Digital literacy and farmers’ land transfer

Digital literacy and farmers’ land transfer | 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Share Download PDF Article Digital literacy and farmers’ land transfer Lili XU, Lei ZHANG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8229633/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract With the rapid popularization of digital tools such as the Internet in rural areas of China, digital literacy has a profound impact on farmers' production, management and consumption behaviors. This study investigates the influence of digital literacy on farmers' land transfer, aiming to reveal the role of farmers' digital literacy in improving the allocation of rural land resources. Based on the China Household Panel Study of the Center for Chinese Social Science Survey of Peking University, this study uses the Probit model and two-stage least squares estimation method to evaluate the impact of digital literacy on farmers' land transfer behavior and discusses its potential mechanism. The results indicate that digital literacy increases the likelihood of land transfer by promoting farmers to engage in non-agricultural employment and non-agricultural entrepreneurship. Heterogeneity analysis indicates that the conclusion drawn in this article is that digital literacy is more beneficial for farmers in economically underdeveloped areas and those with lower education levels. This study provides theoretical insights for policies to promote the development of rural land markets in developing countries and improve farmers' literacy. JEL Classifications: D51 L86 Q12 Social science/Development studies Business and commerce/Economics Social science/Economics Earth and environmental sciences/Environmental social sciences Digital literacy Land transfer Non-agricultural employment Non-agricultural entrepreneurship 1. Introduction Land transfer is critical for enhancing agricultural productivity in developing countries (Lin, 1992 ; McMillan et al., 1989 ), improving resource mismatch (Adamopoulos et al., 2022 ; Chen et al., 2022 ) and improving farmers' welfare (Keswell and Carter, 2014 ). However, the problem of land fragmentation is still serious in developing countries. Taking China as an example, the relevant data of the 2022 "China Rural Policy and Reform Statistical Yearbook" show that the number of farmers operating less than 10 mu of cultivated land still accounts for 74.6%. Many scholars have confirmed that this problem is also common in developing countries such as Estonia (Looga et al., 2018 ), Kenya and Uganda (Djurfeldt, 2020 ). Therefore, how to further promote land transfer in developing countries is of great significance for promoting agricultural economic growth and alleviating poverty in developing countries. Although it is very important to realize land transfer, farmers cannot easily transfer land due to various constraints, including insufficient non-agricultural employment opportunities, high threshold for non-agricultural entrepreneurship, unstable land property rights, and asymmetric information on land transfer (Wang et al., 2020 ; Hu and Chen, 2024 ; Rogers et al., 2021 ; Wang et al., 2015 ; Huy et al., 2016 ; Gao et al., 2020 ). In particular, non-agricultural employment or entrepreneurship has been identified as one of the main obstacles. Wang et al ( 2020 ) pointed out in their study of China that non-agricultural employment opportunities will increase the opportunity cost of farmers not transferring land, thereby increasing farmers' willingness to transfer land. At the same time, farmers' non-agricultural entrepreneurship will reduce the labor force engaged in agricultural production in the family, which will promote land transfer (Hu and Chen, 2024 ). Therefore, how to increase non-agricultural employment opportunities and enhance farmers' non-agricultural entrepreneurship capabilities will be conducive to promoting land transfer. With the continuous improvement of digital infrastructure, digital literacy is believed to greatly facilitate the enhancement of farmers' non-agricultural employment capabilities and the improvement of non-agricultural entrepreneurship capabilities. For example, the improvement of digital literacy is conducive to farmers' use of tools such as mobile phones to obtain non-agricultural employment market information and reduce their transaction costs for obtaining non-agricultural employment (Zhou et al., 2024 ). A series of studies also show that digital literacy will also enhance farmers' ability to identify entrepreneurial opportunities, broaden the path mechanism for obtaining resources such as credit, increase the probability of farmers engaging in non-agricultural entrepreneurship, and improve farmers' entrepreneurial performance (Ji and Zhuang, 2023 ; Bai et al., 2023 ). However, few studies have explored the relationship between digital literacy and farmers' land transfer. Therefore, the main purpose of this study is to use the data from the China Family Panel Studies (CFPS2018) to analyze the relationship between digital literacy and farmers' land transfer behavior. At the same time, this study further analyzes the impact mechanism of digital literacy on farmers' land transfer behavior. This paper contributes to the literature in several ways. First, this paper pioneered the analysis of the impact of digital literacy on farmers' land transfer. Although some scholars have recognized that digital technology has an important impact on farmers' land transfer behavior (Zou and Mishra, 2022 ; Yu et al., 2024 ; Wang et al., 2024 ), the existing literature mainly emphasizes the impact of digital tool supply factors on land transfer. Digital literacy emphasizes the impact of digital tool demanders—farmers' ability to use digital tools on land transfer. Lack of digital literacy will affect farmers' level of use of digital tools and actual use effects, and create a digital use gap (Ragnedda, 2017 ). Therefore, even if the level of digital tool supply is high enough, insufficient digital literacy will affect farmers' use of digital tools to transfer land. Second, this paper uses the instrumental variable method and the omitted variable evaluation model to solve the endogeneity problem. Farmers' use of digital tools depends not only on the level of digital literacy of farmers, but also on the result of self-selection. Therefore, the literature analyzing the impact of digital tool use on land transfer (Zou and Mishra, 2022 ; Yu et al., 2024 ; Wang et al., 2024 ) is prone to serious endogeneity problems. Some literature does not consider the endogeneity problem in empirical analysis (Zou and Mishra, 2022 ). Although another part of the literature uses the PSM model for endogeneity treatment (Wang et al., 2024 ), the PSM method cannot alleviate the selection bias caused by unobserved factors. This study employs strictly exogenous surface relief (Zhou et al., 2024 ) as an instrumental variable to solve the endogeneity problem caused by two-way causality, but also uses the omitted variable evaluation model (Altonji et al., 2005 ) to evaluate the impact of the omitted variable problem. Third, this article examines the impact of digital literacy on land transfer through non-agricultural employment and non-agricultural entrepreneurship paths, filling the gap in research on the land transfer mechanism of digital literacy. Limited research has explored the relationship between digital tools and land transfers. For example, Wang et al. ( 2024 ) found that farmers' use of e-commerce significantly promoted land transfer, but did not further verify the mechanism by which e-commerce affects land transfer. Although another part of the literature found that Internet use has a positive impact on non-agricultural employment (Zou and Mishra, 2022 ), it did not verify the impact of non-agricultural employment on land transfers, and ignored the impact of non-agricultural entrepreneurship on land transfers. This article constructs an analytical framework for the impact of digital literacy on land transfer through non-agricultural employment and non-agricultural entrepreneurship paths, thus further improving and expanding the study of digital literacy on land transfer. The rest of this paper is structured as follows. In Section 2 , we propose hypotheses based on relevant literature and theory. In Section 3 , we introduce variables such as digital literacy and farmers’ land transfer, as well as the empirical model and data used. In Section 4 , we provide quantitative results to explore the impact of digital literacy on farmers’ land transfer. At the same time, we analyze the impact mechanism of digital literacy on farmers’ land transfer and evaluate the heterogeneous impact of digital literacy on farmers’ land transfer behavior. Finally, this paper gives relevant research conclusions and discussions. 2. Theoretical analysis and research hypothesis Information asymmetry is an important factor affecting the effective and reasonable allocation of resources in the market economy. In the traditional rural land transfer market, farmers are often at a disadvantage in obtaining information, and the agricultural market information they receive is delayed and distorted (Li et al., 2020 ; Tang et al., 2015 ). Therefore, in the traditional rural land transfer market, due to the limitation of information transmission, the land transfer objects are mostly acquaintances within the village committee (Qiu et al., 2020), which to a certain extent hinders the development of the land transfer market. However, the improvement of farmers' digital literacy is conducive to the release of land transfer information by both land supply and demand parties through the Internet, and broadens the boundaries of the land transfer transaction market. At the same time, the traditional rural land transfer market mostly uses informal contracts such as oral contracts for land transfer (Fan et al., 2024 ), which to a certain extent increases the transaction costs of land transfer contracts afterwards (Wang et al., 2024 ; Ito, 2024 ). The improvement of farmers' digital literacy is conducive to farmers forming formal contracts with clearer rights and obligations through digital land transfer service platforms (Fan et al., 2024 ), thereby promoting the development of the land transfer market. Based on the above theoretical analysis, digital literacy mainly improves the information transmission boundary of farmers' land transfer and promotes the formalization of land transfer contracts, thereby promoting farmers' land transfer. Therefore, we first propose hypothesis 1 . Hypothesis 1 Digital literacy promotes farmers’ land transfer Non-agricultural employment is considered an important factor in promoting the transfer of rural land. On the one hand, when non-agricultural employment income has a comparative advantage over agricultural employment income, agricultural employment faces higher opportunity costs (Liangjie, 2017 ); on the other hand, non-agricultural employment will promote the transfer of rural labor from rural areas to urban areas (Wang et al., 2020 ), which will further promote the transfer of rural land. The improvement of farmers' digital literacy will improve the transfer of rural land by encouraging farmers to engage in non-agricultural employment. First, the improvement of farmers' digital literacy will help farmers obtain non-agricultural employment information through the Internet. Traditional farmers mainly rely on social network scale factors such as relatives and friends to obtain non-agricultural employment information (Baird and Gray., 2014). The improvement of farmers' digital literacy has prompted farmers to use the Internet or mobile communication tools, breaking the restrictions of traditional social network scale on farmers' access to non-agricultural employment information, reducing farmers' search costs for non-agricultural employment information, and promoting farmers to engage in non-agricultural employment. Secondly, the continuous development of the digital economy has put forward higher requirements for farmers engaged in non-agricultural employment to have digital skills. Having higher digital literacy will not only have a positive impact on farmers' employment ability, efficiency and productivity, but also increase the possibility of farmers finding jobs, promotions or salary increases (Bejaković and Mrnjavac, 2020 ). Therefore, this paper proposes hypothesis 2 : Hypothesis 2 Improving digital literacy can incentivize farmers to transfer their land by encouraging them to engage in non-agricultural employment mechanisms. Farmers’ entrepreneurship is also an important factor in promoting land transfer. In particular, with the improvement of land property rights stability, the possibility of farmers’ entrepreneurship has greatly increased, which in turn has strengthened farmers’ willingness to transfer land (Yang et al., 2022 ). The improvement of farmers' digital literacy will improve land transfer by promoting farmers to engage in entrepreneurship. First, the improvement of farmers' digital literacy promotes farmers' ability to obtain information related to entrepreneurship. Information acquisition is considered to be crucial to the identification of entrepreneurial opportunities (Wang and Ellinger, 2009 ). In the traditional entrepreneurial market, due to geographical constraints, farmers generally have a lag in obtaining information, which hinders the development of entrepreneurial activities (Tan and Li, 2022 ). The improvement of farmers' digital literacy will undoubtedly enhance farmers' ability to obtain information using Internet tools and break the impact of information barriers on farmers' entrepreneurial behavior. Secondly, the continuous development of the digital economy has prompted entrepreneurs to shift their entrepreneurial direction from traditional entrepreneurial fields to Internet fields such as e-commerce. The improvement of entrepreneurs' digital literacy will stimulate entrepreneurs' endogenous motivation to engage in Internet entrepreneurship, thereby enhancing their willingness to engage in Internet entrepreneurship (Mugiono et al., 2021 ). Therefore, the improvement of farmers' digital literacy will broaden the scope of farmers' non-agricultural entrepreneurship and thereby increase the possibility of non-agricultural entrepreneurship. Therefore, this paper proposes Hypothesis 3 : Hypothesis 3 Improving digital literacy can incentivize farmers to transfer their land by promoting non-agricultural entrepreneurship mechanisms. 