农户面临复杂而多变的风险冲击，对其福祉产生深远影响。近年来，新冠疫情、气候变化、自然灾害等协同性风险冲击与健康风险等异质性风险冲击对农户收入水平、营养健康及幸福感产生负面影响，农户甚至由此陷入贫困。造成上述福利损失的重要原因之一是农户缺乏风险应对机制，韧性水平不足。扎实提升农户的韧性水平不仅有助于防止农户因灾因病返贫致贫，巩固脱贫攻坚成果，而且能够增强农户的获得感、幸福感、安全感，提升生活福祉，体现了共同富裕的应有之义。通常来说，韧性被用来描述各类主体面对风险冲击时恢复和发展的能力，是主体在不断变化的环境中生存和发展的内生动力，韧性为分析农户福利遭受冲击和面临压力源时的表现提供新视角。值得注意的是，关于中国农户的地位与命运，长期以来居支配地位的观点是——小农户十分脆弱，难以应对风险，具有脆弱小农的典型特征，故学者们较少关注农户韧性，也尚未对韧性指标体系的构建、韧性的结构等一系列基础问题进行回答。

发达的金融市场是提高农户韧性的重要手段之一。金融市场通过提供农户所需的金融服务或避险工具，帮助农户提升家庭风险应对水平，进而提高其韧性。但是，我国农村金融市场的制度性缺陷尚未充分解决，农村地区金融资源配置效率低下，难以利用金融工具来提高农户的韧性能力。近年来，随着大数据、云计算和人工智能等信息技术的发展，数字金融应运而生，作为一种拓展金融供给和实现金融普惠的变革性模式，数字金融被视为促进农村金融发展和打通农村普惠金融“最后一公里”的有效方式。理论上具有巨大优势的数字金融能否对农户韧性产生促进作用？若有，其作用机制是什么？数字金融参与是否可以降低风险冲击对农户韧性的负面影响，进而发挥风险缓释作用？数字金融参与影响农户韧性的结构效应是否存在，其背后的经济逻辑是怎样的？基于此，本研究使用CHFS（2019）微观调查数据对上述问题展开系统研究。

本研究沿着“问题提出→理论研究→实证研究→政策建议”的思路展开，以联合国粮食及农业组织（FAO）的韧性框架为基础，探究数字金融影响农户韧性的微观基础，剖析数字金融参与影响农户韧性的风险缓释效应，进一步对其结构效应与作用机制展开系统考察，从而为中国特色数字金融发展背景下农户韧性的形成机理及其提升路径提供理论基础与政策建议。主要研究内容与研究结论如下：

研究内容一：中国数字金融特征事实与农户韧性现状分析

基于中国背景，根据FAO的RIMA-II韧性理论基础，从适应能力（AC）、社会安全网（SSN）、资产（AST）、获得基本服务（ABS）韧性四大支柱入手，首次构建中国农户韧性指标体系并使用因子分析法进行测度分析，为传统脆弱小农的定性分析过渡到韧性小农的定量研究提供可行分析框架。通过基本统计分析发现，我国农村数字金融发展总水平、发展广度及发展深度逐年上升，但增幅减缓；农户数字金融参与比例不高，参与种类少；数字金融发展和农户参与数字金融状况具有明显的区域差异性；使用数字金融的农户韧性水平更高；使用不同类型数字金融服务的农户的韧性结构存在显著性差异；家庭遭受风险冲击的农户韧性水平更低。

研究内容二：从理论和实证层面分析数字金融参与对农户韧性的影响

构建了一个两期经济模型，从理论层面证明数字金融与农户韧性之间存在增函数特征，并使用CHFS（2019）微观调查数据进行实证检验。结果表明，农户参与数字金融不仅可以提高其韧性，而且数字金融参与的种类越多，其韧性水平越高，数字金融参与在提升农户韧性方面具有显著的统计意义和经济意义。在解决内生性问题、改变被解释变量的形式、去除金融行业从业样本、解决遗漏变量偏误、更换估计模型五种方式进行稳健性检验后，研究结论仍然成立。

从数字金融参与行为的类型和农户特征两个角度进行异质性分析。从不同类型的数字金融参与行为来看，移动支付、数字理财和数字信贷对农户韧性均具有显著促进作用。从数字鸿沟视角来看，相较于不存在数字鸿沟的农户而言，存在数字鸿沟的农户数字金融参与对其韧性的促进作用更小，数字鸿沟抑制了数字金融普惠效应的扩散。对于存在数字鸿沟的群体来讲，数字金融发展的溢出效应能够改善这类家庭的获益水平，帮助其提高韧性水平。收入（贫困户与非贫困户）、年龄（老年与中青年）、人力资本（高等教育与非高等教育）以及区域差异（东、中、西、东北）四个维度的实证结果显示，数字金融参与能够提升贫困户、老年人、非高等教育群体以及中西部农户等“弱势群体”的韧性水平，具有“雪中送炭”的作用，这表明农村数字金融的发展具有普惠性和益贫性的特征。

研究内容三：系统考察数字金融参与影响农户韧性的风险缓释效应

该部分以RIMA-II韧性评估体系为基础，将“数字金融—风险冲击—农户韧性”纳入统一分析框架，然后将协同性风险冲击与异质性风险冲击两种冲击纳入考察范围进行实证探讨，为理解数字金融的风险平滑功能提供了经验证据。研究发现，风险冲击会降低农户韧性，这种负面影响更多地体现在异质性风险冲击方面，而非协同性风险冲击；数字金融参与广度能够抵御异质性风险冲击对农户韧性的负面影响，数字金融参与广度在应对风险冲击提升农户韧性方面发挥的作用大于是否参与数字金融在这方面的作用，体现了数字金融对农户韧性影响的风险缓释效应。进一步实证考察数字金融参与广度、异质性风险冲击类型与农户韧性的关系发现，数字金融影响农户韧性的风险缓释效应存在风险类型差异，数字金融参与广度对农户韧性影响的风险缓释效应主要体现在重大疾病这种异质性风险冲击，对于其它方面的风险缓释作用较小。

研究内容四：基于RIMA-II韧性四大支柱，全面分析数字金融参与影响农户韧性的结构效应

实证研究发现，数字金融参与对农户韧性具有结构效应。韧性四大支柱包括适应能力、社会安全网、资产和获得基本服务，数字金融参与对韧性的四大支柱中的适应能力、社会安全网以及资产具有显著促进作用，但对获得基本服务没有显著影响。从不同类型的数字金融服务（移动支付、数字理财与数字信贷）对农户韧性结构的影响效应来看，不同类型的数字金融服务对农户韧性结构中的适应能力与资产影响较大，对社会安全网和获得基本服务的影响则相对小一些。从不同的金融服务参与方式来说，数字金融参与对农户韧性的边际影响更大，而传统金融参与对韧性影响的结构范畴更广。

研究内容五：深入探讨数字金融参与影响农户韧性的信息约束缓解、信贷约束缓解及风险管理机制

从信息约束缓解机制来看，数字金融参与拓宽了农户的信息获取渠道，显著提升家庭采取预防性措施来应对未预期风险的能力，有助于韧性水平的提升。从信贷约束缓解机制来看，数字金融参与能够显著提升农户的信贷规模，有助于缓解信贷约束，为缓解风险冲击提供了资本积累，进而提升农户的韧性能力。值得注意的是，信贷约束的缓解也可能引致家庭过度负债，反而使其陷入债务陷阱，削弱家庭的韧性水平，本文将这一可能进行了排除性检验，结果显示，数字金融参与不会导致农户过度负债，本研究的信贷约束缓解机制得到验证。从风险管理机制来看，数字金融参与能够显著提升农户的正规金融借贷偏好和商业保险持有概率，提高了农户的风险管理和自我保险能力。从数字鸿沟视角对机制进行异质性分析发现，数字金融参与影响农户韧性的机制在不同的分组情形下仍然成立，但对存在数字鸿沟的农户的边际影响更小，这表明未来要充分释放农村数字普惠金融的福利效应，仍需重点关注数字排斥群体，这是农户充分共享数字金融发展红利的关键。

本研究以金融中介理论、普惠金融理论、长尾理论与韧性理论为基础，从数字金融视角分析其对农户韧性的影响，同时将“数字金融—风险冲击—农户韧性”纳入统一分析框架，从理论和实证层面廓清了数字金融参与与农户韧性的影响关系、风险缓释效应、结构效应与作用机制，拓宽了对数字金融福利效应的认识，为理解中国特色数字金融发展背景下农户韧性的形成机理以及包容性金融体系的构建提供理论铺垫与经验证据。基于以上分析，本研究从以下四个方面提出政策建议：一是，跨越数字鸿沟，提高农户数字金融参与；二是，多种金融服务方式相结合，助力农村普惠金融发展；三是，加强数字金融监管，守住安全底线，促进农村数字金融健康发展；四是，多渠道、全方位提升农户韧性。

Rural households are exposed to complex and variable risk shocks that have a profound impact on their well-being. In recent years, synergistic risk shocks such as the new epidemic, climate change, natural disasters and heterogeneous risk shocks such as health risks have had a negative impact on the income levels, nutritional health and well-being of farming households and have even driven them into poverty as a result. One of the main reasons for these welfare losses is the lack of risk coping mechanisms and the lack of resilience of farmers. A solid increase in the resilience of rural households will not only help prevent them from falling back into poverty due to disasters and illnesses and consolidate the results of poverty eradication efforts, but will also enhance rural households' sense of access, happiness and security and improve their well-being, reflecting the proper meaning of common prosperity. Generally speaking, resilience is used to describe the ability of various types of subjects to recover and develop in the face of risky shocks, and is the endogenous motivation of subjects to survive and develop in a changing environment. Resilience provides a new perspective for analysing the performance of rural households under shocks and stressors to their welfare. It is worth noting that the dominant view on the status and fate of rural households has long been that smallholders are very vulnerable and find it difficult to cope with risks, with the typical characteristics of vulnerable smallholders.

