When Property Rights Meet Social Capital: The Differentiated Impact of Land Certification on Farm Mechanization

When Property Rights Meet Social Capital: The Differentiated Impact of Land Certification on Farm Mechanization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article When Property Rights Meet Social Capital: The Differentiated Impact of Land Certification on Farm Mechanization Yuyu Wang, Zengli Zhao, Weibing Ji, Chen Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6303702/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Improving agricultural machinery inputs and enhancing the efficiency of agricultural production is an important way to promote agricultural modernization and address food security challenges. Based on a new round of progressive land tenure certification policy (LTCP) in China, this study uses data from the Chinese Family Database (CFD) and the China Household Financial Survey (CHFS) for the period 2011–2015 to analyze the relationship between China's agricultural property ownership reform and farmers' agricultural machinery investments, employing difference-in-difference (DID) methods with fixed effects and clustering. The empirical results show that the implementation of the ownership certification policy increases agricultural machinery investment by improving access to financial credit for households with large-scale agricultural land. Further analysis reveals that the policy has only promoted agricultural machinery investment among farming households with richer social capital. The findings of this study provide a detailed mechanism for understanding the effect of property stabilization on agricultural machinery investment and offer both theoretical and empirical insights for other developing countries seeking to promote agricultural production through improving the stability of land certification programs. Social science/Economics Business and commerce/Economics Land Tenure Certification Policy Agricultural efficiency Agricultural machinery inputs Credit constraints Social networks Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Population pressure has constrained the development of agriculture in China because of the tension between land and humans. Since the Ming and Qing dynasties, China has developed an “over-dense” smallholder economy, which is caused by the fact that the population is increasing faster than the growth rate of agriculture and that the unit land area carries too much labor. Under the Household Contract Responsibility System implemented in China in the early 1980s, although the area allocated to each farmer was very small, it guaranteed their livelihood and development rights, giving China a much higher human development index than other countries with similar levels of economic development (Burgess 2000 ). This system has effectively increased farmers’ production incentives and achieved rapid growth in agricultural output (McMillan et al.,1989). Since industrialization and urbanization in China were relatively backward at that time, labor was cheaper and more common than expensive agricultural machinery, and this fragmented business mode distributed by each head made full use of family labor to alleviate the pressure of human-land relations under socioeconomic conditions, while raising income and increasing the supply of agricultural products. With the rapid progress of industrialization and urbanization, the labor-intensive mode of agricultural production brought about by the household contract responsibility system became a major constraint on the development of agricultural mechanization and scale, leading to low agricultural production efficiency. “Once agricultural production became capital-intensive instead of labor-intensive, the advantages of small farming disappeared” (Weber, 2013 ). The development of industry and technology has made machinery, irrigation equipment, pesticides, and fertilizer substitutes for land and labor key factors in agricultural development and change, while small-scale and fragmented operations restrict the input and commercialization of agricultural mechanization. The dual impact of development stages and ambiguous land property rights has led to the issue of small-scale agricultural operations, which is also the root cause of the insufficient investment in agricultural production machinery in China. According to the World Bank's 2020 survey, China's agricultural labor productivity in China is 0.8 thousand USD per person, whereas in Japan, it is 6.9 thousand USD per person, which is 8.6 times higher than in China. Additionally, China's agricultural labor productivity is 5.2 thousand USD per worker, while Japan's is significantly higher at 22.7 thousand USD per worker, 4.37 times that of China. When compared with other agriculturally advanced countries, China's machinery ownership per laborer is also significantly lower. For example, the average fixed asset formation per person in the Netherlands is 33 thousand USD, which is 41.25 times higher than in China, and the agricultural labor productivity in the Netherlands is 15.42 times higher than in China. This low level of agricultural machinery investment in China has led to low agricultural production efficiency. The agricultural mechanization inputs are closely related to the stability of land property rights. Unstable land property rights led to frequent land disputes, thus reducing the efficiency of farmers’ operations. The comprehensive implementation of the farmland tenure certification policy in China in 2013 is regarded as the most important institutional arrangement for safeguarding the stability and security of rural land rights. The clarification of property rights is fundamental to the contractualization and marketization of land transactions. Although there are a number of studies in the existing literature on agricultural land rights, there are relatively few studies on the impact of land rights and investment behavior of agricultural machinery in the context of China's development of a small land area with a large population and with farmers as the object of the study. The innovation of this paper is mainly in the following two aspects: First, most studies have discussed the mechanisms by which land tenure certification affects investment through the land transfer effect and the mortgage credit effect arising from the stabilization of land rights. However, these studies have overlooked the differential impact of land certification policies on individuals’ borrowing behavior. Formal financial institutions, provide lending services mainly through the assessment of borrowers' collateral, and land certification increases the security and stability of land rights and provides collateral for farmers to borrow from formal financial institutions, such as banks. Land certification policies are more likely to affect the credit behavior of households with larger areas of cultivated land, facilitating their access to the formal credit sector and increasing their machinery investment in agriculture. In this paper, we disaggregate farmers' accessibility into formal and informal financial institutions' credit, and further group farmers by their land area in order clarify through which credit channel China's LTCP makes effects. This approach could be valuable for policymakers and researchers interested in understanding the nuances of agricultural finance and land policy. Second, from the perspective of the substitution relationship between external public power and internal private power, this paper explores the differential impacts of the land tenure certification policy on machinery inputs of farmers with different degrees of social networks. The certification of land property ownership has brought the problems previously solved by "private rights" into the scope of "public rights", thus reducing the expenses incurred by farmers in maintaining external social network. Most studies have overlooked the differential impact of the substitution relationship between property stability and social relations on the level of farmer’s agricultural inputs with different degrees of social network relations. This study investigates whether market transactions can replace relationship-based and acquaintance-based transactions within villages. The paper is organized as follows: Section 2 constructs the theoretical framework and proposes the hypotheses of the paper. Section 3 provides background information and the model setting, and Section 4 presents the empirical research design and main results. Section 5 provides a further discussion concerning heterogeneity and its mechanisms, and Section 6 concludes the paper. 2. Policy background, theoretical framework, data source and model setting 2.1. Policy background Since the reform and opening-up, the establishment of the household joint contract system has granted families the right to use arable land. However, these lands are not only periodically redistributed at the village level, but there is also the risk that they will be withdrawn by the local government at any time, and farmers lack the right to long-term land security. The introduction of the Land Contract Law in 2002 guaranteed the longevity of farmers’ rights to contract land management and strengthened the legitimacy of household and rural land contract transfer systems. This has given farmers the right to transfer farmland, which has facilitated the redistribution of land as a production factor, significantly improved agricultural production efficiency and total output, and improved the responsiveness of land allocation to price fluctuations in agricultural products (Chari et al., 2021 ). However, the implementation of the Land Contract Law has not fully realized the “confirmation of rights to households” and “stabilization of property rights,” and many households have not obtained legal documents and certificates that guarantee farmers’ land contract management rights by law (Wang et al., 2015 ). In this context, the “Decision on Several Major Issues in Promoting Rural Reform and Development” was adopted in 2008 and proposed to “further complete the work of confirming, registering and issuing certificates for rural land, improve the rights to contracted land management, and guarantee the rights and interests of farmers in the possession, use and income of contracted land.” The pilot work began in 2009, and 105 counties in eight villages were selected as pilot areas. In 2014, the pilot areas extended to Anhui, Shandong, and Sichuan provinces, and in 2015, another nine provinces, including Jiangsu, Jilin, Henan, Hubei, Hunan, Gansu, Ningxia, Guizhou, and Jiangxi, were further included, while other non-pilot provinces were allowed to carry out land ownership certification at the county level according to their conditions. According to statistics from the Ministry of Agriculture, as of 2015, 1,988 of 2,856 counties, 13,000 of 30,000 townships, and 195,000 of 600,000 administrative villages across the country have obtained land tenure certificates. 2.2. Theoretical framework As a critical factor of production for farmers, unclear property rights prevent the formation of market transactions in rural areas. As a result, in order to ensure the smooth land transactions, informal social actions need to be constructed through strengthening social network. When farmland transactions are embedded in social factors such as rural values, relationship patterns, the efficiency of market-based land transfers is significantly reduced (Geng and Luo, 2022). Stable property rights increase the exclusivity of land use and reduce the corresponding conservation costs (Galiani and Schargrodsky, 2010 ), enhance the security and stability of land property (Macours et al., 2010 ), promote households’ long-term investment (Besley, 1995 ; Goldstein et al., 2018 ). The establishment of land ownership rights reduces transaction costs and therefore increases the agricultural land transfer (Vranken and Swinnen, 2006 ; Zhang et al., 2022 ) which contributes to the capital investment of agriculture. The impact of property right stability on agricultural investment has been a long-standing focus in economics. However, there is no consensus in existing empirical studies regarding how land right certification affects agricultural machinery investment. Besley ( 1995 ) proposed that farmland property rights influence agricultural investment through mechanisms of land transfer effect and the land mortgage credit effect. In terms of the land transfer effects, it is theoretically argued that land tenure certification could facilitate farmland transfer, promote large-scale agricultural production, and thereby increase investment in agricultural machinery. Chari et al. ( 2021 ) examined that China's ownership policy of agricultural land has improved the efficiency of China's agricultural production by alleviating factor mismatches and facilitating the flow of farmland to more productive farmers. However, the study by Hu and Luo (2016) indicates that land tenure certification has no significant effect on either land transfer-out or land transfer-in among farmers. Similarly, Place et al. (1998), using data from Kenya, found that land tenure certification did not promote farmland transfers between farmers. Moreover, large-scale production does not necessarily equate to increased agricultural investment. Gao et al. (2011) discovered that farmers are less likely to apply organic fertilizer, both in probability and quantity, on rented land compared to their own land, suggesting that accelerated land transfer might also have adverse effects on investment. From the perspective of the land mortgage credit effect, numerous studies suggest that land tenure certification can unlock the collateral value of land, thereby alleviating the credit constraints faced by farmers. The alleviation of credit constraints is positively correlated with increased investment by farmers. However, Sun et al. ( 2020 ) concluded that China's land tenure certification policy could not promote investment in agricultural machinery by easing credit constraints. Their research failed to distinguish the differences between formal and informal credit sectors on the one hand, and to capture the different size of land areas owned by farm households on the other hand. Due to the differing collateral requirements between formal and informal credit for farmers, the land tenure certification policy enables land held by farmers to be recognized as collateral by formal credit institutions, while it has no impact on farmers' access to informal production credit. Existing research has confirmed that Vietnam's land titling program increased farmers' access to formal credit and lowered formal loan interest rates, but it did not affect their access to informal credit (Kemper et al., 2011 ). Currently, rural land mortgage loans in China are predominantly obtained by large landowners, leaving the credit constraints of small-scale farmers unresolved (Jiang and Mi, 2020). Boucher et al. ( 2005 ) and Field et al. (2006) conducted empirical studies on the credit effects of land property rights in Latin America and Africa, respectively, and found that while land property rights improved credit accessibility for both wealthy farmers and large landowners, the number of loans available to small landowners decreased rather than increased. Consequently, this study concludes that the stabilization of farmers’ property rights is more beneficial for the production credit of farmers with larger land scales since they are better positioned to obtain credit. In contrast, small-scale farmers, with limited land resources, face the constrains in the possibility and amount of credit, which in turn affects the capital investment in agricultural production. When the land tenure certification policy allows farmers to access agricultural production credit, this leads to increased investment in either expanding cultivated land or acquiring agricultural machinery, facilitated by the farmers’ enhanced credit availability. If the policy operates through the land transfer mechanism, we should observe a significant rise in the "area of farmers' inward land transfer"; otherwise, land transfer is not the operative mechanism. Additionally, the policy enables farmers to use land as collateral to secure loans from formal financial institutions, as these institutions recognize land rights defined by national policies. In contrast, informal lending relies on personal relationships or community trust, often without formal documentation. Thus, this paper argues that the land tenure certification policy can increase farmers' investment in agricultural machinery by improving their access to formal agricultural production credit. 