3. Materials and methods 3.1 Data source This study uses data from the China Family Panel Studies (CFPS) conducted by the Center for Chinese Social Science Survey at Peking University from June 2018 to March 2019. This survey collects data at three levels: individuals, families, and communities, reflecting changes in Chinese families in terms of economic and non-economic welfare. The China Family Panel Studies is a nationwide, large-scale, multidisciplinary social tracking survey project that records information on sample families in terms of economic activities, educational outcomes, and family relationships. At the same time, the survey uses implicit stratification and multi-stage equal probability sampling methods, covering household data from 25 provinces (municipalities, and autonomous regions) in China, which can fully reflect the digital literacy and land transfer status of Chinese farmers. 3.2 Variable selection 3.2.1 Explained variable Regarding the measurement of land transfer, existing studies have used indicators such as "land transfer in", "land transfer out" and "land transfer in or out" for measurement (Zou and Mishra, 2022 ; Rogers et al., 2021 ; Huy et al., 2016 ; Yu et al., 2024 ; Wang et al., 2024 ). This paper mainly studies the land transfer behavior of farmers, so it refers to the existing literature (Zou and Mishra, 2022 ) and defines the transfer behavior of farmers based on the question of "whether to rent out the land to others". 3.2.2 Core explanatory variables The core explanatory variable is farmers' digital literacy, which reflects the ability or attitude of farmers to use digital devices for work, study, entertainment, socializing with others, etc. Referring to the existing literature (Zhou et al., 2024 ), this paper measures farmers' digital literacy level from five dimensions: "frequency of using the Internet for learning", "frequency of using the Internet for work", "frequency of using the Internet for socializing", "frequency of using the Internet for entertainment", and "frequency of using the Internet for business". The measurement range of frequency of use is from "never" to "almost every day", with a score of 1–7. On this basis, the measured values of the five questions are averaged to obtain the digital literacy score of the head of the farmer's household. Table 1 lists the evaluation dimensions of digital literacy. Table 1 Evaluation dimensions of digital literacy Variable Variable Description Digital_social Frequency of using the Internet for social networking Digital_work Frequency of using the Internet for work Digital_study Frequency of using the Internet for learning Digital_business Frequency of using the Internet for commercial activities Digital_entertainment Frequency of using the Internet for entertainment Digital_literacy The average of the above five items 3.2.3 Control variables Referring to existing literature (Rogers et al., 2021 ; Cao et al., 2024 ; Zou and Mishra, 2022 ), this paper selects control variables from three levels: individual, family, and region, to avoid the deviation of the estimation results of the regression model caused by differences in individual, family, and regional factors. At the individual level, this paper controls the basic characteristics of the household head, such as age (age), gender (sex), education level (edu), marital status (marry), health (health), etc. At the family level, this paper controls the size of family members (family_size), per capita net income of the family (av_inc), whether financial assets are held (finance), and the proportion of labor to family size (labor_rate). At the regional level, this paper controls whether it is in the eastern region (east_region) or the western region (west_region). Variable descriptions and descriptive statistics are presented in Table 2 . Table 2 Variable description Variable Variable Description Obs Mean Std. Dependent variables land_leased Whether to transfer the land (yes = 1; no = 0) 4,327 0.156 0.362 Core Explanatory Variable digital_literacy Digital literacy level of farmers 4,895 4.073 1.473 Control Variables age Age of household head 4,895 37.80 11.29 sex Gender of household head (male = 1; female = 0) 4,895 0.588 0.492 edu Education level of household head (1 = primary school or below; 2 = junior high school; 3 = high school; 4 = college or above) 4,895 2.114 0.976 marry Whether the head of household is married (yes = 1; no = 0) 4,895 0.808 0.394 health Health status of the household head (1 = very healthy; 2 = very healthy; 3 = relatively healthy; 4 = average; 5 = unhealthy) 4,895 2.773 1.141 family size Family size 4,895 4.660 2.015 finance Whether the household holds financial assets (yes = 1; no = 0) 4,895 0.0198 0.139 labor rate Ratio of household labor force to household population size 4,895 0.633 0.237 east region Eastern region (yes = 1; no = 0) 4,895 0.363 0.481 west region Western region (yes = 1; no = 0) 4,895 0.367 0.482 3.3 Empirical model To evaluate the impact of digital literacy on farmers' land transfer, this paper constructs a binary Probit model as follows: $$land\_leased_{i}^{*}={\alpha _0}+{\beta _1}digital\_literac{y_i}+{\beta _2}control\_va{r_i}+{\varepsilon _i}$$ 1 \(land\_lease{d_i}=1(land\_leased_{{_{i}}}^{*}>0)\) In the above formula, \(land\_lease{d_i}\) represents the land transfer behavior of farmers. \(land\_leased_{{_{i}}}^{*}\) represents a latent variable. if \(land\_leased_{{_{i}}}^{*}>0\) , then \(land\_lease{d_i}\) the value is 1, otherwise it is 0. \(digital\_literac{y_i}\) represents the digital literacy level of farmers. \(control\_{\operatorname{var} _i}\) represents the control variables at the individual level, family level and regional level, and represents the random disturbance term. If the coefficient is significantly positive, it means that digital literacy will be beneficial to farmers’ land transfer. 4 Empirical results 4.1 Regression analysis In order to verify whether digital literacy is conducive to farmers' land transfer, this article adopts the stepwise regression method for regression. In columns 1 to 4 of Table 3 , individual-level, household-level, and regional-level control variables are gradually added. It can be seen from the empirical results in column 4 of Table 3 that, with the addition of individual-level, household-level and regional-level control variables, the average marginal effect of digital literacy on farmers' land transfer behavior is 0.035, and at the 10% significance level Significantly. It shows that for every unit increase in the digital literacy level of farmers, the probability of farmers transferring land increases by 3.5%. At the same time, it can be seen from the empirical results in columns 1, 2 and 3 of Table 3 that, while gradually controlling individual-level, household-level and regional-level control variables, the average marginal effect of digital literacy on whether farmers transfer their land is still It is positively significant below the 10% significance level. This shows that the conclusion that digital literacy has a significant positive impact on farmers’ land transfer is relatively robust, and hypothesis 1 has been verified. Table 3 Digital literacy and farmers’ land transfer: basic regression Variables Land_leased (1) (2) (3) (4) digital_literacy 0.038** 0.039** 0.037* 0.035* (0.016) (0.019) (0.019) (0.019) age 0.007*** 0.006** 0.005* (0.002) (0.003) (0.003) sex 0.027 0.025 0.030 (0.048) (0.048) (0.048) edu 0.055** 0.056** 0.041 (0.028) (0.028) (0.028) marry -0.096 -0.076 -0.093 (0.065) (0.067) (0.068) health 0.020 0.021 0.023 (0.021) (0.021) (0.021) family_size -0.031** -0.027* (0.014) (0.014) finance 0.493*** 0.476*** (0.153) (0.154) labor_rate -0.244** -0.219* (0.112) (0.113) east_region -0.080 (0.058) west_region -0.285*** (0.058) Constant -1.166*** -1.555*** -1.220*** -1.054*** (0.068) (0.151) (0.194) (0.199) Observations 4,327 4,327 4,327 4,327 4.2 Endogeneity analysis Previous results demonstrate that digital literacy increases land transfer likelihood. However, this conclusion may have a reverse causal problem. Farmers who transfer their land tend to go to urban areas with higher levels of digital infrastructure to engage in non-agricultural employment or entrepreneurship, thereby acquiring higher levels of digital literacy. Another issue that needs to be considered is the problem of omitted variables. For example, farmers' use of digital tools for consumption, entertainment, and work may be affected by regional customs. 4.2.1 Testing for reverse causality In order to overcome the possible reverse causality problem between digital literacy and farmers' land transfer behavior, we conducted a two-stage least squares estimation (2SLS) using instrumental variables to address potential biases in the estimation results. Referring to existing literature (Zhou et al., 2024 ; You et al., 2018 ), this paper uses county land surface undulation (RDLS) as an instrumental variable. In terms of correlation, the greater the county land surface undulation, the higher the cost of digital infrastructure construction, and the lower the digital literacy level of farmers. In terms of exogeneity, county land surface undulation will not have a reverse causal relationship with farmers' land transfer behavior. From the empirical results in columns 1 and 3 of Table 4 , it can be seen that county land surface relief and farmers' digital literacy level are negatively significant at the 1% significance level. Both the overidentification test and the weak instrumental variable test hold. It shows that the county surface relief is an ideal instrumental variable. At the same time, according to the empirical results in column 4 of Table 4 , after using instrumental variables, the marginal coefficient of farmers' digital literacy increased from 0.035 in the baseline regression to 0.131 in column 2 of Table 4 . This shows that ignoring the endogeneity problem will lead to underestimating the role of digital literacy in promoting farmers’ land transfer. Table 4 Digital literacy and farmers’ land transfer: instrumental variable regression Variables First stage Second stage First stage Second stage (1) (2) (3) (4) digital_literacy land_leased digital_literacy land_leased rdls -0.1467*** -0.1431*** (0.0256) (0.0342) digital_literacy 0.2197*** 0.1310* (0.0596) (0.0728) age -0.0366*** 0.0059** (0.0022) (0.0027) sex 0.1084** -0.0209 (0.0429) (0.0164) edu 0.5810*** -0.0626 (0.0233) (0.0439) marry -0.1002* -0.0128 (0.0607) (0.0214) health -0.0264 0.0085 (0.0185) (0.0066) family_size 0.0065 -0.0012 (0.0122) (0.0042) finance 0.7888*** 0.0753 (0.1594) (0.0834) Labor_rate 0.4162*** -0.0631 (0.1035) (0.0448) east_region -0.1046* -0.0054 (0.0536) (0.0209) west_region 0.1109 -0.0524*** (0.0734) (0.0189) Constant 4.1815*** -0.7243*** 4.1247*** -0.4036 (0.0366) (0.2372) (0.1656) (0.2980) Anderson canon. corr. LM statistic 28.922 (0.0000) 19.394 (0.0000) Cragg-Donald Wald F statistic 29.178 19.441 Observations 3,351 3,073 3,351 3,073 R-squared 0.0097 -0.7544 0.3509 -0.1571 4.2.2 Testing for omitted variables In order to evaluate the severity of the possible omitted variable problem, this paper mainly refers to the existing literature (Altonji et al., 2005 ). Specifically: First, two sets of regression models are designed, one set is a constrained control variable model, and the other set is a full control variable model. Secondly, the core explanatory variable coefficients of the two sets of regression models are calculated as \(\widehat {{{\beta _R}}}\) , \(\widehat {{{\beta _F}}}\) . Finally, the change rate of the core explanatory variable coefficient between the control variable model and the full control variable model is calculated \(Ratio=\left| {{{\widehat {{{\beta _F}}}} \mathord{\left/ {\vphantom {{\widehat {{{\beta _F}}}} {(\widehat {{{\beta _R}}} - \widehat {{{\beta _F}}})}}} \right. \kern-0pt} {(\widehat {{{\beta _R}}} - \widehat {{{\beta _F}}})}}} \right|\) . The larger the change coefficient, the less the core explanatory variable is affected by the omitted variable problem. Based on the above principles, this article designs two sets of omitted variable evaluation models. The first model only includes the core explanatory variables and no additional control variables. The second model includes core explanatory variables and individual level control variables. The third model includes core explanatory variables and control variables at the individual and family levels. The fourth model includes core explanatory variables and control variables at the individual, family, and regional levels. Results in the last row of Table 5 show that, we can see that from the ratio of the last row of the empirical results in the first and second columns, in the first set of omitted variable evaluation models, the deviation caused by the omitted variables in the estimation results will only occur when the potential impact of the omitted variables on the model is at least 39 times that of the control variables in the existing model. In the second set of omitted variable evaluation models, the change rate of the core explanatory variable coefficient between the control variable model and the full control variable model is also 17.5. This shows that the digital literacy of farmers is little affected by the omitted variable problem, and the estimation results of the benchmark regression in this paper are relatively robust. Table 5 Digital literacy and farmers’ land transfer: omitted variable problem test Variables land_leased Group1 Group2 (1) (2) (3) (4) digital_literacy 0.038** 0.039** 0.037* 0.035* (0.016) (0.019) (0.019) (0.019) Individual control variables NO YES YES YES Family control variables NO NO YES YES Regional control variables NO NO NO YES Constant -1.166*** -1.555*** -1.220*** -1.054*** (0.068) (0.151) (0.194) (0.199) Observations 4,327 4,327 4,327 4,327 R2 0.0015 0.0054 0.0099 0.0168 Ratio 39 17.5 4.3 Robustness analysis 4.3.1 Replace explanatory variables In the above, this article mainly uses the digital literacy level of the user as the core explanatory variable. However, the decision-making process of farm households may also be affected by the digital literacy level of other laborers in the family. Therefore, this article uses the average digital literacy level of the farm household labor force to replace the core explanatory variables and conduct a robustness test. The results are shown in column (1) of Table 6 , where the estimated coefficient and significance of digital literacy do not change greatly, which shows that the conclusions of the baseline model are robust. 