Munich Personal RePEc Archive

Farm Development and Rural Poverty Comparison among Villages in Kulon Progo Regency of Yogyakarta Special Province of Indonesia

Nasution, Zamal (2008): Farm Development and Rural Poverty Comparison among Villages in Kulon Progo Regency of Yogyakarta Special Province of Indonesia.

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Abstract

Poverty has always been a concern in Indonesia. More than half of Indonesia's 235 million people are poor. The district of Kulon Progo is the second lowest district in Yogyakarta province both in economic growth and welfare level, so less developed among four others district. This research’s aim is to address factors influence the farm development in poverty alleviation and rural development in Kulon Progo Regency of Yogyakarta Special Province of Indonesia. Statistical data were retrieved from Indonesia’s Central Board of Statistic in range of 2003 through 2006. Primary data comprised of farm development by the government, rural poverty in each village, farmer experience in poverty allevation were derived by conducting direct audience with the government officials, head of villages, field farm officials, farmer group units, and field observation. Using purposive random sampling, this research divides Kulon Progo Regency into north zone, middle zone, and south zone; according to the lowest and highest poverty level of each village. Regression model is developed with classical normal linier regression model to reveal each variable share on rural poverty. Simultaneously, this linear regression model explains 70% of rural poverty caused by all variables. Numbers of farmer positively affects numbers of poor rural inhabitants, where the 1% increasing of numbers of farmer will raise 0.922% numbers of poor rural inhabitants. Irrigated land has a negative impact to rural poverty, where the increasing level of 1% irrigated land will eradicate 0.101% numbers of poor rural inhabitants. Numbers of household member is not significant to influence poor rural inhabitants. In contrary of common belief, the significant role of land ownership has a positive impact to influence rural poverty, where the 1% increasing size of land ownership will raise 0.177% poor rural inhabitants. Regression model results land ownership positively affects rural poverty. Taking interview with some key persons in the six villages compared to statistical data explains that poverty rate is affected by dry land productivity rather than wet land productivity. Based on geographic information system analysis, there are some run-off of water bodies in the north zone. These potential flows should be able to support farm development in the dry land.

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