2.3. Data source This research uses data from the Chinese Family Database (CFD) of Zhejiang University, and the China Household Finance Survey (CHFS) conducted in 2011, 2013 and 2015 by the Survey and Research Center for China Household Finance at the Southwestern University of Finance and Economics China. In the survey conducted in 2011, it covers 25 provinces and autonomous regions, 320 villages with 8438 households. In 2013, the survey covered 29 provinces and autonomous regions (except Xinjiang, Tibet, Hong Kong, Macao and Taiwan), 267 counties, and 1,048 villages nationwide with 28141 households. In 2015, it surveyed 29 provinces and autonomous regions (except Xinjiang, Xizang, Hong Kong, Macao and Taiwan), 351 counties, and 1,396 villages nationwide with 37289 households. making the data nationally representative. In addition, the CHFS survey on rural households recorded detailed information on agricultural investment, land ownership, labor allocation, and household assets, which provided data support for the research in this paper. The data processing steps are as follows: only rural samples from the follow-up survey are retained, while urban samples are excluded. Additionally, farming households not engaged in agricultural production during the 2011, 2013, and 2015 surveys are removed. This exclusion is based on the CHFS questionnaire design, which only collected data on agricultural machinery inputs and sowing areas from households that had “engaged in agricultural production in the past year.” Households “not engaged in agricultural production” did not provide data on these variables, justifying their exclusion. Moreover, since the land rights policy formalizes land ownership and use rights, facilitating land transfer among farmers, those who have transferred out their land and ceased agricultural production are also excluded (Sun et al., 2020 ). To address potential selection bias, the study uses the Heckman correction method alongside the baseline analysis to ensure robust conclusions (see Appendix A). Since the pilot project had already started in some areas before the 2014 survey, the sample households who received the land certificate in 2014 and before were excluded. Since the first batch of pilot areas only covered 8 villages and 105 counties starting in 2009, the policy's impact and scope were initially limited. It wasn't until after 2014 that the policy began to be fully implemented on a national scale. Based on existing literature, excluding the samples households who had already received land certificates prior to 2014 allows for a more accurate assessment of the impact of the new round of tenure certification policies on farmers' agricultural machinery investments. The households surveyed on all three occasions from 2011 to 2015 are kept for our analysis. Finally, the unbalanced panel data in our empirical research has 6639 households. 2.4. Variables selection 2.4.1. Dependent variables The dependent variable in this study is the capital input of farm households, referring to the input of machinery in agricultural production, and is expressed as the value of agricultural machinery input per mu of land (CNY/Mu). This variable is calculated by dividing “the total value of agricultural machinery input per household in the past year (CNY)” by “the area of agricultural production of the household (Mu)” in the survey questionnaire. Given that some households in the sample did not invest in machinery for agricultural production, an inverse hyperbolic sine (IHS) transformation is applied to smooth the variable of average machinery input per acre. Compared to logarithmic transformation, the IHS transformation is more appropriate for variables that include zero values (Chari et al., 2021 ). Additionally, the IHS transformation can enhance the robustness of statistical analyses, particularly when the raw data may include zero or negative values. It also helps reduce skewness, bringing the data closer to a normal distribution. 2.4.2. Explanatory variables The explanatory variables are the interactions between the implementation of the agricultural land certification policy and the year in which the certificate was received. CHFS asked “whether your household obtained the land tenure certificate” and “the year household obtained the land tenure certificate.” Households who did not obtain a land management rights certificate during the sample periods were set as the control group (farmland rights = 0), and households that obtain a land rights certificate after 2014 are set as the treatment group (farmland rights = 1). The year 2013 is considered as the year of implementation and as a result its year dummy is set to 0, and the year 2015 is considered the year after policy implementation and as a result its year dummy is set to 1. The coefficient of the interaction term is used to express the impact of the implementation of the agricultural land certification policy on agricultural mechanization inputs. 2.4.3. Control variables Based on existing literature, control variables are selected at both the household and provincial levels. The control variables at the individual level are the age of the household head, gender of the household head, health level of the household head, years of education of the household head and his/her political status. On the household level, the control variables include total number of household members, the proportion of working labor on total family members and the share of agricultural labor on total family members. Descriptive statistics are shown in Table 1 . This study includes individual control variables related to the characteristics of household heads, such as age, gender, and physical health, as these factors typically influence agricultural inputs. Specifically, female, elderly, or physically unwell laborers may be less able to engage in agricultural production, which in turn affects their investment in such activities. Additionally, whether the household head is a Communist Party member and their level of education may also impact machinery investment. In rural China, households with Party members often have access to more social resources and information, while agricultural producers with higher levels of education tend to have a more comprehensive and scientific understanding of how to enhance agricultural production efficiency. These personal characteristics could potentially confound the effects of land tenure formalization on household machinery investment. To ensure that the findings of this study reflect the impact of the land certification policy on the increase in agricultural machinery investment, rather than being driven by individual differences, these individual control variables are included in the analysis. This study incorporates several control variables at the household level to reflect both the dynamics of household labor allocation and their economic conditions. Variables related to productive labor include household size, the proportion of labor engaged in agricultural versus non-agricultural activities, and the amount of arable land owned by the household. These variables serve as indicators of the household's involvement in agricultural production. Generally, households with larger populations, more extensive land holdings, and a higher proportion of agricultural labor are more likely to engage in large-scale agricultural production, which in turn influences their investment in agricultural machinery. Conversely, a higher proportion of non-agricultural labor within the household is typically associated with reduced agricultural activity, leading to lower levels of investment in agricultural machinery. In addition to labor-related factors, the household's economic condition plays a crucial role in determining the level of investment in agricultural machinery. Therefore, this study includes variables such as household insurance coverage, total household income, and household consumption expenditure as proxies for economic status. By incorporating these variables, the study aims to more accurately assess the impact of land certification policies while minimizing potential confounding effects on household machinery investment and enhancing the robustness of the results. These economic variables reflect the resources available to households and the economic decisions they make in response to policy changes, providing deeper insights into how policies influence investment behavior across households with varying economic conditions. This study also includes household gross agricultural output and expenditures on hired labor as control variables. As the scale of agricultural production increases, both the overall value of agricultural output and the costs associated with hiring labor tend to rise, which generally leads to greater mechanization. Additionally, agricultural subsidies, which significantly influence investment in agricultural machinery, are incorporated as a control variable in our regression analysis. Furthermore, the development of local markets is a crucial factor that directly affects the availability and utilization of agricultural machinery services, thereby influencing farmers' investment decisions regarding such machinery. To account for this, the study includes the regional marketization index as a control variable to further strengthen the robustness of our findings. Table 1 Descriptive Statistics Obs Mean Std. Dev. Min Max Machine (CNY, IHS) 6639 4.284 2.203 0 15.937 Ownership 6639 0.122 0.328 0 1 Age (Years) 6639 54.488 11.421 29 80 Gender (Male = 1, Otherwise = 0) 6639 0.885 0.320 0 1 Edu (1–8) 6611 2.550 0.971 1 8 Health (1–5) 6639 3.081 1.108 1 5 Status (Party_member = 1, Otherwise = 0) 6639 0.047 0.212 0 1 Member_total 6639 4.261 1.813 1 18 Labor_share (%) 6639 0.664 0.255 0 1 Agrilabor_share (%) 6639 0.422 0.303 0 1 Nonagrilabor_share (%) 6639 0.191 0.219 0 1 Insur_coverage (%) 6639 0.598 0.316 0 1 Income_total (Log, CNY per year) 6502 9.973 1.172 0 15.425 Exp_consump (Log, CNY per year) 6639 9.766 1.021 0 13.816 Value_agri (Log, CNY per year) 6637 6.333 2.685 0 16.644 Exp_hiring (Log, CNY per year) 6632 0.598 1.900 0 12.181 Area (Log, Mu) 6639 1.755 0.765 0.001 7.091 Subsidy (Log, CNY per year) 6501 4.722 2.505 0 11.002 Market_index 6639 7.841 1.534 3.371 11.113 Note: 1.Years of education of household head (illiterate = 1, elementary school = 2, junior high school = 3, high school = 4, middle/vocational high school = 5, junior high school/higher vocational high school = 6, bachelor's degree = 7, master's degree = 8, doctoral degree = 9); 2. Health condition of household head (extremely good = 1, very good = 2, good = 3, fair = 4, not good = 5). 3. The average farm size is 10 mu (approximately 0.67 hectares or 1.65 acres); 4. For continuous variables that had a value of 0 before the change, such as machinery inputs per Mu, the amount of subsidies received for agricultural production, and expenditures on hired workers for agricultural production, inverse hyperbolic sine (IHS) changes are applied. For continuous variables that do not have a value of 0, such as incomes, expenditures, and total agricultural output, the logarithm is applied. 5. The marketization index is adopted from the Fan Gang Index which is a comprehensive assessment of multiple dimensions of marketization. It includes multiple sub-indicators, encompassing a wide range of aspects, such as the government-market relationship, the development of non-state-owned economy and intermediary organizations, product and factor markets, and legal environment. This approach enables the Fan Gang Index to comprehensively and objectively reflect the degree of marketization in a given region. 2.5. Research method This study applies the Differences in Differences (DID) method to carry out an empirical analysis by taking time-fixed and region-fixed into consideration. The detailed model is expressed as follows: $${y_{it}}=\alpha +{\beta _0}Pos{t_t}*Trea{t_i}+{\beta _1}Control{s_{it}}+{\gamma _t}+{\mu _i}+{\varepsilon _{it}}$$ 1 Where i , t represents household and year, respectively. \({y_{it}}\) denotes the agricultural investment of household i in year t , \(Pos{t_t}\) denotes the dummy variable representing the time of operation of land policy within \(Pos{t_t}=0\) for period before policy implementation and \(Pos{t_t}=1\) after policy implementation. In addition, \(Trea{t_i}\) represent an individual dummy and \(Trea{t_i}=1\) denotes the treatment of individuals and \(Trea{t_i}=0\) the control individuals. \(Control{s_{it}}\) is the control variable, while \({\gamma _t}\) and \({\mu _i}\) represent year-fixed and individual-fixed effect. \({\varepsilon _{it}}\) is the random error. It should be noted that \({\beta _0}\) this is the parameter of DID estimation and is the main concern of this research. Because the randomness of LTCP shocks is difficult to ensure, the parallel trend assumption for the sample was tested graphically prior to the test. The figure below illustrates the change in the per-acre mechanical inputs for the treatment and control groups. It can be seen from Fig. 1 that before the policy, the mechanical input of households in the treatment group is lower than that in the control group, and two groups have the same trend during 2011–2013. After the implementation of land tenure certification policy, the mechanical input in the treatment group has a significant increase compared to the control group, and eventually exceeds the value of control group. Therefore, Fig. 1 supports our hypothesis of parallel trend. 3. Empirical results In this study, we use a DID approach with control variables as well as individual and time fixed effects and use household level clustering standard errors to test the empirical analysis through model (1). Due to the existence of a small number of households that neither use nor invest in machinery in the agricultural production process, we use an inverse hyperbolic sine (IHS) transformation to smooth the variable of “the acre machinery input per household”. Columns (1)-(3) of Table 2 report the regression results. To compare the reliability of this finding, logarithmic changes are also applied to the machinery inputs per household acre after adding 1. Columns (4)-(6) of Table 2 report the regression results after logarithmic adjustment by adding 1. Table 2 The effect of land property right on machinery input of household. (1) (2) (3) (4) (5) (6) IHS (Input) Ln(1 + Input) Treat_post 0.3401 ** 0.3320 ** 0.3651 ** 0.2562 ** 0.2488 * 0.2779 ** (0.1398) (0.1403) (0.1483) (0.1271) (0.1275) (0.1350) Age -0.0130 *** -0.0090 *** -0.0124 *** -0.0086 *** (0.0028) (0.0030) (0.0027) (0.0028) Gender 0.2076 ** 0.1571 * 0.1944 ** 0.1460 * (0.0887) (0.0918) (0.0832) (0.0864) Edu 0.0721 ** 0.0284 0.0678 ** 0.0266 (0.0342) (0.0361) (0.0323) (0.0340) Health 0.0328 0.0063 0.0354 0.0097 (0.0275) (0.0268) (0.0259) (0.0253) Status 0.3133 ** 0.3252 ** 0.2893 ** 0.2981 ** (0.1331) (0.1267) (0.1228) (0.1173) Member_total 0.0280 0.0257 (0.0229) (0.0212) Labor_share 0.4874 ** 0.4644 ** (0.2414) (0.2292) Agrilabor_share -0.3259 -0.3011 (0.2303) (0.2191) Nonagrilabor_share -0.5628 ** -0.5286 ** (0.2310) (0.2201) Insur_coverage 0.2122 0.1891 (0.1344) (0.1243) Income_total 0.0809 ** 0.0774 ** (0.0344) (0.0323) Exp_consump 0.1326 *** 0.1270 *** (0.0365) (0.0343) Value_agri 0.1363 *** 0.1284 *** (0.0174) (0.0164) Exp_hiring 0.0515 *** 0.0467 *** (0.0139) (0.0129) Area -0.7120*** -0.6599*** (0.0559) (0.0533) Market_index -0.0074*** -0.0066*** (0.0010) (0.0009) Subsidy 0.2180 0.2082 (0.1503) (0.1403) Cons 4.2427 *** 4.4716 *** -0.7041 3.6659 *** 3.8771 *** -1.0450 (0.0171) (0.2347) (1.2614) (0.0156) (0.2219) (1.1806) Household FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes N 6639 6611 6465 6639 6611 6465 R 2 0.1411 0.1499 0.1891 0.1445 0.1536 0.1918 Note: *, **, *** indicates significance level of 10%, 5% and 1%, respectively. The value in parentheses represent robust standard error. Data source: The Chinese Family Database (CFD) of Zhejiang University, and the China Household Finance Survey (CHFS) Table 2 reports the estimation results of model (1). Columns (1)-(3) in Table 2 are the DID estimation results of the machinery input with IHS transformation. Column (1) is the estimation result without adding any control variables on the basis of individual and year fixed effects, and the coefficient 0.3401indicates the land ownership policy significantly contributes to the increase in households’ machinery inputs per acre at the 5% level of statistical significance. Column (3) is the estimation result of adding all control variables as well as area and time fixed effect, and the coefficient of 0.3523 is still significantly positive at the 5% level of statistical significance. Columns (4)-(6) in Table 2 are the results of estimation using log-treated machinery inputs as explanatory variables, and they have significantly positive coefficients at the 5% and 10% statistical significance levels for agricultural land rights after gradually adding control variables, showing again that the policy of agricultural land rights has a significant role in promoting agricultural machinery inputs to farm households' agricultural production. In addition to that, we also replace the acres of machinery inputs by the total household machinery inputs as the explanatory variables to do the test, and the results as shown in Appendix B reveal again that the land ownership policy significantly promotes the total machinery inputs of the farm household at the 1% statistical level. Our analysis reveals that households with older household heads and higher proportions of non-agricultural labor tend to have lower levels of agricultural machinery investment. In contrast, families with male heads of household, party members, high proportion of working labor, high expenditure on hired labor, and better economic conditions exhibit higher levels of machinery investment. Besides, the negative relationship between land area and marketization level with mechanization inputs arises from the nature of the dependent variable: the larger the land area, the lower the level of mechanization inputs per unit area. Furthermore, regions with higher levels of marketization are usually the more developed eastern regions of China. These regions often experience significant rural population outflows and consequently reduce both the agricultural production and machinery investment. 