{"title":"The impact of agricultural technology adoption on income inequality: a propensity score matching analysis for rural Ethiopia","authors":"Aynalem Shita, Nand Kumar, Seema Singh","doi":"10.1504/ijids.2020.10026774","DOIUrl":null,"url":null,"abstract":"This study analyses the impact of agricultural technology adoption on income inequality. Primary data has been collected from 400 sample households in Awi zone of Ethiopia through household survey during agricultural season of 2017/18. The collected data were analysed by using propensity score matching method. The estimated results revealed that adoption of agricultural technologies such as chemical fertiliser and improved seeds significantly increase total household income but worsen income distribution. After adoption of agricultural technologies, income inequality measured by Gini coefficient increased ranged from 0.047 to 0.087. Hence, the government and other concerned authorities should exert more efforts in order to enhance technology adoption status of the poor households by increasing their accessibility for extension and credit services.","PeriodicalId":303039,"journal":{"name":"Int. J. Inf. Decis. Sci.","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Int. J. Inf. Decis. Sci.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijids.2020.10026774","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
This study analyses the impact of agricultural technology adoption on income inequality. Primary data has been collected from 400 sample households in Awi zone of Ethiopia through household survey during agricultural season of 2017/18. The collected data were analysed by using propensity score matching method. The estimated results revealed that adoption of agricultural technologies such as chemical fertiliser and improved seeds significantly increase total household income but worsen income distribution. After adoption of agricultural technologies, income inequality measured by Gini coefficient increased ranged from 0.047 to 0.087. Hence, the government and other concerned authorities should exert more efforts in order to enhance technology adoption status of the poor households by increasing their accessibility for extension and credit services.