1. U SING AD E PT FOR G ENDER A NALYSIS Gender and Development Group World Bank PREM Learning Week 2011 April 20, 2011

2. C ONTEXT AND R ELEVANCE  Persistent gender inequality constrains economic growth  World Bank committed do address gender in its work  This generates greater demand for gender statistics  Poverty, Social and Gender Assessments  Project, Design, Implementation and Monitoring of Impacts  Country Assistance Strategies (CASs)  Millennium Development Goals (MDGs)  MDG3 – Promote Gender Equality and empower women  Monitoring – overtime and across countries

3. C ONTEXT AND R ELEVANCE  Great need to produce relevant gender statistics  BUT resources and knowledge is limited.  Many of the statistics necessary to inform gender issues in PAs, GAs, CASs, CEMs are standard.  Ability to generate timely output is key to  Monitor outcomes (overtime and across countries), and  Identify the right set of policies to influence those outcomes.

4. Illustration from Mozambique (Integrated Poverty, Social and Gender Assessment, 2007) Quintiles AllGirlsBoys 199720031997200319972003 National Poorest Richest All 39.0 62.0 51.0 65.4 79.4 70.5 34.1 55.8 45.1 65.2 79.0 69.0 42.3 66.4 54.1 65.6 80.0 71.8 Rural Poorest Richest All Rural 35.0 45.4 43.6 63.5 72.1 65.3 30.8 39.0 38.5 63.8 70.4 63.5 39.2 53.5 48.7 63.3 73.6 67.0 Urban Poorest Richest All Urban 46.4 80.6 64.4 70.1 90.8 82.5 42.8 78.9 61.2 68.8 90.6 81.4 49.5 82.3 67.8 71.3 91.0 83.6 Region North Center South 45.3 41.8 69.4 60.0 69.4 87.2 40.2 35.7 67.3 58.8 66.3 88.1 50.7 47.8 71.8 61.0 72.4 86.4 Table 2.6: Net Primary School Enrollment, by Sex and Expenditure Quintiles, 1997 and 2003 (percent) Source: Mozambique IAF 1997 and 2003

5. W HY AD E PT? (A UTOMATED DEC P OVERTY T ABLES )  ADePT automates the production of Tables/graphs  Requires limited knowledge of Stata or SPSS  User only needs to prepare the dataset  More people can do the work!  Runs without Stata/SPSS in the user’s computer  Minimizes human error in programming  Generates standardized, comparable results across countries and years  Frees up resources for analysis … interpretation and policy implications

6. F ROM D ATA TO S TATISTICAL R EPORTS

7. AD E PT G ENDER  ADePT Gender is one of 7 ADePT Modules  Objectives of ADePT Gender  Facilitate understanding of the gender dimensions of poverty  Make gender analysis standard practice in poverty diagnostics  Facilitate analysis of the vulnerabilities faced by women, both poor and non-poor, across various dimensions  What is covered in ADePT Gender? I.Gender Dimensions of Poverty II.Gender Dimensions of Labor Market Outcomes

8. I. G ENDER DIMENSIONS OF P OVERTY 1.Poverty by Headship  Poverty (headcount, depth and severity) by head’s sex and age  Poverty Headcount by head’s sex and level of education  Poverty Headcount by head’s sex and sector of economic activity  Poverty Headcount by head’s sex and employment status 2.Education and Literacy  Literacy rates by sex and area of residence (urban/rural) by poverty status  Primary and secondary enrolment rates by sex by urban/rural by poverty status  Primary and secondary completion rates by sex by urban/rural by poverty status  Total years of education by sex by urban/rural by poverty status 3. Utilization of Services  Expenditure quintiles’ shares of education enrolment by urban/rural  Expenditure quintiles’ shares of use of health services and immunization by urban/rural

9. II. G ENDER D IMENSIONS OF L ABOR M ARKET O UTCOMES 1.Labor Force Participation  Labor force participation rates and employment status for males and females  For working age population (15-64 years), (20-24 years) and (25-49)  By Rural/Urban 2.Characteristics of employment  Employment type distribution for male and female workers (across categories) by poverty status  Employment type shares of male and female workers (within categories) by poverty status  Distribution of the employed males and females across sectors of economic activity 3.Earnings  Median and Mean earnings and hours worked by sex and employment categories  Employment type  Sector of economic activity  Formal vs. Informal  Full-time vs. part-time

10. S EX versus G ENDER S EX G ENDER  Innate biological categories of being a male or female  Social roles and identities associated with what it means to be male or female  Sex is does not depend on ideology, religion, ethics, etc  Gender roles are shaped by ideological, religious, ethnic and economic and social factors  Sex is biology  Gender is socio-economy  Sex is fixed  Gender can change overtime through conscious action, public policy. Source: Quisumbing, 1996.

11. Thank You!

12. NOW COMES THE REAL THING !!!!