How good are livestock statistics in Africa? Can nudging and direct counting improve the quality of livestock asset data?

Abay, Kibrom A.; Ayalew, Hailemariam; Terfa, Zelalem; Karguia, Joseph; and Breisinger, Clemens. 2025. How good are livestock statistics in Africa? Can nudging and direct counting improve the quality of livestock asset data? Journal of Development Economics 176(September 2025): 103532. https://doi.org/10.1016/j.jdeveco.2025.103532

Livestock statistics in most low- and middle-income countries rely on self-reported, survey-based measures. However, respondents may have various challenges to accurately report livestock ownership. This study introduces a novel set of survey and measurement experiments to improve livestock statistics in Africa. We introduce two innovations to conventional livestock data collection methods. First, we address some of the sources of potential underreporting in livestock assets by introducing an explicit nudge to a random subset of survey respondents. Second, we arrange for direct counting of livestock assets by enumerators and local livestock experts. We demonstrate that self-reported data on livestock ownership suffer from significant and systematic underreporting. While our nudge affects only the reporting behaviour of households with larger stocks of livestock, direct counting increases total livestock ownership by 39 percent and the reported number of cattle by 43 percent. These impacts are evident at both the extensive and intensive margins of livestock asset ownership, as well as considering the number and value of livestock assets owned. Such mismeasurement in self-reported livestock data can lead to underestimation of the contribution of the livestock sector to national economies. Furthermore, direct counting generates important spillover effects to livestock species not explicitly counted in the survey. We finally show that underreporting in self-reported livestock data is systematic and hence consequential for statistical inferences. Our findings underscore that survey designs that can address specific sources of bias in self-reported livestock data can meaningfully improve livestock asset measurement in Africa.