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More Frequent, More Timely and More Comparable Data for Better Results: The Challenges and How Bank Teams are Responding Washington, April 20 th , 2011 . Cost effective method of collecting quarterly poverty estimates in Peru. Javier HERRERA
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More Frequent, More Timely and More Comparable Data for Better Results: The Challenges and How Bank Teams are Responding Washington, April 20th, 2011 Cost effective method of collecting quarterly poverty estimates in Peru Javier HERRERA Institut de Recherche pour le Développement (IRD-DIAL) herrera@dial.prd.fr
The reasons why poverty is measured provide guidelines for designing household surveys and defining poverty lines & welfare indicator • To compare poverty in different households and in different regions within the country • To compare & monitor poverty indicators over time • To help defining policies to reduce poverty • To evaluate the impact of poverty reduction policies • To compare poverty across countries
The reasons why poverty is measured provides guidelines for designing household surveys and defining poverty lines & welfare indicator • To compare poverty in different households and in different regions within the country • Poverty lines should be welfare-consistent (there would by many poverty lines, mainly due to regional price differences) • Geographic coverage should be national, not just urban (poverty incidence, gap and severity are higher in rural households) • In countries in which the decentralization has taken place, poverty needs to be monitor at a geographic disaggregated level (need for higher sample sizes)
The reasons why poverty is measured provide guidelines for designing household surveys and defining poverty lines & welfare indicator • To compare & monitor poverty indicators over time • Repeated similar household surveys are needed • Methodology for constructing welfare indicator should be the same • Welfare level represented by the poverty line should remain constant
The reasons why poverty is measured provide guidelines for designing household surveys and defining poverty lines & welfare indicator • To help defining policies to reduce poverty • Poverty indicators should be produce timely • The survey should include socio-demographic and economic household characteristics (knowing who are the poor allows to focus on the poor) • Panel data is highly desirable. Analyzing poverty transitions and poverty dynamics allows to identify specific factors associated to moving out of poverty and causes of poverty traps (ie. spatial poverty traps).
The reasons why poverty is measured provide guidelines for designing household surveys and defining poverty lines & welfare indicator • To evaluate the impact of poverty reduction policies • The survey design should ensure the possibility of comparing the “treated” group with a control group before and after the treatment (sample design and questionnaire design).
The Peruvian national Household Survey (ENAHO) Four stages: • 2000 Restoring credibility. Revising poverty lines methodology (poverty consistency, updating sample frame) and publishing new poverty figures within the frame of a participatory process
The Peruvian national Household Survey (ENAHO) Four stages: • 2001-2002 More disagreggated (“Departamental” level) poverty estimates, improving quality control, correcting for non response bias, timely results, participatory process (“informal Poverty Committee”), improving PL methodology, bilateral spatial price deflator, informal sector, multidimensional poverty, fixed panel
The Peruvian national Household Survey (ENAHO) Four stages • 2003-2006 permanent survey (quarterly indicators are allowed, but not published-pending methodological issues) • 2007-2010 rotating panel, new sample based on 2005 Census, using PDA (reducing errors and shortening the time length of data processing), formal Poverty Committee • 2011-… new rotating panel, improved questionnaire (taking into account social programs, conceptual adjustments, etc.,); multilateral regional price index, new poverty line
Why is useful high frequency data collection? • In The ENAHO, there were two purposes for having a permanent survey: • Data collection costs and efficiency: to spread the work implied by the bigger sample size over all months instead of concentrating the field work in 3 months. This also improved quality control (real-time quality indicators) and allowed to have a reduced well-trained supervisors/surveyors team and reduced also fixed costs.
Why is useful high frequency data collection? • The other purpose was to have higher frequency living conditions indicators. Inei published some of them in their (new) quarterly reports. • However, quarterly poverty rates were never published. Why? • Because quarterly poverty rates showed wide –unexpected and unexplained-fluctuations for which the INEI was unable to furnish an explanation (greater seasonal fluctuations in urban than in rural areas).
Quarterly welfare indicator in the ENAHOsurvey • Seasonality of poverty rates is an under-researched topic. Inei was very reluctant to publish a figure that will necessarily be revised by the end of the year. They thought that this will damage their credibility. • There was some seasonality in non-response rates and that there were two kinds of adjustments of expansion (weight) factors: one with partial information and another with full (annual) information and that both figures not necessarily coincide. We need to reconcile adjustments for quarterly and annual non response rates
The Peruvian national Household Survey (ENAHO) • Transparency policy • Microdata and all technical documents are also downloadable (sampling, questionnaires, variable dictionary are readily available for download (3 different access: ANDA, “microdata”, “Poverty results”) through INEI’s web pages • http://www.inei.gob.pe/srienaho/index.htm • http://webinei.inei.gob.pe/anda/ • http://proyectos.inei.gob.pe/DocumentosPublicos/Pobreza/2009/
The Peruvian national Household Survey (ENAHO) National quarterly poverty rates 2006-2009; point estimate and confidence intervals Source: INEI-ENAHO 2004 1st Quart. - 2009 2nd Quart. (unpublished)
The Peruvian national Household Survey (ENAHO) Lima Metrop, quarterly poverty rates 2006-2009; point estimate and confidence intervals Source: INEI-ENAHO 2004 1st Quart. - 2009 2nd Quart. (unpublished)
The Peruvian national Household Survey (ENAHO) Rest urban quarterly poverty rates 2006-2009; point estimate and confidence intervals Source: INEI-ENAHO 2004 1st Quart. - 2009 2nd Quart. (unpublished)
The Peruvian national Household Survey (ENAHO) Rural quarterly poverty rates 2006-2009; point estimate and confidence intervals Source: INEI-ENAHO 2004 1st Quart. - 2009 2nd Quart. (unpublished)
The Peruvian national Household Survey (ENAHO) Source: INEI-ENAHO 2004 1st Quart. - 2009 2nd Quart. (unpublished)
Using the ENAHO for poverty analysis • Herrera, J., F. Roubaud (2007), Urban poverty dynamics in Peru and Madagascar, International Planning Studies, 75(1), 2007, pp.70-95. • De Vreyer, Ph., Herrera, J., S. Mesplé-Somps (2009), Consumption growth and spatial poverty traps : an analysis of the effect of social services and community infrastructures on living standards in rural Peru. In Poverty, Inequality and Policy in Latin America, Klasen S. (ed.), CESifo Series, Harvard: MIT Press, pp.129-155. • Herrera, J., (2009), Reducción de la pobreza urbana y el mercado de trabajo en el Perú: Evolución 2004-2006. In Crecimiento reciente y reducción de la pobreza en el Perú. Una oportunidad que no se puede dejar pasar. Worldbank, Washington DC. pp.32-61.
Using the ENAHO for poverty analysis • Herrera, J., M. Razafindrakoto, F. Roubaud (2008), Poverty, Governance and Democratic Participation in Francophone Africa and the Andean Region, OECD Journal on Development, Special Issue: Measuring Human Rights and Democratic Governance. Experiences and Lessons from Metagora, June, p.99-121. • Herrera, J., M. Razafindrakoto, F. Roubaud (2008), The determinants of subjective poverty: A comparative analysis in Madagascar and Peru. In Poverty, Inequality and Migration Dynamics in Latin America, Stephan Klasen and Felicitas Nowak-Lehmann D., (ed.), Peter Lang Verlag, Frankfurt am Main, p.181-220.