A well-developed financial market is one of the most important means of increasing the resilience of farmers. By providing the financial services or risk-averse instruments that farmers need, financial markets help rural households to improve their level of household risk coping, thereby increasing their resilience. However, the institutional weaknesses of China's rural financial markets have not been adequately addressed, and the inefficient allocation of financial resources in rural areas makes it difficult to use financial tools to improve the resilience of rural households. In recent years, with the development of information technology such as big data, cloud computing and artificial intelligence, digital finance has been applied. As a transformative model to expand financial supply and achieve financial inclusion, digital finance has become the best way to promote rural financial development and bridge the "last mile" of rural inclusive finance. Can digital finance, with its huge theoretical advantages, contribute to the resilience of rural households? If so, what are its mechanisms of action? Can digital financial participation reduce the negative impact of risk shocks on the resilience of rural households and thus play a risk mitigation role? Is there a structural effect of digital financial participation on rural households' resilience, and what is the economic logic behind it? Based on this, this study systematically examines these questions using CHFS (2019) microsurvey data.

Based on the resilience framework of the Food and Agriculture Organization of the United Nations (FAO), this study explores the microfoundations of digital finance's impact on rural households' resilience, analyses the risk mitigation effects of digital finance participation on rural households' resilience, and further examines its structural effects and mechanisms of action. This study will provide a theoretical basis and policy recommendations for the formation mechanism of rural households' resilience and its enhancement path in the context of digital finance development with Chinese characteristics. The main research contents and findings are as follows:

Research content 1: Analysis of the facts on the characteristics of digital finance and the current situation of rural households' resilience in China

Based on the Chinese context and based on FAO's RIMA-II resilience theory, a system of resilience indicators for Chinese rural households' is constructed for the first time and measured using factor analysis, starting from the four pillars of resilience: adaptive capacity (AC), social safety net (SSN), assets (AST) and access to basic services (ABS), which provides a transition from the traditional qualitative analysis of vulnerable smallholder farmers to a quantitative study of resilient smallholder farmers. It provides a feasible analytical framework for the transition from traditional qualitative analysis of vulnerable smallholder farmers to quantitative research on resilient smallholder farmers. The basic statistical analysis reveals that the overall level, breadth and depth of digital financial development in rural China has increased year by year, but the increase has slowed down; the proportion of farmers' digital financial participation is low and the types of participation are few; there are significant regional differences in the development of digital finance and rural households' participation in digital finance; farmers using digital finance have higher levels of resilience; the impact of different types of digital financial services on rural households' resilience structure; there are significant differences in the resilience structure of rural households using different types of digital financial services; rural households exposed to risk shocks have lower levels of resilience.

Research Component 2: Analyzing the effect of digital financial participation on rural households' resilience at the theoretical and empirical levels

A two-period economic model was constructed to demonstrate at a theoretical level the existence of an increasing function characteristic between digital finance and rural households' resilience, and empirically tested using CHFS (2019) microsurvey data. The results indicate that rural households' participation in digital finance can not only improve their resilience, but the more types of digital finance participation, the higher their resilience level. Digital finance participation has significant statistical and economic significance in enhancing farmers' resilience. The findings of the study still hold after robustness testing in five ways: addressing endogeneity, changing the form of the explanatory variables, removing the sample of financial industry practitioners, addressing omitted variable bias, and replacing the estimation model.

Heterogeneity was analysed from two perspectives: types of digital financial participation behaviour and rural households' characteristics. From the perspective of different types of digital financial participation behaviour, mobile payment, digital finance and digital credit all contribute significantly to rural households' resilience. From the perspective of the digital divide, the digital financial participation of rural households with a digital divide contributes less to their resilience than those without a digital divide, which inhibits the spread of the digital financial inclusion effect. For those with a digital divide, the spillover effects of digital financial development can improve the level of benefits for such households, helping them to increase their level of resilience. The empirical results in four dimensions: income (poor vs. non-poor households), age (older vs. middle-aged), human capital (tertiary vs. non-tertiary) and regional differences (East, Central, West and Northeast) show that digital financial participation can improve the "disadvantaged" groups such as poor households, older people, non-tertiary groups and farming households in Central and West "This suggests that the development of digital finance in rural areas is inclusive and pro-poor.

Research Component 3: Systematic examination of the risk mitigation effects of digital finance participation on the rural households' resilience

Based on the RIMA-II resilience assessment system, this section incorporates "digital finance-risk shocks-farm household resilience" into a unified analytical framework, and then includes two types of shocks, namely synergistic and heterogeneous risk shocks, for empirical investigation. The study provides empirical evidence to understand the risk-smoothing function of digital finance. The study finds that risk shocks reduce rural households' resilience, and that this negative impact is more likely to be associated with heterogeneous risk shocks than with synergistic risk shocks; that the breadth of digital financial participation significantly reduces the negative impact of heterogeneous risk shocks on rural households' resilience, and that the breadth of digital financial participation plays a greater role in enhancing rural households' resilience in response to risk shocks than does the presence or absence of digital financial participation in this regard, reflecting The risk mitigation effect of digital finance on the impact of rural households' resilience. Further empirical examination of the relationship between the breadth of digital finance participation, different heterogeneity of risk shocks, and rural households' resilience reveals that there is risk heterogeneity in the risk mitigation effect of digital finance on rural households' resilience. The risk mitigation effect of the breadth of digital finance participation on farmers' resilience is mainly reflected in the heterogeneity of risk shocks such as major diseases, and has a relatively small risk mitigation effect on other aspects.

Research Component 4: A comprehensive analysis of the structural effects of digital financial participation on rural households' resilience based on the four pillars of resilience in RIMA-II

The empirical study finds that digital financial participation has a structural effect on rural households' resilience. The four pillars of resilience include adaptive capacity, social safety net, assets and access to basic services, and digital financial participation significantly contributes to adaptive capacity, social safety net and assets among the four pillars of resilience, but has no significant effect on access to basic services. In terms of the effect of different types of digital financial services (mobile payments, digital finance and digital credit) on the resilience structure of rural households, the effect of different types of digital financial services on the resilience structure of rural households is greater for adaptive capacity and assets, and less for social safety nets and access to basic services. In terms of different types of financial services participation, digital financial participation has a greater marginal impact on rural households' resilience, while traditional financial participation has a broader structural scope of impact on resilience.

Research Component 5: In-depth exploration of information constraint mitigation, credit constraint mitigation and risk management mechanisms of digital financial participation affecting rural households' resilience

In terms of information constraint mitigation mechanisms, digital financial participation broadens rural households' access to information and significantly improves households' ability to take precautionary measures to cope with unanticipated risks, contributing to resilience levels. In terms of credit constraint mitigation mechanisms, digital financial participation can significantly increase the credit size of rural households' households, which helps to alleviate credit constraints and provides capital accumulation for mitigating risk shocks, thus enhancing the resilience of rural households' households. It is worth noting that the alleviation of credit constraints may also lead to over-indebtedness of households, which may in turn lead them to fall into a debt trap and weaken their resilience level. In terms of risk management mechanisms, digital financial participation can significantly increase rural households' preference for formal financial borrowing and the probability of holding commercial insurance, improving their risk management and self-insurance capabilities. A heterogeneity analysis of the mechanisms from a digital divide perspective found that the mechanism of digital financial participation affecting rural households' resilience still holds across different grouping scenarios, but has a smaller marginal impact on farm households with a digital divide, suggesting that to fully unleash the welfare effects of rural digital financial inclusion in the future, there is still a need to focus on the digitally excluded group, which is the key for rural households to fully share the dividends of digital financial development.

Based on financial intermediation theory, financial inclusion theory, long-tail theory and resilience theory, this study analyses the impact of digital finance on rural households' resilience from the perspective of digital finance, and incorporates "digital finance-risk shock-farmers' resilience" into a unified analytical framework, clarifying the relationship between digital financial participation and rural households' resilience, risk mitigation effect, structural effect and mechanism of action at the theoretical and empirical levels, broadening the understanding of the welfare effect of digital finance, and providing theoretical pavement and empirical evidence for understanding the formation mechanism of rural households' resilience and the construction of an inclusive financial system in the context of digital finance development with Chinese characteristics. Based on the above analysis, this study puts forward policy recommendations in the following four aspects: first, to cross the digital divide and increase rural households' digital financial participation; second, to combine various financial services to help the development of rural inclusive finance; third, to strengthen the regulation of digital finance, guard the safety bottom line and promote the healthy development of rural digital finance; fourth, rural households' is enhanced through multiple channels and in all aspects.

