[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3336":3,"related-3336":56},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":55},3336,"Prosper or beggar thy neighbor? non-farm employment effects and spatial spillovers of China's National Digital Village Pilot Program","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1950504","Introduction Counties are a key spatial unit linking cities and villages in China's rural transformation, yet many still lack the industrial base, market scale, and non-farm job opportunities needed to absorb rural labor. This study examines whether China's National Digital Village Pilot Program expands rural non-farm employment and whether its effects extend beyond pilot counties. Methods Using a panel of 1,450 Chinese counties and 24,111 county-year observations from 2007 to 2024, this study exploits the staggered implementation of the 2020 and 2024 pilot cohorts as a quasi-natural experiment. The policy effect is estimated using a multi-period difference-in-differences model and cross-validated with stacked DID and the Callaway-Sant'Anna estimator. Robustness is further assessed through propensity score matching, inverse probability weighting, entropy balancing, placebo tests, and alternative samples and specifications. Transmission channels, heterogeneity, and spatial effects are also examined. Results The National Digital Village Pilot Program increased rural non-farm employment in pilot counties by approximately 25% relative to non-pilot counties, and the result remains robust across alternative identification and estimation strategies. The two-step transmission-channel analysis provides evidence consistent with two potential channels: enhanced county-level innovation and improved mobile network access capability. The policy effect is significantly stronger in central-region counties than in western counties and stronger in smaller counties than in larger ones, with weaker evidence of a larger effect in counties with lower human capital. Spatial analysis shows a positive direct effect on pilot counties but a negative spillover effect on neighboring counties. Discussion The findings indicate that digital village development can strengthen local non-farm employment absorption, but part of the local gain may reflect a spatial reallocation of economic activity rather than net regional job creation. Effective implementation therefore requires not only county-level digital development but also cross-county resource coordination and regional policy integration.","引言 县域是中国城乡转型中连接城市与乡村的关键空间单元，但许多县域仍缺乏吸纳农村劳动力所需的产业基础、市场规模和非农就业机会。本研究考察中国国家数字乡村试点项目是否扩大了农村非农就业，以及其效应是否超出试点县范围。方法 本研究使用2007年至2024年间1，450个中国县域、24，111个县—年观测值的面板数据，将2020年和2024年试点批次的分批实施作为准自然实验。政策效应采用多期双重差分模型估计，并通过堆叠双重差分和Callaway-Sant'Anna估计量进行交叉验证。稳健性进一步通过倾向得分匹配、逆概率加权、熵平衡、安慰剂检验以及替代样本和设定加以评估。研究还考察了传导渠道、异质性和空间效应。结果 国家数字乡村试点项目使试点县的农村非农就业相对于非试点县增加了约25%，且该结果在替代性识别和估计策略下保持稳健。两步传导渠道分析提供的证据与两个潜在渠道相一致：县域创新能力增强和移动网络接入能力改善。政策效应在中部地区县域显著强于西部地区县域，在较小县域强于较大县域，而在人力资本较低县域效应更大的证据较弱。空间分析显示，政策对试点县具有正向直接效应，但对邻近县域产生负向溢出效应。讨论 研究结果表明，数字乡村发展能够增强本地非农就业吸纳能力，但部分本地收益可能反映的是经济活动的空间再配置，而非区域净就业创造。因此，有效实施不仅需要县域层面的数字发展，还需要跨县域资源协调和区域政策整合。",null,"Frontiers in Sustainable Food Systems","2026-09-23T00:00:00Z","论文",10,true,89,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},24,23,19,14,9,1,"基于1450个县2007-2024年面板数据的准自然实验研究，证实数字乡村试点使县域非农就业提升约25%，但存在对邻县的负向溢出，对政策协调具有实质参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字乡村","空间溢出","县域经济","数字乡村试点","非农就业",[33,34],"国家数字乡村试点 非农就业","县域 数字乡村 空间溢出","国家数字乡村试点非农就业-3336",0,"10.3389\u002Ffsufs.2026.1950504",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":48,"direction":52,"ingested_from":54},"W7214093544",[41,44,46],{"name":42,"orcid":43},"Wei Yang","https:\u002F\u002Forcid.org\u002F0000-0002-0540-5934",{"name":45,"orcid":9},"Lijun Wang",{"name":47,"orcid":9},"Yizhuo Ma",{"tldr":49,"method":50,"finding":51,"direction":52,"opportunity":53},"评估国家数字乡村试点对县域非农就业的影响及空间溢出效应。","2007-2024年1450个县面板数据，多期DID与Callaway-Sant","试点县非农就业增约25%，但邻县出现负向溢出，部分为就业空间再分配。","数字乡村与农业信息化","可探究数字乡村试点的跨县协同机制与负溢出治理，及不同区域差异化政策设计。","openalex","2026-09-24T23:30:07.434384Z",{"total":57,"page":22,"page_size":57,"items":58},6,[59,91,118,148,177,217],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":9,"content":9,"source_name":64,"source_url":9,"published_at":65,"category":12,"cover_url":9,"hotness":13,"is_selected":66,"score":67,"score_detail":68,"sources":72,"tags":74,"search_phrases":78,"slug":81,"view_count":36,"doi":9,"paper":82,"created_at":90},3321,"数智化如何提升粮食供应链韧性——基于空间溢出与农业集聚的实证分析（2011—2023省级面板）","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F165475.html","《农业经济与管理》2026年第03期。