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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Sustain. Food Syst.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Sustainable Food Systems</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Sustain. Food Syst.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2571-581X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="doi">10.3389/fsufs.2026.1789435</article-id>
<article-version article-version-type="Version of Record" vocab="NISO-RP-8-2008"/>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Original Research</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Identifying influencing factors and differences in disposal methods for contracted farmland among rural migrants: an investigation based on China&#x2019;s &#x201C;new urban residents&#x201D;</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Zhu</surname>
<given-names>Zhe</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author">
<name>
<surname>Zou</surname>
<given-names>Heran</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/3325496"/>
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<contrib contrib-type="author">
<name>
<surname>Meng</surname>
<given-names>Jiaxin</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Luo</surname>
<given-names>Xiangyu</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<contrib contrib-type="author" corresp="yes">
<name>
<surname>Chen</surname>
<given-names>Shiwen</given-names>
</name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
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<aff id="aff1"><label>1</label><institution>College of Management, Wuhan Institute of Technology</institution>, <city>Wuhan</city>, <country country="cn">China</country></aff>
<aff id="aff2"><label>2</label><institution>Enterprise and Environment Coordinated Development Research Center of Hubei Province</institution>, <city>Wuhan</city>, <country country="cn">China</country></aff>
<author-notes>
<corresp id="c001"><label>&#x002A;</label>Correspondence: Shiwen Chen, <email xlink:href="mailto:22506010009@stu.wit.edu.cn">22506010009@stu.wit.edu.cn</email></corresp>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-02-26">
<day>26</day>
<month>02</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2026</year>
</pub-date>
<volume>10</volume>
<elocation-id>1789435</elocation-id>
<history>
<date date-type="received">
<day>16</day>
<month>01</month>
<year>2026</year>
</date>
<date date-type="rev-recd">
<day>07</day>
<month>02</month>
<year>2026</year>
</date>
<date date-type="accepted">
<day>13</day>
<month>02</month>
<year>2026</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2026 Zhu, Zou, Meng, Luo and Chen.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Zhu, Zou, Meng, Luo and Chen</copyright-holder>
<license>
<ali:license_ref start_date="2026-02-26">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract>
<p>The disposal of contracted land by rural migrants in China is influenced by multidimensional and systemic factors, which result in the gradual formation of a distorted human&#x2013;land relationship that is characterized as &#x201C;leaving farming without leaving rights&#x201D; and &#x201C;abandoning cultivation without abandoning land.&#x201D; This relationship severely restricts the efficiency of rural land use and the structural upgrading of agricultural industries. Using data from the China Migrants Dynamic Survey and applying a multinomial logit model, this study reveals the following findings: (1) individual characteristics, integration status, and migration factors collectively influence land disposal patterns, and they have distinct differences in characteristics, types, and regional variations; (2) in terms of individual characteristics, those rural migrants who are female, in good health, have a high level of education, own homestead land, have a large number of family members, contract a small land area, or face difficulties in dealing with the outflow of land tend to entrust their family members with continued farming rather than engaging in land transfer or abandonment when faced with the choice of contracted land disposal methods, whereas older migrants are more inclined to transfer their contracted land; (3) with respect to integration status in the inflow area, rural migrants with higher levels of economic integration and better identity integration are more likely to choose land transfer or abandonment over continued family cultivation. Conversely, those with greater levels of institutional integration are more likely to opt for continued family cultivation; (4) in terms of mobility factors, rural migrants from western regions and those with shorter migration distances are more likely to continue with family farming or abandon their contracted land. The longer the migration duration, the greater the likelihood of land abandonment. Groups that migrate for family reasons are more inclined toward land transfer or abandonment. The study concludes that the disposal of contracted land by rural migrants serves as a risk-hedging strategy and safety net against urban livelihood vulnerabilities. Multiple measures are needed to optimize disposal decisions and achieve efficient utilization of rural land resources.</p>
</abstract>
<kwd-group>
<kwd>contracted land disposal</kwd>
<kwd>family farming</kwd>
<kwd>land transfer</kwd>
<kwd>land abandonment</kwd>
<kwd>rural migrants</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. This research received funding from the National Social Science Fund of China under Grant (No. 25BJY127).</funding-statement>
</funding-group>
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<fig-count count="3"/>
<table-count count="12"/>
<equation-count count="5"/>
<ref-count count="62"/>
<page-count count="21"/>
<word-count count="12306"/>
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<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Land, Livelihoods and Food Security</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec1">
<label>1</label>
<title>Introduction</title>
<p>&#x201C;Rural migrants&#x201D; refer primarily to those individuals who relocate from rural areas to cities. Since the reform and opening-up, the migration of China&#x2019;s rural labor force to cities has created the largest wave of population migration in global history (<xref ref-type="bibr" rid="ref16">Hu et al., 2011</xref>). China&#x2019;s urbanization rate increased from 17.9% in 1978 to 67% in 2024 [<xref ref-type="bibr" rid="ref32">National Bureau of Statistics of China (NBSC), 2025a</xref>]. A vast cohort of &#x201C;new citizens&#x201D;&#x2014;permanent population consisting of migrant workers and other nonlocal workers with stable urban employment, as well as newly employed college graduates [<xref ref-type="bibr" rid="ref34">National Development and Reform Commission (NDRC), 2021</xref>] &#x2014;entered cities for employment or business, with the rural-registered population residing in urban areas steadily increasing. By 2024, the total number of migrant workers nationwide had reached 290 million, which includes approximately 178 million outbound migrant workers [<xref ref-type="bibr" rid="ref8">Dong, 2025</xref>; <xref ref-type="bibr" rid="ref33">National Bureau of Statistics of China (NBSC), 2025b</xref>].</p>
<p>Theoretically, the Lewis&#x2013;Ranis&#x2013;Fei dual economic structure model posits that as surplus rural labor continuously shifts to modern urban production sectors, the scale of rural land management further expands, agricultural labor productivity improves, and the production efficiency between industries further narrows or even vanishes, as does the urban&#x2013;rural income gap, and the dual economic structure itself transitions toward a single economic structure (<xref ref-type="bibr" rid="ref12">Gao et al., 2025</xref>). Urbanization is an inevitable path toward modernization, and its advancement promotes the concentration and efficient utilization of rural resources, thereby accelerating agricultural industrialization (<xref ref-type="bibr" rid="ref59">Zhu et al., 2024</xref>). Rural labor&#x2019;s spatial &#x201C;disengagement from land&#x201D; and occupational &#x201C;disengagement from agriculture&#x201D; not only facilitate labor transfer but also promote the transfer of contracted land (unlike Western private land ownership, China implements a system of state and collective land ownership in which farmers obtain operational rights by contracting land from village collectives) (<xref ref-type="bibr" rid="ref11">Fu and Xue, 2025</xref>), which enables the optimal allocation of agricultural resources. Furthermore, as rural land becomes available and the rural population decreases, the implementation of this system can alleviate the fragmentation of farmland and support large-scale operations (<xref ref-type="bibr" rid="ref45">Wang et al., 2021</xref>).</p>
<p>However, in practice, the contracted farmland in China has not been well utilized despite rural population migration. Most migrant workers face institutional exclusion, social isolation, and psychological barriers to their own identities, and they must deal with practical challenges such as employment, social security, housing stability, and children&#x2019;s education; thus, their level of urban integration remains low (<xref ref-type="bibr" rid="ref55">Yu et al., 2025</xref>; <xref ref-type="bibr" rid="ref57">Zhang T. et al., 2025</xref>). Rural land is considered as a last resort due to China&#x2019;s unique distorted human&#x2013;land relationship, which is characterized by &#x201C;leaving agriculture without relinquishing rights&#x201D; and &#x201C;abandoning cultivation without abandoning land&#x201D; (<xref ref-type="bibr" rid="ref27">Mei, 2018</xref>). The theoretically anticipated large-scale land utilization on the part of rural laborers who migrate to cities has not materialized. Instead, such migration has led to a decline in the quality of rural labor. Among the rural resident population, 25.86% are 60&#x202F;years old or above, and 18.24% are 65&#x202F;years old or above, which is close to the threshold of a &#x201C;super aging society&#x201D; (<xref ref-type="bibr" rid="ref46">Wang and Cheng, 2023</xref>). Concurrently, this migration has led to a continuous decline in land intensification and refinement, with the scale of abandoned farmland steadily expanding (<xref ref-type="bibr" rid="ref20">Li et al., 2024</xref>; <xref ref-type="bibr" rid="ref44">Wan et al., 2021</xref>). The data indicate that by 2025, the total area of household-contracted farmland transferred nationwide had reached 555 million mu, accounting for only 37% of the total household-contracted farmland area. There is still a significant amount of cultivated land remains underutilized due to the lack of transfer and even faces the risk of abandonment. In 2020, the combined rate of long-term, short-term, and seasonally abandoned farmland in China had reached 20.79%, with the fallow rate in major grain-producing areas reaching as high as 7.38% (<xref ref-type="bibr" rid="ref54">Yin, 2024</xref>). From 1990 to 2020, the proportion of abandoned farmers increased from 1.3 to 21%, with that number having increased by nearly 15 times (<xref ref-type="bibr" rid="ref9">Economic Daily, 2024</xref>). Moreover, unlike major agricultural-producing countries, China faces constraints in terms of its limited arable land resources compared with its large population. The per capita arable land is approximately 1.37 mu, which is far below the global average of 4.8 mu (<xref ref-type="bibr" rid="ref29">Ministry of Natural Resources of the People&#x2019;s Republic of China, 2025</xref>). The limited potential for arable land expansion and high levels of agricultural value added per unit and land productivity make significant increases difficult to achieve (<xref ref-type="bibr" rid="ref12">Gao et al., 2025</xref>; <xref ref-type="bibr" rid="ref5">Cheng et al., 2025</xref>).</p>
<p>Therefore, under the combined influence of the &#x201C;human&#x2013;land relationship&#x201D; related factors, rural migrants&#x2019; disposal methods of contracted land directly impact China&#x2019;s rural land use efficiency and the progress of its agricultural industrial upgrading. The literature indicates that rural migrants&#x2019; disposal methods of contracted land can be categorized into three primary types: continued cultivation by family, land transfer, and abandonment (<xref ref-type="bibr" rid="ref50">Xie, 2012</xref>). However, land disposal methods are influenced by multiple factors. Some scholars have employed qualitative and quantitative methods to explore the disposal approaches, rationales, and intentions of specific rural groups&#x2014;such as &#x201C;extinct households,&#x201D; &#x201C;five-guarantee households,&#x201D; and &#x201C;relocated poverty-alleviation households&#x201D;&#x2014;from various perspectives, such as policy science, psychology, human geography, land resource management, and sociology (<xref ref-type="bibr" rid="ref42">Tang and Zeng, 2025</xref>; <xref ref-type="bibr" rid="ref22">Liu, 2023</xref>; <xref ref-type="bibr" rid="ref28">Mei, 2019</xref>). Others have partially explained the factors that influence land disposal at the provincial level through the integration of demographic characteristics such as sex, age, duration of migrant work, and wage levels (<xref ref-type="bibr" rid="ref23">Liu et al., 2023</xref>). Nevertheless, an in-depth analysis of the factors and variations in land disposal among rural migrants at the national level using large-scale survey data has yet to be conducted. Therefore, this study is focused on the migrant worker population among China&#x2019;s &#x201C;new citizens,&#x201D; uses the multinomial logit model, the factors influencing the disposal of contracted farmland are analyzed via three dimensions: individual characteristics, integration status, and mobility factors. This research is aimed at addressing two key questions: (1) What factors actually influence the disposal methods of contracted farmland among rural migrants? (2) How do disposal patterns differ among rural migrants under distinct individual characteristics, integration status, and mobility factors?</p>
<p>The marginal contributions of this study are as follows: first, at the theoretical level, it adopts a systematic perspective of &#x201C;micro-meso-macro,&#x201D; incorporates individual characteristics, urban integration status, and mobile factors into the analytical framework, systematically explains the complex decision-making of migrant workers&#x2019; land disposal, breaks through the limitation of previous studies that mostly focus on single factors, and enriches and expands the theoretical connotations related to land use and migrant population migration. Second, at the empirical level, it conducts empirical analysis using national large-sample data, further expands and identifies the antecedent variables and heterogeneity characteristics of land disposal, and provides a scientific response to the issue of contract land disposal methods for rural migrants. Third, at the policy level, this study offers theoretical guidance and policy implications for enhancing land use efficiency, promoting large-scale agricultural operations, improving land management systems and strengthening China&#x2019;s food security foundation.</p>
</sec>
<sec id="sec2">
<label>2</label>
<title>Theoretical analysis and research hypotheses</title>
<sec id="sec3">
<label>2.1</label>
<title>Theoretical analysis</title>
<p>From a micro perspective, as rational subjects, farmers typically aim to maximize their income when deciding how to dispose of farmland (<xref ref-type="bibr" rid="ref36">Peng et al., 2025</xref>). The rational choice theory proposed by <xref ref-type="bibr" rid="ref6">Coleman (2008)</xref> also posits that individuals select optimal action plans under constraints imposed by groups, organizations, and institutions, with the ultimate aim of realizing personal interests. Actors operate as purposeful rational agents looking to maximize their individual benefits, and each possesses distinct preferences. Concurrently, from the meso- and macrolevel perspectives, rural migration decisions are influenced by multiple interrelated factors. According to the &#x201C;push-pull&#x201D; theory, migration behavior is shaped by the &#x201C;push factors&#x201D; (rejection forces) of the origin area, the &#x201C;pull factors&#x201D; (attraction forces) of the destination area, intermediate barriers, and individual characteristics (<xref ref-type="bibr" rid="ref1">Bogue, 1959</xref>; <xref ref-type="bibr" rid="ref19">Lee, 1966</xref>). Increased farming costs, low agricultural returns, and insufficient employment opportunities due to agricultural labor surpluses in origin areas, in conjunction with higher income levels, better living environments, infrastructure, educational opportunities, and greater employment prospects in destination areas, collectively drive rural-to-urban migration. However, when there are substantial intermediate barriers between origin and destination areas&#x2014;such as material obstacles, linguistic and cultural differences, migration distance, and migrant capabilities&#x2014;migrants can reduce their social integration during the migration process. This may lead to an inability to continue, thereby resulting in return migration. On top of risk aversion, migrants often treat their contracted land as a last-resort &#x201C;safety net,&#x201D; are unwilling to transfer their land rights, and entrust their contracted fields to the family members that they leave behind&#x2014;such as the elderly&#x2014;for continued farming or even temporarily abandon their land to ensure their basic household livelihoods.</p>
