Living Standards Measurement Study Surveys Development 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 Perspective Development Data Group The World Bank December 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: 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.  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.