李盛竹、王延浩、姜金贵（重庆邮电大学\u002F哈尔滨工程大学）基于2011—2023年中国省级面板数据，构建数智化与粮食供应链韧性综合评价指标体系。结果显示：数智化显著增强了粮食供应链韧性；该结论经过一系列稳健性检验与内生性处理后依然成立，且在粮食主产区和西部地区的促进作用更为突出。机制分析表明，农业生产水平在数智化影响粮食供应链韧性过程中，发挥着正向调节作用；农业产业集聚具有显著的门槛效应，只有跨越特定集聚水平后，数智化的促进作用才更为明显。此外，数智化对粮食供应链韧性的影响存在显著的空间溢出效应。","《农业经济与管理》2026年第03期","2026-09-17T00:00:00Z",false,78,{"impact":69,"substance":70,"depth":69,"authority":20,"freshness":57,"relevant":22,"comment":71},18,22,"基于2011—2023省级面板的实证研究，方法规范、结论有新意，对数字乡村与粮食安全议题有参考价值，但属学术论文，公共传播性有限。",[73],{"name":64,"url":62},[27,75,76,28,77],"智慧农业","粮食安全","农业产业集聚",[79,80],"数智化 粮食供应链韧性","农业集聚 门槛效应","数智化粮食供应链韧性-3321",{"doi":9,"openalex_id":9,"authors":83,"venue":9,"cited_by_count":36,"oa_url":9,"card":84,"direction":52,"ingested_from":89},[],{"tldr":85,"method":86,"finding":87,"direction":52,"opportunity":88},"基于2011—2023省级面板，实证检验数智化对粮食供应链韧性的提升作用及机制。","省级面板数据，构建综合评价指标体系，调节效应、门槛模型与空间溢出分析。","数智化显著增强粮食供应链韧性，主产区和西部更突出，农业集聚存在门槛效应。","可探究数智化空间溢出的衰减边界与跨区域协同机制，及集聚门槛的差异化政策设计。","agent","2026-09-24T00:04:02.337256Z",{"id":92,"title":93,"url":94,"summary":95,"summary_zh":9,"content":9,"source_name":96,"source_url":9,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":66,"score":67,"score_detail":98,"sources":100,"tags":102,"search_phrases":106,"slug":109,"view_count":36,"doi":9,"paper":110,"created_at":117},2404,"数林协同发展的农村居民增收机制——基于浙江省山区26县的实证检验","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F169001.html","林业经济问题2026年第01期。徐彩瑶、严露、吴芸茜等基于浙江省山区26县2001-2023年面板数据，综合运用双向固定效应、中介效应、面板门槛、调节效应与空间杜宾模型实证检验。结果表明：数林协同发展能显著提升山区农村居民收入；人力资本升级与就业机会增加是关键路径；存在双门槛效应；具有正向空间溢出效应，能带动邻近地区农村居民收入增长。","林业经济问题 | 2026-09-08","2026-09-08T00:00:00Z",{"impact":69,"substance":70,"depth":69,"authority":20,"freshness":57,"relevant":22,"comment":99},"基于浙江山区26县长面板数据的实证研究，方法扎实、结论明确，对数字技术与林业协同促进山区增收有参考价值，但属学术论文且时效性一般。",[101],{"name":96,"url":94},[27,28,103,104,105],"林业经济","农村居民增收","浙江山区",[107,108],"农村居民增收 数字乡村 林业经济 浙江山区","农村居民增收 数字乡村","农村居民增收数字乡村林业经济浙江山区-2404",{"doi":9,"openalex_id":9,"authors":111,"venue":9,"cited_by_count":36,"oa_url":9,"card":112,"direction":52,"ingested_from":89},[],{"tldr":113,"method":114,"finding":115,"direction":52,"opportunity":116},"基于浙江山区26县面板数据，实证检验数林协同发展对农村居民增收的机制与空间效应。","2001-2023年面板数据，双向固定效应、中介、门槛、调节与空间杜宾模型。","数林协同显著提升山区农村居民收入，人力资本与就业是关键路径，存在双门槛与正向空间溢出。","可探究数林协同的数字化测度与跨区域溢出边界，及不同山区类型的门槛异质性。","2026-09-14T00:06:33.614332Z",{"id":119,"title":120,"url":121,"summary":122,"summary_zh":9,"content":9,"source_name":123,"source_url":9,"published_at":124,"category":12,"cover_url":9,"hotness":13,"is_selected":66,"score":125,"score_detail":126,"sources":130,"tags":132,"search_phrases":136,"slug":139,"view_count":36,"doi":9,"paper":140,"created_at":147},2403,"乡村治理数字化对农民增收的影响研究——基于县域数字乡村指数与CFPS微观调查数据","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F127622.html","统计与决策2026年第02期。付莎、王军将《县域数字乡村指数》和CFPS微观调查数据进行匹配，实证探究乡村治理数字化对农民增收的影响。结果表明：乡村治理数字化有助于促进农民增收，具有益贫效应（在农村未接入互联网和低收入群体中增收效应更强）；通过提高农民数字素养、提升农民对本地官员的信任度来促进农民增收；对高学历农民的增收效应更显著。","统计与决策 | 2026-09-09","2026-09-09T00:00:00Z",76,{"impact":69,"substance":70,"depth":69,"authority":127,"freshness":128,"relevant":22,"comment":129},13,5,"核心期刊实证研究，将县域数字乡村指数与CFPS微观数据匹配，揭示乡村治理数字化的益贫效应与信任机制，对数字乡村政策有参考价值。",[131],{"name":123,"url":121},[27,133,134,135,29],"乡村治理","数字素养","农民增收",[137,138],"乡村治理 农民增收 县域经济 数字乡村","乡村治理 农民增收","乡村治理农民增收县域经济数字乡村-2403",{"doi":9,"openalex_id":9,"authors":141,"venue":9,"cited_by_count":36,"oa_url":9,"card":142,"direction":52,"ingested_from":89},[],{"tldr":143,"method":144,"finding":145,"direction":52,"opportunity":146},"匹配县域数字乡村指数与CFPS数据，实证检验乡村治理数字化对农民增收的影响。","县域数字乡村指数与CFPS微观调查数据匹配，实证回归分析。","乡村治理数字化促进农民增收且具益贫效应，通过提升数字素养与官员信任实现。","可探究治理数字化益贫效应的长期动态及不同治理场景下的异质性机制。","2026-09-14T00:06:33.536950Z",{"id":149,"title":150,"url":151,"summary":152,"summary_zh":9,"content":9,"source_name":153,"source_url":9,"published_at":154,"category":12,"cover_url":9,"hotness":13,"is_selected":66,"score":155,"score_detail":156,"sources":158,"tags":160,"search_phrases":164,"slug":167,"view_count":36,"doi":168,"paper":169,"created_at":176},2109,"《Digital villages and agricultural green total factor productivity》 数字乡村试点DID评估AGTFP","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1831978\u002Ffull","Guo Hui、Wang Xinyi、Xia He、Jiang Wenjie基于2014—2023年中国县级面板数据,采用超效率SBM-DDF-GML模型测算AGTFP,以国家数字乡村试点为准自然实验,运用双重差分法评估数字乡村建设对AGTFP的影响、机制与空间溢出效应。