3.1. Robust test 3.1.1. PSM-DID To enhance the comparability between the treatment and control groups, we employed propensity score matching (PSM) to balance these samples. We matched the samples on a year-by-year basis, and the kernel density plots for propensity score matching are shown in Appendix C. Many studies tend to run regressions using samples that satisfy the common support assumption after the PSM is completed, which ignores the basic fact that the matched control group samples may serve as matches for more than one treatment group sample, and thus the degree of importance of differently weighted control samples among the overall control group samples is not the same. The greater the weight, the more occasion it is matched on, and so it should be given with more attention in the regression. Therefore, we adopt a frequency weighted variation of the regression to control for the matched samples in the control group. The regression results of PSM-DID are shown in column (1) of Table 3 . 3.1.2. Entropy balanced DID To improve the comparability between the treatment and control groups, we further use the entropy balancing method which uses the first-order moments (means) and second-order moment (variances) of the control variables for matching. Unlike the propensity score matching method, the entropy balancing method is able to better balance the differences between the treatment and control groups without discarding a larger sample. The detailed entropy-balanced matching are shown in Appendix D, and column (2) of Table 3 reports the results of estimation using the balanced samples. 3.1.3. Hierarchy of clustering As can be seen in the benchmark regression, the estimation is clustered at the household level. However, the machinery investment behavior of households may be influenced by farming households in nearby regions. We therefore adjust the level of clustering to check the robustness of our results. Columns (3) and (4) of Table 3 report the estimation results at the village and district levels, respectively. 3.1.4. Incorporate ex ante control variables Since control variables after the policy implementation are also likely to be affected by the agricultural land certification program, the consistency of the estimated coefficients of the model would possibly be affected. Therefore, we replace the original control variables by incorporating an interaction term between the household's control variable in 2011 and a year dummy variable. The results of the new regression are shown in column (5) of Table 3 . Table 3 Robust test IHS(Machinary input per Mu) (1) (2) (3) (4) (5) Ownership 0.3766** 0.2966** 0.4653*** 0.4653** 0.2768* (0.1743) (0.1448) (0.1631) (0.1876) (0.1498) Household FE Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Controls Yes Yes Yes Yes Controls_2011×Year FE Yes PSM Balance Yes Entropy Balance Yes Clustered at HH Yes Yes Yes Clustered at Village Yes Clustered at prefecture Yes N 5537 6331 6285 6285 5703 R 2 0.2596 0.2285 0.2086 0.2086 0.2027 3.2. Placebo testing Estimates for a single treatment time point are vulnerable to omitted contemporaneous policies and variables compared to estimates for multiple time entry treatments. Therefore, a further placebo test is conducted to test whether the presence of omitted contemporaneous policies affects the reliability of the findings by generating "pseudo-treatment" variables by randomly selecting treatment groups and treatment times. Figure 3 shows the Kernel density curves of the estimated coefficients of model (1) obtained by randomly selecting the experimental group and repeating the model 500 times. As can be seen from the figure below, the Kernel density distribution of the estimated coefficients of agricultural land rights coincides almost exactly with the normal distribution with mean zero, and all the estimated coefficients of the "pseudo-processing" are smaller than the real regression coefficients. This indicates that for the randomly generated sample of confirmed farmers, the policy of confirming the right to farmland does not have a significant effect on their input of agricultural machinery, which verifies the robustness of the conclusion. 4. Mechanism analysis 4.1. Mechanism check Farmers obtaining loans to invest in agricultural production through farmland mortgages is an important mechanism for land certification policy to enhance capital investment in agricultural machinery. After the policy came into effect, the stabilization of property rights increased the bank's mortgage loans to farmers. At the same time, the confirmation of the farmland right to the use of agricultural land certification can also help form a reasonable mechanism for the expression of property rights (De Soto, 2000 ), reduce the bank's cost of information search and improve the effectiveness of the collateral, reducing the risk and cost of credit to help farmers access credit. A financial system based on formal financial institutions and supplemented by informal financial institutions has basically been formed in rural China. The formal sector refers to formal financial institutions that are uniformly supervised by government departments, including state-owned banks and joint-stock commercial banks; the informal sector refers to informal financial organizations and their capital-financing activities that are outside the state's financial supervisory system, mainly including cooperatives, pawnbrokers, private fund-raising, free lending by private individuals, and various kinds of foundations. The implementation of the land tenure certification policy has increased the stability of land contracting rights to have a more direct impact on formal credit institutions for farm households, but Boucher et al. ( 2005 ) find in their study of Nicaragua and Honduras that the titling of agricultural land does not significantly improve farmers' access to credit. Piza and Moura ( 2016 ), using data from Brazil, find that the granting of formal land status appeared to have a significant boost to households' formal credit from banks and reduced households' reliance on informal credit, so there is a need to further differentiate between the formal and informal credit behavior of farmers to analytically test the mechanisms by which land certification affects the level of farm inputs. Since the behavior of farm households in obtaining bank loans by mortgage after land certification depends on their original cultivated land endowment, households with less arable land may not be able to pass the bank's asset collateralization threshold even if their right to land property is more stable, thus failing to help farm households with smaller original arable land areas obtain credit. Only with a larger land area can they cross the asset collateralization threshold and meet the asset collateralization requirement. Therefore, we define farm households with a median and below household cultivated land area as small-scale farm households and those above the median as large-scale farm households. A group test of formal versus informal credit access is conducted for these two farmland sizes. Table 4 reports the impact of farmland certification on farmers’ formal and informal credit behavior by production scale. Formal credit refers to loans obtained by farmers from formal institutions such as banks and finance companies, and informal credit refers to loans obtained by farmers from friends, relatives, and private institutions. Columns (1)-(3) in Table 4 report the effect of farmland certification on formal credit access by production scale, and columns (4)-(6) report the effect of farmland certification on informal credit access. The results show that farmland tenure certification policy significantly increases formal credit access of households with large-scale production, but it does not affect farmers’ credit access to the informal credit sector. Table 4 Property rights and credit behavior Accessibility of formal credit Accessibility of informal credit Full_sample Small-scale household Large-scale household Full_sample Small-scale household Large-scale household (1) (2) (3) (4) (5) (6) Ownership 0.0106 -0.0031 0.0299** 0.0066 -0.0099 0.0362 (0.0088) (0.0114) (0.0144) (0.0154) (0.0195) (0.0232) Household FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes Controls Yes Yes Yes Yes Yes Yes N 6331 3458 2873 3779 2038 1741 R2 0.1264 0.1757 0.1767 0.1466 0.2295 0.2181 Note: The informal credit is only surveyed in 2013 and 2015, and therefore column (4)-(6) report the result with households in 2013 and 2015. Based on the land area of the sample farm households, a dummy variable with four equal parts is generated and regressed on the implementation of the agricultural land certification program, where group 4 is the one with the largest cultivated land area and group 1 is the one with the smallest cultivated land area. The interaction terms between the different groups and the implementation of the land certification policy were regressed on access to formal credit and informal credit, respectively, and the results are shown in Fig. 4. The regressions on formal and informal financial credit are conducted by interacting the division of arable land into four groups, based on land size, with the local implementation status of the land rights confirmation policy. In Fig. 4, panel (a) presents access to formal credit as the explanatory variable, while panel (b) focuses on access to informal credit. As shown in Fig. 4, the land certification policy significantly enhances access to formal credit for households with larger landholdings. In contrast, for households with smaller land sizes, although the policy also affects them, it does not significantly increase their access to credit. These findings suggest that the land certification policy improves the level of agricultural inputs by increasing credit accessibility to formal financial institutions for households with larger farmland areas. Formal financial institutions primarily base loan availability on collateral, requiring farmers to have higher income and assets. In contrast, informal financial institutions base lending decisions on factors like the borrower’s family structure, social ties, and long-term residency. Thus, the lending criteria differ significantly between the two. Agricultural land ownership certification policies stabilize property rights and promote formal credit borrowing by using land as collateral. For farmers in developing countries, credit constraints lead to suboptimal agricultural practices, reducing production efficiency and output (Guirkinger and Boucher, 2010). Our analysis shows that property rights certification improves access to formal credit and significantly increases investment in agricultural machinery. 4.2. Heterogeneity analysis Since the reform of China’s rural family contract system, the absence of grassroots governance mechanisms has made the relationship mechanism centered on geography and blood play an important role in maintaining transactional and economic behaviors and rural social order in villages. Land, as the most important means of production for farmers, not only plays a role in maintaining transactional behavior, cooperative relationships, and values among farmers within the village but also to gain more initiative over land, farmers will focus on the maintenance of inter-village social networks, build their social networks, and maintain invisible contractual relationships with compensation mechanisms such as favors and gifts (Kreps, 2013 ). When land ownership is unstable or unclear, they can benefit more in cases of land transfer and disputes by maintaining contractual relationships. The policy of land ownership certification, which was fully implemented in 2013, formalized and legalized property rights security and land rights stability, deconstructed the relationship-based transaction method relying on social networks within villages and reduced disputes between neighbors due to unclear land boundaries, thus improving agricultural production efficiency. An increase in market-based transactions has led to a gradual decrease in the number of “outward” gifts to maintain social networks and an increase in “inward” investments to improve agricultural production efficiency and soil fertility. A clear property rights system is fundamental for resource allocation and efficiency improvements (Alchian A and Demsetz H, 1973; Chen, 2017 ; Geng P and Luo B, 2022). In December 2013, the Chinese government issued the “Opinion on Party Members and Cadres Taking the Lead in Promoting Funeral Reform”. To prevent potential bias resulting from the reduction in social expenditures within the village due to this policy, an interaction term between the implementation year and party member status is incorporated to control for the potential interference of this policy on regression results since the policy primarily affects Chinese Communist Party members. Table 5 Property rights and outward gifts (1) (2) (3) (4) (5) (6) IHS (Expenditure on social interaction among non-relatives) IHS (Expenditure on social interaction among relatives) Treat_post -0.5180*** -0.5560*** -0.5567*** -0.6049** -0.7444*** -0.7383*** (0.1922) (0.2021) (0.2020) (0.2378) (0.2591) (0.2579) Year×Member -0.0931 0.4629 (0.6205) (0.7216) Controls Yes Yes Household FE Yes Yes Yes Yes Yes Yes Year FE Yes Yes Yes Yes Yes Yes N 6639 6611 6331 4029 4025 3779 R2 0.5923 0.5928 0.6145 0.7271 0.7277 0.7529 Note: The expenditure of outward gift for non-relatives are surveyed in 2011, 2013 and 2015, while the expenditure of outward gift for relatives are only surveyed in 2013 and 2015. Table 5 classifies the objects of human interaction expenditures into non-relative human interaction expenditures and relative human interaction expenditures based on blood relations. The test results show that the policy of agricultural land certificates significantly weakens intra-village human interaction expenditures. This suggests that strengthening property rights weakened the effect of social networks on village governance and promoted the development of intra-village economic behavior along the direction of the market economy, and therefore reduced households’ expenditure on human interaction. Traditionally, rural