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Poverty Monitoring

Poverty Monitoring. Taking a Stock of What is Out There Ruslan Yemtsov and Asad Alam. Plan. International publications on poverty monitoring Poverty monitoring: separate activity or integral part of statistical work?

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Poverty Monitoring

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  1. Poverty Monitoring Taking a Stock of What is Out There Ruslan Yemtsov and Asad Alam

  2. Plan • International publications on poverty monitoring • Poverty monitoring: separate activity or integral part of statistical work? • Data quality: is there a difference between what is needed for poverty monitoring and other purposes of statistical work

  3. Sources • World Bank • UN Statistical Commission • UNDP • Eurostat/European Commission

  4. Key elements of poverty monitoring • Key poverty indicator: headcount (and associated measures) is monitored over time • There is (are) regular representative survey (s) done with certain intervals (frequency) • Which allows comparisons over time and with other sources of data – e.g. SNA (consistency) • And in addition to income/consumption poverty contains other policy relevant data • That this is transparent process to derive poverty indicators (access) • There is officially established consistent poverty line which allows comparisons (regularized) • Key elements of poverty monitoring are identical to key elements of good Household survey system

  5. Does it have to be official? • Most poverty indicators in the world are based on surveys conducted by the Government statistical agencies • But a lot of data are supplied by surveys conducted outside statistical offices (often with state support) • What matters is quality of data and transparency of their use

  6. General aspects of quality • Representativeness: surveys are nationally representative and, for larger countries, regionally/sub-nationally representative. • Integrativeness: data on different dimensions of living standards are available from the same survey so as to permit a multidimensional analysis. • Regularity: surveys follow a regular, predictable, preferably annual cycle • Consistency: surveys are comparable over time and with other data • Accessibility: unit record data are publicly available according to a transparent, rules-based system.

  7. Representativeness, frequencyand cost • Representative data requires proper sampling • Larger surveys allow collect representative data for more units / breakdowns • But larger surveys are more difficult/expensive to do frequently • It is hard to get an optimal balance • But it has to be a balance

  8. Household Sample Sizes

  9. Household sample size in EU-SILC - cross-sectional component Household sample size for 27 countries: 127.000 2250 Iceland BelgiumGreeceCzech Rep Hungary 3000 Malta 4750 CyprusLuxembourg 3250 Netherlands Estonia 3500 5000 Ireland LatviaNorway Slovenia Poland 6000 3750 6500 Spain Finland Lithuania France Italy 7250 4000 UnitedKingdom Denmark Slovakia 7500 4250 AustriaPortugalSweden Germany 8250 4500

  10. Consistency • Comparability over time • Comparability with other sources of information • Copnmparabiliity with other countries

  11. Consistency: survey and macro (1) WORLD ECA • Global relationship between survey and NA means holds for ECA; quality problems may be on both sides • Data show a consistent relationship between macro and survey data at the country level Source: Angus Deaton Measuring poverty in a growing world 2004 Note: ECAPOV definition of consumption (no health, durables and rental)

  12. Consistency: Survey and macro (2) WORLD WITHOUT ECA (43 countries, 115 spells) ECA (15 countries, 43 spells) • On average growth rates as measured by survey data in ECA (and globally) are robust to the measurement problems • Tight correlation between changes in household consumption from SNA and survey mean consumption (0.75) • But outliers GSM=-.002+.776GPC R2= 0.354 GSM=-0.634+0.834GPC R2=0.228 Note: $ 2.15 at 2000 PPP as a poverty line Source: M. Ravallion Measuring Aggregate Welfare In Developing Countries: How Well Do National Accounts And Surveys Agree?

  13. Coverage/integrativeness • Covering different dimensions of living standards at once • Covering mobility at the individual level • Compatible with other sources of information • Personal ID • GSD • Info on HH networks

  14. Why coverage is important? Poverty is linked to deprivation in other dimensions Overlapping Poverty Dimensions

  15. EU-SILC : domains coveredHousehold information Housing Dwelling type, tenure status and housing conditions (X,L)Amenities (X)Housing costs (X) Income (X,L)Total household income (gross and disposable)Gross income at component level Basic data (including degree of urbanisation (X,L) Social exclusion Housing and non-housing related arrears (X,L)Non-monetary deprivation indicators (X,L)Physical and social environment (X) Labour information (X)Child care 5

  16. EU-SILC: domains covered - Personal information Basic data (X,L) Income (X,L)(gross personal income,total andcomponents HealthHealth status and chronic illness and condition (X,L)Access to health care (X) Labour Basic information on current activity and current main job, incl on last main job for unemployed (X,L)Basic information on activity status during income reference period (X)Total number of hours worked on current second/third jobs (X)Detailed labour information (X,L)Activity history (L)Calendar of activities (L) Demographic data (for persons aged under 16 (X,L), for persons aged 16+ (X,L) and for former household members (L)) Education (X,L)(including highest ISCED level attained)

  17. Access • Dimensions: • “Internal” access for stat office staff • Access for international organizations • Access for researchers/institutes • Access for other parts of the Government • Elements • Data dissemination standard (see http://www.surveynetwork.org/home/) • Rules and procedure that enable timely access to anonymized micro datasets • Capacity/culture

  18. National poverty lines around 2000, in PPP

  19. Background • Recent history of poverty monitoring • Systematic data collection effort for poverty monitoring initiated within the last few years, with technical and financial support from the donor community: • Albania (2002), Bosnia and Herzegovina (2001), Kosovo (2000), FYR Macedonia (1997), Montenegro (2002), Serbia (2002) • The first Poverty Assessments produced subsequently

  20. Institutional Arrangements • Data collection and poverty monitoring typically led by the main Statistical Agency, with strong donor support • Main actors in the donor community: the World Bank, Italy’s Istituto Nazionale di Statistica (ISTAT), and the UK Department for International Development (DfiD) in two or more countries in the Western Balkans; Statistics Sweden/SIDA in Kosovo and UNDP in Bosnia • Generally very little capacity for poverty monitoring and analysis outside of Government

  21. Poverty monitoring in ECA

  22. Pending Issues in Poverty Monitoring • Sustainability: Generally no provision for the sustained financing of poverty monitoring from central budgetary resources • Access: Access to primary data remains limited in selected countries, constraining evidence-based policymaking and analytical capacity building. • Comparability: The shift from one data source to another (e.g., LSMS to HBS) may hinder the creation of a consistent series of poverty monitoring indicators • Adequacy: HBS, as originally implemented, may not provide sufficient information on broader dimensions of poverty

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