Banks and Microbanks

We combine two datasets to examine whether the presence of banks affects the profitability and outreach of microfinance institutions. We find evidence that competition matters. Greater bank penetration in the overall economy is associated with microbanks pushing toward poorer markets, as reflected in smaller average loans sizes and greater outreach to women. The evidence is particularly strong for microbanks relying on commercial-funding and using traditional bilateral lending contracts (rather than group lending methods favored by microfinance NGOs). We consider plausible alternative explanations for the correlations, including relationships that run through the nature of the regulatory environment and the structure of the banking environment, but we fail to find strong support for these alternative hypotheses.     

Determinants of Geographic Voter Participation Rate Differentials: the 2014 Mid-Term Election

Voter participation rates vary widely across the 50 states and the District of Columbia. This empirical study seeks, within the context of a broadened version of the “rational voter model,” to identify determinants of this geographic variation. Using the 2014 mid-term general election, it was found that the voter participation rate across states and the District of Columbia was positively related to whether there is a close governor’s race or a close U.S. Senate race, the female labor force participation rate, the percent of the population aged 65 and over, the number of referenda on the ballot, and the degree of voting-by-mail usage. In addition, it was found that voter turnout was negatively related to the percentages of the population that are either Hispanic or Afro-American.     

Climate change adaptation actions by fish farmers: evidence from the Niger Delta Region of Nigeria

This paper examined climate change adaptation strategies in fish farming and the effect of such methods on the profit of fish farmers in the Niger Delta region of Nigeria, Africa’s most populous country. Using cross‐sectional data obtained from 420 fish farmers from the region and applying multivariate probit and instrumental variable regressions, the study found that fish farmers have adopted a broad range of strategies to address climate risk and that these have significantly increased farmers’ profit. Our findings indicated important relationships between certain farm, socio‐economic and institutional characteristics and the adaptation actions. The study provides useful insight into factors that potentially encourage the adoption of livelihood‐enhancing climate risk adaptation strategies by fish farmers in the Niger Delta region and similar contexts.     

Modeling the time varying volatility of housing returns: Further evidence from the U.S. metropolitan condominium markets

This study extends the literature on modeling the volatility of housing returns to the case of condominium returns for five major U.S. metropolitan areas (Boston, Chicago, Los Angeles, New York, and San Francisco). Through the estimation of ARMA models for the respective condominium returns, we find volatility clustering of the residuals. The results from an ARMA‐TGARCH‐M model reveal the absence of asymmetry in the conditional variance. Dummy variables associated with the housing market collapse unique to each metropolitan area were statistically insignificant in the conditional variance equation, but negative and statistically significant in the mean equation. Condominium markets in Los Angeles and San Francisco exhibit the greatest persistence to volatility shocks.     

The cost of diabetes and obesity in Australia

Aims: To assess and compare the direct healthcare and non-healthcare costs and government subsidies by body weight and diabetes status.

Methods: The Australian Diabetes, Obesity and Lifestyle study collected health service utilization and health-related expenditure data at the 2011–2012 follow-up surveys. Costing data were available for 4,409 participants. Unit costs for 2016–2017 were used where available or were otherwise inflated to 2016–2017 dollars. Age- and sex-adjusted costs per person were estimated using generalized linear models.

Results: The annual total direct cost ranged from $1,998 per person with normal weight to $2,501 per person with obesity in participants without diabetes. For those with diabetes, total direct costs were $2,353 per person with normal weight, $3,263 per person with overweight, and $3,131 per person with obesity. Additional expenditure as government subsidies ranged from $5,649 per person with normal weight and no diabetes to $8,085 per person with overweight and diabetes. In general, direct costs and government subsidies were higher for overweight and obesity compared to normal weight, regardless of diabetes status, but were more noticeable in the diabetes sub-group. The annual total excess cost compared with normal weight people without diabetes was 26% for obesity alone and 46% for those with obesity and diabetes.

Limitations: Participants included in this study represented a healthier cohort than the Australian population. The relatively small sample of people with both obesity and diabetes prevented a more detailed analysis by obesity class.

Conclusion: Overweight and obesity are associated with increased costs, which are further increased in individuals who also have diabetes. Interventions to prevent overweight and obesity or reduce weight in people who are overweight or obese, and prevent diabetes, should reduce the financial burden.     

The service triangle and power: the role of frontline home support workers and consumer‐directed care—an Australian context

Workplace relational dynamics change when the customer or client takes on greater power in the relationship. Adopting a typology of the service triangle, this qualitative study examines frontline home support workers' perceptions of their power, following recent legislative change from a traditional agency‐directed aged care model to consumer‐directed care.     

Small Area Estimation for Disease Prevalence Mapping

Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available. SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints. We describe design-based and model-based approaches and models that are specified at the area level and at the unit level, focusing on health applications and fully Bayesian spatial models. The use of auxiliary information is a key ingredient for successful inference when response data are sparse, and we discuss a number of approaches that allow the inclusion of covariate data. SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015–2016, is used to illustrate a number of techniques. The potential use of SAE techniques for outcomes related to coronavirus disease 2019 is discussed.