</sec>
<sec id="sec4">
<label>2.2</label>
<title>Research hypothesis</title>
<p>The literature indicates that sex, age, and educational attainment significantly influence farmers&#x2019; decisions regarding the disposal of contracted land (<xref ref-type="bibr" rid="ref25">Luo et al., 2024</xref>; <xref ref-type="bibr" rid="ref23">Liu et al., 2023</xref>; <xref ref-type="bibr" rid="ref30">Mohanty and Lenka, 2023</xref>; <xref ref-type="bibr" rid="ref56">Zhang H. et al., 2025</xref>; <xref ref-type="bibr" rid="ref37">Prishchepov et al., 2021</xref>). Women and elderly people exhibit greater levels of risk aversion, which makes them more likely to make the decision to return to their hometowns (<xref ref-type="bibr" rid="ref43">Tong and Chen, 2025</xref>). Furthermore, better farmer health is positively associated with the transfer-in of farmland management rights, while higher education is negatively associated with it (<xref ref-type="bibr" rid="ref41">Tan et al., 2022</xref>). The number of family members in a household reflects the scale of its available labor, with research indicating that larger households are more inclined to maintain family cultivation (<xref ref-type="bibr" rid="ref50">Xie, 2012</xref>). Concurrently, the amount of cultivated land area, as a fundamental resource endowment, directly influences farmers&#x2019; disposal capacity and choices: smaller household land holdings correlate with a lower willingness to transfer land (<xref ref-type="bibr" rid="ref58">Zhang et al., 2024</xref>; <xref ref-type="bibr" rid="ref53">Xu et al., 2019</xref>). The asset status of rural migrants also warrants attention. For instance, owning residential land may reduce farmers&#x2019; willingness to settle in cities (<xref ref-type="bibr" rid="ref14">Han et al., 2024</xref>; <xref ref-type="bibr" rid="ref10">Fan et al., 2025</xref>; <xref ref-type="bibr" rid="ref52">Xie et al., 2023</xref>), which can indirectly influence how they dispose of their contracted land. Furthermore, difficulties encountered in their hometowns reflect the livelihood vulnerability of rural migrants, and such livelihood pressures can prompt them to adjust their land disposal strategies as a risk-coping technique.</p>
<p>Therefore, the following hypothesis and subhypotheses are proposed:</p>
<disp-quote>
<p><italic>H1</italic>: The individual characteristics of rural migrants influence their choice of contracted farmland disposal.</p>
</disp-quote>
<disp-quote>
<p><italic>H1a</italic>: Female rural migrants are more likely to choose to have their families continue cultivating their contracted land.</p>
</disp-quote>
<disp-quote>
<p><italic>H1b</italic>: Older rural migrants with lower educational levels are more likely to choose continued family cultivation of their contracted land.</p>
</disp-quote>
<disp-quote>
<p><italic>H1c</italic>: Rural migrants who self-assess their health as good are more likely to choose continued family cultivation of their contracted land.</p>
</disp-quote>
<disp-quote>
<p><italic>H1d</italic>: Rural migrants with more family members, smaller individual contracted land areas, and homesteads in their hometowns are more likely to choose continued family cultivation of their contracted land.</p>
</disp-quote>
<disp-quote>
<p><italic>H1e</italic>: Rural migrants facing difficulties in outflow areas are more likely to continued family cultivation of their contracted land.</p>
</disp-quote>
<p>The integration process of rural migrants into cities is essentially a resocialization process. Occupational stability, economic income commensurate with local living standards, and equal social status form the foundational conditions for this process and serve as prerequisites for rural migrants&#x2019; genuine participation in the social lives of their destination communities. The occupational choices, occupational class, educational attainment, and nonagricultural employment income of rural migrant groups significantly influence their decisions regarding farmland transfer (<xref ref-type="bibr" rid="ref48">Wang et al., 2016</xref>; <xref ref-type="bibr" rid="ref21">Li et al., 2025</xref>; <xref ref-type="bibr" rid="ref2">Chang et al., 2025</xref>). Research indicates that new-generation migrant workers with higher household incomes who migrate to urban areas for work possess stronger living capabilities and lower levels of land dependence, which makes them more inclined toward land transfer (<xref ref-type="bibr" rid="ref21">Li et al., 2025</xref>). Suitable housing conditions also serve as key motivators for rural migrants to relinquish land and settle in cities (<xref ref-type="bibr" rid="ref60">Zou et al., 2022</xref>), and migrants who own their own homes in their destination areas are more likely to transfer their land.</p>
<p>However, the current urban&#x2013;rural dual social security system hinders migrant workers&#x2019; equal access to urban public resources, thereby exacerbating the exclusion that migrants&#x2019; face in destination cities and making it difficult for migrant workers to obtain the same social welfare and medical insurance as urban residents have (<xref ref-type="bibr" rid="ref39">Song, 2014</xref>). Furthermore, the community isolation that migrants&#x2019; experienced after migration significantly negatively affects their identity formation and urban settlement (<xref ref-type="bibr" rid="ref49">Wu et al., 2024</xref>). Consequently, farmers without stable urban employment who rely on migrant work for income are more prone to engage in &#x201C;reverse migration.&#x201D; Such migrants are reluctant to relinquish or transfer contracted land and even choose to leave it fallow to preserve their land rights and avoid migration risks (<xref ref-type="bibr" rid="ref40">Sun et al., 2024</xref>).</p>
<p>On the basis of the above analysis, the following hypothesis and subhypotheses are proposed:</p>
<disp-quote>
<p><italic>H2</italic>: The integration status of rural migrants in their inflow area influences their disposal of contracted farmland.</p>
</disp-quote>
<disp-quote>
<p><italic>H2a</italic>: The lower the monthly income level of rural migrants in their inflow area, the more likely they are to choose continued family cultivation of their contracted land.</p>
</disp-quote>
<disp-quote>
<p><italic>H2b</italic>: Rural migrants without housing in their inflow area are more likely to choose continued family cultivation of their contracted land.</p>
</disp-quote>
<disp-quote>
<p><italic>H2c</italic>: Rural migrants with lower perceived institutional security in their inflow area are more likely to choose continued family cultivation of their contracted land.</p>
</disp-quote>
<disp-quote>
<p><italic>H2d</italic>: Rural migrants who experience community isolation in their inflow area are more likely to choose continued family cultivation of their contracted land.</p>
</disp-quote>
<disp-quote>
<p><italic>H2e</italic>: Rural migrants who do not intend to settle in their inflow area are more inclined to choose the continued farming of their contracted land.</p>
</disp-quote>
<p>China has a vast territory featuring significant regional development differences. In noncoastal developed regions such as the western region, large numbers of rural laborers migrate to cities for work, and their land transfer rate is far below the national average, which leads to issues such as rural labor shortages, nongrain use of cultivated land, stagnant land transfers, and abandoned farmland (<xref ref-type="bibr" rid="ref15">Hong et al., 2025</xref>; <xref ref-type="bibr" rid="ref17">Huang et al., 2025</xref>). Moreover, Chinese farmers engage in part-time farming to a high degree, and most farmers engage in agriculture during peak seasons and seek off-farm employment during agricultural off-seasons. Additionally, they often rely on the household division of labor to balance agricultural and nonagricultural production (<xref ref-type="bibr" rid="ref24">Lu et al., 2022</xref>). However, studies have also shown that as the time that farmers&#x2019; spend on nonagricultural employment increases, their willingness to transfer land also increases, and migrant workers who are employed in provinces at great distances from their hometowns exhibit stronger intentions toward land transfer (<xref ref-type="bibr" rid="ref7">Cui et al., 2025</xref>). Therefore, when rural laborers migrate to cities for work, duration and distance significantly influence their willingness to transfer land. Migrant workers with shorter migration periods and smaller migration ranges are less inclined to transfer land and rather opt for family farming.</p>
<p>On the basis of the above analysis, the following hypothesis and subhypotheses are proposed:</p>
<disp-quote>
<p><italic>H3</italic>: Mobility factors faced by rural migrants influence their disposal of contracted land.</p>
</disp-quote>
<disp-quote>
<p><italic>H3a</italic>: Rural migrants originating from western regions are more likely to choose continued family cultivation of their contracted land.</p>
</disp-quote>
<disp-quote>
<p><italic>H3b</italic>: Rural migrants with limited mobility are more likely to choose continued family cultivation of their contracted land.</p>
</disp-quote>
<disp-quote>
<p><italic>H3c</italic>: Rural migrants with shorter migration durations are more likely to choose household-based continued cultivation of their contracted land.</p>
</disp-quote>
<disp-quote>
<p><italic>H3d</italic>: Rural migrants who migrate for employment are more likely to choose continued family cultivation of their contracted land.</p>
</disp-quote>
</sec>
</sec>
<sec id="sec5">
<label>3</label>
<title>Research methodology</title>
<sec id="sec6">
<label>3.1</label>
<title>Sample selection and data collection</title>
<p>&#x201C;New citizens&#x201D; refers to the permanent population consisting of migrant workers and other nonlocal workers with stable urban employment, as well as newly employed college graduates [<xref ref-type="bibr" rid="ref34">National Development and Reform Commission (NDRC), 2021</xref>]. To comprehensively and systematically understand the influencing factors and distinctive characteristics of the disposal methods of contracted land among the rural migrants of China, the migrant worker group among China&#x2019;s &#x201C;new citizens&#x201D; is selected as the research sample for this study. This is based mainly on several key considerations. First, migrant workers in the &#x201C;new citizens&#x201D; group not only possess unique characteristics within the Chinese context but are also more closely aligned with the basic characteristics and connotations of rural migrants internationally. Second, unlike newly employed college graduates and many migrant worker groups who are non-rural registered but employed and living in other places for various reasons, this group retains contracted farmland in their places of origin to support their families&#x2019; livelihoods. Third, this group is massive, totaling 290 million people and accounting for approximately 42.8% of the rural registered population, which makes it as the core constituent group of &#x201C;new citizens,&#x201D; the characteristics of this population provide essential reference points for analyzing land disposition arrangements among rural migrants in China.</p>
<p>The research data are sourced from the China Migrants Dynamic Survey (CMDS), which employs a stratified multistage probability proportional to size (PPS) sampling method to collect samples from China&#x2019;s 31 provinces (autonomous regions and municipalities) and the Xinjiang Production and Construction Corps. This survey targeted migrant populations aged 15 and above in nonsurvey areas (counties and cities) and yielded a total of 169,989 valid questionnaires. Samples of migrant workers from the CMDS (Volume A) who had contracted land in their hometowns were selected. The responses to the questionnaire item &#x201C;Who cultivates your household&#x2019;s contracted land?&#x201D; were categorized into nine types: cultivated by self or family members, cultivated by relatives/friends, cultivated by hired labor, subleased to enterprises, subleased to village collectives, subleased to private individuals, abandoned, used for tree planting, and others. Given the small sample size for &#x201C;tree planting&#x201D; and &#x201C;other&#x201D; uses, these were excluded from the primary disposal methods for contracted land used in this study.</p>
<p>According to the ownership status and actual utilization methods of farmland management rights, and based on the research consensus in the fields of land resource management and agricultural economic management (<xref ref-type="bibr" rid="ref40">Sun et al., 2024</xref>; <xref ref-type="bibr" rid="ref4">Chen et al., 2025</xref>; <xref ref-type="bibr" rid="ref61">Zou et al., 2018</xref>), the disposal methods of migrant workers&#x2019; contracted land are categorized into three types: continued household cultivation, land transfer, and abandonment. When samples were selected, those classified as &#x201C;planting trees&#x201D; or &#x201C;other&#x201D; were excluded, whereas the disposal methods &#x201C;cultivated by oneself or family members,&#x201D; &#x201C;cultivated by hired labor,&#x201D; and &#x201C;cultivated by relatives or friends&#x201D; were grouped under &#x201C;continued household cultivation&#x201D; (all three types share the core characteristic of no substantive transfer of farmland management rights, with the original contractor remaining the core decision-making and responsible entity for land operation); &#x201C;Subleasing to private individuals,&#x201D; &#x201C;subleasing to village collectives,&#x201D; and &#x201C;subleasing to enterprises&#x201D; were categorized as land transfer (all three types realize the legal transfer of farmland management rights from the original contractor to other operating entities (individuals, collective organizations, market entities), which is consistent with the definition of rural land contract management right transfer stipulated in China&#x2019;s Rural Land Contract Law); while &#x201C;abandoning cultivated land&#x201D; was defined as &#x201C;land abandonment&#x201D; (characterized by the original contractor&#x2019;s voluntary or passive abandonment of the production and operation of the contracted land, with no effective operating entity and no actual production input). Through manual collection and collation, samples from the total questionnaire sample that had no contracted land or whose disposal methods were not categorized as continued cultivation by the household, land transfer, or abandonment were excluded, resulting in a final valid sample size of 73,676.</p>
</sec>
<sec id="sec7">
<label>3.2</label>
<title>Research model and method selection</title>
<p>The land disposal methods examined in this study&#x2014;continuing household cultivation, land transfer, and abandonment&#x2014;constitute categorical variables that can take more than two possible values. Considering that the land disposal methods of migrant workers do not overlap and cannot be ranked, the multinomial logit model was selected for quantitative analysis. The model is grounded in the random utility theory framework for discrete choice proposed by <xref ref-type="bibr" rid="ref26">McFadden (1974)</xref>, which serves as a standard and well-established analytical tool for examining individual decision-making among multiple mutually exclusive alternatives. It is specifically designed to estimate how the probabilities of individuals choosing among multiple unordered and mutually exclusive options are influenced by various factors.</p>
<p>Definition:<inline-formula>
<mml:math id="M1">
<mml:mspace width="0.25em"/>
<mml:mi mathvariant="italic">yi</mml:mi>
</mml:math>
</inline-formula>= 1, 2, and 3 represent the i-th migrant&#x2019;s choice of family farming, land transfer, or abandonment, respectively. The constructed model is shown in <xref ref-type="disp-formula" rid="E1">Equation 1</xref>:</p>