societies have relied on close-knit social networks and kinship ties to maintain governance and economic development. However, with the implementation of land certification policies, the clarification and stabilization of land rights have led farmers to focus more on their individual economic interests, reducing their engagement in social activities. This shift helps farmers allocate more resources to agricultural production and other economic activities, advancing the marketization process in rural areas, and enhancing the optimization and efficiency of resource allocation among farmers. Next, we divide the non-relatives' favor gifts into four quantile parts according to the level of expenditure, with group 1 being the group with the lowest expenditure on family favor gifts, and group 4 being the group with the highest. The explanatory variable in the figure is the level of machinery input per mu in the household, and the explanatory variable is the regression of machinery input per mu in the farm household by interacting different groups with the implementation of the agricultural land tenure certification program, and the regression results are shown in Fig. 5 below. The explanatory variable in Fig. 5 is the level of machinery input per household after applying the IHS transformation. The data shows that households with higher spending on social interactions—indicating stronger social networks, tend to invest more in agricultural machinery under the land certification policy. This suggests that the land certification program has varying effects on machinery investment depending on the household's social capital, significantly boosting investment among those with richer social networks. Stronger social networks provide better access to information, exposing farmers to modern agricultural practices. As a result, these farmers increase their machinery investment when the policy is implemented. In contrast, households with weaker social capital do not see the same increase in investment from improved land rights alone, as their decisions are constrained by credit limits, land resources, household size, and limited access to information and modern agricultural concepts influenced by their social networks. Conclusion This study uses Chinese Family Database (CFD) and China Household Financial Survey (CHFS) data from 2011 to 2015 to analyze the relationship between China's ownership reform on agricultural property and farmers' agricultural machinery inputs by the method of difference-in-difference (DID). The empirical study found that the land ownership certificate policy significantly promoted households’ investment in machinery input by increasing the formal credit availability of households with large-scale production. Heterogeneity analysis further showed that the positive effect is even more significant for households with stronger social networks. In many developing countries, much emphasis continues to be placed on land structures with communal ownership or on policies that aim for equality in the distribution of land across farmers rather than on allowing free exchange in land markets (Chari et al., 2021 ). Our analysis suggests that the stability of land property rights is the basis for restructuring intra-rural social relations and facilitating market-based transactions, whereas increased mechanization leads to further efficiency gains in agricultural production. A related explanation is that trading in agricultural land markets is associated with a unique set of coordination problems that cannot be solved by traditional markets (Bryan et al., 2017 ), which is also supported by our research: before the land ownership policy, economic behavior in rural China was mostly based on social networks, and an implicit contractual relationship was maintained through favors and gifts. When the contractual right to land management is legally protected, such non-market behavior would be gradually replaced by formal market rules. China has a unique history and land systems, and its agricultural production is typical of some developing countries, especially for other similarly situated countries with predominantly smallholder economies, small arable land per capita, and reliance mainly on family labor for agricultural production. This study has implications for other countries similar to China on whether they should pursue land certification program to stabilize the expectations of property holders and to promote normative market transactions. Although our study is centered on machinery inputs for agricultural production, the improvement in agricultural productivity is precisely because of the willingness of farmers to invest more capital after land property rights have been clarified. It should also be noted that land tenure certification policies in China clearly have a greater impact on households with large-scale production and on households with more social capital, which may also lead to a widening of income disparities within rural areas. It is important to note that this study is situated within the specific context of rural China, not only due to the typical characteristics and unique aspects of China's rural economic development but also because of the long-standing village civilization in China, which has a rich history and extensive experience in managing the communal living environments of human communities. Villages in China encompass not only the economic logic of efficient resource allocation but also embody emotions, culture, customs, and traditions. While the clarification of property rights can facilitate market-oriented development, it should not completely replace the tools of interpersonal communication and emotional exchange. The transition of farmers from a survival strategy based on land dependence to a property rights approach focused on maximizing village interests may introduce new frictions and risks. Therefore, it is crucial that the fundamental institutional function of agricultural land rights and the interpersonal trust mechanisms within rural communities work in a complementary manner. Declarations Competing interests The authors declare no competing interests. Data availability The data employed in this study are accessible through the Survey and Research Center for China Household Finance (CHFS) and the Chinese Family Database (CFD) at Zhejiang University, contingent upon formal application procedures. In accordance with the data usage agreements of both CHFS and CFD, this research rigorously adheres to confidentiality obligations. We implement secure data storage protocols to safeguard respondent privacy and ensure ethical handling of survey data. Unauthorized disclosure, distribution, or transfer of any data content (including derivative forms) to third parties is strictly prohibited. Owing to legally binding confidentiality requirements, the datasets analyzed in this study are not publicly available. Qualified researchers may submit formal applications through the official CHFS portal (https://chfser.swufe.edu.cn/datas/) to access CHFS data. For CFD data access, applications should be submitted via email to [email protected] . Data access will be granted pending approval from the respective institutions. Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent This article does not contain any studies with human participants or animals performed by any of the authors. Author contributions Y**contributed to the conception and design of the research. All authors contributed to the methodology, data collection, analysis, writing, review, and editing. All authors read and approved the final manuscript. References Adamopoulos, T., Restuccia, D. (2014). The Size Distribution of Farms and International Productivity Differences. American Economic Review , 104(6), 1667-1697. Adamopoulos, T., Brandt, L., Leight, J., Restuccia, D. (2022). Misallocation, selection, and productivity: A quantitative analysis with panel data from China. Econometrica , 90(3), 1261-1282. Alchian, A.A., Demsetz, H. (1973). The Property Right Paradigm. Journal of Economic History , 33(1), 16-27. Besley, T. (1995). Property rights and investment incentives: Theory and evidence from Ghana. Journal of Political Economy , 103(5), 903-937. Boucher, S.R., Barham, B.L., Carter, M.R. (2005). The impact of “market-friendly” reforms on credit and land markets in Honduras and Nicaragua. World Development , 33(1), 107-128. Bryan, G., De Quidt, J., Wilkening, T., Yadav, N. 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Industrial Relocation, Elements Agglomeration and Regional Economic Development. Management World , 34(05), 47-62. (In Chinese) Vranken, L., & Swinnen, J. (2006). Land Rental Markets in Transition: Theory and Evidence from Hungary. World Development , 34(3), 481-500. Wang, H., Riedinger, J., & Jin, S. (2015). Land documents, tenure security and land rental development: Panel evidence from China. China Economic Review , 36, 220-235. Zhang, L., Cao, Y., & Bai, Y. (2022). The impact of the land certificated program on the farmland rental market in rural China. Journal of Rural Studies , 93, 165-175. Additional Declarations No competing interests reported. Supplementary Files Appendices.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6303702","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":476574696,"identity":"50090e9d-baed-45aa-b326-49bb2bd9a0ac","order_by":0,"name":"Yuyu 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version\u003c/p\u003e","description":"","filename":"Figure1.Thetestofhypothesisofparalleltrend.png","url":"https://assets-eu.researchsquare.com/files/rs-6303702/v1/9dd61464572d8584f20fe7d7.png"},{"id":85639807,"identity":"7166e072-8306-411c-b9fa-1dcd7fe36d0a","added_by":"auto","created_at":"2025-06-30 07:07:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":20020,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 3. \u003c/strong\u003e\u0026nbsp;Kernel density curves\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6303702/v1/9ad33ce8fcd9ccca23b3ba83.png"},{"id":85640432,"identity":"75a8a6bb-9f43-4850-849d-a5f594e728ee","added_by":"auto","created_at":"2025-06-30 07:15:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":30838,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 4. \u003c/strong\u003e\u0026nbsp;Property right on credit access by household groups\u003c/p\u003e\n\u003cp\u003eNotes: The figure plots the coefficients and associated 95% confidence intervals from estimating the effect of property right on credit accessibility of household by different production scale.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6303702/v1/17bb17335b78c9cc27f70e8e.png"},{"id":85639809,"identity":"efa7d977-fa6a-408d-8c0a-4eafb0a81527","added_by":"auto","created_at":"2025-06-30 07:07:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":12856,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 5.\u003c/strong\u003e Heterogeneity effect of property right on machinery inputs of households with different social networks\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6303702/v1/a2c9caae3d1632c9b12eb52c.png"},{"id":91448984,"identity":"8f2c621c-de7a-4fbb-88d0-0adca68f8cbe","added_by":"auto","created_at":"2025-09-16 15:08:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1187693,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6303702/v1/14e5bddf-1ea4-406c-ab2d-93b143ff6da8.pdf"},{"id":85639813,"identity":"ef2abc86-e4cf-4924-ace9-f5af7ac37752","added_by":"auto","created_at":"2025-06-30 07:07:55","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":58940,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-6303702/v1/12bef50d394655c29c2938e3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"When Property Rights Meet Social Capital: The Differentiated Impact of Land Certification on Farm Mechanization","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePopulation pressure has constrained the development of agriculture in China because of the tension between land and humans. Since the Ming and Qing dynasties, China has developed an \u0026ldquo;over-dense\u0026rdquo; smallholder economy, which is caused by the fact that the population is increasing faster than the growth rate of agriculture and that the unit land area carries too much labor. Under the Household Contract Responsibility System implemented in China in the early 1980s, although the area allocated to each farmer was very small, it guaranteed their livelihood and development rights, giving China a much higher human development index than other countries with similar levels of economic development (Burgess \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This system has effectively increased farmers\u0026rsquo; production incentives and achieved rapid growth in agricultural output (McMillan et al.,1989). Since industrialization and urbanization in China were relatively backward at that time, labor was cheaper and more common than expensive agricultural machinery, and this fragmented business mode distributed by each head made full use of family labor to alleviate the pressure of human-land relations under socioeconomic conditions, while raising income and increasing the supply of agricultural products.\u003c/p\u003e \u003cp\u003eWith the rapid progress of industrialization and urbanization, the labor-intensive mode of agricultural production brought about by the household contract responsibility system became a major constraint on the development of agricultural mechanization and scale, leading to low agricultural production efficiency. \u0026ldquo;Once agricultural production became capital-intensive instead of labor-intensive, the advantages of small farming disappeared\u0026rdquo; (Weber, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The development of industry and technology has made machinery, irrigation equipment, pesticides, and fertilizer substitutes for land and labor key factors in agricultural development and change, while small-scale and fragmented operations restrict the input and commercialization of agricultural mechanization. The dual impact of development stages and ambiguous land property rights has led to the issue of small-scale agricultural operations, which is also the root cause of the insufficient investment in agricultural production machinery in China.\u003c/p\u003e \u003cp\u003eAccording to the World Bank's 2020 survey, China's agricultural labor productivity in China is 0.8 thousand USD per person, whereas in Japan, it is 6.9 thousand USD per person, which is 8.6 times higher than in China. Additionally, China's agricultural labor productivity is 5.2 thousand USD per worker, while Japan's is significantly higher at 22.7 thousand USD per worker, 4.37 times that of China. When compared with other agriculturally advanced countries, China's machinery ownership per laborer is also significantly lower. For example, the average fixed asset formation per person in the Netherlands is 33 thousand USD, which is 41.25 times higher than in China, and the agricultural labor productivity in the Netherlands is 15.42 times higher than in China. This low level of agricultural machinery investment in China has led to low agricultural production efficiency.\u003c/p\u003e \u003cp\u003eThe agricultural mechanization inputs are closely related to the stability of land property rights. Unstable land property rights led to frequent land disputes, thus reducing the efficiency of farmers\u0026rsquo; operations. The comprehensive implementation of the farmland tenure certification policy in China in 2013 is regarded as the most important institutional arrangement for safeguarding the stability and security of rural land rights. The clarification of property rights is fundamental to the contractualization and marketization of land transactions. Although there are a number of studies in the existing literature on agricultural land rights, there are relatively few studies on the impact of land rights and investment behavior of agricultural machinery in the context of China's development of a small land area with a large population and with farmers as the object of the study.