Assessing Elderly Welfare and PensionPerformance with Survey DataApril 3, 2013 Pensions Core CourseThis presentation build...
Outline• (1) Overview of survey data & social protection– Role of social protection– Application to elderly and pensions– ...
(1) SP Context & Overview ofSurvey Data
Classification of programs4• Social assistance (SocialSafety Nets)SA• Labor Market Programs(active and passive)LM• Social ...
Objectives of pensions• Elderly poverty protection• Consumption smoothing• Encourage savings• Insurance against shocks5
Understanding poverty• No common definition for poverty exists– General agreement: insufficient commoditiesleading to cons...
How poverty is commonly measured• Individuals or households are ranked by incomeor consumption• The measure of income or c...
Poverty measures• Poverty headcount (FGT0) - % of individuals or households with welfarebelow the poverty line• Poverty ga...
Social Protection Objectives9
Why Social Protection and LaborSystems Are Important10
SPL over the life cycle11
How is social protection applied to theelderly?• Equity - Elderly poverty protection• Opportunity– Consumption smoothing i...
Pension Diagnostic Assessment –Evaluation Process & CriteriaEnablingenvironment(Motivating reform,framing & constrainingre...
Diagnostic Assessment – Data,Indicators and ToolsInformation/ DataElderlyincomes,vulnerability &povertyInitialconditionsMa...
Diagnostic Assessment – ToolsToolsAPEXEvaluation ofindividual levelbenefits acrossinstruments + fordifferent incomegroups....
What is survey data?• Examples: HBS, LSMS, DHS• Organization: Household or individual level• Timing: Generally collected e...
Individual Input File17HouseholdIdentificationIndividualIdentificationSTRATA PSUUrban location =1;Rural location=2Househol...
Household Input File18HouseholdIdentificationIndividualIdentificationSTRATA PSUUrban location=1; Rurallocation=2Householde...
Administrative vs Household DataAdministrative data• - Limited populationcoverage - only ‘covered’included• + Comprehensiv...
Uses of survey data (continued)• Quality of data from design, implementationand processing of surveys is critical• Surveys...
ASPIRE Survey Initiative• One of the largest datasets on social protection in theworld: the combined dataset contains info...
ASPIRE household survey components1. Data collection, harmonization and validation2. Tools for data analysis (ADePT and Si...
Types of Social Protection ProgramsCategory I Category II Type of programOld age pensionOld age civil servant pensionVeter...
ASPIRE includes the following 12 indicators formeasuring social protection:• Coverage• Beneficiary Incidence• Benefit Inci...
(2) Surveys specifically for ElderlyWelfare and Pension Analysis
Motivations for work• “Looking forward, this review suggests several concrete areas forfurther work. First, investing in s...
Why use survey data for elderlypoverty and pensions?• Ability to answer new and different policyquestions– Environment – p...
Some practical uses of survey data• Understand characteristics of elderly and non-elderly population (e.g demographics, li...
Pensions Survey Work• East Asia and the Pacific (EAP) – 7 countries,Eastern Europe and Central Asia (ECA)– 20countries, La...
Applications of Survey Data• (1) Environment– Living arrangements– Poverty (elderly and non-elderly)• Design – N/A• (2) Pe...
Characteristics of elderly sample31Variable Obs Mean Std. Dev. Min MaxAge 481,629 69.21 8.09 59.00 119.00Sex (1=male) 480,...
Living Arrangements• What – the structure of households by age,gender, size• Main indicator – Co-residence rate (elderlyli...
Co-residence• Correlates of co-residence in existing studies -lower welfare, living in urban areas, widowers,higher countr...
Co-residence rate, by region340.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00%AFR-MUSAFR-NGAAF...
GNI per capita and co-residence rate35R² = 0.509-2,000.004,000.006,000.008,000.0010,000.0012,000.0014,000.0016,000.0018,00...