[1]阿玛蒂亚·森. 以自由看待发展[M]. 任颐、于真译，北京：中国人民大学出版社，2013.

[2]柴时军.移动支付是否放大了家庭债务风险?——基于家庭财务杠杆视角的微观证据[J].西南民族大学学报(人文社科版)，2020，41(10):122-133.

[3]陈池波，龚政.数字普惠金融能缓解农村家庭金融脆弱性吗?[J]. 中南财经政法大学学报，2021，247(04):132-143.

[4]陈东平，丁力人，高名姿.共同富裕背景下数字金融与城乡收入差距——基于地级市面板数据的实证研究[J].南京农业大学学报(社会科学版)，2022，22(06):171-182.

[5]陈军亚.家户小农：韧性国家的历史社会根基[J].学海，2021(01):14-20.

[6]陈军亚.韧性小农：历史延续与现代转换——中国小农户的生命力及自主责任机制[J].中国社会科学，2019(12)：82-99+201.

[7]陈晓芳，杨建州.数字金融能否提高居民创业的成功率？[J].福建论坛(人文社会科学版)，2021(08):72-84.

[8]陈志武.互联网金融到底有多新[J].新金融，2014(04):9-13.

[9]程名望，Jin Yanhong，盖庆恩，史清华.农村减贫:应该更关注教育还是健康?——基于收入增长和差距缩小双重视角的实证[J].经济研究，2014，49(11):130-144.

[10]程郁，韩俊，罗丹.供给配给与需求压抑交互影响下的正规信贷约束:来自1874户农户金融需求行为考察[J].世界经济，2009(05):73-82.

[11]邓荣荣，张翱祥.中国城市数字金融发展对碳排放绩效的影响及机理[J].资源科学，2021，43(11):2316-2330.

[12]丁骋骋，余欢欢.数字金融对居民的债务扩张效应[J].国际金融研究，2022(10):38-48.

[13]丁杰，袁也，符号亮.金融减贫:数字金融与传统金融的互动关系及相对重要性分析[J].国际金融研究，2022(09):14-24.

[14]董晓林，吴以蛮，熊健.金融服务参与方式对农户多维相对贫困的影响[J].中国农村观察，2021(06):47-64.

[15]杜巍，牛静坤，车蕾.农业转移人口市民化意愿:生计恢复力与土地政策的双重影响[J].公共管理学报，2018，15(3):66-77+157.

[16]方观富，许嘉怡.数字普惠金融促进居民就业吗——来自中国家庭跟踪调查的证据[J].金融经济学研究，2020，35(02):75-86.

[17]方能胜，郭福森，路晓蒙.数字普惠金融会提高老年人幸福感吗——基于CHFS及DFIIC数据的实证研究[J].财经科学，2022(08):32-46.

[18]葛和平，钱宇.数字普惠金融服务乡村振兴的影响机理及实证检验[J].现代经济探讨， 2021(05): 118-126.

[19]郭妍，张立光，王馨.农村数字普惠金融的经济效应与影响因素研究——基于县域调查数据的实证分析[J].山东大学学报(哲学社会科学版)，2020(06):122-132.

[20]何婧，李庆海.数字金融使用与农户创业行为[J].中国农村经济，2019(01)：112-126.

[21]何婧，田雅群，刘甜，李庆海.互联网金融离农户有多远——欠发达地区农户互联网金融排斥及影响因素分析[J].财贸经济，2017，38(11):70-84.

[22]何宗樾，张勋，万广华.数字金融、数字鸿沟与多维贫困[J].统计研究，2020，37(10):79-89.

[23]黄凯南，郝祥如.数字金融是否促进了居民消费升级?[J].山东社会科学，2021(01):117-125.

[24]黄倩，李政，熊德平.数字普惠金融的减贫效应及其传导机制[J].改革，2019(11):90-101.

[25]黄益平，黄卓.中国的数字金融发展:现在与未来[J].经济学(季刊)，2018，17(04)：1489-1502.

[26]江红莉，蒋鹏程.数字普惠金融的居民消费水平提升和结构优化效应研究[J].现代财经(天津财经大学学报)，2020，40(10):18-32.

[27]江鸿泽，梁平汉.数字金融发展与犯罪治理——来自盗窃案刑事判决书的证据[J].数量经济技术经济研究，2022，39(10):68-88.

[28]江艇.因果推断经验研究中的中介效应与调节效应[J].中国工业经济，2022(05):100-120.

[29]克里斯·安德森.长尾理论：为什么商业的未来是小众市场[M].乔江涛，石晓燕译，北京：中信出版社，2015.

[30]科斯. 企业的性质[A].上海：上海三联书店，1994．

[31]李博.后扶贫时代深度贫困地区脱贫成果巩固中的韧性治理[J].南京农业大学学报(社会科学版)，2020，20(04)：172-180.

[32]李超伟，张龙耀.信息通信技术使用、金融交易成本与农户数字金融参与——基于距离、密度与人情成本三重维度的考察[J].南京农业大学学报(社会科学版)：2023(02)：168-177.

[33]李聪，王磊，康博纬，高梦.易地移民搬迁农户的生计恢复力测度及影响因素分析[J].西安交通大学学报(社会科学版)，2019，39(04):38-47.

[34]黎翠梅，周莹.数字普惠金融对农村消费的影响研究——基于空间计量模型[J].经济地理，2021，41(12):177-186.

[35]李晗，陆迁.精准扶贫与贫困家庭复原力——基于CHFS微观数据的分析[J].中国农村观察，2021(02)：28-41.

[36]李继尊.关于互联网金融的思考[J].管理世界，2015(07):1-7+16.

[37]李建军，韩珣.普惠金融、收入分配和贫困减缓——推进效率和公平的政策框架选择[J].金融研究，2019(03):129-148.

[38]李建军，李俊成.普惠金融与创业:“授人以鱼”还是“授人以渔”?[J].金融研究，2020(01):69-87.

[39]李建军，彭俞超.金融科技学[M].北京：高等教育出版社，2021.

[40]李牧辰，封思贤，谢星.数字普惠金融对城乡收入差距的异质性影响研究[J].南京农业大学学报(社会科学版)，2020，20(03):132-145.

[41]李文秀，刘俊杰.数字普惠金融的收入效应与消费不平等——中共二十大报告关于民生福祉视角的阐释[J].金融经济学研究，2023，38(01):34-51.

[42]李小云，林晓莉，徐进.小农的韧性：个体、社会与国家交织的建构性特征——云南省勐腊县河边村疫情下的生计[J].农业经济问题，2022(01)：52-64.

[43]李杏，高登云，尹敬东.数字金融是否改善了居民收入结构？——基于“长尾”理论的视角[J].江苏社会科学，2022(06):118-127+243.

[44]李玉山，卢敏，朱冰洁.多元精准扶贫政策实施与脱贫农户生计脆弱性——基于湘鄂渝黔毗邻民族地区的经验分析[J].中国农村经济，2021，437(05):60-82.

[45]李政，李鑫.数字普惠金融与未预期风险应对：理论与实证[J].金融研究，2022(06):94-114.

[46]刘丹，方锐，汤颖梅.数字普惠金融发展对农民非农收入的空间溢出效应[J].金融经济学研究，2019，34(03):57-66.

[47]刘锦怡，刘纯阳.数字普惠金融的农村减贫效应:效果与机制[J].财经论丛，2020(01):43-53.

[48]刘俊杰，李超伟，韩思敏，张龙耀.农村电商发展与农户数字信贷行为——来自江苏“淘宝村”的微观证据[J].中国农村经济，2020(11):97-112.

[49]刘敏楼，黄旭，孙俊.数字金融对绿色发展的影响机制[J].中国人口·资源与环境，2022，32(06):113-122.

[50]刘魏，张应良，王燕.数字普惠金融发展缓解了相对贫困吗?[J].经济管理，2021，43(07)：44-60.

[51]刘心怡，黄颖，黄思睿，张桃霖.数字普惠金融与共同富裕：理论机制与经验事实[J].金融经济学研究， 2022，37(01): 135-149.

[52]刘征驰，赖明勇.虚拟抵押品、软信息约束与P2P互联网金融[J].中国软科学，2015(01):35-46.

[53]刘忠璐.互联网金融对商业银行风险承担的影响研究[J].财贸经济，2016(04):71-85+115.

[54]鹿光耀，袁云云，吴春雅.数字普惠金融有益于丰富农户收入多样性吗?——基于江西“百村千户”的调研数据[J].江西社会科学，2022，42(06):65-74.

[55]马库斯.布伦纳梅尔.韧性社会[M].余江译，北京：中信出版社，2022.

[56]马述忠，胡增玺.数字金融是否影响劳动力流动?——基于中国流动人口的微观视角[J].经济学(季刊)，2022，22(01):303-322.

[57]马亚明，周璐.基于双创视角的数字普惠金融促进乡村振兴路径与机制研究[J].现代财经(天津财经大学学报)，2022，42(02):3-20.

[58]孟娜娜，粟勤.挤出效应还是鲶鱼效应：金融科技对传统普惠金融影响研究[J].现代财经(天津财经大学学报)，2020，40(01):56-70.

[59]潘爽，魏建国，胡绍波. 互联网金融与家庭正规信贷约束缓解——基于风险偏好异质性的检验[J].经济评论，2020(03)：149-162.