结果显示数字乡村显著提升AGTFP,主要通过绿色技术进步而非绿色技术效率改善;机制上通过优化劳动力配置、激发农业经营创业、扩大消费实现;政策效应在靠近省会城市、地形起伏大、路网密度低的地区更显著。","Frontiers in Sustainable Food Systems 10:1831978","2026-09-05T00:00:00Z",82,{"impact":70,"substance":18,"depth":69,"authority":127,"freshness":57,"relevant":22,"comment":157},"基于县级面板与试点准自然实验，方法规范、结论明确，对数字乡村政策评估有参考价值。",[159],{"name":153,"url":151},[27,29,161,162,163],"农业绿色发展","政策评估","全要素生产率",[165,166],"全要素生产率 农业绿色发展 县域经济 政策评估","全要素生产率 农业绿色发展","全要素生产率农业绿色发展县域经济政策评估-2109","10.3389\u002Ffsufs.2026.1831978\u002Ffull",{"doi":168,"openalex_id":9,"authors":170,"venue":9,"cited_by_count":36,"oa_url":9,"card":171,"direction":52,"ingested_from":89},[],{"tldr":172,"method":173,"finding":174,"direction":52,"opportunity":175},"基于县级面板数据，用DID评估数字乡村试点对农业绿色全要素生产率的影响。","超效率SBM-DDF-GML测算AGTFP，双重差分法，2014—2023年县级","数字乡村显著提升AGTFP，主要靠绿色技术进步，通过劳动力配置、创业和消费实现。","可探究数字乡村对AGTFP的空间溢出边界及绿色技术效率滞后原因。","2026-09-11T00:04:22.864968Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":9,"source_name":183,"source_url":180,"published_at":184,"category":12,"cover_url":9,"hotness":13,"is_selected":66,"score":185,"score_detail":186,"sources":190,"tags":192,"search_phrases":194,"slug":197,"view_count":22,"doi":198,"paper":199,"created_at":216},1745,"National digital village pilot construction alleviates the economic gap between counties in China","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-69800-z","This paper exploits panel data on 1,564 Chinese counties from 2015 to 2023 and applies a difference-in-differences approach to evaluate the impact of digital village pilot on inter-county economic disparities. The results show that the pilots significantly narrow county-level economic gaps, a finding that remains robust across multiple tests. Mechanism analysis suggests that digital village initiatives enhance endogenous county development by promoting technological innovation and rural entrepreneurship, with industrial upgrading playing a moderating role. It has a stronger impact on regions with a lower degree of reliance on agriculture, regions with greater financial support, and the central and western regions. Policy implications include the steady expansion of digital village pilots, strengthening of innovation and entrepreneurship, guidance for industrial upgrading, and improved supportive policies to foster coordinated regional development.","本文利用2015至2023年中国1564个县的面板数据，采用双重差分法评估数字乡村试点对县域间经济差距的影响。结果显示，试点显著缩小了县级经济差距，这一发现在多项检验中依然稳健。机制分析表明，数字乡村建设通过促进技术创新和农村创业来增强县域内生发展动力，其中产业升级发挥了调节作用。对于农业依赖程度较低、金融支持力度较大的地区以及中西部地区，其影响更为显著。政策启示包括稳步扩大数字乡村试点范围、强化创新创业支持、引导产业升级，并完善配套政策以促进区域协调发展。","Scientific Reports","2026-09-04T00:00:00Z",80,{"impact":187,"substance":70,"depth":69,"authority":127,"freshness":188,"relevant":22,"comment":189},25,2,"基于大规模面板数据评估数字乡村试点对县域经济差距的影响，结论可靠，政策启示明确。",[191],{"name":183,"url":180},[27,193,29],"乡村振兴",[195,196],"乡村振兴 县域经济 数字乡村","乡村振兴 县域经济","乡村振兴县域经济数字乡村-1745","10.1038\u002Fs41598-026-69800-z",{"doi":198,"openalex_id":200,"authors":201,"venue":183,"cited_by_count":36,"oa_url":180,"card":211,"direction":52,"ingested_from":54},"W7208750940",[202,205,207,209],{"name":203,"orcid":204},"Huili Yang","https:\u002F\u002Forcid.org\u002F0000-0002-5534-0155",{"name":206,"orcid":9},"Mande Zhu",{"name":208,"orcid":9},"Wanci Tang",{"name":210,"orcid":9},"Chengxiu Li",{"tldr":212,"method":213,"finding":214,"direction":52,"opportunity":215},"数字乡村试点显著缩小了县域经济差距，通过促进技术创新和农村创业实现。","使用2015-2023年1564个县面板数据，采用双重差分法评估政策效应。","数字乡村试点显著缩小县域经济差距，且对非农依赖低、金融支持强及中西部区域效果更明显。","可探究数字乡村试点对农业全要素生产率或农户收入的影响，或结合空间溢出效应分析区域协同发展机制。","2026-09-05T23:30:35.454261Z",{"id":218,"title":219,"url":220,"summary":221,"summary_zh":9,"content":222,"source_name":223,"source_url":9,"published_at":224,"category":12,"cover_url":9,"hotness":13,"is_selected":66,"score":225,"score_detail":226,"sources":230,"tags":232,"search_phrases":236,"slug":239,"view_count":188,"doi":9,"paper":9,"created_at":240},791,"数字乡村发展与牧户非农就业:来自甘青川藏26县2790户牧户调查的证据(原标题:From Grasslands to Markets: Digital Rural Development and Herders' Non-Farm Employment)","https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503","兰州大学草地农业生态系统国家重点实验室吴忠安等团队基于2023-2025年甘青川藏26县2790户牧户调查数据,考察县级数字乡村发展与牧户非农就业关系。