Transcript Key Uses of Household Survey Data Kathleen Beegle Workshop 17, Session 1b Designing and Implementing Household Surveys March 31, 2009

Key Uses of Household
Survey Data
Kathleen Beegle
Workshop 17, Session 1b
Designing and Implementing Household Surveys
March 31, 2009
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Wide range of uses
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Respond to the demand for data for
performance-based management.
But it is also used for other purposes. Broad
categories of use:
Basic Diagnostics of Living Standards:
MDGs, PRSPs, Poverty assessments,
Poverty Maps
Evaluation/development of programs: PSIA,
Proxy Means
Studies of development processes
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Millennium Development Goals (MDGs)
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Many, but not all, MDG indicators are
captured in an LSMS (IS).
Some can be measures with adaptation (if
there is lack of other data sources for that
indicator, such as IMR/immunization
histories).
Other indicators require either larger samples
than a typical LSMS (MMR), or
administrative/other data.
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MDGs 1 -3
GOALS
1. Eradicate extreme
poverty and hunger
2. Achieve universal
primary education
3. Promote gender
equality and
empower women
INDICATORS
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LSMS/IS
(usually)
Proportion of population below $1 a day
Poverty gap ratio (incidence x depth of poverty)
Share of poorest quintile in national consumption
Prevalence of underweight in children (under five years of age)
Proportion of population below minimum level of dietary energy
consumption
 Net enrollment ratio in primary education
Yes
Yes
Yes
Yes
No
 Proportion of pupils starting grade 1 who reach grade 5
Yes
 Literacy rate of 15 to 24-year-olds
 Ratio of girls to boys in primary, secondary, and tertiary
education
 Ratio of literate females to males among 15- to 24-year-olds
 Share of women in wage employment in the nonagricultural
sector
 Proportion of seats held by women in national parliament
Yes
Yes
Yes
Yes
Yes
No
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MDGs 4 -8
GOALS
4. Reduce child
mortality
5. Improve maternal
health
INDICATORS
LSMS/IS
(usually)
IMR and immunizations: usually not, but possible
 Maternal mortality ratio
 Proportion of births attended by skilled health personnel
No
Yes (births in last
2 years)
n/a
6.Combat
 HIV prevalence among 15- to 24-year-old pregnant women
No
HIV/AIDS, malaria,  Contraceptive prevalence rate
No
and other diseases
 Number of children orphaned by HIV/AIDS
No
 Prevalence and death rates associated with malaria
prevention: Yes
 Proportion of population in malaria-risk areas using effective
treatment: No
malaria prevention and treatment measures
No
 TB indicators
7. Ensure
 Land use, GDP per unit of energy use, Carbon dioxide emissions No
environmental
 Proportion of population with sustainable access to an improved Yes
sustainability
water source, access to improved sanitation, access to secure
tenure
8. Develop a global No for most (with exception of unemployment rate of 15- to 24-year-olds)
partnership for
development
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Secondary school enrollments,
12-18 year olds, Albania 2002
100%
90%
80%
70%
Percent
60%
50%
40%
30%
20%
10%
0%
• In almost all countries
we have a single
statistic: mean
enrollment at the
national level. In this
case it is 61%.
Average
•This is interesting for
monitoring purposes,
but it doesn’t say much
about poverty or other
factors.
Secondary school enrollments,
12-18 year olds, Albania 2002
• In many countries we
100%
90%
80%
Urban
have regional
breakdowns, with
marked contrasts
70%
Percent
Average •The contrast between
60%
50%
40%
Rural
urban and rural rates
emphasizes the
disadvantages faced by
rural communities.
30%
20%
10%
0%
• Other breakdowns
would be useful
Secondary school enrollments,
12-18 year olds, Albania 2002
100%
Female, urban
90%
Male, urban
80%
Male, rural
70%
Female, rural
Percent 60%
Average
50%
40%
30%
20%
10%
0%
Q1
Q2
Q3
Q4
Consumption quintile
Q5
•…With the LSMS
survey we can show
enrollment rates
broken down by
consumption level-and thus understand
an additional
dimension
Poverty Maps
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Not necessarily “maps”, rather highly
disaggregated databases of poverty and
inequality. This disaggregation is usually
spatial.