<disp-formula id="E1"><label>(1)</label> <mml:math id="M2">
<mml:msub>
<mml:mi mathvariant="normal">U</mml:mi>
<mml:mi>ij</mml:mi>
</mml:msub>
<mml:mo>=</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">x</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:msub>
<mml:mi>&#x03B2;</mml:mi>
<mml:mi mathvariant="normal">j</mml:mi>
</mml:msub>
<mml:mo>+</mml:mo>
<mml:msub>
<mml:mi>&#x03B5;</mml:mi>
<mml:mi>ij</mml:mi>
</mml:msub>
<mml:mspace width="1em"/>
<mml:mspace width="0.25em"/>
<mml:mi mathvariant="normal">i</mml:mi>
<mml:mo>=</mml:mo>
<mml:mn>1</mml:mn>
<mml:mo>,</mml:mo>
<mml:mn>2</mml:mn>
<mml:mo>,</mml:mo>
<mml:mn>3</mml:mn>
<mml:mo>,</mml:mo>
<mml:mo>&#x2026;</mml:mo>
<mml:mo>;</mml:mo>
<mml:mi mathvariant="normal">j</mml:mi>
<mml:mo>=</mml:mo>
<mml:mn>1</mml:mn>
<mml:mo>,</mml:mo>
<mml:mn>2</mml:mn>
<mml:mo>,</mml:mo>
<mml:mn>3</mml:mn>
</mml:math></disp-formula>
<p>where <inline-formula>
<mml:math id="M3">
<mml:msub>
<mml:mi mathvariant="normal">U</mml:mi>
<mml:mi>ij</mml:mi>
</mml:msub>
</mml:math>
</inline-formula> represents the benefit gained by farmer <inline-formula>
<mml:math id="M4">
<mml:mi mathvariant="normal">i</mml:mi>
</mml:math>
</inline-formula> from choosing disposal method <inline-formula>
<mml:math id="M5">
<mml:mi mathvariant="normal">j</mml:mi>
</mml:math>
</inline-formula>; <inline-formula>
<mml:math id="M6">
<mml:msub>
<mml:mi>&#x03B2;</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
</mml:math>
</inline-formula> denotes a series of factors influencing the farmer&#x2019;s choice of disposal method for contracted land, which vary only with individual <inline-formula>
<mml:math id="M7">
<mml:mi mathvariant="normal">i</mml:mi>
</mml:math>
</inline-formula>; and <inline-formula>
<mml:math id="M8">
<mml:mspace width="0.33em"/>
<mml:msub>
<mml:mi>&#x03B5;</mml:mi>
<mml:mi>ij</mml:mi>
</mml:msub>
</mml:math>
</inline-formula> is the random disturbance term.</p>
<p>From the above equation, it follows that farmer <inline-formula>
<mml:math id="M9">
<mml:mi mathvariant="normal">i</mml:mi>
</mml:math>
</inline-formula> chooses disposal method <inline-formula>
<mml:math id="M10">
<mml:mi mathvariant="normal">j</mml:mi>
</mml:math>
</inline-formula> if and only if the profit from choosing disposal method <inline-formula>
<mml:math id="M11">
<mml:mi mathvariant="normal">j</mml:mi>
</mml:math>
</inline-formula> exceeds the profits from all the other disposal methods. Thus, the probability model for farmer <inline-formula>
<mml:math id="M12">
<mml:mi mathvariant="normal">i</mml:mi>
</mml:math>
</inline-formula> selecting disposal method <inline-formula>
<mml:math id="M13">
<mml:mi mathvariant="normal">j</mml:mi>
</mml:math>
</inline-formula> is shown in <xref ref-type="disp-formula" rid="E2">Equations 2</xref>, <xref ref-type="disp-formula" rid="E3">3</xref>:</p>
<disp-formula id="E2"><label>(2)</label> <mml:math id="M14">
<mml:mi mathvariant="normal">P</mml:mi>
<mml:mo stretchy="true">(</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">y</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:mo>=</mml:mo>
<mml:mi mathvariant="normal">j</mml:mi>
<mml:mo>&#x2223;</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">x</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:mo stretchy="true">)</mml:mo>
<mml:mo>=</mml:mo>
<mml:mi mathvariant="normal">P</mml:mi>
<mml:mo stretchy="true">(</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">U</mml:mi>
<mml:mi>ij</mml:mi>
</mml:msub>
<mml:msub>
<mml:mi mathvariant="normal">U</mml:mi>
<mml:mi>ik</mml:mi>
</mml:msub>
<mml:mo stretchy="true">)</mml:mo>
<mml:mspace width="1em"/>
<mml:mspace width="0.25em"/>
<mml:mi mathvariant="normal">k</mml:mi>
<mml:mo>&#x2260;</mml:mo>
<mml:mi mathvariant="normal">j</mml:mi>
<mml:mspace width="0.25em"/>
</mml:math></disp-formula>
<p>That is:</p>
<disp-formula id="E3"><label>(3)</label> <mml:math id="M15">
<mml:mi mathvariant="normal">P</mml:mi>
<mml:mo stretchy="true">(</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">y</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:mo>=</mml:mo>
<mml:mi mathvariant="normal">j</mml:mi>
<mml:mo>&#x2223;</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">x</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:mo stretchy="true">)</mml:mo>
<mml:mo>=</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:mo>exp</mml:mo>
<mml:mo stretchy="true">(</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">x</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:msub>
<mml:mi>&#x03B2;</mml:mi>
<mml:mi mathvariant="normal">j</mml:mi>
</mml:msub>
<mml:mo stretchy="true">)</mml:mo>
</mml:mrow>
<mml:mrow>
<mml:munderover>
<mml:mo movablelimits="false">&#x2211;</mml:mo>
<mml:mrow>
<mml:mi mathvariant="normal">k</mml:mi>
<mml:mo>=</mml:mo>
<mml:mn>1</mml:mn>
</mml:mrow>
<mml:mi mathvariant="normal">j</mml:mi>
</mml:munderover>
<mml:mo>exp</mml:mo>
<mml:mo stretchy="true">(</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">x</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:msub>
<mml:mi>&#x03B2;</mml:mi>
<mml:mi mathvariant="normal">j</mml:mi>
</mml:msub>
<mml:mo stretchy="true">)</mml:mo>
</mml:mrow>
</mml:mfrac>
</mml:math></disp-formula>
<p>By performing maximum likelihood estimation on the above equation, the estimated value of parameter <inline-formula>
<mml:math id="M16">
<mml:mi>&#x03B2;</mml:mi>
</mml:math>
</inline-formula> can be obtained. However, before maximum likelihood estimation is conducted, a reference group must be selected, and its coefficient must be normalized to zero. Therefore, taking household farming (<inline-formula>
<mml:math id="M17">
<mml:msub>
<mml:mi mathvariant="normal">y</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
</mml:math>
</inline-formula>=1) as the reference group, parameter <inline-formula>
<mml:math id="M18">
<mml:mi>&#x03B2;</mml:mi>
</mml:math>
</inline-formula> represents the propensity for choosing either of the other two land disposal methods relative to migrant workers who opt for household farming. Thus, the ratio of the occurrence of land transfer and abandonment relative to that of household farming is shown in <xref ref-type="disp-formula" rid="E4">Equations 4</xref>, <xref ref-type="disp-formula" rid="E5">5</xref>:</p>
<disp-formula id="E4"><label>(4)</label> <mml:math id="M19">
<mml:mo>ln</mml:mo>
<mml:mo stretchy="true">(</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="normal">p</mml:mi>
<mml:mspace width="0.33em"/>
<mml:mo stretchy="true">(</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">y</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:mo>=</mml:mo>
<mml:mn>2</mml:mn>
<mml:mo>&#x2223;</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">x</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:mo stretchy="true">)</mml:mo>
</mml:mrow>
<mml:mrow>
<mml:mi mathvariant="normal">p</mml:mi>
<mml:mspace width="0.33em"/>
<mml:mo stretchy="true">(</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">y</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:mo>=</mml:mo>
<mml:mn>1</mml:mn>
<mml:mo>&#x2223;</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">x</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:mo stretchy="true">)</mml:mo>
</mml:mrow>
</mml:mfrac>
<mml:mo stretchy="true">)</mml:mo>
<mml:mo>=</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">x</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:mo stretchy="true">(</mml:mo>
<mml:msub>
<mml:mi>&#x03B2;</mml:mi>
<mml:mn>2</mml:mn>
</mml:msub>
<mml:mo>&#x2212;</mml:mo>
<mml:msub>
<mml:mi>&#x03B2;</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
<mml:mo stretchy="true">)</mml:mo>
<mml:mspace width="0.33em"/>
</mml:math></disp-formula>
<disp-formula id="E5"><label>(5)</label> <mml:math id="M20">
<mml:mo>ln</mml:mo>
<mml:mo stretchy="true">(</mml:mo>
<mml:mfrac>
<mml:mrow>
<mml:mi mathvariant="normal">p</mml:mi>
<mml:mspace width="0.33em"/>
<mml:mo stretchy="true">(</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">y</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:mo>=</mml:mo>
<mml:mn>3</mml:mn>
<mml:mo>&#x2223;</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">x</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:mo stretchy="true">)</mml:mo>
</mml:mrow>
<mml:mrow>
<mml:mi mathvariant="normal">p</mml:mi>
<mml:mspace width="0.33em"/>
<mml:mo stretchy="true">(</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">y</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:mo>=</mml:mo>
<mml:mn>1</mml:mn>
<mml:mo>&#x2223;</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">x</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:mo stretchy="true">)</mml:mo>
</mml:mrow>
</mml:mfrac>
<mml:mo stretchy="true">)</mml:mo>
<mml:mo>=</mml:mo>
<mml:msub>
<mml:mi mathvariant="normal">x</mml:mi>
<mml:mi mathvariant="normal">i</mml:mi>
</mml:msub>
<mml:mo stretchy="true">(</mml:mo>
<mml:msub>
<mml:mi>&#x03B2;</mml:mi>
<mml:mn>3</mml:mn>
</mml:msub>
<mml:mo>&#x2212;</mml:mo>
<mml:msub>
<mml:mi>&#x03B2;</mml:mi>
<mml:mn>1</mml:mn>
</mml:msub>
<mml:mo stretchy="true">)</mml:mo>
<mml:mspace width="0.33em"/>
</mml:math></disp-formula>
</sec>
<sec id="sec8">
<label>3.3</label>
<title>Variable definition</title>
<p>The dependent variable of the study is the disposal method of contracted land, which is categorized primarily as either continued family cultivation, land transfer, and abandonment. The independent variables are selected from three dimensions: individual characteristics, integration status, and mobility factors. The specific variable definitions are shown in <xref ref-type="table" rid="tab1">Table 1</xref>.</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Variable names and definitions.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th>Variable category</th>
<th align="left" valign="top" colspan="2">Variable name</th>
<th align="left" valign="top">Definition</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Individual characteristics</td>
<td align="left" valign="top" colspan="2">Gender</td>
<td align="left" valign="top">Male&#x202F;=&#x202F;0; female&#x202F;=&#x202F;1</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top" colspan="2">Age</td>
<td align="left" valign="top">20&#x202F;years old and under&#x202F;=&#x202F;1<break/>21&#x2013;35&#x202F;years old&#x202F;=&#x202F;2<break/>36&#x2013;50&#x202F;years old&#x202F;=&#x202F;3<break/>50&#x202F;years old and above&#x202F;=&#x202F;4</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top" colspan="2">Educational attainment</td>
<td align="left" valign="top">Elementary school or below&#x202F;=&#x202F;1<break/>Junior high school&#x202F;=&#x202F;2<break/>High school/vocational school&#x202F;=&#x202F;3<break/>College/university or higher&#x202F;=&#x202F;4</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top" colspan="2">Self-rated health status</td>
<td align="left" valign="top">Respondent&#x2019;s self-assessment of health status<break/>Healthy&#x202F;=&#x202F;1; unhealthy&#x202F;=&#x202F;0</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top" colspan="2">Number of household members</td>
<td align="left" valign="top">Number of respondents and their spouses and children (excluding children who are married and separated) living in the destination, hometown, and other places, as well as other family members cohabiting in the destination area</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top" colspan="2">Area of contracted farmland</td>
<td align="left" valign="top">Area of contracted farmland personally owned by the respondent in the outflow area</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top" colspan="2">Ownership of residential land</td>
<td align="left" valign="top">Whether the respondent has contracted land in their hometown in the outflow area<break/>Yes&#x202F;=&#x202F;1; no&#x202F;=&#x202F;0</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top" colspan="2">Difficulties existing in hometown</td>
<td align="left" valign="top">Current family difficulties in hometown<break/>No difficulties&#x202F;=&#x202F;1<break/>Difficulties caring for family&#x202F;=&#x202F;2<break/>Financial difficulties&#x202F;=&#x202F;3<break/>Difficulties caring for family and financial difficulties&#x202F;=&#x202F;4</td>
</tr>
<tr>
<td align="left" valign="top">Integration status</td>
<td align="left" valign="top" rowspan="6">Economic<break/>Financial integration<break/>Institutional integration</td>
<td align="left" valign="top">Occupational type</td>
<td align="left" valign="top">White-collar occupation&#x202F;=&#x202F;1<break/>Blue-collar occupation&#x202F;=&#x202F;2<break/>No stable occupation&#x202F;=&#x202F;3</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top">Monthly income</td>
<td align="left" valign="top">Average monthly household income of respondents<break/>&#x2264;4,000 yuan&#x202F;=&#x202F;1<break/>4,001&#x2013;8,000 yuan&#x202F;=&#x202F;2<break/>&#x2265;8,000 yuan&#x202F;=&#x202F;3</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top">Housing situation</td>
<td align="left" valign="top">Nature of respondent&#x2019;s current housing<break/>Rented&#x202F;=&#x202F;1<break/>Self-built/purchased&#x202F;=&#x202F;2<break/>Government or employer-provided housing&#x202F;=&#x202F;3<break/>Other&#x202F;=&#x202F;4</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top">Social medical insurance</td>
<td align="left" valign="top">Has the respondent enrolled in social medical insurance in the destination area?<break/>Enrolled&#x202F;=&#x202F;1; not enrolled&#x202F;=&#x202F;0</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top">Social security card</td>
<td align="left" valign="top">Has the respondent applied for a social security card?<break/>Applied&#x202F;=&#x202F;1; not applied&#x202F;=&#x202F;0</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top">Temporary residence permit/residence permit</td>
<td align="left" valign="top">Has the respondent obtained a temporary residence permit/residence permit in the destination area?<break/>Applied&#x202F;=&#x202F;1; not applied&#x202F;=&#x202F;0</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top" rowspan="4">Identity integration</td>
<td align="left" valign="top">Leisure socializing</td>
<td align="left" valign="top">Who do respondents interact with most locally during their free time?<break/>Fellow townsfolk&#x202F;=&#x202F;1<break/>Locals and other non-locals&#x202F;=&#x202F;2<break/>Rarely socialize with others&#x202F;=&#x202F;3</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top">Participation in social organizations</td>
<td align="left" valign="top">Respondent&#x2019;s participation in local social organizations<break/>Participated&#x202F;=&#x202F;1<break/>Never participated&#x202F;=&#x202F;0</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top">Intention to settle</td>
<td align="left" valign="top">If eligible to meet the residency requirements of the destination area, would the respondent be willing to relocate their household registration to the local area?<break/>Willing&#x202F;=&#x202F;1; unwilling&#x202F;=&#x202F;0</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top">Intention to reside</td>
<td align="left" valign="top">In the coming period, does the respondent plan to continue residing locally?<break/>Stay locally&#x202F;=&#x202F;1<break/>Do not stay locally&#x202F;=&#x202F;0</td>
</tr>
<tr>
<td align="left" valign="top">Mobility factors</td>
<td align="left" valign="top" colspan="2">Outflow region</td>
<td align="left" valign="top">Eastern Region&#x202F;=&#x202F;1<break/>Central Region&#x202F;=&#x202F;2<break/>Western Region&#x202F;=&#x202F;3<break/>Northeastern Region&#x202F;=&#x202F;4</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top" colspan="2">Migration scope</td>
<td align="left" valign="top">Scope of the respondent&#x2019;s current migration<break/>Interprovincial&#x202F;=&#x202F;1<break/>Intraprovincial (intercity)&#x202F;=&#x202F;2<break/>Intra-city (intercounty)&#x202F;=&#x202F;3</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top" colspan="2">Duration of migration</td>
<td align="left" valign="top">Duration of the respondent&#x2019;s current migration<break/>5&#x202F;years or less&#x202F;=&#x202F;1<break/>6 to 15&#x202F;years&#x202F;=&#x202F;2<break/>16&#x202F;years or more&#x202F;=&#x202F;3</td>
</tr>
<tr>
<td align="left" valign="top"></td>
</tr>
<tr>
<td align="left" valign="top" colspan="2">Reason for migration</td>
<td align="left" valign="top">Reason for the respondent&#x2019;s current migration<break/>Work-related&#x202F;=&#x202F;1<break/>Business-related&#x202F;=&#x202F;2<break/>Family-related&#x202F;=&#x202F;3<break/>Other&#x202F;=&#x202F;4</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="3">Disposition of contracted farmland</td>
<td align="left" valign="top" colspan="2">Family continues farming</td>
