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This paper examines household and community characteristics that influence the entry of rural households into non-farm entrepreneurship and investigates the various factors that influence the market exit of non-farm enterprises (NFEs).

The authors use data from three rounds (2011/12, 2013/14 and 2015/16) of the World Bank’s Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). The authors employ panel logit and multilevel logit models to examine the probability of opening one or more enterprises and the enterprise exit rates.

Results indicate that the likelihood of starting a NFE is positively associated with primary education attainment, access to credit, experiencing idiosyncratic shocks and availability of formal financial institutions. Age, higher education attainment and rising farm input prices constrain entry into non-farm entrepreneurship. The enterprise exit rate is negatively associated with small-town residence, wealth, access to tar/gravel roads and cellphone communication.

Policymakers and administrators should strive to address the challenges that communities face in transportation, communication and financial services. Policies aimed at stabilizing prices and increasing access to mobile communication, primary education and road infrastructure could help expand the rural non-farm sector.

Previous studies primarily examined the determinants of participation in NFEs at a given time using cross-sectional data. The current study uses panel data to study the dynamics of NFE ownership by investigating households’ decisions to enter into or exit from the sector.

The peer review history for this article is available at https://publons.com/publon/10.1108/IJSE-09-2022-0611

This study aims to assess housing affordability and estimate demand using a hedonic regression model in the context of Lucknow city, India. This study assesses housing affordability by considering various housing and household-related variables. This study focuses on the impoverished urban population, as they experience the most severe housing scarcity. This study’s primary objective is to understand the demand dynamics within the market comprehensively. An understanding of housing demand can be achieved through an examination of its characteristics and components. Individuals consider the implicit values associated with various components when deciding to purchase or rent a home. The components and characteristics have been obtained from variables relating to housing and households.

A socioeconomic survey was conducted for 450 households from slums in Lucknow city. Two-stage regression models were developed for this research paper. A hedonic price index was prepared for the first model to understand the relationship between housing expenditure and various housing characteristics. The housing characteristics considered for the hedonic model are dwelling unit size, typology, condition, amenities and infrastructure. In the second stage, a regression model is created between household characteristics. The household characteristics considered for the demand estimation model are household size, age, education, social category, income, nonhousing expenditure, migration and overcrowding.

Based on the findings of regression model results, it is evident that the hedonic model is an effective tool for the estimation of housing affordability and housing demand for urban poor. Various housing and household-related variables affect housing expenditure positively or negatively. The two-stage hedonic regression model can define willingness to pay for a particular set of housing with various attributes of a particular household. The results show the significance of dwelling unit size, quality and amenities (R² > 0.9, p < 0.05) for rent/imputed rent. The demand function shows that income has a direct effect, whereas other variables have mixed effects.

This study is case-specific and uses a data set generated from a primary survey. Although household surveys for a large sample size are resource-intensive exercises, they provide an opportunity to exploit microdata for a better understanding of the complex housing situation in slums.

All the stakeholders can use the findings to create an effective housing policy. The variables that are statistically significant and have a positive relationship with housing costs should be deliberated upon to provide the basic standard of living for the urban poor. The formulation of policies should duly include the housing preferences of the economically disadvantaged population residing in slum areas.

This paper uses primary survey data (collected by the authors) to assess housing affordability for the urban poor of Lucknow city. It makes the results of the study credible and useful for further applications.

This study aims to understand the socioeconomic impact of flood events on households, especially household welfare in terms of changes in consumption and coping strategies to deal with flood risk. This study is based on Bihar, one of the most frequently flood-affected, most populous and economically backward states in India.

Primary data were collected from 700 households in the seven most frequently flood-affected districts in Bihar. A total of 100 individuals from each district were randomly selected from flood-affected villages. Based on a detailed literature review, an econometric (probit) model was developed to test the null hypothesis of the availability of consumption insurance, and the multivariate probability approach was used to analyze the various coping strategies of these households.

The results of this study suggest that flood-affected households maintain their consumption by overcoming various losses, including income, house damage and livestock loss. Households depend on financial transfers, borrowings and relief, and migrate to overcome losses. Borrowing could be an extra burden as the government compensates for house damage and crop loss late to the affected households. Again, there is no compensation to overcome livelihood loss and deal with occurrences of post-flood diseases, which further emphasizes the policy implications of strengthening the health infrastructure in the state and generating alternative livelihood opportunities.

