Living Standards
Measurement Study
Surveys
Development Economics Research Group
The World Bank
Goals of LSMS Surveys
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Policy-relevant data on welfare
Welfare: Money-metric measure, key
facets affecting welfare (multi-topic)
Goals:
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Determinants of observed social outcomes
Measuring Welfare
Policy Simulations (ex ante)
Evaluating programs (ex post)
Characteristics
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Complex study:
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Household questionnaire
Community questionnaire
Price questionnaire
Facility questionnaire (not common)
Need for quality control
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Direct informants-all adults provide their own information, data for
children collected for each child
Careful questionnaire design
Small sample size
Concurrent data entry
Training- month not few days
Feedback loop (users-producers-users)- CRITICAL
Topics often covered in LSMS
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Roster
Parents of Hhld members
Housing, utilities
Education
Health
Labor and Other Income
Migration
Fertility
Credit
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Agriculture
Non-Agr. Businesses
Food Expenditures and
Consumption
Other Income Hhld
Anthropometrics
Example of topics within one sector: Health
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Morbidity (self-reported)
Access to health care
services
Use of health services
Cost of health care
Insurance
Disability
Maternal health
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Children:
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Vaccinations
Diarrhea
Anthropometric
Time spent
Quality of health care
Food consumption
Access to water and sanitation
Smoking, alcohol use
Specific Issues for Gender: Advantages
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Data collected about and FROM men and women individually
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All analyses can be done for males and females or controlling for sex
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Wide range of topics related to welfare included and links studied
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Surveys are demand driven: designed to produce data relevant to a
country at a particular point in time
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Being demand driven allows flexibility in questionnaires for meeting
new data/policy demands and/or experimental work
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WB does not own the data sets but works very hard to ensure public
access to data sets
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more than half of LSMS can be downloaded from WB Web Site
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Focus on longer term collaboration, consistency across surveys
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Capacity Building- data collection, analysis, use
Specific Issues for Gender: disadvantages
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The surveys are demand driven: designed to produce data
relevant to a country at a particular point in time
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No central planning or funding mechanism, questionnaire
content a result of negotiation, not imposed
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Each survey reflects country demands, so data are countryspecific – more limited comparability than DHS for example
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Coverage in space and time is again demand driven- not
world coverage or set updates
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Sample size- a bit small if interested in rare events
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Questionnaire breadth can limit depth on specific topics (e.g.
asset issue)
CLSP: a Comparative Data Base
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A database of a subset of variables/indicators from LSMS
Surveys
Goal: increase access to micro-data for users with limited
time or experience in doing micro-data analysis
Focus on comparability across countries, documenting
carefully
Allow ‘on-the-fly’ tables/statistics/regressions within and
among countries (no software needed)
Respecting sampling (weights, disaggregation)
Takes advantage of individually provided data to allow
gender analysis, sex disaggregation
Attention to welfare measures
Measuring Vulnerability
from a Gender Perspective
Development Data Group
The World Bank
December 11, 2007
Vulnerability
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Broadens the definition of poverty to include risk
Risk of poverty: probability of becoming poor in the
future
By quantifying vulnerability:
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Better capture notion of welfare
Greater understanding of poverty dynamics
Supplement poverty estimates by identifying that section
of the population which is not currently poor but would be
if certain risks materialize
Incorporate gender: why?
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Women shoulder a disproportionate burden of
poverty
Because of gender inequality in access to resources,
opportunities and outcomes:
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They might have a higher probability of becoming poor
Their poverty is sometimes invisible
Might experience a longer duration of poverty
Incorporate gender: how?
Approach 1: Intra-household Analysis
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Quantifying poverty and risk outcomes for female and male
members of the household
Advantages:
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Useful in quantifying and interpreting discrimination within
households
Useful for poverty alleviation policies.
Disadvantages:
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Data issues: Little gender disaggregated data on consumption and
food expenditures
Incorporate gender: how?
Approach 2: Inter-household Analysis
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Compare poverty and risk outcomes of female-and maleheaded households
Advantages:
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Reliable data by headship available on income and consumption
Useful as a starting point for quantifying vulnerability by gender
Disadvantages:
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Concept of female headship coming under increasing criticism as a
useful category.
Challenges and constraints
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Since vulnerability is an extension of poverty, subject to
same limitations as income poverty measure
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Poverty lines subjective
Vulnerability measures differ depending on poverty measure used
Analysis based on strong assumptions
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That we can define risks faced by households and individuals using
mathematical functions
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Lack of reliable panel data
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Future directions
 Refine definition of vulnerability
 Improve data collection
Collecting GenderDisaggregated Data on
Access to Economic Assets
Gender and Development Unit
The World Bank
World Bank work on assets
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Gender and Development Unit program on access to assets
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Research Department LSMS group
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Workshop Spring 2007
Inclusion of individual-level questions in Afghanistan and
Tajikistan LSMS Surveys
WBI (in collaboration with UNECE)
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Methodological guidelines “Gender and Access to Assets”
Access to assets - Relevance
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Assets serve multiple functions:
1. Social safety net — strengthening households’ and
individuals’ ability to cope with shocks.
