# <u>Step One Results</u>

The results in Table 2 suggest that farmers who participated in output markets were more likely to use fertilizer and employ workers. There are potentially two channels: the sales may provide the liquidity needed to purchase fertilizer, and market participation may also provide vent-for-surplus opportunities – the ability to profit from producing more than can be consumed (Myint 1971; Hayami 2001). Higher population densities can lower transaction costs through scale and tighter information channels, providing greater opportunities for farmers. Also according to the Boserupean and induced innovation hypotheses (Boserup 1965; Hayami and Ruttan 1985), higher population density stimulates the adoption of land-saving technology, including the application of chemical fertilizer. The associated coefficient, positive and significant in the fertilizer demand regression, is consistent with this notion. Labor markets are likely more fluid where populations are denser. This can make it harder to find farm workers, since farm wages are usually lower than non-farm wages, an idea that is consistent with the negative and significant coefficient in the hired labor regression.

Surprisingly, coefficients on the two variables that might address liquidity constraints, household wealth and non-farm income had no measurable impact on fertilizer demand. In contrast, the variables helped explain choices about hiring workers significantly, suggesting higher opportunity costs for wealthier farmers and farmers who engaged in other money-making activities.

As mentioned, only 23 percent of the households in our sample were visited by extension agents; however, those that were called upon were more likely to use fertilizer and hire workers. This finding is consistent with results reported in Kijima (forthcoming) for rice production in Uganda. The household head’s gender mattered for fertilizer use, with women significantly less likely to use fertilizer than men. In contrast, a farmer’s gender did not appear to affect worker hires.

# <u>Productivity</u>

The estimated yield equation includes five inputs, land, chemical fertilizer, manure, household labor, and hired labor. It also includes additional variables related to risk management, farmer characteristics and weather outcome. The equation’s parameters were estimated using a fixed-effect OLS regression and also using an instrumental variables (IV) regression with fixed effects. As mentioned, predicted values from the fertilizer and hired-labor tobit regressions reported in Table 2 were used as instruments in the IV regression. Before proceeding to a discussion of those results, it is worth explaining why the remaining inputs were not instrumented.<sup>4</sup>

**Maize area** : As has become standard practice, the decision about how much area to plant to maize is treated as non-contemporaneous and therefore predetermined in the regression.<sup>5</sup> The notion here is that cropping decisions are made ahead of choices about inputs. **Manure** is treated as an exogenous household resource, since it is seldom traded. As a consequence, the availability and use of manure depends on a priori decisions about whether or not to include livestock on the farm. In this sense, manure applications are predetermined in a way similar to area planted to maize. **Household labor** : Our survey data on the number

> 4 We also estimated a version of the model that included self-reported use of “improved seeds” as an input. Including the variable had no significant consequences for our analysis; however, we report the results in Annex Table 2 and Annex Table 3.

> 5 See Antle (1983) and related discussion in Larson et al. (2014).

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