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Information about Module14

Published on August 7, 2007

Author: Tarzen


Indicators for Policy Management and Advocacy MDGs and Statistical Literacy:  Indicators for Policy Management and Advocacy MDGs and Statistical Literacy Module 14: Using Indicators to Reflect Diversity What you will be able to do by the end of this module:  What you will be able to do by the end of this module Disaggregate indicators Understand the strengths and limitations of disaggregation of indicators Interpret disaggregated indicators Understand how to identify vulnerable groups or ‘pockets’ Understand how disaggregated indicators can contribute to targeting of policies and advocacy programmes Why Disaggregate?:  Why Disaggregate? To see more detail To investigate pattern To compare across sub-populations To tailor policy effectively To see more detail:  To see more detail Source: KenInfo To Investigate Pattern:  To Investigate Pattern Adult literacy rate, Uganda 2002/03 Source: NHS 2002/03, National Household Survey 2002/3, Uganda Bureau of Statistics, mid-term report, January 2003 To compare across sub-populations:  To compare across sub-populations Source: TSED Does the average suggest the right policies?:  Does the average suggest the right policies? 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 A 277 Region B 271 Region A obviously gets better results Subgroup Analyses:  Subgroup Analyses Let’s look at the data by type of living area - Region B always does better! How can this possibly be?:  How can this possibly be? Consider the distribution the living area of the student population: How can this possibly be?:  How can this possibly be? Region B has considerably more students living in urban slum areas We saw that children living in urban slums had considerably lower scores Mathematical Explanation:  Mathematical Explanation Region A total score = Rural score* Rural %/100 + Urban slum score*Urban slum %/100 + Others score*Others %/100 = 277 And similarly for Region B Policy Implications:  Policy Implications Overall 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 slums and other urban areas Literacy Rate, Kenya, 1989:  Literacy Rate, Kenya, 1989 Literacy rate, by age group and sex, Kenya 1989 overall rate for 1989 was 73.4 but . . . Source: KenInfo Literacy rate, by age Group and Sex, Kenya 1989 :  Literacy rate, by age Group and Sex, Kenya 1989 Source: KenInfo Interpreting disaggregated data for policy:  Interpreting disaggregated data for policy The data show that women are catching up with men in younger age groups, but that there is still a large difference in older cohorts The gov’t may consider adult classes as a way to reduce this gap Advantages of Disaggregation:  Advantages of Disaggregation More detail for reporting, policy, advocacy Understand policy impact mechanisms Feedback to population, providers, funders Identify areas of special success or problems Reflects greater variety of situations – is more likely to catch policymaker’s interest Limitations of Disaggregation:  Limitations of Disaggregation Smaller sample sizes more sampling error bigger confidence intervals Certain interest groups not represented Coverage possibility of more bias Time and cost of analysis, reporting Which Subpopulations?:  Which Subpopulations? Relating to wide national issues: Age Educational attainment Geographical/admin area Ethnic group Employment group Industry sector Poverty Sex Urban/rural Slum dweller Geographical area Disaggregation by Sex:  Disaggregation by Sex Source: KenInfo Disaggregation by Geographical Area:  Disaggregation by Geographical Area Source: UgandaInfo Disaggregation of Poverty Data:  Disaggregation of Poverty Data Income/expenditure quantiles Socio-economic group Income/employment status Education of head of household Sex of head of household Why: to identify groups affected or missed by existing policies Dissagregation by Urban/Rural:  Dissagregation by Urban/Rural Major issues tend to be access to health and education Structure of urban and rural populations may be different in Age group Sex Ethnic group Disaggregation by Sex:  Disaggregation by Sex To disaggregate by sex is just to make two data sets, one for male and one for female. Considering sex-disaggregated data alongside other data such as…: Income Employment status, etc Education Not just disaggregation by sex, but along with other sub-populations which define imbalance between men and women …Allows us to highlight gender issues Gender Mainstreaming Into Statistics:  Gender Mainstreaming Into Statistics Gender mainstreaming is about integrating gender issues and concern into the production and dissemination of statistics Gender mainstreaming into statistics (examples):  Gender mainstreaming into statistics (examples) Pockets:  Pockets Subpopulations 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, advocacy Example: rural disadvantaged, slum dweller Slum Dweller:  Slum Dweller Condition of living associated with poverty, poor health and lack of education It may be an imprecise categorization, but if well defined, does allow for effective targeting Can be determined by, among others, access to: Education Health facilities Clean water Sanitation Targeting:  Targeting Having information about subpopulations allows for much more effective Policy formulation Allocation of resources Advocacy It is important, however, to strike a balance between targeting and staying focused on national priorities, when disaggregating indicators Summary:  Summary Disaggregation Interpretation Targeting Sub-populations Definition Use Practical 14.1:  Practical 14.1 Disaggregation Find in your country DevInfo dataset an indicator which has been recorded by two or more sub-populations on at least three time points. For example, you might have the Poverty head-count index disaggregated by urban and rural and recorded for 4 different years. You should use the metadata in your dataset to ensure that the indicators you select are comparable. Practical 14.1 (continued):  Use DevInfo to produce a table of the indicator by time and sub-population values. Produce a graph which illustrates the values, by sub-population, over time. Practical 14.1 (continued) Practical 14.2:  Practical 14.2 Targeting Construct a brief summary of your poverty profile using available poverty line data. How has poverty changed over time? What can you say about the regional distribution of poverty in your country? 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 14.2 (continued):  Are there any other quantitative or qualitative indicators, or any other disaggregations which would help to explain the patterns you have observed? How would you use this information To feed into national poverty reduction policies and programmes? To target interventions towards specific sub-populations? Practical 14.2 (continued) Practical 14.3:  Practical 14.3 Gender Mainstreaming What are the gender implications of indicators 29, 30 and 31? (*) Indicator numbering refer to the MDG list Practical 14.4:  Practical 14.4 Disaggregate indicator 1, 3, 21 and 23 by age-groups. Also consider literacy rate across age-groups. Build a summary table. Do you notice any variation across age- groups? What can be concluded? (*) Indicator numbering refer to the MDG list

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