[60]彭澎，徐志刚.数字普惠金融能降低农户的脆弱性吗?[J].经济评论，2021(01):82-95.

[61]皮天雷，刘垚森，吴鸿燕.金融科技:内涵、逻辑与风险监管[J].财经科学，2018(09):16-25.

[62]钱海章，陶云清，曹松威，曹雨阳.中国数字金融发展与经济增长的理论与实证[J].数量经济技术经济研究，2020，37(06):26-46.

[63]强国令，商城.数字金融、家庭财富与共同富裕[J].南方经济，2022(08):22-38.

[64]邱泽奇，张樹沁，刘世定，许英康.从数字鸿沟到红利差异——互联网资本的视角[J].中国社会科学，2016(10):93-115+203-204.

[65]邱子迅，周亚虹.电子商务对农村家庭增收作用的机制分析——基于需求与供给有效对接的微观检验[J].中国农村经济，2021(04):36-52.

[66]任静，朱方明.互联网银行的破坏性创新及其对传统银行的挑战[J].现代经济探讨，2016(03):10-14.

[67]宋明月，臧旭恒.我国居民预防性储蓄重要性的测度——来自微观数据的证据[J].经济学家，2016(01):89-97.

[68]宋文豪，黄祖辉，叶春辉.数字金融使用对农村家庭生计策略选择的影响——来自中国农村家庭追踪调查的证据[J].中国农村经济，2023（6）：92-113.

[69]苏芳，尚海洋.农户生计资本对其风险应对策略的影响——以黑河流域张掖市为例[J].中国农村经济，2012(08)：79-87+96.

[70]孙继国，赵俊美.普惠金融是否缩小了城乡收入差距?——基于传统和数字的比较分析[J].福建论坛(人文社会科学版)，2019(10):179-189.

[71]孙学涛，于婷，于法稳.数字普惠金融对农业机械化的影响——来自中国1869个县域的证据[J].中国农村经济，2022(02):76-93.

[72]唐建军，龚教伟，宋清华.数字普惠金融与农业全要素生产率——基于要素流动与技术扩散的视角[J].中国农村经济，2022(07):81-102.

[73]田瑶，赵青，郭立宏.数字普惠金融与共同富裕的实现——基于总体富裕与共享富裕的视角[J].山西财经大学学报，2022，44(09):1-17.

[74]汪亚楠，谭卓鸿，郑乐凯.数字普惠金融对社会保障的影响研究[J].数量经济技术经济研究，2020，37(07):92-112.

[75]王海军，王念，戴冠.“普惠”金融背景的互联网金融——理论解构与政策分析[J].上海金融学院学报，2014(04):32-44.

[76]王晶，吕新业，吕开宇.数字金融使用对农户生计多样化的影响研究[J].农村经济，2021(08):62-71.

[77]王小华，马小珂，何茜.数字金融使用促进农村消费内需动力全面释放了吗？[J].中国农村经济，2022(11):21-39.

[78]王馨.互联网金融助解“长尾”小微企业融资难问题研究[J].金融研究，2015(09):128-139.

[79]王修华，赵亚雄.数字金融发展是否存在马太效应?——贫困户与非贫困户的经验比较[J].金融研究，2020(07):114-133.

[80]王勋，王雪.数字普惠金融与消费风险平滑：中国家庭的微观证据[J].经济学(季刊)，2022，22(05):1679-1698.

[81]王亚柯，刘东亚.信贷约束与家庭金融市场参与[J].金融研究，2023(02):171-188.

[82]王阳，漆雁斌.贫困农户的风险分担网络及其有效性研究——基于中国农村家庭数据的实证[J].四川农业大学学报，2019，37(04):566-578.

[83]王永仓，温涛，王小华.数字金融与农户家庭增收：影响效应与传导机制——基于中国家庭金融调查数据的实证研究[J].财经论丛，2021(09):37-48.

[84]王元彬，张尧，李计广.数字金融与碳排放：基于微观数据和机器学习模型的研究[J].中国人口·资源与环境，2022，32(06):1-11.

[85]王跃生.中国城乡家庭结构变动分析——基于2010年人口普查数据[J].中国社会科学，2013，216(12)：60-77+205-206.

[86]王喆，陈胤默，张明.传统金融供给与数字金融发展：补充还是替代?——基于地区制度差异视角[J].经济管理，2021，43(05)：5-23.

[87]威廉姆森. 资本主义经济制度[M].北京：商务印书馆，2009.

[88]温腾飞，石育中，杨新军，王婷.黄土高原半干旱区农户生计恢复力及其影响因素研究——以榆中县为例[J].中国农业资源与区划，2018，39(5):172-182.

[89]吴静茹，谢家智，涂先进.数字金融、市场参与和农户相对贫困[J].当代财经，2021(08):64-77.

[90]吴晓求.互联网金融:成长的逻辑[J].财贸经济，2015(02):5-15.

[91]吴雨，李晓，李洁，周利.数字金融发展与家庭金融资产组合有效性[J].管理世界，2021， 37(07): 92-104+7.

[92]肖威.数字普惠金融能否改善不平衡不充分的发展局面?[J].经济评论，2021，231(05):50-64.

[93]肖卫，肖琳子.二元经济中的农业技术进步、粮食增产与农民增收——来自2001~2010年中国省级面板数据的经验证据[J].中国农村经济，2013(06):4-13+47.

[94]谢家智，吴静茹.数字金融、信贷约束与家庭消费[J].中南大学学报(社会科学版)，2020，26(02):9-20.

[95]谢平，邹传伟，刘海二.互联网金融监管的必要性与核心原则[J].国际金融研究，2014(08)：3-9.

[96]谢平，邹传伟.互联网金融模式研究[J].金融研究，2012(12)：11-22.

[97]星焱.普惠金融:一个基本理论框架[J].国际金融研究，2016(09):21-37.

[98]熊健，董晓林.数字金融参与促进农户创业决策了吗?——基于融资规模和机会识别视角的实证分析[J].商业研究，2021(05):123-130.

[99]杨汝岱，陈斌开，朱诗娥.基于社会网络视角的农户民间借贷需求行为研究[J].经济研究，2011，46(11):116-129.

[100]杨少雄，孔荣.数字金融市场参与改善农户收入了吗?[J].华中农业大学学报(社会科学版)， 2021(05): 180-190+200.

[101]姚耀军，施丹燕.互联网金融区域差异化发展的逻辑与检验——路径依赖与政府干预视角[J].金融研究，2017(05):127-142.

[102]易行健，周利.数字普惠金融发展是否显著影响了居民消费——来自中国家庭的微观证据[J].金融研究，2018(11):47-67.

[103]尹振涛，李俊成，杨璐.金融科技发展能提高农村家庭幸福感吗？——基于幸福经济学的研究视角[J].中国农村经济，2021(08)：63-79.

[104]尹志超，仇化.金融知识对互联网金融参与重要吗[J].财贸经济，2019，40(06):70-84.

[105]尹志超，公雪，郭沛瑶.移动支付对创业的影响——来自中国家庭金融调查的微观证据[J].中国工业经济，2019(03):119-137.

[106]尹志超，公雪，潘北啸.移动支付对家庭货币需求的影响——来自中国家庭金融调查的微观证据[J].金融研究，2019(10):40-58.

[107]尹志超，王天娇，蒋佳伶.移动支付对中国家庭碳消费的影响——来自家庭碳足迹的证据[J].会计与经济研究，2023，37(01):99-116.

[108]尹志超，文小梅，栗传政.普惠金融、收入差距与共同富裕[J].数量经济技术经济研究，2023，40(01): 109-127.

[109]尹志超，吴子硕，蒋佳伶.移动支付对中国家庭储蓄率的影响[J].金融研究，2022(09):57-74.

[110]尹志超，张栋浩.金融普惠、家庭贫困及脆弱性[J].经济学(季刊)，2020，20(05)：153-172.

[111]岳崴，王雄，张强.健康风险、医疗保险与家庭财务脆弱性[J].中国工业经济，2021(10):175-192.

[112]曾建中，李银珍，刘桂东.数字普惠金融赋能乡村产业兴旺的作用机理和空间效应研究——基于县域空间动态面板数据的实证检验[J].国际金融研究，2023(04):39-49.

[113]曾湘泉，郭晴.数字金融发展能促进返乡农民工再就业吗——基于中国劳动力动态调查(CLDS)的经验分析[J].经济理论与经济管理，2022，42(04):12-26.

[114]张呈磊，郭忠金，李文秀.数字普惠金融的创业效应与收入不平等：数字鸿沟还是数字红利?[J].南方经济，2021(05):110-126.

[115]张东玲，焦宇新.农业保险、农业全要素生产率与农户家庭经济韧性[J].华南农业大学学报(社会科学版)，2022，21(02)：82-97.

[116]张栋浩，尹志超.金融普惠、风险应对与农村家庭贫困脆弱性[J].中国农村经济，2018(04)：54-73.

[117]张海洋，韩晓.数字金融的减贫效应研究——基于贫困脆弱性视角[J].金融评论，2021，13(06): 57-77+119.

[118]张海洋，韩晓.数字金融能缓和社会主要矛盾吗?——消费不平等的视角[J].经济科学，2022(02): 96-109.

