Human Development Indicators Human Development Community of Practice Meeting Bratislava, 19 May 2008

What is HDI? Paradigm shift: people as mean of development vs people as end of development Simple and straightforward summary measure of development to replace GDP HDI a composite index, from 0 to 1 mix both input and output indicators three major areas of development—health, knowledge and decent standards of living not comprehensive measure of development relevant for both developing and developed countries adequate quality of data, for cross-country comparisons

Composite indices Aggregate information from different (incomparable directly) areas Indexing absolute values Issues of discretionary choice: Approaches to aggregation of individual components Weights of individual components Min/max values

Human Development Index Human Development Index (HDI) Dimensions Long and healthy life Knowledge Descent standard of living Dimensions index Life expectancy index Education index GDP index Indicator Life expectancy at birth Adult literacy rate GDP per capita (PPP US$) Gross enrollment ratio

Other Human Development Indices GDI - Gender-related development index average achievement adjusted to gender inequalities focus of ‘functionings’ not opportunities GEM - Gender empowerment measure gender inequality in three dimensions focus on opportunities, not ‘functionings’ HPI - Human Poverty Index deprivations in the basic dimensions of human development HPI1 - for developing countries HPI2 - for developed countries

GDI - Gender-related development index average achievement adjusted to gender inequalities ε—aversion to inequality Estimating female and male earned incomes Total GDP (PPP US$) Ratio female to male non-agriculture wage Female and male share of economically active population

GEM - Gender empowerment measure Gender inequality in three dimensions economic participation and decision-making political participation, and decision making; and power over economic resources Indicators Women’s and men’s share of parliamentary seats Women’s and men’s percentage shares of positions as legislators, senior officials and managers Women’s and men’s percentage shares of professional and technical positions. Estimated female and male earned incomes

Human Poverty Index Human Poverty Index 1 Human Poverty Index 2 People not expected to survive to age 40 Adult illiteracy rate (% age 15 and above) Population without access to safe water Share of under-weight children under age five Human Poverty Index 2 People not expected to survive to age 60 Share of people who are functionally illiterate Long-term unemployment (as % of labour force) Population below income poverty line α is ‘attention to deprivation’—the higher is α, the greater weight of dimension with higher deprivation

Problems with composite indices Bringing together “apples and oranges” Indexing absolute values – choice of min and max thresholds Reliability of data inputs GDP value fluctuates depending on PPP Literacy – from census to census Enrollment – different meaning for different educational levels Depends on the analytical interpretation What does an “HDI value” mean? What does a rank mean?

Data and indicators Data Indicator the status of given phenomenon reflected in number does not mean much out of a context example: number of unemployed; income earned by person; household expenses for food; number of people with flu Indicator instruments that show the status and tendency of a given phenomenon puts data in a context and extracts out of it its meaning used to show progress or regress vis-à-vis certain targets combination of at least two sets of data example: unemployment rate; increase in income earned; share of food in household expenditures; morbidity rate

Indicators based monitoring chains Intermediate Final Input Output Outcome Impact Financial, physical resources Goods and services produced by inputs (classrooms built, textbooks provided) Access to, use of, and satisfaction with services (enrolment, repetition, dropout rates) Effect on dimension of well-being (literacy) Plus sustainability and positive externalities

Examples of indicator types Quantitative Qualitative Input Expenditure on primary education Adequacy of the curriculum Output Number of primary school teachers Quality of teaching atmosphere in the classroom Outcome Enrolment and dropout rates Satisfaction with teaching methods Impact Literacy Change in perception of empowerment and poverty status Both quantitative and qualitative indicators can take any of the following categories: input, output, outcome, and impact. Input: Expenditure on primary education measures the amount, in the appropriate currency and over a defined time period, put into the primary education system. The curriculum is another type of input, but there is no way to measure it directly. One possible indicator is an assessment of its adequacy, for example, by the teachers using it. Output: Part of the financial resources invested is used to pay the teachers employed. Therefore, the number of teachers in service can be considered a direct output of those resources. The quality of teaching is also an output resulting from a number of inputs: expenditure, teacher training systems etc. Outcome: Numbers of children enrolled and the dropout rate are both outputs: they depend, at least in part, upon the quantity and quality of facilities provided. Satisfaction with teaching methods is another type of output, but it is not directly measurable. Impact: One of the ultimate aims of primary education is to improve the literacy of the population. Though with some difficulties, this can be tracked directly. Education is also a means to empower people and affects their ability to participate in the workforce. How people perceive these changes is also an important impact. Both empowerment and perception can not be directly measurable. However, descriptive measurements expressed in the form of non-numeric categories can provide valuable insights. Adapted from: Selecting Indicators, PPT presentation, Aline Coudouel. World Bank Group

Sources of data Administrative or routine data Census data Survey data Surveillance data

Administrative (or routine) data Generated as a byproduct of events and processes Primary purpose is management of processes Event triggers data production Summary and/or dissemination occurs later (but usually within one or two years) Examples: Registration of birth Immunization

