Gender-Sensitive Poverty Mapping for Timor-Leste : Policy Note

• 查看报告

作者：

World Bank

来源地址：

http://hdl.handle.net/10986/32015

关键词：

POVERTY MAP;  GENDER;  SMALL AREA ESTIMATION;  EDUCATION;  HEALTH;  LABOR FORCE;  AGENCY;  POVERTY RATE;  Report;  Rapport;  Informe; 

年份：

2019

出版地：

Washington,USA

语种：

English

摘要：

Timor-Leste has made impressive progress over the past decade in reducing national poverty levels. Geographically, however, this progress has been highly uneven across the country. In addition, concerns exist regarding gender gaps based on broader socioeconomic dimensions, such as access to economic activities, education, health, and power and agency. In response, the Government of Timor-Leste has set a goal of eradicating extreme poverty by introducing more socially inclusive and gender sensitive policies and programs. However, the existing sex-disaggregated statistics and consumption based poverty estimates resulting from the 2014 Survey of Living Standards only provide district-level disaggregation. This limits the government's ability to identify and target pockets of extreme poverty and gender disparity across the country below the district level. To address this gap, the World Bank, in close collaboration with the General Directorate of Statistics Timor-Leste, has generated a new set of sex-disaggregated poverty statistics at the village (suco) level. This work takes a more thoughtful approach to gender-sensitive poverty analyses, beyond the usual household headship, by employing individual-level characteristics of education, health, employment, and power and agency. The analyses employ a small-area estimation (SAE) approach to link the data in the 2015 Population and Housing Census with the 2014 Survey of Living Standards and the 2016 Demographic and Health Survey. The suco-level poverty maps confirm an already known pattern that poverty headcount rates are much higher in western areas of the country. The maps also reveal new findings that were not previously known, namely that there is far more variation in poverty rates within than between districts. For example, while the Dili district-level poverty rate is 29 percent, its suco-level rates range from 8 to 80 percent. Analyzing poverty and gender equality by the gender of the household head, female-headed households are less likely to be poor than those headed by males. However, if poverty and genderequality are assessed using spatially disaggregated evidence of five individual-level gender indicators (education, health, labor force, and power and agency), two interesting patterns emerge. First, poorer areas have higher levels of abuse and domestic violence against women, and females are at a greater educational disadvantage, despite narrowing gaps in the literacy rate among school-aged children and school enrollment. Second, there is an inverse relationship between gender-related labor force gaps and poverty rates: the prevalence of a female labor force disadvantage is higher in more economically developed sucos. However, women do not appear to be disadvantaged in terms of health measures and this pattern has no correlation with poverty. Poverty does not appear to be related to women's autonomy to make decisions. The overall findings suggest the importance of using sex-disaggregated individual level analysis, beyond the male/female household headship, to better assess poverty of women and men and gender disparity. This analysis goes beyond traditional consumption-based poverty analysis by integrating a gender dimension to better capture the standard-of-living and gender disparities in the country. These findings can be used to inform the design of policies and programs that target poverty at the suco level, and to improve resource allocation designed to raise the living standards of the poor, balance the targeting of poor areas and poor people, and close gender gaps in the five dimensions studied here. The poverty maps could also provide a cost-effective way to add value to existing census and survey data, and also serve as a substitute for fielding expensive new censuses or surveys.