Abstract

Abstract The study seeks to analyze impact of livestock credit and simulate the effect of change in covariates of poverty on households‟ consumption expenditure. Data was generated through in person interview of sampled rural households in the Ethiopian Productive Safety Net area. Descriptive statistics, poverty indices, multiple regression, and simulation techniques were applied. The results identified covariates with statistically significant coefficients. The specific contribution in increasing consumption expenditure and reduction in poverty indices as a result of marginal change in covariates was examined. These specific factors need to be considered in designing poverty reduction strategies depending on magnitude of their contribution. Keywords: FGT poverty indices, Household Expenditure, Productive Safety Net, Simulation Introduction The food and nutrition policies and strategies of the 1980s enjoyed great success in most countries as they made food available for growing population. Unfortunately, a number of underlying causes contributed to their recent failure which contributed to nearly one billion people to be food insecure though most developing countries registered significant economic growth of about 6 percent. After many years of neglect, agriculture and food security are back on the development and political agendas and a number of developing countries have continued to expand their spending on food security and agricultural production. Ethiopia is among countries which have adopted national agricultural and food security investment plans to devote at least 10 percent of their national budget to agriculture to achieve agricultural growth of 6 percent a year. However, Ethiopia still remains to be one of the poorest countries in the world and ranks among the lowest for most human development indicators (World Bank, 2010). The Ethiopian economy is highly vulnerable to droughts and adverse terms of trade by virtue of its dependence on primary commodities and rain-fed agriculture. Thus the country‟s growth performance is highly correlated with weather conditions. A one percent change in average

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