MEASUREMENT AND POVERTY MAPPING

1. UPA Package 1, Module 2 MEASUREMENT AND POVERTY MAPPING

2. POVERTY: Theory, Measurement, Policy and Administration - Statistical Methods in Poverty Estimation -

3. Small Area Estimation Background • Small area estimation has received a lot of attention in recent years due to growing demand for reliable small area estimators • Traditional area-specific direct estimators do not provide adequate precision because sample sizes in small areas are seldom large enough • Sample surveys are used to provide estimates not only for the total population but also for a variety of subpopulations (domains)

4. Small Area Estimation Background • “Direct” estimators, based only on the domain-specific sample data, are typically used to estimate parameters for large domains • But sample sizes in small domains, particularly small geographic areas, are rarely large enough to provide direct estimates for specific small domains

5. Small Area Models • It is now generally accepted that when indirect estimators are to be used they should be based on explicit model that related the small areas of interest through supplementary data such as last census data and current administrative data • An advantage of the model approach is that it permits validation of models from the sample data • Small area models may be broadly classified into two types: area level and unit level

6. Types of Small Area Estimation Models • Area-level Model (Fay and Herriot, 1979) ~ known positive constants i ~ IID N(0, b2) • Unit-level Model (Battese et al., 1988) ~ IID N(0, e2) i ~ IID N(0, b2)

7. Area Level Models Sampling model • Area-specific auxiliary data, , are assumed to be available for the sampled areas as well as the non-sampled areas • A basic area level model assumes that the population small area mean or some suitable function , such as , is related to through a linear model with random area effects , where is the p-vector regression parameters and the ’s are uncorrelated with mean zero and variance

8. Area Level Models • Normality of the is also often assumed • The model also holds for non-sampled areas • It is also possible to partition the areas into groups and assume separate models of the same form across groups Linking model • We assume that direct estimators of are available whenever the area sample size . It is also customary to assume that where and the sampling errors are independent with known

9. Area Level Models • Combining the sampling model with the “linking” model, we get the well-known area level mixed model of Fay and Herriot

10. Unit Level Models • A basic unit level population model assumes that the unit y-values , associated with the units in the areas , are related to the auxiliary variables through a one-way nested error regression model where are independent of and Ni is the number of population units in the i-th area

11. Unit Level Models • The parameters of interest are the total or the means • The above model is appropriate for continuous variables y. • To handle count or categorical (e.g. binary) y variables, generalized linear mixed models with random small area effects, are often used.

12. Spatial Microsimulation Approach • Developed by Guy Orcutt in 1957 • ‘A new kind of socio-economic system’ • Directly concerned with microunits such as persons, households, or firms • Models lifecycle by the use of conditional probabilities • One major objective in spatial microsimulation is the estimation of microdata

13. Spatial Microsimulation Approach • Spatial microsimulation is increasingly applied in the quantitative analysis of economic and social policy problems (Clarke, 1996) • Tax benefit incidence • Income • Housing • Water consumption • Transportation • Health

14. Head of household (hh) Steps 1st 2nd 3rd 1. Age,sex, and marital status (M) of hh head Age: 27 Sex: male M: married Age: 32 Sex: male M: married Age: 87 Sex: female M: divorced 2. Probability of hh head of given age, sex, and M being an owner-occupier 0.54 0.7 0.7 3. Random number (computer generated) 0.542 0.823 0.794 4. Tenure assigned to hh on the basis of random sampling owner-occupied rented rented 5. Next hh (keep repeating until a tenure type has been allocated to every hh) Source: Clarke (1996) Example of Spatial Microsimulation

15. Object Representation of Household Microdata Baseline Characteristics Unobserved Characteristics Computational Objects/ Models Target of Microsimulation

16. Zone Zone Data Set Data Set Description/ Coverage Description/ Coverage System System City City 1997 1997 Family Income and Family Income and • • Household demographics, some Household demographics, some Expenditure Survey (FIES) Expenditure Survey (FIES) housing variables housing variables • • Detailed household incomes and Detailed household incomes and expenditures expenditures • • 4,030 samples for Metro Manila 4,030 samples for Metro Manila Traffic Zone Traffic Zone 1996 1996 Metro Manila Urban Metro Manila Urban • • Selected household demographics Selected household demographics Transportation Integration Transportation Integration • • Member/ household income Member/ household income Study (MMUTIS) Study (MMUTIS) • • 50,000 samples for Metro Manila 50,000 samples for Metro Manila Barangay Barangay 1990 1990 Census of Population Census of Population • • Detailed household and housing Detailed household and housing and Housing (CPH) and Housing (CPH) characteristics characteristics • • No income/employment variable No income/employment variable • • Non Non - - response on housing variables response on housing variables • • All households in 1990 (1,567,665 All households in 1990 (1,567,665 households) households) GIS GIS 1996 1996 MMUTIS Land Use GIS MMUTIS Land Use GIS • • Urban land use zoning map for Urban land use zoning map for entire Metro Manila entire Metro Manila 1997 Building Footprint Data 1997 Building Footprint Data • • Building footprints for most cities Building footprints for most cities Available Household Data Sets

17. City Traffic Zone Barangay Households Different Scales of Analysis

18. Spatial Microsimulation of Informal Households in Metro Manila • Manila City (54 traffic zones, 900 barangays, 1.59 million pop. in 1990, 308,874 households)

19. Initialize base households using 1990 CPH data (age, sex, marital status, and education of household head, household size) Compute occupation Compute economic activity probabilities from rate of household head Occupation Choice Model Assign occupation of household head based on Monte Carlo sampling Compute employment probabilities from Employment Choice Model Assign employment sector of household head based on Monte Carlo sampling Compute employment status probabilities and assign employment status by Monte Carlo sampling Compute bias-adjusted household income function based on employment status Estimate household income based on characteristics of household head Estimate permanent Income of household Compute housing tenure status probabilities and assign housing tenure status by Monte Carlo sampling Compute bias-adjusted housing value function based on tenure status Estimate housing tenure and housing value Informal Sim Structure

20. Simulated Mean Household Incomes Middle Low High Low Low High

21. Ground Truths Smokey Mountain Pandacan Port Area, Tondo Punta

22. Simulated Housing Tenure

23. Simulated Informal Employment

24. Inequality Measures