcation, which could be found in Table 1. Table 1. Number of Different Types of Communities in 2009–2020. 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Downtown area 58,264 54,635 56,837 58,218 57,742 58,371 60,009 64,238 66,352 67,940 69,412 66,737 (111) Urban-rural 19,926 25,859 25,734 26,047 25,523 25,292 25,950 27,043 27,695 28,023 28,421 28,042 fringe area (112) Town center (121) 46,416 36,554 40,102 42,024 40,800 44,222 45,126 46,644 49,416 49,920 49,852 48,767 Town-township 48,374 62,801 65,450 67,219 64,401 66,406 65,068 62,918 63,912 60,603 58,507 54,852 fringe area (122) Special zone (123) 6522 6582 7217 7357 5445 1340 1377 5789 5935 6007 6065 5426 Urban total 179,502 186,431 195,340 200,865 193,911 195,631 197,530 206,632 213,310 212,493 212,257 203,824 Rural center (210) 23,198 22,429 21,590 21,346 17,803 19,391 18,385 16,750 16,129 15,454 14,583 12,445 Village(220) 496,507 487,206 477,556 472,459 439,290 455,457 451,604 443,273 443,144 436,983 429,941 399,431 Rural total 519,705 509,635 499,146 493,805 457,093 474,848 469,989 460,023 459,273 452,437 444,524 411,876 Total 699,207 696,066 694,486 694,670 651,004 670,479 667,519 666,655 672,583 664,930 656,781 615,700 Notes: The 3 digit number in brackets in the ﬁrst column represents the urban and rural division codes. This paper uses the change in the rural-urban division code to determine whether a community has undergone in situ urbanization. It is worth noting that if the administrative level or its superior authority changes, a region’s community code will also change. For example, in 2016, Chongming County in Shanghai was changed to Chongming District, and the ﬁrst 6 digits of its community code were changed from 310230 to 310151. This paper only considers those communities whose 12 digit community code has not changed between 2009 and 2020. This is different from Gan et al., who track all community ID even its administrative and upper-level administrative units have changed from 2009 to 2017 [8]. Unlike them, we exclude those communities whose administrative or upper-level adminis- trative units have changed, mainly because the change in (upper-level) administrative level Sustainability 2022, 14, 9058 5 of 21 is also a shock to the region, and taking this into account is not conducive to identifying the effect of in situ urbanization. We collect all community codes that have appeared from 2009 to 2020. The number of various types of communities is shown in Table 1. There are 374,789 communities that have survived from 2009 to 2020, that is, the 12 digit community code has not changed during this period. Excluding the communities that have changed from urban to rural and communities in special zones, the sample size we use is 362,899. We recognize those communities transferred from rural to urban are areas that take place in in situ urbanization. There were 16,082 in situ urbanization communities from 2009 to 2020, of which 10,199 occurred between 2011 and 2015. We also use data from the 2010 and 2015 national consensuses to study well-being. The Chinese national census takes place every 10 years, among which a one-percent national sample census is conducted every 5 years. In this paper, we call both as national census. These data are all sampled and contain 2,105,260 and 2,003,563 samples, respectively. The data collect all kinds of information about the surveyed individual, such as housing, work, location, family and so on; and especially, each person has a speciﬁc address code, so it can clearly identify the community where the individual is located. Therefore, it is possible to inspect whether an individual has undergone in situ urbanization by combining the community code. In addition, we also use county-level variables in the process of PSM later: the number of students in secondary and primary schools, and the number of beds in medical and health institutions to control public service; the per capita GDP, the balance of savings deposits of urban and rural residents, the total investment in ﬁxed assets, and the general budget revenue and expenditure of local ﬁnance to control economic development; the area of the administrative district, the number of registered population at the end of year, and the number of townships to control endowment. The county-level data came from the Chinese Research Data Service Platform (CNRDS), and nominal variables involved are adjusted using the GDP deﬂator. 3.2. Variable Selection Based on the national census data, this paper mainly analyzes the impacts of in situ urbanization on residents’ well-being from three aspects: housing, mobility and employment. The first focuses on the household while the latter two focus on the individual labor. In the 2010 and 2015 national census questionnaires, the common questions about the housing conditions include: building area, number of rooms, number of ﬂoors, whether there is a private kitchen and a private toilet, and the source of housing. Based on this, we sort out three types of characteristics to reﬂect housing conditions. The ﬁrst is the physical properties of housing, including building area, number of rooms, and whether it is a high- rise building. The second is the living conditions of the housing. The dummy variables, whether there is a private kitchen or a private toilet, will be used as indicators. The third is the source of housing. In the census questionnaires, housing sources are divided into four categories: purchase, self-build, lease and others. In situ urbanization often induces farmers to move out of self-built housing to commercial building, so we choose whether live in purchased or self-built housing as dependent variables. Regarding the control variables in housing condition analysis, this paper considers the family size due to the availability of data. When discussing individuals’ employment and mobility, we ﬁrstly limit the sample range from 16 to 60 years old, which is usually seen as the working-age in China. At the same time, we excluded unemployed individuals whose reasons are as follows: studying at school, incapacity for work, retirement, doing household chores, and others. When examin- ing in situ urbanization’s impacts on mobility, the dependent variables include whether the individual stays in the household registered (hukou) county, whether the individual left household registered county more than half a year, and whether the individual comes from other communities. When examining in situ urbanization’s impacts on employment, the Sustainability 2022, 14, 9058 6 of 21 sample includes all the population staying in the county at the time of ﬁlling in the national census questionnaires, that is, the population who stayed in the county with local hukou and the migrant population who ﬂowed into the county without local hukou. We inspect the change in employment structure in the county after in situ urbanization. Individual control variables included gender, age, ethnicity, education, marital status, literacy, and family size. Data description of the main variables is shown in Table 2. Table 2. The deﬁnition and data description of variables. Variables Deﬁnition Observation Mean S.D. Minimum Maximum Area House area 354,730 103.011 72.384 1 360 Rooms The number of rooms 354,730 3.239 2.205 1 12 1 if family live in the building with more High-rise 