\u003c/p\u003e \u003cp\u003eThe innovation of this paper is mainly in the following two aspects: First, most studies have discussed the mechanisms by which land tenure certification affects investment through the land transfer effect and the mortgage credit effect arising from the stabilization of land rights. However, these studies have overlooked the differential impact of land certification policies on individuals\u0026rsquo; borrowing behavior. Formal financial institutions, provide lending services mainly through the assessment of borrowers' collateral, and land certification increases the security and stability of land rights and provides collateral for farmers to borrow from formal financial institutions, such as banks. Land certification policies are more likely to affect the credit behavior of households with larger areas of cultivated land, facilitating their access to the formal credit sector and increasing their machinery investment in agriculture. In this paper, we disaggregate farmers' accessibility into formal and informal financial institutions' credit, and further group farmers by their land area in order clarify through which credit channel China's LTCP makes effects. This approach could be valuable for policymakers and researchers interested in understanding the nuances of agricultural finance and land policy.\u003c/p\u003e \u003cp\u003eSecond, from the perspective of the substitution relationship between external public power and internal private power, this paper explores the differential impacts of the land tenure certification policy on machinery inputs of farmers with different degrees of social networks. The certification of land property ownership has brought the problems previously solved by \"private rights\" into the scope of \"public rights\", thus reducing the expenses incurred by farmers in maintaining external social network. Most studies have overlooked the differential impact of the substitution relationship between property stability and social relations on the level of farmer\u0026rsquo;s agricultural inputs with different degrees of social network relations. This study investigates whether market transactions can replace relationship-based and acquaintance-based transactions within villages.\u003c/p\u003e \u003cp\u003eThe paper is organized as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e constructs the theoretical framework and proposes the hypotheses of the paper. Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides background information and the model setting, and Section 4 presents the empirical research design and main results. Section 5 provides a further discussion concerning heterogeneity and its mechanisms, and Section \u003cspan refid=\"Sec21\" class=\"InternalRef\"\u003e6\u003c/span\u003e concludes the paper.\u003c/p\u003e"},{"header":"2. Policy background, theoretical framework, data source and model setting","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Policy background\u003c/h2\u003e \u003cp\u003eSince the reform and opening-up, the establishment of the household joint contract system has granted families the right to use arable land. However, these lands are not only periodically redistributed at the village level, but there is also the risk that they will be withdrawn by the local government at any time, and farmers lack the right to long-term land security. The introduction of the Land Contract Law in 2002 guaranteed the longevity of farmers\u0026rsquo; rights to contract land management and strengthened the legitimacy of household and rural land contract transfer systems. This has given farmers the right to transfer farmland, which has facilitated the redistribution of land as a production factor, significantly improved agricultural production efficiency and total output, and improved the responsiveness of land allocation to price fluctuations in agricultural products (Chari et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the implementation of the Land Contract Law has not fully realized the \u0026ldquo;confirmation of rights to households\u0026rdquo; and \u0026ldquo;stabilization of property rights,\u0026rdquo; and many households have not obtained legal documents and certificates that guarantee farmers\u0026rsquo; land contract management rights by law (Wang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, the \u0026ldquo;Decision on Several Major Issues in Promoting Rural Reform and Development\u0026rdquo; was adopted in 2008 and proposed to \u0026ldquo;further complete the work of confirming, registering and issuing certificates for rural land, improve the rights to contracted land management, and guarantee the rights and interests of farmers in the possession, use and income of contracted land.\u0026rdquo; The pilot work began in 2009, and 105 counties in eight villages were selected as pilot areas. In 2014, the pilot areas extended to Anhui, Shandong, and Sichuan provinces, and in 2015, another nine provinces, including Jiangsu, Jilin, Henan, Hubei, Hunan, Gansu, Ningxia, Guizhou, and Jiangxi, were further included, while other non-pilot provinces were allowed to carry out land ownership certification at the county level according to their conditions. According to statistics from the Ministry of Agriculture, as of 2015, 1,988 of 2,856 counties, 13,000 of 30,000 townships, and 195,000 of 600,000 administrative villages across the country have obtained land tenure certificates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Theoretical framework\u003c/h2\u003e \u003cp\u003eAs a critical factor of production for farmers, unclear property rights prevent the formation of market transactions in rural areas. As a result, in order to ensure the smooth land transactions, informal social actions need to be constructed through strengthening social network. When farmland transactions are embedded in social factors such as rural values, relationship patterns, the efficiency of market-based land transfers is significantly reduced (Geng and Luo, 2022). Stable property rights increase the exclusivity of land use and reduce the corresponding conservation costs (Galiani and Schargrodsky, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), enhance the security and stability of land property (Macours et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), promote households\u0026rsquo; long-term investment (Besley, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Goldstein et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The establishment of land ownership rights reduces transaction costs and therefore increases the agricultural land transfer (Vranken and Swinnen, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) which contributes to the capital investment of agriculture.\u003c/p\u003e \u003cp\u003eThe impact of property right stability on agricultural investment has been a long-standing focus in economics. However, there is no consensus in existing empirical studies regarding how land right certification affects agricultural machinery investment. Besley (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) proposed that farmland property rights influence agricultural investment through mechanisms of land transfer effect and the land mortgage credit effect. In terms of the land transfer effects, it is theoretically argued that land tenure certification could facilitate farmland transfer, promote large-scale agricultural production, and thereby increase investment in agricultural machinery. Chari et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) examined that China's ownership policy of agricultural land has improved the efficiency of China's agricultural production by alleviating factor mismatches and facilitating the flow of farmland to more productive farmers. However, the study by Hu and Luo (2016) indicates that land tenure certification has no significant effect on either land transfer-out or land transfer-in among farmers. Similarly, Place et al. (1998), using data from Kenya, found that land tenure certification did not promote farmland transfers between farmers. Moreover, large-scale production does not necessarily equate to increased agricultural investment. Gao et al. (2011) discovered that farmers are less likely to apply organic fertilizer, both in probability and quantity, on rented land compared to their own land, suggesting that accelerated land transfer might also have adverse effects on investment.\u003c/p\u003e \u003cp\u003eFrom the perspective of the land mortgage credit effect, numerous studies suggest that land tenure certification can unlock the collateral value of land, thereby alleviating the credit constraints faced by farmers. The alleviation of credit constraints is positively correlated with increased investment by farmers. However, Sun et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) concluded that China's land tenure certification policy could not promote investment in agricultural machinery by easing credit constraints. Their research failed to distinguish the differences between formal and informal credit sectors on the one hand, and to capture the different size of land areas owned by farm households on the other hand. Due to the differing collateral requirements between formal and informal credit for farmers, the land tenure certification policy enables land held by farmers to be recognized as collateral by formal credit institutions, while it has no impact on farmers' access to informal production credit. Existing research has confirmed that Vietnam's land titling program increased farmers' access to formal credit and lowered formal loan interest rates, but it did not affect their access to informal credit (Kemper et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrently, rural land mortgage loans in China are predominantly obtained by large landowners, leaving the credit constraints of small-scale farmers unresolved (Jiang and Mi, 2020). Boucher et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and Field et al. (2006) conducted empirical studies on the credit effects of land property rights in Latin America and Africa, respectively, and found that while land property rights improved credit accessibility for both wealthy farmers and large landowners, the number of loans available to small landowners decreased rather than increased. Consequently, this study concludes that the stabilization of farmers\u0026rsquo; property rights is more beneficial for the production credit of farmers with larger land scales since they are better positioned to obtain credit. In contrast, small-scale farmers, with limited land resources, face the constrains in the possibility and amount of credit, which in turn affects the capital investment in agricultural production.\u003c/p\u003e \u003cp\u003eWhen the land tenure certification policy allows farmers to access agricultural production credit, this leads to increased investment in either expanding cultivated land or acquiring agricultural machinery, facilitated by the farmers\u0026rsquo; enhanced credit availability. If the policy operates through the land transfer mechanism, we should observe a significant rise in the \"area of farmers' inward land transfer\"; otherwise, land transfer is not the operative mechanism. Additionally, the policy enables farmers to use land as collateral to secure loans from formal financial institutions, as these institutions recognize land rights defined by national policies. In contrast, informal lending relies on personal relationships or community trust, often without formal documentation. Thus, this paper argues that the land tenure certification policy can increase farmers' investment in agricultural machinery by improving their access to formal agricultural production credit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data source\u003c/h2\u003e \u003cp\u003eThis research uses data from the Chinese Family Database (CFD) of Zhejiang University, and the China Household Finance Survey (CHFS) conducted in 2011, 2013 and 2015 by the Survey and Research Center for China Household Finance at the Southwestern University of Finance and Economics China. In the survey conducted in 2011, it covers 25 provinces and autonomous regions, 320 villages with 8438 households. In 2013, the survey covered 29 provinces and autonomous regions (except Xinjiang, Tibet, Hong Kong, Macao and Taiwan), 267 counties, and 1,048 villages nationwide with 28141 households. In 2015, it surveyed 29 provinces and autonomous regions (except Xinjiang, Xizang, Hong Kong, Macao and Taiwan), 351 counties, and 1,396 villages nationwide with 37289 households. making the data nationally representative. In addition, the CHFS survey on rural households recorded detailed information on agricultural investment, land ownership, labor allocation, and household assets, which provided data support for the research in this paper.\u003c/p\u003e \u003cp\u003eThe data processing steps are as follows: only rural samples from the follow-up survey are retained, while urban samples are excluded. Additionally, farming households not engaged in agricultural production during the 2011, 2013, and 2015 surveys are removed. This exclusion is based on the CHFS questionnaire design, which only collected data on agricultural machinery inputs and sowing areas from households that had \u0026ldquo;engaged in agricultural production in the past year.\u0026rdquo; Households \u0026ldquo;not engaged in agricultural production\u0026rdquo; did not provide data on these variables, justifying their exclusion. Moreover, since the land rights policy formalizes land ownership and use rights, facilitating land transfer among farmers, those who have transferred out their land and ceased agricultural production are also excluded (Sun et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To address potential selection bias, the study uses the Heckman correction method alongside the baseline analysis to ensure robust conclusions (see Appendix A).\u003c/p\u003e \u003cp\u003eSince the pilot project had already started in some areas before the 2014 survey, the sample households who received the land certificate in 2014 and before were excluded. Since the first batch of pilot areas only covered 8 villages and 105 counties starting in 2009, the policy's impact and scope were initially limited. It wasn't until after 2014 that the policy began to be fully implemented on a national scale. Based on existing literature, excluding the samples households who had already received land certificates prior to 2014 allows for a more accurate assessment of the impact of the new round of tenure certification policies on farmers' agricultural machinery investments. The households surveyed on all three occasions from 2011 to 2015 are kept for our analysis. Finally, the unbalanced panel data in our empirical research has 6639 households.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Variables selection\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1. Dependent variables\u003c/h2\u003e \u003cp\u003eThe dependent variable in this study is the capital input of farm households, referring to the input of machinery in agricultural production, and is expressed as the value of agricultural machinery input per mu of land (CNY/Mu). This variable is calculated by dividing \u0026ldquo;the total value of agricultural machinery input per household in the past year (CNY)\u0026rdquo; by \u0026ldquo;the area of agricultural production of the household (Mu)\u0026rdquo; in the survey questionnaire.\u003c/p\u003e \u003cp\u003eGiven that some households in the sample did not invest in machinery for agricultural production, an inverse hyperbolic sine (IHS) transformation is applied to smooth the variable of average machinery input per acre. Compared to logarithmic transformation, the IHS transformation is more appropriate for variables that include zero values (Chari et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, the IHS transformation can enhance the robustness of statistical analyses, particularly when the raw data may include zero or negative values. It also helps reduce skewness, bringing the data closer to a normal distribution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2. Explanatory variables\u003c/h2\u003e \u003cp\u003eThe explanatory variables are the interactions between the implementation of the agricultural land certification policy and the year in which the certificate was received. CHFS asked \u0026ldquo;whether your household obtained the land tenure certificate\u0026rdquo; and \u0026ldquo;the year household obtained the land tenure certificate.