USING ADEPT FOR GENDER ANALYSIS

Gender and Development GroupWorld Bank

PREM Learning Week 2011April 20, 2011

CONTEXT AND RELEVANCE

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

CONTEXT AND RELEVANCE

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.

Illustration from Mozambique(Integrated Poverty, Social and Gender Assessment, 2007)

Quintiles All Girls Boys1997 2003 1997 2003 1997 2003

National Poorest Richest All

39.062.051.0

65.479.470.5

34.155.845.1

65.279.069.0

42.366.454.1

65.680.071.8

Rural Poorest Richest All Rural

35.045.443.6

63.572.165.3

30.839.038.5

63.870.463.5

39.253.548.7

63.373.667.0

Urban Poorest Richest All Urban

46.480.664.4

70.190.882.5

42.878.961.2

68.890.681.4

49.582.367.8

71.391.083.6

Region North Center South

45.341.869.4

60.069.487.2

40.235.767.3

58.866.388.1

50.747.871.8

61.072.486.4

Table 2.6: Net Primary School Enrollment, by Sex and Expenditure Quintiles, 1997 and 2003(percent)

Source: Mozambique IAF 1997 and 2003

WHY ADEPT?(AUTOMATED DEC POVERTY TABLES)

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

FROM DATA TO STATISTICAL REPORTS

Inside ADePT:

User Computational interface kernel (Stata)

ADePT GenderUser micro-level data: LSMS …

Print-ready output

Database with the necessary

variables prepared by the user

ADEPT GENDER

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 PovertyII. Gender Dimensions of Labor Market Outcomes

I. GENDER DIMENSIONS OF POVERTY

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

II. GENDER DIMENSIONS OF LABOR MARKET OUTCOMES

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

SEX versus GENDER

SEX GENDER 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.

Thank You!

NOW COMES THE REAL THING !!!!