Why Digital Literacy Widens the Income Gap wi thin Chinese Farming Households

Why Digital Literacy Widens the Income Gap wi thin Chinese Farming Households | 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 Research Article Why Digital Literacy Widens the Income Gap wi thin Chinese Farming Households 永奇 张 This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3272248/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Whether or not we can narrow the urban-rural "digital literacy gap" and improve the digital literacy and skills of China's rural households so that digital technology can truly empower people, is crucial to improving the well-being of the population. Using data from the China Family Tracking Survey (CFPS), the article explores the construction of digital literacy indicators and identifies the key issues for improving the income of rural households through digital literacy, starting with the depth of information application. The findings show that improved digital literacy has a significant positive effect on increasing farm household income, and the study's conclusions remain valid after utilizing instrumental variables as well as multiple methodological tests. The quantile model shows that the income-enhancing performance of digital literacy is uneven across regions and significantly widens the income gap within farm households. The income-generating benefits of digital literacy have a "threshold effect" within farming households, with low-income farmers mainly obtaining wages and business income through "entertainment apps" and high-income farmers accomplishing wealth accumulation through "serious apps". High-income farmers use "serious applications" to complete wealth accumulation. Mechanism analysis shows that digital literacy can reduce the cost of acquiring knowledge and effective information, improve the better management of individual resources, and realize income expansion. Heterogeneity analysis finds that digital literacy has a more pronounced income-enhancing effect on middle-aged and older farmers and those with low levels of education. Focusing on low-income households with multiple vulnerabilities, the use of household "digital feedback" can further reduce the income gap within the household. This study helps to examine the economic effects of digital literacy on farm household income under the "winner-takes-all" market structure and provides evidence to support the use of digital literacy as a tool to promote the digital village and commonwealth in China. farmers' digital literacy depth of information application digital literacy divide commonwealth 1. Introduction In the digital era, digital literacy has become a basic survival skill and essential literacy for citizens. The level of digital literacy is directly related to the willingness and ability of farmers to continue using digital resources after they have been "connected to the Internet" (Hargittai, 2005 ), which fundamentally affects the role of farmers and the improvement of their overall welfare level. Breaking the huge digital literacy gap between urban and rural areas due to "information polarization" (Zhang, 2022) is of great practical significance for China, the world's largest developing country. important practical significance for China, the world's largest developing country. The emergence of China's "rural netizens" has led to a series of short videos on rural life and the sale of local specialities, which has led to a development path of rural revitalization with its village characteristics, and provided more development opportunities for rural residents to increase production and income (Peng et al., 2022). However, only "local capable people" with certain digital skills and high digital literacy can better apply digital technology, integrate into the digital economy, and share digital dividends (Liu et al., 2020; Oria, 2020), and the possibility of new digital poverty arising from differences in the use of the Internet among individuals is rapidly increasing (Berkowsky et al., 2022). increases (Berkowsky et al., 2018). At present, based on realizing "the same network and the same speed in urban and rural areas", many villages are still in the forgotten corners, the depth of information application of most farmers is low, and the awareness and ability to solve problems and create income by using digital technologies and tools such as personal computers is still a shortcoming (Chen et al., 2022) so that the vast number of farmers have "opportunities" in the digital era (Chen et al., 2022). In the digital era, the problem of "not being able to grasp the opportunities and not being able to use the conditions" has not been truly solved. According to the Report on Survey and Analysis of Digital Literacy in Rural China under the Background of Rural Revitalization Strategy released by the Informatization Research Center of the Chinese Academy of Social Sciences, nearly one-third of rural residents believe that the application of cell phones or computers does not have any effect on personal employment, entrepreneurship and income enhancement. In addition, some farmers recklessly "brush gifts" to the host, which triggers family conflicts and increases family debts. High-quality digital village development remains a serious challenge. Even though digital devices have become highly accessible in rural areas and the Internet has become a new force for equalizing opportunities. However, there are different choices between digitally advantaged and digitally disadvantaged groups in terms of "serious applications" and "entertainment applications" (Mikołajczyk, 2023) due to differences in personalities and imaginations, leading to a large gap in the role of capital and resources in digital applications. The dominant role of capital and resources presents a large gap, which in turn has a direct impact on the pattern of income distribution within the farming community. Recognizing that the pure use of digital technology does not automatically improve people's ability to apply digital technology, how to effectively improve the ability of low-income farmers to use digital "new agricultural tools" (UNESCO, 2017), cultivate and improve the overall digital literacy and digital skills of farmers, and stimulate the deep application and value creation of personal computers has become a major challenge for farmers. In-depth application and value creation of personal computers by farmers has become a major issue for China in practising the two strategies of digital villages and promoting common prosperity. Compared with previous studies, the possible improvement of this study lies in the following: based on the new scenario that information services have fully penetrated the full range of rural residents, based on the increasing importance of the digital economy to economic growth (Herman and Oliver, 2023), focusing on the indisputable fact that the income disparity within the farming households has been increasing during the process of urban-rural integration, utilizing the data from the two issues of the China Family Panel Survey ( China Family Panel Studies, CFPS) data, from the perspective of rural residents as the main users of information resources, the welfare effects of digital literacy, a front-end condition for the depth of information application, in the process of value re-creating and value re-distributing are systematically sorted out and elaborated... It is hoped to construct the intrinsic connection between digital literacy and farm household income, and based on different application levels, through the results of empirical analysis, it is proposed to promote the overall income growth of farm households with precise digital increase and shrinkage distance policies, while forming a virtuous circle of economic return to income equality. 2. Literature review and theoretical mechanisms 2.1 Literature review Bawden (2008) systematically summarized the definitions and sources of digital literacy, defining digital literacy as the ability to read and comprehend multimedia and online texts. While digital literacy in the current context usually refers to the ability to read, write and process information based on the context of the digital economy, which was originally defined by (Gilster, 1997), stating that digital literacy is "mastering ideas, not keyboarding". Eshet-Alkalai (2004), on the other hand, states that digital literacy should refer to the ability to understand and properly use computers to store digital resources and information. Synthesizing the aforementioned typical frameworks, UNESCO proposes a literacy domain that includes related domains such as device operation, information processing, and communication and collaboration. Although previous studies have used different descriptions of the concept of digital literacy, these concepts are based on "the skills needed to effectively utilize computers and the Internet" (Soroya et al., 2021). These skills are a necessity for everyone to survive in the current information and communication technology (ICT) era. Digital literacy has been measured in more specific ways than the vagueness of its definition. Initially, digital literacy was categorized into five dimensions, namely "picture-visual literacy," "reproduction literacy," "categorical thinking literacy, " information literacy" and "socio-emotional literacy" (Eshet-Alkalai, 2004). After that, the Digital Intelligence Alliance (DIA), a world organization established by the World Economic Forum and other world organizations, published the DQ Global Standards Report 2019, which is the first attempt to develop global standards for digital literacy and competencies in education and technology, proposing 24 digital literacy competencies covering knowledge, skills, attitudes, and values at three levels and in eight domains. Soroya et al. (2021) examined the information screening ability of Pakistani university students to identify their digital literacy using the indicator of "frequency of Internet use" by individuals. Overall, along with the continuous penetration of global digital technology, the connotation of digital literacy is getting deeper and deeper, the research groups are gradually focusing on it, and the measurement framework is becoming clearer and clearer. At the same time, however, given that digital literacy measurement frameworks need to be integrated with researchers' living environments, digital literacy framework models are often characterized by cross-national institutions. Relevant studies show that digital literacy models and frameworks have now reached more than 100 (Tabusum et al., 2014). Therefore, in the new stage of digital village construction to drive and promote common prosperity in agriculture and rural areas, there is a need to focus on constructing digital literacy frameworks that are more practical, unified and coordinated for Chinese rural residents. "The Fourteenth Five-Year Plan is a critical period for China to bridge the digital literacy gap between urban and rural areas, as well as a new stage in the digital economy's shift to universal sharing. The Outline of Actions for Enhancing Digital Literacy and Skills of the Whole Population says, "Efforts will be made to expand the four major scenarios of digital life, digital learning, digital work and digital innovation for the whole population, enhance the literacy and skills of the whole population in digital learning, work, life and innovation, and set up a system of indicators for evaluating the development of the whole population's digital literacy and skills in line with China's national conditions, to continually bridge the digital divide between urban and rural areas, regions and populations, and promote common prosperity. digital divide and promote common prosperity." Inadequate ability to create value using personal computers is the main shortcoming that limits the improvement of rural residents' digital ① literacy1. As a result, this study defines rural households' digital literacy as an action ability to realize productive and creative value through the specific application of the Internet based on the four scenarios of digital life, digital learning, digital work, and digital innovation. On this basis, this study utilizes the CFPS microdata in an attempt to explore the economic and social effects of rural residents' income enhancement from the perspective of digital literacy, and to explain in-depth the relevant impacts and theoretical mechanisms of digital literacy on farmers' income. 2.2 Theoretical mechanisms The economic impact of digital literacy is an important theme in related research, and existing studies generally agree that changes in the degree of inequality and changes in digital literacy are intertwined, but the conclusions of existing studies are widely divergent (Bauer, 2018). Based on the reality of China's large urban-rural digital literacy gap and the low digital literacy of rural residents, this study focuses on the impact of digital literacy on the income of farm households, intending to provide more specific and reliable empirical evidence for existing aggregate studies. The theoretical analysis is mainly derived from the Nelson - Phelps model of human capital. The model argues that the most important role of human capital is not to increase the productive capacity of existing work tasks, but to facilitate the adoption of technology. That is, compared to Becker and Mincer's basis that human capital can drive productivity gains, this notion favours the idea that the primary role of human capital can provide important safeguards for workers to cope with change, disruption, and especially new technologies. This study argues that the potential of digital literacy to boost farm incomes lies in the fact that it is a kind of human capital in the digital age and an important way to expand the quality dividend of the population. Based on the micro level, the continued promotion of digital villages provides a new direction for rural economic growth, and digital literacy will be an ability for households to use digital services based on digital scenarios and to allocate resources in the digital era (Hargittai et al., 2018). Having a certain degree of digital literacy is a prerequisite for individual farmers to be able to integrate into the digital society, enjoy digital dividends, and open up space for employment, entrepreneurship, and income generation. However, individual farmers who want to have a higher marginal rate of return in digital scenarios will inevitably require a stronger digital literacy that combines the needs of information integration, screening, and decision-making in the dynamic evolution of the digital economy. The impact of digital literacy on farmers' income is mainly reflected in the integration of digital scenarios and response to digital behaviours, while the underlying logic is reflected in the reduction of the cost of acquiring knowledge and information and the better management of individual resources. The accumulation of digital literacy improves farmers' understanding and cognitive level of digital technology, bridges the mismatch between awareness and skills, and thus improves their awareness and ability to make economic decisions such as choosing careers and investments based on digital scenarios. From the supply side, digital literacy corresponds to the ability to acquire and process information, which is conducive to transforming traditional agriculture, changing the mode of agricultural production, accelerating the digital transformation of planting and animal husbandry, accelerating the linkage of e-commerce systems and express logistics and distribution systems in counties and villages, and realizing industrial empowerment with the help of digital technology. From the demand side, digital literacy is also able to stimulate the enthusiasm, initiative and creativity of farmers to build a digital countryside with the help of human capital accumulation and peer effect, and through the ripple effect of the diffusion of digital technology, they have the option to participate in broader market transactions. Farmers can not only make use of the larger market to sell their products and provide labour and professional skill services to increase their income through e-commerce live broadcasting but also obtain a more diversified supply and lower transaction costs under the "buyer" advantage of the consumer market. Both a wider range of income channels and a lower cost of living will lead to higher real incomes. While the strengthening of digital literacy is conducive to increasing farmers' acceptance and support for digital transformation, it may also lead to higher issues such as privacy leakage and financial security. Due to security concerns, households are more conservative in promoting and using digital products. In the case of mobile payment, whether an individual adopts mobile payment technology depends not only on the ease of use and usefulness of mobile payment but also on its privacy and security and social norms. The lower the individual's risk appetite, the lower the internet usage and the less willing they are to take the risk of using financial services and products (Zhao et al., 2023). However, another study suggests that individuals' lack of understanding and identification with digital services and products changes significantly as digital literacy improves across the ability threshold. Farmers with high resource endowments and comprehensive capabilities perform more prominently in terms of utilizing digital literacy to achieve resource allocation efficiency. When faced with the economic choice between "entertainment applications" and "serious applications", high-income farmers are more likely to cross the cost threshold and ability threshold of information utilization and choose the latter, which improves the efficiency of asset allocation and property income, and makes farmers Thus, in the process of improving the efficiency of asset allocation and increasing property income, the likelihood of the widening of the internal income gap among farm households increases significantly. 