Using satellite data to track socio-economic outcomes: a case study of Namibia

Efforts to improve the livelihoods of the poor in sub-Saharan Africa are hindered by data deficiencies. Surveys on socio-economic outcomes, for example, are generally conducted infrequently and are only statistically representative for relatively large geographic areas. To overcome these data limitations, researchers are increasingly turning to satellites which capture data for small areas at high frequencies. Night lights satellite data has particularly drawn interest and growth in lights have been shown to be a useful proxy for GDP growth (Henderson et al., 2012). However, in poor agricultural regions, night lights data might be less useful in explaining variation in socio-economic outcomes because such regions are generally under-electrified. Daytime satellite data measuring land use and vegetation quality, have been used to model socio-economic outcomes across regions, but no studies have explored whether daytime satellite data can be used to track welfare longitudinally. This paper argues that indicators of vegetation quality can be used to track welfare over time in agriculturally dominant areas. Such indicators are used extensively to predict agricultural yields and thus should correlate with welfare, as agriculture is an important source of income. This paper presents results from a small study in Namibia, that explores whether this is the case. Firstly, it is shown using classification of cropland, that daytime satellite data can identify areas of economic activity where night lights cannot. Secondly the relationship between vegetation quality and welfare is studied. Cross-sectionally, increases in vegetation quality correlate negatively with welfare. This is expected as the poor are more likely to live in rural areas. Within rural areas, however, vegetation quality correlates positively with welfare. This study thus supports the hypothesis that satellite based indicators of vegetative health can be used to track welfare over time in areas where night lights are not present.

I32, O13, Q56

Satellites, Night Lights, Normalised Differenced Vegetation Index, Agriculture, Poverty, Namibia