[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3599":3,"related-3599":80},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":8,"secondary_topics":31,"classification":32,"search_phrases":65,"slug":68,"view_count":41,"doi":8,"paper":69,"created_at":79},3599,"数字赋能何以激发内生动力？——数字乡村建设的扶智扶志效应及其协同机制研究","https:\u002F\u002Fwww.toutiao.com\u002Farticle\u002F7689001773884834319","周迪、李书曼、王雪芹在《数量经济技术经济研究》2026年第9期发表。文章基于2018年与2020年县域数字乡村指数与中国家庭追踪调查（CFPS）的匹配数据，构建包含数字乡村建设、人力资本积累与个体努力程度的世代交叠理论模型，运用数值模拟呈现不同数字化水平下个体技能与信心的动态演化路径，并采用双重机器学习模型进行因果推断。研究发现：数字乡村建设整体上对脱贫地区个体的技能提升与未来信心均具有显著正向作用；扶智效应主要通过拓宽信息渠道与促进非农就业实现，扶志效应借助改善职业环境与提升收入达成；对低技能、原深度连片贫困县和山区县的群体产生更为明显的扶志扶智效应。",null,"三农直通车（今日头条）2026-09-24","2026-09-24T00:00:00Z","论文",10,true,84,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},22,23,19,14,6,1,"核心期刊论文，基于CFPS与县域数字乡村指数匹配数据，方法新颖、结论可靠，对数字乡村激发内生动力具有政策参考价值。",[24],{"name":9,"url":6},[26,27,28,29,30],"数字乡村","双重机器学习","非农就业","CFPS","扶智扶志",[],{"status":33,"locked":34,"version":35,"method":36,"evidence":37},"needs_review",false,"2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":34,"reason":64},[39,45,52,59],{"code":40,"score":41,"supported":13,"title":42,"abstract":43,"tags":44},"I6",12,[26],[26],[26],{"code":46,"score":47,"supported":13,"title":48,"abstract":49,"tags":51},"I7",4,[],[50],"因果推断",[27],{"code":53,"score":54,"supported":13,"title":55,"abstract":56,"tags":58},"I2",3,[],[57],"机器学习",[],{"code":60,"score":21,"supported":34,"title":61,"abstract":62,"tags":63},"G6",[],[],[28],"农业相关性需要复核",[66,67],"数字乡村 扶智扶志 CFPS","县域数字乡村指数 双重机器学习","数字乡村扶智扶志CFPS-3599",{"doi":8,"openalex_id":8,"authors":70,"venue":8,"cited_by_count":71,"oa_url":8,"card":72,"direction":76,"ingested_from":78},[],0,{"tldr":73,"method":74,"finding":75,"direction":76,"opportunity":77},"基于县域数字乡村指数与CFPS数据，实证检验数字乡村建设对脱贫地区个体的扶智扶志效应及协同机制。","世代交叠模型数值模拟与双重机器学习因果推断，匹配2018、2020年县域数字乡村","数字乡村建设显著提升脱贫地区个体技能与未来信心，扶智靠信息渠道与非农就业，扶志靠职业环境与收入改善。","数字乡村与农业信息化","可探究数字乡村扶智扶志效应的长期动态演化及不同数字化水平的门槛与空间溢出效应。","agent","2026-09-27T00:05:18.097742Z",{"total":20,"page":21,"page_size":20,"items":81},[82,136,190,260,304,340],{"id":83,"title":84,"url":85,"summary":86,"summary_zh":8,"content":8,"source_name":87,"source_url":8,"published_at":88,"category":11,"cover_url":8,"hotness":12,"is_selected":34,"score":89,"score_detail":90,"sources":94,"tags":96,"primary_topic":8,"secondary_topics":100,"classification":101,"search_phrases":124,"slug":127,"view_count":71,"doi":8,"paper":128,"created_at":135},4282,"数字乡村建设如何影响县域创业及其福利变化","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F165609.html","黄赜琳、牛昱佳(上海财经大学公共管理学院)基于县域数字乡村指数实证检验数字乡村建设对县域创业的影响效应。