In Peru, a country with an astonishing variety of different ecological areas, including 84 different climate zones and landscapes, with rainforests, high mountain ranges and dry deserts, the geographical context may not be all that matters, but it could be very significant in explaining regional variations in income and welfare. The major question this paper tries to answer is: what role do geographic variables, both natural and manmade, play in explaining per capita expenditure differentials across regions within Peru? How have these influences changed over time, through what channels have they been transmitted, and has access to private and public assets compensated for the effects of an adverse geography? We have shown that what seem to be sizable geographic differences in living standards in Peru can be almost fully explained when one takes into account the spatial concentration of households with readily observable non-geographic characteristics, in particular public and private assets. In other words, the same observationally equivalent household has a similar expenditure level in one place as another with different geographic characteristics such as altitude or temperature. This does not mean, however that geography is not important but that its influence on expenditure level and growth differential comes about through a spatially uneven provision of public infrastructure. Furthermore, when we measured the expected gain (or loss) in consumption from living in one geographic region (i. e. , coast) as opposed to living in another (i. e. , highlands), we found that most of the difference in log per-capita expenditure between the highland and the coast can be accounted for by the differences in infrastructure endowments and private assets. This could be an indication that the availability of infrastructure could be limited by the geography and therefore the more adverse geographic regions are the ones with less access to public infrastructure. It is important to note that there appear to be non-geographic, spatially correlated, omitted variables that need to be taken into account in our expenditure growth model. Therefore policy programs that use regional targeting do have a rationale even if geographic variables do not explain the bulk of the difference in regional growth, once we have taken into account differentials in access to private and public assets
