In Africa, most development strategies include efforts to improve the productivity of staple crops grown on smallholder farms. An underlying premise is that small farms are productive in the African context and that smallholders do not forgo economies of scale—a premise supported by the often observed phenomenon that staple cereal yields decline as the scale of production increases. This article explores a research design conundrum that encourages researchers who study the relationship between productivity and scale to use surveys with a narrow geographic reach in order to produce more reliable results, even though results are better suited for policy decisions when they are based on data that are broadly representative. Using a model of endogenous technology choice, we explore the relationship between maize yields and scale using alternative data. Since rich descriptions of the decision environments that farmers face are needed to identify the applied technologies that generate the data, improvements in the location specificity of the data should reduce the likelihood of identification errors and biased estimates. However, our analysis finds that the inverse-productivity hypothesis holds up well across a broad platform of data, despite obvious shortcomings with some components. It also finds surprising consistency in the estimated scale elasticities.