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COVID-19 which became a global pandemic in 2020 has had tremendous impacts on several sectors including agriculture. Farm household capability is said to play a major role in ensuring that individuals develop some level of resilience towards the pandemic. This study assesses the perceived impacts of the pandemic on livelihood activities and examines the effects of farm household capability on food security resilience.

The factor analysis, 2SLS and RIMA methodology were employed to assess the perceived impacts of the pandemic on livelihood activities and examine the effects of farm households' capability on food security resilience. The study is based on farm households in the Northern region of Ghana.

The study revealed that on a scale of 0 (no impact of the COVID-19 pandemic) to 1 (high impact of the COVID-19 pandemic), a value of 0.641 was obtained which means that the COVID-19 pandemic had a moderate (average) impact on farm households. Farm household capability also increases the food security resilience of farm households.

Government and local leaders should ensure that basic life-sustaining conditions are provided for rural farmers, and farm household capabilities are considered during crises.

Studies have examined impacts of the COVID-19 but little or no attention has been given to farm households' capability to remain resilient amid the COVID-19 shocks on livelihoods.

In this paper, we explore the role of education in household financial technology (FinTech) adoption.

Using representative nationwide household data from the 2017 China Household Finance Survey, we employ the change in China’s compulsory schooling law in the 1980s as an instrumental variable for educational attainment.

We find that among Chinese households, education has statistically significant and economically important effects on the use of various FinTech services, including digital banking, mobile payment, digital wealth management and digital consumer credit. Further analysis indicates that exogeneous increases in education lead to higher levels of financial literacy and social trust, both of which are potential drivers of FinTech adoption. Our findings provide new insights into the importance of education for household financial decision-making and technology adoption.

The contribution of our study is mainly twofold. First, we provide evidence on the role of education in household financial decision making. Second, this study adds to the literature on household adoption of technological innovation in finance. Our findings are also policy-relevant.

Agricultural production in Ethiopia is largely dependent on seasonal rainfall. This has made the agriculture-based economy of the country extremely vulnerable to the impacts of climatic change. To reduce this risk, the government has introduced strategies that promote irrigation as a key policy priority. Despite this, there has been limited empirical evidence on the impact of practicing small-scale irrigation on household income. This study was, therefore, conducted to examine the impact of access to small-scale irrigation on farm-household income in GannaBossa district, Ethiopia.

A multistage stratified random sampling technique was adopted to select 289 study participants. The data were analysed using both descriptive and inferential data analysis techniques. Propensity score matching (PSM) and logit regression were applied to analyse the impact of irrigation utilization on farm households’ income and identify factors influencing farm households’ decisions about irrigation-utilization, respectively.

The results of the study indicated that access to irrigation has a positive and significant impact on farm-household income. Estimates of the PSM model also confirmed that the mean income of irrigating households is significantly higher than that of non-irritating households. Further analysis of the results of the regression model revealed that amongst the hypothesized predictors of irrigation utilization, family size, soil fertility status, access to credit, access to extension services and access to the market were found to be positive and significant determinants of irrigation utilization, while distance to a water source was found to be a negative and significant predictor of irrigation utilization.

The results of the study can inform development practitioners on how expanding access to irrigation can serve as one key driver in enhancing growth in agricultural productivity, increasing household income and contributing to the alleviation of rural poverty in areas with irregular rainfall and a high risk of drought.

Given the country’s irrigation potential and policy priorities, such empirical evidence informs decision-makers to make informed decisions regarding prioritizing irrigation interventions and expanding access and management in Ethiopia.

This study assesses the impact of online grocery shopping and food delivery services on food insecurity before and during the COVID-19 pandemic.

This study uses an online survey of 1,532 respondents. Respondents’ sociodemographics, food consumption, purchasing behavior, food security status, food insecurity coping mechanisms and concerns associated with food safety were asked before and during COVID-19.

Online grocery shopping and food delivery services increase food insecurity. Moreover, households with female primary shoppers were less likely to be food insecure than households with male primary shoppers. Furthermore, households with children were more likely to be food insecure. Minority households, such as Black, Hispanic, Native American and younger households, were more likely to be food insecure.

Panel data with the same households surveyed before and after COVID-19 would be a better approach. Similarly, the impact of online shopping on food insecurity needs further research, as many factors could be associated with online shopping that impact food insecurity, especially during a crisis like the COVID-19 pandemic. Additionally, a study of the long-term impact of online shopping on food insecurity would be interesting and could present broader and more generalizable results.