3. Data Sources and Modeling The main source of data used for the empirical analysis of this study is the 2016 and 2018 China Family Tracking Survey (CFPS). The main reasons for choosing this database are: first, the CFPS survey database is executed by the China Social Science Survey Center (ISSS) of Peking University, which covers a wide range of provinces, has a high sample retention rate, and is considered an authoritative research database. It can meet the analytical needs of this study. Second, the questionnaire contains sufficient questions about the use of digital application scenarios and variables such as individual income, and it can demonstrate the relationship between digital literacy and farm household income on a national and large scale, reducing the small sample measurement error. Based on this, this study matches the merged sample of farm household families with some regional macro data in the corresponding period after deletion and reassignment of values, to include individual mobility, and finally obtains 10,540 unbalanced panel survey data for the two periods. Among them, 5,433 farm household samples in 2016 and 5,107 farm household samples in 2018. Explanatory variables: The explanatory variable in this study is "farm household income". Regarding the income of agricultural households, the question "total tax income from all jobs in the past year" in the CFPS questionnaire was used as a measure. To avoid the effect of heteroskedasticity, this variable was logarithmized and included in the analysis model. Core explanatory variable: digital literacy. Relying on the previous theoretical analysis and related research, this study starts from the realization scenario of digital literacy and measures the level of digital literacy of farmers with the help of five questions in the questionnaire, namely, "Frequency of using the Internet for learning", "Frequency of using the Internet for work", "Frequency of using the Internet for The digital literacy level of farmers is measured with the help of five questions in the questionnaire: "Frequency of using the Internet for learning", "Frequency of using the Internet for work", "Frequency of using the Internet for entertainment", "Frequency of using the Internet for socializing", "Frequency of using the Internet for business" (the frequency takes the value from 0 to 6). Among them, "frequency of using the Internet for entertainment" and "frequency of using the Internet for socializing" can be attributed to the digital life scenarios. Considering the inherent differences in infrastructure between provinces and cities, it is inappropriate to adopt a uniform standard for measuring digital literacy, which is likely to exacerbate the digital divide among farmers. Therefore, this study draws on the research idea of Asfaw et al (2018) to calculate the digital literacy of farm households in three steps: first, the different structural literacies of each province and city are summed up separately; second, the proportion of farm households in different digital scenarios in each province is calculated; and third, an equal-weighted assignment is carried out based on the aforementioned proportions of different application scenarios, and the actual digital literacy of the farm households is finally deduced. It should be noted that although the digital literacy indicators are still constructed based on "Internet use", the focus of the study has shifted from the stage of Internet popularization to the assessment of digital literacy. According to the statistical results in Table 1 , it can be seen that, firstly, the digital literacy of farmers in the East is significantly higher than that of farmers in the West, which is reflected not only in the absolute change but also in the relative change. However, at the same time, it is also found that the digital literacy of a larger number of farmers is very close to 0. Farm households have a higher frequency of exposure to digital life scenarios, and a weaker degree of digital literacy enhancement achieved through the integration of digital application scenarios. This suggests that many farmers have a single way of using the Internet and lack effective utilization of applications. Second, although the digital economy, with its innovative capacity, has led to the rapid growth of China's economy through both industrial digitization and digital industrialization, the basic trend of the digital economy development index, which is decreasing from the East coast to the west and inland, has not changed significantly over time. Digital infrastructure is likely to be a necessary but not sufficient condition for boosting farmers' incomes. Third, based on the income data of farm household subgroups published by the National Bureau of Statistics (NBS) for the same period, the absolute income gap between the incomes of farm households in the lower subgroups and those in the higher subgroups shows a widening trend. This further supports the policy reflection that China's toughest task of promoting common prosperity is in rural areas. Since the digital economy was first included in a government report in 2017, it has already had a significant impact on the future direction of China's economy. But at the same time, the digital economy has also shaped a new distribution system through a new production relationship. Under this new distribution system, it is mainly reflected in the difference in the ability to benefit between people. Therefore, in the era of the digital economy, it is necessary to analyze the relationship between digital literacy and the income of rural households to be highly vigilant against the misperception that the use of Internet services, which is promoted by the "reduction of costs and increase of speed" of the communication infrastructure in rural areas, is in sync with the acquisition of digital literacy. Table 1 Descriptive analysis of the digital economy, digital literacy and farm household income in the sub-region digital economy Farmers' income 2016 Eastern Average 52.75 low packet high clustering 2016 Western Average 27.05 3006.50 28448 2018 Eastern Average 47.63 Gap (2016) Gap (2018) 2018 Western Average 24.29 8116.60 8821.83 digital literacy Farmers' income 2016 Eastern Average 0.03 low packet high clustering 2016 Western Average 0.01 3666.20 34042.60 2018 Eastern Average 0.11 Absolute income gap 2018 Western Average 0.04 25441.50 30376.40 Note: The digital economy index here is from the China Digital Economy Index White Paper, and farm household income is from the China Statistical Yearbook as well as CFPS integrated data. Income unit: yuan. Same below. To show the relationship between rural residents' digital literacy and farm household income more intuitively, this study divides farm household income into 2 intervals according to the mean value, indicating the distribution of changes in farm household income from low to high. On this basis, the combination of farm household income with different digital application scenarios is described in joint statistics. According to the statistical results in Table 2 , it can be seen that, firstly, from the front-end of the accumulation of digital literacy: different scenarios of digital application, it can be found that the application of whatever digital scenario can enhance the probability of the farm household to enter the high-income group from the low-income group. Second, comparing the income groups of farmers under different digital application scenarios, it can be found that, no matter the high-income group or low-income group, farmers prefer digital entertainment, and the reach of digital innovation and digital learning scenarios is still not high. This shows that the government needs to target this weakness at a later stage, through the construction of scenarios from "entertainment applications" to "serious applications", and implement precise measures against the standard and table, to effectively bring into play the role of digital scenarios in knowledge and information acquisition, to ensure that farmers can gradually narrow the income gap in the process of sustained income increase. This is the only way to ensure that farmers can gradually narrow the income gap in the process of sustained income growth. However, whether this income-generating effect driven by digital literacy can stand up to the test at a later stage requires the introduction of other control variables and an in-depth analysis of the relationship between the two to obtain more reliable empirical evidence. Table 2 Description of separate joint statistics Digital Application Scenarios digital literacy digital learning flimsy general high Farm household income (range 1) 74.81 19.48 5.71 Farm household income (range 2) 56.90 29.69 13.41 Working with numbers flimsy general high Farm household income (range 1) 81.97 7.77 10.26 Farm household income (range 2) 63.46 11.88 24.66 digital socialization flimsy general high Farm household income (range 1) 56.26 21.94 21.80 Farm household income (range 2) 30.83 28.70 40.47 digital entertainment flimsy general high Farm household income (range 1) 52.97 14.58 32.45 Farm household income (range 2) 28.92 17.33 53.75 Digital Innovation flimsy general high Farm household income (range 1) 20.81 77.50 1.69 Farm household income (range 2) 20.22 71.61 8.17 Note: The digital literacy distinction here is the frequency of each function of Internet use: (0) is weak; (1–5) is average; (6) is high. Control variables: concerning existing studies (Guess A et al., 2019; Chetty et al., 2018), this study, based on the questionnaire question availability and appropriateness, starts from the three dimensions of individual characteristics, family characteristics, and provincial characteristics, and adds corresponding control variables to try to cut down the regression bias of digital literacy on the income of farmers due to the omission of variables. Specific descriptive statistics are shown in Table 3 . Table 3 Descriptive statistics variable name Variable Definition observed value average value Farmers' income Gross income from work after taxes (log) 10540 6.754 digital literacy The proportion of each farm household in the sum of digital literacy in each province 10540 0.010 (a person's) age Age of respondents 10540 39.370 (math.) age squared (math.) age squared 10540 1817 distinguishing between the sexes 1 for males, 0 for females 10540 0.459 educational attainment Years of education completed 10540 8.239 marital status Married is assigned a value of 1, otherwise 0 10540 0.709 social security Pension and medical participation is assigned a value of 1, otherwise 0 10540 0.820 Family size Number of persons in the household 10540 4.921 Family care Household chores or parental care is assigned a value of 1, otherwise, it is 0. 10540 0.202 Total household assets Total household financial assets (log) 10540 7.094 geographic capital Expenditures on family favours (log) 10540 7.262 Strip Capital Social trust in the family (grossed up) 10540 13.810 Education costs Total household expenditure on education (log) 10540 0.056 economic level The gross product of provinces and municipalities (logarithmic) 10540 10.350 The estimated equation for the impact of digital literacy on farm household income is as follows: Where, denotes farm household income, denotes digital literacy, represents the control variables affecting farm household income, and is a random disturbance term. denotes the coefficient of influence of digital literacy on farm household income, which is also the focus of this study. 4. Empirical analysis 4.1 Baseline analysis Given that the CFPS two-period data used in this study are unbalanced panel data, the use of mixed OLS (POLS) tests is more appropriate here. Table 4 Models (1) to (4) represent the empirical results obtained from the regression test by introducing only core variables, adding individual characteristic variables, adding household characteristic variables, and adding regional characteristic variables, respectively. In addition, this study was conducted to further find out whether the economic impact of digital literacy on farm household income would be policy-driven. Regression analyses were conducted using ordinary least squares (OLS) using cross-sectional data from 2016 and 2018, respectively. The results are shown in Table 4 models (5) to model (6). The results of the mixed OLS test for models (1) to (4) in Table 4 show that digital literacy has a significant contribution to farmers' income growth with or without the addition of the control characteristic variables, and they are all significant at the 1% statistical level. It should be noted that compared with the promotion effect of digital literacy on farm household income increase in 2018, digital literacy in 2016 did not serve the function of farm household income increase. The reason is that China's action plan to implement "Internet Plus" and promote the construction of "Digital China" began in 2015. Since then, with the strong support of national policies, the use of the Internet has gradually penetrated from e-government and e-commerce to socialization and entertainment, and China's national digital literacy has seen an accelerated period of improvement after 2016. The overall findings suggest that focusing on low-income populations and underdeveloped regions, building digital scenarios closely related to farmers' production and life as soon as possible, and narrowing the gaps in the use of smart devices and the knowledge gap of digital skills are conducive to the realization of the long-term empowerment of digital literacy to increase the incomes of farmers. Table 4 Benchmarking variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 digital literacy 10.100*** 3.869*** 3.560*** 3.983*** 2.546 1.791*** (10.01) (4.03) (3.74) (4.23) (1.28) (3.66) (a person's) age 0.199*** 0.175*** 0.136*** 0.140*** 0.088*** (11.66) (10.00) (7.83) (4.10) (5.46) (math.) age squared -0.003*** -0.002*** -0.002*** -0.002*** -0.002*** (-13.92) (-12.56) (-11.10) (-6.06) (-9.25) distinguishing between the sexes 1.197*** (13.18) 1.177*** (13.05) 1.161*** (13.09) 1.199*** (7.69) 0.867*** (12.02) educational attainment 0.266*** (22.49) 0.260*** (21.85) 0.184*** (15.21) 0.207*** (9.59) 0.102*** (10.24) marital status 0.281** 0.335*** 0.382*** -0.957*** 1.641*** (2.43) (2.83) (3.26) (-4.72) (14.72) social security 1.660*** 1.591*** 1.988*** 0.393 3.596*** (12.14) (11.69) (14.87) (1.45) (20.47) Family size -0.120*** -0.110*** -0.091** -0.047*** (-5.67) (-5.21) (-2.37) (-2.81) Family care -0.191* 0.095 -0.343* 0.214** (-1.75) (0.87) (-1.77) (2.46) Total household assets 0.098*** (10.05) 0.061*** (6.33) 0.068*** (4.05) 0.012 (1.44) geographic capital -0.031* 0.030 0.018 0.043*** (-1.67) (1.63) (0.49) (2.92) Strip Capital 0.014*** 0.011*** 0.014 0.003*** (5.52) (4.59) (1.63) (2.95) Education costs -0.040 -0.011 0.014 -0.003 (-0.72) (-0.18) (0.21) (-0.06) economic level 1.187*** 0.979*** 0.140*** (20.38) (9.95) (2.75) Note: t-statistics are in parentheses; differences in confidence level significance are indicated by ***, **, and * at the 1%, 5%, and 10% levels, respectively. Year characteristics are controlled for, as below. 4.2 Quantile regression The previous tests have confirmed the income-enhancing effect of digital literacy, but since the impact of digital literacy on income varies according to the stock of household income, it produces either an increasing scale effect or a decreasing marginal effect, which leads to the economic impact of digital literacy on the increase in income of farm households, which exhibits differences in returns among different groups of farm households. Thus, this study uses the quantile regression method to expand the test of the relationship between digital literacy and farm household income. According to the regression analysis in Table 5 , it can be seen that digital literacy has a positive impact on the income of farm households in different quartiles. From the trend change, middle-income farm households may be affected by lower digital literacy. However, the credibility of digital literacy on farm household income increases significantly with higher quantile points. This suggests that, in the face of the digital economy wave, differences in digital literacy will make the risk of widening the income gap within farm households, i.e. the inclusiveness of the digital economy still needs to be strengthened. Therefore, in addition to considering how to improve the digital literacy of rural residents at the overall level, governments at all levels should also examine the differences in the digital use of rural residents, to prevent the widening of the "digital literacy gap" within the countryside, which will make the bottom line of the construction of the digital countryside and the effectiveness of the strategy for revitalization of the countryside unstable, and thus hurt China's overall pattern of income distribution. and thus adversely affect China's overall income distribution pattern. Table 5 Quantile regression model variable Model 1 Model 2 Model 3 Model 4 Farm household income (Q_10) Farm household income (Q_50) Farm household income (Q_90) Farm household income (Q_99) digital literacy 4.513* 1.038** 1.945*** 2.591*** (1.93) (1.97) (3.93) (3.25) control variable Controlled Controlled Controlled Controlled 4.3 Dimensional tests This study has constructed digital literacy indicators from four types of digital application scenarios as well as a relatively comprehensive empirical test of farm household income. However, to further identify the reasons why digital literacy tends to widen the income gap within farm households and to provide precise policy recommendations, it is also necessary to clarify which types of digital literacy applications should be targeted by corresponding policies, and which sources of income are reflected in the income gap caused by digital literacy. Considering that serious apps have a stronger resource