4.3.2 Eliminate non-cultivated land transfer samples In the above, this paper regards the transfer of land such as "cultivated land, forest land, pasture and pond" as the transfer of land by farmers. However, for most farmers, cultivated land is the most important land resource. Therefore, this paper only retains the sample of farmers who "received cultivated land from the collective" and conducts a robustness test. The results are shown in column (2) of Table 6 . It can be seen that when the sample of farmers who "received cultivated land from the collective" is used for regression, digital literacy has a promoting effect on farmers' land transfer, which shows that the conclusion of the baseline model is robust. 4.3.3 Excluding samples from regions with developed digital infrastructure The formation of farmers' digital literacy is easily affected by the level of external digital infrastructure. The more developed the digital infrastructure is, the higher the digital literacy level of farmers may be. In order to avoid the impact of samples from provinces or regions with more developed digital infrastructure on the overall regression results. According to the "Digital China Construction and Development Report (2018)" released by the Cyberspace Administration of China, the two provinces or regions with the highest scores in China's information infrastructure are Shanghai and Zhejiang. This article eliminates the samples from the two provinces and regions with the highest level of information infrastructure construction and performs regression. The results are shown in column (3) of Table 6 . It can be seen that even if the samples of two provinces and regions with relatively developed information infrastructure construction levels are excluded, the positive impact of digital literacy on farmers' land transfer is still at the 10% level. is significant, indicating that the conclusions of the baseline model are robust. Table 6 Farmers’ digital literacy and land transfer: robustness test Variables land_leased Cultivated_land_leased land_leased (1) (2) (3) digital_literacy_av 0.040*** (0.010) digital_literacy 0.035* 0.035* (0.019) (0.020) Contorl YES YES YES Constant -0.794*** -1.078*** -1.035*** (0.116) (0.200) (0.202) Observations 11,085 4,271 4,225 4.4 Mechanism analysis In the above, we have proved that the improvement of digital literacy can promote farmers to transfer land, but the specific path that affects farmers' land transfer still needs further testing. Based on the theoretical analysis above and referring to the approach of Chen et al. ( 2020 ), we constructed a mechanism testing model for how digital literacy affects land transfer through farmers' non-agricultural employment and non-agricultural entrepreneurship. The mechanism analysis includes four steps: (1) Testing the impact of explanatory variables on explained variables. (2) Examining the impact of explanatory variables on mediating variables. (3) Including both explanatory variables and mediating variables in the regression. The benchmark regression verifies the first step. 4.4.1 Non-agricultural employment channels This article uses whether the farmer is engaged in non-agricultural employment (work_noag) as a variable to indicate that farmers are engaged in non-agricultural employment. The empirical results in column (1) of Table 7 show that the impact of digital literacy on non-agricultural employment of rural households is positively significant at the 1% significance level. This shows that digital literacy can influence farmers’ decisions to engage in non-agricultural employment. The empirical results in column (2) of Table 7 show that the impact of non-agricultural employment on farmers' land transfer is positively significant at the 1% significance level, which shows that engaging in non-agricultural employment is conducive to farmers' land transfer. Based on the benchmark regression results, it can be found that digital literacy can promote farmers to engage in non-agricultural employment, which in turn is conducive to farmers' land transfer. Hypothesis 2 was verified. 4.4.2 Non-agricultural Entrepreneurship Channels This paper examines the mechanism of non-agricultural entrepreneurship among farmers, using whether they engage in individual entrepreneurship (entrepreneurship) as an intermediary variable. The empirical results in column (3) of Table 7 show that the impact of digital literacy on farmers' non-agricultural entrepreneurship is positively significant at the 1% significance level. This shows that digital literacy can promote farmers to engage in non-agricultural entrepreneurship. The empirical results in column (4) of Table 7 show that the impact of non-agricultural entrepreneurship on farmers' transfer out is positively significant at the 1% significance level, which shows that engaging in non-agricultural entrepreneurship is conducive to farmers' transfer of land. Based on the benchmark regression results, it can be found that digital literacy can promote farmers to engage in non-agricultural entrepreneurship, which in turn is conducive to farmers' land transfer. Hypothesis 3 was verified. Table 7 Digital literacy and farmers’ land transfer: mechanism test Variables work_noag land_leased entrepreneurship land_leased (1) (2) (3) (4) digital_literacy 0.223*** 0.113*** (0.017) (0.019) work_noag 0.521*** (0.038) entrepreneurship 0.231*** (0.044) Contorl YES YES YES YES Constant -0.248 -1.149*** -2.567*** -0.764*** (0.178) (0.123) (0.203) (0.078) Observations 4,639 10,820 4,895 14,693 4.5 Heterogeneity analysis Above, we have proven that digital literacy has a significant role in promoting farmers’ land transfer, but it is unclear whether this impact differs under different macro and micro environments. Therefore, this article will next conduct heterogeneity analysis from macro and micro perspectives. At the macro level, this article studies the differences in the impact of digital literacy on farmers' land transfer under different regional conditions with different levels of economic development. At the micro level, this article analyzes how the labor endowment of rural households affects land transfer behavior in digital literacy. 4.5.1 Regions with different economic development levels In the above, we have proved that digital literacy can have a positive impact on farmers’ land transfer through non-agricultural employment and non-agricultural entrepreneurship, but we assumed that all farmers are in the same external non-agricultural employment and non-agricultural entrepreneurship environment. In theory, the level of regional economic development will affect farmers’ access to non-agricultural employment opportunities and the institutional costs of engaging in non-agricultural entrepreneurship. Therefore, we need to further analyze whether digital literacy has a heterogeneous effect on farmers’ land transfer from the perspective of regions with different economic development levels. According to the 2018 provincial per capita GDP data released by the National Bureau of Statistics of China, we divided the sample into economically developed regions and underdeveloped regions according to the median of provincial per capita GDP. Economically developed regions include "Shanghai, Inner Mongolia, Beijing, Sichuan, Tianjin, Anhui, Shandong, Guangdong, Jiangsu, Zhejiang, Hubei, Hunan, Fujian, Liaoning, Chongqing, and Shaanxi." Economically underdeveloped areas include "Hebei, Hainan, Shanxi, Jilin, Heilongjiang, Jiangxi, Henan, Guangxi, Guizhou, Yunnan, Tibet, Gansu, Qinghai, Ningxia and Xinjiang". According to data from the National Bureau of Statistics of China, calculations show that the average per capita GDP of economically developed and underdeveloped provinces or regions is 86912.35714 yuan/person and 45798.64706 yuan/person respectively. Therefore, there are significant differences in the level of economic development between economically developed and underdeveloped provinces or regions. The regression results are shown in columns (1) and (2) of Table 8 . Research results show that compared with economically developed areas, improving digital literacy is more effective in transferring land from farmers in underdeveloped areas. This may be because the secondary and tertiary industries such as the digital economy in economically developed areas are more developed, and digital literacy accelerates the flow of rural households in underdeveloped areas to economically developed areas through non-agricultural employment or non-agricultural entrepreneurship (Zhang et al., 2024 ). At the same time, the coefficient difference statistics (chi-square value) between columns (1) and (2) passed the 5% significance level test. This shows that under conditions of heterogeneous economic development, digital literacy can significantly promote farmers to transfer land. Table 8 Digital literacy and farmers’ land transfer: regional heterogeneity variables land_leased Developed provinces Developing provinces (1) (2) digital_literacy -0.025 0.071*** (0.032) (0.024) Control YES YES Constant -1.138*** -1.310*** (0.335) (0.240) Observations 1,527 2,800 chi-squared 5.70** 4.5.2 Different human capital endowments Human capital is not only an important factor affecting farmers’ land transfer behavior (Deininger and Jin, 2005 ), but also the basis for digital literacy to play a role (Bejaković and Mrnjavac, 2020 ). Therefore, this paper attempts to further evaluate the heterogeneity of digital literacy in affecting farmers’ land transfer under conditions of different labor force sizes, education levels, and aging levels. In terms of labor force size, this paper mainly uses the number of family members between the ages of 16 and 65 (labor_size). In terms of labor force education level, the head of the household is the leader of family decision-making, so this paper mainly considers the head of the household's education level (education). In terms of the degree of aging of the labor force, this paper mainly calculates the average age of the family labor force (aging_labor). The samples are divided according to the median of the above variables. Those above the median are classified as high human capital endowment group, and those below the median are classified as low human capital endowment group. The regression results are shown in columns (1) and (2) of Table 9 . At the level of labor force scale, digital literacy is more conducive to promoting land transfer in rural households with more labor force, rather than in rural households with less labor force. However, the coefficient difference (chi square value) between column (1) and column (2) did not pass the significance level test. This indicates that under conditions of heterogeneous labor force size, digital literacy does not significantly promote farmers' land transfer. The regression results are shown in columns (3) and (4) of Table 9 . From the perspective of the education level of household heads, compared with highly educated household heads, the improvement of digital literacy is more conducive to the land transfer of rural households with low education levels. Meanwhile, the coefficient differences (chi square values) between the groups in columns (3) and (4) passed the 10% significance test. This indicates that under the condition of heterogeneous education levels among household heads, digital literacy can significantly promote land transfer among farmers. The regression results are shown in columns (5) and (6) of Table 9 . In terms of the degree of aging of the labor force, the coefficient difference statistics (chi-square value) between groups in columns (5) and (6) did not pass the significance test. This shows that under the conditions of heterogeneity in the aging of the labor force, digital literacy cannot significantly promote the transfer of land by farmers. Table 9 Digital literacy and farmers’ land transfer: Human capital heterogeneity Variables land_leased labor size labor education aging labor Low LS High LS Low LE High LE Low AL High AL (1) (2) (3) (4) (5) (6) Digital_literacy 0.029 0.058** 0.102*** 0.026 0.040* 0.057* (0.026) (0.025) (0.039) (0.021) (0.023) (0.029) Control YES YES YES YES YES YES Constant -0.827*** -0.809*** -0.841** -0.964*** -0.957*** -1.178*** (0.317) (0.298) (0.372) (0.232) (0.233) (0.391) Observations 2,042 2,285 1,375 2,952 2,787 1,540 chi-squared 0.64 3.29* 0.24 5 Conclusion and recommendations 5.1 Conclusion and discussion In particular, we use the China Family Panel Studies (CFPS2018) data released by the China Social Science Survey Center to empirically analyze the impact of digital literacy on farmers' land transfer and the transmission path. At the same time, we also analyze the heterogeneity and marginal coefficient of digital literacy on farmers' land transfer under different economic environments and different labor endowments. The empirical analysis draws the following conclusions: First, in general, digital literacy has a significant role in promoting the transfer of farmers' land. For every percentage point increase in digital literacy, the probability of farmers transferring their land will increase by 3.5%. Second, digital literacy can promote the transfer of land by promoting farmers to engage in non-agricultural employment or non-agricultural entrepreneurship. Third, heterogeneity analysis shows that improving digital literacy is more conducive to farmers in underdeveloped areas transferring land. With the improvement of digital literacy, farmers with lower education levels are more likely to transfer their land. 5.2 Policy recommendations Based on the above analysis, we put forward the following suggestions. First, in promoting the transfer of rural land resources, in addition to continuing to increase investment in rural digital infrastructure and general digital application platforms, policy makers should also focus on improving farmers' digital literacy. Although more and more developing countries are aware of the important impact of digital technology on improving the efficiency of land resource allocation and agricultural productivity, improving farmers' ability to use digital technology is a very important prerequisite. Therefore, government departments need to improve farmers' digital literacy by leveraging the role of other institutions such as training institutions, schools, and social organizations. Secondly, non-agricultural employment and non-agricultural entrepreneurship are important ways for farmers to promote land transfer through digital literacy. Therefore, government departments should provide farmers with more job opportunities for non-agricultural employment and create a better non-agricultural entrepreneurial environment. For example, establish a non-agricultural employment information release platform to reduce the cost of farmers' non-agricultural employment information search and storage. Provide farmers with low-interest loans for non-agricultural entrepreneurship to reduce the capital threshold for farmers to engage in non-agricultural entrepreneurship. Finally, policies to improve farmers' digital literacy need to pay more attention to farmers in economically underdeveloped areas. Digital literacy has a greater marginal effect in improving farmers' land transfer in economically underdeveloped areas. At the same time, economically underdeveloped areas are often also areas with a lower proportion of land transfer. Therefore, paying more attention to improving the digital literacy of farmers in economically underdeveloped areas will be able to play a greater policy effect. 