研究发现数字乡村发展与户主主要从事非农就业的可能性正相关,在系列稳健性和内生性检验后结果稳定;在第一产业基础较大的地区以及从事饲草种植的牧户中,正向关联较弱,表明当地生产结构和家庭劳动配置制约了数字机会向就业多元化转化。进一步分析表明数字金融和数字消费是数字乡村发展支持非农就业的两种潜在渠道。研究强调牧区就业收益既取决于数字接入,又取决于家庭劳动配置灵活性及非农机会可得性。","Logical Operator Operator\n\nSearch Text\n\nSearch Type\n\n_add\\_circle\\_outline_\n\n_remove\\_circle\\_outline_\n\n[![Image 1: sustainability-logo](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fjournals\u002Fsustainability-logo.png?3798e4e58c765aed)](https:\u002F\u002Fwww.mdpi.com\u002Fjournal\u002Fsustainability)\n\n## Article Menu\n\nFont Type:\n\n_Arial_ _Georgia_ _Verdana_\n\nFont Size:\n\nAa Aa Aa\n\nLine Spacing:\n\n__ __ __\n\nColumn Width:\n\n__ __ __\n\nBackground:\n\nOpen Access Article\n\nby \nZhongan Wu\n\n *[](mailto:wuzha2023@lzu.edu.cn), \nToba Stephen Olasehinde\n\n and \nYubing Fan\n\nState Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, Chinese Grass Industry Development Strategy Research Center, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China\n\n*\n\nAuthor to whom correspondence should be addressed.\n\nSubmission received: 3 July 2026 \u002F Revised: 11 August 2026 \u002F Accepted: 12 August 2026 \u002F Published: 19 August 2026\n\n## Abstract\n\nAlthough natural resources and geographical factors limit herders’ employment options, the development of digital rural helps herding households compete on an equal footing by expanding employment opportunities. Using survey data collected from 2790 herder households in 26 counties across Gansu, Qinghai, Sichuan and Tibet from 2023 to 2025, this study examines the relationship between county-level digital rural development and non-farm employment. We find that digital rural development is positively associated with the likelihood that household heads engage primarily in non-farm employment. This finding remains stable across a series of robustness and endogeneity checks. Moreover, the positive association is weaker in areas with a larger primary-industry base and among households engaged in forage cultivation, suggesting that local production structures and household labor commitments constrain the conversion of digital opportunities into employment diversification. Further analysis indicates that digital finance and digital consumption are two potential channels through which digital rural development supports non-farm employment. These findings highlight that the employment benefits of digital development depend not only on digital access but also on the flexibility of household labor allocation and the availability of non-farm opportunities in pastoral areas.\n\n## 1. Introduction\n\nArtificial intelligence, Big Data, and mobile internet are examples of digital technology that are spreading to all parts of the economy and society, changing how production is organized and people work [[1](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B1-sustainability-18-08503)]. Given the above, China released the Digital Rural Development Plan in 2018 to promote the construction of a digital rural society and rural revitalization. As 5G networks expand into rural and pastoral areas, the all-weather demonstration project for e-commerce in rural areas continues to advance, and the coverage of digital inclusive finance is gradually expanding. At the same time, digital infrastructure and services are being rolled out in the grassland region at an ever-increasing rate [[2](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B2-sustainability-18-08503)]. However, because of their remote areas, fragile ecosystems and unique cultures, these large pastoral areas have been lacking in development resources for a long time. Traditional livestock farming is limited by the productivity of natural grasslands, ecological red lines and fluctuations in market prices, and a single grazing-based livelihood model cannot provide stable income growth for herder households [[3](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B3-sustainability-18-08503)]. Therefore, expanding non-farm employment opportunities and promoting the transfer of the pastoral workforce to the secondary and tertiary sectors are urgently needed to consolidate the results of poverty alleviation and prevent a relapse into poverty, as well as to achieve all-round rejuvenation of pastoral areas.