1. Small-area estimation
of income statistics
Coro Chasco Yrigoyen
Ana López García
Título
Instituto Lawrence R. Klein
Department of Applied Economics
Universidad Autónoma de Madrid
C.Chasco & A.López. Auton.Univ.Madrid

2. Small-area estimation methods
Direct method
Sectorial employment matrices
Huge information database, not always very precise
Cannot reflect the underground economy nor non-wages earnings
Indirect estimations
Relation between income and socio-economic variables
Variables available for all the small-area units
Don’t require excessively ardous proceedings
C.Chasco & A.López. Auton.Univ.Madrid

3. Availability of statistical data
Official data: INE data
Supplies provincial income data
in the Regional Accounts, for the period of
Socio-economic indicators
Related to income
Exogenous variables in a multivariate regression model
Available for both the provincial and the small-area level
C.Chasco & A.López. Auton.Univ.Madrid

4. The process Aggregate –ecological- level (provinces)
Dissagregate –microdata- level (municipalities)
Ecological inference or spatial prediction
C.Chasco & A.López. Auton.Univ.Madrid

5. Spatial extrapolation problems
MAUP: some statistics can vary in quantity or even in sign!
Spatial effects can also be different in both aggregate and disaggregate level.
Economic macro and micro theories are not always coincident.
C.Chasco & A.López. Auton.Univ.Madrid

6. Spatial extrapolation solutions
Added information about income distribution in different geographic levels.
Good selection of explicative variables
Exploratory spatial data analysis can detect spatial spillovers and spatial regimes
Spatial econometrics models can specify, estimate and test spatial effects correctly
C.Chasco & A.López. Auton.Univ.Madrid

7. Spatial extrapolation process
The basis: theoretical principles
Selection and manipulation of explicative data
Exploratory spatial data analysis
Econometric estimation of the ecological model
Spatial extrapolation of small-area data
Analysis and validation (?)
C.Chasco & A.López. Auton.Univ.Madrid

8. Theoretical principles
Explicative variables of household income in small areas
1. Educational qualifications
5. Locacional factors
6. Historical factors
7. Sex and age
8. Public sector investment
2. Professional category
3. Economic activity
4. Employment
Household income = wages + non-wages earnings –
– taxes – social security contributions
C.Chasco & A.López. Auton.Univ.Madrid

9. Selection of household income explicative variables
1. Consumption/saving indicators
Telephone lines
Cars
Total electricity use
Bank offices
Average price for a house/m2
2. Employment
Unemployment rates
Occupation rates
Activity rates
Professional category
3. Economic activity
Retailing outlets
Tourism outlets
Industry outlets
4. Others
Educational qualifications
Distance to service centres
C.Chasco & A.López. Auton.Univ.Madrid

10. Exploratory spatial data analysis
Periphery
Core
Periphery
“Wealthy”
Spatial autocorrelation
Spatial heterogeneity: spatial regimes
“Poor”
C.Chasco & A.López. Auton.Univ.Madrid

11. Confirmatory spatial analysis
Estimation and testing of spatial effects in spatial regression models
New specifications
New estimation methods
OLS
C.Chasco & A.López. Auton.Univ.Madrid

12. C.Chasco & A.López. Auton.Univ.Madrid
Spatial model
Spatial lag
C.Chasco & A.López. Auton.Univ.Madrid

13. Spatial extrapolation
C.Chasco & A.López. Auton.Univ.Madrid

14. Analysis and validation
C.Chasco & A.López. Auton.Univ.Madrid