Co-residence by pension receipt360%10%20%30%40%50%60%70%80%90%100%AFR-MWIAFR-MUSEAP-KHMEAP-LAOEAP-PHLEAP-THAEAP-VNMECA-ALB...
Coresidence rate by keycharacteristics- country level370.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%To...
Poverty of elderly• Are elderly household more poor then non-elderly households?• Are elderly individuals more poor then n...
Welfare distribution by age group –individual level390.2.4.6.8Density8 10 12 14Log of percapita welfareYouth Working AgeEl...
Global comparison – elderly poverty %400.000 0.050 0.100 0.150 0.200 0.250AFR-GHAAFR-MWIAFR-MUSAFR-NGAAFR-RWAEAP-KHMEAP-LA...
Regional comparison – poverty % byage group410.0000.0500.1000.1500.2000.250Chart TitleOverall Youth Working Age Elderly
Country level - Poverty Headcount byHousehold Type420%5%10%15%20%25%30%Average 1) Elderly:lone2) Elderly:2+5) ElderlywithW...
Poverty rate by age and gender43
Select performance indicators– Coverage - receipt– Adequacy – transfer amount/ poverty line or/welfare– Poverty impact – r...
Simulated poverty impact45Poverty Rate (FGT0)* Poverty Gap (FGT1)* Poverty Severity (FGT2)*All Income No pension transfer ...
(3) Pipeline Work
Pipeline work• Using survey data for country work,particularly environment and performancediagnostics – poverty profile, l...
ADePT Training• There will be a hands-on computer trainingnext week for those interested.• Please sign-up48
Select References• Cameron. 2000. “The Residency Decision of Elderly Indonesians: A Nested Logit Analysis”. Demography. Vo...
Thank you!50• If your country is interested in survey training on SocialProtection and Poverty (1/2 day to 3 day courses):...
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Pensions Core Course 2013: Assessing Elderly Welfare and Pension Performance with Survey Data

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Pensions Core Course 2013: Assessing Elderly Welfare and Pension Performance with Survey Data

  1. 1. Assessing Elderly Welfare and PensionPerformance with Survey DataApril 3, 2013 Pensions Core CourseThis presentation builds on the work of colleagues in HDN and PREM
  2. 2. Outline• (1) Overview of survey data & social protection– Role of social protection– Application to elderly and pensions– Pensions framework applied to survey data– Understanding survey data strengths and weaknesses• (2) Application: Survey data for elderly welfare andpensions analysis –– Environment• Living arrangements• Poverty– Performance• (3) Future work2
  3. 3. (1) SP Context & Overview ofSurvey Data
  4. 4. Classification of programs4• Social assistance (SocialSafety Nets)SA• Labor Market Programs(active and passive)LM• Social InsuranceSI
  5. 5. Objectives of pensions• Elderly poverty protection• Consumption smoothing• Encourage savings• Insurance against shocks5
  6. 6. Understanding poverty• No common definition for poverty exists– General agreement: insufficient commoditiesleading to constrained choices (Harold Watts)– More narrow definition: lack of specificconsumptions (e.g. too little food energy intake)– Less narrow definition: Poverty as lack of“welfare” e.g., lack of “capabilitiy”: inability toachieve certain “functionings” (“beings anddoings”) (Amartya Sen)6 Based on DEC presentation
  7. 7. How poverty is commonly measured• Individuals or households are ranked by incomeor consumption• The measure of income or consumption isreferred to as the ‘welfare aggregate’• Poverty lines are then set either on a relative orabsolute basis, often based on an extreme andbasic standard of living• Those with income or consumption (welfareaggregate) below a given poverty line areconsidered poor7 Based on DEC presentation
