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The availability of weather data from remotely sensed Earth observation (EO) data has reduced the cost of including weather variables in econometric models. Weather variables are common instrumental variables used to predict economic outcomes and serve as an input into modelling crop yields for rainfed agriculture. The use of EO data in econometric applications has only recently been met with a critical assessment of the suitability and quality of this data in economics. We quantify the significance and magnitude of the effect of measurement error in EO data in the context of smallholder agricultural productivity. We find that different measurement methods from different EO sources: findings are not robust to the choice of EO dataset and outcomes are not simply affine transformations of one another. This begs caution on the part of researchers using these data and suggests that robustness checks should include testing alternative sources of EO data., Comment: arXiv admin note: substantial text overlap with arXiv:2202.05220

We examine the impact of livelihood diversification on food insecurity amid the COVID-19 pandemic. Our analysis uses household panel data from Ethiopia, Malawi, and Nigeria in which the first round was collected immediately prior to the pandemic and extends through multiple rounds of monthly data collection during the pandemic. Using this pre- and post-outbreak data, and guided by a pre-analysis plan, we estimate the causal effect of livelihood diversification on food insecurity. Our results do not support the hypothesis that livelihood diversification boosts household resilience. Though income diversification may serve as an effective coping mechanism for small-scale shocks, we find that for a disaster on the scale of the pandemic this strategy is not effective. Policymakers looking to prepare for the increased occurrence of large-scale disasters will need to grapple with the fact that coping strategies that gave people hope in the past may fail them as they try to cope with the future.

When publishing socioeconomic survey data, survey programs implement a variety of statistical methods designed to preserve privacy but which come at the cost of distorting the data. We explore the extent to which spatial anonymization methods to preserve privacy in the large-scale surveys supported by the World Bank Living Standards Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) introduce measurement error in econometric estimates when that survey data is integrated with remote sensing weather data. Guided by a pre-analysis plan, we produce 90 linked weather-household datasets that vary by the spatial anonymization method and the remote sensing weather product. By varying the data along with the econometric model we quantify the magnitude and significance of measurement error coming from the loss of accuracy that results from protect privacy measures. We find that spatial anonymization techniques currently in general use have, on average, limited to no impact on estimates of the relationship between weather and agricultural productivity. However, the degree to which spatial anonymization introduces mismeasurement is a function of which remote sensing weather product is used in the analysis. We conclude that care must be taken in choosing a remote sensing weather product when looking to integrate it with publicly available survey data.

This paper quantifies the significance and magnitude of the effect of measurement error in remote sensing weather data in the analysis of smallholder agricultural productivity. The analysis leverages 17 rounds of nationally-representative, panel household survey data from six countries in Sub-Saharan Africa. These data are spatially-linked with a range of geospatial weather data sources and related metrics. We provide systematic evidence on measurement error introduced by 1) different methods used to obfuscate the exact GPS coordinates of households, 2) different metrics used to quantify precipitation and temperature, and 3) different remote sensing measurement technologies. First, we find no discernible effect of measurement error introduced by different obfuscation methods. Second, we find that simple weather metrics, such as total seasonal rainfall and mean daily temperature, outperform more complex metrics, such as deviations in rainfall from the long-run average or growing degree days, in a broad range of settings. Finally, we find substantial amounts of measurement error based on remote sensing product. In extreme cases, data drawn from different remote sensing products result in opposite signs for coefficients on weather metrics, meaning that precipitation or temperature draw from one product purportedly increases crop output while the same metrics drawn from a different product purportedly reduces crop output. We conclude with a set of six best practices for researchers looking to combine remote sensing weather data with socioeconomic survey data.

High resolution datasets of population density which accurately map sparsely-distributed human populations do not exist at a global scale. Typically, population data is obtained using censuses and statistical modeling. More recently, methods using remotely-sensed data have emerged, capable of effectively identifying urbanized areas. Obtaining high accuracy in estimation of population distribution in rural areas remains a very challenging task due to the simultaneous requirements of sufficient sensitivity and resolution to detect very sparse populations through remote sensing as well as reliable performance at a global scale. Here, we present a computer vision method based on machine learning to create population maps from satellite imagery at a global scale, with a spatial sensitivity corresponding to individual buildings and suitable for global deployment. By combining this settlement data with census data, we create population maps with ~30 meter resolution for 18 countries. We validate our method, and find that the building identification has an average precision and recall of 0.95 and 0.91, respectively and that the population estimates have a standard error of a factor ~2 or less. Based on our data, we analyze 29 percent of the world population, and show that 99 percent lives within 36 km of the nearest urban cluster. The resulting high-resolution population datasets have applications in infrastructure planning, vaccination campaign planning, disaster response efforts and risk analysis such as high accuracy flood risk analysis.