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“Building a ‘fit-for-purpose’ demographic evidence base for the post-2015 agenda”
Neil Fantom, World Bank
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
“Building a ‘fit-for-purpose’ demographic evidence base for the post-2015 agenda” - Neil Fantom 1
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Our goals are to:
- eradicate extreme poverty by 2030
- promote shared prosperity
And do so sustainably
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
“Building a ‘fit-for-purpose’ demographic evidence base for the post-2015 agenda” - Neil Fantom 2
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We are data users
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
“Building a ‘fit-for-purpose’ demographic evidence base for the post-2015 agenda” - Neil Fantom 3
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We are data users
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
“Building a ‘fit-for-purpose’ demographic evidence base for the post-2015 agenda” - Neil Fantom 4
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2015 Global Monitoring Report
“Development Goals in and Era of Demographic Change”
Examines how demographic shifts will alter the course of global development over the SDG period
First flagship report since the 1985 World Development Report on population change
Draws on the July 2015 UNPD population estimates and projections
Joint WB/IMF product, will launch in Lima on October 7
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
“Building a ‘fit-for-purpose’ demographic evidence base for the post-2015 agenda” - Neil Fantom 5
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2015 Global Monitoring Report
Develops a new demographic typology of countries based on their ability to harness demographic dividends
Finds that demographic change presents risk
But with good policies it will be one of the most consequential development opportunities of the SDG period
Calls for development policies to be better informed by demographic evidence
GMR team will present results to EGM on Nov 2-3 on Policy Responses to Low Fertility
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
“Building a ‘fit-for-purpose’ demographic evidence base for the post-2015 agenda” - Neil Fantom 6
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We are data producers
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
“Building a ‘fit-for-purpose’ demographic evidence base for the post-2015 agenda” - Neil Fantom 7
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New $1.90 poverty line using 2011 ICP
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
“Building a ‘fit-for-purpose’ demographic evidence base for the post-2015 agenda” - Neil Fantom 8
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Population data and poverty data
Household sample surveys are used to measure poverty and shared prosperity
To estimate numbers of poor and rates of poverty, need population data for all countries
Population sampling frame is also needed to produce unbiased estimates
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
“Building a ‘fit-for-purpose’ demographic evidence base for the post-2015 agenda” - Neil Fantom 9
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Population data and poverty data
UN WPP 2012 Rev. / WDI 2014 and later
UN WPP 2008 Rev. / WDI 2011 and earlier
UN WPP 2010 Rev. / WDI 2012 and 2013
71m poor
65m poor
64m poor
Poverty rate in 2010 43%
Bangladesh, 2005 to 2015
Census in 2011
UN WPP pre-census estimates significantly higher than post-census estimates
With each revision, number of poor in Povcal changes, even at given poverty rate
Bangladesh not exceptional
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
“Building a ‘fit-for-purpose’ demographic evidence base for the post-2015 agenda” - Neil Fantom 10
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Fit for purpose for the SDGs
Monitoring the SDGs means indicators
Implementing the SDGs means data and statistics for policy and program development and management
Expectations: more indicators – better timeliness – better accuracy – better coverage – more disaggregation – better access and openness...
“Just good enough” is never good enough...!
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
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Local demand for local data is high
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
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Data sources: what matters?
Cost: is it expensive?
Monitoring: can it measure change over short periods?
Scope: can it be used for detailed analysis – are there enough variables?
Coverage: does it cover the population of interest?
Disaggregation: does it measure small groups?
Access: can we get the data?
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
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Household surveys: censuses
Cost: “big-ticket”
Monitoring: infrequent; many countries will do just one in the SDG period
Scope: cross-sectional, but limited variables
Coverage: all population (but can miss some)
Disaggregation: small area
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
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Household sample surveys
Cost: “big-ticket”
Monitoring: varies, but many poor countries have infrequent surveys - 77 lack data to monitor poverty trends in MDG period
Scope: cross-sectional, can have many variables
Coverage: all population (but can miss some)
Disaggregation: difficult for small population groups
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
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Administrative records
Cost: low, usually
Monitoring: good – routine records
Scope: often limited
Coverage: often limited, sometimes biased
Disaggregation: good, usually
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
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“New” data sources from technology
Cost: varies (and access not guaranteed)
Monitoring: good, routine records
Scope: often limited
Coverage: limited, biased (“big data, big bias” – Nate Silver)
Disaggregation: unclear
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
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Improving data sources
5 ideas:
1. Invest in civil and vital registration
2. Leverage technology
3. Archiving, openness, and metadata
4. Combining sources
5. Fix the boundary problem
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
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1. Civil and vital registration
UN EGM on Strengthening the Demographic Evidence Base For The Post-2015 Development Agenda, New York, 5-6 October 2015
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1. Civil and vital registration
SDG targets: 16.9 on legal identity and birth registration, 17.18 on disaggregated high quality data
Vital registration is key demographic data source
110 countries have deficient systems, only 8 African countries have 80 percent birth registration rate by age 5
10 year investment plan: projects starting in a few countries
Using technology, linking with electronic ID scheme, will help
New center of excellence at IDRC, Canada
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2. Use of technology: crowdsourcing
Citizens Mapping Services Linking Citizen Voice to Public Service Provision in Dar Es Salaam
August 2011
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2. Use of technology: crowdsourcing
September 2011
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2. Use of technology: CDRs
Mobile phones can be used to estimate population movements (e.g. call detail records)
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3. Data access and use: don’t have DbHd
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Archive microdata
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Build data time machines
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Build data time machines
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Label your stuff
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Label your stuff
(prefer standard formats like DDI and SDMX, prefer long notes to short notes...)
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4. Combine data sources (a)
Achieving SDG 1 (eliminate poverty everywhere) requires identification of “pockets” of poverty
Conventional data sources not fit for purpose: cannot estimate reliably sub-national poverty and income Small-area estimation methods, combining e.g. censuses and sample surveys, seeing increased application Methods applicable to non-spatially defined population groups and non-income dimensions
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Small area estimation
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Changes in poverty in Vietnam
1999 2009
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Malnutrition in Nepal
Source: Central Bureau of Statistics - Nepal, UN - WFP, The World Bank, 2006
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Targetting programs in Bangladesh
Poverty Market accessibility
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4. Combine data sources (b)
Better use of satellite imagery (e.g. LandScan database possible alternative to census for stage 1 sampling frames)
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5. Better boundaries
Inconsistent geographical coding hampers sub-national statistics, nationally and internationally
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Partnership will help us
Joy’s Law: No matter who you are, most of the smart people always work for someone else.
New opportunities are emerging from the SDGs – e.g. the UN IAEG on SDG monitoring, and the new Global Partnership for Sustainable Development Data launched in September
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