348,069 0.436 0.496 0 1 than 3 ﬂoors, 0 otherwise 1 if the house has private kitchen, Kitchen 348,069 0.865 0.342 0 1 0 otherwise Toilet 1 if the house has private toilet, 0 otherwise 348,069 0.530 0.499 0 1 Self-built 1 if the family built the house, 0 otherwise 348,069 0.469 0.499 0 1 1 if the family bought the house, Bought 348,069 0.156 0.363 0 1 0 otherwise 1 if individual stays in the household In-county 682,119 0.904 0.294 0 1 registered county, 0 otherwise 1 if the individual left household registered Migrant 682,119 0.054 0.226 0 1 county more than half a year, 0 otherwise 1 if individual comes from other Inﬂow 682,119 0.303 0.459 0 1 communities in the county, 0 otherwise Unemployment 1 if individual loses job, 0 otherwise 682,119 0.050 0.217 0 1 1 if individual works in primary industry, Ind1 648,168 0.287 0.452 0 1 0 otherwise 1 if individual works in secondary industry, Ind2 648,168 0.290 0.454 0 1 0 otherwise 1 if individual works in tertiary industry, Ind3 648,168 0.403 0.490 0 1 0 otherwise Male 1 if individual is male, 0 otherwise 682,119 0.562 0.496 0 1 Minority 1 if individual is a minority, 0 otherwise 682,119 0.048 0.214 0 1 1 if individual holds junior college degrees Education 682,119 0.174 0.379 0 1 or above, 0 otherwise Married 1 if individual is married, 0 otherwise 682,119 0.762 0.426 0 1 Literacy 1 if individual is literate, 0 otherwise 682,119 0.989 0.102 0 1 Age Individual’s age 682,119 37.251 10.997 16 60 Familysize Number of family members 682,119 4.047 1.790 1 10 3.3. Methods Our research procedure consists of two parts. First, we use Propensity Score Matching (PSM) to construct data that could be used to compare among counties. Second, we apply Differences-in-Difference (DID) to estimate the impacts of in situ urbanization. 3.3.1. Propensity Score Matching (PSM) To protect individual privacy, the census data does not provide a code that can uniquely identify each person, so we could not match them year by year. Instead, we can only use pooled cross-sectional data for research. Additionally, the rural-urban division code has been published and implemented since 2009, so only the 2010 and 2015 census data can Sustainability 2022, 14, 9058 7 of 21 be used for empirical analysis, and parallel trend tests also cannot be carried out with two-period data. In order to make up for the above deﬁciencies, this paper refers to Xu et al. and Wan & Li, applying the Propensity Score Matching (PSM) and Differences- in-Difference (DID) method to evaluate [22,40]. Note that counties that implement in situ urbanization are selective in our study. For example, counties with higher economic development level are more likely to implement in situ urbanization because of stronger public service affordability. Hence, we match data based on the county-level characteristics when using PSM. Based on the above reasons, this paper, taking 2010 as the base period, matches the counties with in situ urbanization (treatment group) and without (control group) during 2011–2015 by using one-to-one nearest-neighbor logistic model. We also use other matching methods and obtain robust results. The matching results and balancing test are shown in Table 3. We take logarithms on all variables in Table 3 when we run PSM model. As can be seen in Table 3, the p value of each variable after matching is greater than 0.1, indicating that there is no signiﬁcant difference between the variables in the treatment group and the control group after matching; and the standardized bias is signiﬁcantly reduced, much less than the threshold value (20%). This indicates that we pass the balancing test, that is, the treatment group and the control group have very close characteristics in all aspects after matching. Finally, the treatment group of this paper consists of 139,245 households and 298,387 individuals in 253 counties, and the control group consists of 205,485 households and 383,732 individuals in 358 counties, as shown in Table 4. Table 3. PSM results and balancing test. Mean t Test Unmatched % Reduct V(T)/ Variable % Bias Matched |Bias| V(C) Treated Control t p > |t| Number of students in U 10.732 10.777 5.100 0.790 0.432 0.62 * 81.600 secondary schools M 10.732 10.740 0.900 0.150 0.879 0.92 Number of students in U 10.743 10.783 4.100 0.640 0.523 0.59 * 56.000 primary schools M 10.742 10.760 1.800 0.300 0.762 0.92 U 7.774 7.929 14.800 2.310 0.021 0.70 * Number of beds in medical and 98.900 M 7.772 7.774 0.200 0.030 0.980 0.93 health institutions U 5.115 5.224 7.800 1.210 0.225 0.75 * Total investment in ﬁxed assets 69.500 M 5.114 5.148 2.400 0.370 0.713 0.91 U 12.685 12.988 26.800 4.220 0.000 0.85 General budget expenditure of 99.600 local ﬁnance M 12.686 12.687 0.100 0.010 0.989 0.97 U 11.959 12.299 19.600 3.070 0.002 0.80 * General budget revenue of 86.800 local ﬁnance M 11.959 11.914 2.600 0.400 0.690 0.98 U 8.136 9.025 52.100 8.170 0.000 0.80 * Area of the administrative district 97.800 M 8.137 8.118 1.100 0.170 0.865 0.87 Number of registered population U 4.383 4.673 21.700 3.360 0.001 0.54 * 70.200 at the end of the year M 4.383 4.296 6.500 1.120 0.262 0.95 U 3.035 3.177 24.600 3.860 0.000 0.84 Number of townships 98.600 M 3.036 3.038 0.300 0.050 0.959 0.96 U 14.272 14.518 16.600 2.590 0.010 0.71 * Balance of savings deposits of 99.200 urban and rural residents M 14.271 14.269 0.100 0.020 0.983 0.92 U 1.814 2.130 34.500 5.420 0.000 0.88 Per capita GDP 97.600 M 1.815 1.822 0.800 0.120 0.902 0.85 Notes: * indicates p < 0.1. Sustainability 2022, 14, 9058 8 of 21 Table 4. Description of treatment and control group after PSM. County A: 253 B: 358 A1 A2 A3 B1 B2 Household 86,685 58,064 4496 153,110 52,375 Total: 139,245 Total: 205,485 A1 A2 A3 B1 B2 Individual 159,188 129,242 9957 261,292 122,440 Total: 298,387 Total: 383,732 In addition to studying the direct impacts of in situ urbanization on residents who live in the communities transferred from rural to non-agricultural, we also study the spillover effects, that is, the impacts of in situ urbanization on other residents who live in the urban communities or rural communities that has not transferred. For the convenience of explanation, the counties containing the rural-urban conversion community are referred to as A, and A is divided into urban community A1, rural community A2 without in situ urbanization, and A3, the original rural community that has been transferred into urban community. The counties that do not contain rural-to-urban communities are referred to as B, and B includes urban communities B1 and rural communities B2. Table 4 shows the sample size of each group. The direct and spillover effects of in situ urbanization are explored by using different combinations of treatment and control group. 