The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

No abstract

Understanding the determinants of agricultural productivity requires accurate measurement of crop output and yield. In smallholder production systems across low-and middle-income countries, crop yields have traditionally been assessed based on farmer-reported production and land areas in household/farm surveys, occasionally by objective crop cuts for a sub-section of a farmer's plot, and rarely using full-plot harvests. In parallel, satellite data continue to improve in terms of spatial, temporal, and spectral resolution needed to discern performance on smallholder plots. This study evaluates ground-and satellite-based approaches to estimating crop yields and yield responsiveness to inputs, using data on maize from Eastern Uganda. Using unique, simultaneous ground data on yields based on farmer reporting, sub-plot crop cutting, and full-plot harvests across hundreds of smallholder plots, we document large discrepancies among the ground-based measures, particularly among yields based on farmer-reporting versus sub-plot or full-plot crop cutting. Compared to yield measures based on either farmer-reporting or sub-plot crop cutting, satellite-based yield measures explain as much or more variation in yields based on (gold-standard) full-plot crop cuts. Further, estimates of the association between maize yield and various production factors (e.g., fertilizer, soil quality) are similar across crop cut-and satellite-based yield measures, with the use of the latter at times leading to more significant results due to larger sample sizes. Overall, the results suggest a substantial role for satellite-based yield estimation in measuring and understanding agricultural productivity in the developing world.

Following the onset of the COVID-19 pandemic, face-to-face survey data collection efforts came to a halt due to lockdowns, limitations on mobility and social distancing requirements. What followed was a surge in phone surveys to fulfill rapidly evolving needs for timely and policy-relevant microdata for understanding the socioeconomic impacts of and responses to the pandemic. Even as the face-to-face survey data collection efforts are resuming in different parts of the world with COVID-19 safety protocols, the rapidly-acquired experience with phone surveys on the part of national statistical offices and survey practitioners in low- and middle-income countries appears to have formed the foundation for phone surveys to be more commonly implemented in the post-pandemic era, in response to other shocks and as complementary efforts to face-to-face surveys. Informed by the practical experience with the high-frequency phone surveys that have been implemented with support from the World Bank Living Standards Measurement Study (LSMS) to monitor the socioeconomic impacts of the COVID-19 pandemic, this paper provides an overview of options for the design and implementation of phone surveys to collect representative data from households and individuals. Further, the discussion identifies the requirements for phone surveys to be a mainstay in the toolkits of national statistical offices and the directions for future research on the design and implementation of phone surveys.