Nighttime lights satellite imagery (DMSP-NTL) are now a popular data source among economists. In a sentence, these imagery encompass almost all inhabited areas of the globe, and record the average quantity of light observed at each pixel (nominal size ~1km2) across cloud-free nights for every year, 1992-2012. In under-developed or conflicted regions, where survey or census data at a fine level of spatial and temporal disaggregation are seldom available or reliable or comparable over space or time, NTL and other satellite imagery can be an excellent resource. Recent economics papers have used NTL to study growth of cities in sub-Saharan Africa (Storeygard (2015)), production activity in blockaded Palestinian towns of the West Bank (Abrahams (2015), van der Weide et al (2015)), and urban form in China (Baum-Snow & Turner (2015)) and India (Harari (2015)).
Today in the Upshot, Justin Wolfers heavily criticizes a recent study that has received lots of media attention claiming that child outcomes are barely correlated with the time that parents spend with their children. He writes:
This post is co-authored with Thomas Pave Sohnesen
Since 2011, we have struggled to reconcile the poverty trends from two complementary poverty monitoring sources in Malawi. From 2005 to 2009, the Welfare Monitoring Survey (WMS) was used to predict consumption and showed a solid decline in poverty. In contrast, the 2004/05 and 2010/11 rounds of the Integrated Household Survey (IHS) that measured consumption through recall-based modules showed no decline.
Today’s blog post is about a household survey experiment and our working paper, which can, at least partially, explain why complementary monitoring tools could provide different results. The results are also relevant for other tools that rely on vastly different instruments to measure the same outcomes.
This post is co-authored with Marshall Burke.
One morning last August a number of economists, engineers, Silicon Valley players, donors, and policymakers met on the UC-Berkeley campus to discuss frontier topics in measuring development outcomes. The idea behind the event was not that economists could ask experts to create measurement tools they need, but instead that measurement scientists could tell economists about what was going on at the frontier of measuring development-related outcomes. Instead of waiting for pilot results, we decided to blog about some of these ideas and get inputs from Development Impact readers. In this series, we start with recent progress on measuring (“remote-sensing”) agricultural crop yields from space.
Yesterday the World Bank released their first report on the socioeconomic impacts of Ebola that was based on household data. The report provides a number of new insights into the crisis in Liberia, showing, for example, an unexpected resiliency in agriculture, and broader economic impacts than previously believed in areas outside the main zones of infection. As widely reported, prices for staple crops (such as rice) have jumped well above seasonal increases, but additionally we find an important income effect. We also find the highest prices in the remote southeast of the country, an area that has been relatively unaffected by the disease. The link to the full report can be found here.
Paper 1: List randomization for measuring illegal migration
International mobility of people is measured much less accurately than that of goods or finances. The most common sources of global data are from national censuses, which occur only every 10 years (and take years more to come out). Specialized surveys in some countries allow more frequent measurement of some flows, but such data are still relatively rare, and poorly suited to studying short-term migration movements.