\u0026rdquo; Households who did not obtain a land management rights certificate during the sample periods were set as the control group (farmland rights\u0026thinsp;=\u0026thinsp;0), and households that obtain a land rights certificate after 2014 are set as the treatment group (farmland rights\u0026thinsp;=\u0026thinsp;1). The year 2013 is considered as the year of implementation and as a result its year dummy is set to 0, and the year 2015 is considered the year after policy implementation and as a result its year dummy is set to 1. The coefficient of the interaction term is used to express the impact of the implementation of the agricultural land certification policy on agricultural mechanization inputs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3. Control variables\u003c/h2\u003e \u003cp\u003eBased on existing literature, control variables are selected at both the household and provincial levels. The control variables at the individual level are the age of the household head, gender of the household head, health level of the household head, years of education of the household head and his/her political status. On the household level, the control variables include total number of household members, the proportion of working labor on total family members and the share of agricultural labor on total family members. Descriptive statistics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThis study includes individual control variables related to the characteristics of household heads, such as age, gender, and physical health, as these factors typically influence agricultural inputs. Specifically, female, elderly, or physically unwell laborers may be less able to engage in agricultural production, which in turn affects their investment in such activities. Additionally, whether the household head is a Communist Party member and their level of education may also impact machinery investment. In rural China, households with Party members often have access to more social resources and information, while agricultural producers with higher levels of education tend to have a more comprehensive and scientific understanding of how to enhance agricultural production efficiency. These personal characteristics could potentially confound the effects of land tenure formalization on household machinery investment. To ensure that the findings of this study reflect the impact of the land certification policy on the increase in agricultural machinery investment, rather than being driven by individual differences, these individual control variables are included in the analysis.\u003c/p\u003e \u003cp\u003eThis study incorporates several control variables at the household level to reflect both the dynamics of household labor allocation and their economic conditions. Variables related to productive labor include household size, the proportion of labor engaged in agricultural versus non-agricultural activities, and the amount of arable land owned by the household. These variables serve as indicators of the household's involvement in agricultural production. Generally, households with larger populations, more extensive land holdings, and a higher proportion of agricultural labor are more likely to engage in large-scale agricultural production, which in turn influences their investment in agricultural machinery. Conversely, a higher proportion of non-agricultural labor within the household is typically associated with reduced agricultural activity, leading to lower levels of investment in agricultural machinery.\u003c/p\u003e \u003cp\u003eIn addition to labor-related factors, the household's economic condition plays a crucial role in determining the level of investment in agricultural machinery. Therefore, this study includes variables such as household insurance coverage, total household income, and household consumption expenditure as proxies for economic status. By incorporating these variables, the study aims to more accurately assess the impact of land certification policies while minimizing potential confounding effects on household machinery investment and enhancing the robustness of the results. These economic variables reflect the resources available to households and the economic decisions they make in response to policy changes, providing deeper insights into how policies influence investment behavior across households with varying economic conditions.\u003c/p\u003e \u003cp\u003eThis study also includes household gross agricultural output and expenditures on hired labor as control variables. As the scale of agricultural production increases, both the overall value of agricultural output and the costs associated with hiring labor tend to rise, which generally leads to greater mechanization. Additionally, agricultural subsidies, which significantly influence investment in agricultural machinery, are incorporated as a control variable in our regression analysis. Furthermore, the development of local markets is a crucial factor that directly affects the availability and utilization of agricultural machinery services, thereby influencing farmers' investment decisions regarding such machinery. To account for this, the study includes the regional marketization index as a control variable to further strengthen the robustness of our findings.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMachine (CNY, IHS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.937\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwnership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male\u0026thinsp;=\u0026thinsp;1, Otherwise\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdu (1\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatus (Party_member\u0026thinsp;=\u0026thinsp;1, Otherwise\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMember_total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabor_share (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgrilabor_share (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonagrilabor_share (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsur_coverage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome_total (Log, CNY per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExp_consump (Log, CNY per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValue_agri (Log, CNY per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExp_hiring (Log, CNY per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea (Log, Mu)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubsidy (Log, CNY per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket_index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: 1.Years of education of household head (illiterate\u0026thinsp;=\u0026thinsp;1, elementary school\u0026thinsp;=\u0026thinsp;2, junior high school\u0026thinsp;=\u0026thinsp;3, high school\u0026thinsp;=\u0026thinsp;4, middle/vocational high school\u0026thinsp;=\u0026thinsp;5, junior high school/higher vocational high school\u0026thinsp;=\u0026thinsp;6, bachelor's degree\u0026thinsp;=\u0026thinsp;7, master's degree\u0026thinsp;=\u0026thinsp;8, doctoral degree\u0026thinsp;=\u0026thinsp;9); 2. Health condition of household head (extremely good\u0026thinsp;=\u0026thinsp;1, very good\u0026thinsp;=\u0026thinsp;2, good\u0026thinsp;=\u0026thinsp;3, fair\u0026thinsp;=\u0026thinsp;4, not good\u0026thinsp;=\u0026thinsp;5). 3. The average farm size is 10 mu (approximately 0.67 hectares or 1.65 acres); 4. For continuous variables that had a value of 0 before the change, such as machinery inputs per Mu, the amount of subsidies received for agricultural production, and expenditures on hired workers for agricultural production, inverse hyperbolic sine (IHS) changes are applied. For continuous variables that do not have a value of 0, such as incomes, expenditures, and total agricultural output, the logarithm is applied. 5. The marketization index is adopted from the Fan Gang Index which is a comprehensive assessment of multiple dimensions of marketization. It includes multiple sub-indicators, encompassing a wide range of aspects, such as the government-market relationship, the development of non-state-owned economy and intermediary organizations, product and factor markets, and legal environment. This approach enables the Fan Gang Index to comprehensively and objectively reflect the degree of marketization in a given region.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Research method\u003c/h2\u003e \u003cp\u003eThis study applies the Differences in Differences (DID) method to carry out an empirical analysis by taking time-fixed and region-fixed into consideration. The detailed model is expressed as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${y_{it}}=\\alpha +{\\beta _0}Pos{t_t}*Trea{t_i}+{\\beta _1}Control{s_{it}}+{\\gamma _t}+{\\mu _i}+{\\varepsilon _{it}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cem\u003ei\u003c/em\u003e\u003c/span\u003e, \u003cem\u003et\u003c/em\u003e represents household and year, respectively. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({y_{it}}\\)\u003c/span\u003e\u003c/span\u003edenotes the agricultural investment of household \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Pos{t_t}\\)\u003c/span\u003e\u003c/span\u003e denotes the dummy variable representing the time of operation of land policy within\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Pos{t_t}=0\\)\u003c/span\u003e\u003c/span\u003efor period before policy implementation and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Pos{t_t}=1\\)\u003c/span\u003e\u003c/span\u003eafter policy implementation. In addition, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Trea{t_i}\\)\u003c/span\u003e\u003c/span\u003e represent an individual dummy and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Trea{t_i}=1\\)\u003c/span\u003e\u003c/span\u003e denotes the treatment of individuals and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Trea{t_i}=0\\)\u003c/span\u003e\u003c/span\u003e the control individuals. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Control{s_{it}}\\)\u003c/span\u003e\u003c/span\u003e is the control variable, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\gamma _t}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu _i}\\)\u003c/span\u003e\u003c/span\u003erepresent year-fixed and individual-fixed effect. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varepsilon _{it}}\\)\u003c/span\u003e\u003c/span\u003e is the random error. It should be noted that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta _0}\\)\u003c/span\u003e\u003c/span\u003e this is the parameter of DID estimation and is the main concern of this research.\u003c/p\u003e\u003cp\u003eBecause the randomness of LTCP shocks is difficult to ensure, the parallel trend assumption for the sample was tested graphically prior to the test. The figure below illustrates the change in the per-acre mechanical inputs for the treatment and control groups. It can be seen from Fig.\u0026nbsp;1 that before the policy, the mechanical input of households in the treatment group is lower than that in the control group, and two groups have the same trend during 2011\u0026ndash;2013. After the implementation of land tenure certification policy, the mechanical input in the treatment group has a significant increase compared to the control group, and eventually exceeds the value of control group. Therefore, Fig.\u0026nbsp;1 supports our hypothesis of parallel trend.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Empirical results","content":"\u003cp\u003eIn this study, we use a DID approach with control variables as well as individual and time fixed effects and use household level clustering standard errors to test the empirical analysis through model (1). Due to the existence of a small number of households that neither use nor invest in machinery in the agricultural production process, we use an inverse hyperbolic sine (IHS) transformation to smooth the variable of \u0026ldquo;the acre machinery input per household\u0026rdquo;. Columns (1)-(3) of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e report the regression results. To compare the reliability of this finding, logarithmic changes are also applied to the machinery inputs per household acre after adding 1. Columns (4)-(6) of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e report the regression results after logarithmic adjustment by adding 1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe effect of land property right on machinery input of household.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd 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align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2488\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2779\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.1398)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.1403)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.1483)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.1271)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.1275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.1350)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0130\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0090\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0124\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0086\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0028)\u003c/p\u003e \u003c/td\u003e 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\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1460\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0887)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0918)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0832)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0864)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0721\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0678\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0342)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0361)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e 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colname=\"c3\"\u003e \u003cp\u003e(0.0275)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0268)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0259)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0253)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3133\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3252\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2893\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2981\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.1331)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.1267)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.1228)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.1173)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMember_total\u003c/p\u003e \u003c/td\u003e 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colname=\"c7\"\u003e \u003cp\u003e(0.0212)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabor_share\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4874\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4644\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.2414)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.2292)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgrilabor_share\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.3259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.3011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.2303)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.2191)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonagrilabor_share\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.5628\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.5286\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.2310)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.2201)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsur_coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.1344)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.1243)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome_total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0809\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0774\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0344)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0323)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExp_consump\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1326\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1270\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0365)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0343)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValue_agri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1363\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1284\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0164)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExp_hiring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0515\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0467\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0139)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0129)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.7120***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.6599***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0559)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0533)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket_index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0074***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0066***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0009)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubsidy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.1503)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.1403)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2427\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4716\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.7041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.6659\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.8771\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.0450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.2347)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1.2614)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.2219)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.1806)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1918\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: *, **, *** indicates significance level of 10%, 5% and 1%, respectively. The value in parentheses represent robust standard error. Data source: The Chinese Family Database (CFD) of Zhejiang University, and the China Household Finance Survey (CHFS)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the estimation results of model (1). Columns (1)-(3) in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e are the DID estimation results of the machinery input with IHS transformation. Column (1) is the estimation result without adding any control variables on the basis of individual and year fixed effects, and the coefficient 0.3401indicates the land ownership policy significantly contributes to the increase in households\u0026rsquo; machinery inputs per acre at the 5% level of statistical significance. Column (3) is the estimation result of adding all control variables as well as area and time fixed effect, and the coefficient of 0.3523 is still significantly positive at the 5% level of statistical significance. Columns (4)-(6) in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e are the results of estimation using log-treated machinery inputs as explanatory variables, and they have significantly positive coefficients at the 5% and 10% statistical significance levels for agricultural land rights after gradually adding control variables, showing again that the policy of agricultural land rights has a significant role in promoting agricultural machinery inputs to farm households' agricultural production. In addition to that, we also replace the acres of machinery inputs by the total household machinery inputs as the explanatory variables to do the test, and the results as shown in Appendix B reveal again that the land ownership policy significantly promotes the total machinery inputs of the farm household at the 1% statistical level.\u003c/p\u003e \u003cp\u003eOur analysis reveals that households with older household heads and higher proportions of non-agricultural labor tend to have lower levels of agricultural machinery investment. In contrast, families with male heads of household, party members, high proportion of working labor, high expenditure on hired labor, and better economic conditions exhibit higher levels of machinery investment. Besides, the negative relationship between land area and marketization level with mechanization inputs arises from the nature of the dependent variable: the larger the land area, the lower the level of mechanization inputs per unit area. Furthermore, regions with higher levels of marketization are usually the more developed eastern regions of China. These regions often experience significant rural population outflows and consequently reduce both the agricultural production and machinery investment.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Robust test\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. PSM-DID\u003c/h2\u003e \u003cp\u003eTo enhance the comparability between the treatment and control groups, we employed propensity score matching (PSM) to balance these samples. We matched the samples on a year-by-year basis, and the kernel density plots for propensity score matching are shown in Appendix C. Many studies tend to run regressions using samples that satisfy the common support assumption after the PSM is completed, which ignores the basic fact that the matched control group samples may serve as matches for more than one treatment group sample, and thus the degree of importance of differently weighted control samples among the overall control group samples is not the same. The greater the weight, the more occasion it is matched on, and so it should be given with more attention in the regression. Therefore, we adopt a frequency weighted variation of the regression to control for the matched samples in the control group. The regression results of PSM-DID are shown in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. Entropy balanced DID\u003c/h2\u003e \u003cp\u003eTo improve the comparability between the treatment and control groups, we further use the entropy balancing method which uses the first-order moments (means) and second-order moment (variances) of the control variables for matching. Unlike the propensity score matching method, the entropy balancing method is able to better balance the differences between the treatment and control groups without discarding a larger sample. The detailed entropy-balanced matching are shown in Appendix D, and column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the results of estimation using the balanced samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3. Hierarchy of clustering\u003c/h2\u003e \u003cp\u003eAs can be seen in the benchmark regression, the estimation is clustered at the household level. However, the machinery investment behavior of households may be influenced by farming households in nearby regions. We therefore adjust the level of clustering to check the robustness of our results. Columns (3) and (4) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e report the estimation results at the village and district levels, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4. Incorporate ex ante control variables\u003c/h2\u003e \u003cp\u003eSince control variables after the policy implementation are also likely to be affected by the agricultural land certification program, the consistency of the estimated coefficients of the model would possibly be affected. Therefore, we replace the original control variables by incorporating an interaction term between the household's control variable in 2011 and a year dummy variable. The results of the new regression are shown in column (5) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobust test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eIHS(Machinary input per Mu)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwnership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.3766**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2966**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4653***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4653**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2768*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.1743)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.1448)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.1631)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.1876)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.1498)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls_2011\u0026times;Year FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSM Balance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEntropy Balance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClustered at HH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClustered at Village\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClustered at prefecture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Placebo testing\u003c/h2\u003e \u003cp\u003eEstimates for a single treatment time point are vulnerable to omitted contemporaneous policies and variables compared to estimates for multiple time entry treatments. Therefore, a further placebo test is conducted to test whether the presence of omitted contemporaneous policies affects the reliability of the findings by generating \"pseudo-treatment\" variables by randomly selecting treatment groups and treatment times. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the Kernel density curves of the estimated coefficients of model (1) obtained by randomly selecting the experimental group and repeating the model 500 times. As can be seen from the figure below, the Kernel density distribution of the estimated coefficients of agricultural land rights coincides almost exactly with the normal distribution with mean zero, and all the estimated coefficients of the \"pseudo-processing\" are smaller than the real regression coefficients. This indicates that for the randomly generated sample of confirmed farmers, the policy of confirming the right to farmland does not have a significant effect on their input of agricultural machinery, which verifies the robustness of the conclusion.\u003c/p\u003e "},{"header":"4. Mechanism analysis","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Mechanism check\u003c/h2\u003e \u003cp\u003eFarmers obtaining loans to invest in agricultural production through farmland mortgages is an important mechanism for land certification policy to enhance capital investment in agricultural machinery. After the policy came into effect, the stabilization of property rights increased the bank's mortgage loans to farmers. At the same time, the confirmation of the farmland right to the use of agricultural land certification can also help form a reasonable mechanism for the expression of property rights (De Soto, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), reduce the bank's cost of information search and improve the effectiveness of the collateral, reducing the risk and cost of credit to help farmers access credit.\u003c/p\u003e \u003cp\u003eA financial system based on formal financial institutions and supplemented by informal financial institutions has basically been formed in rural China. The formal sector refers to formal financial institutions that are uniformly supervised by government departments, including state-owned banks and joint-stock commercial banks; the informal sector refers to informal financial organizations and their capital-financing activities that are outside the state's financial supervisory system, mainly including cooperatives, pawnbrokers, private fund-raising, free lending by private individuals, and various kinds of foundations. The implementation of the land tenure certification policy has increased the stability of land contracting rights to have a more direct impact on formal credit institutions for farm households, but Boucher et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) find in their study of Nicaragua and Honduras that the titling of agricultural land does not significantly improve farmers' access to credit. Piza and Moura (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), using data from Brazil, find that the granting of formal land status appeared to have a significant boost to households' formal credit from banks and reduced households' reliance on informal credit, so there is a need to further differentiate between the formal and informal credit behavior of farmers to analytically test the mechanisms by which land certification affects the level of farm inputs.\u003c/p\u003e \u003cp\u003eSince the behavior of farm households in obtaining bank loans by mortgage after land certification depends on their original cultivated land endowment, households with less arable land may not be able to pass the bank's asset collateralization threshold even if their right to land property is more stable, thus failing to help farm households with smaller original arable land areas obtain credit. Only with a larger land area can they cross the asset collateralization threshold and meet the asset collateralization requirement. Therefore, we define farm households with a median and below household cultivated land area as small-scale farm households and those above the median as large-scale farm households. A group test of formal versus informal credit access is conducted for these two farmland sizes.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the impact of farmland certification on farmers\u0026rsquo; formal and informal credit behavior by production scale. Formal credit refers to loans obtained by farmers from formal institutions such as banks and finance companies, and informal credit refers to loans obtained by farmers from friends, relatives, and private institutions. Columns (1)-(3) in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e report the effect of farmland certification on formal credit access by production scale, and columns (4)-(6) report the effect of farmland certification on informal credit access. The results show that farmland tenure certification policy significantly increases formal credit access of households with large-scale production, but it does not affect farmers\u0026rsquo; credit access to the informal credit sector.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProperty rights and credit behavior\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAccessibility of formal credit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAccessibility of informal credit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull_sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmall-scale household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLarge-scale household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFull_sample\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSmall-scale household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLarge-scale household\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOwnership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0299**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0088)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0144)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0195)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0232)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: The informal credit is only surveyed in 2013 and 2015, and therefore column (4)-(6) report the result with households in 2013 and 2015.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the land area of the sample farm households, a dummy variable with four equal parts is generated and regressed on the implementation of the agricultural land certification program, where group 4 is the one with the largest cultivated land area and group 1 is the one with the smallest cultivated land area. The interaction terms between the different groups and the implementation of the land certification policy were regressed on access to formal credit and informal credit, respectively, and the results are shown in Fig.\u0026nbsp;4.\u003c/p\u003e \u003cp\u003eThe regressions on formal and informal financial credit are conducted by interacting the division of arable land into four groups, based on land size, with the local implementation status of the land rights confirmation policy. In Fig.\u0026nbsp;4, panel (a) presents access to formal credit as the explanatory variable, while panel (b) focuses on access to informal credit. As shown in Fig.