\n\nNon-farm employment provides herder households with an alternative to relying entirely on traditional livestock production. This resulting income mix can reduce exposure to livestock losses and strengthen resilience to market, climatic and health shocks [[4](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B4-sustainability-18-08503)]. Especially against the backdrop of increasing resource and environmental constraints in pastoral areas and shrinking growth prospects for traditional livestock farming, promoting the rational flow of labor into non-farm sectors is not only a practical necessity for herding households to enhance the resilience of their livelihoods, but also a key direction for industrial restructuring and socioeconomic transformation in pastoral areas. However, the formation of non-farm employment is not simply a matter of individual choice, as herders’ decision-making is influenced by a range of factors, including their ability to access information, job search costs, financial constraints, and the extent of market access [[5](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B5-sustainability-18-08503),[6](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B6-sustainability-18-08503)]. The reason why the development of digital rural may have an impact on herders’ non-farm employment is that it can, to some extent, alleviate these constraints.\n\nExisting research has provided a wealth of discussion on the relationship between the digital economy, information and communication technologies, and rural labor migration, as well as the growth of farm household income [[7](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B7-sustainability-18-08503),[8](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B8-sustainability-18-08503),[9](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B9-sustainability-18-08503),[10](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B10-sustainability-18-08503)]. A large body of the literature indicates that digital development can improve the information environment, expand market access, alleviate financial exclusion, and, to some extent, facilitate the shift in rural labor toward the non-farm sector [[11](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B11-sustainability-18-08503),[12](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B12-sustainability-18-08503),[13](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B13-sustainability-18-08503)]. Some studies have also indicated that the development of internet usage, digital finance, and e-commerce has a positive impact on farmers’ non-farm employment, entrepreneurial activities, and income growth [[14](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B14-sustainability-18-08503),[15](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B15-sustainability-18-08503),[16](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B16-sustainability-18-08503),[17](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B17-sustainability-18-08503)]. More broadly, infrastructure development and technological progress can promote economic diversification and strengthen livelihood resilience in peripheral and resource-dependent regions [[18](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B18-sustainability-18-08503)]. In terms of research subjects, most of the existing studies have focused on farmers and have paid relatively little attention to herders, another group in pastoral areas. Herders’ employment decisions are influenced by the general labor market, but they are also restricted by other factors, such as grassland management systems, livestock production cycles, household livestock assets and grazing practices; therefore, their non-farm employment behavior is relatively context-specific and constrained [[19](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B19-sustainability-18-08503),[20](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B20-sustainability-18-08503)]. Most studies have focused on the general inclusive benefits of digital technology for research, but there is a lack of specific research on how digital rural initiatives operate in the particular spatial conditions of pastoral areas and whether their effects differ among different groups and regions.