release function than entertainment apps (James, 2019), the "serious apps" indicator is constructed by using "individual digital literacy in both years exceeds the average value of digital literacy in each province" (greater than the assigned value of is 1, and vice versa is 0), and "individual digital life (entertainment, social) literacy in two years exceeds the average value of digital literacy in each province" is used to construct the "entertainment application" indicator (greater than the assigned value is 1, and vice versa is 0). According to the regression results in Table 6 , following the previous scholars' quintile grouping method (Rooij et al., 2011), from low to high, those in the interval 1 and 2 are redefined as low-income farmers, and those in the interval 4 and 5 are redefined as high-income farmers. High-income farmers realized more significant income increase through the application of serious category. And in addition, this study also found that the digital literacy of high-income farmers is mainly focused on the increase of property income (wage and business coefficients are significant but lower than that of low-income farmers), and the positive effect of digital literacy on the low-income farmers' access to wage and business incomes is more pronounced (only the most significant coefficients are retained for the wage results). With the continuous development of the digital economic system, the improvement of digital literacy strengthens rural residents' understanding and cognitive level of digital technology, thus enhancing their awareness and ability to make economic decisions such as choosing a career and making investments based on digital scenarios. However, at present, the role of digital literacy in increasing the income of rural households is more often reflected in high-income households, which have more resource endowments, and are also in a position to obtain stronger digital literacy and digital financial capabilities, and cross the scene threshold from "entertainment applications" to "serious applications" as soon as possible, from "entertainment applications" to "serious applications". As soon as possible, they can cross the threshold from "entertainment apps" to "serious apps", and gradually transition from production benefits to creation benefits. With the help of "serious applications", we can realize the structural transformation from wage income and business income to property income, and promote the accumulation of wealth through the investment of financial products and the transfer of land. But this in turn will also affect the further widening of the income gap. The essence behind this phenomenon lies in the growing importance of digital innovation capabilities in the digital transformation of agriculture and rural areas, which increases market rents to the benefit of the highest-income groups. This, in turn, reaffirms that differences in digital adoption, externalized by differences in digital literacy, are an important reason for widening the income gap within farming households. Table 6 Dimensionality test of digital literacy on farm household income variable Model 1 Model 2 Model 3 Model 4 Low-income farmers High-income farmers Low-income farmers High-income farmers Serious apps 0.289 (1.26) 0.265** (2.32) Entertainment apps 0.302*** (3.59) 0.123** (2.00) digital literacy 4.792*** 5.076*** (3.08) (3.10) control variable Controlled Controlled Controlled Controlled 4.4 Robustness testing and endogeneity treatment To develop empirical analysis using microdata, the issues of robustness and endogeneity should be considered to further enhance the scientific validity of the research findings. The test ideas of this study are as follows: first, try to replace the core variables. The logarithmic value of farm household income is divided according to 0–1, and the value equal to 0 is attributed to 0. The specific regression results are shown in Table 7 model (1). In addition, this study also re-measured the digital literacy variable by assigning those who did not participate in digital application scenarios to carry out various online activities to 0, and those whose values are greater than 0 to 1. Then the regression was conducted again, and the results are shown in Table 7 Model (2). Second, considering that the relationship between digital literacy and farmers' income will be plagued by the problem of endogeneity, this study utilizes the indicator of "the average value of digital literacy in the same province and city, except for the farmers themselves" as an instrumental variable, which to some extent meets the homogeneity and relevance of the instrumental variable, and the specific results of the first-phase and second-phase regressions are shown in Table 7 , Model (3) and Table 7 , Model (3) and Table 7 , Model (2). The specific results of the first-stage and second-stage regressions are shown in Table Model (3) and Model (4). Third, digital literacy has a heterogeneous effect on the existence of income of different groups. The generally low level of education of rural residents is the most important reason that triggers the lower digital literacy of rural residents, and as the mainstay of the long-term rural resident population, the rural elderly are even less educated and are the typical digital poor group (Bonami & Nemorin, 2021; Prodromou & amp; Lavranos, 2019). As a result, this study categorized farm households into young and middle-aged and old according to whether they reached 40 years of age in the year of birth, and the results are shown in Table 8 Model (1) and Model (2). And based on the number of years of education, the agricultural households were categorized into those who have completed compulsory education and those who have not completed compulsory education, and the results are shown in Table 8 Model (3) and Model (4). Based on the regression results in Table 7 , it can be seen that after attempting to replace the core variables, the positive effect of digital literacy on farmers' income increase remains significant, and the aforementioned empirical evidence is further supported. For the endogeneity issue, the F-statistic value in the one-stage regression test is much more than 10, indicating that the selected instrumental variables meet the requirements. The higher the mean value of digital literacy of the remaining farmers in each region, the easier it is for farmers to realize digital transformation, and thus the effect of digital literacy of farmers is more obvious. The results of the two-stage regression show that the coefficient of digital literacy is significantly positive, and this coefficient is much higher than the benchmark regression coefficient. This means that if the endogeneity issue is not considered, it will largely reduce the promotion effect of digital literacy on farm household income. The heterogeneity test in Table 8 shows that digital literacy promotion has a more significant economic effect on the income promotion of middle-aged and elderly farmers. In the division of years of education, the effect of digital literacy on income enhancement is more pronounced for farm households with lower educational experience. The main reason is related to the existing knowledge and information stock of each group, and digital literacy has a greater effect on the group that lacks the corresponding knowledge and information and has a more significant income-enhancing effect. The improvement of digital literacy can alleviate the plight of such groups that do not have enough effective information to recognize digital scenarios, and with the "latecomer's advantage", they can realize cost reduction and income increase. In addition, the study also takes into account that middle-aged and old-aged farmers and young farmers do not exist independently, and in rural areas, these two groups of people usually live in the same space or even in the same family, and they should have a kind of "driving" each other, so it further tries to analyze whether the latter can play a kind of regulating effect on the income of the former, to provide theoretical wisdom for the subsequent countermeasure suggestions. Therefore, we further try to analyze whether the latter can have a moderating effect on the income of the former, to better provide theoretical wisdom for the subsequent countermeasures. Given the availability of data, this study constructs the indicator of "per capita household digital literacy". Taking families as a group, after summing up individual digital literacy and dividing it by family size, we constructed a family per capita digital literacy indicator to further test whether digital literacy has a spatial "driving" effect on the income of middle-aged and old-aged farmers. According to the regression results of model 5 in Table 8 , the effect of household per capita digital literacy on the income of middle-aged and old-aged farmers is significantly higher than that of digital literacy itself. This means that the transmission and flow of digital literacy within the family can realize the effective bridging of intergenerational relations, deepen and dissolve the differences in digital literacy and value, and further strengthen the income-generating effect of digital literacy on middle-aged and elderly farmers. In the new era of the convergence of the active response to population ageing and the digital village strategy, governments at all levels can further utilize the function of family "digital feedback", focusing on low-income groups with low years of education and mainly middle-aged and elderly farmers, to strengthen the income-enhancing effects of digital literacy on such groups. Table 7 Robustness test and endogeneity treatment variable Model 1 Model 2 Model 3 Model 4 Ownership income Farmers' income digital literacy Farmers' income digital literacy 1.579** 42.64*** (2.45) (4.74) Digital Literacy Alternative 0.595*** (6.34) instrumental variable 0.056*** (16.14) control variable Controlled Controlled Controlled Controlled Table 8 Heterogeneity analysis variable Model 1 Model 2 Model 3 Model 4 Model 5 Digital literacy (home) 3.377*** (3.38) 6.498*** (4.15) 3.252*** (2.95) 4.736*** (5.31) 8.062*** (4.20) control variable Controlled Controlled Controlled Controlled Controlled Table 9 Statistical Distribution of Education, Age and Income variable Low-income farmers High-income farmers Non-completion of compulsory education 36.61 63.39 Completion of compulsory education 46.83 53.17 youthful years 46.50 53.50 middle and old age 24.44 75.56 4.5 Impact mechanisms The previous studies have examined the direct impact of the application of digital scenarios on the income of farm households based on the application of digital scenarios, but have not focused on the indirect impact of digital literacy on the income of farm households through digital behaviour. To improve the income transformation effect of digital literacy, the mechanism of influence is further explored here based on the significant contribution of risk preference level to the source of income realization of farm households, and the results are shown in Table 10 . Risk appetite is derived from the natural logarithm of the amount of financial investment. The results of model (1) in Table 10 show that the economic effect of digital literacy on the risk appetite of farm households is not significant. This suggests that there is a lot of room to explore the quality of the application of digital scenarios by farmers. Subdividing the high- and low-income farm households and comparing the results of the study, it is found that digital literacy can significantly improve the income level of high-income farm households through the mechanism of risk preference, and for low-income farm households, risk preference still can't be an influential mechanism for digital literacy to affect the income of farm households. This means that relying on the development of Internet technology and the application of smartphones provides a feasible path for farmers to reach the digital economy and improve digital literacy. By reducing information asymmetry and lowering transaction costs, the marginal returns of rural economic agents have been enhanced. However, at present, the overall ability of rural residents to identify, control and utilize risks is insufficient, and the resources belonging to them have not been optimally managed. It is necessary to give full play to the role of village leaders in demonstrating the cognitive and behavioural biases of low- and middle-income rural households in the promotion of digital villages, such as "unwillingness to use" and "not daring to use" "serious applications", to further unleash the power of the rural economy. and behavioural bias, to further release the "digital dividend" and universalization effect of digital literacy in rural areas. Table 10 Analysis of impact mechanisms variable Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 risk appetite Farmers' income risk appetite Low-income farmers risk appetite High-income farmers digital literacy 0.224 3.957*** 0.163 0.590** 0.360*** 1.205** (1.40) (4.20) (0.56) (2.22) (2.68) (2.14) risk appetite 0.119* -0.202 0.090* (1.77) (-0.95) (1.91) control variable Controlled Controlled Controlled Controlled Controlled Controlled 5. Research findings and policy recommendations 5.1 Conclusions of the study Against the backdrop of increasing penetration of digital devices and communication networks, the scale efficiency disadvantage of digital financial services exhibited by less developed regions is worth reflecting on. Bridging the urban-rural digital divide, offsetting the negative effects of epidemic shocks, and fostering high-quality farmers has become an inevitable trend of digital transformation in rural areas. This study empirically examines the direction and mechanism of the impact of digital literacy on the income enhancement of rural households based on two periods of microdata from the China Household Tracking Survey. The main research conclusions are: From a statistical point of view, the income of agricultural households is characterized by imbalance and insufficiency, which is an important factor constraining the process of common prosperity. The intra-farm household income gap tends to increase, and the income gap between eastern and western farm households is also more prominent, showing more obvious group differences and regional differences. In addition, digital literacy is more closely related to intra-farm household income disparity than the digital economy. The empirical results show that the improvement of digital literacy has a significant role in increasing the income of farm households, but it widens the income gap between farm households. That is, although digital literacy can help low- and middle-income farmers reduce the cost of acquiring knowledge and effective information, and create a useful path to increase wages and business income, there is a larger income gap between the two compared to high-income farmers who utilize "serious applications" to realize property income and wealth accumulation through risk appetite. As an effective means of acquiring knowledge and information, "entertainment apps" can provide effective training for farmers in production and management and asset allocation, but there is still a gap between the creative income-generating effects of "serious apps". Digital literacy has a more significant positive effect on the income improvement of digitally disadvantaged groups in rural areas. The coefficient of influence of digital literacy on the income enhancement of middle-aged and old-aged farmers as well as farmers who have not completed compulsory education is even greater. These groups are typical of the digitally disadvantaged, and positive interaction with such groups to achieve digital literacy enhancement can further reduce digital illiteracy and truly realize digital empowerment through digital literacy education. 