Adoption of rice cultivation technologies and its effect on technical efficiency in Sagnarigu District of Ghana

This study examined the adoption of rice cultivation technologies on farmers’ technical efficiency in Sagnarigu District. The stochastic frontier model was used to estimate the determinants of output and technical inefficiency while propensity score matching was also used to analyse the average treatment effect (ATE) and the average treatment effect on the treated (ATT). A total of 120 respondents comprising 60 adopters and 60 non-adopters were randomly selected from six communities in the District and interviewed using semi-structured questionnaires. Farm size, fertilizer, weedicides and household labour had positive and significant effect on rice output. Farmers who adopted the rice cultivation techniques were less technically inefficient than those who did not adopt. The ATT was 0.121 which implies that farmers who adopted the rice technologies increased their technical efficiency by about 12% and this was significant at 10% for the PSM with similar results obtained for the nearest neighbour matching. The ATE value of 0.102 which was also statistically significant at 10% means that farmers on the whole increased their technical efficiency by 10.2%. Moreover, the mean technical efficiency estimates for adopters and non-adopters were about 58% and 48% respectively under regression adjustment and inverse-probability weights. The existence of a technical efficiency gap of 10% between adopters and non-adopters of rice technologies emphasized the significant effect of technology adoption on farmer’s technical efficiency. The study recommends that more rice farmers should be encouraged to adopt the rice production technologies in order to improve their technical efficiency levels

Small Area Estimation of Latent Economic Well-being

© The Author(s) 2019. Small area estimation (SAE) plays a crucial role in the social sciences due to the growing need for reliable and accurate estimates for small domains. In the study of well-being, for example, policy makers need detailed information about the geographical distribution of a range of social indicators. We investigate data dimensionality reduction using factor analysis models and implement SAE on the factor scores under the empirical best linear unbiased prediction approach. We contrast this approach with the standard approach of providing a dashboard of indicators or a weighted average of indicators at the local level. We demonstrate the approach in a simulation study and a real data application based on the European Union Statistics for Income and Living Conditions for the municipalities of Tuscany

Efficiency of Ontario primary care physicians across payment models : a stochastic frontier analysis

Objective The study examines the relationship between the primary care model that a physician belongs to and the efficiency of the primary care physician in Ontario, Canada. Methods Survey data were collected from 183 self-selected physicians and linked to administrative databases to capture the provision of services to the patients served for the 12 month period ending June 30, 2013, and the characteristics of the patients at the beginning of the study period. Two stochastic frontier regression models were used to estimate efficiency scores and parameters for two separate outputs: the number of distinct patients seen and the number of visits. Results Because of missing data, only 165 physicians were included in the analyses. The average efficiency was 0.72 for both outputs with scores varying from 4 % to 93 % for the visits and 5 % to 94 % for the number of patients seen. We observed that there were both very low and very high efficiency scores within each model. These variations were larger than variations in average scores across models