Measuring poverty is a first step to the design of effective public policies, however, it is also essential to know where the poor are located. The main objective of this research is to evaluate the spatial heterogeneity of the factors that influence monetary poverty for each district in Peru. We apply a Geographically Weighted Regression (GWR) approach, which allows us to capture the non-stationarity of the hidden data and to provide coefficients for each district, unlike the OLS model. This research mainly uses the Poverty Map and the Population and Household Census of Peru, both from 2007 and 2017. The overriding findings of our results indicate that female headship, secondary education, electricity, and sanitation services are directly associated with poverty reduction at the local level. For 2007, significant effects are mainly concentrated in the districts of Pasco, Lima and Cajama...

Measuring poverty is a first step to the design of effective public policies, however, it is also essential to know where the poor are located. The main objective of this research is to evaluate the spatial heterogeneity of the factors that influence monetary poverty for each district in Peru. We apply a Geographically Weighted Regression (GWR) approach, which allows us to capture the non-stationarity of the hidden data and to provide coefficients for each district, unlike the OLS model. This research mainly uses the Poverty Map and the Population and Household Census of Peru, both from 2007 and 2017. The overriding findings of our results indicate that female headship, secondary education, electricity, and sanitation services are directly associated with poverty reduction at the local level. For 2007, significant effects are mainly concentrated in the districts of Pasco, Lima and Cajama...

Measuring poverty is a first step to the design of effective public policies, however, it is also essential to know where the poor are located. The main objective of this research is to evaluate the spatial heterogeneity of the factors that influence monetary poverty for each district in Peru. We apply a Geographically Weighted Regression (GWR) approach, which allows us to capture the non-stationarity of the hidden data and to provide coefficients for each district, unlike the OLS model. This research mainly uses the Poverty Map and the Population and Household Census of Peru, both from 2007 and 2017. The overriding findings of our results indicate that female headship, secondary education, electricity, and sanitation services are directly associated with poverty reduction at the local level. For 2007, significant effects are mainly concentrated in the districts of Pasco, Lima and Cajama...