5.3 Limitations and future prospects This study has the following limitations. On the one hand, this paper only considers the sample of farmers in China, and further empirical tests are needed to determine whether the conclusions of this study are applicable to other developing countries. On the other hand, this paper only analyzes the impact of digital literacy on farmland transfer behavior. Further research is needed to determine whether digital literacy affects the duration and rent of rural land transfer contracts. Therefore, future research can be further expanded to the impact of digital literacy on the characteristics of farmers' land transfer contracts. Declarations Ethical approval: Ethical approval was not required as the study did not involve human participants. Informed consent: This article does not contain any studies with human participants performed by any of the authors. Competing interests: The authors declare no competing interests. Data availability All data generated or analysed during this study are included in this published article [and its supplementary information files] Author Contribution L.X.wrote the main manuscript text and L.Z.complete the design of the thesis.All authors reviewed the manuscript. Acknowledgement This work was supported by the National Natural Science Foundation of China (Grant No. 72363026; 71963026) and the Philosophy and Social Sciences Research Project of Jiangsu Province (Grant No. 2024SJYB0211). Data Availability All data generated or analysed during this study are included in this published article [and its supplementary information files] References Adamopoulos T, Brandt L, Leight J, et al. Misallocation, selection, and productivity: A quantitative analysis with panel data from China[J]. 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Wang J, Xin L, Wang Y. How farmers’ non-agricultural employment affects rural land circulation in China?[J]. Journal of geographical sciences, 2020, 30: 378-400. Wang Y, Wang H, Wen B, et al. Influences of Farmland Transfer Transaction Costs on Contract Choices from the Perspective of Differential Order Governance[J]. Authorea Preprints, 2024. Wang Y, Wang W, Jiang X, et al. The Impact of Farmers’ E-Commerce Adoption on Land Transfer: Evidence from Ten Provinces across China[J]. Land, 2024, 13(7): 1066. Wang Y L, Ellinger A D. Examining the relationships between information acquisition, entrepreneurial opportunity recognition, and innovation performance through the development and validation of a new measure to assess information acquisition in the high technology sector in Taiwan[J]. International Journal of Entrepreneurship and Innovation Management, 2009, 9(3): 313-342. Yang F, Liu W, Wen T. The rural household’s entrepreneurship under the land certification in China[J]. 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Introduction","content":"\u003cp\u003eLand transfer is critical for enhancing agricultural productivity in developing countries (Lin, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; McMillan et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), improving resource mismatch (Adamopoulos et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and improving farmers' welfare (Keswell and Carter, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, the problem of land fragmentation is still serious in developing countries. Taking China as an example, the relevant data of the 2022 \"China Rural Policy and Reform Statistical Yearbook\" show that the number of farmers operating less than 10 mu of cultivated land still accounts for 74.6%. Many scholars have confirmed that this problem is also common in developing countries such as Estonia (Looga et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Kenya and Uganda (Djurfeldt, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, how to further promote land transfer in developing countries is of great significance for promoting agricultural economic growth and alleviating poverty in developing countries.\u003c/p\u003e \u003cp\u003eAlthough it is very important to realize land transfer, farmers cannot easily transfer land due to various constraints, including insufficient non-agricultural employment opportunities, high threshold for non-agricultural entrepreneurship, unstable land property rights, and asymmetric information on land transfer (Wang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hu and Chen, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rogers et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Huy et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In particular, non-agricultural employment or entrepreneurship has been identified as one of the main obstacles. Wang et al (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) pointed out in their study of China that non-agricultural employment opportunities will increase the opportunity cost of farmers not transferring land, thereby increasing farmers' willingness to transfer land. At the same time, farmers' non-agricultural entrepreneurship will reduce the labor force engaged in agricultural production in the family, which will promote land transfer (Hu and Chen, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, how to increase non-agricultural employment opportunities and enhance farmers' non-agricultural entrepreneurship capabilities will be conducive to promoting land transfer.\u003c/p\u003e \u003cp\u003eWith the continuous improvement of digital infrastructure, digital literacy is believed to greatly facilitate the enhancement of farmers' non-agricultural employment capabilities and the improvement of non-agricultural entrepreneurship capabilities. For example, the improvement of digital literacy is conducive to farmers' use of tools such as mobile phones to obtain non-agricultural employment market information and reduce their transaction costs for obtaining non-agricultural employment (Zhou et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A series of studies also show that digital literacy will also enhance farmers' ability to identify entrepreneurial opportunities, broaden the path mechanism for obtaining resources such as credit, increase the probability of farmers engaging in non-agricultural entrepreneurship, and improve farmers' entrepreneurial performance (Ji and Zhuang, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bai et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, few studies have explored the relationship between digital literacy and farmers' land transfer.\u003c/p\u003e \u003cp\u003eTherefore, the main purpose of this study is to use the data from the China Family Panel Studies (CFPS2018) to analyze the relationship between digital literacy and farmers' land transfer behavior. At the same time, this study further analyzes the impact mechanism of digital literacy on farmers' land transfer behavior.\u003c/p\u003e \u003cp\u003eThis paper contributes to the literature in several ways.\u003c/p\u003e \u003cp\u003eFirst, this paper pioneered the analysis of the impact of digital literacy on farmers' land transfer. Although some scholars have recognized that digital technology has an important impact on farmers' land transfer behavior (Zou and Mishra, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the existing literature mainly emphasizes the impact of digital tool supply factors on land transfer. Digital literacy emphasizes the impact of digital tool demanders\u0026mdash;farmers' ability to use digital tools on land transfer. Lack of digital literacy will affect farmers' level of use of digital tools and actual use effects, and create a digital use gap (Ragnedda, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, even if the level of digital tool supply is high enough, insufficient digital literacy will affect farmers' use of digital tools to transfer land.\u003c/p\u003e \u003cp\u003eSecond, this paper uses the instrumental variable method and the omitted variable evaluation model to solve the endogeneity problem. Farmers' use of digital tools depends not only on the level of digital literacy of farmers, but also on the result of self-selection. Therefore, the literature analyzing the impact of digital tool use on land transfer (Zou and Mishra, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) is prone to serious endogeneity problems. Some literature does not consider the endogeneity problem in empirical analysis (Zou and Mishra, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although another part of the literature uses the PSM model for endogeneity treatment (Wang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the PSM method cannot alleviate the selection bias caused by unobserved factors. This study employs strictly exogenous surface relief (Zhou et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) as an instrumental variable to solve the endogeneity problem caused by two-way causality, but also uses the omitted variable evaluation model (Altonji et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) to evaluate the impact of the omitted variable problem.\u003c/p\u003e \u003cp\u003eThird, this article examines the impact of digital literacy on land transfer through non-agricultural employment and non-agricultural entrepreneurship paths, filling the gap in research on the land transfer mechanism of digital literacy. Limited research has explored the relationship between digital tools and land transfers. For example, Wang et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that farmers' use of e-commerce significantly promoted land transfer, but did not further verify the mechanism by which e-commerce affects land transfer. Although another part of the literature found that Internet use has a positive impact on non-agricultural employment (Zou and Mishra, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), it did not verify the impact of non-agricultural employment on land transfers, and ignored the impact of non-agricultural entrepreneurship on land transfers. This article constructs an analytical framework for the impact of digital literacy on land transfer through non-agricultural employment and non-agricultural entrepreneurship paths, thus further improving and expanding the study of digital literacy on land transfer.\u003c/p\u003e \u003cp\u003eThe rest of this paper is structured as follows. In Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, we propose hypotheses based on relevant literature and theory. In Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we introduce variables such as digital literacy and farmers\u0026rsquo; land transfer, as well as the empirical model and data used. In Section \u003cspan refid=\"Sec10\" class=\"InternalRef\"\u003e4\u003c/span\u003e, we provide quantitative results to explore the impact of digital literacy on farmers\u0026rsquo; land transfer. At the same time, we analyze the impact mechanism of digital literacy on farmers\u0026rsquo; land transfer and evaluate the heterogeneous impact of digital literacy on farmers\u0026rsquo; land transfer behavior. Finally, this paper gives relevant research conclusions and discussions.\u003c/p\u003e"},{"header":"2. Theoretical analysis and research hypothesis","content":"\u003cp\u003eInformation asymmetry is an important factor affecting the effective and reasonable allocation of resources in the market economy. In the traditional rural land transfer market, farmers are often at a disadvantage in obtaining information, and the agricultural market information they receive is delayed and distorted (Li et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, in the traditional rural land transfer market, due to the limitation of information transmission, the land transfer objects are mostly acquaintances within the village committee (Qiu et al., 2020), which to a certain extent hinders the development of the land transfer market. However, the improvement of farmers' digital literacy is conducive to the release of land transfer information by both land supply and demand parties through the Internet, and broadens the boundaries of the land transfer transaction market. At the same time, the traditional rural land transfer market mostly uses informal contracts such as oral contracts for land transfer (Fan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which to a certain extent increases the transaction costs of land transfer contracts afterwards (Wang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ito, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The improvement of farmers' digital literacy is conducive to farmers forming formal contracts with clearer rights and obligations through digital land transfer service platforms (Fan et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), thereby promoting the development of the land transfer market.\u003c/p\u003e \u003cp\u003eBased on the above theoretical analysis, digital literacy mainly improves the information transmission boundary of farmers' land transfer and promotes the formalization of land transfer contracts, thereby promoting farmers' land transfer. Therefore, we first propose hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 1\u003c/strong\u003e \u003cp\u003eDigital literacy promotes farmers\u0026rsquo; land transfer\u003c/p\u003e \u003c/p\u003e \u003cp\u003eNon-agricultural employment is considered an important factor in promoting the transfer of rural land. On the one hand, when non-agricultural employment income has a comparative advantage over agricultural employment income, agricultural employment faces higher opportunity costs (Liangjie, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); on the other hand, non-agricultural employment will promote the transfer of rural labor from rural areas to urban areas (Wang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which will further promote the transfer of rural land.