2003; Morris et al. 2007). Some argued that private market agents had failed to fill the void left by market reforms and called for greater state intervention (Winter-Nelson and Temu 2005; Poulton, Kydd, and Dorward 2006; Mosely, Carney and Becker 2010). When food prices surged in 2008 and again in 2010, governments in Africa and elsewhere intervened in a variety of ways, and some instituted fertilizer promotion programs that included subsidies (Chamberlin and Jayne 2013; Shively and Ricker-Gilbert 2013).<sup>5</sup>

# <u>Farming methods and fertilizer demand</u>

One unambiguous finding from the many household-based studies cited above is that African farmers use a mix of applied technologies, including those that do not rely on chemical fertilizer. Consequently, applied papers on fertilizer demand in Africa must reconcile the heterogeneous choices about farming practices. In many studies, the mixed outcomes are viewed in terms of a constant sample-wide production function, and zero-valued observations of fertilizer demand are implicitly treated as corner-solutions that are optimal from the household’s perspective and randomly observed from a statistical perspective.

In a subset of papers however, the adoption decision is distinguished from the intensification decision, at least in the econometric sense. Implicitly or explicitly, market performance is also a consideration in these models, and proxies for high transaction costs or poor market access are included to explain choices about using fertilizer. While the root causes of market performance are not necessarily explicit, the applied models contain a mix of variables related to transportation costs, market organization and household characteristics.<sup>6</sup> Consequently, these models are closest in spirit to ours and share some structural components. What’s more, their results are consistent with our conceptual starting point that heterogeneous decision environments result in heterogeneous fertilizer adoption rates.

Using cross-sectional household data from Eastern Zambia, Jha and Hojjati (1993) estimated a two-stage Heckman (1979) model of fertilizer adoption and intensity. They found that the distance traveled to obtain fertilizer was a barrier to adoption and that membership in a cooperative facilitated adoption. Regional dummies, meant to capture differences in markets and agro-ecological conditions, mattered as well. Using a 1994 representative cross-section of farmers in Ethiopia, Croppenstedt et al. (2003) found significant regional effects in their double hurdle model of fertilizer adoption, which they attributed to differing marketing conditions. Winter-Nelson and Temu (2005) employed a two-stage selection model to examine input expenditure levels among 250 coffee-growing households in Tanzania. They found that fertilizer adoption rates fell as the distance traveled to input markets increased. The same was true for their indicator of remoteness, the time it took to travel to the nearest cooperative office or commercial center. Membership in producer associations and spatial dummies were significant as well. Alene et al. (2008) also used a similar model to look at market participation and fertilizer use among 802 maize producers in Kenya. They found

> 5 A literature on the costs and efficacy of the programs is beginning to emerge, for example, Ricker-Gilbert et al. (2013) provide a recent review.

> 6 For early discussion on how transaction costs affect household choices see Pollak (1985), Binswanger and McIntire (1987), Rosenzweig (1988), Bromley and Cavas (1989) and Goetz (1992).

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