The impact of online shopping on food insecurity before and during COVID-19 has yet to be studied sufficiently. Given the increase in popularity of online grocery shopping, its impact on food insecurity still needs to be discovered. Besides online grocery shopping, we also study online food delivery services whose demand has gained momentum over the past few years, including during the pandemic.

Climate change is an undeniable reality that threatens people’s livelihoods. Flooding and saltwater intrusion, along with the rising sea levels, are affecting agricultural and aquaculture livelihoods in Myanmar’s coastal areas. Although climate change adaptation is gaining popularity as a resilience strategy to cope with the negative effects of climate change, both agriculture- and aquaculture-farmers are more often deterred from implementing climate change adaptation strategies due to practical availability and socioeconomic barriers to adaptation. This study aims to evaluate the barriers and factors that influence farm household’ choice of climate change adaptation measures.

This study was conducted with 599 farm households (484 rice-farmers and 115 fish farmers) based in the coastal areas of Myanmar during 2021–2022 to explore the farmer’s choice of climate change adaptation measures and the determining factors. The multinomial logit regression (MLR) model was used to examine the factors influencing the farmers’ choice of climate change adaptation strategies.

The study found out that farm households use a variety of adaptation methods at the farm level, with building embankment strategy (23.4%) in agriculture and net-fencing measure (33.9%) in fish farming being the most popular adaptation strategies. Farmers’ decisions to adopt climate change adaptation strategies are influenced by factors such as distance to market, education level of the household head, remittance income and the availability of early warning information, among others. The study also discovered that COVID-19 has had an impact on the employment opportunities of household members and the income from farming as well had a consequential effect on the adoption of climate change adaptation measures. Furthermore, lack of credit (42.4%), labor shortage (52.8%), pest and disease infestation (58.9%), high input costs (81%) and lower agricultural product prices (73%) were identified as major barriers to the adoption of climate change adaptation measures by both agriculture and aquaculture farm households.

This study demonstrates that the COVID-19 pandemic and farm-level barriers are the major factors influencing farm households’ choice of climate change adaptation measures, and that removing practical farm-level barriers and encouraging the adoption of adaptation techniques as potential COVID-19 recovery actions are required. This study also highlighted that the adaptive capacity of agriculture and aquaculture farm households should be strengthened through formal and informal training programs, awareness raising, the exchange of early warning information and the development of proper credit scheme programs.

Rural development programs are widely used policy instruments mitigating rural-urban economic disparities. Yet, little research has examined their effect on rural labor. This study fills this knowledge gap by quantifying the causal impact of such programs on the labor allocation of farm households in Taiwan.

A theoretical framework based on the agricultural household model is constructed to guide the empirical specification. A unique dataset compiles administrative data on the program’s subsidies with farm household surveys across seven years. To cope with endogeneity bias, an instrumental variables model is applied. The eligibility rule for a township to participate in the program is used as the instrument.

We find that the program increases the labor supply of farm household members. These effects are more pronounced for off-farm work, particularly non-heads of farm households. The program’s subsidies supporting culture and promotion-related activities have larger effects. Finally, females benefited more from the program.

We focus on farm households since this group is the target of place-based rural development programs. In addition, we identify the causal impact of place-based development programs on rural labor. Finally, this study is relevant to the literature on intra-household models by demonstrating that place-based rural development programs can affect the labor supply of farm household members.

The study aims to explore the discrepancy between the subjective and objective debt burdens across various household socio-demographic and debt characteristics. Additionally, it seeks to establish an optimal debt service-to-income ratio (DSTI) threshold for identifying over-indebtedness.

This study utilized a sample of 1,004 respondents from a nationwide survey conducted among Polish indebted households. A discrepancy ratio (DR) measure was proposed to evaluate the divergence between subjective and objective over-indebtedness. Binary logistic regression was employed to estimate the probability of being subjectively and objectively over-indebted, as well as the discrepancy between the two measures of over-indebtedness. The study also employed numerical simulations to determine the optimal DSTI threshold for identifying over-indebted households in general and based on their socio-economic characteristics.