\u003c/p\u003e \u003cp\u003eThe improvement of farmers' digital literacy will improve the transfer of rural land by encouraging farmers to engage in non-agricultural employment. First, the improvement of farmers' digital literacy will help farmers obtain non-agricultural employment information through the Internet. Traditional farmers mainly rely on social network scale factors such as relatives and friends to obtain non-agricultural employment information (Baird and Gray., 2014). The improvement of farmers' digital literacy has prompted farmers to use the Internet or mobile communication tools, breaking the restrictions of traditional social network scale on farmers' access to non-agricultural employment information, reducing farmers' search costs for non-agricultural employment information, and promoting farmers to engage in non-agricultural employment. Secondly, the continuous development of the digital economy has put forward higher requirements for farmers engaged in non-agricultural employment to have digital skills. Having higher digital literacy will not only have a positive impact on farmers' employment ability, efficiency and productivity, but also increase the possibility of farmers finding jobs, promotions or salary increases (Bejaković and Mrnjavac, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, this paper proposes hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 2\u003c/strong\u003e \u003cp\u003eImproving digital literacy can incentivize farmers to transfer their land by encouraging them to engage in non-agricultural employment mechanisms.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFarmers\u0026rsquo; entrepreneurship is also an important factor in promoting land transfer. In particular, with the improvement of land property rights stability, the possibility of farmers\u0026rsquo; entrepreneurship has greatly increased, which in turn has strengthened farmers\u0026rsquo; willingness to transfer land (Yang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe improvement of farmers' digital literacy will improve land transfer by promoting farmers to engage in entrepreneurship. First, the improvement of farmers' digital literacy promotes farmers' ability to obtain information related to entrepreneurship. Information acquisition is considered to be crucial to the identification of entrepreneurial opportunities (Wang and Ellinger, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In the traditional entrepreneurial market, due to geographical constraints, farmers generally have a lag in obtaining information, which hinders the development of entrepreneurial activities (Tan and Li, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The improvement of farmers' digital literacy will undoubtedly enhance farmers' ability to obtain information using Internet tools and break the impact of information barriers on farmers' entrepreneurial behavior. Secondly, the continuous development of the digital economy has prompted entrepreneurs to shift their entrepreneurial direction from traditional entrepreneurial fields to Internet fields such as e-commerce. The improvement of entrepreneurs' digital literacy will stimulate entrepreneurs' endogenous motivation to engage in Internet entrepreneurship, thereby enhancing their willingness to engage in Internet entrepreneurship (Mugiono et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, the improvement of farmers' digital literacy will broaden the scope of farmers' non-agricultural entrepreneurship and thereby increase the possibility of non-agricultural entrepreneurship. Therefore, this paper proposes Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis 3\u003c/strong\u003e \u003cp\u003eImproving digital literacy can incentivize farmers to transfer their land by promoting non-agricultural entrepreneurship mechanisms.\u003c/p\u003e \u003c/p\u003e"},{"header":"3. Materials and methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data source\u003c/h2\u003e \u003cp\u003eThis study uses data from the China Family Panel Studies (CFPS) conducted by the Center for Chinese Social Science Survey at Peking University from June 2018 to March 2019. This survey collects data at three levels: individuals, families, and communities, reflecting changes in Chinese families in terms of economic and non-economic welfare. The China Family Panel Studies is a nationwide, large-scale, multidisciplinary social tracking survey project that records information on sample families in terms of economic activities, educational outcomes, and family relationships. At the same time, the survey uses implicit stratification and multi-stage equal probability sampling methods, covering household data from 25 provinces (municipalities, and autonomous regions) in China, which can fully reflect the digital literacy and land transfer status of Chinese farmers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Variable selection\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Explained variable\u003c/h2\u003e \u003cp\u003eRegarding the measurement of land transfer, existing studies have used indicators such as \"land transfer in\", \"land transfer out\" and \"land transfer in or out\" for measurement (Zou and Mishra, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rogers et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Huy et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This paper mainly studies the land transfer behavior of farmers, so it refers to the existing literature (Zou and Mishra, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and defines the transfer behavior of farmers based on the question of \"whether to rent out the land to others\".\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Core explanatory variables\u003c/h2\u003e \u003cp\u003eThe core explanatory variable is farmers' digital literacy, which reflects the ability or attitude of farmers to use digital devices for work, study, entertainment, socializing with others, etc. Referring to the existing literature (Zhou et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), this paper measures farmers' digital literacy level from five dimensions: \"frequency of using the Internet for learning\", \"frequency of using the Internet for work\", \"frequency of using the Internet for socializing\", \"frequency of using the Internet for entertainment\", and \"frequency of using the Internet for business\". The measurement range of frequency of use is from \"never\" to \"almost every day\", with a score of 1\u0026ndash;7. On this basis, the measured values of the five questions are averaged to obtain the digital literacy score of the head of the farmer's household. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the evaluation dimensions of digital literacy.\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\u003eEvaluation dimensions of digital literacy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable Description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital_social\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency of using the Internet for social networking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital_work\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency of using the Internet for work\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital_study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency of using the Internet for learning\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital_business\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency of using the Internet for commercial activities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital_entertainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency of using the Internet for entertainment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital_literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe average of the above five items\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 \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e3.2.3 Control variables\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eReferring to existing literature (Rogers et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cao et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zou and Mishra, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), this paper selects control variables from three levels: individual, family, and region, to avoid the deviation of the estimation results of the regression model caused by differences in individual, family, and regional factors.\u003c/p\u003e \u003cp\u003eAt the individual level, this paper controls the basic characteristics of the household head, such as age (age), gender (sex), education level (edu), marital status (marry), health (health), etc. At the family level, this paper controls the size of family members (family_size), per capita net income of the family (av_inc), whether financial assets are held (finance), and the proportion of labor to family size (labor_rate). At the regional level, this paper controls whether it is in the eastern region (east_region) or the western region (west_region).\u003c/p\u003e \u003cp\u003eVariable descriptions and descriptive statistics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003eVariable description\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDependent variables\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eland_leased\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether to transfer the land (yes\u0026thinsp;=\u0026thinsp;1; no\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eCore Explanatory Variable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edigital_literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital literacy level of farmers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eControl Variables\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender of household head (male\u0026thinsp;=\u0026thinsp;1; female\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.492\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 \u003cp\u003eEducation level of household head (1\u0026thinsp;=\u0026thinsp;primary school or below; 2\u0026thinsp;=\u0026thinsp;junior high school; 3\u0026thinsp;=\u0026thinsp;high school; 4\u0026thinsp;=\u0026thinsp;college or above)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emarry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether the head of household is married (yes\u0026thinsp;=\u0026thinsp;1; no\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth status of the household head (1\u0026thinsp;=\u0026thinsp;very healthy; 2\u0026thinsp;=\u0026thinsp;very healthy; 3\u0026thinsp;=\u0026thinsp;relatively healthy; 4\u0026thinsp;=\u0026thinsp;average; 5\u0026thinsp;=\u0026thinsp;unhealthy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efinance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhether the household holds financial assets (yes\u0026thinsp;=\u0026thinsp;1; no\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elabor rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRatio of household labor force to household population size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeast region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEastern region (yes\u0026thinsp;=\u0026thinsp;1; no\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewest region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWestern region (yes\u0026thinsp;=\u0026thinsp;1; no\u0026thinsp;=\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.482\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=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Empirical model\u003c/h2\u003e \u003cp\u003eTo evaluate the impact of digital literacy on farmers' land transfer, this paper constructs a binary Probit model as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$land\\_leased_{i}^{*}={\\alpha _0}+{\\beta _1}digital\\_literac{y_i}+{\\beta _2}control\\_va{r_i}+{\\varepsilon _i}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(land\\_lease{d_i}=1(land\\_leased_{{_{i}}}^{*}\u0026gt;0)\\)\u003c/span\u003e \u003c/span\u003e \u003c/p\u003e \u003cp\u003eIn the above formula, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(land\\_lease{d_i}\\)\u003c/span\u003e\u003c/span\u003erepresents the land transfer behavior of farmers.\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(land\\_leased_{{_{i}}}^{*}\\)\u003c/span\u003e\u003c/span\u003erepresents a latent variable. if \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(land\\_leased_{{_{i}}}^{*}\u0026gt;0\\)\u003c/span\u003e\u003c/span\u003e, then \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(land\\_lease{d_i}\\)\u003c/span\u003e\u003c/span\u003ethe value is 1, otherwise it is 0. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(digital\\_literac{y_i}\\)\u003c/span\u003e\u003c/span\u003erepresents the digital literacy level of farmers. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(control\\_{\\operatorname{var} _i}\\)\u003c/span\u003e\u003c/span\u003erepresents the control variables at the individual level, family level and regional level, and represents the random disturbance term. If the coefficient is significantly positive, it means that digital literacy will be beneficial to farmers\u0026rsquo; land transfer.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Empirical results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Regression analysis\u003c/h2\u003e \u003cp\u003eIn order to verify whether digital literacy is conducive to farmers' land transfer, this article adopts the stepwise regression method for regression. In columns 1 to 4 of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, individual-level, household-level, and regional-level control variables are gradually added. It can be seen from the empirical results in column 4 of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e that, with the addition of individual-level, household-level and regional-level control variables, the average marginal effect of digital literacy on farmers' land transfer behavior is 0.035, and at the 10% significance level Significantly. It shows that for every unit increase in the digital literacy level of farmers, the probability of farmers transferring land increases by 3.5%. At the same time, it can be seen from the empirical results in columns 1, 2 and 3 of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e that, while gradually controlling individual-level, household-level and regional-level control variables, the average marginal effect of digital literacy on whether farmers transfer their land is still It is positively significant below the 10% significance level. This shows that the conclusion that digital literacy has a significant positive impact on farmers\u0026rsquo; land transfer is relatively robust, and hypothesis \u003cspan refid=\"FPar1\" class=\"InternalRef\"\u003e1\u003c/span\u003e has been verified.