  8. 8. Poverty measures• Poverty headcount (FGT0) - % of individuals or households with welfarebelow the poverty line• Poverty gap (FGT1) - mean shortfall of poor from the poverty line,expressed as a percentage of the poverty line• Poverty severity (FGT2) – average of squared poverty gap ratio8Distancesquared% Below line Avg distance below line
  9. 9. Social Protection Objectives9
  10. 10. Why Social Protection and LaborSystems Are Important10
  11. 11. SPL over the life cycle11
  12. 12. How is social protection applied to theelderly?• Equity - Elderly poverty protection• Opportunity– Consumption smoothing in retirement– Encourage savings• Resilience - Insurance against shocks12
  13. 13. Pension Diagnostic Assessment –Evaluation Process & CriteriaEnablingenvironment(Motivating reform,framing & constrainingreform options)Existing designDemographicprofileMacro-economicenvironmentInstitutionalCapacityFinancial marketstatusPolitical economyInitialConditions &InheritedSystemDemand -consumptionsmoothing &elderly povertyprotectionSupply -mandatory &voluntarypension & socialsecurity schemesFamily &communitysupportReformobjectivesPrimary:improvingcoverage,adequacy, &sustain-abilityfor the long-termSecondary:improving labormarkets,macro/fiscalposition, &contributing tofinancial marketdevelopment.Reform Design &ImplementationOptionsDesign reforms -introduce newschemes,parametric &structuralreformsGovernance,Institutional andregulatoryreformsStrengtheninginstitutions &implementation
  14. 14. Diagnostic Assessment – Data,Indicators and ToolsInformation/ DataElderlyincomes,vulnerability &povertyInitialconditionsMandatory &voluntarypensionsystems &social securityschemesToolsCountry HH survey dataADEPT-SP x/country dataEnvironment –UN Population ProjectionsCountry admin dataFinancial market dataMacro & fiscal data (country/IMF)System design –Admin data/country lawsWB database comparatorsPerformance –Admin data/country lawsWB database comparatorsHH survey dataAdministrative data from social welfareschemes, housing, health provision.HH survey data.Additionalstate supportIndicatorsEnvironmentDemographicEconomicFinancialInformal Support ADEPT-SPApexPROSTASPIRE & ext.x-countrydataDesignStructure of pension systemQualifying conditionsParametersPerformanceCoverageAdequacyFinancial sustainability
  15. 15. Diagnostic Assessment – ToolsToolsAPEXEvaluation ofindividual levelbenefits acrossinstruments + fordifferent incomegroups.Individualreplacement ratesReplacement ofaverage wagePension wealthADEPT-SP Elderly welfare Elderly poverty Co-residence Elderly incomegeneration Comparisons of welfare,poverty across elderly,non-elderly & householdtypes.PROSTBaseline. Long-term projectionsof financing gap for existingschemes + replacement ratesfor current and futureretireesReform scenarios. Long-termprojections financing gap +replacement rates forparametric and/or structuralreformsOutputs to simulate otherinstruments (social pensions,voluntary savings)WB Database & ExternalX-Country DataCross-countrycomparisons Demographics Coverage Adequacy Affordability Sustainability
  16. 16. What is survey data?• Examples: HBS, LSMS, DHS• Organization: Household or individual level• Timing: Generally collected ever 2-3 years,more frequent than census (~ 10 years)• Information: Core demographics (eg age andgender), expenditure/ income, employmentstatus, public and private transfers, etc16