[119]张海洋.融资约束下金融互助模式的演进——从民间金融到网络借贷[J].金融研究，2017(03): 101-115.

[120]张金林，董小凡，李健.数字普惠金融能否推进共同富裕？——基于微观家庭数据的经验研究[J].财经研究，2022，48(07)：4-17+123.

[121]张林，温涛.数字普惠金融如何影响农村产业融合发展[J].中国农村经济，2022(07):59-80.

[122]张龙耀，李超伟，王睿.金融知识与农户数字金融行为响应——来自四省农户调查的微观证据[J].中国农村经济，2021(05)：83-101.

[123]张龙耀，邢朝辉.中国农村数字普惠金融发展的分布动态、地区差异与收敛性研究[J].数量经济技术经济研究，2021，38(03):23-42.

[124]张龙耀，徐曼曼，刘俊杰.自然灾害冲击与农户信贷获得水平——基于CFPS数据的实证研究[J].中国农村经济，2019(03):36-52.

[125]张龙耀，张海宁.金融约束与家庭创业——中国的城乡差异[J].金融研究，2013(09):123-135.

[126]张龙耀，张静.健康冲击、数字普惠金融与家庭消费[J].南方经济，2022(08):39-51.

[127]张五常. 供应行为（经济解释卷二）[M].香港：花千树出版有限公司，2002

[128]张行，陈海，耿甜伟，史琴琴，刘迪.黄土丘陵沟壑区农户生计恢复力空间分异及影响因素——以陕北米脂县石沟镇为例[J].地理与地理信息科学，2020，36(1):100-106.

[129]张勋，万广华，吴海涛.缩小数字鸿沟:中国特色数字金融发展[J].中国社会科学，2021(08) : 35-51+204-205.

[130]张勋，万广华，张佳佳，何宗樾.数字经济、普惠金融与包容性增长[J].经济研究，2019，54(08)：71-86.

[131]张勋，杨桐，汪晨，万广华.数字金融发展与居民消费增长：理论与中国实践[J].管理世界，2020，36(11):48-63.

[132]张正平，卢欢.数字鸿沟对家庭金融投资的影响——基于CFPS数据的实证研究[J].福建论坛(人文社会科学版)，2021(03):57-70.

[133]周月书，苗哲瑜.数字普惠金融对农户生产经营投资的影响[J].中国农村观察，2023(01):40-58.

[134]周云波，黄祺雨.数字普惠金融与居民社会信任——基于经济公平和机会公平的双视角[J].现代经济探讨，2023(01):15-28.

[135]邹新月，王旺.数字普惠金融对居民消费的影响研究——基于空间计量模型的实证分析[J].金融经济学研究，2020，35(04):133-145.

[136]Adato, M., & Meizen-Dick, R. (2002). Assessing the impact of agricultural research on poverty using the sustainable livelihoods framework (FCND discussion paper 128, EPTD discussion paper 89). Washington, DC: International Food Policy Research Institute.

[137]Adger, N. (2003). Social capital, collective action, and adaptation to climate change. Economic Geography, 79(4), 387–404.

[138]Agarwal S. Skiba P. Tobacman J. (2009). Payday Loans and Credit Cards: New Liquidity and Credit Scoring Puzzles? The American Economic Review, 99(2), 412-417.

[139]Ahmed, H., & Cowan B. (2021) .Mobile Money and Healthcare Use: Evidence from East Africa. World Development, 141(3):1-13.

[140]Akerlof, G.(1970) The market for‘lemon ':Qualitative uncertainty and the market mechanism. Quarterly Journal of Economics, (84):488-500.

[141]Alfani, F., Dabalen, A., Fisker, P., and Molini, V. (2015). Can we measure resilience? A proposed method and evidence from countries in the Sahel. Policy Research Working Paper Series.

[142]Alinovi, L., D’errico, M., Mane, E., and Romano, D. (2010). Livelihoods strategies and household resilience to food insecurity: An empirical analysis to Kenya. In "Conference on “promoting resilience through social protection in sub-Saharan Africa”, organised by the European report of development in Dakar, Senegal", 28-30.

[143]Alinovi, L., Mane, E., & Romano, D. (2008). Towards the measurement of household resilience to food insecurity: Applying a model to Palestinian household data. Deriving food security information from national household budget surveys. In Food and Agriculture Organization of the United Nations, 137–152.

[144]Allen, F., & Santomero, A. M. (1997). The theory of financial intermediation. Journal of Banking & Finance, 21(12): 1461-1485.

[145]Alshehri, S. A., Rezgui, Y., & Li, H. (2015). Disaster community resilience assessment method: A consensus-based Delphi and AHP approach. Natural Hazards, 78(1): 395–416

[146]Altonji,J.G., Elder., & Taber,C.R. (2005).Selection on observed and unobserved variables： assessing the effectiveness of catholic Schools. Journal of Political Economy,113(1):151-184.

[147]Amoako, C. (2018). Emerging grassroots resilience and flood responses in informal settlements in Accra, Ghana. GeoJournal, 83(5): 949–965.

[148]Anand, S. K., & Chhikara K. S. (2012) .A theoretical and quantitative analysis of financial inclusion and economic growth. Management and Labour Studies, (1):103-133.

[149]Ansah, I. G. K., Gardebroek, C., & Ihle, R. (2019). Resilience and household food security: A review of concepts, methodological approaches and empirical evidence. Food Security, 11(6), 1187-1203.

[150]Antle, J. M. (1983). Testing the stochastic structure of production: A flexible moment-based approach”, Journal of Business & Economic Statistics, 1(3): 192.

[151]Arner, D. W., Buckley, R. P., Zetzsche, D. A., & Veidt, R. (2020). Sustainability, FinTech and financial inclusion. European Business Organization Law Review, 21(1), 7–35.

[152]Arouri, M., C. Nguyen, and A. B. Youssef. (2015). Natural disasters, household welfare, and resilience: evidence from rural Vietnam. World Development ,70:59-77

[153]Bahadur, A., Ibrahim, M., & Tanner, T. (2013). Characterising resilience: unpacking the concept for tackling climate change and development. Climate and Development, 5(1), 55–65.

[154]Barnett, Barry J., Christopher B. Barrett, & Jerry, R. S. (2008) .Poverty traps and index based risk transfer products. World Development 36(10): 1766-85.

[155]Barrett, C. B., & Constas, M. A. (2014). Toward a theory of resilience for international development applications. Proceedings of the National Academy of Sciences of the United States of America, 111(40), 14625–14630.

[156]Barrett, C. B., Ghezzi-Kopel, K., Hoddinott, J., Homami, N., Tennant, E., Upton, J., & Wu, T. (2021). A scoping review of the development resilience literature: Theory, methods and evidence. World Development, 146, 105612.

[157]Basely T. (1995). Nonmarket institutions for credit and risk sharing in low-income country[J]. Journal of Economic Perspectives, 9(3):115-127.

[158]Beck, T., Demirguc-Kunt, A., & Martinez Peria, M. S. (2007). Reaching out: access to and use of banking services across countries. Journal of Financial Economics, 85(1), 234-266.

[159]Beck,T., Pamuk, H., Ramrattan,R., & Burak, R.U. (2018). Payment instruments, finance and development. Journal of Development Economics, 133:162-186.

[160]Béné, C., Al-Hassan, R. M., Amarasinghe, O., Fong, P., Ocran, J.,Onumah, E., Ratuniata, R., Tuyen, T. V., McGregor, J. A., & Mills, D. J. (2016). Is resilience socially constructed? Empirical evidence from Fiji, Ghana, Sri Lanka, and Vietnam. Global Environmental Change, 38, 153-170.

[161]Béné, C., Chowdhury, F. S., Rashid, M., Dhali, S. A., & Jahan, F. (2017). Squaring the circle: Reconciling the need for rigor with the reality on the ground in resilience impact assessment. World Development, 97, 212–231.

[162]Béné, C., Newsham, A., Davies, M., Ulrichs, M., & Godfrey-Wood, R. (2014). Resilience, poverty and development. Journal of International Development, 26(5), 598-623.

[163]Béné, C., Wood, R.G., Newsham, A. & Davies, M. (2012). Resilience: new utopia or new tyranny? reflection about the potentials and limits of the concept of resilience in relation to vulnerability reduction programmes. IDS Working Paper 405(2012): 1-61.

[164]Bensten George. (1976). A Transaction Cost Approach to the Theoryof Financial Intermediation. Journal of Finance,31,215-231.

[165]Berg T., Burg V., Gombovic A., Puri M. (2020). On the rise of FinTech: Credit scoring using digital footprints. Review of Financial Studies, 33(7):2845-2897.

[166]Birhanu, Z., Ambelu, A., Berhanu, N., Tesfaye, A., & Woldemichael, K. (2017). Understanding resilience dimensions and adaptive strategies to the impact of recurrent droughts in borana zone, oromia region, ethiopia: a grounded theory approach. International Journal of Environmental Research and Public Health, 14(2), 118.

[167]Björkegren, D., & D. Grissen. (2018). The potential of digital credit to bank the poor[J]. AEA Papers and Proceedings, 108: 68-71.

