1. 2015 1990 1 Module 12: Module 12: Using Indicators to Reflect Diversity Tools for Civil Society to Understand and Use Development Data: Improving MDG Policymaking and Monitoring

2. 2015 1990 2 What you will be able to do by the end of this module: Understand the strengths and limitations of disaggregation of indicatorsUnderstand the strengths and limitations of disaggregation of indicators Interpret disaggregated indicatorsInterpret disaggregated indicators Understand how to identify vulnerable groups or ‘pockets’Understand how to identify vulnerable groups or ‘pockets’ Understand how disaggregated indicators can contribute to targeting of policies and advocacy programmesUnderstand how disaggregated indicators can contribute to targeting of policies and advocacy programmes

3. 2015 1990 3 Why Disaggregate? To see more detail, to investigate pattern, to compare across sub-populationsTo see more detail, to investigate pattern, to compare across sub-populations → in other words, to dig under the surface! → in other words, to dig under the surface! This is important to understand social and economic realityThis is important to understand social and economic reality But also to tailor policies effectivelyBut also to tailor policies effectively

4. 2015 1990 4 Does the average suggest the right policies? An example: Consider a set of examination results for 8th grade from two different regions of a certain country. The numbers are averages of students’ aggregate scores on mathematics examinations. Region A277 Region A277 Region B272 Region A obviously gets better results

5. 2015 1990 5 Subgroup Analysis Let’s look at the data by type of living area Surprisingly, region B always does better! Rural Urban Slum Other Urban Region A 281236259 Region B 283242260

6. 2015 1990 6 How can this possibly be? Consider the distribution of the student population by the living area: Rural Urban Slum Other Urban Region A 87%5%8% Region B 66%15%19%

7. 2015 1990 7 How can this possibly be? (2) Region B has considerably more students living in urban slum areasRegion B has considerably more students living in urban slum areas Children living in urban slums had considerably lower scoresChildren living in urban slums had considerably lower scores Larger share of students with lower scores in region B results in lower average score for this regionLarger share of students with lower scores in region B results in lower average score for this region

8. 2015 1990 8 Policy Implications Overall averages → need to improve the standards in Region B’s schoolsOverall averages → need to improve the standards in Region B’s schools Subgroup analyses → need to address the reasons behind the low scores for students living in urban slumsSubgroup analyses → need to address the reasons behind the low scores for students living in urban slums

9. 2015 1990 9 Unemployment in Belarus by gender and education, 2004 Education Thousand people % of total in the education category WomenMenWomenMen Higher5.72.86733 Professional secondary 12.32.88119 General secondary 21.19.07030 Basic secondary and primary 5.65.15248 All categories 44.719.76931 Source: Social Environment and Living Standards in the Republic of Belarus. Statistical Book, 2005, Minsk

10. 2015 1990 10 Interpreting disaggregated data for policy: Belorussian example The data show that in general women register as unemployed much more frequently than menThe data show that in general women register as unemployed much more frequently than men However, share of women among unemployed with professional secondary education is particularly highHowever, share of women among unemployed with professional secondary education is particularly high Quality of education in secondary vocational schools in “women’s” professions may be lower than national averageQuality of education in secondary vocational schools in “women’s” professions may be lower than national average → The government may consider actions aimed at improvement of quality of education in these vocational schools

11. 2015 1990 11 Poverty in Moldova by region, 1999-2005 Source: World Bank, 2006, “Moldova: Poverty Update”

12. 2015 1990 12 Interpreting disaggregated data for policy: Moldavian example In 1999-2003, poverty reduced in all regions of the country → economic growth had clear pro-poor patternIn 1999-2003, poverty reduced in all regions of the country → economic growth had clear pro-poor pattern Later, poverty reduction continued in large cities only, while in other regions in 2004-2005 it started to increase again → the growth benefits were unevenly distributed among population of different regionsLater, poverty reduction continued in large cities only, while in other regions in 2004-2005 it started to increase again → the growth benefits were unevenly distributed among population of different regions → The government may consider specific measures of poverty alleviation in small towns and rural areas

