قارن الطرق

	Gender Budgeting Analysis	Gender Inequality Index
المجال	Gender Studies	Gender Studies
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	2002	2010
صاحب الطريقة	Diane Elson (analytical tools); Debbie Budlender, Diane Elson, Guy Hewitt & Tanni Mukhopadhyay (Commonwealth synthesis)	UNDP Human Development Report Office (Gaye, Klugman et al.)
النوع	Policy and fiscal gender analysis method	Composite inequality index
المصدر التأسيسي	Budlender, D., Elson, D., Hewitt, G., & Mukhopadhyay, T. (2002). Gender Budgets Make Cents: Understanding Gender Responsive Budgets. Commonwealth Secretariat, London. ISBN: 9780850926811	Gaye, A., Klugman, J., Kovacevic, M., Twigg, S., & Zambrano, E. (2010). Measuring key disparities in human development: The Gender Inequality Index. Human Development Research Paper 2010/46. UNDP Human Development Report Office.
الأسماء البديلة	Gender-Responsive Budgeting, Gender Budget Analysis, GRB	GII, UNDP Gender Inequality Index
ذات صلة	4	4
الملخص	Gender budgeting analysis, also called gender-responsive budgeting (GRB), is a method for examining government budgets to reveal their differing impacts on women and men and to reallocate resources toward gender equality. It is emphatically not about creating separate budgets for women; instead it applies a gender lens to the whole of public revenue and expenditure, using a set of analytical tools — pioneered by Diane Elson — including gender-aware policy appraisal, beneficiary assessment, expenditure incidence analysis, revenue incidence analysis, and the gender-aware budget statement, and it links fiscal choices to the often-invisible unpaid care economy.	The Gender Inequality Index (GII) is a composite measure introduced by the UNDP in the 2010 Human Development Report to capture the loss in potential human development due to inequality between women and men. It combines three dimensions — reproductive health, empowerment, and labour-market participation — into a single index ranging from 0 (perfect equality) to 1 (complete inequality), using an association-sensitive aggregation that penalises both gaps between the sexes and inequality across dimensions.