The study established a debt service-to-income ratio (DSTI) threshold of 20% to minimize the discrepancy between subjective and objective debt burden, which is lower than thresholds found in other studies aimed at identifying over-indebted households. Age, number of loans, self-perceived needs satisfaction and type of debt were identified as significant socio-economic and debt-related determinants of over-indebtedness. Household socio-economic and debt-related characteristics significantly influence the threshold for identifying over-indebtedness using DSTI. It can vary widely, ranging from as low as 11% for well-educated women with multiple loan commitments to 43.7% for young males with vocational education, high incomes and originating from households with four or more members.

The paper proposes a more comprehensive approach to debt burden analysis by introducing a new methodology for determining a debt service-to-income (DSTI) threshold that could serve as a measure of over-indebtedness based on the discrepancy between subjective and objective over-indebtedness. It also emphasizes the significance of socio-economic and debt-related factors in evaluating subjective and objective over-indebtedness.

Financial inclusion and digital finance go side by side and help enhance agricultural activities; however, the magnitude of digital financial services varies across countries. In line with this argument, this study aims to examine whether financial inclusion enhances agricultural participation and decompose the significance of the difference in determinants of agricultural participation between financially included – not financially included households and digital finance – no digital finance households.

This study uses Pakistan’s household integrated economic survey 2018/19 to test hypotheses. The logit model is used to examine the effect of financial inclusion on agriculture participation. Moreover, this study employs a nonlinear Fairlie Oaxaca Blinder technique to investigate the difference in determinants of agricultural participation.

This study reports that financial inclusion positively influences agricultural participation, meaning households may have access to financial services and participate in agricultural activities. The results suggest that the likelihood of participating in agriculture in households with mobiles and smartphones is higher. Moreover, household size, income, age, gender, education, urban, remittances from abroad, fertilizer, pesticides, wheat, cotton, sugarcane, fruits and vegetables are the significant determinants of agricultural participation. To distinguish the financially included – not financially included households’ gap, this study employs a nonlinear Fairlie Oaxaca Blinder decomposition and finds that differences in fertilizer explain the substantial gap in agricultural participation. Likewise, this study tests the digital finance – no digital finance gap and finds that the difference in fertilizer is a significant contributor, describing a considerable gap in agricultural participation.

Empirically identified that various factors cause agricultural participation including financial inclusion and digital finance. Regarding the research limitation, this study only considers a developing country to analyze the findings. However, for future research, scholars may consider some other countries to compare the results and identify their differences.

The accessibility of fertilizer can reduce the agricultural participation gap. However, increased income level, education and cotton and sugar production can also overcome the differences in agriculture participation between digital finance and no digital finance households.

This is the first study to decompose the difference in determinants of agricultural participation between financially and not financially included households.

The main purpose of this paper is to examine the status of poverty and its reduction by following the inclusive development approach. This study is designed to examine the benefits obtained from development programs, assess the government’s commitment to alleviating social inequality, and its impacts on the redistribution of wealth and poverty reduction.

To evaluate the implementation of the various development schemes and enhance grass-roots participation, a survey was carried out on 540 households, selected through multistage stratified sampling techniques in three different states of Punjab. The study employed an exploratory factor analysis on 21 independent variables to identify the key factors influencing poverty reduction subsequently followed by the binary logistic regression to access the sectoral impact of inclusiveness on poverty reduction in Punjab.

Exploratory Factor analysis extracted six key factors from the selected 21 variables, also called statements: “'Housing Development Resources”; “Human Capital Variables”; “Livelihood Essentials”, “Medical and Family Welfare Benefits”; “Receiving Educational Benefits”; and Social Security Benefits’. Binary logistic regression revealed that Housing Development Resources, Human Capital Variables, and Receiving Educational Facilities, significantly predict the likelihood of poverty reduction with inclusive growth in Punjab.

To provide basic amenities to rural people, increased people’s participation, decentralized planning, extended irrigation facilities, improved equipped facilities, and improved cultivation techniques are pivotal. The Indian Government has implemented several programs and projects to develop and support rural households. However, these schemes have faced many challenges such as rigidity, non-adaptability to local conditions, late disbursements of funds, reallocation of funds to unrelated expenditures by some states, embezzlement, and bribery demands. Hence, the findings indicate the presence of pseudo-inclusivity in Punjab’s growth.

The study’s uniqueness lies in its focus on selected districts of Punjab and also its application of exploratory factor analysis and binary logistic regression to construct a statistical model from the selected variables.