Living Standards Measurement Study SurveysDevelopment Economics Research Group

The World Bank

Goals of LSMS Surveys Policy-relevant data on welfare Welfare: Money-metric measure, key

facets affecting welfare (multi-topic) Goals:

Determinants of observed social outcomes Measuring Welfare Policy Simulations (ex ante) Evaluating programs (ex post)

Characteristics Complex study:

Household questionnaire Community questionnaire Price questionnaire Facility questionnaire (not common)

Need for quality control 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 Roster Parents of Hhld members Housing, utilities Education Health Labor and Other Income Migration Fertility Credit

Agriculture Non-Agr. Businesses Food Expenditures and

Consumption Other Income Hhld Anthropometrics

Example of topics within one sector: Health

Morbidity (self-reported) Access to health care

services Use of health services Cost of health care Insurance Disability Maternal health

Children: Vaccinations Diarrhea Anthropometric

Time spent Quality of health care Food consumption Access to water and sanitation Smoking, alcohol use

Specific Issues for Gender: Advantages Data collected about and FROM men and women individually All analyses can be done for males and females or controlling for sex Wide range of topics related to welfare included and links studied Surveys are demand driven: designed to produce data relevant to a

country at a particular point in time Being demand driven allows flexibility in questionnaires for meeting

new data/policy demands and/or experimental work WB does not own the data sets but works very hard to ensure public

access to data sets more than half of LSMS can be downloaded from WB Web Site

Focus on longer term collaboration, consistency across surveys Capacity Building- data collection, analysis, use

Specific Issues for Gender: disadvantages

The surveys are demand driven: designed to produce data relevant to a country at a particular point in time

No central planning or funding mechanism, questionnaire content a result of negotiation, not imposed

Each survey reflects country demands, so data are country-specific – more limited comparability than DHS for example

Coverage in space and time is again demand driven- not world coverage or set updates

Sample size- a bit small if interested in rare events Questionnaire breadth can limit depth on specific topics (e.g.

asset issue)

CLSP: a Comparative Data Base 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 PerspectiveDevelopment Data Group

The World BankDecember 11, 2007

Vulnerability Broadens the definition of poverty to include risk Risk of poverty: probability of becoming poor in the

future By quantifying vulnerability:

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?

Women shoulder a disproportionate burden of poverty

Because of gender inequality in access to resources, opportunities and outcomes: 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

Quantifying poverty and risk outcomes for female and male members of the household

Advantages: Useful in quantifying and interpreting discrimination within

households Useful for poverty alleviation policies.

Disadvantages: Data issues: Little gender disaggregated data on consumption and

food expenditures

Incorporate gender: how?

Approach 2: Inter-household Analysis Compare poverty and risk outcomes of female-and male-

headed households Advantages:

Reliable data by headship available on income and consumption Useful as a starting point for quantifying vulnerability by gender

Disadvantages: Concept of female headship coming under increasing criticism as a

useful category.

Challenges and constraints Since vulnerability is an extension of poverty, subject to

same limitations as income poverty measure Poverty lines subjective Vulnerability measures differ depending on poverty measure used

Analysis based on strong assumptions That we can define risks faced by households and individuals using

mathematical functions

Lack of reliable panel data

Future directions Refine definition of vulnerability Improve data collection

Collecting Gender-Disaggregated Data on Access to Economic Assets

Gender and Development Unit

The World Bank

World Bank work on assets

Gender and Development Unit program on access to assets Workshop Spring 2007

Research Department LSMS group Inclusion of individual-level questions in Afghanistan and

Tajikistan LSMS Surveys

WBI (in collaboration with UNECE) Methodological guidelines “Gender and Access to Assets”

Access to assets - Relevance

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: Vulnerability Income generating potential and poverty Bargaining power …

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)

Tangible assets: Real: housing, land, livestock, businesses, equipment,

tools, vehicles, consumer durables. Financial: cash, accounts, stocks, pensions. Natural resources: water, trees, etc.

Intangible assets: Human capital, intellectual abilities, reputation, social

capital (networks, information, etc.)

Assets - Definition

Individual- vs. household-level information

Similarly to income and consumption, assets can be distributed unevenly across household members;

Ad-hoc surveys and qualitative data indicate that: Women are less likely than men to own and control assets,

especially productive assets; Men and women often own different types of assets; Channels for acquiring assets differ by gender; 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);

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:Land Ownership: Women are less likely to own land, and their plots are likely to Women are less likely to own land, and their plots are likely to be smaller and of poorer quality than men’s.be smaller and of poorer quality than men’s.

In Cameroon, over 75% of the agricultural work is done by women, but In Cameroon, over 75% of the agricultural work is done by women, but women hold less than 10% of land certificates.women hold less than 10% of land certificates.

Housing:Housing: Rarely do surveys asks which household member(s) owns the dwelling Rarely do surveys asks which household member(s) owns the dwelling and/or who has title to the houseand/or who has title to the house

In Nicaragua, women owned 44% of owned residences, men owned 50%, In Nicaragua, women owned 44% of owned residences, men owned 50%, and 6% were held jointly by both spouses (2001 ENHMNV).and 6% were held jointly by both spouses (2001 ENHMNV).

Livestock Ownership:Livestock Ownership: A general pattern is for men to own large livestock A general pattern is for men to own large livestock (particularly work animals) while women own smaller livestock and yard animals.(particularly work animals) while women own smaller livestock and yard animals.

In Nicaragua, men owned 23% of livestock and women owned 37%. In Nicaragua, men owned 23% of livestock and women owned 37%. However, women were more likely to own pigs and poultry, while men were However, women were more likely to own pigs and poultry, while men were more likely to own donkeys, horses and cattle.more likely to own donkeys, horses and cattle.

Gender Dimensions of Asset Ownership

Business Assets:Business Assets: Not much research has focused on gender gaps. Not much research has focused on gender gaps. Research in Ghana found that although women were more likely to own 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 business assets, the mean value of the assets owned by men was much higher than that owned by women.higher than that owned by women.In Nicaragua, women owned 49% of household businesses and men 37%.In Nicaragua, women owned 49% of household businesses and men 37%.

Financial Assets:Financial Assets: Research on pensions reveals that men are more likely to hold Research on pensions reveals that men are more likely to hold jobs that provide access to pensions, and among those with pensions, average jobs that provide access to pensions, and among those with pensions, average pensions are larger for men than for women. pensions are larger for men than for women.

There has been little research on other financial assets owned by men and There has been little research on other financial assets owned by men and women.women.

Other Physical Assets:Other Physical Assets: Women and men own other physical assets such as Women and men own other physical assets such as vehicles, jewelry and culturally specific items. These types of assets may differ by vehicles, jewelry and culturally specific items. These types of assets may differ by gender.gender.A UNICEF/IFPRI, UDS survey in Savelugu and Nanton Districts in Ghana 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 showed that men were more likely than women to own bicycles, cars or motorcycles.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.

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 Individual rights such as…

Ownership data; whether a formal title exists; whether the asset is owned individually or jointly;

Management of the asset (“access”, “control”, “decision making”):

• 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

Most data on assets are collected only at the household level: 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

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

Which survey is best? Multipurpose surveys Ad-hoc surveys Panel data

It depends on what we want to measure! 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

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.