\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\u003eDigital literacy and farmers\u0026rsquo; land transfer: basic regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eLand_leased\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edigital_literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.038**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.037*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035*\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(0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.019)\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\u003e0.007***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005*\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(0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.003)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esex\u003c/p\u003e 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colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.056**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.041\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(0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.028)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emarry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.093\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(0.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.067)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.068)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.023\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(0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efamily_size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.031**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.027*\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(0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efinance\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\u003e0.493***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.476***\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(0.153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.154)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elabor_rate\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-0.244**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.219*\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(0.112)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.113)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeast_region\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\u003e-0.080\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.058)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewest_region\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\u003e-0.285***\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.058)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.166***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.555***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.220***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.054***\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(0.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.199)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,327\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 \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Endogeneity analysis\u003c/h2\u003e \u003cp\u003ePrevious results demonstrate that digital literacy increases land transfer likelihood. However, this conclusion may have a reverse causal problem. Farmers who transfer their land tend to go to urban areas with higher levels of digital infrastructure to engage in non-agricultural employment or entrepreneurship, thereby acquiring higher levels of digital literacy. Another issue that needs to be considered is the problem of omitted variables. For example, farmers' use of digital tools for consumption, entertainment, and work may be affected by regional customs.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e4.2.1 Testing for reverse causality\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn order to overcome the possible reverse causality problem between digital literacy and farmers' land transfer behavior, we conducted a two-stage least squares estimation (2SLS) using instrumental variables to address potential biases in the estimation results.\u003c/p\u003e \u003cp\u003eReferring to existing literature (Zhou et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; You et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), this paper uses county land surface undulation (RDLS) as an instrumental variable. In terms of correlation, the greater the county land surface undulation, the higher the cost of digital infrastructure construction, and the lower the digital literacy level of farmers. In terms of exogeneity, county land surface undulation will not have a reverse causal relationship with farmers' land transfer behavior.\u003c/p\u003e \u003cp\u003eFrom the empirical results in columns 1 and 3 of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, it can be seen that county land surface relief and farmers' digital literacy level are negatively significant at the 1% significance level. Both the overidentification test and the weak instrumental variable test hold. It shows that the county surface relief is an ideal instrumental variable. At the same time, according to the empirical results in column 4 of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, after using instrumental variables, the marginal coefficient of farmers' digital literacy increased from 0.035 in the baseline regression to 0.131 in column 2 of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. This shows that ignoring the endogeneity problem will lead to underestimating the role of digital literacy in promoting farmers\u0026rsquo; land transfer.\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\u003eDigital literacy and farmers\u0026rsquo; land transfer: instrumental variable regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst stage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecond stage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFirst stage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSecond stage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\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 \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edigital_literacy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eland_leased\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edigital_literacy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eland_leased\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erdls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.1467***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.1431***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0256)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0342)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edigital_literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2197***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1310*\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(0.0596)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0728)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0366***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0059**\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(0.0022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0027)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esex\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\u003e0.1084**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0209\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(0.0429)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0164)\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5810***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0626\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(0.0233)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0439)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emarry\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-0.1002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0128\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(0.0607)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0214)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehealth\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-0.0264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0085\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(0.0185)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0066)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efamily_size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0012\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(0.0122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0042)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efinance\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\u003e0.7888***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0753\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(0.1594)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0834)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLabor_rate\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\u003e0.4162***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0631\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(0.1035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0448)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeast_region\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-0.1046*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0054\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(0.0536)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0209)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewest_region\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\u003e0.1109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0524***\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(0.0734)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0189)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1815***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.7243***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.1247***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.4036\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(0.0366)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.2372)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.1656)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.2980)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnderson canon. corr. LM statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e28.922\u003c/p\u003e \u003cp\u003e(0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e19.394\u003c/p\u003e \u003cp\u003e(0.0000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCragg-Donald Wald F statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e29.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e19.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.7544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.1571\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 \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Testing for omitted variables\u003c/h2\u003e \u003cp\u003eIn order to evaluate the severity of the possible omitted variable problem, this paper mainly refers to the existing literature (Altonji et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Specifically: First, two sets of regression models are designed, one set is a constrained control variable model, and the other set is a full control variable model. Secondly, the core explanatory variable coefficients of the two sets of regression models are calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\widehat {{{\\beta _R}}}\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\widehat {{{\\beta _F}}}\\)\u003c/span\u003e\u003c/span\u003e. Finally, the change rate of the core explanatory variable coefficient between the control variable model and the full control variable model is calculated\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Ratio=\\left| {{{\\widehat {{{\\beta _F}}}} \\mathord{\\left/ {\\vphantom {{\\widehat {{{\\beta _F}}}} {(\\widehat {{{\\beta _R}}} - \\widehat {{{\\beta _F}}})}}} \\right. \\kern-0pt} {(\\widehat {{{\\beta _R}}} - \\widehat {{{\\beta _F}}})}}} \\right|\\)\u003c/span\u003e\u003c/span\u003e. The larger the change coefficient, the less the core explanatory variable is affected by the omitted variable problem.\u003c/p\u003e \u003cp\u003eBased on the above principles, this article designs two sets of omitted variable evaluation models. The first model only includes the core explanatory variables and no additional control variables. The second model includes core explanatory variables and individual level control variables. The third model includes core explanatory variables and control variables at the individual and family levels. The fourth model includes core explanatory variables and control variables at the individual, family, and regional levels.\u003c/p\u003e \u003cp\u003eResults in the last row of Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show that, we can see that from the ratio of the last row of the empirical results in the first and second columns, in the first set of omitted variable evaluation models, the deviation caused by the omitted variables in the estimation results will only occur when the potential impact of the omitted variables on the model is at least 39 times that of the control variables in the existing model. In the second set of omitted variable evaluation models, the change rate of the core explanatory variable coefficient between the control variable model and the full control variable model is also 17.5. This shows that the digital literacy of farmers is little affected by the omitted variable problem, and the estimation results of the benchmark regression in this paper are relatively robust.