This study discusses flood risk in terms of changes in household welfare, identifies the most effective risk-coping capabilities of rural communities and contributes to the shortcomings of the government insurance and relief model.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-07-2023-0569

The purpose of this study is to examine the influence of conscientiousness on entrepreneurship over and above the impact of other factors that are associated with entrepreneurship in the literature.

The design uses household responses from the Panel Study of Income Dynamics (PSID) biennial survey that follows the same heads of households over time to measure their conscientiousness, businesses owned and other demographic and financial characteristics. Ordinary least squares (OLS), Probit and Poisson regression techniques are applied at the head of household and state level to examine the relationship.

The results show heads of households’ conscientiousness positively relating to the average number of businesses owned, beyond other Big Five traits and the impact of other characteristics. A one-standard deviation increase in conscientiousness is significantly associated with a 0.012 increase in the number of businesses owned. This association is robust to alternative regression specifications and variable measurements.

The results are original to the finance literature, complementing studies by linking intrinsic head of household-level traits to entrepreneurship while controlling for external financial and demographic factors. The study also attempts to externally validate previous findings using aggregate-level outcomes. The data and setting used to measure personality traits as well as entrepreneurial outcomes are original to the entrepreneurship literature, validating previous findings.

The purpose of the study is to estimate the willingness to pay (WTP) for major chemical fertilizers and revisit the fertilizer subsidy policy in Nepal.

We surveyed 619 households from six districts and assessed farmers’ WTP for urea, diammonium phosphate (DAP) and muriate of potash (MOP) during the fertilizer crisis. Our study elicited the WTP for fertilizers when fertilizers were not available on the market. A modified payment card approach was used to elicit farmers’ WTP.

The study found that farmers who buy fertilizer from agrodealers, buy from gray markets, have bank accounts, are willing to take a risk, have strong or medium economic conditions and incur higher travel costs have a higher WTP for fertilizers. Farmers in sampled areas, on average, are willing to pay 31 percent more for urea, 13 percent more for DAP and 19 percent more for MOP than the government recommended fertilizer price.

The design of the payment card and the estimation techniques used to fit the valuation function are likely to influence WTP.

Overall, literature on households’ WTP for fertilizers in developing countries is scarce. Our study contributes to the knowledge of WTP for fertilizers.

With the rapid development of the economy in China, the wealth of residents has continued to increase, and most families have gradually been aware of the importance of commercial insurance. The family purchase of insurance in China was still not optimistic. Many scholars focus on wealth allocation, but the attention to the commercial insurance market was still less. Based on previous research studies, this study aims to investigate the impact of education and financial literacy on the commercial insurance purchase in China.

China Household Finance Survey data was used to investigate the purchase of commercial insurance in Mainland Chinese families. Factor analysis was used to construct financial literacy, and the education data were combined to analyze the commercial insurance purchase using the Probit model and the Tobit model. Finally, the contributions of education and financial literacy to commercial insurance purchases were analyzed.

Both education and financial literacy exerted a positive impact on the purchase of commercial insurance in China. Individual characteristics such as gender, age, marital status, risk attitude, purchase of social insurance and consultation with a financial advisor possessed significant effects; household factors like household size and assets, macro factors such as the density of financial institutions and the density of financial industry staff, and regional factors as local unemployment rate excreted influences on the commercial insurance purchase.

Based on the current economic development in China, this study investigated and expressed opinions on the public and insurance companies regarding commercial insurance purchases. It accentuated financial literacy and education as factors that facilitated commercial insurance development.

This paper aims to explore the role of digital inclusive finance (DIF) in influencing household tourism consumption, whether this influence differs between households with different characteristics and determining the intermediate mechanisms that influence the relationship.

The conceptual framework of this study was designed on the basis of the research on DIF in residential consumption practices. The China Household Finance Survey (CHFS) and the Peking University DIF Index were used in the study, which included four years of unbalanced panel data from 25 provinces in China. A fixed effects model was used to validate the conceptual framework and hypothesis testing.

Both hypothesis paths proposed in this study were supported. Results of this study show that DIF has a significant contribution to household tourism consumption and shows a positive impact in terms of both breadth of coverage and depth of use, and that Internet usage is an important mediating mechanism for DIF to promote household tourism consumption. Thus, the use of DIF as a tool can have a positive impact on tourism consumption.