  17. 17. Individual Input File17HouseholdIdentificationIndividualIdentificationSTRATA PSUUrban location =1;Rural location=2HouseholdexpansionfactorHouseholdSizeAdultequivalentscaleHead of thehouseholdAge of thehouseholdmemberTotalhouseholdincomePovertylineAmountreceivedfrom oldagepensionsParticipation inscholarshipprogramsAmount receivedby the householdfromOportunidadesAmountreceived by thehousehold fromPro-Campoid_hh id_ind strata psu urban hhweight hhsize adul_eq head age hh_income pob_ing apos becas_ toport tprocam20060150282 1 1 2 2 305 3 2 1 18 2459.34 938.61 0 180.4920060150282 2 1 2 2 305 3 2 0 18 2459.34 938.61 0 180.4920060150282 3 1 2 2 305 3 2 0 1 2459.34 938.61 0 180.4920060150280 1 1 2 2 305 7 6 1 56 9094.69 938.61 0 334.2420060150280 2 1 2 2 305 7 6 0 53 9094.69 938.61 0 334.2420060150280 3 1 2 2 305 7 6 0 29 9094.69 938.61 0 334.2420060150280 4 1 2 2 305 7 6 0 26 9094.69 938.61 0 334.2420060150280 5 1 2 2 305 7 6 0 15 9094.69 938.61 0 334.2420060150280 6 1 2 2 305 7 6 0 13 9094.69 938.61 0 334.2420060150280 7 1 2 2 305 7 6 0 7 9094.69 938.61 1 334.2420060150030 1 1 1 1 777 4 3 1 77 18183.37 938.61 1403.81 020060150030 2 1 1 1 777 4 3 0 51 18183.37 938.61 020060150030 3 1 1 1 777 4 3 0 43 18183.37 938.61 020060150030 4 1 1 1 777 4 3 0 9 18183.37 938.61 020060150040 1 1 1 1 777 1 1 1 92 4458.78 938.61 1604.35 020060150050 1 1 1 1 777 2 2 1 83 6397.05 938.61 1640.45 020060150050 2 1 1 1 777 2 2 0 39 6397.05 938.61 020060150060 1 1 1 1 859 5 2 1 41 12988.27 938.61 020060150060 2 1 1 1 859 5 2 0 32 12988.27 938.61 020060150060 3 1 1 1 859 5 2 0 11 12988.27 938.61 020060140410 1 1 7 1 638 10 6 1 56 10730.62 938.61 0 514.1820060140410 2 1 7 1 638 10 6 0 58 10730.62 938.61 0 514.1820060140410 3 1 7 1 638 10 6 0 86 10730.62 938.61 1411.48 0 514.1820060140410 4 1 7 1 638 10 6 0 30 10730.62 938.61 0 514.1820060140410 5 1 7 1 638 10 6 0 29 10730.62 938.61 0 514.1820060140410 6 1 7 1 638 10 6 0 10 10730.62 938.61 0 514.1820060140410 7 1 7 1 638 10 6 0 9 10730.62 938.61 0 514.1820060140410 8 1 7 1 638 10 6 0 4 10730.62 938.61 0 514.18
  18. 18. Household Input File18HouseholdIdentificationIndividualIdentificationSTRATA PSUUrban location=1; Rurallocation=2HouseholdexpansionfactorHouseholdSizeAdultequivalentscaleHead of thehouseholdAge of thehouseholdmemberTotalhouseholdincomePovertylineAmountreceivedfrom oldagepensionsParticipation inscholarshipprogramsAmount receivedby the householdfromOportunidadesAmountreceived by thehousehold fromPro-Campoid_hh id_ind strata psu urban hhweight hhsize adul_eq head age hh_income pob_ing apos becas_ toport tprocam20060150282 1 1 2 2 305 3 2 1 18 2459.34 938.61 0 180.4920060150280 1 1 2 2 305 7 6 1 56 9094.69 938.61 1 334.2420060150030 1 1 1 1 777 4 3 1 77 18183.37 938.61 1403.81 020060150040 1 1 1 1 777 1 1 1 92 4458.78 938.61 1604.35 020060150050 1 1 1 1 777 2 2 1 83 6397.05 938.61 1640.45 020060150060 1 1 1 1 859 5 2 1 41 12988.27 938.61 020060140410 1 1 7 1 638 10 6 1 56 10730.62 938.61 1411.48 0 514.18
  19. 19. Administrative vs Household DataAdministrative data• - Limited populationcoverage - only ‘covered’included• + Comprehensive data oncontributors, beneficiaries• + Cumulative (over lifecycle)• - Narrow variables (eg age,gender, contribution)Household data• + Entire populationrepresented• -/+ Generally lack data oncontributors, though extensiveinfo on recipients (and non-recipients)• - Static (singe year, usually notpanel)• + Much more comprehensive(demographic, poverty, public& private transfers)19
