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

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

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

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

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 Region B

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%

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

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

Unemployment in Belarus by gender and education, 2004 Education Thousand people % of total in the education category WomenMenWomenMen Higher Professional secondary General secondary Basic secondary and primary All categories Source: Social Environment and Living Standards in the Republic of Belarus. Statistical Book, 2005, Minsk

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

Poverty in Moldova by region, Source: World Bank, 2006, “Moldova: Poverty Update”

Interpreting disaggregated data for policy: Moldavian example In , poverty reduced in all regions of the country → economic growth had clear pro-poor patternIn , 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 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 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

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

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

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

Disaggregation by region Source: National MDG Report of the Republic of Belarus, 2005

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

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

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

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

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.

Summary Disaggregation Disaggregation - Interpretation - Interpretation - Targeting - Targeting Sub-populations Sub-populations - Definition - Definition - Use - Use

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?

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?