5.2 Policy implications Digital literacy can provide endogenous motivation for farmers to increase their income. To better utilize the positive role of digital literacy in promoting high-quality development in agriculture and rural areas, this study proposes the following policy recommendations: The construction of digital rural areas should emphasize not only the creation of digital communication infrastructure and common scenarios but also the guidance and cultivation of digital literacy among farmers. Whether people can enjoy the dividends brought by the digital era ultimately depends on the awareness and ability to utilize digital services on the demand side, i.e., the digital literacy of individuals. In addition to promoting the digital village strategy in an orderly manner, helping farmers learn basic Internet operations, software for basic applications, and the use of security services through systematic digital literacy education, and realizing the coverage of digital literacy education for rural residents with the help of digital literacy programs, governments at all levels should also combine the resources of the government, NGOs, and individuals, and guide enterprises and public interest organizations to effectively participate in the digital skills upgrading of farmers, to Promote the vertical extension of digital services and training to rural areas. Through public service centres and professional farmers' portals, we can enhance rural residents' recognition and support of the digital scene, build a framework for a cultivation system that matches the "five forces" model of digital literacy: perception, integration, absorption, innovation and development, and help rural residents better integrate into the digital era by taking the initiative of awakening their consciousness. The active behaviour of awakening awareness helps rural residents to better integrate into the digital era, thus forming high-quality living and employment habits. The supply of policies to improve digital literacy should focus on key subjects and main dimensions, reflecting the precise application of policies. Low- and middle-income farmers, middle-aged and elderly farmers, and farmers with lower education levels are the key groups that the relevant policies need to focus on, with more room for policymaking and tapping into potential. First of all, to address the inherent disadvantage of low- and middle-income farmers' digital literacy accumulation, the government needs to preemptively demonstrate the benefits of digitization and implement the "better of the best" support policy to encourage and guide high-income farmers to land in enterprises and transform their industries, etc., to provide more opportunities for low- and middle-income farmers to participate in training for digital transformation through the strengthening of the trust mechanism. Through the strengthening of the trust mechanism, provide more opportunities for middle- and low-income farmers to participate in digital transformation training, strongly attract the visible poverty caused by insufficient income and the invisible poverty group identified by the time deficit, and minimize the gap between the labour resource endowment of the middle- and low-income groups and that of the high-income groups. Second, it is necessary to improve the ability of middle-aged and elderly farmers to use digital "new agricultural tools" and narrow the gap between the digital integration of middle-aged and elderly people. On the one hand, it is necessary to improve the suitability of relevant applications for the elderly; on the other hand, it is necessary to enhance the relevant skills of middle-aged and elderly farmers. Specific measures include simplifying the operational steps of application settings and increasing learning facilities and opportunities. Real-life experience and the introduction and expansion of family "digital feedback" should be utilized to enhance the sense of information participation and experience of this group, strengthen the self-efficacy of middle- and old-aged farmers, and weaken the extension of the traditional generation gap in the digital era, which will ultimately contribute to the development of China's silver economy. While improving the targeting of digital literacy and focusing on improving the digital literacy of the "long-tail group", it is also necessary to consider how to effectively reduce the risk of exclusionary behaviour of the long-tail group, to make the focus of the use of digital technology in such groups shift from "entertainment applications" to "serious applications". "Serious applications". First, the government and the corresponding digital supplying organizations need to guard the bottom line of security and improve the security coefficient of digital products and services. Secondly, the digital literacy policy should be tailored to the specific characteristics and needs of rural residents in each province and city. Compared with the developed regions in the east, the digital literacy level of rural residents in the western region is on the low side. By "promoting the West with the East" and "supporting the weak with the strong", we can further adjust and transform the low level of satisfaction of rural residents through unidirectional entertainment, and improve the social environment by stimulating the spirit of the hidden high level of entertainment. high level of spirit, and improve the imbalance of social wealth distribution. Finally, improving digital literacy and promoting commonwealth is a long-term systematic project. At different stages of digital development, the accumulation of digital literacy of farmers will change with the progress and lag of digital technology, and the types of digital applications will also show dynamic changes. In the face of new digital service scenarios and modes, governments at all levels and relevant parts need to expand the population quality dividend, so that digital literacy can be turned into a practical logic to accelerate the promotion of farmers to share the digital dividend. Declarations Author Contributions All the work was done independently by myself. Funding The authors would like to acknowledge support from the Major Program of National Social Science Foundation of China (Project No.20BJY142,20AZD079). Data availability Data will be made available on request. Ethics approval Ethics approval was not required for this study/Not Applicable. Conflict of interest The authors have no conflicts of interest to declare.The authors certify that the submission is original work and is not under review at any other publication. References Asfaw,S.,Pallante,G.,Palma,A.,2018.Diversification Strategies and Adaptation Deficit: Evidence from Rural Communities in Niger. World development.101(1),219-234. Bauer,J.M.,2018.The Internet and income inequality: socio-economic challenges in a hyperconnected society. Telecommunications Policy.42(4),333-343. Bawden,D.,2008.Origins and concepts of digital literacy. New York: Peter Lang. Berkowsky, R.W., Sharit, J., Czaja,S.J.,2018.Factors predicting decisions about technology adoption among older adults. Innovation in Aging.1(3),1-12. Bonami,B.,Nemorin,S.,2021.Through three levels of abstraction: Towards an ecological framework for making sense of new technologies in education. Education and Information Technologies.26(1):1-18. Chen,H.,Chen,C.,Li,Y.,Qin,L.,Qin,M.,2022.How Internet usage contributes to the livelihood resilience of migrant peasant workers? Evidence from China. Journal of Rural Studies.96,112-120. Chetty,K.,Liu,Q.,Gcora,N.,Josie,J.,Li,W.,Chen,F.,2018.Bridging the digital divide: measuring digital literacy. Economics:The Open-Access,Open-Assessment E-Journal.12(1). Eshet-Alkalai,Y.,2004.Digital literacy: A conceptual framework for survival skills in the digital era. Journal of Educational Multimedia and Hypermedia.13(1),93-106. Gilster,P.,1997.Digital Literacy.New York: Wiley Publishing. Guess,A.,Nagler,J.,Tucker,J.,2019.Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Science Advances.5(1),eaau4586. Hargittai,E.,2005.Survey measures of web-oriented digital literacy. Soc Sci Comput Rev.23(3),371-379, Hargittai,E.,Füchslin,T., Schäfer,MS.,2018.How Do Young Adults Engage With Science and Research on Social Media? Some Preliminary Findings and an Agenda for Future Research. Social Media + Society.4(3),205630511879772. Herman,P.R.,Oliver,S.,2023.Trade, policy, and economic development in the digital economy. Journal of Development Economics.164,103135. James, J.,2019.Confronting the scarcity of digital skills among the poor in developing countries. Development Policy Review.39(2),324-339. Liu,ZJ.,Tretyakova,N.,Fedorov,V.,Kharakhordina,M.,2020.Digital literacy and digital didactics as the basis for new learning models development. Int J Emerg Technol Learn.15(14),4-18. Mikołajczyk,B.,2023.Universal human rights instruments and digital literacy of older persons. The International Journal of Human Rights.27(3),403-424. Oria,B.,2020.Edmodo como herramienta de aprendizaje telecolaborativo online en el aula de inglés. Encuentro.28,49-70. Peng,X.,Zhang,J.,Peng,G.,2022.Does Internet Use Improve the Income of Residents? -Empirical Evidence from CGSS2017. China Finance and Economic Review.10(4),96-114. Prodromou,M., Lavranos,G.,2019.Identifying latent needs in elderly digital literacy: the PROADAS study. European Journal of Public Health.29(4), Rooij,M.V., Lusardi,A.,Alessie,R.,2011.Financial literacy and stock market participation. Journal of Financial Economics.101(2),449-472. Soroya,SH.,Ahmad,AS.,Ahmad,S.,Soroya,MS.,2021.Mapping internet literacy skills of digital natives: a developing country perspective. PLOS ONE.16(4),e0249495. Tabusum,S.,Saleem, A.,Batcha,M.S.,2014.Digital literacy awareness among arts and science college students in Tiruvallur district: a study. International Journal of Managerial Studies and Research.2(4),61-67. UNESCO.,2017.Working group on education: Digital skills for life and work. In: Broadband commission for sustainable development. Zhang,Y.,2022.Measuring and applying digital literacy: Implications for access for the elderly in rural China. Educ Inf Technol. Zhao,H.,Li,Y.,Sai,Q.,Ren,Y.,2023.Cross-border credit networks, banking risk contagion and suppression effects. Social Networks.73,130-141. Footnotes ①Source: Survey and Analysis Report on Digital Literacy in Chinese Villages in the Context of Rural Revitalization Strategy, published by the Informatization Research Center of the Chinese Academy of Social Sciences. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3272248","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":227273980,"identity":"0cae2371-8cf2-4a13-a12b-febd164cfa01","order_by":0,"name":"永奇 张","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYDACCTiLsYHhQ4UNDz9/AwlaGGecSZORnHGAaC0MDMycLYdtDBoS8OuQn9387OHXNps8effkBmbGhvM8BgwHGD98zMGthXHOMXNj2ba0YsMzDxuYC3fc5jFnbmCWnLkNtxZmiQQzacm2w4kbZyQ2MM88c5vHsuEAGzMvHi1sEunfEFp4287xGBxIwK+FRyLHTPIjUMt8CbCWA4S1SEjklEkznEtL3MDzEBTIyTySMw424/WL/Iz0bZI/ymwS57enPwBGpZ09P3/zwQ8f8WgBBwEvGwMD0D3sPyB8YDIgBBh//AFaRygGR8EoGAWjYOQCAIbLVZdzFuW4AAAAAElFTkSuQmCC","orcid":"","institution":"Sichuan Agricultural University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"永奇","middleName":"","lastName":"张","suffix":""}],"badges":[],"createdAt":"2023-08-17 12:44:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3272248/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3272248/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":45631719,"identity":"623fe65b-2e81-4c31-a506-27ee68f6ef1a","added_by":"auto","created_at":"2023-11-01 08:46:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":499375,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3272248/v1/229d0eb9-0bd4-4b16-8708-bd68b9c478f3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Why Digital Literacy Widens the Income Gap wi thin Chinese Farming Households","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the digital era, digital literacy has become a basic survival skill and essential literacy for citizens. The level of digital literacy is directly related to the willingness and ability of farmers to continue using digital resources after they have been \"connected to the Internet\" (Hargittai, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), which fundamentally affects the role of farmers and the improvement of their overall welfare level. Breaking the huge digital literacy gap between urban and rural areas due to \"information polarization\" (Zhang, 2022) is of great practical significance for China, the world's largest developing country. important practical significance for China, the world's largest developing country.\u003c/p\u003e \u003cp\u003eThe emergence of China's \"rural netizens\" has led to a series of short videos on rural life and the sale of local specialities, which has led to a development path of rural revitalization with its village characteristics, and provided more development opportunities for rural residents to increase production and income (Peng et al., 2022). However, only \"local capable people\" with certain digital skills and high digital literacy can better apply digital technology, integrate into the digital economy, and share digital dividends (Liu et al., 2020; Oria, 2020), and the possibility of new digital poverty arising from differences in the use of the Internet among individuals is rapidly increasing (Berkowsky et al., 2022). increases (Berkowsky et al., 2018). At present, based on realizing \"the same network and the same speed in urban and rural areas\", many villages are still in the forgotten corners, the depth of information application of most farmers is low, and the awareness and ability to solve problems and create income by using digital technologies and tools such as personal computers is still a shortcoming (Chen et al., 2022) so that the vast number of farmers have \"opportunities\" in the digital era (Chen et al., 2022). In the digital era, the problem of \"not being able to grasp the opportunities and not being able to use the conditions\" has not been truly solved. According to the Report on Survey and Analysis of Digital Literacy in Rural China under the Background of Rural Revitalization Strategy released by the Informatization Research Center of the Chinese Academy of Social Sciences, nearly one-third of rural residents believe that the application of cell phones or computers does not have any effect on personal employment, entrepreneurship and income enhancement. In addition, some farmers recklessly \"brush gifts\" to the host, which triggers family conflicts and increases family debts. High-quality digital village development remains a serious challenge.\u003c/p\u003e \u003cp\u003eEven though digital devices have become highly accessible in rural areas and the Internet has become a new force for equalizing opportunities. However, there are different choices between digitally advantaged and digitally disadvantaged groups in terms of \"serious applications\" and \"entertainment applications\" (Mikołajczyk, 2023) due to differences in personalities and imaginations, leading to a large gap in the role of capital and resources in digital applications. The dominant role of capital and resources presents a large gap, which in turn has a direct impact on the pattern of income distribution within the farming community. Recognizing that the pure use of digital technology does not automatically improve people's ability to apply digital technology, how to effectively improve the ability of low-income farmers to use digital \"new agricultural tools\" (UNESCO, 2017), cultivate and improve the overall digital literacy and digital skills of farmers, and stimulate the deep application and value creation of personal computers has become a major challenge for farmers. In-depth application and value creation of personal computers by farmers has become a major issue for China in practising the two strategies of digital villages and promoting common prosperity.\u003c/p\u003e \u003cp\u003eCompared with previous studies, the possible improvement of this study lies in the following: based on the new scenario that information services have fully penetrated the full range of rural residents, based on the increasing importance of the digital economy to economic growth (Herman and Oliver, 2023), focusing on the indisputable fact that the income disparity within the farming households has been increasing during the process of urban-rural integration, utilizing the data from the two issues of the China Family Panel Survey ( China Family Panel Studies, CFPS) data, from the perspective of rural residents as the main users of information resources, the welfare effects of digital literacy, a front-end condition for the depth of information application, in the process of value re-creating and value re-distributing are systematically sorted out and elaborated... It is hoped to construct the intrinsic connection between digital literacy and farm household income, and based on different application levels, through the results of empirical analysis, it is proposed to promote the overall income growth of farm households with precise digital increase and shrinkage distance policies, while forming a virtuous circle of economic return to income equality.\u003c/p\u003e"},{"header":"2. Literature review and theoretical mechanisms","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Literature review\u003c/h2\u003e \u003cp\u003eBawden (2008) systematically summarized the definitions and sources of digital literacy, defining digital literacy as the ability to read and comprehend multimedia and online texts. While digital literacy in the current context usually refers to the ability to read, write and process information based on the context of the digital economy, which was originally defined by (Gilster, 1997), stating that digital literacy is \"mastering ideas, not keyboarding\". Eshet-Alkalai (2004), on the other hand, states that digital literacy should refer to the ability to understand and properly use computers to store digital resources and information. Synthesizing the aforementioned typical frameworks, UNESCO proposes a literacy domain that includes related domains such as device operation, information processing, and communication and collaboration. Although previous studies have used different descriptions of the concept of digital literacy, these concepts are based on \"the skills needed to effectively utilize computers and the Internet\" (Soroya et al., 2021). These skills are a necessity for everyone to survive in the current information and communication technology (ICT) era.\u003c/p\u003e \u003cp\u003eDigital literacy has been measured in more specific ways than the vagueness of its definition. Initially, digital literacy was categorized into five dimensions, namely \"picture-visual literacy,\" \"reproduction literacy,\" \"categorical thinking literacy, \" information literacy\" and \"socio-emotional literacy\" (Eshet-Alkalai, 2004). After that, the Digital Intelligence Alliance (DIA), a world organization established by the World Economic Forum and other world organizations, published the DQ Global Standards Report 2019, which is the first attempt to develop global standards for digital literacy and competencies in education and technology, proposing 24 digital literacy competencies covering knowledge, skills, attitudes, and values at three levels and in eight domains. Soroya et al. (2021) examined the information screening ability of Pakistani university students to identify their digital literacy using the indicator of \"frequency of Internet use\" by individuals. Overall, along with the continuous penetration of global digital technology, the connotation of digital literacy is getting deeper and deeper, the research groups are gradually focusing on it, and the measurement framework is becoming clearer and clearer. At the same time, however, given that digital literacy measurement frameworks need to be integrated with researchers' living environments, digital literacy framework models are often characterized by cross-national institutions. Relevant studies show that digital literacy models and frameworks have now reached more than 100 (Tabusum et al., 2014). Therefore, in the new stage of digital village construction to drive and promote common prosperity in agriculture and rural areas, there is a need to focus on constructing digital literacy frameworks that are more practical, unified and coordinated for Chinese rural residents.