  20. 20. Uses of survey data (continued)• Quality of data from design, implementationand processing of surveys is critical• Surveys do have limitations, such ascomparability across countries (eg income vsconsumption), recall, survey non-response,comparability over time• Nonetheless, survey data can provide awealth of information for welfare andprogram assessment20
  21. 21. ASPIRE Survey Initiative• One of the largest datasets on social protection in theworld: the combined dataset contains information onalmost 5 million individuals in 1.3 mln. households.• Reveals large information gaps in standard householdsurveys, many of which do not include questions aboutparticipation in social protection programs.• Advocates for greater use of surveys for assessingsocial protection policies, and provides tools forenhanced data collection (survey modules, links to dataanalysis software).21
  22. 22. ASPIRE household survey components1. Data collection, harmonization and validation2. Tools for data analysis (ADePT and Simulators)3. Training in tools (Bank and non-Bank clients)4. Analytical notes and reports5. External partnership and internalcollaboration6. Dissemination22
  23. 23. Types of Social Protection ProgramsCategory I Category II Type of programOld age pensionOld age civil servant pensionVeterans old age pensionEarly retirement pensionSurvivors pensionSurvivors civil servant pensionOccupational injury benefits/ pensionPaid sick leaveDisability Disability pensionUnemployment compensationSeverance payEarly retirement for labor market reasonsLabor market trainingYouth measuresSubsided employmentEmployment measures for disabledEmployment service and administrationLow income/ Last-resort programNon-contributory/ Social pensionFamily allowances*Disability benefitsHousing allowancesFood stamps/ VouchersConditional cash transfers Conditional cash transfersFood rationsSupplementary feedingSchool feedingEnergency food distributionFee waivers, educationScholarshipsFee waivers, healthFood price subsidiesPublic distribution systemsEnergy and utility subsidiesPublic works Public worksIn-kind food transfersFee waivers and scholarshipsGeneral subsidiesSocial safety net programsLabor market programs UnemploymentActive labor market programsCash or near-cash transfersPensions and other socialinsuranceOld ageSurvivorsOccupational injury/sicknessbenefits23
  24. 24. ASPIRE includes the following 12 indicators formeasuring social protection:• Coverage• Beneficiary Incidence• Benefit Incidence• Generosity• Leakage• Targeting differential• Impact on Poverty• Impact on Inequality• Coady-Grosh-Hoddinott indicator• Program duplication• Social protection overlap• Cost benefit ratio24
  25. 25. (2) Surveys specifically for ElderlyWelfare and Pension Analysis
  26. 26. Motivations for work• “Looking forward, this review suggests several concrete areas forfurther work. First, investing in strategic knowledge productsparticularly in areas related to coverage where there are significantgaps including, but not limited to:– Old age poverty, informal support and pension systems, especially incountries with large agricultural and informal sectors such as in SouthAsia and Sub-Saharan Africa. This requires efforts in several areasincluding exploiting existing survey data, collecting administrative datafrom country sources and micro-simulations with the APEX model.”• “Measurement of performance must come from systematicevidence provided by household survey data. The surveys provideinformation on the incidence of benefits and the impact ofprograms on household poverty, among other things.”Source: Dorfman and Palacios. 2012. “World Bank Support for Pensionsand Social Security”26