研究发现：数字乡村建设能够促进县域创业且对第三产业创业带动效应尤为显著；主要通过缓解融资约束、推动企业创新和促进非农就业来驱动；乡村生活数字化、数字基础设施和治理数字化显著促进县域创业；政策效应在城乡要素流动通畅地区、数字鸿沟较浅地区、新型城镇化试点区更强。","农业技术经济 2026年第01期","2026-10-01T00:00:00Z",86,{"impact":16,"substance":17,"depth":91,"authority":19,"freshness":92,"relevant":21,"comment":93},18,9,"核心期刊实证研究，基于县域数字乡村指数揭示创业促进机制与福利效应，结论可靠、政策参考价值高。",[95],{"name":87,"url":85},[26,97,98,28,99],"数字普惠金融","城乡融合","县域创业",[],{"status":33,"locked":34,"version":35,"method":36,"evidence":102},{"candidates":103,"agricultural_evidence":34,"reason":64},[104,110,115,120],{"code":40,"score":105,"supported":13,"title":106,"abstract":107,"tags":109},15,[26],[26,108],"数字鸿沟",[26],{"code":111,"score":21,"supported":34,"title":112,"abstract":113,"tags":114},"G3",[],[],[97],{"code":116,"score":21,"supported":34,"title":117,"abstract":118,"tags":119},"G5",[],[],[98],{"code":60,"score":21,"supported":34,"title":121,"abstract":122,"tags":123},[],[],[28],[125,126],"上海财经大学 县域数字乡村指数","数字乡村 县域创业","上海财经大学县域数字乡村指数-4282",{"doi":8,"openalex_id":8,"authors":129,"venue":8,"cited_by_count":71,"oa_url":8,"card":130,"direction":76,"ingested_from":78},[],{"tldr":131,"method":132,"finding":133,"direction":76,"opportunity":134},"基于县域数字乡村指数，实证检验数字乡村建设对县域创业的影响及福利变化。","县域数字乡村指数与实证计量模型，分析创业效应及机制。","数字乡村建设促进县域创业，尤以第三产业显著，通过缓解融资约束、创新和非农就业驱动。","可探究数字乡村创业效应在农业内部及不同产业间的异质性，及长期福利动态。","2026-10-06T00:05:04.608387Z",{"id":137,"title":138,"url":139,"summary":140,"summary_zh":141,"content":8,"source_name":142,"source_url":139,"published_at":143,"category":11,"cover_url":8,"hotness":12,"is_selected":34,"score":144,"score_detail":145,"sources":150,"tags":152,"primary_topic":8,"secondary_topics":156,"classification":157,"search_phrases":170,"slug":173,"view_count":54,"doi":174,"paper":175,"created_at":189},3765,"Why empowerment, not relief? Business networks and the economic resilience of disaster-prone areas households in rural China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1932544","Introduction The “differential mode of association” theory describes a traditional Chinese society centered around personal, family, and local relationships. However, due to market changes, population movement, and networking, these traditional ties are weakening, and business networks are becoming more important. Methods Using data from 2016 to 2022 China Family Panel Studies (CFPS), we investigated the impact of business networks on the economic resilience of disaster-prone areas households in rural China. Results and discussion Results show that business networks enhance the economic resilience of disaster-prone areas households in rural China by providing off-farm employment. Heterogeneity analysis shows that business networks have a double effect, boosting gains for better-off families while still providing crucial support for weaker families to meet basic needs. Further analysis shows that business networks have a significant positive effect on job security and promotion satisfaction, but their impact on income, work environment, and work-life balance is not statistically significant. Conclusion These findings remain robust across various tests, offering new insights into how informal institutions can foster economic resilience of disaster-prone areas households in rural China.","引言 “差序格局”理论描述的是一个以个人、家庭和乡土关系为核心的传统中国社会。然而，由于市场变迁、人口流动和网络化，这些传统纽带正在弱化，业缘网络正变得更为重要。