3.3.2. Differences-in-Difference (DID) After the PSM, we use the DID model to identify the impacts of in situ urbanization on family housing, individual mobility and employment. The DID model is constructed as follows: Y = a + bD + gT + l(D T ) + dX + j + # (1) it it it it it it it In Formula (1), the subscripts (c, i and t) represent counties, families or individuals, and years, respectively. Y is the outcome variable affected by in situ urbanization, including family housing, individual mobility and employment. Due to the existence of continuous variables in the outcome variables, we use linear probability models for convenience. D represents the binary variable indicating whether the individual or family has undergone in situ urbanization. T is the binary variable of whether in situ urbanization has occurred at the current. The coefﬁcient l before D T is what we pay attention to, which shows the exactly change after in situ urbanization. According to our hypotheses, housing, mobility and employment conditions will be improved after in situ urbanization, so we expect those regression model with positive independent variable would result l > 0, vice versa. X represents the control variable. When we analyze the impacts of in situ urbanization on housing, X consists of family size due to the data availability. When we analyze the impacts of in situ urbanization on individual mobility and employment, X consists of gender, age, ethnicity, education, marital status, literacy, and family size. The deﬁnition and data description of X could be seen in Table 2. The county ﬁxed effects are controlled by j, while time ﬁxed effects have been controlled by T because there are only two-year cross-sectional data. It should be noted that the characteristics of cross-sectional data lead to the inability to precisely control the individual ﬁxed effects in the model, and we can only control the ﬁxed effects at a higher level. We choose to control the county ﬁxed effects because this paper focuses on county. And we adopt robust standard errors by clustering to individual or family level to solve the problem of heteroscedasticity in all regressions. Sustainability 2022, 14, 9058 9 of 21 4. Results 4.1. Impacts of In Situ Urbanization on Housing This section explores the impacts of in situ urbanization on household housing con- ditions by combining different treatment and control groups, and full regression results are shown in Appendix A, Tables A1–A6. For convenience to compare, we organize the results in Table 5. In the table, the characters in front of “&” represent the treatment group, while in the back represents the control group. For instance, A3&A1A2, means that A3 is the treatment group and A1 and A2 are control group in Formula (1). The following “&” in the table has the same meaning. Table 5. Impacts of In situ Urbanization on Housing. (1) (2) (3) (4) (5) (6) A3&A1A2 A3&A2 A3&B2 A2&B2 A1& B1 A&B 3.032 0.095 0.085 1.812 * 4.913 *** 3.654 *** Area (2.693) (2.729) (2.943) (0.933) (0.669) (0.542) 0.064 0.179 ** 0.215 *** 0.073 ** 0.159 *** 0.123 *** Room number (0.072) (0.074) (0.078) (0.030) (0.020) (0.017) 0.022 ** 0.055 *** 0.029 *** 0.002 0.008 ** 0.002 High-rise (0.011) (0.009) (0.010) (0.002) (0.004) (0.003) 0.049 *** 0.042 *** 0.049 *** 0.007 0.011 *** 0.009 *** Kitchen (0.011) (0.012) (0.012) (0.005) (0.002) (0.002) 0.095 *** 0.079 *** 0.086 *** 0.009* 0.008 ** 0.012 *** Toilet (0.014) (0.014) (0.015) (0.005) (0.004) (0.003) 0.047 *** 0.053 *** 0.040 *** 0.003 0.012 *** 0.011 *** Self-built (0.012) (0.011) (0.012) (0.003) (0.003) (0.003) 0.005 0.013 ** 0.005 0.001 0.011 *** 0.006 ** Bought (0.006) (0.006) (0.006) (0.001) (0.004) (0.003) Notes: ***, **, *, respectively, indicate p < 0.01, p < 0.05, p < 0.1. The standard errors are given in parentheses. Control variables include family size. The results of column (1) in Table 5 show that, compared with A1 and A2, households in A3 are more likely to move into high-rise buildings after in situ urbanization, and those buildings are less likely built by themselves. In addition, the possibility of households with private kitchens and toilets increases. Changing the control group to A2, we will ﬁnd that the number of rooms occupied by A3 families has decreased, but the probability of A3 staying in purchased high-rise buildings with better facilities has increased signiﬁcantly. This is inconsistent with existing studies which indicate that in situ urbanization will reduce the living area but improve living conditions, and veriﬁes out Hypothesis 1. This also indirectly shows that our method of using the transfer of rural-urban division codes to measure in situ urbanization is reasonable. Then we consider spillover effects. The ﬁrst is to consider whether other rural commu- nities in the county will be affected by in situ urbanization. The control group was replaced by B2, and the regression results are shown in column (3). We can ﬁnd that column (3) is basically the same as that of column (2) in coefﬁcient sign and signiﬁcance, but the coefﬁ- cients vary, indicating that A2 may have been affected by in situ urbanization. Therefore, we directly compare A2 with B2, and the results are shown in column (4). The housing area and the number of rooms of A2 families have decreased, but there is no signiﬁcant change in housing ﬂoors and housing sources, and their living conditions of housing is hardly improved. This shows that in situ urbanization may compress the living area of other rural communities in the county, but the housing sources and living conditions of other rural Sustainability 2022, 14, 9058 10 of 21 communities will not be signiﬁcantly affected. The second is to consider spillover effects on urban communities. The results of column (5) show that the housing area and the number of rooms of the A1 families have decreased signiﬁcantly, but the possibility of living in purchased high-rise buildings with better living facilities has increased signiﬁcantly. The improvement in living conditions and housing source of A1 is not surprising, but the decline in housing area and number of rooms is somewhat odd. We think the reason behind that is the mobility of the labor. The following analysis of mobility shows that A1 will have more inﬂows after in situ urbanization, so the demand for housing in A1 will increase, accompanied with a decline in the housing area and the number of rooms. Finally, we study the spillover effects on the entire county. Column (6) shows that after conducting in situ urbanization, the non-self-built housing in counties and the living conditions will improve comparing to B, but the area and number of rooms will decrease. 4.2. Impacts of In Situ Urbanization on Mobility This section studies the impacts of in situ urbanization on the mobility of residents, full regression results are shown in Appendix A, Tables A7 and A8. It can be found that an individual who is female, unmarried, younger, and well-educated is more likely to leave the hometown and become a migrant worker. We rearrange the results in Table 6. The results in column (1) show that after in situ urbanization, the probability of A3 farmers staying in the county increases signiﬁcantly, while the probability of becoming an outﬂow population decreases signiﬁcantly. Hypoth- esis 2 is veriﬁed. And similar results are shown in column (2). We need to explain why there are signiﬁcant differences between A2 and A3 even if they are both farmers with similar qualities. Three reasons lie behind; ﬁrst is the wealth effect. In situ urbanization is usually accompanied by land expropriation, so A3 residents may reduce motivation to go out to look for jobs after receiving monetary or property compensation. The second is the information advantage. Compared with other labors in the county, A3 residents have more information on the development of counties (such as information about building factories