\u003c/p\u003e \u003cp\u003e\"The Fourteenth Five-Year Plan is a critical period for China to bridge the digital literacy gap between urban and rural areas, as well as a new stage in the digital economy's shift to universal sharing. The Outline of Actions for Enhancing Digital Literacy and Skills of the Whole Population says, \"Efforts will be made to expand the four major scenarios of digital life, digital learning, digital work and digital innovation for the whole population, enhance the literacy and skills of the whole population in digital learning, work, life and innovation, and set up a system of indicators for evaluating the development of the whole population's digital literacy and skills in line with China's national conditions, to continually bridge the digital divide between urban and rural areas, regions and populations, and promote common prosperity. digital divide and promote common prosperity.\" Inadequate ability to create value using personal computers is the main shortcoming that limits the improvement of rural residents' digital \u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e①\u003c/a\u003e literacy1. As a result, this study defines rural households' digital literacy as an action ability to realize productive and creative value through the specific application of the Internet based on the four scenarios of digital life, digital learning, digital work, and digital innovation. On this basis, this study utilizes the CFPS microdata in an attempt to explore the economic and social effects of rural residents' income enhancement from the perspective of digital literacy, and to explain in-depth the relevant impacts and theoretical mechanisms of digital literacy on farmers' income.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Theoretical mechanisms\u003c/h2\u003e \u003cp\u003eThe economic impact of digital literacy is an important theme in related research, and existing studies generally agree that changes in the degree of inequality and changes in digital literacy are intertwined, but the conclusions of existing studies are widely divergent (Bauer, 2018). Based on the reality of China's large urban-rural digital literacy gap and the low digital literacy of rural residents, this study focuses on the impact of digital literacy on the income of farm households, intending to provide more specific and reliable empirical evidence for existing aggregate studies. The theoretical analysis is mainly derived from the Nelson - Phelps model of human capital. The model argues that the most important role of human capital is not to increase the productive capacity of existing work tasks, but to facilitate the adoption of technology. That is, compared to Becker and Mincer's basis that human capital can drive productivity gains, this notion favours the idea that the primary role of human capital can provide important safeguards for workers to cope with change, disruption, and especially new technologies. This study argues that the potential of digital literacy to boost farm incomes lies in the fact that it is a kind of human capital in the digital age and an important way to expand the quality dividend of the population.\u003c/p\u003e \u003cp\u003eBased on the micro level, the continued promotion of digital villages provides a new direction for rural economic growth, and digital literacy will be an ability for households to use digital services based on digital scenarios and to allocate resources in the digital era (Hargittai et al., 2018). Having a certain degree of digital literacy is a prerequisite for individual farmers to be able to integrate into the digital society, enjoy digital dividends, and open up space for employment, entrepreneurship, and income generation. However, individual farmers who want to have a higher marginal rate of return in digital scenarios will inevitably require a stronger digital literacy that combines the needs of information integration, screening, and decision-making in the dynamic evolution of the digital economy. The impact of digital literacy on farmers' income is mainly reflected in the integration of digital scenarios and response to digital behaviours, while the underlying logic is reflected in the reduction of the cost of acquiring knowledge and information and the better management of individual resources.\u003c/p\u003e \u003cp\u003eThe accumulation of digital literacy improves farmers' understanding and cognitive level of digital technology, bridges the mismatch between awareness and skills, and thus improves their awareness and ability to make economic decisions such as choosing careers and investments based on digital scenarios. From the supply side, digital literacy corresponds to the ability to acquire and process information, which is conducive to transforming traditional agriculture, changing the mode of agricultural production, accelerating the digital transformation of planting and animal husbandry, accelerating the linkage of e-commerce systems and express logistics and distribution systems in counties and villages, and realizing industrial empowerment with the help of digital technology. From the demand side, digital literacy is also able to stimulate the enthusiasm, initiative and creativity of farmers to build a digital countryside with the help of human capital accumulation and peer effect, and through the ripple effect of the diffusion of digital technology, they have the option to participate in broader market transactions. Farmers can not only make use of the larger market to sell their products and provide labour and professional skill services to increase their income through e-commerce live broadcasting but also obtain a more diversified supply and lower transaction costs under the \"buyer\" advantage of the consumer market. Both a wider range of income channels and a lower cost of living will lead to higher real incomes.\u003c/p\u003e \u003cp\u003eWhile the strengthening of digital literacy is conducive to increasing farmers' acceptance and support for digital transformation, it may also lead to higher issues such as privacy leakage and financial security. Due to security concerns, households are more conservative in promoting and using digital products. In the case of mobile payment, whether an individual adopts mobile payment technology depends not only on the ease of use and usefulness of mobile payment but also on its privacy and security and social norms. The lower the individual's risk appetite, the lower the internet usage and the less willing they are to take the risk of using financial services and products (Zhao et al., 2023). However, another study suggests that individuals' lack of understanding and identification with digital services and products changes significantly as digital literacy improves across the ability threshold. Farmers with high resource endowments and comprehensive capabilities perform more prominently in terms of utilizing digital literacy to achieve resource allocation efficiency. When faced with the economic choice between \"entertainment applications\" and \"serious applications\", high-income farmers are more likely to cross the cost threshold and ability threshold of information utilization and choose the latter, which improves the efficiency of asset allocation and property income, and makes farmers Thus, in the process of improving the efficiency of asset allocation and increasing property income, the likelihood of the widening of the internal income gap among farm households increases significantly.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data Sources and Modeling","content":"\u003cp\u003eThe main source of data used for the empirical analysis of this study is the 2016 and 2018 China Family Tracking Survey (CFPS). The main reasons for choosing this database are: first, the CFPS survey database is executed by the China Social Science Survey Center (ISSS) of Peking University, which covers a wide range of provinces, has a high sample retention rate, and is considered an authoritative research database. It can meet the analytical needs of this study. Second, the questionnaire contains sufficient questions about the use of digital application scenarios and variables such as individual income, and it can demonstrate the relationship between digital literacy and farm household income on a national and large scale, reducing the small sample measurement error. Based on this, this study matches the merged sample of farm household families with some regional macro data in the corresponding period after deletion and reassignment of values, to include individual mobility, and finally obtains 10,540 unbalanced panel survey data for the two periods. Among them, 5,433 farm household samples in 2016 and 5,107 farm household samples in 2018.\u003c/p\u003e \u003cp\u003eExplanatory variables: The explanatory variable in this study is \"farm household income\". Regarding the income of agricultural households, the question \"total tax income from all jobs in the past year\" in the CFPS questionnaire was used as a measure. To avoid the effect of heteroskedasticity, this variable was logarithmized and included in the analysis model.\u003c/p\u003e \u003cp\u003eCore explanatory variable: digital literacy. Relying on the previous theoretical analysis and related research, this study starts from the realization scenario of digital literacy and measures the level of digital literacy of farmers with the help of five questions in the questionnaire, namely, \"Frequency of using the Internet for learning\", \"Frequency of using the Internet for work\", \"Frequency of using the Internet for The digital literacy level of farmers is measured with the help of five questions in the questionnaire: \"Frequency of using the Internet for learning\", \"Frequency of using the Internet for work\", \"Frequency of using the Internet for entertainment\", \"Frequency of using the Internet for socializing\", \"Frequency of using the Internet for business\" (the frequency takes the value from 0 to 6). Among them, \"frequency of using the Internet for entertainment\" and \"frequency of using the Internet for socializing\" can be attributed to the digital life scenarios. Considering the inherent differences in infrastructure between provinces and cities, it is inappropriate to adopt a uniform standard for measuring digital literacy, which is likely to exacerbate the digital divide among farmers. Therefore, this study draws on the research idea of Asfaw et al (2018) to calculate the digital literacy of farm households in three steps: first, the different structural literacies of each province and city are summed up separately; second, the proportion of farm households in different digital scenarios in each province is calculated; and third, an equal-weighted assignment is carried out based on the aforementioned proportions of different application scenarios, and the actual digital literacy of the farm households is finally deduced. It should be noted that although the digital literacy indicators are still constructed based on \"Internet use\", the focus of the study has shifted from the stage of Internet popularization to the assessment of digital literacy.\u003c/p\u003e \u003cp\u003eAccording to the statistical results in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, it can be seen that, firstly, the digital literacy of farmers in the East is significantly higher than that of farmers in the West, which is reflected not only in the absolute change but also in the relative change. However, at the same time, it is also found that the digital literacy of a larger number of farmers is very close to 0. Farm households have a higher frequency of exposure to digital life scenarios, and a weaker degree of digital literacy enhancement achieved through the integration of digital application scenarios. This suggests that many farmers have a single way of using the Internet and lack effective utilization of applications. Second, although the digital economy, with its innovative capacity, has led to the rapid growth of China's economy through both industrial digitization and digital industrialization, the basic trend of the digital economy development index, which is decreasing from the East coast to the west and inland, has not changed significantly over time. Digital infrastructure is likely to be a necessary but not sufficient condition for boosting farmers' incomes. Third, based on the income data of farm household subgroups published by the National Bureau of Statistics (NBS) for the same period, the absolute income gap between the incomes of farm households in the lower subgroups and those in the higher subgroups shows a widening trend. This further supports the policy reflection that China's toughest task of promoting common prosperity is in rural areas. Since the digital economy was first included in a government report in 2017, it has already had a significant impact on the future direction of China's economy. But at the same time, the digital economy has also shaped a new distribution system through a new production relationship. Under this new distribution system, it is mainly reflected in the difference in the ability to benefit between people. Therefore, in the era of the digital economy, it is necessary to analyze the relationship between digital literacy and the income of rural households to be highly vigilant against the misperception that the use of Internet services, which is promoted by the \"reduction of costs and increase of speed\" of the communication infrastructure in rural areas, is in sync with the acquisition 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\u003eDescriptive analysis of the digital economy, digital literacy and farm household income in the sub-region\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003edigital economy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFarmers' income\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016 Eastern Average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elow packet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehigh clustering\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016 Western Average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3006.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018 Eastern Average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGap (2016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGap (2018)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018 Western Average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8116.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8821.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003edigital literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFarmers' income\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016 Eastern Average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003elow packet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehigh clustering\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016 Western Average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3666.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34042.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018 Eastern Average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAbsolute income gap\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018 Western Average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25441.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30376.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: The digital economy index here is from the China Digital Economy Index White Paper, and farm household income is from the China Statistical Yearbook as well as CFPS integrated data. Income unit: yuan. Same below.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo show the relationship between rural residents' digital literacy and farm household income more intuitively, this study divides farm household income into 2 intervals according to the mean value, indicating the distribution of changes in farm household income from low to high. On this basis, the combination of farm household income with different digital application scenarios is described in joint statistics. According to the statistical results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it can be seen that, firstly, from the front-end of the accumulation of digital literacy: different scenarios of digital application, it can be found that the application of whatever digital scenario can enhance the probability of the farm household to enter the high-income group from the low-income group. Second, comparing the income groups of farmers under different digital application scenarios, it can be found that, no matter the high-income group or low-income group, farmers prefer digital entertainment, and the reach of digital innovation and digital learning scenarios is still not high. This shows that the government needs to target this weakness at a later stage, through the construction of scenarios from \"entertainment applications\" to \"serious applications\", and implement precise measures against the standard and table, to effectively bring into play the role of digital scenarios in knowledge and information acquisition, to ensure that farmers can gradually narrow the income gap in the process of sustained income increase. This is the only way to ensure that farmers can gradually narrow the income gap in the process of sustained income growth. However, whether this income-generating effect driven by digital literacy can stand up to the test at a later stage requires the introduction of other control variables and an in-depth analysis of the relationship between the two to obtain more reliable empirical evidence.