<td align="left" valign="top">Family continues farming method (self/family farming&#x202F;+&#x202F;hired labor farming&#x202F;+&#x202F;relatives/friends farming)&#x202F;=&#x202F;1</td>
</tr>
<tr>
<td align="left" valign="top" colspan="2">Land transfer</td>
<td align="left" valign="top">Land transfer method (subleased to private individuals&#x202F;+&#x202F;subleased to village collective&#x202F;+&#x202F;subleased to enterprises)&#x202F;=&#x202F;2</td>
</tr>
<tr>
<td align="left" valign="top" colspan="2">Land abandonment</td>
<td align="left" valign="top">Abandoned land disposal method&#x202F;=&#x202F;3</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec id="sec9">
<label>4</label>
<title>Research findings</title>
<sec id="sec10">
<label>4.1</label>
<title>Descriptive statistics</title>
<sec id="sec11">
<label>4.1.1</label>
<title>Individual characteristics</title>
<p>The study sample comprised 42,094 males, which accounts for 57.13%, and 31,582 females, which accounts for 42.87% of the sample. The age distribution was predominantly concentrated among young and middle-aged laborers aged 21&#x2013;50, which accounts for approximately 84.64% of the sample. Migrant workers with only junior high school education or below constituted the largest proportion (70.74%). A total of 71,344 respondents (96.83%) reported being in good health. With respect to contracted farmland area, approximately 78.38% of the sample owned less than 2&#x202F;mu (approximately 0.33 acres), whereas only 9.58% owned more than 4 mu (approximately 0.67 acres). Among rural migrant workers, 84.94% owned residential land in their hometowns, and 32,171 individuals, accounting for 43.67% of the sample, faced difficulties caring for family members or experienced economic hardship in their hometowns. The specific results are shown in <xref ref-type="table" rid="tab2">Table 2</xref>.</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Descriptive statistics of individual characteristics.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Characteristic</th>
<th align="left" valign="top">Description</th>
<th align="center" valign="top">Sample size</th>
<th align="center" valign="top">Proportion</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" rowspan="2">Gender</td>
<td align="left" valign="top">Male</td>
<td align="center" valign="top">42,094</td>
<td align="char" valign="top" char=".">57.13%</td>
</tr>
<tr>
<td align="left" valign="top">Female</td>
<td align="center" valign="top">31,582</td>
<td align="char" valign="top" char=".">42.87%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="4">Age</td>
<td align="left" valign="top">20&#x202F;years old and under</td>
<td align="center" valign="top">1,511</td>
<td align="char" valign="top" char=".">2.05%</td>
</tr>
<tr>
<td align="left" valign="top">21 to 35&#x202F;years old</td>
<td align="center" valign="top">30,822</td>
<td align="char" valign="top" char=".">41.83%</td>
</tr>
<tr>
<td align="left" valign="top">36 to 50&#x202F;years old</td>
<td align="center" valign="top">31,540</td>
<td align="char" valign="top" char=".">42.81%</td>
</tr>
<tr>
<td align="left" valign="top">50&#x202F;years old and above</td>
<td align="center" valign="top">9,803</td>
<td align="char" valign="top" char=".">13.31%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="4">Educational attainment</td>
<td align="left" valign="top">Elementary school and below</td>
<td align="center" valign="top">15,894</td>
<td align="char" valign="top" char=".">21.57%</td>
</tr>
<tr>
<td align="left" valign="top">Junior high school</td>
<td align="center" valign="top">36,226</td>
<td align="char" valign="top" char=".">49.17%</td>
</tr>
<tr>
<td align="left" valign="top">High school/vocational school</td>
<td align="center" valign="top">14,287</td>
<td align="char" valign="top" char=".">19.39%</td>
</tr>
<tr>
<td align="left" valign="top">College degree or higher</td>
<td align="center" valign="top">7,269</td>
<td align="char" valign="top" char=".">9.87%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="2">Self-reported health status</td>
<td align="left" valign="top">Unhealthy</td>
<td align="center" valign="top">2,332</td>
<td align="char" valign="top" char=".">3.17%</td>
</tr>
<tr>
<td align="left" valign="top">Healthy</td>
<td align="center" valign="top">71,344</td>
<td align="char" valign="top" char=".">96.83%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="4">Contracted land area</td>
<td align="left" valign="top">1 mu or less</td>
<td align="center" valign="top">38,704</td>
<td align="char" valign="top" char=".">52.53%</td>
</tr>
<tr>
<td align="left" valign="top">Greater than 1 mu but less than or equal to 2 mu</td>
<td align="center" valign="top">19,044</td>
<td align="char" valign="top" char=".">25.85%</td>
</tr>
<tr>
<td align="left" valign="top">Greater than 2 mu and less than or equal to 4 mu</td>
<td align="center" valign="top">8,872</td>
<td align="char" valign="top" char=".">12.04%</td>
</tr>
<tr>
<td align="left" valign="top">Over 4 mu</td>
<td align="center" valign="top">7,056</td>
<td align="char" valign="top" char=".">9.58%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="2">Whether residential land is available</td>
<td align="left" valign="top">No</td>
<td align="center" valign="top">11,095</td>
<td align="char" valign="top" char=".">15.06%</td>
</tr>
<tr>
<td align="left" valign="top">Yes</td>
<td align="center" valign="top">62,581</td>
<td align="char" valign="top" char=".">84.94%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="4">Challenges in hometown</td>
<td align="left" valign="top">No difficulties</td>
<td align="center" valign="top">41,505</td>
<td align="char" valign="top" char=".">56.33%</td>
</tr>
<tr>
<td align="left" valign="top">Difficulty caring for family members</td>
<td align="center" valign="top">8,207</td>
<td align="char" valign="top" char=".">11.14%</td>
</tr>
<tr>
<td align="left" valign="top">Financial hardship</td>
<td align="center" valign="top">6,091</td>
<td align="char" valign="top" char=".">8.27%</td>
</tr>
<tr>
<td align="left" valign="top">Family responsibilities and financial hardship</td>
<td align="center" valign="top">17,873</td>
<td align="char" valign="top" char=".">24.26%</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="sec12">
<label>4.1.2</label>
<title>Integration status</title>
<p>With respect to the economic integration of the &#x201C;new citizens&#x201D; group, the majority of household monthly incomes fall below 8,000 yuan. According to China&#x2019;s classification of per capita disposable income for urban residents, the lower-middle income group earning less than 2,000 yuan accounts for 33.11% of the sample. In terms of housing conditions in the destination areas, 58.35% of the group rents housing. The specific results are shown in <xref ref-type="table" rid="tab3">Table 3</xref>.</p>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>Descriptive statistics on economic integration status.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Characteristic</th>
<th align="left" valign="top">Description</th>
<th align="center" valign="top">Sample Size</th>
<th align="center" valign="top">Proportion</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" rowspan="3">Monthly household income</td>
<td align="left" valign="top">&#x2264;4,000 yuan</td>
<td align="center" valign="top">20,770</td>
<td align="char" valign="top" char=".">28.19%</td>
</tr>
<tr>
<td align="left" valign="top">4,001 to 8,000 yuan</td>
<td align="center" valign="top">36,870</td>
<td align="char" valign="top" char=".">50.04%</td>
</tr>
<tr>
<td align="left" valign="top">Over 8,000 yuan</td>
<td align="center" valign="top">16,036</td>
<td align="char" valign="top" char=".">21.77%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="4">Housing situation</td>
<td align="left" valign="top">Rented</td>
<td align="center" valign="top">42,992</td>
<td align="char" valign="top" char=".">58.35%</td>
</tr>
<tr>
<td align="left" valign="top">Self-built/self-purchased housing</td>
<td align="center" valign="top">18,886</td>
<td align="char" valign="top" char=".">25.63%</td>
</tr>
<tr>
<td align="left" valign="top">Government or employer-provided housing</td>
<td align="center" valign="top">8,044</td>
<td align="char" valign="top" char=".">10.92%</td>
</tr>
<tr>
<td align="left" valign="top">Other</td>
<td align="center" valign="top">3,754</td>
<td align="char" valign="top" char=".">5.10%</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>In terms of institutional integration, only 18.13% of the surveyed migrant workers participated in social medical insurance in their destination areas, and less than half had obtained a personal social security card. Temporary and standard residence permits serve as territorial management tools for the floating population, enabling them to enjoy benefits in employment, medical insurance, children&#x2019;s education, housing rentals, and vehicle/property purchases. However, 35.34% of the surveyed migrant workers did not have a residence permit. The specific results are shown in <xref ref-type="table" rid="tab4">Table 4</xref>.</p>
<table-wrap position="float" id="tab4">
<label>Table 4</label>
<caption>
<p>Descriptive statistics on institutional integration.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Characteristic</th>
<th align="left" valign="top">Description</th>
<th align="center" valign="top">Sample size</th>
<th align="center" valign="top">Proportion</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" rowspan="2">Participation in social medical insurance in destination areas</td>
<td align="left" valign="top">Already participated</td>
<td align="center" valign="top">13,361</td>
<td align="char" valign="top" char=".">18.13%</td>
</tr>
<tr>
<td align="left" valign="top">Not enrolled</td>
<td align="center" valign="top">60,315</td>
<td align="char" valign="top" char=".">81.87%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="2">Social security card application status</td>
<td align="left" valign="top">Already processed</td>
<td align="center" valign="top">33,520</td>
<td align="char" valign="top" char=".">45.50%</td>
</tr>
<tr>
<td align="left" valign="top">Not applied for</td>
<td align="center" valign="top">40,156</td>
<td align="char" valign="top" char=".">54.50%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="2">Temporary residence permit/residence permit processing status</td>
<td align="left" valign="top">Processed</td>
<td align="center" valign="top">47,636</td>
<td align="char" valign="top" char=".">64.66%</td>
</tr>
<tr>
<td align="left" valign="top">Not processed</td>
<td align="center" valign="top">26,040</td>
<td align="char" valign="top" char=".">35.34%</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>In terms of identity integration, 23.12% of the migrant workers remained self-isolated and rarely interacted with their fellow villagers, locals, or other nonresidents, while 42.42% had never participated in local social organizations. Moreover, when eligible to register as permanent residents in their destination areas, 68.26% of migrant workers are unwilling to relinquish their rural household registration and transfer it to their destination, and a staggering 82.7% choose to remain in their place of origin. The specific results are shown in <xref ref-type="table" rid="tab5">Table 5</xref>.</p>
<table-wrap position="float" id="tab5">
<label>Table 5</label>
<caption>
<p>Descriptive statistics on identity integration.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Characteristic</th>
<th align="left" valign="top">Description</th>
<th align="center" valign="top">Sample size</th>
<th align="center" valign="top">Proportion</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" rowspan="3">Amateur friendship status</td>
<td align="left" valign="top">Fellow townspeople</td>
<td align="center" valign="top">27,024</td>
<td align="char" valign="top" char=".">36.68%</td>
</tr>
<tr>
<td align="left" valign="top">Locals and other non-locals</td>
<td align="center" valign="top">29,616</td>
<td align="char" valign="top" char=".">40.20%</td>
</tr>
<tr>
<td align="left" valign="top">Rarely socialize with others</td>
<td align="center" valign="top">17,036</td>
<td align="char" valign="top" char=".">23.12%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="2">Participation in social organizations</td>
<td align="left" valign="top">Participated</td>
<td align="center" valign="top">42,423</td>
<td align="char" valign="top" char=".">57.58%</td>
</tr>
<tr>
<td align="left" valign="top">Never participated</td>
<td align="center" valign="top">31,253</td>
<td align="char" valign="top" char=".">42.42%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="2">Willingness to settle</td>
<td align="left" valign="top">Willing</td>
<td align="center" valign="top">23,387</td>
<td align="char" valign="top" char=".">31.74%</td>
</tr>
<tr>
<td align="left" valign="top">Not willing</td>
<td align="center" valign="top">50,289</td>
<td align="char" valign="top" char=".">68.26%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="2">Willing to stay</td>
<td align="left" valign="top">Stay locally</td>
<td align="center" valign="top">60,928</td>
<td align="char" valign="top" char=".">82.70%</td>
</tr>
<tr>
<td align="left" valign="top">Other</td>
<td align="center" valign="top">12,748</td>
<td align="char" valign="top" char=".">17.30%</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="sec13">
<label>4.1.3</label>
<title>Mobile factors</title>
<p>To further explore the regional characteristics of the mobility of &#x201C;new urban residents&#x201D;, survey data from 31 provinces [<xref ref-type="bibr" rid="ref31">National Bureau of Statistics of China (NBSC), 2021</xref>], including the Eastern Region (Beijing, Shanghai, Tianjin, Shandong, Hebei, Zhejiang, Jiangsu, Fujian, Guangdong, Hainan), Central Region (Henan, Anhui, Shanxi, Jiangxi, Hubei, Hunan), Western Region (Inner Mongolia, Xinjiang, Xizang, Yunnan, Guizhou, Chongqing, Shaanxi, Gansu, Qinghai, Ningxia, Guangxi), and Northeastern Region (Heilongjiang, Jilin, Liaoning) were first categorized according to the standards of the National Bureau of Statistics. The data indicate that, on the basis of the outflow regions for the surveyed samples, migrant workers originating from the Central and Western Regions constitute the largest proportion, accounting for 72.37%, while those from the Eastern Region account for only 19.93%. The specific results are shown in <xref ref-type="table" rid="tab6">Table 6</xref>.</p>
<table-wrap position="float" id="tab6">
<label>Table 6</label>
<caption>
<p>Outflow regions of migrant workers.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Region</th>
<th align="center" valign="top">Sample size</th>
<th align="center" valign="top">Proportion</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Eastern Region</td>
<td align="center" valign="top">14,681</td>
<td align="char" valign="top" char=".">19.93%</td>
</tr>
<tr>
<td align="left" valign="top">Central Region</td>
<td align="center" valign="top">26,893</td>
<td align="char" valign="top" char=".">36.50%</td>
</tr>
<tr>
<td align="left" valign="top">Western Region</td>
<td align="center" valign="top">26,426</td>
<td align="char" valign="top" char=".">35.87%</td>
</tr>
<tr>
<td align="left" valign="top">Northeast Region</td>
<td align="center" valign="top">5,676</td>
<td align="char" valign="top" char=".">7.70%</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>In terms of migration scope, nearly half of the migrant workers moved across provincial borders, while those migrating within provinces across cities or within cities across counties accounted for smaller proportions. With respect to migration duration, most migrant workers had relatively short migration periods, with the majority having migrated for less than 5&#x202F;years. Only 11.35% had migrated for more than 15 years. With respect to migration purposes, 86.83% of the migrant workers migrated for employment or business activities. The detailed results are presented in <xref ref-type="table" rid="tab7">Table 7</xref>.</p>
<table-wrap position="float" id="tab7">
<label>Table 7</label>
<caption>
<p>Migration scope, duration, and type of migrant workers.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Characteristics</th>
<th align="left" valign="top">Description</th>
<th align="center" valign="top">Sample size</th>
<th align="center" valign="top">Proportion</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" rowspan="3">Migration scope</td>
<td align="left" valign="top">Interprovincial mobility</td>
<td align="center" valign="top">35,694</td>
<td align="char" valign="top" char=".">48.45%</td>
</tr>
<tr>
<td align="left" valign="top">Intra-provincial inter-city mobility</td>
<td align="center" valign="top">23,518</td>
<td align="char" valign="top" char=".">31.92%</td>
</tr>
<tr>