or commercial districts on their occupied land), and they are more likely to return to or stay in their hometowns for employment. The third is the protection for land-losing farmers by local governments. For the sake of farmers’ interests and social stability, local governments may require factories or commercial companies occupying A3 land to hire more A3 residents to solve the employment problem of landless farmers. Table 6. Impacts of In situ Urbanization on Mobility. (1) (2) (3) (4) (5) (6) A3&A1A2 A3&A2 A3&B2 A2&B2 A1& B1 A&B 0.035 *** 0.054 *** 0.036 *** 0.027 *** 0.003 0.008 *** In-county (0.009) (0.009) (0.010) (0.004) (0.002) (0.002) 0.028 *** 0.037 *** 0.033 *** 0.011 *** 0.002 0.003 Migrant (0.007) (0.007) (0.008) (0.004) (0.002) (0.002) 0.014 0.005 0.001 0.001 0.023 *** 0.022 *** Inﬂow (0.013) (0.013) (0.013) (0.003) (0.005) (0.003) Notes: *** indicates p < 0.01. The standard errors are given in parentheses. Control variables include gender, age, ethnicity, education, marital status, literacy, and family size. Next the control group of A3 was replaced by B2, and the same conclusion was obtained in column (2) and column (3), but the absolute value of the coefﬁcient in column (2) was larger than that in column (3), so it is reasonable to study spillover effects of in situ urbanization. By comparing A2 and B2 directly, column (4) shows that the probability of A2 residents staying in the county is signiﬁcantly lower than that of B2, and the probability of going out to become migrant workers is signiﬁcantly higher. A2 and A3 are both farmers Sustainability 2022, 14, 9058 11 of 21 in essence, which means they may seek similar work in the county. However, there are limited job opportunities for A3 and A2 in the county. When A3 labor increases, both A2 and A3 would face ﬁercer competition in the job market. And from the reasons we have mentioned above, A3 residents would be more likely to stay in their hometown county with land expropriation compensation even if they are out of work, so A2 farmers would be crowded out and are more likely to become migrant workers compared with B2. Besides spillover effects on the mobility of other agricultural communities, in situ urbanization can also affect the mobility of residents in non-agricultural communities. The results in column (5) show that after the in situ urbanization, the probability of A1 citizens leaving the county did not change signiﬁcantly, but the population ﬂowing into A1 from other communities increased signiﬁcantly. Moreover, the results in column (6) show that the total population remaining in the county A decreases, and this is resulted from the population outﬂow in A2 exceeds the population return in A3. 4.3. Impacts of In Situ Urbanization on Employment This section studies the impacts of in situ urbanization on the employment of residents in the county. The sample in this section consists of all the labor, including local residents and migrants in the county. Appendix A, Tables A9–A11 show the regression results. We can ﬁnd that ethnic minorities are more engaged in primary production because of differences in language and culture, and younger men are more likely to work in non-agricultural industries, especially in the secondary industry compared with women. Moreover, well- educated laborers appeal more to jobs in the tertiary industry. Table 7 shows the abbreviated regression results. Column (1) in Table 7 shows that compared with other labors in the county, the probability of A3 residents engaging in the primary industry has decreased signiﬁcantly, while the probability of engaging in the tertiary industry has increased signiﬁcantly. Hypothesis 3 is veriﬁed. The results of column (2) show that the A2 and A3 populations who stay in the county have no signiﬁcant difference in their working industries, which is in line with the above point of view, that is, A2 and A3 are farmers with similar qualities and compete for employment. When the control group is replaced by B2, A3 labors are less likely to engage in the primary industry, while are more likely to engage in the tertiary industry. Table 7. Impacts of in situ Urbanization on Employment. (1) (2) (3) (4) (5) (6) A3&A1A2 A3&A2 A3&B2 A2&B2 A1& B1 A&B 0.008 0.001 0.004 0.004 * 0.004 * 0.001 Unemployment (0.006) (0.006) (0.007) (0.002) (0.002) (0.002) 0.051 *** 0.013 0.031 ** 0.050 *** 0.025 *** 0.053 *** Ind1 (0.013) (0.014) (0.015) (0.006) (0.003) (0.003) 0.014 0.015 0.022 0.003 0.002 0.007 ** Ind2 (0.014) (0.014) (0.015) (0.005) (0.004) (0.003) 0.030 ** 0.011 0.041 *** 0.038 *** 0.010 ** 0.027 *** Ind3 (0.013) (0.013) (0.014) (0.005) (0.005) (0.004) Notes: ***, **, *, respectively, indicate p < 0.01, p < 0.05, p < 0.1. The standard errors are given in parentheses. Control variables include gender, age, ethnicity, education, marital status, literacy, and family size. Then we examine the spillover effects. Taking A2 as the treatment group and B2 as the control group, the results in column (4) show that the unemployment probability of A2 farmers has increased compared with that of B2 farmers. This comes from the crowding out effect which has not yet been fully “digested”, that is, the unemployed A2 workers do not have enough time to leave the county for work. Moreover, the probability of A2 engaging in the primary industry has relatively decreased, while the probability of engaging in the tertiary industry has relatively increased. For non-agricultural communities, the unemployment rate of A1 will drop slightly compared to B1, and the probability of Sustainability 2022, 14, 9058 12 of 21 engaging in the tertiary industry will increase signiﬁcantly. This implicates the development of non-agricultural industries in the county, especially in A1. On the whole, column (6) shows that the employment structure of the county has shifted from the primary industry to the secondary and tertiary industries. This paper argues that such a shift consists of the following two parts: one is the increase in the population ﬂowing into the A1 community, and the other is the mobility of A2 and A3 farmers. Speciﬁcally, the results of column (5) show that there is no signiﬁcant difference between A1 and B1 in the probability to engage in the secondary industry, but the results in the previous part show that labor will ﬂow into A1. Although the probability remains the same, the total of labor in A1 increases, so the number of people engaged in the secondary industry will also increase after the in situ urbanization. It is obvious that the tertiary industry attracts more laborers because the results of A1&B1 show that the coefﬁcient of Ind3 is signiﬁcant while Ind2 is not. In addition to that, the shift also consists of internal change among rural labors, that is, due to A3 labors are more likely to stay in the county so the secondary and tertiary industry in the county will attract more A3 and less A2 relatively. 