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

Laura Estrada Sandra Liliana Moreno

December 2013 Aguascalientes, Mexico Content

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

1. Spatial dimension of poverty and regional disparities 2. Methodology 3. Results 4. Conclusions Short description • Approximately 47 million of – 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. 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? Content

1. Spatial dimension of poverty and regional disparities 2. Methodology 3. Results 4. Conclusions Census Data 2005

Standard Sample Minimum Maximum Range Average Deviation MPI Total 1114 14.27 100 85.73 69.47 16.39 MPI Urban 1097 13.23 100 86.77 53.54 18.49 MPI Rural 1112 22.99 100 77.01 79.91 13.54 Multidimensional Poverty Index (MPI)

0,2 0,2 0,2 0,2 0,2 Housing and Educational Childhood Work Health Public Conditions and youth services

School Schooling Long-term Healthcare Access to attendance access drinking water

At the right Formal Healthcare access Literacy Sanitation level employment given a necessity

0,1 Access to 0,1 0,1 childcare Flooring services

No Child labour Exterior walls

0,05 No Overcrowding

0,04 Spatial Analysis

Thematic • Quantile and standard Maps deviation maps.

Global trends • Trend in data Analysis

Spatial autocorrelation • Moran’s I • Anselin local and cluster Moran’s I location Content

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

Standard deviation maps

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

Trend analysis

Municipal level Rural level Urban level

Global spatial autocorrelation

Global Moran's I MPI Municipal MPI Rural MPI Urban Moran's Index 0,602393 0,692594 0,39589 Expected Index -0,000898 -0,0009 -0,000912 Variance 0,000335 0,000338 0,000021 Z-Score 32,98314 37,748742 87,328941 P-Value* 0,000000 0,000000 0,000000 Local spatial autocorrelation: Anselin local Moran’s I

Fig 1 Clusters and Spatial outliers MPI 2005, Fig 2 Clusters and Spatial outliers MPI 2005, Fig 3 Clusters and Spatial outliers MPI 2005, municipal level rural level urban level.

Content

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

• Multidimensional poverty demonstrates positive spatial autocorrelation, meaning poorer municipalities tend to cluster around each other.

• At the total and rural levels, 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. 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. Contact

LAURA ESTRADA ARBELAEZ

Organization: DANE Mail address : Carrera 59 No.26-70 Interior I - CAN Department: DIMPE Email address : [email protected] Phone number: 5978300 Ext. 2463 Fax number : 5978399