[168]Bodin & Wiman. (2006). Resilience and other stability concepts in ecology: Notes on their origin, validity, and usefulness. ESS Bulletin,(2):33–43.

[169]Bonfadelli, H. (2002). The internet and knowledge gaps: A theoretical and empirical investigation. European Journal of Communication, 17(1): 65-84.

[170]Boyd,J.H., Prescott, E.C. (1986). Financial intermediary-coalitions. Journal of Economic Theory, 38(2):211-232.

[171]Browne, M., Ortmann, G. F., & Hendriks, S. L. (2014). Household food security monitoring and evaluation using a resilience indicator: An application of categorical principal component analysis and simple sum of assets in five African countries. Agrekon, 53(2), 25–46.

[172]Brück, T., d’Errico, M., & Pietrelli, R. (2019). The effects of violent conflict on household resilience and food security: Evidencze from the 2014 Gaza conflict. World Development, 119, 203–223.

[173]Burgess, R., & Pande, R. (2005). Do Rural Banks Matter? Evidence from the Indian Social Banking Experiment. American Economic Review, 95(3), 780-795.

[174]Campbell, B., J. A. Sayer, P. Frost, S. Vermeulen, M. Ruiz Pérez, A. Cunningham, & R. Prabhu. (2001). Assessing the performance of natural resource systems. Conservation Ecology, 5(2):22.

[175]Carpenter, S., B. Walker, J. M. Anderies, & N. Abel. (2001). From metaphor to measurement: resilience of what to what? Ecosystems, 4(8):765-781.

[176]Carter, Michael R. & Christopher B. Barrett. (2006). The economics of poverty traps and persistent poverty: An asset-based approach. Journal of Development Studies ,42(2): 178-99.

[177]Catholic Relief Services(CRS). (2013). Niger resilience study. Baltimore, MA: Catholic Relief Services.

[178]Chambers,R. & Conway,G.R. (1992). Sustainable rural livelihoods: practical concepts for the 21st century[R]. JIDS Discussion Paper 296. Brighton,England:Institute of Development Studies, 9-12.

[179]Chaudhuri, S., (2000). Empirical methods for assessing household vulnerability to poverty. mimeo, department of economics. Columbia University, New York.

[180]Choudhury, M. (2014). Poverty, vulnerability and financial inclusion: the context of bangladesh. Journal of Politics and Administration, 2(1):1-13.

[181]Christiaensen, L., & K. Sabbarao. (2004). Toward an understanding of household vulnerability in rural Kenya, World Bank Policy Research Working Paper 3326, Washington DC: World Bank.

[182]Cissé, J. D., & Barrett, C. B. (2018). Estimating development resilience: A conditional moments-based approach. Journal of Development Economics, 135, 272–284.

[183]Cissé, J.D. & Ikegami, M. (2016). Does insurance improve resilience? Measuring the impact of index-based livestock insurance on development resilience in Northern Kenya. International Livestock Research Institute working paper.

[184]Clare, A., Sagynbekova, L., Singer, G., Bene, B., & Rahmanberdi, A. (2018). Can subjective resilience indicators predict future food security? Evidence from three communities in rural Kyrgyzstan Centre for Climate Change Economics and Policy Working Paper 342/Grantham Research Institute on Climate Change and the Environment Working Paper 308. London: London School of Economics and Political Science.

[185]Coase, R H. (1937). The Nature of the Firm. Economica, 4(16): 386-405.

[186]Cooper, S. J., & T. Wheeler. (2015). Adaptive governance: livelihood innovation for climate resilience in Uganda. Geoforum, 65:96–107.

[187]Costa, A.,Deb, A., & Kubzansky, M. (2015). Big data, small credit: the digital revolution and its impact on emerging market consumers. Innovations: Technology, Governance, Globalization, 10(3): 49-80.

[188]Cox, R. S., & Perry, K. M. E. (2011). Like a fish out of water: Reconsidering disaster recovery and the role of place and social capital in community disaster resilience. American Journal of Community Psychology, 48: 395-411.

[189]Cuesta, J., & Maratou-Kolias, L. (2018). WASH and nutrition synergies: The case of tunisia. The Journal of Development Studies, 102(2): 1–22.

[190]Cullen,R. (2003). Addresing the digital divide[J].Scientific American, 289(2):311-320.

[191]Cutter, S. L., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E., & Webb, J. (2008). A place-based model for understanding community resilience to natural disasters. Global Environmental Change, 18(4):598-606.

[192]d’Errico, M., & Di Giuseppe, S. (2018). Resilience mobility in Uganda: A dynamic analysis. World Development, 104: 78-96.

[193]d’Errico, M., Garbero, A., Letta, M., & Winters, P. (2020). Evaluating program impact on resilience: Evidence from Lesotho’s child grants programme. The Journal of Development Studies, 56(12), 2212-2234.

[194]d’Errico, M., Pietrelli, R. & Romano, D. (2016). Household resilience to food insecurity: evidence from Tanzania and Uganda. Paper presented at the 5th AIEAA Conference, Bologna, Italy. June 16-17, 2016. www.aieaa.org/node/144.

[195]d’Errico, M., Romano, D., & Pietrelli, R. (2018). Household resilience to food insecurity: Evidence from Tanzania and Uganda. Food Security, 10(4), 1033-1054.

[196]Danquah, M., & Iddrisu, A. M. (2018). Access to mobile phones and the wellbeing of non-farm enterprise households: Evidence from Ghana. Technology in Society, 54:1-9.

[197]Datt, G., & Hoogeveen, H.(2003). El Niño or El Peso? Crisis,Poverty, and Income Distribution in the Philippines.World Development, 31 (7):1103-1124.

[198]Demertzis, M., Merler, S., & Wolff, G. B. (2018). Capital Markets union and the fintech opportunity. Journal of Financial Regulation, 4(1):157-165.

[199]d'Errico, M., & Pietrelli, R. (2017). Resilience and child malnutrition in Mali. Food Security,(9) :355-370.

[200]DFID (Department for International Development. (2011). Defining disaster resilience: a DFID approach paper. London, UK,1-20. Retrieved from: https://www.gov.uk/government/publications/defining-disaster-resilience-adfid-approach-paper.

[201]Di Maggio, M., & Yao, V. (2020). Fintech borrowers: lax screening or cream-skimming? The Review of Financial Studies,1-54.

[202]Diamond, Douglas W., and Philip H. Dybvig. (1983). Bank runs, deposit insurance, and liquidity. Journal of Political Economy , 91 (5): 401-19.

[203]Dupas P. and Robinson J. (2013). Savings constraints and microenterprise development: evidence from a field experiment in Kenya. Applied Economics,(1):163-192.

[204]Dupas, P. (2009). What matters (and what does not) in households’ decision to invest in malaria prevention?. American Economic Review, 99(2),:224-230.

[205]Dupas, P., & Robinson, J. (2013). Why don’t the poor save more? evidence from health savings experiments. American Economic Review,103(4):1138-1171．

[206]Ellis, F. (2000). Rural livelihoods and diversity in developing countries. Oxford, UK: Oxford University Press.

[207]Enns, C., & Bersaglio, C. (2015). Exploring the implications of oil and gas development for livelihood resilience in Turkana, Kenya. For the PRISE small grant programme of the Overseas Development Institute. http://www.academia.edu/13047117/Exploring_the_implications_of_oil_and_gas_development_for_livelihood_resilience_in_Turkana_Kenya.

[208]Erenstein, O., J. Hellin, & Chandna, P. (2010). Poverty mapping based on livelihood assets: a meso-level application in the Indo-Gangetic Plains, India. Applied Geography, 30(1):112-125.

[209]European Commission, 2016, ‘Building resilience: The EU’s approach’, http:// ec.europa.eu/echo/files/aid/countries/factsheets/thematic/resilience_en.pdf.

[210]Fama, F. E. (1980). Agency problems and the theory of the firm. Journal of Political Economy, 88(2):288-307.

[211]FAO. RIMA-II: Resilience index measurement and analysis 2. Rome: Italy: United Nations Food and Agriculture Organisation. 2016, http://www.fao.org/3/a-i5665e.pdf.

[212]Fauci, A. J., Bonciani, M., & Guerra, R. (2012). Quality of life, vulnerability and resilience: A qualitative study of the tsunami impact on the affected population of Sri Lanka. Annali Dell’Istituto Superiore Di Sanita, 48(2), 177-188.

[213]Folke, C. (2016). Resilience (republished). Ecology and Society, 21(4).

[214]Frankenberger, T., Langworthy, M., Spangler, T., Nelson, S., Campbell,J., & Njoka, J. (2012). Enhancing resilience to food security shocks. White Paper (Draft). Tucson: TANGO International.

[215]Frankenberger, T., Mueller, M., Spangler, T., & Alexander, S. (2013). Community resilience: Conceptual framework and measurement feed the future learning agenda. Rockville, MD: Westat.

[216]FSIN. 2014. Resilience measurement principles’, www.fao.org/fileadmin/user_upload/drought/docs/FSIN%20Resilience%20Measurement%20201401.pdf.

[217]Fuster A., Plosser M., Schnabl P., Vickery J. (2019). The role of technology in mortgage lending. Review of Financial Studies,32(5):1854-1899.

