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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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?

10. 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

11. 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

12. 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

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

14. 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

15. 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)

16. 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

17. 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

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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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

27. 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)

28. 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

29. 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

30. 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