<td align="left" valign="top">Intra-city cross-county migration</td>
<td align="center" valign="top">14,464</td>
<td align="char" valign="top" char=".">19.63%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="3">Duration of migration</td>
<td align="left" valign="top">5&#x202F;years or less</td>
<td align="center" valign="top">38,116</td>
<td align="char" valign="top" char=".">51.73%</td>
</tr>
<tr>
<td align="left" valign="top">6 to 15&#x202F;years</td>
<td align="center" valign="top">27,196</td>
<td align="char" valign="top" char=".">36.91%</td>
</tr>
<tr>
<td align="left" valign="top">Over 15&#x202F;years</td>
<td align="center" valign="top">8,364</td>
<td align="char" valign="top" char=".">11.35%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="4">Type of migration</td>
<td align="left" valign="top">Labor</td>
<td align="center" valign="top">44,648</td>
<td align="char" valign="top" char=".">60.60%</td>
</tr>
<tr>
<td align="left" valign="top">Business</td>
<td align="center" valign="top">19,326</td>
<td align="char" valign="top" char=".">26.23%</td>
</tr>
<tr>
<td align="left" valign="top">Family relocation</td>
<td align="center" valign="top">8,371</td>
<td align="char" valign="top" char=".">11.36%</td>
</tr>
<tr>
<td align="left" valign="top">Other</td>
<td align="center" valign="top">1,331</td>
<td align="char" valign="top" char=".">1.81%</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="sec14">
<label>4.1.4</label>
<title>Descriptive statistics and spatial distribution characteristics of contracted land disposal methods</title>
<p>Among the study sample, 78.08% chose to have family members or relatives continue cultivating their contracted land. Only 14.92% of the sampled migrant workers opted to transfer their contracted land, while 7.01% left their contracted land fallow. The specific results are shown in <xref ref-type="table" rid="tab8">Table 8</xref>.</p>
<table-wrap position="float" id="tab8">
<label>Table 8</label>
<caption>
<p>Descriptive statistics of contracted land disposal methods.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Disposal method</th>
<th align="center" valign="top">Sample size</th>
<th align="center" valign="top">Proportion</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Continued cultivation by family</td>
<td align="center" valign="top">57,524</td>
<td align="char" valign="top" char=".">78.08%</td>
</tr>
<tr>
<td align="left" valign="top">Land transfer</td>
<td align="center" valign="top">10,990</td>
<td align="char" valign="top" char=".">14.92%</td>
</tr>
<tr>
<td align="left" valign="top">Abandoned farmland</td>
<td align="center" valign="top">5,162</td>
<td align="char" valign="top" char=".">7.01%</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>In terms of the spatial distribution of land disposal methods among the &#x201C;new urban residents&#x201D;, migrant workers in the Eastern Region most frequently chose family cultivation, those in the Northeastern Region reported the highest proportion of land transfers and the lowest proportion of land abandonment, and those in the Western Region reported the highest proportion of land abandonment. The specific results are shown in <xref ref-type="table" rid="tab9">Tables 9</xref> and <xref ref-type="table" rid="tab10">10</xref>.</p>
<table-wrap position="float" id="tab9">
<label>Table 9</label>
<caption>
<p>Disposal methods for contracted farmland among migrant workers in different regions.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Region</th>
<th align="center" valign="top" colspan="2">Family farming</th>
<th align="center" valign="top" colspan="2">Land transfer</th>
<th align="center" valign="top" colspan="2">Land abandonment</th>
</tr>
<tr>
<th align="center" valign="top">Sample size</th>
<th align="center" valign="top">Percentage</th>
<th align="center" valign="top">Sample size</th>
<th align="center" valign="top">Percentage</th>
<th align="center" valign="top">Sample size</th>
<th align="center" valign="top">Percentage</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Eastern Region</td>
<td align="center" valign="top">11,807</td>
<td align="char" valign="top" char=".">80.42%</td>
<td align="center" valign="top">2,198</td>
<td align="char" valign="top" char=".">14.97%</td>
<td align="center" valign="top">676</td>
<td align="char" valign="top" char=".">4.60%</td>
</tr>
<tr>
<td align="left" valign="top">Central Region</td>
<td align="center" valign="top">21,251</td>
<td align="char" valign="top" char=".">79.02%</td>
<td align="center" valign="top">4,334</td>
<td align="char" valign="top" char=".">16.12%</td>
<td align="center" valign="top">1,308</td>
<td align="char" valign="top" char=".">4.86%</td>
</tr>
<tr>
<td align="left" valign="top">Western Region</td>
<td align="center" valign="top">20,645</td>
<td align="char" valign="top" char=".">78.12%</td>
<td align="center" valign="top">2,633</td>
<td align="char" valign="top" char=".">9.96%</td>
<td align="center" valign="top">3,148</td>
<td align="char" valign="top" char=".">11.91%</td>
</tr>
<tr>
<td align="left" valign="top">Northeast Region</td>
<td align="center" valign="top">3,822</td>
<td align="char" valign="top" char=".">67.34%</td>
<td align="center" valign="top">1824</td>
<td align="char" valign="top" char=".">32.14%</td>
<td align="center" valign="top">30</td>
<td align="char" valign="top" char=".">0.53%</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap position="float" id="tab10">
<label>Table 10</label>
<caption>
<p>Disposition methods for contracted farmland among migrant workers in 31 provinces.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Region</th>
<th align="left" valign="top" rowspan="2">Province</th>
<th align="center" valign="top" colspan="2">Family farming</th>
<th align="center" valign="top" colspan="2">Land transfer</th>
<th align="center" valign="top" colspan="2">Land abandonment</th>
</tr>
<tr>
<th align="center" valign="top">Sample size</th>
<th align="center" valign="top">Percentage</th>
<th align="center" valign="top">Sample size</th>
<th align="center" valign="top">Percentage</th>
<th align="center" valign="top">Sample size</th>
<th align="center" valign="top">Percentage</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" rowspan="10">Eastern Region</td>
<td align="left" valign="top">Beijing</td>
<td align="center" valign="top">2</td>
<td align="char" valign="top" char=".">66.67%</td>
<td align="center" valign="top">1</td>
<td align="char" valign="top" char=".">33.33%</td>
<td align="center" valign="top">0</td>
<td align="char" valign="top" char=".">0.00%</td>
</tr>
<tr>
<td align="left" valign="top">Tianjin Municipality</td>
<td align="center" valign="top">23</td>
<td align="char" valign="top" char=".">85.19%</td>
<td align="center" valign="top">4</td>
<td align="char" valign="top" char=".">14.81%</td>
<td align="center" valign="top">0</td>
<td align="char" valign="top" char=".">0.00%</td>
</tr>
<tr>
<td align="left" valign="top">Hebei Province</td>
<td align="center" valign="top">2,953</td>
<td align="char" valign="top" char=".">86.60%</td>
<td align="center" valign="top">383</td>
<td align="char" valign="top" char=".">11.23%</td>
<td align="center" valign="top">74</td>
<td align="char" valign="top" char=".">2.17%</td>
</tr>
<tr>
<td align="left" valign="top">Shanghai</td>
<td align="center" valign="top">2</td>
<td align="char" valign="top" char=".">100.00%</td>
<td align="center" valign="top">0</td>
<td align="char" valign="top" char=".">0.00%</td>
<td align="center" valign="top">0</td>
<td align="char" valign="top" char=".">0.00%</td>
</tr>
<tr>
<td align="left" valign="top">Jiangsu Province</td>
<td align="center" valign="top">1,645</td>
<td align="char" valign="top" char=".">73.73%</td>
<td align="center" valign="top">556</td>
<td align="char" valign="top" char=".">24.92%</td>
<td align="center" valign="top">30</td>
<td align="char" valign="top" char=".">1.34%</td>
</tr>
<tr>
<td align="left" valign="top">Zhejiang Province</td>
<td align="center" valign="top">818</td>
<td align="char" valign="top" char=".">64.46%</td>
<td align="center" valign="top">275</td>
<td align="char" valign="top" char=".">21.67%</td>
<td align="center" valign="top">176</td>
<td align="char" valign="top" char=".">13.87%</td>
</tr>
<tr>
<td align="left" valign="top">Fujian Province</td>
<td align="center" valign="top">1,276</td>
<td align="char" valign="top" char=".">74.62%</td>
<td align="center" valign="top">207</td>
<td align="char" valign="top" char=".">12.11%</td>
<td align="center" valign="top">227</td>
<td align="char" valign="top" char=".">13.27%</td>
</tr>
<tr>
<td align="left" valign="top">Shandong Province</td>
<td align="center" valign="top">4,318</td>
<td align="char" valign="top" char=".">85.42%</td>
<td align="center" valign="top">687</td>
<td align="char" valign="top" char=".">13.59%</td>
<td align="center" valign="top">50</td>
<td align="char" valign="top" char=".">0.99%</td>
</tr>
<tr>
<td align="left" valign="top">Guangdong Province</td>
<td align="center" valign="top">626</td>
<td align="char" valign="top" char=".">79.85%</td>
<td align="center" valign="top">63</td>
<td align="char" valign="top" char=".">8.04%</td>
<td align="center" valign="top">95</td>
<td align="char" valign="top" char=".">12.12%</td>
</tr>
<tr>
<td align="left" valign="top">Hainan Province</td>
<td align="center" valign="top">144</td>
<td align="char" valign="top" char=".">75.79%</td>
<td align="center" valign="top">22</td>
<td align="char" valign="top" char=".">11.58%</td>
<td align="center" valign="top">24</td>
<td align="char" valign="top" char=".">12.63%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="6">Central Region</td>
<td align="left" valign="top">Shanxi Province</td>
<td align="center" valign="top">1,403</td>
<td align="char" valign="top" char=".">85.65%</td>
<td align="center" valign="top">141</td>
<td align="char" valign="top" char=".">8.61%</td>
<td align="center" valign="top">94</td>
<td align="char" valign="top" char=".">5.74%</td>
</tr>
<tr>
<td align="left" valign="top">Anhui Province</td>
<td align="center" valign="top">5,314</td>
<td align="char" valign="top" char=".">71.44%</td>
<td align="center" valign="top">1852</td>
<td align="char" valign="top" char=".">24.90%</td>
<td align="center" valign="top">272</td>
<td align="char" valign="top" char=".">3.66%</td>
</tr>
<tr>
<td align="left" valign="top">Jiangxi Province</td>
<td align="center" valign="top">2061</td>
<td align="char" valign="top" char=".">70.44%</td>
<td align="center" valign="top">603</td>
<td align="char" valign="top" char=".">20.61%</td>
<td align="center" valign="top">262</td>
<td align="char" valign="top" char=".">8.95%</td>
</tr>
<tr>
<td align="left" valign="top">Henan Province</td>
<td align="center" valign="top">6,376</td>
<td align="char" valign="top" char=".">87.06%</td>
<td align="center" valign="top">851</td>
<td align="char" valign="top" char=".">11.62%</td>
<td align="center" valign="top">97</td>
<td align="char" valign="top" char=".">1.32%</td>
</tr>
<tr>
<td align="left" valign="top">Hubei Province</td>
<td align="center" valign="top">2,457</td>
<td align="char" valign="top" char=".">80.64%</td>
<td align="center" valign="top">378</td>
<td align="char" valign="top" char=".">12.41%</td>
<td align="center" valign="top">212</td>
<td align="char" valign="top" char=".">6.96%</td>
</tr>
<tr>
<td align="left" valign="top">Hunan Province</td>
<td align="center" valign="top">3,640</td>
<td align="char" valign="top" char=".">80.53%</td>
<td align="center" valign="top">509</td>
<td align="char" valign="top" char=".">11.26%</td>
<td align="center" valign="top">371</td>
<td align="char" valign="top" char=".">8.21%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="12">Western Region</td>
<td align="left" valign="top">Inner Mongolia Autonomous Region</td>
<td align="center" valign="top">1,656</td>
<td align="char" valign="top" char=".">74.19%</td>
<td align="center" valign="top">449</td>
<td align="char" valign="top" char=".">20.12%</td>
<td align="center" valign="top">127</td>
<td align="char" valign="top" char=".">5.69%</td>
</tr>
<tr>
<td align="left" valign="top">Guangxi Zhuang Autonomous Region</td>
<td align="center" valign="top">1838</td>
<td align="char" valign="top" char=".">85.45%</td>
<td align="center" valign="top">161</td>
<td align="char" valign="top" char=".">7.48%</td>
<td align="center" valign="top">152</td>
<td align="char" valign="top" char=".">7.07%</td>
</tr>
<tr>
<td align="left" valign="top">Chongqing Municipality</td>
<td align="center" valign="top">2,135</td>
<td align="char" valign="top" char=".">70.21%</td>
<td align="center" valign="top">268</td>
<td align="char" valign="top" char=".">8.81%</td>
<td align="center" valign="top">638</td>
<td align="char" valign="top" char=".">20.98%</td>
</tr>
<tr>
<td align="left" valign="top">Sichuan Province</td>
<td align="center" valign="top">5,019</td>
<td align="char" valign="top" char=".">78.90%</td>
<td align="center" valign="top">594</td>
<td align="char" valign="top" char=".">9.34%</td>
<td align="center" valign="top">748</td>
<td align="char" valign="top" char=".">11.76%</td>
</tr>
<tr>
<td align="left" valign="top">Guizhou Province</td>
<td align="center" valign="top">2094</td>
<td align="char" valign="top" char=".">78.22%</td>
<td align="center" valign="top">239</td>
<td align="char" valign="top" char=".">8.93%</td>
<td align="center" valign="top">344</td>
<td align="char" valign="top" char=".">12.85%</td>
</tr>
<tr>
<td align="left" valign="top">Yunnan Province</td>
<td align="center" valign="top">1978</td>
<td align="char" valign="top" char=".">83.53%</td>
<td align="center" valign="top">195</td>
<td align="char" valign="top" char=".">8.23%</td>
<td align="center" valign="top">195</td>
<td align="char" valign="top" char=".">8.23%</td>
</tr>
<tr>
<td align="left" valign="top">Tibet Autonomous Region</td>
<td align="center" valign="top">120</td>
<td align="char" valign="top" char=".">93.75%</td>
<td align="center" valign="top">6</td>
<td align="char" valign="top" char=".">4.69%</td>
<td align="center" valign="top">2</td>
<td align="char" valign="top" char=".">1.56%</td>
</tr>
<tr>
<td align="left" valign="top">Shaanxi Province</td>
<td align="center" valign="top">2,149</td>
<td align="char" valign="top" char=".">80.07%</td>
<td align="center" valign="top">205</td>
<td align="char" valign="top" char=".">7.64%</td>
<td align="center" valign="top">330</td>
<td align="char" valign="top" char=".">12.30%</td>
</tr>
<tr>
<td align="left" valign="top">Gansu Province</td>
<td align="center" valign="top">2,293</td>
<td align="char" valign="top" char=".">83.26%</td>
<td align="center" valign="top">204</td>
<td align="char" valign="top" char=".">7.41%</td>
<td align="center" valign="top">257</td>
<td align="char" valign="top" char=".">9.33%</td>
</tr>
<tr>
<td align="left" valign="top">Qinghai Province</td>
<td align="center" valign="top">624</td>
<td align="char" valign="top" char=".">77.90%</td>
<td align="center" valign="top">85</td>
<td align="char" valign="top" char=".">10.61%</td>
<td align="center" valign="top">92</td>
<td align="char" valign="top" char=".">11.49%</td>
</tr>
<tr>
<td align="left" valign="top">Ningxia Hui Autonomous Region</td>
<td align="center" valign="top">455</td>
<td align="char" valign="top" char=".">54.23%</td>
<td align="center" valign="top">123</td>
<td align="char" valign="top" char=".">14.66%</td>
<td align="center" valign="top">261</td>
<td align="char" valign="top" char=".">31.11%</td>
</tr>
<tr>
<td align="left" valign="top">Xinjiang Uygur Autonomous Region</td>
<td align="center" valign="top">284</td>
<td align="char" valign="top" char=".">72.82%</td>
<td align="center" valign="top">104</td>
<td align="char" valign="top" char=".">26.67%</td>
<td align="center" valign="top">2</td>
<td align="char" valign="top" char=".">0.51%</td>
</tr>
<tr>
<td align="left" valign="top" rowspan="3">Northeast Region</td>
<td align="left" valign="top">Liaoning Province</td>
<td align="center" valign="top">978</td>
<td align="char" valign="top" char=".">83.30%</td>
<td align="center" valign="top">187</td>
<td align="char" valign="top" char=".">15.93%</td>
<td align="center" valign="top">9</td>
<td align="char" valign="top" char=".">0.77%</td>
</tr>
<tr>
<td align="left" valign="top">Jilin Province</td>
<td align="center" valign="top">1,102</td>
<td align="char" valign="top" char=".">66.91%</td>
<td align="center" valign="top">538</td>
<td align="char" valign="top" char=".">32.67%</td>
<td align="center" valign="top">7</td>
<td align="char" valign="top" char=".">0.43%</td>
</tr>
<tr>
<td align="left" valign="top">Heilongjiang Province</td>
<td align="center" valign="top">1,742</td>
<td align="char" valign="top" char=".">61.02%</td>
<td align="center" valign="top">1,099</td>
<td align="char" valign="top" char=".">38.49%</td>
<td align="center" valign="top">14</td>