\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\u003eDescription of separate joint statistics\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\"\u003e \u003cp\u003eDigital Application Scenarios\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003edigital literacy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edigital learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eflimsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003egeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm household income (range 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm household income (range 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking with numbers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eflimsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003egeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm household income (range 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm household income (range 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edigital socialization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eflimsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003egeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm household income (range 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm household income (range 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.47\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\u003eflimsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003egeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm household income (range 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm household income (range 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Innovation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eflimsy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003egeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm household income (range 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm household income (range 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: The digital literacy distinction here is the frequency of each function of Internet use: (0) is weak; (1\u0026ndash;5) is average; (6) is high.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eControl variables: concerning existing studies (Guess A et al., 2019; Chetty et al., 2018), this study, based on the questionnaire question availability and appropriateness, starts from the three dimensions of individual characteristics, family characteristics, and provincial characteristics, and adds corresponding control variables to try to cut down the regression bias of digital literacy on the income of farmers due to the omission of variables. Specific descriptive statistics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics\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=\"char\" char=\".\" 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\"\u003e \u003cp\u003evariable name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable Definition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eobserved value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eaverage value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmers' income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGross income from work after taxes (log)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.754\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 proportion of each farm household in the sum of digital literacy in each province\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(a person's) age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge of respondents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(math.) age squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(math.) age squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edistinguishing between the sexes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 for males, 0 for females\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeducational attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYears of education completed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried is assigned a value of 1, otherwise 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esocial security\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePension and medical participation is assigned a value of 1, otherwise 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.820\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\u003eNumber of persons in the household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousehold chores or parental care is assigned a value of 1, otherwise, it is 0.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal household assets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal household financial assets (log)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egeographic capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpenditures on family favours (log)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.262\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrip Capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial trust in the family (grossed up)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.810\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation costs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal household expenditure on education (log)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeconomic level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe gross product of provinces and municipalities (logarithmic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe estimated equation for the impact of digital literacy on farm household income is as follows:\u003c/p\u003e \n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/span\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eWhere, denotes farm household income, denotes digital literacy, represents the control variables affecting farm household income, and is a random disturbance term. denotes the coefficient of influence of digital literacy on farm household income, which is also the focus of this study.\u003c/p\u003e"},{"header":"4. Empirical analysis","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Baseline analysis\u003c/h2\u003e \u003cp\u003eGiven that the CFPS two-period data used in this study are unbalanced panel data, the use of mixed OLS (POLS) tests is more appropriate here. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e Models (1) to (4) represent the empirical results obtained from the regression test by introducing only core variables, adding individual characteristic variables, adding household characteristic variables, and adding regional characteristic variables, respectively. In addition, this study was conducted to further find out whether the economic impact of digital literacy on farm household income would be policy-driven. Regression analyses were conducted using ordinary least squares (OLS) using cross-sectional data from 2016 and 2018, respectively. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e models (5) to model (6). The results of the mixed OLS test for models (1) to (4) in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show that digital literacy has a significant contribution to farmers' income growth with or without the addition of the control characteristic variables, and they are all significant at the 1% statistical level. It should be noted that compared with the promotion effect of digital literacy on farm household income increase in 2018, digital literacy in 2016 did not serve the function of farm household income increase. The reason is that China's action plan to implement \"Internet Plus\" and promote the construction of \"Digital China\" began in 2015. Since then, with the strong support of national policies, the use of the Internet has gradually penetrated from e-government and e-commerce to socialization and entertainment, and China's national digital literacy has seen an accelerated period of improvement after 2016. The overall findings suggest that focusing on low-income populations and underdeveloped regions, building digital scenarios closely related to farmers' production and life as soon as possible, and narrowing the gaps in the use of smart devices and the knowledge gap of digital skills are conducive to the realization of the long-term empowerment of digital literacy to increase the incomes of farmers.\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\u003eBenchmarking\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.100***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.869***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.560***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.983***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.791***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(10.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(4.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(3.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(4.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(3.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(a person's) age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.199***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.175***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.136***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.140***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.088***\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=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(11.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(10.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(7.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(4.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(5.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(math.) age squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.003***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.002***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.002***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.002***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.002***\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=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(-13.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-12.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(-11.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(-6.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(-9.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edistinguishing between the sexes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.197***\u003c/p\u003e \u003cp\u003e(13.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.177***\u003c/p\u003e \u003cp\u003e(13.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.161***\u003c/p\u003e \u003cp\u003e(13.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.199***\u003c/p\u003e \u003cp\u003e(7.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.867***\u003c/p\u003e \u003cp\u003e(12.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeducational attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.266***\u003c/p\u003e \u003cp\u003e(22.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.260***\u003c/p\u003e \u003cp\u003e(21.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.184***\u003c/p\u003e \u003cp\u003e(15.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.207***\u003c/p\u003e \u003cp\u003e(9.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.102***\u003c/p\u003e \u003cp\u003e(10.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.281**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.335***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.382***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.957***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.641***\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=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(3.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(-4.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(14.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esocial security\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.660***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.591***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.988***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.596***\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=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(12.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(11.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(14.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(20.47)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.120***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.110***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.091**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.047***\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-5.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(-5.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(-2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(-2.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily care\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.191*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.343*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.214**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-1.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(-1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(2.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal household assets\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.098***\u003c/p\u003e \u003cp\u003e(10.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.061***\u003c/p\u003e \u003cp\u003e(6.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.068***\u003c/p\u003e \u003cp\u003e(4.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003cp\u003e(1.44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egeographic capital\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.031*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.043***\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(2.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrip Capital\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003***\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(5.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(4.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(2.95)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation costs\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.003\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e(-0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(-0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(-0.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeconomic level\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.187***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.979***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.140***\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e(20.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e(9.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e(2.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: t-statistics are in parentheses; differences in confidence level significance are indicated by ***, **, and * at the 1%, 5%, and 10% levels, respectively. Year characteristics are controlled for, as below.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Quantile regression\u003c/h2\u003e \u003cp\u003eThe previous tests have confirmed the income-enhancing effect of digital literacy, but since the impact of digital literacy on income varies according to the stock of household income, it produces either an increasing scale effect or a decreasing marginal effect, which leads to the economic impact of digital literacy on the increase in income of farm households, which exhibits differences in returns among different groups of farm households. Thus, this study uses the quantile regression method to expand the test of the relationship between digital literacy and farm household income. According to the regression analysis in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, it can be seen that digital literacy has a positive impact on the income of farm households in different quartiles. From the trend change, middle-income farm households may be affected by lower digital literacy. However, the credibility of digital literacy on farm household income increases significantly with higher quantile points. This suggests that, in the face of the digital economy wave, differences in digital literacy will make the risk of widening the income gap within farm households, i.e. the inclusiveness of the digital economy still needs to be strengthened. Therefore, in addition to considering how to improve the digital literacy of rural residents at the overall level, governments at all levels should also examine the differences in the digital use of rural residents, to prevent the widening of the \"digital literacy gap\" within the countryside, which will make the bottom line of the construction of the digital countryside and the effectiveness of the strategy for revitalization of the countryside unstable, and thus hurt China's overall pattern of income distribution. and thus adversely affect China's overall income distribution pattern.\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\u003eQuantile regression model\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\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarm household income (Q_10)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFarm household income (Q_50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFarm household income (Q_90)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarm household income (Q_99)\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\u003e4.513*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.038**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.945***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.591***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(3.