5. Conclusions and Implications In situ urbanization can be considered as beneﬁcial to the local community, and thus is an example of sustainable urbanization in China. Existing studies about in situ urbanization place emphasis on empirical cases and ignore the spillover effects on other residents in the county. This paper ﬁrst ﬁnds a suitable measure: the transfer of rural-urban division codes to identify in situ urbanization. On this basis, combined with the 2010 and 2015 census data, we study the impacts of in situ urbanization on farmers’ well-being from the perspective of housing, mobility and employment. Moreover, we explore the spillover effects of in situ urbanization on residents of other rural and urban communities by combining different treatment and control groups. The main conclusions of this paper are as follows: First, farmers are more likely to move into non-self-built high-rise buildings after in situ urbanization, and their living conditions will be improved, while their housing area and number of rooms will decrease. Furthermore, similar results could be observed for the other rural community without in situ urbanization and urban community in the county, with advancing living conditions and decreasing living areas. Second, the probability of farmers becoming migrant workers after in situ urbanization decreases, while the probability of other farmers in the county increases. Due to the wealth effect, information advantages and the protection by local governments, farmers after in situ urbanization would become more “sticky” to their hometowns, and farmers in other communities with similar qualities are more likely to go out for work due to ﬁercer job competition. Moreover, in situ urbanization will attract laborers inﬂowing non-agricultural communities in the county due to the non-agricultural industrial development. Third, in situ urbanization will promote the shift of employment structure in the county from the primary to the secondary and tertiary industries, of which the tertiary industry is the main direction of the shift. Such a shift consists of two major parts. One comes from the urban community with more people inﬂowing and being engaged in secondary and tertiary industries. The other comes from the internal change between farmers with in situ urbanization and the other farmers without in the county. Overall, our study shows that in situ urbanization can improve residents’ well-being from the perspective of housing, mobility and employment. Concluded from this, in situ urbanization in China is an integrated development of land and human urbanization. Although in situ urbanization in China is always initiated by government, which is different from classic urbanization processes, it leads to similar results; i.e., urbanization provides more non-agricultural job opportunities for residents, attracts rural people ﬂowing, and promoting structural transformation [41–44]. Our research has two theoretical contributions. First, we use an appropriate indicator to measure in situ urbanization. Second, we not only study the effects of in situ urbanization on farmers, but also spillover effects of in situ urbanization on other residents in the county. Sustainability 2022, 14, 9058 13 of 21 As for the practical implication, we suggest that moderate promotion for urbaniza- tion by government is beneﬁcial for developing countries. Existing studies show that urbanization in developing countries could shift labor from agricultural sectors to non- agricultural sectors, promoting non-agricultural development and reducing poverty [42,43]. But urbanization needs some conditions to trigger, which may be difﬁcult or need long time to meet for developing countries. Therefore, some promotions should be made by governments, such as the in situ urbanization conducted by the government in China. Of course, we should pay attention to the phenomenon that “urbanization of people lags behind urbanization of land”, that is, the welfare of people was ignored during the planned urbanization. Establishing a sound and fair social security system, including medical care, education, pension and so on, is the responsibility and obligation of the government during urbanization. Author Contributions: Conceptualization, Y.L. and Z.X.; methodology, Y.L.; software, Y.L.; validation, Y.L., and Z.X.; formal analysis, Y.L.; investigation, Z.X.; resources, B.L.; data curation, Z.X. and M.M. writing—original draft preparation, Y.L.; writing—review and editing, B.L., Z.X. and M.M.; visualization, B.L. and M.M.; supervision, B.L. and M.M.; project administration, Z.X.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by National Social Science Foundation of China [grant number 18BJY061]. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The rural-urban division codes could be obtained from http://www. stats.gov.cn/tjsj/tjbz/tjyqhdmhcxhfdm. The national census data is freely accessible to registered users at https://microdata.stats.gov.cn. Conﬂicts of Interest: The authors declare no conﬂict of interest. Appendix A Table A1. Impacts of In situ Urbanization on Housing (A3&A1A2). (1) (2) (3) (4) (5) (6) (7) Area Rooms High-Rise Kitchen Toilet Self-Built Bought 13.335 *** 0.338 *** 0.198 *** 0.034 *** 0.134 *** 0.193 *** 0.090 *** (1.274) (0.035) (0.006) (0.007) (0.008) (0.007) (0.003) 23.876 *** 0.773 *** 0.045 *** 0.059 *** 0.177 *** 0.000 0.012 *** (0.415) (0.013) (0.002) (0.002) (0.002) (0.002) (0.002) 3.032 0.064 0.022 ** 0.049 *** 0.095 *** 0.047 *** 0.005 D T (2.693) (0.072) (0.011) (0.011) (0.014) (0.012) (0.006) 13.351 *** 0.389 *** 0.028 *** 0.022 *** 0.000 0.059 *** 0.008 *** Familysize (0.126) (0.004) (0.001) (0.001) (0.001) (0.001) (0.000) 59.426 *** 1.985 *** 0.414 *** 0.760 *** 0.416 *** 0.378 *** 0.156 *** Constant (0.449) (0.014) (0.003) (0.003) (0.003) (0.003) (0.002) County Fixed YES YES YES YES YES YES YES Effects N 149,245 149,245 146,925 146,925 146,925 146,925 146,925 adj. R 0.237 0.254 0.336 0.144 0.297 0.399 0.082 Notes: ***, ** respectively, indicate p < 0.01, p < 0.05. The standard errors are given in parentheses. Sustainability 2022, 14, 9058 14 of 21 Table A2. Impacts of In situ Urbanization on Housing (A3&A2). (1) (2) (3) (4) (5) (6) (7) Area Rooms High-Rise Kitchen Toilet Self-Built Bought 6.379 *** 0.020 0.030 *** 0.011 0.045 *** 0.070 *** 0.015 *** (1.333) (0.037) (0.004) (0.008) (0.007) (0.006) (0.003) 25.839 *** 0.858 *** 0.006 *** 0.063 *** 0.180 *** 0.002 0.002 *** (0.644) (0.021) (0.001) (0.003) (0.003) (0.002) (0.001) 0.095 0.179 ** 0.055 *** 0.042 *** 0.079 *** 0.053 *** 0.013 ** D T (2.729) (0.074) (0.009) (0.012) (0.014) (0.011) (0.006) 12.055 *** 0.367 *** 0.002 *** 0.020 *** 0.016 *** 0.015 *** 0.001 *** Familysize (0.174) (0.006) (0.000) (0.001) (0.001) (0.001) (0.000) 70.547 *** 2.509 *** 0.025 *** 0.696 *** 0.076 *** 0.888 *** 0.007 *** Constant (0.715) (0.023) (0.002) (0.004) (0.004) (0.003) (0.001) County Fixed YES YES YES YES YES YES YES Effects N 62,558 62,558 61,622 61,622 61,622 61,622 61,622 adj. R 0.282 0.255 0.142 0.206 0.344 0.201 0.049 Notes: ***, ** respectively, indicate p < 0.01, p < 0.05. The standard errors are given in parentheses. Table A3. Impacts of In situ Urbanization on Housing (A3&B2). (1) (2) (3) (4) (5) (6) (7) Area Rooms High-Rise Kitchen Toilet Self-Built Bought - - - - - - - - - - - - - - 27.386 *** 0.924 *** 0.009 *** 0.070 *** 0.171 *** 0.005 ** 0.001 (0.672) (0.022) (0.001) (0.004) (0.003) (0.002) (0.001) 0.085 0.215 *** 0.029 *** 0.049 *** 0.086 *** 0.040 *** 0.005 D T (2.943) (0.078) (0.010) (0.012) (0.015) (0.012) (0.006) 10.521 *** 0.338 *** 0.003 *** 0.020 *** 0.011 *** 0.018 *** 0.001 *** Familysize (0.173) (0.005) (0.000) (0.001) (0.001) (0.001) (0.000) 65.407 *** 2.198 *** 0.032 *** 0.690 *** 0.083 *** 0.865 *** 0.009 *** Constant (0.724) (0.023) (0.002) (0.005) (0.004) (0.003) (0.001) County Fixed YES YES YES YES YES YES YES Effects N 56,857 56,857 55,871 55,871 55,871 55,871 55,871 adj. R 0.268 0.272 0.188 0.191 0.328 0.194 0.101 Notes: ***, ** respectively, indicate p < 0.01, p < 0.05. The standard errors are given in parentheses. Sustainability 2022, 14, 9058 15 of 21 Table A4. Impacts of In situ Urbanization on Housing (A2&B2). (1) (2) (3) (4) (5) (6) (7) Area Rooms High-Rise Kitchen Toilet Self-Built Bought - - - - - - - - - - - - - - 27.440 *** 0.925 *** 0.009 *** 0.070 *** 0.171 *** 0.005 ** 0.001 (0.672) (0.022) (0.001) (0.004) (0.003) (0.002) (0.001) 1.812 * 0.073 ** 0.002 0.007 0.009 * 0.003 0.001 D T (0.933) (0.030) (0.002) (0.005) (0.005) (0.003) (0.001) 10.919 *** 0.346 *** 0.002 *** 0.019 *** 0.013 *** 0.014 *** 0.000 *** Familysize (0.126) (0.004) (0.000) (0.001) (0.001) (0.000) (0.000) 68.715 *** 2.377 *** 0.021 *** 0.697 *** 0.071 *** 0.895 *** 0.005 *** Constant (0.520) (0.017) (0.001) (0.003) (0.003) (0.002) (0.001) County Fixed YES YES YES YES YES YES YES Effects N 110,435 110,435 108,605 108,605 108,605 108,605 108,605 adj. R 0.277 0.264 0.107 0.202 0.328 0.130 0.013 Notes: ***, **, *, respectively, indicate p < 0.01, p < 0.05, p < 0.1. The standard errors are given in parentheses. Table A5. Impacts of In situ Urbanization on Housing (A1&B1). (1) (2) (3) (4) (5) (6) (7) Area Rooms High-Rise Kitchen Toilet Self-Built Bought - - - - - - - - - - - - - - 26.998 *** 0.869 *** 0.061 *** 0.043 *** 0.166 *** 0.012 *** 0.007 *** (0.408) (0.013) (0.002) (0.001) (0.002) (0.002) (0.002) 4.913 *** 0.159 *** 0.008 ** 0.011 *** 0.008 ** 0.012 *** 0.011 *** D T (0.669) (0.020) (0.004) (0.002) (0.004) (0.003) (0.004) 10.900 *** 0.295 *** 0.017 *** 0.027 *** 0.011 *** 0.055 *** 0.004 *** Familysize (0.108) (0.003) (0.001) (0.000) (0.001) (0.001) (0.001) 53.842 *** 1.723 *** 0.666 *** 0.803 *** 0.609 *** 0.074 *** 0.214 *** Constant (0.340) (0.010) (0.002) (0.002) (0.002) (0.002) (0.002) County Fixed YES YES YES YES YES YES YES Effects N 239,795 239,795 235,010 235,010 235,010 235,010 235,010 adj. R 0.196 0.197 0.268 0.098 0.185 0.343 0.067 Notes: ***, **, respectively, indicate p < 0.01, p < 0.05. The standard errors are given in parentheses. Sustainability 2022, 14, 9058 16 of 21 Table A6. Impacts of In situ Urbanization on Housing (A&B). (1) (2) (3) (4) (5) (6) (7) Area Rooms High-Rise Kitchen Toilet Self-Built Bought - - - - - - - - - - - - - - 27.516 *** 0.892 *** 0.048 *** 0.051 *** 0.168 *** 0.010 *** 0.006 *** (0.352) (0.011) (0.002) (0.001) (0.002) (0.002) (0.002) 3.654 *** 0.123 *** 0.002 0.009 *** 0.012 *** 0.011 *** 0.006 ** D T (0.542) (0.017) (0.003) (0.002) (0.003) (0.003) (0.003) 11.383 *** 0.336 *** 0.025 *** 0.021 *** 0.000 0.056 *** 0.005 *** Familysize (0.082) (0.002) (0.000) (0.000) (0.000) (0.000) (0.000) 57.176 *** 1.862 *** 0.504 *** 0.777 *** 0.474 *** 0.284 *** 0.169 *** Constant (0.283) (0.008) (0.002) (0.002) (0.002) (0.002) (0.001) County Fixed YES YES YES YES YES YES YES Effects N 354,730 354,730 348,069 348,069 348,069 348,069 348,069 adj. R 0.223 0.244 0.405 0.140 0.318 0.482 0.090 Notes: ***, **, respectively, indicate p < 0.01, p < 0.05. The standard errors are given in parentheses. Table A7. Impacts of In situ Urbanization on Mobility (A3&A12; A3&A2; A3&B2). A3&A1A2 A3&A2 A3&B2 (1) (2) (3) (1) (2) (3) (1) (2) (3) In-County Migrant Inﬂow In-County Migrant Inﬂow In-County Migrant Inﬂow D 0.005 0.001 0.037 *** 0.038 *** 0.020 *** 0.088 *** - - - (0.005) (0.004) (0.007) (0.006) (0.005) (0.007) - - - T 0.010 *** 0.011 *** 0.008 *** 0.030 *** 0.020 *** 0.002 0.002 0.009 *** 0.000 (0.002) (0.001) (0.003) (0.003) (0.003) (0.002) (0.003) (0.003) (0.002) D T 0.035 *** 0.028 *** 0.014 0.054 *** 0.037 *** 0.005 0.036 *** 0.033 *** 0.001 (0.009) (0.007) (0.013) (0.009) (0.007) (0.013) (0.010) (0.008) (0.013) Male 0.024 *** 0.022 *** 0.007 *** 0.040 *** 0.035 *** 0.005 *** 0.028 *** 0.025 *** 0.004 *** (0.001) (0.001) (0.001) (0.002) (0.001) (0.001) (0.002) (0.001) (0.001) Minority 0.013 *** 0.010 *** 0.081 *** 0.018 ** 0.017 *** 0.072 *** 0.008 0.008 0.009 (0.004) (0.003) (0.007) (0.007) (0.005) (0.008) (0.006) (0.005) (0.007) Education 0.003 * 0.004 *** 0.005 0.086 *** 0.050 *** 0.050 *** 0.075 *** 0.045 *** 0.078 *** (0.002) (0.001) (0.004) (0.007) (0.006) (0.007) (0.008) (0.006) (0.009) Married 0.034 *** 0.025 *** 0.073 *** 0.040 *** 0.030 *** 0.051 *** 0.050 *** 0.039 *** 0.052 *** (0.002) (0.002) (0.003) (0.003) (0.003) (0.002) (0.003) (0.003) (0.002) Literacy 0.058 *** 0.036 *** 0.036 *** 0.065 *** 0.043 *** 0.021 *** 0.051 *** 0.033 *** 0.010 ** (0.004) (0.003) (0.006) (0.005) (0.004) (0.004) (0.005) (0.004) (0.005) Age 0.004 *** 0.003 *** 0.006 *** 0.007 *** 0.004 *** 0.002 *** 0.006 *** 0.003 *** 0.002 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Familysize 0.017 *** 0.010 *** 0.033 *** 0.016 *** 0.010 *** 0.005 *** 0.014 *** 0.009 *** 0.008 *** (0.001) (0.000) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Sustainability 2022, 14, 9058 17 of 21 Table A7. Cont. A3&A1A2 A3&A2 A3&B2 (1) (2) (3) (1) (2) (3) (1) (2) (3) In-County Migrant Inﬂow In-County Migrant Inﬂow In-County Migrant Inﬂow Constant 0.838 *** 0.095 *** 0.597 *** 0.698 *** 0.172 *** 0.160 *** 0.718 *** 0.161 *** 0.167 *** (0.005) (0.004) (0.008) (0.008) (0.006) (0.008) (0.008) (0.006) (0.008) County Fixed YES YES YES YES YES YES YES YES YES Effects N 298,387 298,387 298,387 139,198 139,198 139,198 132,392 132,392 132,392 adj. R 0.150 0.132 0.273 0.176 0.152 0.192 0.152 0.127 0.198 Notes: ***, **, *, respectively, indicate p < 0.01, p < 0.05, p < 0.1. The standard errors are given in parentheses. Table A8. Impacts of In situ Urbanization on Mobility (A2&B2; A1&B1; A&B). A2&B2 A1&B1 A&B (1) (2) (3) (1) (2) (3) (1) (2) (3) In-County Migrant Inﬂow In-County Migrant Inﬂow In-County Migrant Inﬂow D - - - - - - - - - - - - - - - - - - T 0.003 0.009 *** 0.000 0.005 *** 0.003 *** 0.036 *** 0.001 0.006 *** 0.027 *** (0.003) (0.003) (0.002) (0.001) (0.001) (0.003) (0.001) (0.001) (0.002) D T 0.027 *** 0.011 *** 0.001 0.003 0.002 0.023 *** 0.008 *** 0.003 0.022 *** (0.004) (0.004) (0.003) (0.002) (0.002) (0.005) (0.002) (0.002) (0.003) Male 0.035 *** 0.031 *** 0.005 *** 0.003 *** 0.006 *** 0.016 *** 0.018 *** 0.017 *** 0.007 *** (0.001) (0.001) (0.001) (0.001) (0.000) (0.001) (0.001) (0.000) (0.001) Minority 0.001 0.000 0.016 *** 0.003 * 0.001 0.050 *** 0.003 0.003 * 0.016 *** (0.005) (0.004) (0.005) (0.002) (0.001) (0.005) (0.002) (0.002) (0.004) Education 0.087 *** 0.052 *** 0.063 *** 0.013 *** 0.004 *** 0.096 *** 0.004 *** 0.001 0.035 *** (0.006) (0.005) (0.006) (0.001) (0.001) (0.002) (0.001) (0.001) (0.002) Married 0.045 *** 0.035 *** 0.049 *** 0.018 *** 0.014 *** 0.073 *** 0.030 *** 0.023 *** 0.074 *** (0.002) (0.002) (0.002) (0.001) (0.001) (0.002) (0.001) (0.001) (0.002) Literacy 0.061 *** 0.039 *** 0.012 *** 0.029 *** 0.013 *** 0.144 *** 0.060 *** 0.036 *** 0.051 *** (0.004) (0.003) (0.003) (0.003) (0.002) (0.009) (0.003) (0.002) (0.004) Age 0.006 *** 0.004 *** 0.002 *** 0.001 *** 0.001 *** 0.009 *** 0.004 *** 0.002 *** 0.007 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Familysize 0.015 *** 0.010 *** 0.005 *** 0.014 *** 0.006 *** 0.042 *** 0.016 *** 0.008 *** 0.036 *** (0.001) (0.001) (0.001) (0.000) (0.000) (0.001) (0.000) (0.000) (0.001) Constant 0.706 *** 0.167 *** 0.140 *** 0.975 *** 0.026 *** 1.057 *** 0.885 *** 0.072 *** 0.691 *** (0.006) (0.005) (0.006) (0.004) (0.003) (0.011) (0.003) (0.003) (0.006) County Fixed YES YES YES YES YES YES YES YES YES Effects N 251,679 251,679 251,679 420,480 420,480 420,480 682,119 682,119 682,119 adj. R 0.166 0.142 0.140 0.075 0.074 0.198 0.137 0.123 0.266 Notes: ***, *, respectively, indicate p < 0.01, p < 0.1. The standard errors are given in parentheses. Sustainability 2022, 14, 9058 18 of 21 Table A9. Impacts of In situ Urbanization on Employment (A3&A1A2; A3&A2). A3&A1A2 A3&A2 (1) (2) (3) (4) (1) (2) (3) (4) Unemployment Ind1 Ind2 Ind3 Unemployment Ind1 Ind2 Ind3 D 0.014 *** 0.058 *** 0.020 ** 0.075 *** 0.000 0.096 *** 0.058 *** 0.037 *** (0.002) (0.008) (0.008) (0.007) (0.002) (0.008) (0.008) (0.007) T 0.018 *** 0.098 *** 0.030 *** 0.091 *** 0.024 *** 0.157 *** 0.000 0.101 *** (0.001) (0.002) (0.003) (0.003) (0.002) (0.004) (0.003) (0.004) D T 0.008 0.051 *** 0.014 0.030 ** 0.001 0.013 0.015 0.011 (0.006) (0.013) (0.014) (0.013) (0.006) (0.014) (0.014) (0.013) Male 0.002 *** 0.080 *** 0.090 *** 0.028 *** 0.003 *** 0.107 *** 0.080 *** 0.007 *** (0.001) (0.001) (0.002) (0.002) (0.001) (0.002) (0.002) (0.002) Minority 0.009 *** 0.022 *** 0.021 *** 0.042 *** 0.009 *** 0.025 ** 0.001 0.025 *** (0.003) (0.006) (0.006) (0.007) (0.003) (0.010) (0.008) (0.009) Education 0.004 ** 0.171 *** 0.110 *** 0.279 *** 0.038 *** 0.257 *** 0.066 *** 0.331 *** (0.001) (0.002) (0.003) (0.004) (0.005) (0.009) (0.009) (0.011) Married 0.057 *** 0.062 *** 0.021 *** 0.041 *** 0.045 *** 0.027 *** 0.019 *** 0.006 * (0.001) (0.002) (0.003) (0.003) (0.002) (0.004) (0.004) (0.003) Literacy 0.003 0.144 *** 0.043 *** 0.099 *** 0.004 * 0.058 *** 0.020 *** 0.036 *** (0.003) (0.006) (0.006) (0.006) (0.002) (0.007) (0.005) (0.004) Age 0.001 *** 0.008 *** 0.004 *** 0.004 *** 0.001 *** 0.008 *** 0.005 *** 0.004 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Familysize 0.001 *** 0.016 *** 0.001 0.016 *** 0.000 0.004 *** 0.002 * 0.001 (0.000) (0.001) (0.001) (0.001) (0.000) (0.001) (0.001) (0.001) Constant 0.108 *** 0.264 *** 0.366 *** 0.374 *** 0.095 *** 0.500 *** 0.299 *** 0.211 *** (0.004) (0.008) (0.008) (0.008) (0.004) (0.010) (0.009) (0.008) County Fixed YES YES YES YES YES YES YES YES Effects N 264,057 252,677 252,677 252,677 114,047 111,389 111,389 111,389 adj. R 0.034 0.375 0.171 0.195 0.049 0.306 0.202 0.116 Notes: ***, **, *, respectively, indicate p < 0.01, p < 0.05, p < 0.1. The standard errors are given in parentheses. Table A10. Impacts of In situ Urbanization on Employment (A3&B2; A2&B2). A3&B2 A2&B2 (1) (2) (3) (4) (1) (2) (3) (4) Unemployment Ind1 Ind2 Ind3 Unemployment Ind1 Ind2 Ind3 D - - - - - - - - - - - - - - - - T 0.021 *** 0.104 *** 0.001 0.062 *** 0.021 *** 0.105 *** 0.002 0.062 *** (0.001) (0.004) (0.003) (0.003) (0.001) (0.004) (0.003) (0.003) D T 0.004 0.031 ** 0.022 0.041 *** 0.004 * 0.050 *** 0.003 0.038 *** (0.007) (0.015) (0.015) (0.014) (0.002) (0.006) (0.005) (0.005) Male 0.002 ** 0.079 *** 0.060 *** 0.004 ** 0.002 *** 0.095 *** 0.069 *** 0.009 *** (0.001) (0.002) (0.002) (0.002) (0.001) (0.002) (0.001) (0.001) Minority 0.000 0.036 *** 0.018 *** 0.013 ** 0.003 0.034 *** 0.014 *** 0.017 *** (0.002) (0.008) (0.007) (0.006) (0.002) (0.006) (0.005) (0.005) Education 0.042 *** 0.338 *** 0.017 0.361 *** 0.046 *** 0.318 *** 0.027 *** 0.351 *** (0.006) (0.010) (0.011) (0.012) (0.004) (0.007) (0.008) (0.009) Married 0.048 *** 0.001 0.002 0.002 0.046 *** 0.011 *** 0.009 *** 0.003 (0.002) (0.004) (0.003) (0.003) (0.001) (0.003) (0.002) (0.002) Sustainability 2022, 14, 9058 19 of 21 Table A10. Cont. A3&B2 A2&B2 (1) (2) (3) (4) (1) (2) (3) (4) Unemployment Ind1 Ind2 Ind3 Unemployment Ind1 Ind2 Ind3 Literacy 0.000 0.068 *** 0.023 *** 0.042 *** 0.002 0.060 *** 0.021 *** 0.037 *** (0.002) (0.006) (0.004) (0.004) (0.002) (0.005) (0.004) (0.003) Age 0.001 *** 0.006 *** 0.003 *** 0.003 *** 0.001 *** 0.007 *** 0.004 *** 0.003 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Familysize 0.001 *** 0.001 0.002 0.001 0.001 *** 0.003 *** 0.002 ** 0.001 (0.000) (0.001) (0.001) (0.001) (0.000) (0.001) (0.001) (0.001) Constant 0.084 *** 0.605 *** 0.228 *** 0.175*** 0.089 *** 0.572 *** 0.252 *** 0.185 *** (0.004) (0.010) (0.008) (0.008) (0.003) (0.007) (0.006) (0.006) County Fixed YES YES YES YES YES YES YES YES Effects N 111,886 109,303 109,303 109,303 208,344 203,672 203,672 203,672 adj. R 0.058 0.345 0.239 0.132 0.054 0.305 0.209 0.109 Notes: ***, **, *, respectively, indicate p < 0.01, p < 0.05, p < 0.1. The standard errors are given in parentheses. Table A11. Impacts of In situ Urbanization on Employment (A1&B1; A&B). A1&B1 A&B (1) (2) (3) (4) (1) (2) (3) (4) Unemployment Ind1 Ind2 Ind3 Unemployment Ind1 Ind2 Ind3 D - - - - - - - - - - - - - - - - T 0.016 *** 0.019 *** 0.056 *** 0.068 *** 0.018 *** 0.047 *** 0.038 *** 0.067 *** (0.001) (0.002) (0.003) (0.003) (0.001) (0.002) (0.002) (0.002) D T 0.004 * 0.025 *** 0.002 0.010 ** 0.001 0.053 *** 0.007 ** 0.027 *** (0.002) (0.003) (0.004) (0.005) (0.002) (0.003) (0.003) (0.004) Male 0.003 *** 0.031 *** 0.095 *** 0.076 *** 0.002 *** 0.062 *** 0.089 *** 0.040 *** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) Minority 0.001 0.003 0.018 *** 0.018 *** 0.004 ** 0.052 *** 0.003 0.045 *** (0.002) (0.003) (0.005) (0.005) (0.002) (0.004) (0.004) (0.004) Education 0.021 *** 0.057 *** 0.109 *** 0.161 *** 0.012 *** 0.130 *** 0.094 *** 0.220 *** (0.001) (0.001) (0.002) (0.002) (0.001) (0.001) (0.002) (0.002) Married 0.079 *** 0.028 *** 0.016 *** 0.015 *** 0.066 *** 0.044 *** 0.020 *** 0.026 *** (0.001) (0.001) (0.002) (0.003) (0.001) (0.001) (0.002) (0.002) Literacy 0.000 0.145 *** 0.041 *** 0.100 *** 0.007 *** 0.155 *** 0.043 *** 0.108 *** (0.005) (0.009) (0.010) (0.011) (0.002) (0.005) (0.004) (0.004) Age 0.000 *** 0.004 *** 0.002 *** 0.002 *** 0.000 *** 0.006 *** 0.003 *** 0.003 *** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Familysize 0.000 0.012 *** 0.005 *** 0.017 *** 0.001 *** 0.018 *** 0.002 ** 0.019 *** (0.000) (0.000) (0.001) (0.001) (0.000) (0.001) (0.001) (0.001) Constant 0.120 *** 0.124 *** 0.323 *** 0.549 *** 0.108 *** 0.265 *** 0.304 *** 0.431 *** (0.006) (0.010) (0.011) (0.012) (0.003) (0.005) (0.005) (0.006) County Fixed YES YES YES YES YES YES YES YES Effects N 399,762 372,402 372,402 372,402 616,906 584,590 584,590 584,590 adj. R 0.034 0.257 0.130 0.147 0.039 0.436 0.161 0.232 Notes: ***, **, *, respectively, indicate p < 0.01, p < 0.05, p < 0.1. The standard errors are given in parentheses. Sustainability 2022, 14, 9058 20 of 21 References 1. Guo, S.; Zou, J. Study and Enlightenment of the in-Situ Urbanization of Rural Areas in China in the Background of New Pattern Urbanization-Taking Zhanqi Village, Pi County for Instance. Open J. Soc. Sci. 2015, 3, 137–144. [CrossRef] 2. Dociu, M.; Dunarintu, A. The Socio-Economic Impact of Urbanization. Int. J. Acad. Res. Account. Financ. Manag. Sci. 2012, 2, 47–52. 3. Gross, J.; Ouyang, Y. Types of Urbanization and Economic Growth. Int. J. Urban Sci. 2021, 25, 71–85. [CrossRef] 4. Kan, K.; Chen, J. Rural Urbanization in China: Administrative Restructuring and the Livelihoods of Urbanized Rural Residents. J. Contemp. China 2021, 31, 1–18. [CrossRef] 5. Henderson, J.V. Urbanization and Economic Development. Ann. Econ. Financ. 