  27. 27. Why use survey data for elderlypoverty and pensions?• Ability to answer new and different policyquestions– Environment – poverty, distribution ofincome/consumption, living arrangements, keydemographics– Design – N/A– Performance – coverage (receipt), poverty impact,adequacy, targeting, etc• Cross-tabulate by key characteristics, eg geography, gender,age, income• More breadth of information on individuals andhouseholds27
  28. 28. Some practical uses of survey data• Understand characteristics of elderly and non-elderly population (e.g demographics, livingarrangements and welfare)• Produce evidence- based findings on pensionand other programs, such as coverage,adequacy, poverty impact, etc• Provide snapshot of income by sources(public, private) for different age groups28
  29. 29. Pensions Survey Work• East Asia and the Pacific (EAP) – 7 countries,Eastern Europe and Central Asia (ECA)– 20countries, Latin America and Caribbean (LAC)– 20 countries, Middle East and NorthernAfrica (MNA) – 5 countries, South Asia (SAR) –7 countries. The surveys vary in size fromapproximately 15,000 in Albania to 600,000individuals in India.29
  30. 30. Applications of Survey Data• (1) Environment– Living arrangements– Poverty (elderly and non-elderly)• Design – N/A• (2) Performance– Coverage– Adequacy– Poverty impact– Program overlap– Cost-benefit– Targeting30
  31. 31. Characteristics of elderly sample31Variable Obs Mean Std. Dev. Min MaxAge 481,629 69.21 8.09 59.00 119.00Sex (1=male) 480,278 0.45 0.50 0.00 1.00Household Size 481,629 4.03 2.79 1.00 48.00Urban 469,719 0.57 0.49 0.00 1.00Pension received 450,279 0.44 0.50 0.00 1.00Remittances received 344,945 0.16 0.36 0.00 1.00Co-resident 464,384 0.70 0.46 0.00 1.00Employed 213,625 0.34 0.47 0.00 1.00Widower 249,264 0.33 0.47 0.00 1.00Age60_69 481,629 0.52 0.50 0.00 1.00Age70_79 481,629 0.30 0.46 0.00 1.00Age80_plus 481,629 0.13 0.33 0.00 1.00Quintile welfare 473,053 3.88 1.40 1.00 5.00Decile welfare 473,053 7.35 2.90 1.00 10.00
  32. 32. Living Arrangements• What – the structure of households by age,gender, size• Main indicator – Co-residence rate (elderlyliving with non-elderly)• Why – proxy for informal support by non-elderly, ‘voluntary pillar’ of family support cancomplement formal pension systems32
  33. 33. Co-residence• Correlates of co-residence in existing studies -lower welfare, living in urban areas, widowers,higher country level GDP per capita)• Initial findings– Co-residence more likely for: urban, not receivinga pension, not receiving remittances, lowerwelfare deciles, lower income countries– Wide variation between, and within some regions(eg ECA)33
  34. 34. Co-residence rate, by region340.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00%AFR-MUSAFR-NGAAFR-RWAEAP-MNGEAP-VNMEAP-TMPEAP-LAOECA-HUNECA-BLRECA-HRVECA-POLECA-BGRECA-MNEECA-KRZECA-GEOECA-ARMECA-TJKLAC-URYLAC-BRALAC-CRILAC-PANLAC-MEXLAC-PERLAC-COLLAC-SLVLAC-HNDLAC-NICMNA-WBGMNA-MARMNA-DJISAR-INDSAR-NPLSAR-MDVSAR-AFGAverage
  35. 35. GNI per capita and co-residence rate35R² = 0.509-2,000.004,000.006,000.008,000.0010,000.0012,000.0014,000.0016,000.0018,000.000.4 0.5 0.6 0.7 0.8 0.9 1