方法 利用2016年至2022年中国家庭追踪调查（CFPS）数据，我们考察了业缘网络对中国农村灾害易发地区家庭经济韧性的影响。结果与讨论 结果表明，业缘网络通过提供非农就业增强了中国农村灾害易发地区家庭的经济韧性。异质性分析表明，业缘网络具有双重效应，既能为较富裕家庭增加收益，又能为较弱势家庭满足基本需求提供关键支持。进一步分析表明，业缘网络对工作保障和晋升满意度具有显著正向影响，但其对收入、工作环境和工作与生活平衡的影响在统计上不显著。结论 这些发现在各种检验中保持稳健，为 informal institutions（非正式制度）如何促进中国农村灾害易发地区家庭的经济韧性提供了新的见解。","Frontiers in Sustainable Food Systems","2026-09-28T00:00:00Z",77,{"impact":146,"substance":147,"depth":91,"authority":148,"freshness":92,"relevant":21,"comment":149},16,21,13,"基于CFPS 2016—2022年数据的实证研究，揭示商业网络通过非农就业提升灾害频发区农户经济韧性，对乡村社会资本与防灾减贫政策有参考价值。",[151],{"name":142,"url":139},[26,28,153,154,155],"农户经济韧性","社会网络","灾害风险",[],{"status":158,"locked":34,"version":35,"method":36,"evidence":159},"unclassified",{"candidates":160,"agricultural_evidence":13,"reason":169},[161,165],{"code":60,"score":21,"supported":34,"title":162,"abstract":163,"tags":164},[],[],[28],{"code":40,"score":21,"supported":34,"title":166,"abstract":167,"tags":168},[],[],[26],"缺少足够的主题证据",[171,172],"CFPS 商业网络 农户经济韧性","灾害频发区 非农就业","CFPS商业网络农户经济韧性-3765","10.3389\u002Ffsufs.2026.1932544",{"doi":174,"openalex_id":176,"authors":177,"venue":142,"cited_by_count":71,"oa_url":182,"card":183,"direction":76,"ingested_from":188},"W7214658065",[178,180],{"name":179,"orcid":8},"Zhidong Wu",{"name":181,"orcid":8},"Junyi Wan","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1932544\u002Fpdf",{"tldr":184,"method":185,"finding":186,"direction":76,"opportunity":187},"基于CFPS数据，研究商业网络如何通过非农就业提升中国农村灾害易发区家庭的经济韧性。","使用2016-2022年CFPS数据，实证分析商业网络对经济韧性的影响。","商业网络通过提供非农就业增强经济韧性，对富裕和弱势家庭有双重效应。","可探究数字平台如何构建或替代传统商业网络，以增强灾害易发区家庭韧性。","openalex","2026-09-29T23:30:07.424931Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":8,"source_name":142,"source_url":193,"published_at":196,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":197,"score_detail":198,"sources":201,"tags":203,"primary_topic":207,"secondary_topics":212,"classification":213,"search_phrases":239,"slug":242,"view_count":54,"doi":243,"paper":244,"created_at":259},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。","2026-09-23T00:00:00Z",89,{"impact":199,"substance":17,"depth":18,"authority":19,"freshness":92,"relevant":21,"comment":200},24,"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。",[202],{"name":142,"url":193},[26,204,205,206,28],"空间溢出","县域经济","数字乡村试点",{"code":40,"slug":208,"name":209,"path":210,"parent_code":211},"digital-village","数字乡村与数据要素","\u002Ftopic\u002Fdigital-agriculture\u002Fdigital-village","I",[],{"status":214,"locked":34,"version":35,"method":36,"evidence":215},"classified",{"candidates":216,"agricultural_evidence":13},[217,222,230,235],{"code":40,"score":146,"supported":13,"title":218,"abstract":220,"tags":221},[219],"digital village",[26,219],[26,206],{"code":223,"score":224,"supported":13,"title":225,"abstract":226,"tags":229},"I4",7,[],[227,228],"空间分析","spatial