Empirically based analysis of households coping with unexpected shocks in the central Himalayas
Lea Ravnkilde MøllerCarsten Smith‐HallHenrik MeilbySantosh RayamajhiLise HerslundHelle Overgaard LarsenØystein Juul NielsenAnja Byg
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Climate change may significantly impact the large number of households in developing countries depending on agricultural production, not least through changes in the frequency and/or magnitude of climatic hazards resulting in household income shocks. This paper analyses rural households' responses to past experiences of and future expectations to substantial and unexpected negative and positive agricultural income shocks. Empirical data is derived from an environmentally-augmented structured household (n = 112) survey in the high mountains of central Nepal. Multinomial logit regression, using data on rural household demographics, assets (agricultural land, livestock), value of other assets such as furniture, bicycles, and agricultural implements, and income sources showed that household coping choices are determined by opportunities to generate cash. We argue that public policies should enhance the ability of rural household to generate cash income, including through environmental products.Keywords:
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Factor, multinomial logistic regression and cluster analyses are used in combination to provide a predictive model of store patronage behaviour for consumers in Cardiff, Wales. A subset of variables and factors that are important for consumers when choosing a supermarket were used to provide a picture of each store’s clientele. Multinomial logistic regression allowed an overall model of supermarket choice to be developed and also enabled comparisons to be made of individual supermarkets within the sample. A detailed picture of store patronage is presented along with predictions about store choice for a number of “consumer clusters”. The results demonstrate the utility of the predictive multinomial models when used in conjunction with other analytical techniques and reinforces a number of studies that have investigated patronage behaviour.
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This chapter discusses the logit link function of logistic regression to the multinomial situation where a categorical response variable can take on one of several outcomes. It talks about an approach of estimating the class probabilities for a multicategory response, and uses these probabilities to classify new cases into one of several outcome groups. Several choices are available to estimate multinomial logistic regression models in R. For example, one can use the command mlogit in the package mlogit, the command vglm in the package VGAM, or the mnlm function in the package textir. The chapter illustrates an example: forensic glass. The three features, proportions of Na, Mg, and Al, are used to illustrate the multinomial logistic regression model. The R function mnlm makes use of simple triplet matrices.
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Achieving agricultural productivity growth will not be possible without developing and disseminating improved agricultural technologies that can increase productivity to smallholder agriculture farm. It is significantly important to identify the factors that affect agricultural productivity and find the methods of the rural household income improvements. Therefore, the objective of this article is to review and summarize the different factors that affects agricultural productivity and rural household income in Ethiopia based on studies conducted by different scholars so far. Land-labor ratio, use of fertilizer, use of extension service, use of pesticide, manure, number of oxen used and household size are found to be the most significant variables that affect agricultural labor and land productivity. However, drought has statistically significant and has negative effect on both labor and land productivity by the same magnitude. Labor productivity, non-farm income and land productivity are found to be the most determinants of household income. However, number of dependency ratio is significantly and negatively affecting the rural household income. Sex of the household head is the main socio-economic factor for the variation of income among the rural households. The review also concludes that, Labor productivity is the most potent for factor of production and rural household income enhancement. Then increasing land-labor ratio is important for agricultural productivity enhancement and promotion of both farm labor and non-farm income are best focusing to speed up for the enhancement of rural household income. Keywords : labor productivity; land productivity, rural household income DOI: 10.7176/JESD/11-18-01 Publication date: September 30 th 2020
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Abstract Everyone joints go through a cycle of damage and repair during their lifetime, but sometimes the body’s process to repair our joints can cause changes in their shape or structure. When these changes happen, it’s known as osteoarthritis. Osteoarthritis is the most common form of arthritis, affecting millions of people worldwide. Osteoarthritis causes pain, swelling, stiffness in the areas, and decreased the ability to move for the sufferers. Therefore it requires accurate method of classification. Many methods have been used to classify osteoarthritis, but this study will apply Multinomial Logistic Regression and Super Vector Machine (SVM) as the machine learning methods. We used CT scan result data from RSUPN dr. Cipto Mangunkusumo, Central Jakarta. The results show the SVM provides better results than Multinomial Logistic Regression in terms of classification accuracy. The highest accuracy of SVM reaches around 85%, while Multinomial Logistic Regression only 71%.
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This paper seeks to identify the determining control factors that influence the fuel energy choice for lighting purposes in Rwanda by applying the Multinomial Logit Regression to the national representative survey at household level data. The study revealed that the households with higher income adopt the use the cleaner and modern fuel energy sources, confirming the hypothesis for the energy ladder. Not only household income exerting impact on the fuel energy choice for lighting, but also the other fuel choices that are the significant determining variables in Rwanda are the number of the rooms occupied by household, type of dwelling for household, age of the household head, whether the household head has the formal education, the household size, type of the habitat for the household and the location of the household. This paper suggests deployment and utilisation of solar potential for supplying the cleaner and modern fuel energy for lighting purposes in the remote area of Rwanda (Africa) which may be replicated in other developing countries in the world.
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Self- reported health status is the most commonly used measures of subjective and global measure of health because it is simple, economical and easy to administer. The objective of the study is to compare the performance of logistic regression models having multinomial response and identify the factors affecting health status of adolescents. Based on two stage sampling technique 2084 adolescents were interviewed to study the health status of teenagers in Jimma zone. In this article, we reviewed the most important logistic regression model and common approaches used to verify goodness-of-fit, using software R. We performed formal as well as graphical analyses to compare ordinal logistic regression models using data sets of health status. The results obtained from both baseline category logit model and ordinal logistic regression showed that sex of adolescents, source of drinking water and educational status significantly affect health status of teenagers. It was also found that a cumulative logit model containing these predictors provided the best description of the dataset among baseline category logit model, adjacent category logit model and continuation ratio model. Key words: Adolescents’ health status, multinomial logistic regression and ordinal logistic regression models, model comparisons using Akakie information criteria (AIC), goodness of fit.
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