  36. 36. Co-residence by pension receipt360%10%20%30%40%50%60%70%80%90%100%AFR-MWIAFR-MUSEAP-KHMEAP-LAOEAP-PHLEAP-THAEAP-VNMECA-ALBECA-ARMECA-BIHECA-BGRECA-HRVECA-HUNECA-KSVECA-KRZECA-LTUECA-MDAECA-MNEECA-POLECA-SRBECA-SVKECA-TJKECA-TURECA-UKRLAC-ARGLAC-BOLLAC-BRALAC-CHLLAC-COLLAC-CRILAC-DOMLAC-ECULAC-SLVLAC-GTMLAC-HNDLAC-JAMLAC-MEXLAC-NICLAC-PANLAC-PRYLAC-URYLAC-VENMNA-MARMNA-IRQMNA-JORSAR-AFGSAR-BTNSAR-INDSAR-MDVSAR-NPLSAR-PAKSAR-LKATotalNo pension Yes pension
  37. 37. Coresidence rate by keycharacteristics- country level370.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%TotalUrbanRuralMaleFemale60-7475+Bottom QuintileTop QuintilePension ReceiptNo Pension ReceiptRemittance ReceiptNo Remittance ReceiptEmployedNot Employed
  38. 38. Poverty of elderly• Are elderly household more poor then non-elderly households?• Are elderly individuals more poor then non-elderly?• Preliminary findings (point estimates robust, though less for s.e.)– By household type ( any elderly, only elderly, someelderly, no elderly)– By individual – youth, working age, elderly38
  39. 39. Welfare distribution by age group –individual level390.2.4.6.8Density8 10 12 14Log of percapita welfareYouth Working AgeElderlyPoverty line
  40. 40. Global comparison – elderly poverty %400.000 0.050 0.100 0.150 0.200 0.250AFR-GHAAFR-MWIAFR-MUSAFR-NGAAFR-RWAEAP-KHMEAP-LAOEAP-MNGEAP-PHLEAP-TMPEAP-VNMECA-ALBECA-ARMECA-BLRECA-BIHECA-BGRECA-HRVECA-GEOECA-HUNECA-KSVECA-KRZECA-LTUECA-MDAECA-MNEECA-POLECA-SRBECA-SVKECA-TJKECA-TURECA-UKRLAC-ARGLAC-BOLLAC-BRALAC-CHLLAC-COLLAC-CRILAC-DOMLAC-ECULAC-SLVLAC-GTMLAC-HNDLAC-MEXLAC-NICLAC-PANLAC-PRYLAC-PERLAC-URYLAC-VENMNA-MARMNA-DJIMNA-JORMNA-WBGSAR-AFGSAR-BTNSAR-INDSAR-MDVSAR-NPLSAR-PAKSAR-LKA
  41. 41. Regional comparison – poverty % byage group410.0000.0500.1000.1500.2000.250Chart TitleOverall Youth Working Age Elderly
  42. 42. Country level - Poverty Headcount byHousehold Type420%5%10%15%20%25%30%Average 1) Elderly:lone2) Elderly:2+5) ElderlywithWorkingAge7) ElderlywithWorkingAge andYouth6) Elderlywith Youth3) Workingage only8) Workingage and/orYouthHH OnlyElderlyHH SomeElderlyHH NoElderly
  43. 43. Poverty rate by age and gender43
  44. 44. Select performance indicators– Coverage - receipt– Adequacy – transfer amount/ poverty line or/welfare– Poverty impact – reduction in poverty due totransfer– Program overlap – receipt of 2+ programs– Cost-benefit – reduction in poverty gap for each $spent– Targeting - % intended beneficiaries receivingtransfers44
  45. 45. Simulated poverty impact45Poverty Rate (FGT0)* Poverty Gap (FGT1)* Poverty Severity (FGT2)*All Income No pension transfer All Income No pension transfer All Income No pension transferIndividual levelPopulation 0.5077 0.5878 0.2312 0.3265 0.1487 0.2435Youth (0-14) 0.6196 0.6651 0.2873 0.3617 0.1860 0.2642Working Age (15-60) 0.4718 0.5248 0.2157 0.2789 0.1405 0.2024Elderly (60+) 0.3502 0.7611 0.1330 0.5455 0.0687 0.4685Elderly (60-74) 0.3562 0.7380 0.1279 0.5224 0.0656 0.4485Elderly (75+) 0.3331 0.8279 0.1476 0.6124 0.0777 0.5262Non-Elderly (0-59) 0.5218 0.5723 0.2400 0.3069 0.1559 0.2233Household levelElderly-only households 0.0410 0.8512 0.0177 0.7742 0.0126 0.7337Some elderly households 0.4817 0.7283 0.1817 0.4697 0.0917 0.3767Non-Elderly Households 0.4325 0.4550 0.2088 0.2335 0.1452 0.1690Total 0.4197 0.5321 0.1924 0.3116 0.1269 0.2429NotesThe poverty line is set at total median income (deflated) – Note that forillustration purposes as typical poverty line is 50% medianThe simulated welfare subtracts pension income (deflated)Weights are applied for statistical accuracy
