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# 1 Introduction

Stylized facts drive research agendas and policy debates. They provide a sense of importance, help frame the inquiry and are used to galvanize resources. So, the notion that 60‐80 percent of work in African agriculture is done by women has often been quoted to motivate a greater gender focus in agricultural research and policy making. Similarly, the observation that one‐third of the world’s food is lost post‐harvest is used to rally the world around a food waste reduction agenda (Chaboud and Daviron, 2017).

Yet, robust stylized facts, systematically obtained with reliable methodologies and comparable data across countries, and settings within countries, are often hard to come by. Partly this is because some concepts are simply difficult to measure. Not everything important can come packaged as a neat statistic. In the face of pressure to produce statistics irrespectively, wrong‐headed numbers may then arise as a result. It partly also reflects the lack of regular, representative and reliable data to compile these facts. Finally, preoccupation with causal inference often leaves few incentives to produce a ground‐truthed description of reality. With stylized facts often constituting the very starting point of research itself, this is odd at best.

As a result, academic debates and policies rely too often on outdated or poor quality statistics, or just unrepresentative case study evidence. In Jerven’s words (2016, p343): “ _... the numerical basis on which we study African economies is poorer than we would like to think._ ” Sometimes numbers have even evolved into zombie statistics, numbers that live a life on their own, with their empirical basis undocumented and their origins unknown, though widely accepted as conventional wisdom. One example is the notion that 70 percent of the world’s extreme poor are women.<sup>2</sup> Devarajan (2013) calls for urgent action to remedy such “statistical tragedy”. After all, research, policies and investments can only be as good and effective as the data and evidence informing them (Beegle et al., 2016; Jerven, 2016).

The topic of quality data and measurement has recently started to receive more attention, in the literature and in policy circles, especially for macroeconomic statistics (Jerven, 2013), literacy (UNESCO, 2015) and poverty (Beegle, et al., 2016). But the need to revisit common wisdom applies equally to agriculture, and in particular, African agriculture (Carletto, Joliffe and Banerjee, 2015). In part this is because the world in which African agriculture operates has been changing dramatically over the past two decades, following robust economic growth, rapid urbanization, and climate change. But the information base on African agriculture has been limited for a long time, often even lacking reliable statistics on basic metrics such as the country’s agricultural yields. More generally, translating economic concepts into numbers, such as the notions of productivity, seasonality, commercialization, or a household’s net food marketing position (net buyer/seller), remains intrinsically challenging,<sup>3</sup> often requiring special data that are not standardly collected at scale, forcing analysts to rely on outdated or case study evidence instead.

The household survey panel data collected under the Living Standards Measurement Study–Integrated Surveys on Agriculture (LSMS‐ISA) Initiative provide a unique opportunity to take up this challenge. Over the period 2008‐2020, nationally representative surveys are to be conducted in 8 African countries, representing 45 percent of Sub‐Saharan Africa’s (SSA) population. In these countries, four or more waves of detailed information are collected on households’ economic activities, their income and well‐being, with

> 2 http://www.politifact.com/punditfact/article/2014/jul/03/meet‐zombie‐stat‐just‐wont‐die/

> 3 See for example Rao (2007, Ch3) on measuring seasonality.