\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\u003eDigital literacy and farmers\u0026rsquo; land transfer: omitted variable problem test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eland_leased\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGroup1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eGroup2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edigital_literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.038**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.037*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035*\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(0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual control variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily control variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegional control variables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.166***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.555***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.220***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.054***\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(0.068)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.194)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.199)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,327\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\u003e0.0015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e17.5\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=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Robustness analysis\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e4.3.1 Replace explanatory variables\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn the above, this article mainly uses the digital literacy level of the user as the core explanatory variable. However, the decision-making process of farm households may also be affected by the digital literacy level of other laborers in the family. Therefore, this article uses the average digital literacy level of the farm household labor force to replace the core explanatory variables and conduct a robustness test. The results are shown in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, where the estimated coefficient and significance of digital literacy do not change greatly, which shows that the conclusions of the baseline model are robust.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e4.3.2 Eliminate non-cultivated land transfer samples\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn the above, this paper regards the transfer of land such as \"cultivated land, forest land, pasture and pond\" as the transfer of land by farmers. However, for most farmers, cultivated land is the most important land resource. Therefore, this paper only retains the sample of farmers who \"received cultivated land from the collective\" and conducts a robustness test. The results are shown in column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. It can be seen that when the sample of farmers who \"received cultivated land from the collective\" is used for regression, digital literacy has a promoting effect on farmers' land transfer, which shows that the conclusion of the baseline model is robust.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e4.3.3 Excluding samples from regions with developed digital infrastructure\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe formation of farmers' digital literacy is easily affected by the level of external digital infrastructure. The more developed the digital infrastructure is, the higher the digital literacy level of farmers may be. In order to avoid the impact of samples from provinces or regions with more developed digital infrastructure on the overall regression results. According to the \"Digital China Construction and Development Report (2018)\" released by the Cyberspace Administration of China, the two provinces or regions with the highest scores in China's information infrastructure are Shanghai and Zhejiang. This article eliminates the samples from the two provinces and regions with the highest level of information infrastructure construction and performs regression. The results are shown in column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. It can be seen that even if the samples of two provinces and regions with relatively developed information infrastructure construction levels are excluded, the positive impact of digital literacy on farmers' land transfer is still at the 10% level. is significant, indicating that the conclusions of the baseline model are robust.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFarmers\u0026rsquo; digital literacy and land transfer: robustness test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eland_leased\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCultivated_land_leased\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eland_leased\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edigital_literacy_av\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.040***\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.010)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edigital_literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.035*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.035*\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(0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContorl\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.794***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.078***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.035***\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(0.116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.202)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,225\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=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Mechanism analysis\u003c/h2\u003e \u003cp\u003eIn the above, we have proved that the improvement of digital literacy can promote farmers to transfer land, but the specific path that affects farmers' land transfer still needs further testing. Based on the theoretical analysis above and referring to the approach of Chen et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), we constructed a mechanism testing model for how digital literacy affects land transfer through farmers' non-agricultural employment and non-agricultural entrepreneurship. The mechanism analysis includes four steps: (1) Testing the impact of explanatory variables on explained variables. (2) Examining the impact of explanatory variables on mediating variables. (3) Including both explanatory variables and mediating variables in the regression. The benchmark regression verifies the first step.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e4.4.1 Non-agricultural employment channels\u003c/h2\u003e \u003cp\u003eThis article uses whether the farmer is engaged in non-agricultural employment (work_noag) as a variable to indicate that farmers are engaged in non-agricultural employment. The empirical results in column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e show that the impact of digital literacy on non-agricultural employment of rural households is positively significant at the 1% significance level. This shows that digital literacy can influence farmers\u0026rsquo; decisions to engage in non-agricultural employment. The empirical results in column (2) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e show that the impact of non-agricultural employment on farmers' land transfer is positively significant at the 1% significance level, which shows that engaging in non-agricultural employment is conducive to farmers' land transfer. Based on the benchmark regression results, it can be found that digital literacy can promote farmers to engage in non-agricultural employment, which in turn is conducive to farmers' land transfer. Hypothesis \u003cspan refid=\"FPar2\" class=\"InternalRef\"\u003e2\u003c/span\u003e was verified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e4.4.2 Non-agricultural Entrepreneurship Channels\u003c/h2\u003e \u003cp\u003eThis paper examines the mechanism of non-agricultural entrepreneurship among farmers, using whether they engage in individual entrepreneurship (entrepreneurship) as an intermediary variable. The empirical results in column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e show that the impact of digital literacy on farmers' non-agricultural entrepreneurship is positively significant at the 1% significance level. This shows that digital literacy can promote farmers to engage in non-agricultural entrepreneurship. The empirical results in column (4) of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e show that the impact of non-agricultural entrepreneurship on farmers' transfer out is positively significant at the 1% significance level, which shows that engaging in non-agricultural entrepreneurship is conducive to farmers' transfer of land. Based on the benchmark regression results, it can be found that digital literacy can promote farmers to engage in non-agricultural entrepreneurship, which in turn is conducive to farmers' land transfer. Hypothesis \u003cspan refid=\"FPar3\" class=\"InternalRef\"\u003e3\u003c/span\u003e was verified.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDigital literacy and farmers\u0026rsquo; land transfer: mechanism test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ework_noag\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eland_leased\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eentrepreneurship\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eland_leased\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edigital_literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.223***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.113***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ework_noag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.521***\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 \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(0.038)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eentrepreneurship\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\u003e0.231***\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.044)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContorl\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.149***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.567***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.764***\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(0.178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.203)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.078)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14,693\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=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e4.5 Heterogeneity analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eAbove, we have proven that digital literacy has a significant role in promoting farmers\u0026rsquo; land transfer, but it is unclear whether this impact differs under different macro and micro environments. Therefore, this article will next conduct heterogeneity analysis from macro and micro perspectives. At the macro level, this article studies the differences in the impact of digital literacy on farmers' land transfer under different regional conditions with different levels of economic development. At the micro level, this article analyzes how the labor endowment of rural households affects land transfer behavior in digital literacy.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e4.5.1 Regions with different economic development levels\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn the above, we have proved that digital literacy can have a positive impact on farmers\u0026rsquo; land transfer through non-agricultural employment and non-agricultural entrepreneurship, but we assumed that all farmers are in the same external non-agricultural employment and non-agricultural entrepreneurship environment. In theory, the level of regional economic development will affect farmers\u0026rsquo; access to non-agricultural employment opportunities and the institutional costs of engaging in non-agricultural entrepreneurship. Therefore, we need to further analyze whether digital literacy has a heterogeneous effect on farmers\u0026rsquo; land transfer from the perspective of regions with different economic development levels.\u003c/p\u003e \u003cp\u003eAccording to the 2018 provincial per capita GDP data released by the National Bureau of Statistics of China, we divided the sample into economically developed regions and underdeveloped regions according to the median of provincial per capita GDP. Economically developed regions include \"Shanghai, Inner Mongolia, Beijing, Sichuan, Tianjin, Anhui, Shandong, Guangdong, Jiangsu, Zhejiang, Hubei, Hunan, Fujian, Liaoning, Chongqing, and Shaanxi.\" Economically underdeveloped areas include \"Hebei, Hainan, Shanxi, Jilin, Heilongjiang, Jiangxi, Henan, Guangxi, Guizhou, Yunnan, Tibet, Gansu, Qinghai, Ningxia and Xinjiang\". According to data from the National Bureau of Statistics of China, calculations show that the average per capita GDP of economically developed and underdeveloped provinces or regions is 86912.35714 yuan/person and 45798.64706 yuan/person respectively. Therefore, there are significant differences in the level of economic development between economically developed and underdeveloped provinces or regions.\u003c/p\u003e \u003cp\u003eThe regression results are shown in columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Research results show that compared with economically developed areas, improving digital literacy is more effective in transferring land from farmers in underdeveloped areas. This may be because the secondary and tertiary industries such as the digital economy in economically developed areas are more developed, and digital literacy accelerates the flow of rural households in underdeveloped areas to economically developed areas through non-agricultural employment or non-agricultural entrepreneurship (Zhang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). At the same time, the coefficient difference statistics (chi-square value) between columns (1) and (2) passed the 5% significance level test. This shows that under conditions of heterogeneous economic development, digital literacy can significantly promote farmers to transfer land.