<td align="char" valign="top" char=".">0.49%</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Specifically, after seeking employment elsewhere, migrant workers from Xizang, Henan, Hebei, Shanxi, Guangxi, Tianjin, and Shandong Provinces are more likely to choose continued family cultivation. In contrast, the proportion of contracted farmland entrusted to family cultivation is lower in Jilin, Zhejiang, Ningxia, and Heilongjiang Provinces, as shown in <xref ref-type="fig" rid="fig1">Figure 1</xref>.</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>Continuation of contracted land cultivation by family in 31 provinces.</p>
</caption>
<graphic xlink:href="fsufs-10-1789435-g001.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Choropleth map of China divided into Northeast, Western, Eastern, and Central regions with provinces shaded by family farming ratio: lighter areas indicate less than seventy percent, medium for seventy to eighty-five percent, and darkest for eighty-five to one hundred percent; some regions lack data.</alt-text>
</graphic>
</fig>
<p>In terms of land transfer, the proportion of contracted land that is transferred by migrant workers&#x2019; households in Heilongjiang, Beijing, Jilin, Xinjiang, Jiangsu, Anhui, Zhejiang, Jiangxi, and Inner Mongolia exceeded 20%. In contrast, Xizang, Gansu, Guangxi, Shaanxi, Guangdong, Yunnan, Shanxi, Chongqing, Guizhou, and Sichuan presented poor transfer rates, all of which were less than 10%. As shown in <xref ref-type="fig" rid="fig2">Figure 2</xref>.</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Contracted land transfer rates across 31 provinces.</p>
</caption>
<graphic xlink:href="fsufs-10-1789435-g002.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Choropleth map of China showing provincial land transfer ratios, with regions labeled as Western, Central, Eastern, and Northeast China. Darker shades indicate a higher land transfer ratio, over 20 percent, while lighter shades represent lower ratios, from zero to ten percent. A legend and scale bar are included for reference.</alt-text>
</graphic>
</fig>
<p>With respect to the abandonment of contracted farmland, Ningxia and Chongqing have the highest proportions of contracted land that has been abandoned by rural migrants. Zhejiang, Fujian, Guizhou, Hainan, Shaanxi, Guangdong, Sichuan, and Qinghai also experience relatively severe levels of abandonment. The three northeastern provinces, Xinjiang, and Shandong, have lower abandonment rates, as shown in <xref ref-type="fig" rid="fig3">Figure 3</xref>.</p>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Abandoned contracted farmland in 31 provinces.</p>
</caption>
<graphic xlink:href="fsufs-10-1789435-g003.tif" mimetype="image" mime-subtype="tiff">
<alt-text content-type="machine-generated">Choropleth map of China divided into four regions&#x2014;Western China, Central China, Eastern China, and Northeast&#x2014;showing abandoned land ratio by prefecture using three color categories: light beige for zero to five percent, light brown for five to ten percent, and dark brown for above ten percent. Western and Central China display the highest abandoned land ratios, while Eastern China and Northeast mostly exhibit lower ratios. Scale bar and legend are included for context.</alt-text>
</graphic>
</fig>
</sec>
</sec>
<sec id="sec15">
<label>4.2</label>
<title>Multinomial logit model analysis results</title>
<p>Prior to conducting the multinomial logit regression analysis, multicollinearity among the variables was first examined. The results indicated that the variance inflation factor (VIF) for all the selected variables were less than 10, confirming the absence of multicollinearity issues. The test results are presented in <xref ref-type="table" rid="tab11">Table 11</xref>.</p>
<table-wrap position="float" id="tab11">
<label>Table 11</label>
<caption>
<p>Multicollinearity test.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variable name</th>
<th align="center" valign="top">VIF</th>
<th align="center" valign="top">1/VIF</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Gender</td>
<td align="char" valign="top" char=".">1.21</td>
<td align="char" valign="top" char=".">0.83</td>
</tr>
<tr>
<td align="left" valign="top">Age</td>
<td align="char" valign="top" char=".">1.27</td>
<td align="char" valign="top" char=".">0.79</td>
</tr>
<tr>
<td align="left" valign="top">Educational attainment</td>
<td align="char" valign="top" char=".">1.41</td>
<td align="char" valign="top" char=".">0.71</td>
</tr>
<tr>
<td align="left" valign="top">Self-rated health</td>
<td align="char" valign="top" char=".">1.08</td>
<td align="char" valign="top" char=".">0.93</td>
</tr>
<tr>
<td align="left" valign="top">Number of household members</td>
<td align="char" valign="top" char=".">1.17</td>
<td align="char" valign="top" char=".">0.85</td>
</tr>
<tr>
<td align="left" valign="top">Area of contracted farmland</td>
<td align="char" valign="top" char=".">1.02</td>
<td align="char" valign="top" char=".">0.98</td>
</tr>
<tr>
<td align="left" valign="top">Whether residential land is available</td>
<td align="char" valign="top" char=".">1.09</td>
<td align="char" valign="top" char=".">0.91</td>
</tr>
<tr>
<td align="left" valign="top">Difficulties in the hometown</td>
<td align="char" valign="top" char=".">1.04</td>
<td align="char" valign="top" char=".">0.96</td>
</tr>
<tr>
<td align="left" valign="top">Occupational type</td>
<td align="char" valign="top" char=".">1.22</td>
<td align="char" valign="top" char=".">0.82</td>
</tr>
<tr>
<td align="left" valign="top">Monthly household income</td>
<td align="char" valign="top" char=".">1.42</td>
<td align="char" valign="top" char=".">0.71</td>
</tr>
<tr>
<td align="left" valign="top">Personal monthly income</td>
<td align="char" valign="top" char=".">1.66</td>
<td align="char" valign="top" char=".">0.60</td>
</tr>
<tr>
<td align="left" valign="top">Housing in destination areas</td>
<td align="char" valign="top" char=".">1.09</td>
<td align="char" valign="top" char=".">0.91</td>
</tr>
<tr>
<td align="left" valign="top">Social medical insurance coverage in destination area</td>
<td align="char" valign="top" char=".">1.33</td>
<td align="char" valign="top" char=".">0.75</td>
</tr>
<tr>
<td align="left" valign="top">Social security card</td>
<td align="char" valign="top" char=".">1.22</td>
<td align="char" valign="top" char=".">0.82</td>
</tr>
<tr>
<td align="left" valign="top">Temporary residence permit/residence permit</td>
<td align="char" valign="top" char=".">1.12</td>
<td align="char" valign="top" char=".">0.90</td>
</tr>
<tr>
<td align="left" valign="top">Amateur friendship</td>
<td align="char" valign="top" char=".">1.07</td>
<td align="char" valign="top" char=".">0.93</td>
</tr>
<tr>
<td align="left" valign="top">Social organization participation</td>
<td align="char" valign="top" char=".">1.13</td>
<td align="char" valign="top" char=".">0.88</td>
</tr>
<tr>
<td align="left" valign="top">Willingness to settle</td>
<td align="char" valign="top" char=".">1.09</td>
<td align="char" valign="top" char=".">0.92</td>
</tr>
<tr>
<td align="left" valign="top">Desire to reside</td>
<td align="char" valign="top" char=".">1.08</td>
<td align="char" valign="top" char=".">0.93</td>
</tr>
<tr>
<td align="left" valign="top">Outflow region</td>
<td align="char" valign="top" char=".">1.15</td>
<td align="char" valign="top" char=".">0.87</td>
</tr>
<tr>
<td align="left" valign="top">Flow range</td>
<td align="char" valign="top" char=".">1.12</td>
<td align="char" valign="top" char=".">0.89</td>
</tr>
<tr>
<td align="left" valign="top">Flow time</td>
<td align="char" valign="top" char=".">1.01</td>
<td align="char" valign="top" char=".">0.99</td>
</tr>
<tr>
<td align="left" valign="top">Flow type</td>
<td align="char" valign="top" char=".">1.15</td>
<td align="char" valign="top" char=".">0.87</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Since the independent variable is a multicategorical variable, to scientifically examine the factors influencing the three disposal methods of contracted farmland&#x2014;continued household cultivation, land transfer, and abandonment&#x2014;the study treats continued household cultivation is used as the reference group for farmland disposal. Individual characteristics, urban integration status, and migration factors across dimensions were incorporated into a multinomial logit model, and the occurrence ratio was used to explain the marginal impact of land disposal methods relative to the reference group (or the change in one unit of the independent variable). The regression results are shown in <xref ref-type="table" rid="tab12">Table 12</xref>.</p>
<table-wrap position="float" id="tab12">
<label>Table 12</label>
<caption>
<p>Regression results for contracted land disposition methods.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2">Independent variables</th>
<th align="center" valign="top" colspan="3">Land transfer: household continued cultivation</th>
<th align="center" valign="top" colspan="3">Abandoned land: households continue cultivation</th>
</tr>
<tr>
<th align="center" valign="top">Coefficient</th>
<th align="center" valign="top">Standard error</th>
<th align="center" valign="top">Incidence ratio</th>
<th align="center" valign="top">Coefficient</th>
<th align="center" valign="top">Standard error</th>
<th align="center" valign="top">Incidence ratio</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" colspan="7">Gender (male)</td>
</tr>
<tr>
<td align="left" valign="top">Female</td>
<td align="char" valign="top" char=".">&#x2212;0.050&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.024</td>
<td align="char" valign="top" char=".">0.951</td>
<td align="char" valign="top" char=".">&#x2212;0.079&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.033</td>
<td align="char" valign="top" char=".">0.924</td>
</tr>
<tr>
<td align="left" valign="top">Age (20&#x202F;years and younger)</td>
<td colspan="6"/>
</tr>
<tr>
<td align="left" valign="top">21&#x2013;35</td>
<td align="char" valign="top" char=".">0.189&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.092</td>
<td align="char" valign="top" char=".">1.207</td>
<td align="char" valign="top" char=".">&#x2212;0.143</td>
<td align="char" valign="top" char=".">0.101</td>
<td align="char" valign="top" char=".">0.867</td>
</tr>
<tr>
<td align="left" valign="top">36&#x2013;50&#x202F;years old</td>
<td align="char" valign="top" char=".">0.430&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.093</td>
<td align="char" valign="top" char=".">1.538</td>
<td align="char" valign="top" char=".">&#x2212;0.239&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.103</td>
<td align="char" valign="top" char=".">0.787</td>
</tr>
<tr>
<td align="left" valign="top">50&#x202F;years and older</td>
<td align="char" valign="top" char=".">0.842&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.095</td>
<td align="char" valign="top" char=".">2.320</td>
<td align="char" valign="top" char=".">0.023</td>
<td align="char" valign="top" char=".">0.107</td>
<td align="char" valign="top" char=".">1.023</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Educational attainment (primary school and below)</td>
</tr>
<tr>
<td align="left" valign="top">Junior high school</td>
<td align="char" valign="top" char=".">&#x2212;0.060&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.029</td>
<td align="char" valign="top" char=".">0.942</td>
<td align="char" valign="top" char=".">&#x2212;0.379&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.0377</td>
<td align="char" valign="top" char=".">0.685</td>
</tr>
<tr>
<td align="left" valign="top">High school/vocational school</td>
<td align="char" valign="top" char=".">&#x2212;0.110&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.037</td>
<td align="char" valign="top" char=".">0.900</td>
<td align="char" valign="top" char=".">&#x2212;0.435&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.0507</td>
<td align="char" valign="top" char=".">0.647</td>
</tr>
<tr>
<td align="left" valign="top">College degree and above</td>
<td align="char" valign="top" char=".">&#x2212;0.072</td>
<td align="char" valign="top" char=".">0.049</td>
<td align="char" valign="top" char=".">0.930</td>
<td align="char" valign="top" char=".">&#x2212;0.509&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.069</td>
<td align="char" valign="top" char=".">0.601</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Self-rated health (poor)</td>
</tr>
<tr>
<td align="left" valign="top">Healthy</td>
<td align="char" valign="top" char=".">&#x2212;0.143&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.059</td>
<td align="char" valign="top" char=".">0.867</td>
<td align="char" valign="top" char=".">&#x2212;0.398&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.0698</td>
<td align="char" valign="top" char=".">0.672</td>
</tr>
<tr>
<td align="left" valign="top">Number of household members</td>
<td align="char" valign="top" char=".">&#x2212;0.023&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.010</td>
<td align="char" valign="top" char=".">0.977</td>
<td align="char" valign="top" char=".">0.093&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.013</td>
<td align="char" valign="top" char=".">1.098</td>
</tr>
<tr>
<td align="left" valign="top">Contracted land area</td>
<td align="char" valign="top" char=".">0.006&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.001</td>
<td align="char" valign="top" char=".">1.006</td>
<td align="char" valign="top" char=".">&#x2212;0.001</td>
<td align="char" valign="top" char=".">0.003</td>
<td align="char" valign="top" char=".">0.999</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Ownership of residential land (no)</td>
</tr>
<tr>
<td align="left" valign="top">Yes</td>
<td align="char" valign="top" char=".">&#x2212;0.101&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.031</td>
<td align="char" valign="top" char=".">0.904</td>
<td align="char" valign="top" char=".">&#x2212;0.247&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.042</td>
<td align="char" valign="top" char=".">0.781</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Difficulties in hometown (no difficulties)</td>
</tr>
<tr>
<td align="left" valign="top">Difficulty caring for family members</td>
<td align="char" valign="top" char=".">&#x2212;0.067&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.034</td>
<td align="char" valign="top" char=".">0.935</td>
<td align="char" valign="top" char=".">&#x2212;0.050</td>
<td align="char" valign="top" char=".">0.051</td>
<td align="char" valign="top" char=".">0.951</td>
</tr>
<tr>
<td align="left" valign="top">Financial hardship</td>
<td align="char" valign="top" char=".">&#x2212;0.193&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.041</td>
<td align="char" valign="top" char=".">0.824</td>
<td align="char" valign="top" char=".">0.224&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.049</td>
<td align="char" valign="top" char=".">1.252</td>
</tr>
<tr>
<td align="left" valign="top">Family care and financial hardship</td>
<td align="char" valign="top" char=".">&#x2212;0.424&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.029</td>
<td align="char" valign="top" char=".">0.654</td>
<td align="char" valign="top" char=".">&#x2212;0.070</td>
<td align="char" valign="top" char=".">0.037</td>
<td align="char" valign="top" char=".">0.933</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Occupational type (white-collar occupations)</td>
</tr>
<tr>
<td align="left" valign="top">Blue-collar occupations</td>
<td align="char" valign="top" char=".">&#x2212;0.020</td>
<td align="char" valign="top" char=".">0.030</td>
<td align="char" valign="top" char=".">0.980</td>
<td align="char" valign="top" char=".">0.018</td>
<td align="char" valign="top" char=".">0.043</td>
<td align="char" valign="top" char=".">1.018</td>
</tr>
<tr>
<td align="left" valign="top">No stable occupation</td>
<td align="char" valign="top" char=".">0.043</td>
<td align="char" valign="top" char=".">0.042</td>
<td align="char" valign="top" char=".">1.044</td>
<td align="char" valign="top" char=".">0.064</td>
<td align="char" valign="top" char=".">0.057</td>
<td align="char" valign="top" char=".">1.066</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Monthly household income (&#x2264;4,000 yuan)</td>
</tr>
<tr>
<td align="left" valign="top">4,001&#x2013;8,000 yuan</td>
<td align="char" valign="top" char=".">0.169&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.029</td>
<td align="char" valign="top" char=".">1.184</td>
<td align="char" valign="top" char=".">&#x2212;0.064</td>
<td align="char" valign="top" char=".">0.037</td>
<td align="char" valign="top" char=".">0.938</td>
</tr>
<tr>
<td align="left" valign="top">Greater than 8,000 yuan</td>
<td align="char" valign="top" char=".">0.439&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.036</td>
