Area models for estimating poverty and labor market indicators
Workshop material
1
Session 1- Census and satellite information
1.1
Use of Satellite Imagery and SAE
1.2
Satellite Image Data Sources
1.3
Google Earth Engine
1.4
Installing rgee
1.4.1
Downloading Satellite Information
1.4.2
Night Lights
1.4.3
Crop Cover
1.4.4
Urban Cover
1.4.5
Human Modification
1.4.6
Average Travel Time to Hospital
1.4.7
Average Travel Time to Hospital by Non-Motorized Vehicle
1.5
Population and Housing Censuses
1.5.1
Mapas de las variables con información censal.
2
Session 2- Generalized Variance Function
2.1
Graphical Analysis
2.2
Variance Model
3
Session 3- Fay Herriot Model - Poverty Estimation
Area Model for Poverty Estimation
Optimal Predictor of
\(P_d\)
3.1
Estimation Procedure
3.2
Preparing the supplies for
STAN
3.2.1
Results of the model for observed domains.
3.3
Area models - ArcSin transformation.
3.3.1
Estimation procedure
3.3.2
Preparing Inputs for
STAN
3.4
Benchmark Process
3.4.1
Results Validation
3.5
Poverty Map
4
Session 4 - Area model for labor market statistics
4.1
Definition of the Multinomial Model
4.2
Loading Libraries
4.3
Reading the survey and direct estimates
4.4
Domain Selection
4.5
Modeling in
STAN
4.6
Preparing supplies for
STAN
4.7
Model validation
Fixed effects
Random effects
4.7.1
Posterior predictive distribution
4.8
Parameter estimation.
4.9
Estimation of Standard Deviation and Coefficient of Variation
4.10
Metodología de Benchmarking
4.10.1
Grafico de validación del Benchmarking
4.11
Labor market maps.
Occupancy Rate
Unemployment Rate
Inactivo
Area models for estimating poverty and labor market indicators
Chapter 1
Session 1- Census and satellite information