\n\nThis paper takes as its focus the non-farm employment of herders and studies the effect of digital rural development on the transformation of herders’ employment, as well as the mechanisms through which this change occurs. Based on this, this paper seeks to answer the following questions: can digital rural development promote non-farm employment among herders?; through what mechanisms does it primarily achieve this?; and does this impact vary depending on individual characteristics, spatial conditions, and the level of development in each county? This study makes three contributions. First, it conceptualizes digital rural development in pastoral areas as a mechanism for translating digital access into non-farm employment opportunities, emphasizing the role of household labor constraints in livestock-based livelihoods. In doing so, it extends the literature on digital rural development and labor transition by showing that improved digital access does not automatically lead to labor reallocation, because pastoral production commitments may constrain herders’ ability to respond to new employment opportunities. Second, it integrates survey data from 2790 herding households across Gansu, Qinghai, Sichuan, and Tibet, with a county-level Digital Rural Development Index, providing evidence from underrepresented pastoral production systems. Third, it examines how structural and household-level conditions shape the relationship between digital rural development and non-farm employment, highlighting the importance of contextual heterogeneity in pastoral regions. By linking digital development with livelihood diversification, the study also contributes to the rural sustainability literature by showing that the employment benefits of digitalization depend on household labor flexibility and local economic conditions.\n\n## 2. Theoretical Framework\n\n### 2.1. Direct Effects of Digital Rural Development on Herders’ Non-Farm Employment\n\nCompared to typical rural areas, pastoral regions face long-standing constraints such as geographical dispersion, poor transportation, limited access to information and insufficient market access. As a result, employment options for herding households are often confined to traditional livestock farming. Access to non-farm employment depends largely on personal network or occasional face-to-face contact [[21](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B21-sustainability-18-08503)]. In light of the above, the construction of an information infrastructure, the extension of platform coverage and the broad application of digital tools in the development of digital rural have created new employment opportunities for herders outside of traditional sectors.\n\nAccording to the theory of information asymmetry, if market participants cannot obtain the required information in time and to their full satisfaction, they will bear a higher search cost, be less efficient in matching, and thus lose opportunities in their decision-making [[22](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B22-sustainability-18-08503),[23](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B23-sustainability-18-08503)]. The development of digital rural is changing how herders get jobs and market information. New technologies are now available to help herders stay informed about new employment prospects outside farming, wage levels, working locations and required skills [[24](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B24-sustainability-18-08503)]. In this way, information barriers between herders and the outside labor market have been reduced. Thus, employment information is no longer scarce and fragmented, but rather open and accessible. As a result, herders’ awareness of non-farm employment and their opportunities for employment have significantly increased [[25](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B25-sustainability-18-08503)].