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtrol variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControlled\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=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Dimensional tests\u003c/h2\u003e \u003cp\u003eThis study has constructed digital literacy indicators from four types of digital application scenarios as well as a relatively comprehensive empirical test of farm household income. However, to further identify the reasons why digital literacy tends to widen the income gap within farm households and to provide precise policy recommendations, it is also necessary to clarify which types of digital literacy applications should be targeted by corresponding policies, and which sources of income are reflected in the income gap caused by digital literacy. Considering that serious apps have a stronger resource release function than entertainment apps (James, 2019), the \"serious apps\" indicator is constructed by using \"individual digital literacy in both years exceeds the average value of digital literacy in each province\" (greater than the assigned value of is 1, and vice versa is 0), and \"individual digital life (entertainment, social) literacy in two years exceeds the average value of digital literacy in each province\" is used to construct the \"entertainment application\" indicator (greater than the assigned value is 1, and vice versa is 0). According to the regression results in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, following the previous scholars' quintile grouping method (Rooij et al., 2011), from low to high, those in the interval 1 and 2 are redefined as low-income farmers, and those in the interval 4 and 5 are redefined as high-income farmers. High-income farmers realized more significant income increase through the application of serious category. And in addition, this study also found that the digital literacy of high-income farmers is mainly focused on the increase of property income (wage and business coefficients are significant but lower than that of low-income farmers), and the positive effect of digital literacy on the low-income farmers' access to wage and business incomes is more pronounced (only the most significant coefficients are retained for the wage results). With the continuous development of the digital economic system, the improvement of digital literacy strengthens rural residents' understanding and cognitive level of digital technology, thus enhancing their awareness and ability to make economic decisions such as choosing a career and making investments based on digital scenarios. However, at present, the role of digital literacy in increasing the income of rural households is more often reflected in high-income households, which have more resource endowments, and are also in a position to obtain stronger digital literacy and digital financial capabilities, and cross the scene threshold from \"entertainment applications\" to \"serious applications\" as soon as possible, from \"entertainment applications\" to \"serious applications\". As soon as possible, they can cross the threshold from \"entertainment apps\" to \"serious apps\", and gradually transition from production benefits to creation benefits. With the help of \"serious applications\", we can realize the structural transformation from wage income and business income to property income, and promote the accumulation of wealth through the investment of financial products and the transfer of land. But this in turn will also affect the further widening of the income gap. The essence behind this phenomenon lies in the growing importance of digital innovation capabilities in the digital transformation of agriculture and rural areas, which increases market rents to the benefit of the highest-income groups. This, in turn, reaffirms that differences in digital adoption, externalized by differences in digital literacy, are an important reason for widening the income gap within farming households.\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\u003eDimensionality test of digital literacy on farm household income\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\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow-income farmers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh-income farmers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow-income farmers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh-income farmers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerious apps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003cp\u003e(1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.265**\u003c/p\u003e \u003cp\u003e(2.32)\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\u003eEntertainment apps\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.302***\u003c/p\u003e \u003cp\u003e(3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.123**\u003c/p\u003e \u003cp\u003e(2.00)\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\u003edigital literacy\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\u003e4.792***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.076***\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(3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(3.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtrol variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControlled\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=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Robustness testing and endogeneity treatment\u003c/h2\u003e \u003cp\u003eTo develop empirical analysis using microdata, the issues of robustness and endogeneity should be considered to further enhance the scientific validity of the research findings. The test ideas of this study are as follows: first, try to replace the core variables. The logarithmic value of farm household income is divided according to 0\u0026ndash;1, and the value equal to 0 is attributed to 0. The specific regression results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e model (1). In addition, this study also re-measured the digital literacy variable by assigning those who did not participate in digital application scenarios to carry out various online activities to 0, and those whose values are greater than 0 to 1. Then the regression was conducted again, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e Model (2). Second, considering that the relationship between digital literacy and farmers' income will be plagued by the problem of endogeneity, this study utilizes the indicator of \"the average value of digital literacy in the same province and city, except for the farmers themselves\" as an instrumental variable, which to some extent meets the homogeneity and relevance of the instrumental variable, and the specific results of the first-phase and second-phase regressions are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Model (3) and Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Model (3) and Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Model (2). The specific results of the first-stage and second-stage regressions are shown in Table Model (3) and Model (4). Third, digital literacy has a heterogeneous effect on the existence of income of different groups. The generally low level of education of rural residents is the most important reason that triggers the lower digital literacy of rural residents, and as the mainstay of the long-term rural resident population, the rural elderly are even less educated and are the typical digital poor group (Bonami \u0026amp; Nemorin, 2021; Prodromou \u0026amp; amp; Lavranos, 2019). As a result, this study categorized farm households into young and middle-aged and old according to whether they reached 40 years of age in the year of birth, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e Model (1) and Model (2). And based on the number of years of education, the agricultural households were categorized into those who have completed compulsory education and those who have not completed compulsory education, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e Model (3) and Model (4).\u003c/p\u003e \u003cp\u003eBased on the regression results in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, it can be seen that after attempting to replace the core variables, the positive effect of digital literacy on farmers' income increase remains significant, and the aforementioned empirical evidence is further supported. For the endogeneity issue, the F-statistic value in the one-stage regression test is much more than 10, indicating that the selected instrumental variables meet the requirements. The higher the mean value of digital literacy of the remaining farmers in each region, the easier it is for farmers to realize digital transformation, and thus the effect of digital literacy of farmers is more obvious. The results of the two-stage regression show that the coefficient of digital literacy is significantly positive, and this coefficient is much higher than the benchmark regression coefficient. This means that if the endogeneity issue is not considered, it will largely reduce the promotion effect of digital literacy on farm household income. The heterogeneity test in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows that digital literacy promotion has a more significant economic effect on the income promotion of middle-aged and elderly farmers. In the division of years of education, the effect of digital literacy on income enhancement is more pronounced for farm households with lower educational experience. The main reason is related to the existing knowledge and information stock of each group, and digital literacy has a greater effect on the group that lacks the corresponding knowledge and information and has a more significant income-enhancing effect. The improvement of digital literacy can alleviate the plight of such groups that do not have enough effective information to recognize digital scenarios, and with the \"latecomer's advantage\", they can realize cost reduction and income increase.\u003c/p\u003e \u003cp\u003eIn addition, the study also takes into account that middle-aged and old-aged farmers and young farmers do not exist independently, and in rural areas, these two groups of people usually live in the same space or even in the same family, and they should have a kind of \"driving\" each other, so it further tries to analyze whether the latter can play a kind of regulating effect on the income of the former, to provide theoretical wisdom for the subsequent countermeasure suggestions. Therefore, we further try to analyze whether the latter can have a moderating effect on the income of the former, to better provide theoretical wisdom for the subsequent countermeasures. Given the availability of data, this study constructs the indicator of \"per capita household digital literacy\". Taking families as a group, after summing up individual digital literacy and dividing it by family size, we constructed a family per capita digital literacy indicator to further test whether digital literacy has a spatial \"driving\" effect on the income of middle-aged and old-aged farmers. According to the regression results of model 5 in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the effect of household per capita digital literacy on the income of middle-aged and old-aged farmers is significantly higher than that of digital literacy itself. This means that the transmission and flow of digital literacy within the family can realize the effective bridging of intergenerational relations, deepen and dissolve the differences in digital literacy and value, and further strengthen the income-generating effect of digital literacy on middle-aged and elderly farmers. In the new era of the convergence of the active response to population ageing and the digital village strategy, governments at all levels can further utilize the function of family \"digital feedback\", focusing on low-income groups with low years of education and mainly middle-aged and elderly farmers, to strengthen the income-enhancing effects of digital literacy on such groups.\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\u003eRobustness test and endogeneity treatment\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\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOwnership income\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFarmers' income\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\u003eFarmers' income\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\u003e1.579**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.64***\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(2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Literacy Alternative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.595***\u003c/p\u003e \u003cp\u003e(6.34)\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\u003einstrumental variable\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\u003e0.056***\u003c/p\u003e \u003cp\u003e(16.14)\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\u003econtrol variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eHeterogeneity analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 5\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 (home)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.377***\u003c/p\u003e \u003cp\u003e(3.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.498***\u003c/p\u003e \u003cp\u003e(4.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.252***\u003c/p\u003e \u003cp\u003e(2.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.736***\u003c/p\u003e \u003cp\u003e(5.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.062***\u003c/p\u003e \u003cp\u003e(4.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtrol variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eStatistical Distribution of Education, Age and Income\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \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\u003eLow-income farmers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh-income farmers\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-completion of compulsory education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompletion of compulsory education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyouthful years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiddle and old age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75.56\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=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Impact mechanisms\u003c/h2\u003e \u003cp\u003eThe previous studies have examined the direct impact of the application of digital scenarios on the income of farm households based on the application of digital scenarios, but have not focused on the indirect impact of digital literacy on the income of farm households through digital behaviour. To improve the income transformation effect of digital literacy, the mechanism of influence is further explored here based on the significant contribution of risk preference level to the source of income realization of farm households, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. Risk appetite is derived from the natural logarithm of the amount of financial investment. The results of model (1) in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e show that the economic effect of digital literacy on the risk appetite of farm households is not significant. This suggests that there is a lot of room to explore the quality of the application of digital scenarios by farmers. Subdividing the high- and low-income farm households and comparing the results of the study, it is found that digital literacy can significantly improve the income level of high-income farm households through the mechanism of risk preference, and for low-income farm households, risk preference still can't be an influential mechanism for digital literacy to affect the income of farm households. This means that relying on the development of Internet technology and the application of smartphones provides a feasible path for farmers to reach the digital economy and improve digital literacy. By reducing information asymmetry and lowering transaction costs, the marginal returns of rural economic agents have been enhanced. However, at present, the overall ability of rural residents to identify, control and utilize risks is insufficient, and the resources belonging to them have not been optimally managed. It is necessary to give full play to the role of village leaders in demonstrating the cognitive and behavioural biases of low- and middle-income rural households in the promotion of digital villages, such as \"unwillingness to use\" and \"not daring to use\" \"serious applications\", to further unleash the power of the rural economy. and behavioural bias, to further release the \"digital dividend\" and universalization effect of digital literacy in rural areas.