analysis",[204],{"code":231,"score":21,"supported":34,"title":232,"abstract":233,"tags":234},"G4",[],[],[205],{"code":60,"score":21,"supported":34,"title":236,"abstract":237,"tags":238},[],[],[28],[240,241],"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","10.3389\u002Ffsufs.2026.1950504",{"doi":243,"openalex_id":245,"authors":246,"venue":142,"cited_by_count":71,"oa_url":193,"card":254,"direction":76,"ingested_from":188},"W7214093544",[247,250,252],{"name":248,"orcid":249},"Wei Yang","https:\u002F\u002Forcid.org\u002F0000-0002-0540-5934",{"name":251,"orcid":8},"Lijun Wang",{"name":253,"orcid":8},"Yizhuo Ma",{"tldr":255,"method":256,"finding":257,"direction":76,"opportunity":258},"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。","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。","2026-09-24T23:30:07.434384Z",{"id":261,"title":262,"url":263,"summary":264,"summary_zh":8,"content":8,"source_name":265,"source_url":8,"published_at":266,"category":11,"cover_url":8,"hotness":12,"is_selected":34,"score":267,"score_detail":268,"sources":271,"tags":273,"primary_topic":8,"secondary_topics":275,"classification":276,"search_phrases":292,"slug":295,"view_count":47,"doi":8,"paper":296,"created_at":303},3005,"数字乡村建设能否提升县域经济韧性——基于双重机器学习与县级面板数据","https:\u002F\u002Fgcxb.gufe.edu.cn\u002FCN\u002FPDF\u002F9619","郑州轻工业大学陈昱等基于2014—2022年县级面板数据采用双重机器学习模型探究数字乡村建设对县域经济韧性的影响效应及作用机制，并进一步考察不同层级数字鸿沟产生的异质性影响。研究表明：数字乡村建设显著提升县域经济韧性经稳健性检验后结果依然成立；数字乡村建设以技术为核心通过破除市场壁垒、优化产业结构、提升非农创业活力提升县域经济韧性；但数字乡村建设对县域经济韧性的促进作用受到三级数字鸿沟制约，导致其在数字发展低水平地区产生抑制效应。","贵阳学院学报(社会科学版)2026","2026-09-16T00:00:00Z",79,{"impact":91,"substance":16,"depth":91,"authority":148,"freshness":269,"relevant":21,"comment":270},8,"基于县级面板数据与双重机器学习方法，实证揭示数字乡村建设对县域经济韧性的提升效应及数字鸿沟的制约机制，方法新颖、结论可靠，对数字乡村政策具有参考价值。",[272],{"name":265,"url":263},[26,27,108,274],"县域经济韧性",[],{"status":33,"locked":34,"version":35,"method":36,"evidence":277},{"candidates":278,"agricultural_evidence":34,"reason":64},[279,283,288],{"code":40,"score":146,"supported":13,"title":280,"abstract":281,"tags":282},[26],[26,108],[26,108],{"code":231,"score":284,"supported":13,"title":285,"abstract":286,"tags":287},11,[205],[205],[],{"code":53,"score":284,"supported":13,"title":289,"abstract":290,"tags":291},[57],[57],[],[293,294],"郑州轻工业大学 数字乡村 县域经济韧性","县域经济韧性 双重机器学习 数字乡村 数字鸿沟","郑州轻工业大学数字乡村县域经济韧性-3005",{"doi":8,"openalex_id":8,"authors":297,"venue":8,"cited_by_count":71,"oa_url":8,"card":298,"direction":76,"ingested_from":78},[],{"tldr":299,"method":300,"finding":301,"direction":76,"opportunity":302},"基于县级面板数据，用双重机器学习检验数字乡村建设对县域经济韧性的因果效应与机制。","2014—2022年县级面板数据，双重机器学习因果推断模型。","数字乡村建设显著提升县域经济韧性，但三级数字鸿沟会削弱甚至逆转该效应。","可探究数字鸿沟的微观形成机制及弥合路径，或分区域设计差异化数字乡村政策。","2026-09-20T00:03:08.550563Z",{"id":305,"title":306,"url":307,"summary":308,"summary_zh":8,"content":8,"source_name":309,"source_url":8,"published_at":8,"category":11,"cover_url":8,"hotness":12,"is_selected":34,"score":310,"score_detail":311,"sources":314,"tags":316,"primary_topic":319,"secondary_topics":320,"classification":321,"search_phrases":328,"slug":331,"view_count":21,"doi":8,"paper":332,"created_at":339},2736,"弥合数字鸿沟:数字经济参与能否提升农村家庭发展韧性?","http:\u002F\u002Fwww.qikanvip.com\u002Fqkml\u002F165479.html","华中农业大学陈万红等基于2012—2022年中国家庭追踪调查(CFPS)农村面板数据,运用双重差分、倾向得分匹配、工具变量法等计量手段,实证检验数字经济参与对农村家庭发展韧性的作用。