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In this paper, we try to understand whether measures of GDP per capita taken from national accounts or measures of mean income or consumption derived from household surveys better proxy for true income per capita. We propose a data-driven method to assess the relative quality of GDP per capita versus survey means by comparing the evolution of each series to the evolution of satellite-recorded nighttime lights. Our main assumption, which is robust to a variety of specification checks, is that the measurement error in nighttime lights is unrelated to the measurement errors in either national accounts or survey means. We obtain estimates of weights on national accounts and survey means in an optimal proxy for true income; these weights are very large for national accounts and very modest for survey means. We conclusively reject the null hypothesis that the optimal weight on surveys is greater than the optimal weight on national accounts, and we generally fail to reject the null hypothesis that the optimal weight on surveys is zero. Using the estimated optimal weights, we compute estimates of true income per capita and $1-a-day poverty rates for the developing world and its regions. We obtain poverty estimates that are substantially lower, and that fall substantially faster, than those of Chen and Ravallion (2010) specifically or of the survey-based poverty literature more generally. Our result is mainly driven by the finding that economic growth has been higher in poor countries than the surveys suggest. We also find that living standards in the developing world have risen faster, and the world income distribution has become more equal, than would be suggested by surveys alone. Additionally, we provide evidence that national accounts are good indicators of desirable outcomes for the poor (such as longer life expectancy, better education, and access to safe water), and we show that surveys appear to perform worse in developing countries that are richer and that are growing faster.