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of impact mechanisms\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=\"1\" rowspan=\"2\"\u003e \u003cp\u003evariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003erisk appetite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFarmers' income\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003erisk appetite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow-income farmers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003erisk appetite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHigh-income farmers\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\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.957***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.590**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.360***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.205**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003erisk appetite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.119*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.090*\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(1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(-0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econtrol variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eControlled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eControlled\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"},{"header":"5. Research findings and policy recommendations","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Conclusions of the study\u003c/h2\u003e \u003cp\u003eAgainst the backdrop of increasing penetration of digital devices and communication networks, the scale efficiency disadvantage of digital financial services exhibited by less developed regions is worth reflecting on. Bridging the urban-rural digital divide, offsetting the negative effects of epidemic shocks, and fostering high-quality farmers has become an inevitable trend of digital transformation in rural areas. This study empirically examines the direction and mechanism of the impact of digital literacy on the income enhancement of rural households based on two periods of microdata from the China Household Tracking Survey. The main research conclusions are:\u003c/p\u003e \u003cp\u003eFrom a statistical point of view, the income of agricultural households is characterized by imbalance and insufficiency, which is an important factor constraining the process of common prosperity. The intra-farm household income gap tends to increase, and the income gap between eastern and western farm households is also more prominent, showing more obvious group differences and regional differences. In addition, digital literacy is more closely related to intra-farm household income disparity than the digital economy.\u003c/p\u003e \u003cp\u003eThe empirical results show that the improvement of digital literacy has a significant role in increasing the income of farm households, but it widens the income gap between farm households. That is, although digital literacy can help low- and middle-income farmers reduce the cost of acquiring knowledge and effective information, and create a useful path to increase wages and business income, there is a larger income gap between the two compared to high-income farmers who utilize \"serious applications\" to realize property income and wealth accumulation through risk appetite. As an effective means of acquiring knowledge and information, \"entertainment apps\" can provide effective training for farmers in production and management and asset allocation, but there is still a gap between the creative income-generating effects of \"serious apps\".\u003c/p\u003e \u003cp\u003eDigital literacy has a more significant positive effect on the income improvement of digitally disadvantaged groups in rural areas. The coefficient of influence of digital literacy on the income enhancement of middle-aged and old-aged farmers as well as farmers who have not completed compulsory education is even greater. These groups are typical of the digitally disadvantaged, and positive interaction with such groups to achieve digital literacy enhancement can further reduce digital illiteracy and truly realize digital empowerment through digital literacy education.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Policy implications\u003c/h2\u003e \u003cp\u003eDigital literacy can provide endogenous motivation for farmers to increase their income. To better utilize the positive role of digital literacy in promoting high-quality development in agriculture and rural areas, this study proposes the following policy recommendations:\u003c/p\u003e \u003cp\u003eThe construction of digital rural areas should emphasize not only the creation of digital communication infrastructure and common scenarios but also the guidance and cultivation of digital literacy among farmers. Whether people can enjoy the dividends brought by the digital era ultimately depends on the awareness and ability to utilize digital services on the demand side, i.e., the digital literacy of individuals. In addition to promoting the digital village strategy in an orderly manner, helping farmers learn basic Internet operations, software for basic applications, and the use of security services through systematic digital literacy education, and realizing the coverage of digital literacy education for rural residents with the help of digital literacy programs, governments at all levels should also combine the resources of the government, NGOs, and individuals, and guide enterprises and public interest organizations to effectively participate in the digital skills upgrading of farmers, to Promote the vertical extension of digital services and training to rural areas. Through public service centres and professional farmers' portals, we can enhance rural residents' recognition and support of the digital scene, build a framework for a cultivation system that matches the \"five forces\" model of digital literacy: perception, integration, absorption, innovation and development, and help rural residents better integrate into the digital era by taking the initiative of awakening their consciousness. The active behaviour of awakening awareness helps rural residents to better integrate into the digital era, thus forming high-quality living and employment habits.\u003c/p\u003e \u003cp\u003eThe supply of policies to improve digital literacy should focus on key subjects and main dimensions, reflecting the precise application of policies. Low- and middle-income farmers, middle-aged and elderly farmers, and farmers with lower education levels are the key groups that the relevant policies need to focus on, with more room for policymaking and tapping into potential. First of all, to address the inherent disadvantage of low- and middle-income farmers' digital literacy accumulation, the government needs to preemptively demonstrate the benefits of digitization and implement the \"better of the best\" support policy to encourage and guide high-income farmers to land in enterprises and transform their industries, etc., to provide more opportunities for low- and middle-income farmers to participate in training for digital transformation through the strengthening of the trust mechanism. Through the strengthening of the trust mechanism, provide more opportunities for middle- and low-income farmers to participate in digital transformation training, strongly attract the visible poverty caused by insufficient income and the invisible poverty group identified by the time deficit, and minimize the gap between the labour resource endowment of the middle- and low-income groups and that of the high-income groups. Second, it is necessary to improve the ability of middle-aged and elderly farmers to use digital \"new agricultural tools\" and narrow the gap between the digital integration of middle-aged and elderly people. On the one hand, it is necessary to improve the suitability of relevant applications for the elderly; on the other hand, it is necessary to enhance the relevant skills of middle-aged and elderly farmers. Specific measures include simplifying the operational steps of application settings and increasing learning facilities and opportunities. Real-life experience and the introduction and expansion of family \"digital feedback\" should be utilized to enhance the sense of information participation and experience of this group, strengthen the self-efficacy of middle- and old-aged farmers, and weaken the extension of the traditional generation gap in the digital era, which will ultimately contribute to the development of China's silver economy.\u003c/p\u003e \u003cp\u003eWhile improving the targeting of digital literacy and focusing on improving the digital literacy of the \"long-tail group\", it is also necessary to consider how to effectively reduce the risk of exclusionary behaviour of the long-tail group, to make the focus of the use of digital technology in such groups shift from \"entertainment applications\" to \"serious applications\". \"Serious applications\". First, the government and the corresponding digital supplying organizations need to guard the bottom line of security and improve the security coefficient of digital products and services. Secondly, the digital literacy policy should be tailored to the specific characteristics and needs of rural residents in each province and city. Compared with the developed regions in the east, the digital literacy level of rural residents in the western region is on the low side. By \"promoting the West with the East\" and \"supporting the weak with the strong\", we can further adjust and transform the low level of satisfaction of rural residents through unidirectional entertainment, and improve the social environment by stimulating the spirit of the hidden high level of entertainment. high level of spirit, and improve the imbalance of social wealth distribution. Finally, improving digital literacy and promoting commonwealth is a long-term systematic project. At different stages of digital development, the accumulation of digital literacy of farmers will change with the progress and lag of digital technology, and the types of digital applications will also show dynamic changes. In the face of new digital service scenarios and modes, governments at all levels and relevant parts need to expand the population quality dividend, so that digital literacy can be turned into a practical logic to accelerate the promotion of farmers to share the digital dividend.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e All the work was done independently by myself.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e The authors would like to acknowledge support from the Major Program of National Social Science Foundation of China (Project No.20BJY142,20AZD079).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e Data will be made available on request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003eEthics approval was not required for this study/Not Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors have no conflicts of interest to declare.The authors certify that the submission is original work and is not under review at any other publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAsfaw,S.,Pallante,G.,Palma,A.,2018.Diversification Strategies and Adaptation Deficit: Evidence from Rural Communities in Niger. World development.101(1),219-234.\u003c/li\u003e\n\u003cli\u003eBauer,J.M.,2018.The Internet and income inequality: socio-economic challenges in a hyperconnected society. Telecommunications Policy.42(4),333-343.\u003c/li\u003e\n\u003cli\u003eBawden,D.,2008.Origins and concepts of digital literacy. New York: Peter Lang.\u003c/li\u003e\n\u003cli\u003eBerkowsky, R.W., Sharit, J., Czaja,S.J.,2018.Factors predicting decisions about technology adoption among older adults. Innovation in Aging.1(3),1-12.\u003c/li\u003e\n\u003cli\u003eBonami,B.,Nemorin,S.,2021.Through three levels of abstraction: Towards an ecological framework for making sense of new technologies in education. Education and Information Technologies.26(1):1-18.\u003c/li\u003e\n\u003cli\u003eChen,H.,Chen,C.,Li,Y.,Qin,L.,Qin,M.,2022.How Internet usage contributes to the livelihood resilience of migrant peasant workers? Evidence from China. Journal of Rural Studies.96,112-120.\u003c/li\u003e\n\u003cli\u003eChetty,K.,Liu,Q.,Gcora,N.,Josie,J.,Li,W.,Chen,F.,2018.Bridging the digital divide: measuring digital literacy. Economics:The Open-Access,Open-Assessment E-Journal.12(1). \u003c/li\u003e\n\u003cli\u003eEshet-Alkalai,Y.,2004.Digital literacy: A conceptual framework for survival skills in the digital era. Journal of Educational Multimedia and Hypermedia.13(1),93-106.\u003c/li\u003e\n\u003cli\u003eGilster,P.,1997.Digital Literacy.New York: Wiley Publishing.\u003c/li\u003e\n\u003cli\u003eGuess,A.,Nagler,J.,Tucker,J.,2019.Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Science Advances.5(1),eaau4586.\u003c/li\u003e\n\u003cli\u003eHargittai,E.,2005.Survey measures of web-oriented digital literacy. Soc Sci Comput Rev.23(3),371-379, \u003c/li\u003e\n\u003cli\u003eHargittai,E.,F\u0026uuml;chslin,T., Sch\u0026auml;fer,MS.,2018.How Do Young Adults Engage With Science and Research on Social Media? Some Preliminary Findings and an Agenda for Future Research. Social Media + Society.4(3),205630511879772.\u003c/li\u003e\n\u003cli\u003eHerman,P.R.,Oliver,S.,2023.Trade, policy, and economic development in the digital economy. Journal of Development Economics.164,103135.\u003c/li\u003e\n\u003cli\u003eJames, J.,2019.Confronting the scarcity of digital skills among the poor in developing countries. Development Policy Review.39(2),324-339.\u003c/li\u003e\n\u003cli\u003eLiu,ZJ.,Tretyakova,N.,Fedorov,V.,Kharakhordina,M.,2020.Digital literacy and digital didactics as the basis for new learning models development. Int J Emerg Technol Learn.15(14),4-18.\u003c/li\u003e\n\u003cli\u003eMikołajczyk,B.,2023.Universal human rights instruments and digital literacy of older persons. The International Journal of Human Rights.27(3),403-424. \u003c/li\u003e\n\u003cli\u003eOria,B.,2020.Edmodo como herramienta de aprendizaje telecolaborativo online en el aula de ingl\u0026eacute;s. Encuentro.28,49-70.\u003c/li\u003e\n\u003cli\u003ePeng,X.,Zhang,J.,Peng,G.,2022.Does Internet Use Improve the Income of Residents? -Empirical Evidence from CGSS2017. China Finance and Economic Review.10(4),96-114.\u003c/li\u003e\n\u003cli\u003eProdromou,M., Lavranos,G.,2019.Identifying latent needs in elderly digital literacy: the PROADAS study. European Journal of Public Health.29(4), \u003c/li\u003e\n\u003cli\u003eRooij,M.V., Lusardi,A.,Alessie,R.,2011.Financial literacy and stock market participation. Journal of Financial Economics.101(2),449-472.\u003c/li\u003e\n\u003cli\u003eSoroya,SH.,Ahmad,AS.,Ahmad,S.,Soroya,MS.,2021.Mapping internet literacy skills of digital natives: a developing country perspective. PLOS ONE.16(4),e0249495.\u003c/li\u003e\n\u003cli\u003eTabusum,S.,Saleem, A.,Batcha,M.S.,2014.Digital literacy awareness among arts and science college students in Tiruvallur district: a study. International Journal of Managerial Studies and Research.2(4),61-67.\u003c/li\u003e\n\u003cli\u003eUNESCO.,2017.Working group on education: Digital skills for life and work. In: Broadband commission for sustainable development.\u003c/li\u003e\n\u003cli\u003eZhang,Y.,2022.Measuring and applying digital literacy: Implications for access for the elderly in rural China. Educ Inf Technol.\u003c/li\u003e\n\u003cli\u003eZhao,H.,Li,Y.,Sai,Q.,Ren,Y.,2023.Cross-border credit networks, banking risk contagion and suppression effects. Social Networks.73,130-141.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003cp\u003e①Source: Survey and Analysis Report on Digital Literacy in Chinese Villages in the Context of Rural Revitalization Strategy, published by the Informatization Research Center of the Chinese Academy of Social Sciences.\u003c/span\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"farmers' digital literacy, depth of information application, digital literacy divide, commonwealth","lastPublishedDoi":"10.21203/rs.3.rs-3272248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3272248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhether or not we can narrow the urban-rural \"digital literacy gap\" and improve the digital literacy and skills of China's rural households so that digital technology can truly empower people, is crucial to improving the well-being of the population. Using data from the China Family Tracking Survey (CFPS), the article explores the construction of digital literacy indicators and identifies the key issues for improving the income of rural households through digital literacy, starting with the depth of information application. The findings show that improved digital literacy has a significant positive effect on increasing farm household income, and the study's conclusions remain valid after utilizing instrumental variables as well as multiple methodological tests. The quantile model shows that the income-enhancing performance of digital literacy is uneven across regions and significantly widens the income gap within farm households. The income-generating benefits of digital literacy have a \"threshold effect\" within farming households, with low-income farmers mainly obtaining wages and business income through \"entertainment apps\" and high-income farmers accomplishing wealth accumulation through \"serious apps\". High-income farmers use \"serious applications\" to complete wealth accumulation. Mechanism analysis shows that digital literacy can reduce the cost of acquiring knowledge and effective information, improve the better management of individual resources, and realize income expansion. Heterogeneity analysis finds that digital literacy has a more pronounced income-enhancing effect on middle-aged and older farmers and those with low levels of education. Focusing on low-income households with multiple vulnerabilities, the use of household \"digital feedback\" can further reduce the income gap within the household. This study helps to examine the economic effects of digital literacy on farm household income under the \"winner-takes-all\" market structure and provides evidence to support the use of digital literacy as a tool to promote the digital village and commonwealth in China.\u003c/p\u003e","manuscriptTitle":"Why Digital Literacy Widens the Income Gap wi thin Chinese Farming Households","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-23 05:51:06","doi":"10.21203/rs.3.rs-3272248/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a7c12764-baf5-4520-9570-833d6e7ed04e","owner":[],"postedDate":"August 23rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-01T08:45:39+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-23 05:51:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3272248","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3272248","identity":"rs-3272248","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}