\n\nAs is well-known, non-farm employment not only involves the movement of labor across sectors, but also entails costs associated with information search, transportation, payment settlements and job matching. Transaction cost theory suggests that improvements in market efficiency depend largely on reductions in transaction costs [[26](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B26-sustainability-18-08503),[27](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B27-sustainability-18-08503)]. The development of digital rural has reduced the time costs, communication costs, and institutional friction faced by herders when seeking non-farm employment through information dissemination, digital payments and platform integration. Looking further, improvements in the information environment and reductions in transaction costs do not stop at the level of access to employment opportunities. Rather, they continue to influence labor allocation within herding households. In this context, non-farm employment reflects the reallocation of household labor from livestock production to non-farm sector [[28](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B28-sustainability-18-08503),[29](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B29-sustainability-18-08503)]. Consequently, household labor may gradually shift away from traditional livestock production toward a wider range of market-based non-farm activities. In light of the above, this paper proposes the following hypothesis:\n\n**H1.**\n\nDigital rural development is positively associated with household non-farm employment.\n\n### 2.2. Boundary Conditions of the Employment Effects of Digital Rural Development\n\nDigital rural development can improve information access, reduce job-search costs, and strengthen links to external markets. Nevertheless, these improvements do not automatically generate non-farm employment. Whether herders convert digital opportunities into employment also depends on local industrial structure and household production systems. We therefore examine the moderating roles of primary-industry development and forage cultivation.\n\nStructural transformation involves the reallocation of labor from primary production toward secondary and tertiary activities [[30](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B30-sustainability-18-08503)]. Primary-industry value added at the county level measures the absolute scale of agricultural and livestock output, rather than the sector’s share of the local economy or its relative return. A higher value may nevertheless indicate a larger agricultural and livestock production base. In such counties, digital tools may be used more extensively for production management, input procurement and product marketing within the primary sector [[31](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B31-sustainability-18-08503)]. Digitalization may lower search, matching, and market-access costs while also improving production within the primary sector. Where digital services are used mainly for production management and product marketing, these benefits may be absorbed within agricultural and pastoral activities rather than translated into labor reallocation [[32](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B32-sustainability-18-08503)]. The positive association between digital rural development and herders’ participation in non-farm employment may therefore be weaker in counties with a larger primary industry base. We propose:\n\n**H2a.**\n\nHigher primary industry value added in a county weakens the positive association between digital rural development and household non-farm employment.\n\nForage cultivation is closely integrated with livestock production and requires labor for planting, management, harvesting, transport and storage [[33](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B33-sustainability-18-08503)]. These tasks increase household labor demands, particularly during busy seasons. With limited household labor, forage cultivation may reduce the time available for non-farm employment [[34](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B34-sustainability-18-08503)]. It also binds cropland, forage, livestock and family labor into a more integrated production system. Households may therefore have less flexibility to act on digital employment information and market opportunities.