\u0026nbsp;4, the land certification policy significantly enhances access to formal credit for households with larger landholdings. In contrast, for households with smaller land sizes, although the policy also affects them, it does not significantly increase their access to credit. These findings suggest that the land certification policy improves the level of agricultural inputs by increasing credit accessibility to formal financial institutions for households with larger farmland areas.\u003c/p\u003e \u003cp\u003eFormal financial institutions primarily base loan availability on collateral, requiring farmers to have higher income and assets. In contrast, informal financial institutions base lending decisions on factors like the borrower\u0026rsquo;s family structure, social ties, and long-term residency. Thus, the lending criteria differ significantly between the two. Agricultural land ownership certification policies stabilize property rights and promote formal credit borrowing by using land as collateral. For farmers in developing countries, credit constraints lead to suboptimal agricultural practices, reducing production efficiency and output (Guirkinger and Boucher, 2010). Our analysis shows that property rights certification improves access to formal credit and significantly increases investment in agricultural machinery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Heterogeneity analysis\u003c/h2\u003e \u003cp\u003eSince the reform of China\u0026rsquo;s rural family contract system, the absence of grassroots governance mechanisms has made the relationship mechanism centered on geography and blood play an important role in maintaining transactional and economic behaviors and rural social order in villages. Land, as the most important means of production for farmers, not only plays a role in maintaining transactional behavior, cooperative relationships, and values among farmers within the village but also to gain more initiative over land, farmers will focus on the maintenance of inter-village social networks, build their social networks, and maintain invisible contractual relationships with compensation mechanisms such as favors and gifts (Kreps, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). When land ownership is unstable or unclear, they can benefit more in cases of land transfer and disputes by maintaining contractual relationships.\u003c/p\u003e \u003cp\u003eThe policy of land ownership certification, which was fully implemented in 2013, formalized and legalized property rights security and land rights stability, deconstructed the relationship-based transaction method relying on social networks within villages and reduced disputes between neighbors due to unclear land boundaries, thus improving agricultural production efficiency. An increase in market-based transactions has led to a gradual decrease in the number of \u0026ldquo;outward\u0026rdquo; gifts to maintain social networks and an increase in \u0026ldquo;inward\u0026rdquo; investments to improve agricultural production efficiency and soil fertility. A clear property rights system is fundamental for resource allocation and efficiency improvements (Alchian A and Demsetz H, 1973; Chen, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Geng P and Luo B, 2022).\u003c/p\u003e \u003cp\u003eIn December 2013, the Chinese government issued the \u0026ldquo;Opinion on Party Members and Cadres Taking the Lead in Promoting Funeral Reform\u0026rdquo;. To prevent potential bias resulting from the reduction in social expenditures within the village due to this policy, an interaction term between the implementation year and party member status is incorporated to control for the potential interference of this policy on regression results since the policy primarily affects Chinese Communist Party members.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProperty rights and outward gifts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eIHS (Expenditure on social interaction among non-relatives)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eIHS (Expenditure on social interaction among relatives)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreat_post\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.5180***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5560***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.5567***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.6049**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.7444***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.7383***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.1922)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.2378)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.2591)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.2579)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u0026times;Member\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.4629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.6205)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.7216)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear FE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3779\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: The expenditure of outward gift for non-relatives are surveyed in 2011, 2013 and 2015, while the expenditure of outward gift for relatives are only surveyed in 2013 and 2015.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e classifies the objects of human interaction expenditures into non-relative human interaction expenditures and relative human interaction expenditures based on blood relations. The test results show that the policy of agricultural land certificates significantly weakens intra-village human interaction expenditures. This suggests that strengthening property rights weakened the effect of social networks on village governance and promoted the development of intra-village economic behavior along the direction of the market economy, and therefore reduced households\u0026rsquo; expenditure on human interaction.\u003c/p\u003e \u003cp\u003eTraditionally, rural societies have relied on close-knit social networks and kinship ties to maintain governance and economic development. However, with the implementation of land certification policies, the clarification and stabilization of land rights have led farmers to focus more on their individual economic interests, reducing their engagement in social activities. This shift helps farmers allocate more resources to agricultural production and other economic activities, advancing the marketization process in rural areas, and enhancing the optimization and efficiency of resource allocation among farmers.\u003c/p\u003e \u003cp\u003eNext, we divide the non-relatives' favor gifts into four quantile parts according to the level of expenditure, with group 1 being the group with the lowest expenditure on family favor gifts, and group 4 being the group with the highest. The explanatory variable in the figure is the level of machinery input per mu in the household, and the explanatory variable is the regression of machinery input per mu in the farm household by interacting different groups with the implementation of the agricultural land tenure certification program, and the regression results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe explanatory variable in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e5\u003c/span\u003e is the level of machinery input per household after applying the IHS transformation. The data shows that households with higher spending on social interactions\u0026mdash;indicating stronger social networks, tend to invest more in agricultural machinery under the land certification policy. This suggests that the land certification program has varying effects on machinery investment depending on the household's social capital, significantly boosting investment among those with richer social networks. Stronger social networks provide better access to information, exposing farmers to modern agricultural practices. As a result, these farmers increase their machinery investment when the policy is implemented. In contrast, households with weaker social capital do not see the same increase in investment from improved land rights alone, as their decisions are constrained by credit limits, land resources, household size, and limited access to information and modern agricultural concepts influenced by their social networks.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study uses Chinese Family Database (CFD) and China Household Financial Survey (CHFS) data from 2011 to 2015 to analyze the relationship between China's ownership reform on agricultural property and farmers' agricultural machinery inputs by the method of difference-in-difference (DID). The empirical study found that the land ownership certificate policy significantly promoted households\u0026rsquo; investment in machinery input by increasing the formal credit availability of households with large-scale production. Heterogeneity analysis further showed that the positive effect is even more significant for households with stronger social networks.\u003c/p\u003e \u003cp\u003eIn many developing countries, much emphasis continues to be placed on land structures with communal ownership or on policies that aim for equality in the distribution of land across farmers rather than on allowing free exchange in land markets (Chari et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our analysis suggests that the stability of land property rights is the basis for restructuring intra-rural social relations and facilitating market-based transactions, whereas increased mechanization leads to further efficiency gains in agricultural production. A related explanation is that trading in agricultural land markets is associated with a unique set of coordination problems that cannot be solved by traditional markets (Bryan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), which is also supported by our research: before the land ownership policy, economic behavior in rural China was mostly based on social networks, and an implicit contractual relationship was maintained through favors and gifts. When the contractual right to land management is legally protected, such non-market behavior would be gradually replaced by formal market rules.\u003c/p\u003e \u003cp\u003eChina has a unique history and land systems, and its agricultural production is typical of some developing countries, especially for other similarly situated countries with predominantly smallholder economies, small arable land per capita, and reliance mainly on family labor for agricultural production. This study has implications for other countries similar to China on whether they should pursue land certification program to stabilize the expectations of property holders and to promote normative market transactions. Although our study is centered on machinery inputs for agricultural production, the improvement in agricultural productivity is precisely because of the willingness of farmers to invest more capital after land property rights have been clarified. It should also be noted that land tenure certification policies in China clearly have a greater impact on households with large-scale production and on households with more social capital, which may also lead to a widening of income disparities within rural areas.\u003c/p\u003e \u003cp\u003eIt is important to note that this study is situated within the specific context of rural China, not only due to the typical characteristics and unique aspects of China's rural economic development but also because of the long-standing village civilization in China, which has a rich history and extensive experience in managing the communal living environments of human communities. Villages in China encompass not only the economic logic of efficient resource allocation but also embody emotions, culture, customs, and traditions. While the clarification of property rights can facilitate market-oriented development, it should not completely replace the tools of interpersonal communication and emotional exchange. The transition of farmers from a survival strategy based on land dependence to a property rights approach focused on maximizing village interests may introduce new frictions and risks. Therefore, it is crucial that the fundamental institutional function of agricultural land rights and the interpersonal trust mechanisms within rural communities work in a complementary manner.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data employed in this study are accessible through the Survey and Research Center for China Household Finance (CHFS) and the Chinese Family Database (CFD) at Zhejiang University, contingent upon formal application procedures. In accordance with the data usage agreements of both CHFS and CFD, this research rigorously adheres to confidentiality obligations. We implement secure data storage protocols to safeguard respondent privacy and ensure ethical handling of survey data. Unauthorized disclosure, distribution, or transfer of any data content (including derivative forms) to third parties is strictly prohibited. Owing to legally binding confidentiality requirements, the datasets analyzed in this study are not publicly available. Qualified researchers may submit formal applications through the official CHFS portal (https://chfser.swufe.edu.cn/datas/) to access CHFS data. For CFD data access, applications should be submitted via email to [email protected]. Data access will be granted pending approval from the respective institutions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Informed consent\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY**contributed to the conception and design of the research. All authors contributed to the methodology, data collection, analysis, writing, review, and editing. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdamopoulos, T., Restuccia, D. (2014). 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The impact of the land certificated program on the farmland rental market in rural China. \u003cem\u003eJournal of Rural Studies\u003c/em\u003e, 93, 165-175.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Land Tenure Certification Policy, Agricultural efficiency, Agricultural machinery inputs, Credit constraints, Social networks","lastPublishedDoi":"10.21203/rs.3.rs-6303702/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6303702/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImproving agricultural machinery inputs and enhancing the efficiency of agricultural production is an important way to promote agricultural modernization and address food security challenges. Based on a new round of progressive land tenure certification policy (LTCP) in China, this study uses data from the Chinese Family Database (CFD) and the China Household Financial Survey (CHFS) for the period 2011\u0026ndash;2015 to analyze the relationship between China's agricultural property ownership reform and farmers' agricultural machinery investments, employing difference-in-difference (DID) methods with fixed effects and clustering. The empirical results show that the implementation of the ownership certification policy increases agricultural machinery investment by improving access to financial credit for households with large-scale agricultural land. Further analysis reveals that the policy has only promoted agricultural machinery investment among farming households with richer social capital. The findings of this study provide a detailed mechanism for understanding the effect of property stabilization on agricultural machinery investment and offer both theoretical and empirical insights for other developing countries seeking to promote agricultural production through improving the stability of land certification programs.\u003c/p\u003e","manuscriptTitle":"When Property Rights Meet Social Capital: The Differentiated Impact of Land Certification on Farm Mechanization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-30 07:07:50","doi":"10.21203/rs.3.rs-6303702/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"153e526a-d4ee-4c5e-8195-8f9fe2f5cdf3","owner":[],"postedDate":"June 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50606583,"name":"Social science/Economics"},{"id":50606584,"name":"Business and commerce/Economics"}],"tags":[],"updatedAt":"2025-09-16T15:08:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-30 07:07:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6303702","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6303702","identity":"rs-6303702","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}