Administrative sources Vital registration (births, deaths, etc) Health systems (immunization rates, mortality rates, maternal health data, etc.) Education registries (Enrollment and completion data, student-teacher ratio, etc.) Employment registries (numbers employed, industry, level of participation) Business (Industry, sector, size)

Administrative sources: advantages Less expensive than surveys, censuses (provided it already exists and they usually don’t in areas or countries that most need them) Relatively up to date (usually available within one to two years after event) Useful for short to medium term policy development Often produced by agencies who are stakeholders in the policy process, e.g., health providers, schools, industry bodies, so incentive to participate Good source for small-area disaggregated data

Administrative sources: disadvantages Very expensive to set up Coverage may be insufficient or biased Limited set of information collected Put in one basket input and output indicators (some data may depend upon uptake of services) May measure service provision rather than demand, and uptake rather than impact Numbers may be inflated or missing in some areas (female infant mortality, custom marriages) People may be reluctant to register

Censuses Collect data from every unit in the population 100% coverage (in theory) Expensive Time consuming

Population census Identify each member of the population Collect certain basic data about them age, gender, location, etc. Modules to collect data on specific topics may be added Normally about every 10 years Modeling methods used to generate population estimates between censuses Good for small-area poverty mapping

Population census data Advantage Excellent coverage Creates sampling frame for household surveys Disadvantage Potential for some bias – for example, could miss nomadic groups or homeless May be inaccurate due to infrequency Limited data collected Lag before data produced

Establishments censuses Censuses of businesses, hospitals, other organisations Provide a frame for later surveys Collect basic data, as for population census Problems: Smaller or informal establishments often excluded Establishments may change more frequently than households

Sample survey data Advantages Disadvantages Major types Cheaper and quicker than census, conducted more frequent than census, though usually only every 3 to 5 years Can collect wider range of data than census and administrative systems Reduced potential for bias than administrative data Disadvantages Sampling error since coverage < 100% Requires more sophisticated design to ensure consistency and accuracy Major types Household surveys Labor force surveys Perception surveys

Household surveys Usually carried out every 3 to 5 months Reporting usually takes about 1 year after completion of data collection Sample of households drawn, and data collected about each member of the household Focus is on socio-economic and health issues Examples: Multi Indicator Cluster Surveys (MICS) Demographic and Health Surveys (DHS) Living Standards Measurement Study Surveys (LSMS) World Health Surveys (WHS) Core Welfare Indicators Questionnaires (CWIQ) Household budget surveys (HBS) Household income and expenditure surveys

Demographic Surveillance Systems (DSS) Longitudinal monitoring of sentinel populations (60,000 to 100,000) Follow same people every year through life of survey 100% event registration Advantages Coverage of sentinel pop = 100% Rapid data availability Facilitates targeting and short term monitoring Disadvantages Few large clusters – potential for serious bias Expensive to include many clusters

Data sources compared Characteristic Admin Survey DSS Census Inclusion criterion All ‘noticed’ events Designated units All events in clusters All units Coverage Variable, depending upon system % coverage specified Coverage of clusters only ~100% coverage Bias May be biased Designed to minimize bias Urban/rural included, but may not be sufficiently representative Lack of coverage may lead to some bias

Data sources compared Characteristic Admin Survey DSS Census Cost Cheap Medium Expensive Time Ongoing, + 1-2 years for reporting 3-5 years + 1 year for reporting Ongoing, report in < 1 year 10 years + 2 years for reporting Potential for Policy/advocacy targeting V good, but limited info, and problem if coverage poor Good, but only for medium to long term V good, but only for clusters and similar populations Good for long term and as input with other data

When data is not there… Proxy indicators Every indicator is a “proxy” of something. In current context, proxy indicators : Reflect those aspects indirectly (its “face value” may mean different things) Are contextually linked to certain aspects of reality Highly dependent on contextual interpretation, need broader background Can be used for estimates, perhaps for indices Have advantages and problems that should be clearly stated Both quantitative (“hard statistics”) and qualitative (“soft” data but also quantifiable)

Examples of proxies – poverty Quantitative: Food share of household expenditures Level of outstanding payments per category per household (HH member) Eligibility for bank loan (share of approved loan applications of all submitted) Usage of dental services (per capita in municipality) Qualitative (perception based) Can you afford… Where spend vacations

Metadata – data on data Provides info on how data were collected, when, by whom Give a clue on potential for bias Assessment of quality of data Tells how data items are defined, what methodology was used Confirm definitions, facilitate decisions about compatibility of data from different sources Guides and validates the interpretation of data and their indicators

Abuse of indicators Wrong indicator Wrong interpretation Comparing unlike scales (for example, comparing CPI of two countries which use different consumer baskets) Errors in data or analysis methods Using out of date values Inappropriate extrapolation Ignoring variability