\n\n**H2b.**\n\nForage cultivation weakens the positive association between digital rural development and household non-farm employment.\n\n### 2.3. Transmission Mechanisms of Digital Rural Development\n\nIn most cases, funding is a key factor limiting herders’ ability to engage in any activities. Continuing livestock production, starting a business, and seeking employment elsewhere may all require financial resources, while reliable payment services can facilitate related transactions [[35](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B35-sustainability-18-08503),[36](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B36-sustainability-18-08503)]. However, due to factors such as remote locations, a lack of financial service outlets and exclusion from the traditional financial system, herders have long faced challenges in accessing financial services [[37](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B37-sustainability-18-08503)]. The development of digital rural has driven the shift in financial services toward online, convenient and grassroots-level delivery, providing herders with more accessible channels for payments, credit and financial services. The expansion of financial services to grassroots levels represents not only an increase in the scale of financial operations but also an extension of service coverage and an improvement in the efficiency of resource allocation. Digital rural development can facilitate the expansion of digital finance. Greater access to digital financial services may ease the credit and liquidity constraints faced by herding households. This, in turn, can lower the financial barriers to participation in non-farm employment and entrepreneurial activities [[38](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B38-sustainability-18-08503)]. At the same time, development of digital finance has reduced the transaction costs for labor transfer and provided a relatively easy way for herders to access external labor markets [[39](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B39-sustainability-18-08503)]. Therefore, the development of digital rural areas should not only increase the supply of financial products but also improve the financial situation for herders’ non-market activities at a deeper level and enhance their ability to engage in non-farm employment. Based on the above analysis, this study puts forward the following hypothesis:\n\n**H3.**\n\nDigital rural development promotes herders’ non-farm employment by expanding digital finance.\n\nWith the spread of online shopping, digital payments, and platform services, herding households have become more closely connected to digital markets. These developments have changed how households purchase goods, make payments, and access consumer services [[40](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B40-sustainability-18-08503)].\n\nThe shift in consumption patterns is not merely a change in lifestyle. It also influences household employment choices through adjustments in income needs and labor allocation. Traditional herding households often rely on self-produced goods and local supplies. Digital rural development has expanded their access to external markets. Their consumption may therefore become more dependent on purchased goods and services. While households now have a wider range of choices regarding goods and services, they also have a greater need for a stable cash flow to support these choices [[41](https:\u002F\u002Fwww.mdpi.com\u002F2071-1050\u002F18\u002F16\u002F8503#B41-sustainability-18-08503)]. The increase in digital consumption suggests that more money will be needed by households. As a result, there has been a change in demand for labor, and many people have begun to leave traditional pastoralism for non-agricultural work in search of a more stable in","MDPI Sustainability 18(16):8503 2026-08-19","2026-08-19T00:00:00Z",81,{"impact":69,"substance":70,"depth":227,"authority":127,"freshness":228,"relevant":22,"comment":229},20,8,"基于2790户牧户调查的实证研究，揭示数字乡村对非农就业的促进作用及条件，数据详实，结论可靠，对牧区数字乡村政策有参考价值。",[231],{"name":223,"url":220},[27,233,234,31,235],"Sustainability","数字金融","牧户",[237,238],"数字乡村 数字金融 非农就业 牧户","数字乡村 数字金融","数字乡村数字金融非农就业牧户-791","2026-08-21T00:05:57.571383Z"]