2003, 4, 275–341. 6. Laumas, P.; Williams, M. 1984, “Urbanization and Economic Development”. East. Econ. J. 2008, 10, 325–332. 7. Mayer-Foulkes, D. Efﬁcient Urbanization for Mexican Development. Int. J. Econ. Financ. 2018, 10, 1–27. [CrossRef] 8. Gan, L.; He, Q.; Si, R.; Yi, D. Relocating or Redeﬁned: A New Perspective on Urbanization in China; Working Paper Series; National Bureau of Economic Research: Cambridge, MA, USA, 2019. 9. Bertinelli, L.; Black, D. Urbanization and Growth. J. Urban Econ. 2004, 56, 80–96. [CrossRef] 10. Iyer, C.G. Urbanization in India and Productivity of Manufacturing Industries: An Empirical Study. Indian Econ. Rev. 2013, 48, 297–322. 11. Rosero, G.C.G.; Riou, S.; Autant-Bernard, C. Agglomeration Externalities and Urbanization in Ecuador; European Regional Science Association: Louvain-la-Neuve, Belgium, 2015. 12. Tran, T.B. Informal Sector and Urbanization Externalities in Vietnam. Eurasian J. Econ. Financ. 2018, 6, 56–63. [CrossRef] 13. Bosker, M.; Deichmann, U.; Roberts, M. Hukou and Highways the Impact of China’s Spatial Development Policies on Urbanization and Regional Inequality. Reg. Sci. Urban Econ. 2018, 71, 91–109. [CrossRef] 14. Dreger, C.; Wang, T.; Zhang, Y. Understanding Chinese Consumption: The Impact of Hukou. Dev. Change 2015, 46, 1331–1344. [CrossRef] 15. Hsu, W.; Ma, L. Urbanization Policy and Economic Development: A Quantitative Analysis of China’s Differential Hukou Reforms. Reg. Sci. Urban Econ. 2021, 91, 103639. [CrossRef] 16. Ahlers, A.L. Weaving the Chinese Dream on the Ground? Local Government Approaches to “New-Typed” Rural Urbanization. J. Chin. Political Sci. 2015, 20, 121–142. [CrossRef] 17. Chen, M.; Liu, W.; Lu, D. Challenges and the Way Forward in China’s New-Type Urbanization. Land Use Policy 2016, 55, 334–339. [CrossRef] 18. Guan, X.; Wei, H.; Lu, S.; Dai, Q.; Su, H. Assessment On the Urbanization Strategy in China: Achievements, Challenges and Reﬂections. Habitat Int. 2018, 71, 97–109. [CrossRef] 19. Lu, J.; Li, B.; Li, H. The Inﬂuence of Land Finance and Public Service Supply On Peri-Urbanization: Evidence From the Counties in China. Habitat Int. 2019, 92, 102039. [CrossRef] 20. Gao, J.; Liu, Y.; Chen, J. China’s Initiatives Towards Rural Land System Reform. Land Use Policy 2020, 94, 104567. [CrossRef] 21. Tao, R.; Xu, Z. Urbanization, Rural Land System and Social Security for Migrants in China. J. Dev. Stud. 2007, 43, 1301–1320. [CrossRef] 22. Xu, Z.; Ning, K.; Zhong, F.; Ji, Y. New Rural Pension Insurance and Land Transfer: Can Institutional Pension Replace Land Pension? Based On the Perspectives of Family Demographic Structure and Liquidity Constraint. Manag. World 2018, 34, 86–97. 23. Chen, J.; Chen, S. Mental Health Effects of Perceived Living Environment and Neighborhood Safety in Urbanizing China. Habitat Int. 2015, 46, 101–110. [CrossRef] 24. Chen, Y.; Chen, H.; Liu, J. Household Split, Income, and Migrants’ Life Satisfaction: Social Problems Caused by Rapid Urbanization in China. Sustainability 2019, 11, 3415. [CrossRef] 25. Liang, Y.; Zhu, D. Subjective Well-Being of Chinese Landless Peasants in Relatively Developed Regions: Measurement Using Panas and Swls. Soc. Indic. Res. 2015, 123, 817–835. [CrossRef] 26. Zhou, S.; Yu, X. Regional Heterogeneity of Life Satisfaction in Urban China: Evidence From Hierarchical Ordered Logit Analysis. Soc. Indic. Res. 2017, 132, 25–45. [CrossRef] 27. Xie, S.; Chen, J. Housing and Identity Expression Among in-Situ Urbanized Rural Residents in China. Popul. Space Place 2021, 28, e2548. 28. Zhu, Y. In Situ Urbanization in China: Processes, Contributing Factors, and Policy Implications. China Popul. Dev. Stud. 2017, 1, 45–66. [CrossRef] 29. Zhu, Y.; Wang, W.W.; Lin, L.; Shen, J.; Ren, Q. Return Migration and in Situ Urbanization of Migrant Sending Areas: Insights from a Survey of Seven Provinces in China. Cities 2021, 115, 103242. [CrossRef] 30. Zhu, Y. In Situ Urbanization in Rural China: Case Studies from Fujian Province. Dev. Change 2000, 31, 413–434. [CrossRef] 31. Chen, A. How Has the Abolition of Agricultural Taxes Transformed Village Governance in China? Evidence From Agricultural Regions. China Q. 2014, 219, 715–735. [CrossRef] 32. Chen, J.; Davis, D.S.; Landry, P.F. Beyond Hukou Reform: Enhancing Human-Centered Urbanization in China. Paulson Policy Memo. 2017, 3–5. Available online: https://www.paulsoninstitute.org/wp-content/uploads/2017/02/PPM_Beyond-Hukou_ Chen_Davis_Landry_English.pdf (accessed on 7 December 2020). Sustainability 2022, 14, 9058 21 of 21 33. Chen, J.; Chen, S.; Landry, P.F.; Davis, D.S. How Dynamics of Urbanization Affect Physical and Mental Health in Urban China. China Q. 2014, 220, 988–1011. [CrossRef] 34. Chen, J.; Davis, D.S.; Wu, K.; Dai, H. Life Satisfaction in Urbanizing China: The Effect of City Size and Pathways to Urban Residency. Cities 2015, 49, 88–97. [CrossRef] 35. Liu, S.; Zhang, P.; Lo, K. Urbanization in Remote Areas: A Case Study of the Heilongjiang Reclamation Area, Northeast China. Habitat Int. 2014, 42, 103–110. [CrossRef] 36. Tong, W.; Lo, K.; Zhang, P. Land Consolidation in Rural China: Life Satisfaction Among Resettlers and its Determinants. Land 2020, 9, 118. [CrossRef] 37. Hou, B.; Nazroo, J.; Banks, J.; Marshall, A. Are Cities Good for Health? A Study of the Impacts of Planned Urbanization in China. Int. J. Epidemiol. 2019, 48, 1083–1090. [CrossRef] 38. Sato, Y.; Zenou, Y. How Urbanization Affect Employment and Social Interactions. Eur. Econ. Rev. 2015, 75, 131–155. [CrossRef] 39. Evers, H. The Formation of a Social Class Structure: Urbanization, Bureaucratization and Social Mobility in Thailand. Am. Sociol. Rev. 1966, 31, 480–488. [CrossRef] 40. Wan, H.; Li, S. The Effects of Household Registration System Discrimination on Urban-Rural Income Inequality in China. Econ. Res. J. 2013, 48, 43–55. 41. Sendut, H. Contemporary Urbanization in Malaysia. Asian Surv. 1966, 6, 484–491. [CrossRef] 42. Mellor, J.W.; Khan, M.H.; Qureshi, S.K. Agricultural Links to Nonagricultural Growth: Urbanization, Employment, Poverty. Pak. Dev. Rev. 1991, 30, 439–456. [CrossRef] 43. Khan, H. Challenges for Sustainable Development: Rapid Urbanization, Poverty and Capabilities in Bangladesh. MPRA Paper No. 9290. Available online: https://mpra.ub.uni-muenchen.de/9290/1/MPRA_paper_9290.pdf (accessed on 7 December 2020). 44. Michaels, G.; Rauch, F.; Redding, S.J. Urbanization and Structural Transformation. Q. J. Econ. 2012, 127, 535–586. [CrossRef]

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Sustainability – Multidisciplinary Digital Publishing Institute

Published: Jul 24, 2022

Keywords: housing; mobility; employment; spillover effects; in situ urbanization

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The Impacts of In Situ Urbanization on Housing, Mobility and Employment of Local Residents in China

The Impacts of In Situ Urbanization on Housing, Mobility and Employment of Local Residents in China

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The Impacts of In Situ Urbanization on Housing, Mobility and Employment of Local Residents in China

The Impacts of In Situ Urbanization on Housing, Mobility and Employment of Local Residents in China

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