cite as: Duque, J. C.; Royuela, V.; Norenña, M. (2010) A stepwise tool for spatial delimitation of poverty areas. Medellín (Colombia) as a case study. In Seminar of Spatial Econometrics in honor of Doctor J.H.P. Paelinck, Oviedo, Spain. 22 October.

1. A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY. Juan Carlos Duque (RISE) Vicente Royuela (AQR-IREA) Miguel Noreña (RISE) 2010

5. A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Objective Detect poverty in Medellín. As usual, the point is how to do it We present a stepwise procedure that efficiently delimits intra-urban slums. We aim supersede the use of administrative regions using analytical regions. We use the max-p region (available at Geogrouper ) to build analytical regions

6. A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Case of study: Medellín 2007 data (Quality of Life Survey of Medellín). 184 questions on nine dimensions: housing, households, demography, education, social security, income and employment, participation, gender and family violence, and nutrition. The survey includes 21,861 households , representing 79,912 persons

7. A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Case of study: Medellín

10. A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY The alternative: using analytical regions Analytical homogeneous regions seek to divide the area of study into regions that are homogeneous in terms of a set of socioeconomic variables that are considered relevant to the phenomenon under study (Fischer, 1980; Duque et al. 2006). Analytical regions not only provide an alternative to deal with several statistical challenges, but also ensure that specific public policy actions implemented in a region will have an homogeneous impact throughout the region (Fischer, 1980).

11. A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Before applying the Max-P, a list of aspects needs to be solved. 0. Choosing the relevant information 1. Small numbers problem 2. Too many variables 3. Selecting the neighbourhood structure 4. Selecting the minimum sample size

12. A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Global Empirical Bayes This technique adjusts the raw rate in each area, ri , by taking into account information of the rest of the sample. The adjustment procedure operates in such a way that the raw rates that come from small populations are moved towards the overall mean, while the raw rates that come from large populations remain unchanged For each area i, the new estimated rate,  i, is calculated as follows: where…

13. A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Factor analysis We perform the factor analysis on the spatially smoothed rates of the socioeconomic variables. We choose factors with eigen value above 1 and summing up to 63% of total variance. All of them has some spatial pattern .

14. A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Factor analysis We perform the factor analysis on the spatially smoothed rates of the socioeconomic variables. We choose factors with eigen value above 1 and summing up to 63% of total variance. All of them has some spatial pattern .

15. A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Factor analysis We perform the factor analysis on the spatially smoothed rates of the socioeconomic variables. We choose factors with eigen value above 1 and summing up to 63% of total variance. All of them has some spatial pattern .

16. A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Factor analysis We perform the factor analysis on the spatially smoothed rates of the socioeconomic variables. We choose factors with eigen value above 1 and summing up to 63% of total variance. All of them has some spatial pattern .

17. A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Factor analysis We perform the factor analysis on the spatially smoothed rates of the socioeconomic variables. We choose factors with eigen value above 1 and summing up to 63% of total variance. All of them has some spatial pattern .

18. A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Neighbourhood structure Queen-based spatial contiguity matrix (W) Minimum population threshold We fix the requirement of at least one hundred surveyed households per regions (100)

20. A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Building analytical regions Analytical regions coming from Max-P: 139 regions (between 16 communes and 243 neigh.)

23. A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Delineation of marginal areas We build the spatial clusters using two complementary measures: Local Moran’s I, and Getis Gi*: We identify statistically significant clusters using spatial permutation and we present two different significance level s: the usual 5% and the False Discovery Rate (FDR) (see Caldas de Castro and Singer, 2006)

24. A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Raw data Spatially Smoothed data Analytical Regions Local Moran’s I, Getis Gi*: Raw data Spatially Smoothed data Analytical Regions

25. A STEPWISE TOOL FOR SPATIAL DELINEATION OF MARGINAL AREAS. MEDELLÍN (COLOMBIA) AS A CASE STUDY Concluding… Working with analytical regions helps to find where is poverty and then, where should we concentrate public efforts. Finding different clusters should lead policy makers to think on the different nature of poverty in these areas. Probably causes of poverty may differ in space, and consequently different policies should be applied. Our procedures can be easily applied in many other areas (remember.. Geogrouper!!)