  46. 46. (3) Pipeline Work
  47. 47. Pipeline work• Using survey data for country work,particularly environment and performancediagnostics – poverty profile, livingarrangements, coverage, adequacy, etc• Regional and cross-regional comparison• Multi-country indicators and researchexamining factors such as correlates tocoresidence & elderly poverty47
  48. 48. ADePT Training• There will be a hands-on computer trainingnext week for those interested.• Please sign-up48
  49. 49. Select References• Cameron. 2000. “The Residency Decision of Elderly Indonesians: A Nested Logit Analysis”. Demography. Volume 37-No. 1.• Costa 1998.The Evolution of Retirement: An American Economic History, 1980-1990. University of Chicago Press. Chicago.• Deaton. 2000. The Analysis of Household Surveys: A Microeconmetric Approach to Development. World Bank.• Dorfman et al. 2012. “Malaysia Elderly Protection Study”. World Bank. Washington DC.• Evans, M. 2012. “Profiling Elderly Populations and Elderly Poverty – Practice Note”.• Giles et al. 2010. “Can China’s Rural Elderly Count on Support from Adult Children?”. World Bank. Washington DC.• Holzman, R. and Hinz, R. “Old Age Income Support in the 21st Century: An International Perspective on Pension Systems and Reform”, World Bank,2005.• OECD. 2011. Pensions at a Glance 2011. Paris.• Rofman, R. et al. 2008. Pension Systems in Latin America: Concepts and Measurements of Coverage. World Bank.• Whitehouse, E. 2007. Pensions Panaroma. World Bank. Washington DC.• World Bank. 2009. Closing the Coverage Gap. Washington DC.• World Bank. “International Patterns of Pension Provision II: A Worldwide Overview of Facts and Figures”. Washington DC.• World Bank. “Pension Indicators: reliable statistics to improve pension policymaking”, World Bank Pension Indicators and Database, Briefing 1,Washington DC.• World Bank. “Coverage: How much of the labor force is covered by the pension system?”, World Bank Pension Indicators and Database, Briefing 2,Washington DC.• World Bank. “Adequacy (1): Pension entitlements, replacement rates and pension wealth”, World Bank Pension Indicators and Database, Briefing 3,Washington DC.• World Bank. “Adequacy (2): Pension entitlements of recent retirees”, World Bank Pension Indicators and Database, Briefing 4, Washington DC.• World Bank. “Financial sustainability: Assessing the finances of pension systems over the long term”, World Bank Pension Indicators and Database,Briefing 6, Washington DC.• World Bank. “Economic efficiency: Minimizing the pension system’s distortions of individual choices”, World Bank Pension Indicators and Database,Briefing 7, Washington DC.• World Bank. “Administrative efficiency: assessing the cost of running public pension systems”, World Bank Pension Indicators and Database, Briefing8, Washington DC.• World Bank. World Bank, “Security: Risk and uncertainty in retirement-income systems” , World Bank Pension Indicators and Database, Briefing 9,Washington DC.• World Bank. 2012. “Pensions Database”. Washington DC.49
  50. 50. Thank you!50• If your country is interested in survey training on SocialProtection and Poverty (1/2 day to 3 day courses):– Please contact Mr. Ruslan Yemtsov, ryemstov@worldbank.orgMr. Robert Palacions rpalacios@worldbank.org or Mr. BrooksEvans bevans2@worldbank.org

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