Demand for poverty maps: geographic
targeting of anti-poverty programs,
decentralization and evidence-based
policy,…
Linking LSMS/IS data to Census data to
impute welfare levels in small areas
www.worldbank.org/povertymapping
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Example: Yunnan Province (China)
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Poverty and Social Impact Analysis:
PSIA
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Analysis of consequences and distributional impacts
of policy interventions/reforms, such as:
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Utilities
Pension reforms
Civil service reform
Ag reform
Education/health (fees, decentralization)
Fiscal (VAT, other taxes)
Land reforms
Etc…
http: //www.worldbank.org/psia
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Tools for PSIA
Types
Direct impact analysis
Examples
 Incidence tools
 Poverty mapping
Behavioral models
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Supply and demand analysis
Household models
Partial equilibrium tools
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Multi-market models
General equilibrium tools
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CGEs
SAM-IO
Macro-micro models
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1-2-3 PRSP
PAMS
Volume of case studies (Coudouel, Dani and Paternostro
2006)
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Example: Malawi ADMARC reforms
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Restructuring marketing functions of
ADMARC (closing loss-making markets for
inputs and outputs)
Objective: Investigate the importance of
ADMARC services for various groups
Data: 1997/98 Malawi Integrated
Household Survey, merged with location of
ADMARC markets and roads network
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Malawi ADMARC reforms
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Proximity has a larger positive effect in remote
areas:
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Impact of markets on maize yields, demand for fertilizer
farm profits and consumption is significant only in remote
areas.
Policy recommendations:
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In areas where the private sector operates and which are
close to a main road, loss-making markets could be closed
without major distributional impacts.
In areas where the private sector does not operate and
where households are isolated, subsidy to loss-making
markets could be justified for their social role.
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Such analysis can also be used for ex-post
evaluation/assessment of policy/events
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Evaluation of impact of Malawi fertilizer
voucher scheme by re-surveying subset of IHS
2004/05 households in 2006/07.
Rapid assessment of impact of Hurricane
Mitch: Shortly after completing the 1998 LSMS
Nicaragua, returned to households in sample
in the areas affected.
Nicaragua Social Fund (FISE) evaluation by
over-sampling FISE areas (booster sample in
1st stage) and linking with 1998 LSMS
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Proxy Means Testing for Programs
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Who should be beneficiaries? How to identify these people?
(Other uses of household survey data that influence program
design: Geographic coverage; level benefits people receive)
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Using household survey data to develop short list of simple
indicator that can be collected in the field to “proxy” the
household income/consumption.
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Compile long list of possible indicators, then use econometrics to
determine which indicators are useful and the weight to place on
these indicators.
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Analysis can be made more accurate by using more specific
geographic regions (urban/rural, districts, etc) but this depends
on the level at which results can be generalized from household
data.
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Proxy Means Testing: Examples
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KIHBS 2007 data being used to create
targeting system for OVC CCT program that
targets poorest 20%.
Panama Red de Oportunidades CCT
program, developed with input from the 2003
Panama Living Standards Survey (Encuesta
de Niveles de Vida, ENV)
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Understanding development & living
standards
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Example from Vietnam: What are the longlasting effects of conflict events, in levels
(poverty), and in changes (growth)?
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Detailed information on health and disability from
VHLSS 2006
Panel structure to look at changes
Data on US military activities (bombing and
herbicide spray applications)
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Summary
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Household surveys like an LSMS can help monitor
welfare, as well as influence the design and
implementation of social policy.
They are also a tool for studying development and
living standards more generally.
The extent of these applications will depend on, among
other factors:
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Comparability with existing data
Developing questionnaire/sample to respond to needs
Coordination with others (country teams, other groups)
Public availability of well-documented data
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Summary
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Using existing data as a source for conducting
an evaluation: Need to understand the sample
design and content of the questionnaire.
There is potential to embed an evaluation into a
household survey, through piggy-backing
(adding questions or a booster sample) or
creating a panel (fielding a subsequent round).
To be discussed later.
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