[218]Dohmen, T., A. Falk, D. Huffman, & Sunde, U. (2010). Are risk aversion and impatience related to cognitive ability?. American Economic Review,(3): 1238-1260

[219]Garmezy, N. (1974). The study of competence in children at risk for severe psychopathology. In E. J. Anthony & C. Koupernik (Eds.). The child in his family: Children at psychiatric risk (Vol(3) 77-97). New York: Wiley.

[220]Gertler, P., Levine, D. I., & Moretti, E. (2009). Do microfinance programs help families insure consumption against illness?. Health Economics, 18(3), 257-273.

[221]Goulden, M. C., Adger, W. N., Allison, E. H., & Conway, D. (2013). Limits to resilience from livelihood diversification and social capital in lake social-ecological systems. Annals of the Association of American Geographers, 103(4), 906-924.

[222]Grossman,S., & Stiglitz,J. (1976). Information and competitive price systems.American Economic Review,(66): 246-253.

[223]Grossman, S. (1977). The existence of futures markets, noisy rational expectations and informational externalities. Review of Economic Studies, (64):431-449.

[224]Gubbins, Paul, & Edoardo,T. (2018). Digital credit in Kenya: Evidence from demand side-survey. financial sector depening (fsd)kenyan. Available online: https:/fsdkenya.org /publication/digital-credit-in-kenya-evidence-from-demand-side-surveys/

[225]Guerrieri,V., & Lorenzoni, G.(2017). Credit crises, precautionary savings，and the liquidity' trap. QuarterlyJournal of Economics, 32(3) :1427-1467.

[226]Günther, I. & H. Kenneth. (2009). Estimating households vulnerability to idiosyncratic and covariate shocks: A novel method applied in madagascar. World Development, 37(7): 1222-1234.

[227]Gurley, J. G., & Shaw,E.S. (1956). Financial intermediaries and the saving-investment process. Journal of Finance, 11(2):257-276.

[228]Gurley, J. G., & Shaw, E. S. (1955). Financial aspects of economic development. The American Economic Review, 45(4), 515-538.

[229]Hanning,A. & Jansen,S.(2015). Financial inclusion and financial stability:Current policy issues[J]. ADBI Working Paper.

[230]Hodbod, J., & Eakin, H. (2015). Adapting a social-ecological resilience framework for food systems. Journal of Environmental Studies and Science, 5, 474-484.

[231]Holling, C.S. (1973). Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 4(1), 1-23.

[232]Hua,X., Huang,Y., & Zheng Y. (2019). Current practices, new insights, and emerging trends of financial technologies. Industrial management & data systems, 119(7):1401-1410.

[233]Hurley,M., & Adebayo,J. (2017). Credit scoring in the era of big data. Yale Journal of Law&Technology, (1):148-155.

[234]Ibarrarán, M. E., Ruth, M., Ahmad, S., & London, M. (2009). Climate change and natural disasters: Macroeconomic performance and distributional impacts. Environment, Development and Sustainability, 11(3), 549-569.

[235]International Federation of Red Cross and Red Crescent Societies (IFRC). (2015). The one billion coalition for resilience: discussion paper on levels of resilience. Dalberg Analysis.

[236]Jabeen, H., Johnson, C., & Allen, A. (2010). Built-in resilience: Learning from grassroots coping strategies for climate variability. Environment and Urbanization, 22(2), 415-431.

[237]Jack, W., and Suri, T. (2014). Risk sharing and transaction costs: evidence from Kenya’s mobile money revolution. American Economic Review, 104(1):183-223

[238]Jagtiani, J., and C. Lemieux. (2018). Do fintech lenders penetrate areas that are underserved by traditional banks?. Journal of Economics and Business, 100: 43-54.

[239]Johnson, Susan, & Nino-Zarazua, Max (2011). Financial access and inclusion in Kenya and Uganda. Journal of Development Studies, (3):475-496.

[240]Jones, L., & d’ Errico, M. (2019). Whose resilience matters? Like-for-like comparison of objective and subjective evaluations of resilience. World Development, 124, 104632.

[241]Jones, L., & Samman, E. (2016). Measuring subjective household resilience: Insights from Tanzania. London: UK: Overseas Development Institute (ODI).

[242]Jones, L., & Tanner, T. (2017). Subjective resilience’: using perceptions to quantify household resilience to climate extremes and disasters. Regional Environmental Change, 17: 229-243.

[243]Just, R. E.& R. D. Pope.(1979), Production function estimation and related risk considerations. American Journal of Agricultural Economics, 61(2): 276.

[244]Kahn, M. E. (2005). The death toll from natural disasters: The role of income, geography, and institutions. Review of Economics and Statistics, 87(2),271–284.

[245]Karlan,D., Kendall,J., & Mann R.(2016). Research and impacts of digital financial services. Working Paper Series.

[246]Karlan,D., & Zinman,J.(2010). Expanding credit access: using randomized supply decisions to estimate the impacts. The review of financial studies, 23(1):433-464.

[247]Karlan, D., & Zinman, J. (2011). Microcredit in theory and practice: using randomized credit scoring for impact evaluation. Science, 332(6035), 1278-1284.

[248]Kikulwe,E.M., Fischer, E., & Qaim,M. (2014). Mobile money, smallholder farmers, and household welfare in Kenya. PLOS ONE, 9(10):1-13.

[249]Kirui, O. K., & Njiraini G W. (2013). Determinants of agricultural commercialization among the rural poor: Role of ICT and collective action initiatives and gender perspective in Kenya. Oliver Kirui, 1747-1754.

[250]Knippenberg, E., & Hoddinott, J. (2017). Shocks, social protection, and resilience: Evidence from Ethiopia. International Food Policy Research Institute (IFPRI).

[251]Knippenberg, E., Jensen, N., & Constas, M. (2019). Quantifying household resilience with high frequency data: Temporal dynamics and methodological options.World Development, 121, 1-15.

[252]Leslie, P., and J. T. McCabe. (2013). Response diversity and resilience in social-ecological systems. Current Anthropology,54(2):114-143.

[253]Ligon, E., & L. Schechter. (2003). Measuring vulnerability. The Economic Journal, 113(486): 95-102.

[254]Lokosang, L. B., Ramroop, S., & Zewotir, T. (2014). Indexing household resilience to food insecurity shocks: The case of South Sudan.Agrekon, 53, 137-159.

[255]LWR. (2016). Part 2: Applying resilience in development practice, https://lwr.org/wpcontent/uploads/Applying-Resilience-in-Development-Practice.pdf.

[256]Manyena,B., & S.Gordon. (2015). Resilience, panarchy andcustomary structures in Afghanistan. Resilience, 3(1):1-15.

[257]Manyena S. B. (2006). The concept of resilience revisited. Disasters, 30:433-450.

[258]Marshall, N. A., Marshall, P. A., Tamelander, J., Obura, D., Malleret-King, D., & Cinner,J. E. (2010). A framework for social adaptation to climate change: Sustaining tropical coastal communities [sic] and industries. Gland, Switzerland:International Union of Conservation of Nature (IUCN).

[259]Maxwell, D., Constas, M., Frankenberger, T., Klaus, D., & Mock, M. (2015). Qualitative data and subjective indicators for resilience measurement. Resilience measurement technical working group. Technical Series No. 4. Rome: Food Security Information Network. Available online at:http://www.odi.org/sites/odi.org.uk/files/odiassets/publications-opinion-files/9049.pdf.

[260]McAslan, A. (2010). The concept of resilience: Understanding its origins, meaning and utility. Adelaide: Torrens Resilience Insitute.

[261]Melzer, B. T. (2011). The real costs of credit access: Evidence from the payday lending market. The Quarterly Journal of Economics, 126(1), 517-555.

[262]Meng, K.X & Xiao, J.J. (2023). Digital finance and happiness: evidence from China. Information Technology for Development, 51-169.

[263]Moshy, V. H., Bryceson, I., & Mwaipopo, R. (2015). Social-ecological changes, livelihoods and resilience among fishing communities in Mafia Island Marine Park, Tanzania. Forum for Development Studies, 42(3), 529-553.

[264]Munyegera,G.K. & Matsumoto, T. (2016). Mobile money, remittances, and household welfare: panel evidence from rural Uganda. World Development, 79:127-137.

[265]Murendo, C., Kairezi, G., & Mazvimavi, K. Resilience capacities and household nutrition in the presence of shocks. evidence from Malawi. World Development Perspectives, 2020, 20, 100241.

[266]Narayan,G., & Nerurkar, A.N. (2008). Value-proposition of E-governmence services:Bridging rural-urban Divide in developing cournties. Internaional Journal of Education and Development Using Information and Communication Technology,2(3):33-34.

[267]Nelson, V., & Stathers, T. (2009). Resilience, power, culture, and climate: a case study from semi-arid Tanzania, and new research directions. Gender and Development, 17(1):81-94.

[268]Nguyen, K. V., & James, H. (2013). Measuring resilience to floods: A case study in the Vietnamese Mekong River Delta. Ecology and Society, 18.3, 13.

[269]OECD. (2014). Guidelines for resilience systems analysis: How to analyze risk and build a road map to Resilience’, www.oecd.org/dac/Resilience%20Systems%20Analysis%20FINAL.pdf.