2. Income generating mechanism — providing
productive capacity and additional consumption, ensuring
access to credit, capital, etc.
3. Accumulation and power — increasing the ability of
accumulating more assets and increasing bargaining
power.
Assets can therefore be a measure of:
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Vulnerability
Income generating potential and poverty
Bargaining power …
Assets - Definition
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Assets can be defined as
“stocks of financial, human, natural or social resources
that can be acquired, developed, improved and
transferred across generations”
(Ford Foundation, 2004)
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Tangible assets:
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Real: housing, land, livestock, businesses, equipment,
tools, vehicles, consumer durables.
Financial: cash, accounts, stocks, pensions.
Natural resources: water, trees, etc.
Intangible assets:
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Human capital, intellectual abilities, reputation, social
capital (networks, information, etc.)
Individual- vs. household-level information
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Similarly to income and consumption, assets can be
distributed unevenly across household members;
Ad-hoc surveys and qualitative data indicate that:
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Women are less likely than men to own and control assets,
especially productive assets;
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Men and women often own different types of assets;
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Channels for acquiring assets differ by gender;
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Social norms, intra-family arrangements and civil codes
can limit the ownership and control of assets by women
(i.e. inheritance laws, family laws, and type of marriage);
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Lack of ownership and control of assets results in greater
poverty and economic vulnerability for women, especially
in the event of a divorce or the death of the husband.
Gender Dimensions of Asset Ownership
Land Ownership: Women are less likely to own land, and their plots are likely to
be smaller and of poorer quality than men’s.
In Cameroon, over 75% of the agricultural work is done by women, but
women hold less than 10% of land certificates.
Housing: Rarely do surveys asks which household member(s) owns the dwelling
and/or who has title to the house
In Nicaragua, women owned 44% of owned residences, men owned 50%,
and 6% were held jointly by both spouses (2001 ENHMNV).
Livestock Ownership: A general pattern is for men to own large livestock
(particularly work animals) while women own smaller livestock and yard animals.
In Nicaragua, men owned 23% of livestock and women owned 37%.
However, women were more likely to own pigs and poultry, while men were
more likely to own donkeys, horses and cattle.
Gender Dimensions of Asset Ownership
Business Assets: Not much research has focused on gender gaps.
Research in Ghana found that although women were more likely to own
business assets, the mean value of the assets owned by men was much
higher than that owned by women.
In Nicaragua, women owned 49% of household businesses and men 37%.
Financial Assets: Research on pensions reveals that men are more likely to hold
jobs that provide access to pensions, and among those with pensions, average
pensions are larger for men than for women.
There has been little research on other financial assets owned by men and
women.
Other Physical Assets: Women and men own other physical assets such as
vehicles, jewelry and culturally specific items. These types of assets may differ by
gender.
A UNICEF/IFPRI, UDS survey in Savelugu and Nanton Districts in Ghana
showed that men were more likely than women to own bicycles, cars or
motorcycles.
Implications for data collection
What do we need to know/1
To understand gender patterns of asset
ownership, it is important to know who in the
household owns, uses, and control a particular
asset, as well as the value of the assets.
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We need information on all relevant assets
We need information on all the relevant rights
We need information on the value of the assets
Implications for data collection
What do we need to know/2
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Individual rights such as…
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Ownership data; whether a formal title exists; whether
the asset is owned individually or jointly;
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Management of the asset (“access”, “control”, “decision
making”):
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Ability to use;
Ability to rent;
Ability to use as a collateral;
Ability to bequest;
Ability to keep the income originating from the asset;
Ability to sell; …
Secure tenure on the asset;
Origin of the asset (mode and timing of acquisition)
Why individual-level data are not commonly
collected/1
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Most data on assets are collected only at the household
level:
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Individual ownership/control are usually not the main
focus (in LSMS, Income and exp survey, Household
Budget Surveys, DHS, LFS, MICS, etc);
Conceptually difficult to assign all assets to individuals;
Many questions are needed to disentangle all possible
‘rights’ over the asset;
Additional information is required to fully exploit and
interpret individual-level information on access to assets
(e.g. marital regime)
Why individual-level data are not commonly
collected/2
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While 82%, 81% and 96% LSMS questionnaires
collected household level data on land, livestock
and housing, respectively, only 22%, 7% and 21%
of the LSMS questionnaires did so at the individual
level data.
Over 40% collected data on financial assets,
specifically on pension income and rent, interest
and dividends, but
Fewer LSMS questionnaires collected data on
business and other physical assets at the individual
level.
Potential strategies
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Which survey is best?
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Multipurpose surveys
Ad-hoc surveys
Panel data
It depends on what we want to measure!
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Indicator (gender asset gap)?
Assets as a proxy for vulnerability, income generating
capacity, bargaining power?
Impact evaluation of increased access to assets by
women on a set of outcomes?
Implications for data collection in LSMS
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Review available evidence, research, data, and
experience in assets measurement to decide which
information to collect;
Use existing modules of LSMS strategically to
incorporate individual-level questions;
Prioritize;
Exploit synergies across modules;
Collect complementary information — type of marriage,
marital regime, etc.
Use community questionnaire to complement LSMS
questionnaire.