结果表明:数字经济参与能够显著提升农村家庭发展韧性;该赋能效应存在区域、家庭生命周期异质性,华北、华东、华中南、西北地区以及抚养期、负担期、稳定期家庭提升效果显著。","农业经济与管理 2026年第3期",74,{"impact":91,"substance":16,"depth":91,"authority":19,"freshness":312,"relevant":21,"comment":313},2,"基于CFPS十年面板数据的实证研究，方法规范、结论有新意，但时效性偏弱，适合主题聚合而非每日精选。",[315],{"name":309,"url":307},[26,317,108,29,318],"数字经济","农村家庭韧性",{"code":40,"slug":208,"name":209,"path":210,"parent_code":211},[],{"status":214,"locked":34,"version":35,"method":36,"evidence":322},{"candidates":323,"agricultural_evidence":13},[324],{"code":40,"score":284,"supported":13,"title":325,"abstract":326,"tags":327},[108],[],[26,317,108],[329,330],"农村家庭韧性 数字乡村 数字经济 数字鸿沟","农村家庭韧性 数字乡村","农村家庭韧性数字乡村数字经济数字鸿沟-2736",{"doi":8,"openalex_id":8,"authors":333,"venue":8,"cited_by_count":71,"oa_url":8,"card":334,"direction":76,"ingested_from":78},[],{"tldr":335,"method":336,"finding":337,"direction":76,"opportunity":338},"基于CFPS农村面板数据，实证检验数字经济参与对农村家庭发展韧性的提升作用。","2012—2022年CFPS农村面板数据，双重差分、倾向得分匹配、工具变量法。","数字经济参与显著提升农村家庭发展韧性，且存在区域和家庭生命周期异质性。","可探究数字经济参与提升韧性的具体机制，并关注数字鸿沟下弱势家庭的差异化干预策略。","2026-09-17T00:04:40.873674Z",{"id":341,"title":342,"url":343,"summary":344,"summary_zh":8,"content":345,"source_name":346,"source_url":8,"published_at":347,"category":11,"cover_url":8,"hotness":12,"is_selected":34,"score":348,"score_detail":349,"sources":352,"tags":354,"primary_topic":358,"secondary_topics":359,"classification":360,"search_phrases":399,"slug":402,"view_count":403,"doi":8,"paper":8,"created_at":404},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":91,"substance":16,"depth":350,"authority":148,"freshness":269,"relevant":21,"comment":351},20,"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。",[353],{"name":346,"url":343},[26,355,356,28,357],"Sustainability","数字金融","牧户",{"code":40,"slug":208,"name":209,"path":210,"parent_code":211},[],{"status":214,"locked":34,"version":35,"method":36,"evidence":361},{"candidates":362,"agricultural_evidence":13},[363,367,373,379,385,391,395],{"code":40,"score":41,"supported":13,"title":364,"abstract":365,"tags":366},[26],[26],[26],{"code":368,"score":269,"supported":13,"title":369,"abstract":371,"tags":372},"G",[370],"rural development",[],[],{"code":374,"score":54,"supported":13,"title":375,"abstract":376,"tags":378},"C4",[],[377],"饲草",[],{"code":380,"score":54,"supported":13,"title":381,"abstract":382,"tags":384},"F5",[],[383],"农机",[],{"code":386,"score":54,"supported":13,"title":387,"abstract":388,"tags":390},"H6",[],[389],"农业生态",[],{"code":111,"score":21,"supported":34,"title":392,"abstract":393,"tags":394},[],[],[356],{"code":60,"score":21,"supported":34,"title":396,"abstract":397,"tags":398},[],[],[28],[400,401],"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",5,"2026-08-21T00:05:57.571383Z"]