<td align="char" valign="top" char=".">1.551</td>
<td align="char" valign="top" char=".">0.134&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.050</td>
<td align="char" valign="top" char=".">1.143</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Destination housing (rental)</td>
</tr>
<tr>
<td align="left" valign="top">Self-built and self-purchased housing</td>
<td align="char" valign="top" char=".">0.063&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.027</td>
<td align="char" valign="top" char=".">1.065</td>
<td align="char" valign="top" char=".">0.028</td>
<td align="char" valign="top" char=".">0.038</td>
<td align="char" valign="top" char=".">1.028</td>
</tr>
<tr>
<td align="left" valign="top">Government or employer-provided housing</td>
<td align="char" valign="top" char=".">&#x2212;0.150&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.040</td>
<td align="char" valign="top" char=".">0.861</td>
<td align="char" valign="top" char=".">&#x2212;0.028</td>
<td align="char" valign="top" char=".">0.051</td>
<td align="char" valign="top" char=".">0.972</td>
</tr>
<tr>
<td align="left" valign="top">Other</td>
<td align="char" valign="top" char=".">0.040</td>
<td align="char" valign="top" char=".">0.048</td>
<td align="char" valign="top" char=".">1.040</td>
<td align="char" valign="top" char=".">&#x2212;0.105</td>
<td align="char" valign="top" char=".">0.071</td>
<td align="char" valign="top" char=".">0.900</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Participating in social medical insurance in the destination area (not participating)</td>
</tr>
<tr>
<td align="left" valign="top">Enrolled</td>
<td align="char" valign="top" char=".">&#x2212;0.067&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.033</td>
<td align="char" valign="top" char=".">0.935</td>
<td align="char" valign="top" char=".">0.040</td>
<td align="char" valign="top" char=".">0.045</td>
<td align="char" valign="top" char=".">1.040</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Social security card (not yet processed)</td>
</tr>
<tr>
<td align="left" valign="top">Issued</td>
<td align="char" valign="top" char=".">0.021</td>
<td align="char" valign="top" char=".">0.024</td>
<td align="char" valign="top" char=".">1.022</td>
<td align="char" valign="top" char=".">0.217&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.032</td>
<td align="char" valign="top" char=".">1.242</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Temporary residence permit/residence permit (not obtained)</td>
</tr>
<tr>
<td align="left" valign="top">Applied for</td>
<td align="char" valign="top" char=".">&#x2212;0.079&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.024</td>
<td align="char" valign="top" char=".">0.924</td>
<td align="char" valign="top" char=".">&#x2212;0.221&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.032</td>
<td align="char" valign="top" char=".">0.802</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Casual dating (rarely socializing)</td>
</tr>
<tr>
<td align="left" valign="top">Locals and other non-locals</td>
<td align="char" valign="top" char=".">0.079&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.026</td>
<td align="char" valign="top" char=".">1.083</td>
<td align="char" valign="top" char=".">0.023</td>
<td align="char" valign="top" char=".">0.036</td>
<td align="char" valign="top" char=".">1.023</td>
</tr>
<tr>
<td align="left" valign="top">Fellow townsman</td>
<td align="char" valign="top" char=".">0.095&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.030</td>
<td align="char" valign="top" char=".">1.100</td>
<td align="char" valign="top" char=".">0.039</td>
<td align="char" valign="top" char=".">0.040</td>
<td align="char" valign="top" char=".">1.039</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Social organization participation (did not participate)</td>
</tr>
<tr>
<td align="left" valign="top">Participated</td>
<td align="char" valign="top" char=".">&#x2212;0.009</td>
<td align="char" valign="top" char=".">0.023</td>
<td align="char" valign="top" char=".">0.991</td>
<td align="char" valign="top" char=".">&#x2212;0.105&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.032</td>
<td align="char" valign="top" char=".">0.900</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Willingness to settle (unwilling)</td>
</tr>
<tr>
<td align="left" valign="top">Willing</td>
<td align="char" valign="top" char=".">&#x2212;0.044</td>
<td align="char" valign="top" char=".">0.024</td>
<td align="char" valign="top" char=".">0.957</td>
<td align="char" valign="top" char=".">0.040</td>
<td align="char" valign="top" char=".">0.033</td>
<td align="char" valign="top" char=".">1.040</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Resident preference (not staying locally)</td>
</tr>
<tr>
<td align="left" valign="top">Intention to remain locally</td>
<td align="char" valign="top" char=".">0.057</td>
<td align="char" valign="top" char=".">0.030</td>
<td align="char" valign="top" char=".">1.059</td>
<td align="char" valign="top" char=".">&#x2212;0.050</td>
<td align="char" valign="top" char=".">0.040</td>
<td align="char" valign="top" char=".">0.951</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Outflow area (eastern)</td>
</tr>
<tr>
<td align="left" valign="top">Central</td>
<td align="char" valign="top" char=".">0.090&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.030</td>
<td align="char" valign="top" char=".">1.095</td>
<td align="char" valign="top" char=".">0.030</td>
<td align="char" valign="top" char=".">0.050</td>
<td align="char" valign="top" char=".">1.031</td>
</tr>
<tr>
<td align="left" valign="top">Western</td>
<td align="char" valign="top" char=".">&#x2212;0.349&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.033</td>
<td align="char" valign="top" char=".">0.705</td>
<td align="char" valign="top" char=".">0.861&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.046</td>
<td align="char" valign="top" char=".">2.367</td>
</tr>
<tr>
<td align="left" valign="top">Northeast</td>
<td align="char" valign="top" char=".">0.777&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.042</td>
<td align="char" valign="top" char=".">2.176</td>
<td align="char" valign="top" char=".">&#x2212;2.165&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.190</td>
<td align="char" valign="top" char=".">0.115</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Interprovincial mobility</td>
</tr>
<tr>
<td align="left" valign="top">Intra-provincial (inter-city)</td>
<td align="char" valign="top" char=".">&#x2212;0.069&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.026</td>
<td align="char" valign="top" char=".">0.934</td>
<td align="char" valign="top" char=".">&#x2212;0.082&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.035</td>
<td align="char" valign="top" char=".">0.921</td>
</tr>
<tr>
<td align="left" valign="top">Intra-city cross-county</td>
<td align="char" valign="top" char=".">&#x2212;0.001</td>
<td align="char" valign="top" char=".">0.031</td>
<td align="char" valign="top" char=".">0.999</td>
<td align="char" valign="top" char=".">&#x2212;0.100&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.043</td>
<td align="char" valign="top" char=".">0.905</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Liquidity period (5&#x202F;years or less)</td>
</tr>
<tr>
<td align="left" valign="top">6 to 15&#x202F;years</td>
<td align="char" valign="top" char=".">0.014</td>
<td align="char" valign="top" char=".">0.023</td>
<td align="char" valign="top" char=".">1.015</td>
<td align="char" valign="top" char=".">0.022</td>
<td align="char" valign="top" char=".">0.032</td>
<td align="char" valign="top" char=".">1.023</td>
</tr>
<tr>
<td align="left" valign="top">16&#x202F;years or more</td>
<td align="char" valign="top" char=".">0.035</td>
<td align="char" valign="top" char=".">0.035</td>
<td align="char" valign="top" char=".">1.035</td>
<td align="char" valign="top" char=".">0.115&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.047</td>
<td align="char" valign="top" char=".">1.122</td>
</tr>
<tr>
<td align="left" valign="top" colspan="7">Type of migration (labor)</td>
</tr>
<tr>
<td align="left" valign="top">Business</td>
<td align="char" valign="top" char=".">0.017</td>
<td align="char" valign="top" char=".">0.031</td>
<td align="char" valign="top" char=".">1.018</td>
<td align="char" valign="top" char=".">0.018</td>
<td align="char" valign="top" char=".">0.045</td>
<td align="char" valign="top" char=".">1.018</td>
</tr>
<tr>
<td align="left" valign="top">Family reasons</td>
<td align="char" valign="top" char=".">0.135&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.039</td>
<td align="char" valign="top" char=".">1.144</td>
<td align="char" valign="top" char=".">0.178&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.051</td>
<td align="char" valign="top" char=".">1.195</td>
</tr>
<tr>
<td align="left" valign="top">Other</td>
<td align="char" valign="top" char=".">0.319&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.074</td>
<td align="char" valign="top" char=".">1.375</td>
<td align="char" valign="top" char=".">0.531&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.094</td>
<td align="char" valign="top" char=".">1.701</td>
</tr>
<tr>
<td align="left" valign="top">Constant</td>
<td align="char" valign="top" char=".">&#x2212;1.853&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.129</td>
<td align="char" valign="top" char=".">0.157</td>
<td align="char" valign="top" char=".">&#x2212;1.965&#x002A;&#x002A;&#x002A;</td>
<td align="char" valign="top" char=".">0.157</td>
<td align="char" valign="top" char=".">0.140</td>
</tr>
<tr>
<td align="left" valign="top">Log likelihood</td>
<td align="center" valign="top" colspan="6">&#x2212;46335.786</td>
</tr>
<tr>
<td align="left" valign="top">Prob&#x202F;&#x003E;chi<sup>2</sup></td>
<td align="center" valign="top" colspan="6">0.0000</td>
</tr>
<tr>
<td align="left" valign="top">Pseudo <italic>R</italic><sup>2</sup></td>
<td align="center" valign="top" colspan="6">0.0518</td>
</tr>
<tr>
<td align="left" valign="top">Note</td>
<td align="center" valign="top" colspan="6">&#x002A;&#x002A;&#x002A; and &#x002A;&#x002A; indicate statistical significance at the 1 and 5% levels, respectively. The values displayed in parentheses represent the reference group.</td>
</tr>
</tbody>
</table>
</table-wrap>
<sec id="sec16">
<label>4.2.1</label>
<title>Impact and differences of individual characteristics on contracted land disposal methods</title>
<p>The findings reveal that the various dimensions of the individual characteristic variables collectively constitute the significant factors influencing rural migrants&#x2019; disposal of contracted land, which validates Hypothesis 1. Specifically, compared with male migrants, female migrants exhibit a stronger tendency to transfer contracted land to family members for continued cultivation when faced with land transfer or abandonment, with behavioral occurrence ratios of 0.951 and 0.924, respectively, which validates Hypothesis 1a. Compared with migrant workers 20&#x202F;years old and younger, older migrant workers show a stronger preference for land transfer, and the probability of transferring contracted land increases with age. Further, compared to those aged 20 and below, people aged 36&#x2013;50 have a lower probability of choosing to abandon farmland over the alternative of continued family cultivation. Compared with those with primary school education or below, migrant workers with high school/vocational school or junior high school education backgrounds are more inclined to choose continued family cultivation over land transfer or abandonment. Those with college degrees or higher did not show significant differences between their preferences for land transfer and continued family cultivation, but they were more likely to choose continued family cultivation over abandonment, which indicates that Hypothesis 1b is not supported. Those migrant workers who self-reported good health were more inclined to choose continued family cultivation over land transfer or abandonment, which validates Hypothesis 1c. The more family members there are, the lower that the probability of choosing land transfer compared to leaving the family to continue farming and fallow; as the amount of contracted land area increases, the probability of migrant workers choosing land transfer over family continued cultivation also increases, and those migrant workers who own homestead land in their hometowns exhibit are less likely to choose land transfer and abandonment than they are to choose family continued cultivation, thereby confirming Hypothesis 1d. Migrant workers who face difficulties in their destination areas are more likely to choose continued family cultivation than land transfer, but those experiencing economic hardship are more likely to abandon their land, which partially validates Hypothesis 1e.</p>
</sec>
<sec id="sec17">
<label>4.2.2</label>
<title>Impact and differences of integration status on contracted land disposal methods</title>
<p>The research findings indicate that the integration status of rural migrants in their destination areas significantly influences their disposal choice for contracted land, thus validating Hypothesis 2. Specifically, rural migrants with a higher monthly income have an increased probability of choosing land transfer. When a migrant&#x2019;s income exceeds 8,000 yuan, the probabilities of abandonment and land transfer are 1.143 times and 1.551 times higher than those of continued family cultivation, respectively, which validates Hypothesis 2a. Compared with renters, those who built or purchased housing were more likely to transfer their land rights. Conversely, those migrants who are provided temporary housing by the government or employers are more inclined to choose continued family cultivation of their contracted land, confirming Hypothesis 2b. Compared to those without medical insurance or temporary/residence permits, migrant workers with medical insurance and permits are more likely to choose continued family cultivation. Having social security does not significantly affect the land disposal decisions of migrants. Hypothesis 2c is not validated. Compared with migrant workers who rarely socialize, those who frequently interact with locals or fellow villagers during their leisure time are more likely to transfer their land than to have their families continue farming it, which confirms Hypothesis 2d. The desire to settle permanently and the hospital where they receive treatment did not significantly influence migrant workers&#x2019; decisions regarding their contracted land, thus failing to confirm Hypothesis 2e.</p>
</sec>
<sec id="sec18">
<label>4.2.3</label>
<title>Impact and differences of mobility factors on contracted land disposal methods</title>
<p>The findings indicate that the various dimensions of the mobile factor variable collectively serve as important determinants of rural migrants&#x2019; disposal of contracted land, confirming Hypothesis 3. Specifically, compared with those in the Eastern Region, rural migrants in the Western Region are more likely to choose continued family cultivation, but they also have a higher probability of land abandonment. Hypothesis 3a is validated: migrants moving within the same province but across cities are more inclined to choose continued family cultivation than land transfer or abandonment than those moving across provinces. Hypothesis 3b was verified: the duration of migration did not significantly affect land transfer or continued family cultivation among migrant workers, but longer migration periods were associated with higher probabilities of land abandonment, leading to the rejection of Hypothesis 3c. Compared with rural migrants who moved for employment, those who migrated for family reasons were more likely to transfer or abandon their contracted land, confirming Hypothesis 3d.</p>
</sec>
</sec>
</sec>
<sec id="sec19">
<label>5</label>
<title>Conclusions and discussion</title>
<sec id="sec20">
<label>5.1</label>
<title>Main findings</title>
<p>(1) The choice of disposal of contracted land by rural migrants is influenced by multidimensional and systematic factors. Individual characteristics, integration status, and various migration factors collectively shape migrants&#x2019; land disposal choices, revealing distinct patterns, types, and regional variations. Currently, a significantly greater proportion of rural migrants in China who choose to continue cultivating land through their households (including cultivation by family members themselves or through hired labor) than to transfer or abandon it. The underlying decision-making logic reflects a risk-hedging strategy and a &#x201C;safety net&#x201D; choice that serves as a response to the vulnerability of urban livelihoods.</p>