13. 2015 1990 13 Advantages of Disaggregation More detail for reporting, policy, advocacyMore detail for reporting, policy, advocacy Understand policy impact mechanismsUnderstand policy impact mechanisms Feedback to population, providers, fundersFeedback to population, providers, funders Identify areas of special success or problemsIdentify areas of special success or problems Reflects greater variety of situations – is more likely to catch policymaker’s interestReflects greater variety of situations – is more likely to catch policymaker’s interest

14. 2015 1990 14 Some limitations of disaggregation CoverageCoverage - possibility of more bias when dealing with sub- populations - possibility of more bias when dealing with sub- populations Identify the ‘good’ variable to disaggregateIdentify the ‘good’ variable to disaggregate Definition of the sub-population not always simpleDefinition of the sub-population not always simple Problems with sample survey dataProblems with sample survey data - more sampling error - more sampling error - certain sub-populations not represented - certain sub-populations not represented Confidentiality issuesConfidentiality issues Time and cost of analysis, reportingTime and cost of analysis, reporting

15. 2015 1990 15 Which Subpopulations? Relating to wide national issues: Age Age Educational attainment Educational attainment Geographical/admin area Geographical/admin area Ethnic group Ethnic group Employment group Employment group Economic sector Economic sector Poverty status Poverty status Sex Sex Urban/rural Urban/rural Geographical area Geographical area

16. 2015 1990 16 Disaggregation by region Source: National MDG Report of the Republic of Belarus, 2005

17. 2015 1990 17 Disaggregation of Poverty Data Income/consumption quantilesIncome/consumption quantiles Socio-economic groupSocio-economic group - Urban/rural - Urban/rural - Income/employment status - Income/employment status - Education of head of household - Education of head of household - Sex of head of household (?) - Sex of head of household (?) Why: to identify groups affected or missed by existing policiesWhy: to identify groups affected or missed by existing policies

18. 2015 1990 18 Disaggregation by Sex Source: National Statistical Bureau of the Republic of Moldova, 2005, “Women and Men in the Republic of Moldova”

19. 2015 1990 19 Disaggregation by Sex Contrary to other disaggregation criteria, sex clearly cuts in two each society Cultural and social norms, alongside with biological differences, have built different roles for women and men in the society, in the economy, in the familyCultural and social norms, alongside with biological differences, have built different roles for women and men in the society, in the economy, in the family Gender mainstreaming into statistics is about integrating gender issues and concern into the production and dissemination of statisticsGender mainstreaming into statistics is about integrating gender issues and concern into the production and dissemination of statistics

20. 2015 1990 20 Gender mainstreaming into statistics (examples) ISSUE CONVENTIONAL STATISTICS GENDER ISSUE TO CONSIDER GENDER MAINSTREAMING Land holders by sex Only land above a certain size are considered in the sample Women more often than men hold plots of small size All holding sizes are included in the sample Economic activity by sex No specific precautions are taken to include informal employment A number of productive activities carried out by women are informal Questionnaires explicitly include informal employment as farming in family plots

21. 2015 1990 21 Pockets Subpopulations which do not correspond directly to simple disaggregation, but to new categories derived from combinations of other subpopulationsSubpopulations which do not correspond directly to simple disaggregation, but to new categories derived from combinations of other subpopulations Relate to groups meaningful for planning, policy, advocacyRelate to groups meaningful for planning, policy, advocacy Example: old people in rural areas, specific ethnic groups, etc.Example: old people in rural areas, specific ethnic groups, etc.

22. 2015 1990 22 Summary Disaggregation Disaggregation - Interpretation - Interpretation - Targeting - Targeting Sub-populations Sub-populations - Definition - Definition - Use - Use

23. 2015 1990 23 Practical 12 1.Construct a brief summary of poverty profile in your country using available poverty data 2.How has poverty changed over time? 3.What can you say about the regional distribution of poverty in your country? 4.Are there differences amongst other sub- populations (rural/urban, education level, socio- economic status etc)? If so, what are these differences? How would you explain them?

24. 2015 1990 24 Practical 12 5.Are there any other quantitative or qualitative indicators, or any other disaggregations which would help to explain the patterns you have observed? 6. How would you use this information To feed into national development policies and programmes?To feed into national development policies and programmes? To target interventions towards specific sub- populations?To target interventions towards specific sub- populations?