Types of Research
- (-) Remove Household Well-Being & Equity filter Household Well-Being & Equity
- (-) Remove 2016 filter 2016
- (-) Remove Farmer First filter Farmer First
- (-) Remove Information & Mobile Technology filter Information & Mobile Technology
- (-) Remove East Africa Region and Selected Countries filter East Africa Region and Selected Countries
- (-) Remove West Africa Region and Selected Countries filter West Africa Region and Selected Countries
- (-) Remove Other Datasets filter Other Datasets
Previous research has shown that men and women, on average, have different risk attitudes and may therefore see different value propositions in response to new opportunities. We use data from smallholder farm households in Mali to test whether risk perceptions differ by gender and across domains. We model this potential association across six risks (work injury, extreme weather, community relationships, debt, lack of buyers, and conflict) while controlling for demographic and attitudinal characteristics. Factor analysis highlights extreme weather and conflict as eliciting the most distinct patterns of participant response. Regression analysis for Mali as a whole reveals an association between gender and risk perception, with women expressing more concern except in the extreme weather domain; however, the association with gender is largely absent when models control for geographic region. We also find lower risk perception associated with an individualistic and/or fatalistic worldview, a risk-tolerant outlook, and optimism about the future, while education, better health, a social orientation, self-efficacy, and access to information are generally associated with more frequent worry— with some inconsistency. Income, wealth, and time poverty exhibit complex associations with perception of risk. Understanding whether and how men’s and women’s risk preferences differ, and identifying other dominant predictors such as geographic region and worldview, could help development organizations to shape risk mitigation interventions to increase the likelihood of adoption, and to avoid inadvertently making certain subpopulations worse off by increasing the potential for negative outcomes.
We use OLS and logistic regression to investigate variation in husband and wife perspectives on the division of authority over agriculture-related decisions within households in rural Tanzania. Using original data from husbands and wives (interviewed separately) in 1,851 Tanzanian households, the analysis examines differences in the wife’s authority over 13 household and farming decisions. The study finds that the level of decision-making authority allocated to wives by their husbands, and the authority allocated by wives to themselves, both vary significantly across households. In addition to commonly considered assets such as women’s age and education, in rural agricultural households women’s health and labour activities also appear to matter for perceptions of authority. We also find husbands and wives interviewed separately frequently disagree with each other over who holds authority over key farming, family, and livelihood decisions. Further, the results of OLS and logistic regression suggest that even after controlling for various individual, household, and regional characteristics, husband and wife claims to decision-making authority continue to vary systematically by decision – suggesting decision characteristics themselves also matter. The absence of spousal agreement over the allocation of authority (i.e., a lack of “intrahousehold accord”) over different farm and household decisions is problematic for interventions seeking to use survey data to develop and inform strategies for reducing gender inequalities or empowering women in rural agricultural households. Findings provide policy and program insights into when studies interviewing only a single spouse or considering only a single decision may inaccurately characterize intra-household decision-making dynamics.
Mobile technology is associated with a variety of positive development and social outcomes, and as a result reaching the “final frontier” of uncovered populations is an important policy issue. We use proprietary 2012 data on mobile coverage from Collins Bartholomew to estimate the proportion of the population living in areas without mobile coverage globally and in selected regions and countries, and use spatial analysis to identify where these populations are concentrated. We then compare our coverage estimates to data from previous years and estimates from the most recent literature to provide a picture of recent trends in coverage expansion, considering separately the trends for coverage of urban and rural populations. We find that mobile coverage expansion rates are slowing, as easier to reach urban populations in developing countries are now almost entirely covered and the remaining uncovered populations are more dispersed in rural areas and therefore more difficult and costly to reach. This analysis of mobile coverage trends was the focus of an initial report on mobile coverage estimates. In a follow-up paper prepared for presentation at the 2016 APPAM International Conference, we investigate the assumption that levels of mobile network coverage are related to the degree of market liberalization at the country level.