Multidimensional Poverty in Colombia: Identifying Regional Disparities using GIS and Population Census Data (2005)

Transcription

1 Multidimensional Poverty in Colombia: Identifying Regional Disparities using GIS and Population Census Data (2005) Laura Estrada Sandra Liliana Moreno December 2013 Aguascalientes, Mexico

2 Content 1. Spatial dimension of poverty and regional disparities 2. Methodology 3. Results 4. Conclusions

3 Content 1. Spatial dimension of poverty and regional disparities 2. Methodology 3. Results 4. Conclusions

4 Short description Approximately 47 million of Colombians 75% living in urban areas 25% living in rural areas The Andean region is the most populated region 70% of Colombians The main capital cities in the: Andean region are Bogotá, Medellín, Cali and Bucaramanga. Caribbean region are Barranquilla, Santa Marta and Cartagena. Poverty levels have decreased but the poverty rate of the rural area is about twofold the poverty rate of the urban area.

5 For a more disaggregated poverty assessment: GIS + Census Data Spatial dimension of poverty and regional disparities Is multidimensional poverty randomly distributed across geographic space or does it have a spatial pattern? Are there and can we locate clusters of high and low multidimensional poverty rates? Are results different when analyzing municipal, urban and rural levels?

6 Content 1. Spatial dimension of poverty and regional disparities 2. Methodology 3. Results 4. Conclusions

7 Census Data 2005 Sample Minimum Maximum Range Average Standard Deviation MPI Total MPI Urban MPI Rural

8 Multidimensional Poverty Index (MPI) Educational Conditions 0,2 0,2 0,2 0,2 0,2 Childhood and youth Work Health Housing and Public services Schooling School attendance Long-term unemployment Healthcare access Access to drinking water Literacy At the right level Formal employment Healthcare access given a necessity Sanitation 0,1 Access to childcare services 0,1 0,1 Flooring No Child labour Exterior walls 0,05 No Overcrowding 0,04

9 Spatial Analysis Thematic Maps Quantile and standard deviation maps. Global trends in data Trend Analysis Spatial autocorrelation and cluster location Moran s I Anselin local Moran s I

10 Content 1. Spatial dimension of poverty and regional disparities 2. Methodology 3. Results 4. Conclusions

11 Quantile maps Fig 1 Quantile Map MPI 2005, municipal level. Fig 2 Quantile Map MPI 2005, rural level. Fig 3 Quantile Map MPI 2005, urban level.

12 Standard deviation maps Fig 1 Standard Deviation Map MPI 2005, municipal level. Fig 2 Standard Deviation Map MPI 2005, rural level. Fig 3 Standard Deviation Map MPI 2005, urban level.

13 Trend analysis Municipal level Rural level Urban level

14 Global spatial autocorrelation Global Moran's I MPI Municipal MPI Rural MPI Urban Moran's Index 0, , ,39589 Expected Index -0, ,0009-0, Variance 0, , , Z-Score 32, , , P-Value* 0, , ,000000

15 Local spatial autocorrelation: Anselin local Moran s I Fig 1 Clusters and Spatial outliers MPI 2005, municipal level Fig 2 Clusters and Spatial outliers MPI 2005, rural level Fig 3 Clusters and Spatial outliers MPI 2005, urban level.

16 Content 1. Spatial dimension of poverty and regional disparities 2. Methodology 3. Results 4. Conclusions

17 Conclusions Multidimensional poverty demonstrates positive spatial autocorrelation, meaning poorer municipalities tend to cluster around each other. At the total and rural levels, Welfare Hubs can be found in the main capital cities and surrounding municipalities Welfare Paths can be found at an urban level related to the main capital cities which enclose the municipalities between them. High poverty rate clusters can be found in the south of the country and along the Pacific and Caribbean coasts. - These clusters are discontinuous and show a notable difference in poverty rates between total municipal, urban, and rural levels. Spatial outliers are identified especially in the Caribbean coast and the south of the country.

18 Conclusions Further research should address the evolution of multidimensional poverty spatial patterns, outliers, and clusters using data from the next Population Census, as well as explanatory variables (road infrastructure, ethnic groups, natural resources, etc.). The spatial dimension of multidimensional poverty and regional disparities study leads to question spatially differentiated impact of socio economic variables such as education, health, labor, infrastructure, fiscal policy; of environmental variables such as climate, biodiversity, soil quality, among others.

19 Contact LAURA ESTRADA ARBELAEZ Organization: DANE Mail address : Carrera 59 No Interior I - CAN Department: DIMPE address : lestradaa@dane.gov.co Phone number: Ext Fax number :