[270]Okoye, B. C., Abass, A., Bachwenkizi, B., Asumugha, G., Alenkhe, B., Ranaivoson, R., Ralimanana, I. (2016). Effect of transaction costs on market participation among smallholder cassava farmers in Central Madagascar. Cogent Economics & Finance, 4(1),318.

[271]Opiyo, F., Wasonga, O., Nyangito, M., Schilling, J., & Munang, R. (2015). Drought adaptation and coping strategies among the Turkana pastoralists of northern Kenya. International Journal of Disaster Risk Science, 6(3), 295–309.

[272]Oster, E. (2017). Unobservable selection and coefficient stability: theory and evidence, Journal of Business & Economic Statistics, 35(4):1-18.

[273]Otchere, F. & Handa, S. (2022). Building resilience through social protection:evidence from Malawi.The Journal of Development Studies,58(10), 1958-1980.

[274]OXFAM. (2015). Measuring resilience. Lessons learned from measuring resilience in Oxfam’s large-n effectiveness reviews. OXFAM Discussion Paper.

[275]Perrings, C. (2006). Resilience and sustainable development. Environment and Development Economics, 11(4), 417–427.

[276]Petryniak,O., Kurtz,J., & Frischknecht S. (2015). What next for nepal? evidence of what matters for building resilience after the gorkha earthquake, www.mercycorps.org/research-resources/what-next-for-nepal.

[277]Phadera, L., Michelson, H., Winter-Nelson, A., & Goldsmith, P. (2019). Do asset transfers build household resilience? Journal of Development Economics, 138:205-227.

[278]Pimm, S. L. (1984). The complexity and stability of ecosystems. Nature, 307(5949):321-326.

[279]Pingali, P., Alinovi, L. & Sutton, J. (2005). Food security in complex emergencies: enhancing food system resilience. Disaster. 29(S1): 5-24.

[280]Qin, X., Wu, H., & Li, R. (2022). Digital finance and household carbon emissions in China. China Economic Review, 101872

[281]Quandt, A. (2018). Measuring livelihood resilience: the household livelihood resilience approach (HLRA). World Development, 107:253-263.

[282]Quandt, A., Neufeldt, H., & McCabe, J. T. (2017). The role of agroforestry in building livelihood resilience to floods and drought in semiarid Kenya. Ecology and Society, 22(3)10.

[283]Quandt, A., Neufeldt, H., & McCabe, J. T. (2018). Building livelihood resilience: what role does agroforestry play? Climate and Development, 1-16.

[284]Resilience Alliance. (2002) . Key concepts. http://www.resalliance.org/index.php/key_concepts.

[285]Riley, E. (2018). Mobile money and risk sharing against village shocks. Journal of Development Economics, 135:43–58.

[286]Rockefeller Foundation, www.rockefellerfoundation.org/our-work/topics/resilience/.

[287]Sawada, Y.. (2017). Disasters, household decisions, and insurance mechanisms: A review of evidence and a case study from a developing country in Asia. Asian Economic Policy Review, 12(1):18-40.

[288]Seara, T., Clay, P. M., & Colburn, L. (2016). Perceived adaptive capacity and natural disasters: A fisheries case study. Global Environmental Change, 38, 49–57

[289]Sen, K.(2010). Towards inclusive financial development for achieving the MDGs in Asia and the Pacifc.MPDD Working Papers WP/10/07,2010.

[290]Serfilippi E., & Ramnath G. (2018). Resilience measurement and conceptual frameworks: a review of the literature. Annals of Public and Cooperative Economics, 89(4):645–664.

[291]Silbert, M., & Useche, M. (2012). Repeated natural disasters and poverty in Island nations: A decade of evidence from Indonesia PURC Working Paper. University of Florida, Department of Economics.

[292]Sina, D., Chang-Richards, A. Y., Wilkinson, S., & Potangaroa, R. (2019). A conceptual framework for measuring livelihood resilience: Relocation experience from Aceh, Indonesia. World Development, 117: 253–265.

[293]Smith, L. C., & Frankenberger, T. R. (2018). Does resilience capacity reduce the negative impact of shocks on household food security? Evidence from the 2014 floods in Northern Bangladesh. World Development, 102: 358–376.

[294]Solorzano, A. (2016). Can social protection increase resilience to climate change? A case study of oportunidades in rural Yucatan, Mexico. IDS Working Paper - Institute for Development Studies, 465, 1-65.

[295]Spence, M., & Zeckhauser,R. (1971). Insurance, information, and individual action.American Economic Review,(61):380-387.

[296]Spence M. (1973). Job market signalling.Quarterly Journal of Economics, (87):355-374.

[297]Speranza, C. I., Wiesmann, U., & Rist, S. (2014). An indicator framework for assessing livelihood resilience in the context of social-ecological dynamics. Global Environmental Change, 28, 109-119.

[298]Stiglitz, J.E., & Weiss.A.(1981). Credit rationing in markets with imperfect information.The American Economic Review, 71(3) :393-410.

[299]Suri, T., and Jack, W. (2016). The long-run poverty and gender impacts of mobile money. Science, 354(6317), 1288-1292.

[300]Suri, T., Bharadwaj, P., & Jack, W. (2021). Fintech and household resilience to shocks: Evidence from digital loans in Kenya. Journal of Development Economics, 153, 102697.

[301]Tabetando, R., & Matsumoto, T. (2020). Mobile money, risk sharing, and educational investment: Panel evidence from rural Uganda. Review of Development Economics, 24(1):84-105.

[302]Tang, H. (2019). Peer-to-Peer lenders versus banks: substitutes or complements? The Review of Financial Studies, 32(5):1900-1938.

[303]Tanner, T., Lewis, D., Wrathall, D., Bronen, R., Cradock-Henry, N., Huq, S., Thomalla, F. (2015). Livelihood resilience in the face of climate change. Nature Climate Change, 5(1):23–26.

[304]Tawodzera, G. (2012). Urban household survival and resilience to food insecurity in crisis conditions: The case of Epworth in Harare, Zimbabwe. Journal of Hunger & Environmental Nutrition, 7(2/3): 293–320.

[305]TULANE UNIVERSITY. (2012). Haiti humanitarian assistance evaluation from a resilience perspective, www.tulane.edu/drla/upload/UEH-Tulane-DRLA-Haiti Humanitarian-Aid-Evaluation-ENGLISH-May-2012.pdf.

[306]Tyler, S., & Moench, M. (2012). A framework for urban climate resilience. Climate and Development, 4(4): 311–326.

[307]Udry,C. (1994). Risk and insurance in a rural credit market.an empirical lnvestigation in Northern Nigeri . Review of Economic Studies,61 (3):495-526.

[308]UNDP. (2013). Community based resilience assessment (COBRA) conceptual framework and methodology,www.undp.org/content/undp/en/home/librarypage/environmentenergy/sustainable_land_management/CoBRA/cobra-conceptual-framework.html.

[309]Upton, J. B., Constenla-Villoslada, S., & Barrett, C. B. (2020). Caveat utilitor: A comparative assessment of resilience measurement approaches. Cornell University working paper.

[310]Urrea, M., &Maldonado,J. (2011). Vulnerability and risk management: the importance of financial inclusion for beneficiaries of conditional transfers in colombia. Canadian Journal of Development Studies, 32(4): 381-398.

[311]USAID, 2015, ‘Ethiopia pastoralist areas resilience improvement and market expansion (PRIME). Project impact evaluation’, agrilinks.org/sites/default/files/resource/files/EthiopiaPRIMEVol1final.pdf.

[312]Vaitla, B., Cissé, J. D., Upton, J., Tesfay, G., Abadi, N., & Maxwell, D. (2020). How the choice of food security indicators affects the assessment of resilience—an example from northern ethiopia. Food Security, 12(1):137-150.

[313]Walker, B.H., & Salt,D. (2006). Resilience thinking: sustaining ecosystems and people in a changing world (Washington, DC: Island Press).

[314]Walker, B., Gunderson, L., Kinzig, A., Folke, C., Carpenter, S., & Schultz, L. (2006). A handful of heuristics and some propositions for understanding resilience in social-ecological systems. Ecology and Society, 11(1).

[315]Waller, M.(2001).Resilience in ecosystemic context: evolution of the concept’, American Journal of Orthopsychiatry, 71, 290–297.

[316]Wang, J.R.,Tian, W.L.,&Ma, T. Y. (2023). Can digital finance promote the happiness of residents? Evidence from China. Applied Economics Letters,(3):1-19.

[317]Winderl, T. (2014). Disaster resilience measurements: Stocktaking of ongoing efforts in developing systems for measuring resilience. United Nations Development Programme (UNDP). Retrieved from http://www.preventionweb.net/files/37916_disasterresiliencemeasurementsundpt.pdf.

[318]Wuepper, D., Yesigat, A. H., & Sauer, J.(2018). Social capital, income diversification and climate change adaptation: Panel data evidence from rural Ethiopia. Journal of Agricultural Economics, 69(2): 458-475

[319]Yao, B., Shanoyan,A., Schwab, Ben.,&Amanor-Boadu, V. (2023). The role of mobile money in household resilience: Evidence from Kenya. World Development,165:106198.

[320]Zinman, J. (2010).Restricting consumer credit access: Household survey evidence on effects around the oregon rate cap. Journal of Banking & Finance, 34(3):546-556.