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDigital literacy and farmers\u0026rsquo; land transfer: regional heterogeneity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003evariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eland_leased\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeveloped provinces\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDeveloping provinces\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edigital_literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.071***\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(0.032)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.138***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.310***\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(0.335)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.240)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echi-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5.70**\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 \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e4.5.2 Different human capital endowments\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eHuman capital is not only an important factor affecting farmers\u0026rsquo; land transfer behavior (Deininger and Jin, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), but also the basis for digital literacy to play a role (Bejaković and Mrnjavac, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, this paper attempts to further evaluate the heterogeneity of digital literacy in affecting farmers\u0026rsquo; land transfer under conditions of different labor force sizes, education levels, and aging levels.\u003c/p\u003e \u003cp\u003eIn terms of labor force size, this paper mainly uses the number of family members between the ages of 16 and 65 (labor_size). In terms of labor force education level, the head of the household is the leader of family decision-making, so this paper mainly considers the head of the household's education level (education). In terms of the degree of aging of the labor force, this paper mainly calculates the average age of the family labor force (aging_labor). The samples are divided according to the median of the above variables. Those above the median are classified as high human capital endowment group, and those below the median are classified as low human capital endowment group.\u003c/p\u003e \u003cp\u003eThe regression results are shown in columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. At the level of labor force scale, digital literacy is more conducive to promoting land transfer in rural households with more labor force, rather than in rural households with less labor force. However, the coefficient difference (chi square value) between column (1) and column (2) did not pass the significance level test. This indicates that under conditions of heterogeneous labor force size, digital literacy does not significantly promote farmers' land transfer.\u003c/p\u003e \u003cp\u003eThe regression results are shown in columns (3) and (4) of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. From the perspective of the education level of household heads, compared with highly educated household heads, the improvement of digital literacy is more conducive to the land transfer of rural households with low education levels. Meanwhile, the coefficient differences (chi square values) between the groups in columns (3) and (4) passed the 10% significance test. This indicates that under the condition of heterogeneous education levels among household heads, digital literacy can significantly promote land transfer among farmers.\u003c/p\u003e \u003cp\u003eThe regression results are shown in columns (5) and (6) of Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. In terms of the degree of aging of the labor force, the coefficient difference statistics (chi-square value) between groups in columns (5) and (6) did not pass the significance test. This shows that under the conditions of heterogeneity in the aging of the labor force, digital literacy cannot significantly promote the transfer of land by farmers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDigital literacy and farmers\u0026rsquo; land transfer: Human capital heterogeneity\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\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eland_leased\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003elabor size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003elabor education\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eaging labor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow LS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh LS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow LE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh LE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLow AL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh AL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\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 \u003cp\u003eDigital_literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.058**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.102***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.040*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.057*\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(0.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.029)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\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\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.827***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.809***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.841**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.964***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.957***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.178***\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(0.317)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.298)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.372)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.232)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.233)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.391)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2,787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,540\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echi-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.29*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.24\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"},{"header":"5 Conclusion and recommendations","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e5.1 Conclusion and discussion\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eIn particular, we use the China Family Panel Studies (CFPS2018) data released by the China Social Science Survey Center to empirically analyze the impact of digital literacy on farmers' land transfer and the transmission path. At the same time, we also analyze the heterogeneity and marginal coefficient of digital literacy on farmers' land transfer under different economic environments and different labor endowments.\u003c/p\u003e \u003cp\u003eThe empirical analysis draws the following conclusions: First, in general, digital literacy has a significant role in promoting the transfer of farmers' land. For every percentage point increase in digital literacy, the probability of farmers transferring their land will increase by 3.5%. Second, digital literacy can promote the transfer of land by promoting farmers to engage in non-agricultural employment or non-agricultural entrepreneurship. Third, heterogeneity analysis shows that improving digital literacy is more conducive to farmers in underdeveloped areas transferring land. With the improvement of digital literacy, farmers with lower education levels are more likely to transfer their land.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e5.2 Policy recommendations\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eBased on the above analysis, we put forward the following suggestions. First, in promoting the transfer of rural land resources, in addition to continuing to increase investment in rural digital infrastructure and general digital application platforms, policy makers should also focus on improving farmers' digital literacy. Although more and more developing countries are aware of the important impact of digital technology on improving the efficiency of land resource allocation and agricultural productivity, improving farmers' ability to use digital technology is a very important prerequisite. Therefore, government departments need to improve farmers' digital literacy by leveraging the role of other institutions such as training institutions, schools, and social organizations. Secondly, non-agricultural employment and non-agricultural entrepreneurship are important ways for farmers to promote land transfer through digital literacy. Therefore, government departments should provide farmers with more job opportunities for non-agricultural employment and create a better non-agricultural entrepreneurial environment. For example, establish a non-agricultural employment information release platform to reduce the cost of farmers' non-agricultural employment information search and storage. Provide farmers with low-interest loans for non-agricultural entrepreneurship to reduce the capital threshold for farmers to engage in non-agricultural entrepreneurship. Finally, policies to improve farmers' digital literacy need to pay more attention to farmers in economically underdeveloped areas. Digital literacy has a greater marginal effect in improving farmers' land transfer in economically underdeveloped areas. At the same time, economically underdeveloped areas are often also areas with a lower proportion of land transfer. Therefore, paying more attention to improving the digital literacy of farmers in economically underdeveloped areas will be able to play a greater policy effect.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e5.3 Limitations and future prospects\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThis study has the following limitations. On the one hand, this paper only considers the sample of farmers in China, and further empirical tests are needed to determine whether the conclusions of this study are applicable to other developing countries. On the other hand, this paper only analyzes the impact of digital literacy on farmland transfer behavior. Further research is needed to determine whether digital literacy affects the duration and rent of rural land transfer contracts. Therefore, future research can be further expanded to the impact of digital literacy on the characteristics of farmers' land transfer contracts.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical approval:\u003c/h2\u003e \u003cp\u003eEthical approval was not required as the study did not involve human participants.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInformed consent:\u003c/strong\u003e \u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests:\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eData availability\u003c/strong\u003e \u003cp\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files]\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.X.wrote the main manuscript text and L.Z.complete the design of the thesis.All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Grant No. 72363026; 71963026) and the Philosophy and Social Sciences Research Project of Jiangsu Province (Grant No. 2024SJYB0211).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files]\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003eAdamopoulos T, Brandt L, Leight J, et al. 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Computers and Electronics in Agriculture, 2022, 198: 107075.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Digital literacy, Land transfer, Non-agricultural employment, Non-agricultural entrepreneurship","lastPublishedDoi":"10.21203/rs.3.rs-8229633/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8229633/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the rapid popularization of digital tools such as the Internet in rural areas of China, digital literacy has a profound impact on farmers' production, management and consumption behaviors. This study investigates the influence of digital literacy on farmers' land transfer, aiming to reveal the role of farmers' digital literacy in improving the allocation of rural land resources. Based on the China Household Panel Study of the Center for Chinese Social Science Survey of Peking University, this study uses the Probit model and two-stage least squares estimation method to evaluate the impact of digital literacy on farmers' land transfer behavior and discusses its potential mechanism. The results indicate that digital literacy increases the likelihood of land transfer by promoting farmers to engage in non-agricultural employment and non-agricultural entrepreneurship. Heterogeneity analysis indicates that the conclusion drawn in this article is that digital literacy is more beneficial for farmers in economically underdeveloped areas and those with lower education levels. This study provides theoretical insights for policies to promote the development of rural land markets in developing countries and improve farmers' literacy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classifications:\u003c/strong\u003e D51 L86 Q12\u003c/p\u003e","manuscriptTitle":"Digital literacy and farmers’ land transfer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-29 08:41:51","doi":"10.21203/rs.3.rs-8229633/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-28T16:25:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-07T08:44:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-04T16:44:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-30T12:13:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303343096724351411441805637688222780204","date":"2025-12-28T11:23:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-28T06:21:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17639776889161599284764151985813405345","date":"2025-12-27T09:05:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125896521836658448824990794311644810907","date":"2025-12-26T13:37:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229256008740833906108054907341972512310","date":"2025-12-26T11:34:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-26T11:15:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-20T03:13:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-20T03:11:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-14T09:29:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-12-14T09:22:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"159ce31f-355c-462b-86b8-b301601718ce","owner":[],"postedDate":"December 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":60303118,"name":"Social science/Development studies"},{"id":60303119,"name":"Business and commerce/Economics"},{"id":60303120,"name":"Social science/Economics"},{"id":60303121,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2026-05-15T12:55:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-29 08:41:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8229633","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8229633","identity":"rs-8229633","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}