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It is often assumed that transfers received from governments, nongovernment organizations (NGOs), friends and relatives help rural households to pool risk. In this paper I investigate two functions of transfers in Ethiopia: risk pooling and income redistribution. Unlike most of the literature this paper investigates not only whether but also how much risk pooling is achieved. I find evidence that transfers from governments/NGOs play a role in insuring covariant income shocks, (weak) evidence that transfers from friends/relatives insure idiosyncratic income shocks and evidence that transfers target the poor households. However, the contributions of transfers to risk pooling and income redistribution are economically very limited.

It is often assumed that transfers received from governments, nongovernment organizations (NGOs), friends and relatives help rural households to pool risk. In this paper I investigate two functions of transfers in Ethiopia: risk pooling and income redistribution. Unlike most of the literature this paper investigates not only whether but also how much risk pooling is achieved. I find evidence that transfers from governments/NGOs play a role in insuring covariant income shocks, (weak) evidence that transfers from friends/relatives insure idiosyncratic income shocks and evidence that transfers target the poor households. However, the contributions of transfers to risk pooling and income redistribution are economically very limited.

AbstractThis paper examines the impact of access to electricity on financial development. In doing so, we use a number of instrumental variables (IV) approaches. Using panel data for 38 countries in Sub‐Saharan Africa over the period 2000–2018, the results suggest that more people having access to electricity can promote financial development. In addition, mobile phone and commercial bank branches diffusion serve as potential channels through which access to electricity affects financial development. Our results are robust to sample‐splitting and different estimation techniques. The results have important implications for policies in overcoming barriers to electricity access.

Assessing the scope for insurance in rural communities usually requires a structural model of household behavior under risk. One of the few empirical applications of such models is the study by Rosenzweig and Wolpin (1993) who conclude that Indian farmers in the ICRISAT villages would not benefit from the introduction of formal weather insurance. In this paper we investigate how models such as theirs can be estimated from panel data on production and assets. We show that if assets can take only a limited number of values the coefficients of the model cannot be estimated with reasonable precision. We also show that this can affect the conclusion that insurance would not be welfare improving.

Kang Sun,1,2 Wangping Li,1 Yu Li,3,4 Guangyu Li,5 Lei Pan,1 Faguang Jin1 1Department of Respiratory and Critical Care Medicine, Tang Du Hospital, Air Force Military Medical University, Xi'an, Shaanxi Province, 710038, People's Republic of China; 2Department of Respiratory and Critical Care Medicine, The 989th Hospital of Joint Support Force of Chinese People's Liberation Army, Luoyang, Henan Province, 471003, People's Republic of China; 3Department of Infectious Diseases, Shaanxi Provincial People's Hospital and The Affiliated Hospital of Xi'an Medical University, Xi'an, Shaanxi Province, 710068, People's Republic of China; 4Shaanxi Center for Models of Clinical Medicine in International Cooperation of Science and Technology, Xi'an, Shaanxi Province, 710068, People's Republic of China; 5Department of Pathology, University of Texas Medical Branch, Galveston, TX, 77555, USACorrespondence: Lei Pan; Wangping Li, Department of Respiratory and Critical Care Medicine, Tang Du Hospital, Air Force Military Medical University, Xi'an, Shaanxi Province, 710038, People's Republic of China, Email panlei@fmmu.edu.cn; qxd25@163.comBackground: The prognosis of ABA-HAP patients is very poor. This study aimed to develop a scoring model to predict ABA-HAP in patients with GNB-HAP.Methods: A single center retrospective cohort study was performed among patients with HAP caused by GNB in our hospital during January 2019 to June 2019 (the derivation cohort, DC). The variables were assessed on the day when qualified respiratory specimens were obtained. A prediction score was formulated by using independent risk factors obtained from logistic regression analysis. It was prospectively validated with a subsequent cohort of GNB-HAP patients admitted to our hospital during July 2019 to Dec 2019 (the validation cohort, VC).Results: The final logistic regression model of DC included the following variables: transferred from other hospitals (3 points); blood purification (3 points); risk for aspiration (4 points); immunocompromised (3 points); pulmonary interstitial fibrosis (3 points); pleural effusion (1 points); heart failure (3 points); encephalitis (5 points); increased monocyte count (2 points); and increased neutrophils count (2 points). The AUROC of the scoring model was 0.845 (95% CI, 0.796 ∼ 0.895) in DC and 0.807 (95% CI, 0.759 ∼ 0.856) in VC. The scoring model clearly differentiated the low-risk patients (the score < 8 points), moderate-risk patients (8 ≤ the score < 12 points) and high-risk patients (the score ≥ 12 points), both in DC (P < 0.001) and in VC (P < 0.001).Conclusion: This simple scoring model could predict ABA-HAP with high predictive value and help clinicians to choose appropriate empirical antibiotic therapy.Keywords: Acinetobacter baumannii, hospital acquired pneumonia, Gram-negative bacilli, predictive scoring model, empirical antibiotic therapy