(Jerven 2013), or studies using small or purposively chosen samples, which may not be reliably scalable for informing national-level policy priorities.

We aim to fill this gap using newly available data from the Living Standard Measurement StudyIntegrated Surveys on Agriculture Initiative (LSMS-ISA). With these nationally representative, recently collected, agriculturally intensive, and cross-country comparable data sets, we can provide an updated and unprecedentedly broad-scale picture related to a bundle of inputs used by farming households in Africa. We utilize one cross section of data collected between 2010 and 2012 in each of six countries (Niger, Nigeria, Ethiopia, Malawi, Tanzania, Uganda) encompassing over <mark>22,000 cultivating households and 62,000 agricultural plots</mark> representing a large section—both geographically and population-wise—of Sub-Saharan Africa. Specifically, we investigate the validity of the assumption that African farmers use few modern inputs, especially those inputs typically believed to embody improved technologies. To test this overarching hypothesis, we produce thick descriptive statistics related to a number of inputs often cited as “under-used” in Sub-Saharan Africa: fertilizer, improved seed varieties, agrochemicals (pesticides, herbicides, and fungicides), irrigation, and animal power and mechanized farm equipment. This allows us to provide evidence for or against a number of sub-hypotheses related to input use among different cross-sections of the population derived from long-held beliefs about the types of farmers and households who adopt yield-enhancing inputs (e.g., Feder and Umali 1993; Feder, Just, Zilberman 1985; Sunding and Zilberman 2001). Our aim is purely descriptive and therefore modest. We test no hypotheses as to what gives rise to the patterns we describe. But getting the basic facts right seems an essential and to-date-overlooked step in intensifying debates about how to stimulate African agricultural development.

We begin in Section 2 by reviewing the available literature and macro-statistics that are the sources of most common conceptions of input use and then describe in Section 3 our data and sample of agricultural households. In Section 4, we disaggregate data on both binary input use decisions and continuous application rates using a number of regional, household, and plot-level characteristics; investigate the joint distributions of uptake in order to identify prospective patterns of synergies among distinct “packages” of modern inputs; and dissect some of the available information on input provisioning, including input/output price ratios, the accessibility of input markets, and the incidence of purchasing inputs on credit. In Section 5, we use an R<sup>2</sup> decomposition technique in an attempt to uncover the primary source(s) of most of the betweenand within-household level variation in binary input use decisions. The ten most striking and important findings from our analysis are distilled in Section 6.

# **2. Prevailing prior beliefs on the modern input use context in Sub-Saharan Africa**

In this section, we describe the importance of each of the five chosen inputs to agricultural productivity and present the figures that guide “conventional wisdoms” surrounding input use patterns in SSA. Mostly, prevailing prior beliefs come from macro-level data. Because microlevel studies also inform our common conceptions and a host of work has been done on the topic, we also highlight some of the larger studies that speak broadly about national and multinational patterns based on plot- or farm-level data collected in the recent past and include a listing of such studies in Appendix 1. Where possible, we focus on nationally-representative or near-nationallyrepresentative data, although equivalents do not exist across all countries and inputs.

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