of days household members devoted to maize production proved problematic, which ultimately led us to use an exogenous proxy in its place.

To understand this last point better, it is important to note that household labor measures are notoriously inaccurate, often with a bias toward inflating reported labor days (Beegle, Carletto and Himelein 2012). We find indirect evidence of this in our sample, with the distribution of household labor days per hectare skewed by a string of high-valued observations. This is illustrated by the box-plot shown in Figure 5, which condenses key aspects of our sample labor data into a single form. The top of the rectangular box shaded in the figure marks the 75<sup>th</sup> percentile of the data range, while the bottom “hinge” markets the lower 25<sup>th</sup> percentile. The “whiskers” extend another 1.5 times the interquartile range of the nearest quartile. The white line marks the median of the data. Intuitively, the range of the box delineates observations that are typical. The whiskers contain values that are somewhat atypical relative to most observations, while the dots mark observations that are extreme. Consistent with the tendency to over-report, the observations contain a number of suspiciously large values. Consequently, we decided to use an exogenous measure, available labor – that is the number of household members between the ages of 14 and 60 – to proxy household labor input, obviating the need to include household labor days as an instrumented variable.

With this as background, mean-valued elasticities from the second-stage are reported in Table 3.<sup>6</sup> For comparison purposes, elasticities from a corresponding OLS model are reported as well. In both estimation exercises, enumeration-area dummies were included to account for location effects; the effects are expected to sweep up the effects of any unmeasured differences in relative prices, market conditions, travel times and average soil and climate endowments. The regression suggests that location matters as the location dummies were statistically significant and explained a significant portion in the variation in yields.

Focusing first on the IV estimates, the results show that using fertilizer boosts yields in a statistically significant way. However, the average effect is not large, with an elasticity less than 0.10 evaluated at average values for yield and fertilizer use. There is a small, but statistically insignificant impact on yields from manure use. Higher levels of available family labor and hiring farm workers boost yields in a measurable way; at mean levels, the effects are not large and similar to the elasticity for fertilizer. As is often the case with maize in Africa, the elasticity of area is negative, although not significantly so.<sup>7</sup> In terms of risk management strategies, the results suggest that growing maize in dedicated plots does not boost yields but rather reduces them. It is difficult to say exactly why this is the case, but it is consistent with the fact that very little of the maize grown in Uganda is harvested from pure-stand plots. It is also possible that nitrogen-fixing crops, such as beans, help compensate for low levels of applied chemical fertilizers. As discussed earlier, this type of intercropping is prevalent in the Kenyan highlands.

The IV results also suggest that farmers that diversify their risks by growing several crops also achieve higher yields, perhaps because they are able to use riskier-but-more profitable production strategies. However, diversification comes at a cost as it also results in fragmentation; the diversification elasticity is estimated at 0.167, while the fragmentation elasticity is at -0.084, suggesting that, in practice, the diversification benefits are partially off-set.

> 6 The estimated parameters themselves are reported in Annex Table A.1.

> 7 See Larson et al. (2014) for a related discussion.

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