Our regression estimates and estimated Shapley values for the binary inorganic fertilizer use decision can be found in Table 29. Quite surprisingly, the overwhelming amount of variation, indeed nearly half (45 percent), is accounted for by the country dummy variables. Even controlling for a wide range of important observable household-level variables, some combination of other policy, institutional, or macroeconomic variables explain most of the micro-scale variation in inorganic fertilizer use in this unprecedentedly large sample. Since our dependent variable is the binary input use decision, differences in survey design, which may lead to differences in measurement of continuous input volumes, cannot plausibly account for the importance of the country-level variables. This is an important finding, as clearly the policy and operating environments facilitated by governments matter.

Biophysical variables account for 24 percent of the explained variation in fertilizer use, followed by farm operation characteristics accounting for 16 percent, market and accessibility variables accounting for nearly 10 percent, and socio-economic variables less than 4 percent. The fact that geography and biophysical characteristics (accounting for a combined 70 percent of variation) matter so much to the fertilizer use decisions mirrors, to a large extent, findings by McCord and Sachs (2013) on the importance of the same factors in explaining variations in macroeconomic development conditions across countries. Together, these findings suggest the need to for broadbased policy reform at the country level, which are likely to have tangible impacts on spurring input use and staple grain productivity (e.g., Sheahan, Ariga, Jayne 2013).

When running this analysis with the same variables (apart from the fertilizer price) for agrochemical use instead (Table 30), the same qualitative patterns emerge. Country-level variables account for 43 percent of the variation, farm operation variables account for 20 percent, biophysical variables account for 15 percent, market and accessibility variables account for 15 percent, and socio-economic variables account for 7 percent. Holding constant other factors, both cash crops and maize production are negative and statistically significantly related to agrochemical use, while the number of crops cultivated is positively associated with input use and accounts for 17 percent of the variation alone, suggesting that diversified producers may be more likely to use agro-chemicals across these six countries.

# **_5.2. Within-household variation_**

The richness of the LSMS-ISA data allows us to further explore the within-household variation, namely households’ choice to apply inorganic fertilizer to one plot over another after controlling for characteristics of the household. To accomplish this, we first estimate a binary linear probability model at the plot level using household level fixed effects. Then, we “de-mean” the plot level values using household level averages to decompose the R<sup>2</sup> using the same technique just described.<sup>21</sup> Because we explore within-household variation, it is important to note that these results apply only to the vast majority of households that cultivate more than one plot in the main season.

> 21 For categorical variables, like plot type and soil quality, we “de-mean” by treating the categories as continuous variables for the R<sup>2</sup> decomposition exercise. As such, the coefficient estimates on these resulting values have no economic meaning but the Shapley value provides an indication on the overall contribution of this category of variables to the fit of the model.

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