<p>(2) At the level of individual characteristics, rural migrants who are female, in good health, have a high level of education, own homestead land, have a large number of family members, have a small contracted land area, or face difficulties in their place of origin tend to entrust family members to continue farming rather than engaging in land transfer or abandonment when faced with the choice of contracted land disposal methods. This reflects the rational decision-making process of individuals who prioritize family security, institutional constraints, and emotional ties. First, female migrants have a heightened sensitivity to the risks associated with land rights. Rural females have long been disadvantaged in land contracting and management. Informal institutional practices that perpetuate gender discrimination have resulted in the land rights of rural females being neither enduring nor stable (<xref ref-type="bibr" rid="ref13">Goli et al., 2025</xref>). Their choices are primarily made to hedge against land right uncertainties, urban life vulnerabilities, and the additional risks that stem from their gender roles. Second, the family functions as an economic community, integrating the &#x201C;high returns&#x201D; of external labor migration with the &#x201C;low risk&#x201D; of internal farming. Migrant workers allocate their &#x201C;health capital&#x201D; to the urban labor market to obtain higher returns and assign &#x201C;land assets&#x201D; to family members to ensure safety and maintain family bonds. This is particularly evident among migrant workers with large families, where decisions regarding contracted land serve as a proactive strategy based on optimal resource allocation. Finally, the disposal of contracted land represents a rational decision to minimize risks while maximizing benefits and security. On the one hand, as the knowledge levels of farmers increase, their understanding of land value and stable land rights deepens, which reduces their willingness to adjust their land holdings. On the other hand, land is viewed as the ultimate safeguard against life risks such as unemployment and illness, which is particularly true among rural migrants who face difficulties in their places of origin and possess limited land holdings. They would rather leave their land fallow or allow inefficient use than accept one-time compensation for the transfer of their land.</p>
<p>Furthermore, unlike the perspective of land attachment among the elderly group identified in previous studies (<xref ref-type="bibr" rid="ref38">Purc-Stephenson et al., 2025</xref>; <xref ref-type="bibr" rid="ref9001">Conway et al., 2021</xref>; <xref ref-type="bibr" rid="ref18">Kong et al., 2025</xref>), this research reveals that older migrants are more inclined to transfer their contracted land. This tendency may stem from two factors: first, as their labor capacity diminishes, stable rental income received from transferred land becomes a crucial supplementary source of pension income; second, against the backdrop of China&#x2019;s unique cultural context, family traditions and population migration characteristics, older migrants often relocate to join their children who work or live in cities, and such migrants are more likely to transfer their land and exit agricultural production when alternative economic support becomes available.</p>
<p>(3) In terms of migrant integration status in destination areas, higher levels of economic and identity integration increase the probability of rural migrants choosing land transfer or abandonment over continued family farming. This stems from two factors: on one hand, increased household income enhances farmers&#x2019; risk resilience, improves their living standards, and facilitates their land transfer decisions. On the other hand, as social networks expand, rural migrants no longer have a sole reliance on kinship and geographical ties for spiritual support and emotional comfort, which weakens their attachment to their hometowns and thereby facilitates land transfer (<xref ref-type="bibr" rid="ref35">Pan et al., 2025</xref>).</p>
<p>Concurrently, research has indicated that groups with high institutional integration are more likely to choose continued family farming. This phenomenon stems primarily from institutional design, perceived benefits, and the comprehensive considerations of migrant workers. Both field and existing research have revealed that despite the expansion of medical insurance coverage, migrant workers receive insufficient tangible benefits after enrolling. While participation rates have risen in recent years, the protection levels for migrants still lag behind those of urban residents. Inter-provincial migrants have a 42.7% lower immediate medical insurance reimbursement rate compared to local residents (<xref ref-type="bibr" rid="ref47">Wang et al., 2022</xref>). Only 28.6% of migrants enjoy outpatient reimbursement parity with urban residents, while 61.3% forego medical treatment or return to their place of household registration for care due to barriers in transferring medical insurance relations (<xref ref-type="bibr" rid="ref3">Chen et al., 2020</xref>). Additionally, issues such as cross-regional settlement and reimbursement rates create barriers to utilization. Furthermore, residence permits provide only basic registration and fail to address the underlying inequality of rights that is associated with household registration. Overall, migrant workers prioritize direct livelihood concerns such as income stability and career development. The current policies are only somewhat effective in facilitating land transfers for migrant workers, and they have relatively low marginal utility. Comprehensive measures that integrate income enhancement and rights protection are necessary to effectively advance urbanization.</p>
<p>(4) In terms of the various dimensions of mobility factors, rural migrants from Western Regions are more likely to choose either continued family cultivation of contracted land or land abandonment. Second, rural migrants with shorter migration distances prefer continued family cultivation, but land abandonment increases with longer migration duration. Third, compared with rural migrants who are actively seeking employment, those who migrate for family reasons are more inclined toward land transfer or abandonment (<xref ref-type="bibr" rid="ref51">Xie et al., 2025</xref>). This stems primarily from the high proportion of agriculture in the Western Region&#x2019;s economy, where land serves as a vital source of income for many households and encourages family members to either remain behind or entrust cultivation to relatives and friends. In addition, the natural resource endowment in the Western Region is poor and mostly tied to mountainous and hilly areas, and the economic foundation is weak and has a high incidence of return to poverty. Rural migrants, guided by economic rationality and practical constraints, can retain land as a livelihood buffer through either explicit or implicit abandonment via inefficient utilization. Moreover, migrant workers with shorter mobility distances are typically temporary or seasonal laborers whose face return costs that are significantly lower than those of interprovincial migrants. However, with increasing migration durations, migrant workers face multiple challenges, including high relational costs, poor trust, and inefficient land transfer markets, which makes them more inclined to temporarily set aside their land as a low-cost safeguard. Furthermore, household migration signifies longer-term and relatively stable urban settlement plans. With the complete transfer of labor, agricultural production has gradually transitioned from a &#x201C;sideline&#x201D; to a &#x201C;burden.&#x201D; However, in reality, such land abandonment behavior is motivated by a strong desire to retain contracted land rights.</p>
<p>In summary, the following insights and recommendations are offered. First, the precision of policy implementation should be enhanced. During the execution of contracted land transfer policies, tailored approaches should be adopted on the basis of individual migrant workers&#x2019; circumstances to facilitate the transfer of their family-contracted land, which avoids taking a one-size-fits-all approach. For rural migrant workers with stable employment and no conditions for entrusted land cultivation, priority should be given to pushing forward targeted information on land transfer cooperation with large-scale agricultural business entities; for those with flexible employment and partial capacity for self-cultivation, support should be provided for them to adopt a hybrid model of partial land transfer and partial self-cultivation; for those with no immediate intention of land transfer, supporting services such as the connection of entrusted cultivation services and agricultural production trusteeship should be offered, and mandatory guidance for land transfer shall be avoided. Second, a land transfer market that benefits both farmers and the public should be established. To address current issues such as low transfer efficiency, further efforts should be taken to advance pilot projects designed for standardizing rural property rights transfer markets, explore diverse models for transferring rural land contracting rights, for instance, short-term trusteeship-based land transfer, seasonal land transfer and shareholding cooperative land transfer are tailored to the diverse employment cycle needs of rural migrant workers, accelerate the institutional innovation in agricultural land systems, and improve all of the systems related to rural land transfers. Third, the transformation of migrant workers into &#x201C;new citizens&#x201D; should be facilitated. Vocational skills training is provided in response to the employment needs of rural migrant workers, multiple measures should be taken to improve migrant workers&#x2019; employment conditions in cities, consolidate the connection mechanism for their social security, enhance their substantive social security coverage, ensure that they and their accompanying family members enjoy equal welfare benefits as urban residents after enrollment, and increase their level of social integration in their destination areas. Fourth, social security for the family members that migrant workers leave behind should be improved. Inclusive, fundamental, and bottom-line social welfare programs need to be improved; convenient services and social work services need to be enhanced at the township level; and vulnerable groups such as left-behind children, left-behind elderly, and left-behind women require special attention. Comprehensive service stations for left-behind groups should be established in administrative villages. These measures can improve the living standards and well-being of family members left behind in migrant workers&#x2019; areas of origin. Fifth, multiple measures should be adopted to reduce the abandonment of contracted farmland. In collaboration with village collectives, a tripartite governance system for cultivated land abandonment integrating monitoring, disposal and guarantee should be constructed. The oversight of abandoned farmland should be strengthened, identification and diagnosis systems should be refined, land transfer mechanisms social security should be enhanced for economically vulnerable groups affected by land abandonment, and agricultural technology upgrades should be promoted through initiatives such as the transition of science and technology into rural areas. Farmers engaged in land reclamation are provided with subsidies for improved crop varieties and agricultural machinery operation; rural migrant worker households that have abandoned cultivated land due to family financial hardships are prioritarily included in the coverage of support measures such as rural minimum living security and temporary assistance, thereby reducing their land reclamation costs.</p>
</sec>
<sec id="sec21">
<label>5.2</label>
<title>Limitations and future research prospects</title>
<p>Although this study reveals the influencing factors and differential characteristics of rural migrant workers&#x2019; contracted land disposal based on nationwide large-sample data, it still has obvious limitations: the study adopts cross-sectional data for static analysis, which fails to track the dynamic evolution process of contracted land disposal methods and reflect the long-term effects of exogenous policy shocks; some key variables such as &#x201C;difficulties in hometown&#x201D; and &#x201C;self-reported health status&#x201D; rely on subjective evaluation based on single items, leading to insufficient measurement accuracy. In addition, meso-level variables such as the level of land transfer services provided by village collectives and the intensity of local policies are not included in the analysis, resulting in an inadequate discussion on the interaction mechanism between individual decision-making and institutional environments; although the sample covers all 31 provinces in China, it does not conduct special analysis on such subgroups as new-generation rural migrant workers, high-skilled rural migrant workers and migrant workers in different industries, and inadequately explores the differentiated origins of disposal behaviors of specific groups.</p>
<p>Future research can further deepen and expand the study in response to the above limitations: panel data can be used to track the dynamic changes of contracted land disposal of the same group, and a causal identification framework can be constructed by combining quantitative methods such as the Difference-in-Differences (DID) method and Propensity Score Matching (PSM) to accurately evaluate the long-term effects of policies such as rural land titling and national overall planning of basic medical insurance, so as to clarify the influence mechanism of exogenous shocks on rural migrant workers&#x2019; land disposal decisions; meso-level variables such as the service level of village collectives and the intensity of local policies should be introduced to explore the impact of supply&#x2013;demand mismatch on land disposal efficiency from the dual perspectives of the supply side and the demand side, so as to improve the multi-level analytical framework; the research can focus on subgroups including new-generation and older-generation rural migrant workers, inter-provincial and intra-provincial migrant workers, and migrant workers in the Eastern, Central and Western regions of China, and conduct comparative studies combined with industrial differences to explore the core demands and obstacles of disposal behaviors of various groups, thus expanding the theoretical boundary and practical value of the research.</p>
</sec>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="sec22">
<title>Data availability statement</title>
<p>The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.</p>
</sec>
<sec sec-type="ethics-statement" id="sec23">
<title>Ethics statement</title>
<p>Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the [patients/participants OR patients/participants legal guardian/next of kin] was not required to participate in this study in accordance with the national legislation and the institutional requirements.</p>
</sec>
<sec sec-type="author-contributions" id="sec24">
<title>Author contributions</title>
<p>ZZ: Methodology, Conceptualization, Writing &#x2013; review &#x0026; editing, Investigation, Funding acquisition. HZ: Formal analysis, Writing &#x2013; original draft, Conceptualization, Data curation. JM: Project administration, Conceptualization, Visualization, Writing &#x2013; original draft. XL: Writing &#x2013; review &#x0026; editing, Conceptualization, Supervision. SC: Validation, Supervision, Writing &#x2013; review &#x0026; editing, Software.</p>
</sec>
<sec sec-type="COI-statement" id="sec25">
<title>Conflict of interest</title>
<p>The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="sec26">
<title>Generative AI statement</title>
<p>The author(s) declared that Generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="disclaimer" id="sec27">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<ref-list>
<title>References</title>
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<fn-group>
<fn fn-type="custom" custom-type="edited-by" id="fn0001">
<p>Edited by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1693334/overview">Liye Wang</ext-link>, Shandong University of Finance and Economics, China</p>
</fn>
<fn fn-type="custom" custom-type="reviewed-by" id="fn0002">
<p>Reviewed by: <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2314330/overview">Lv Tiangui</ext-link>, Jiangxi University of Finance and Economics, China</p>
<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3356131/overview">Xuehan Lin</ext-link>, Lund University, Sweden</p>
</fn>
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