The Importance of Disaggregated Data in Learning from Gender Mainstreaming Poverty Reduction Program of Sumogawe Village Semarang

3rd Geoplanning-International Conference on Geomatics and Planning

Esariti, L.^1 ; Sabana, M.R.^1

Department of Urban and Regional Planning, Diponegoro University, Indonesia^1

关键词: Analysis and evaluation;    Effectiveness indicators;    Good governances;    Poverty alleviation;    Poverty reduction;    Quality of life;    Road-maps;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/313/1/012025/pdf
DOI  :  10.1088/1755-1315/313/1/012025

来源: IOP

【 摘 要 】

Gender-based poverty alleviation program is important for performing good governance as it is ensuring the achievement of program road map related to development outcomes. It was established that the lives and realities of women and men, girls and boys are often shaped very differently. Therefore, it is necessary to compile, analyse and publish data separately for both sexes. An example of disaggregated data method is poverty alleviation program at Sumogawe village, Semarang which uses the asset based poverty reduction program method. This program looks at observations of human, natural, financial, infrastructure and social livelihood assets. In its implementation, this approach method is able to describe the relationship between the usages of these assets in improving the quality of life of the poor. But there are several pitfalls related to gender-based disaggregated data. One, it has not been clearly identified how the roles, treatment and response of each population based on age and sex in utilizing these assets. Secondly, asset management has not been linked to gender indicators set by Bappenas in 2012, as one of the effectiveness indicators of poverty alleviation program achievements. Third, it has not been identified how an explanation of the relationship between each livelihood asset in the context of the hierarchy of utilization at the level of individuals, households and communities in relation to the role and the ability to perform agency for the poor. In conclusion, this article recommends 4 steps to ensure that gender disaggregated data is really clearly designed and prepared before analysis and evaluation activities are carried out.