Results of this study will help researchers and tourism businesses understand the relationship and mechanisms at play between DIF and household tourism consumption and leverage financial tools to drive tourism revival. However, the lack of third-country data for comparative analysis may render the conclusions inapplicable to every economy.

This study is the first to examine the relationship between DIF and household tourism consumption, using an “individual + time + region” fixed effects model to conduct specific empirical tests.

This paper aims at understanding the causes of low adoption of hybrid rice technology. The paper also assesses the impact of adoption of hybrids and modern varieties on crop yield, vis-à-vis the old or traditional varieties.

Using unit-level data from a large-scale survey of farm households (19,877 paddy cultivators), the authors applied multi-nomial regression method to understand the factors for adoption of hybrid rice and instrumental variable method of regression to estimate its impact.

The findings demonstrate that in India, hybrid rice is often grown on relatively poor soils, resulting in greater irrigation costs and for other inputs, such as fertilizers. Further, farmers' poor access to information on the traits of hybrid rice and the associated agronomic practices, as well as poor access to financial resources, hampers efforts to scale up its adoption. More importantly, the findings reveal that the relative yield advantage of hybrids over open-pollinated modern varieties is not large enough to incentivize the rapid adoption of hybrid rice technology.

Given the higher cost of hybrids than the inbred varieties, enhancing paddy cultivators' access to financial resources can accelerate the adoption of hybrid rice in India.

The study is based on unit level data from a large-scale, nationally representative survey of farm households, comprising a sample of 19,877 paddy cultivators, spread across states in India.

Brazil’s regional inequality is an important topic due to the large and persistent differences in development between states and the high levels of inequality in the country. These variations in development can potentially render survey data inaccurate since the significance of capital income varies across the states. Besides, previous studies incorporating tax and national accounts data globally have mainly focused on measuring the income distribution at the country-level. This approach can limit the understanding of inequality, especially when considering large countries such as Brazil.

The methodology used to construct these estimates follows the guidelines of the Distributional National Accounts, whose core goal is to provide income distribution measures consistent with macroeconomic aggregates and harmonized across countries and time. The procedure has three main steps: first, it corrects the survey’s underrepresentation of top incomes using tax data. Then, it accounts for national income items not included in the survey or tax data, such as imputed rents and undistributed profits. Finally, it ensures that all components match the national income.

Compared to survey-based estimations, the results reveal a new angle on the state-level inequality. This study indicates that Amazonas, Rio de Janeiro and São Paulo have a more concentrated income distribution. The top 1\% of earners in these states receives around 28\% of total pre-tax income, while the top 10\% receive nearly 60\%. On the other end, Amapá (AP), Acre (AC), Rondônia (RO) and Santa Catarina (SC) are the states where the income distribution is less concentrated. There were no significant changes in the income distribution across the states during the period analyzed.

This study combines survey, tax and national accounts data to construct new estimates of Brazil’s state-level income distribution from 2006 to 2019. Previous results only considered income captured in surveys, which usually misses a significant part of capital incomes. This limitation may bias comparisons as capital income has different importance across the states. The new estimates represent the income of top groups more accurately, account for the entire national income and enable to compare regional inequality levels consistently with other countries.

This study aims to measure the status of rural household food security across regions using multidimensional indicators. It also aims to identify the determinants of rural household food security in Ethiopia.

The study adopted descriptive and explanatory designs. It used data from the fourth wave of the Ethiopian socioeconomic survey that has 3,115 respondents. The authors constructed household food security index using variables that capture availability, access, utilization and stability dimensions of food security. The authors categorized households into relative food security groups, namely, alarming and moderately food insecure, as well as moderately and highly food secure. Beta regression model, which is widely used to analyze response variables that assume values between 0 and 1, is used to estimate the determinants of food security.

The study finds that 77.7% of rural households are food insecure. Of this, 90% are moderately food insecure. Regional variations in magnitude of food security showed that Harari, Gambella and Benshanguel Gumuz regional states are relatively better-off than other regions in Ethiopia. The study identified sex, education level, marital status, location and wealth status of households as significant determinants of food security.

This study sheds light on regional variations in multidimensional food security in Ethiopia. It thus challenged previous estimates of food security using uni-dimensional indicator. It highlighted the need for region-specific analysis of determinants and a follow up of tailored regional interventions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0139