what is included in the aggregation of each input type by country. For differences in how the questions were asked, it is best to refer to the survey instruments and enumerator manuals themselves, available through the World Bank’s LSMS-ISA web site.

Table 6: Number of households and plots included in this analysis versus overall survey sample

||S|Name of|Overa<br>sa|ll survey<br>mple|Sub-sa|mple used in this an|alysis (main s|eason)|
|---|---|---|---|---|---|---|---|---|
|Country|urvey<br>year|main ag<br>season|No. of<br>hh|% of hh in<br>“rural”<br>areas|No. of<br>hh|% of overall<br>survey sample in<br>analysis|% of hh in<br>“rural”<br>areas|No. of<br>plots|
|Ethiopia|2011/12|Meher|3,969|98.9|2,852|86.6|99.7|23,051|
|Malawi|2010/11|Rainy|12,271|84.4|10,086|83.2|93.5|18,598|
|Niger|2011/12|Rainy|3,968|61.2|2,208|77.9|93.8|6,109|
|Nigeria|2010/11|-|5,000|59.0|2,939|49.9|84.6|5,546|
|Tanzania|2010/11|Long rainy|3,924|69.1|2,372|66.6|85.9|4,794|
|Uganda|2010/11|First|2,716|83.5|2,108|73.8|93.7|4,289|
|_Sample siz_|_e across co_|_untries_|_31,848_|_76.0_|_22,565_|_73.0_|_91.9_|_62,387_|

Note: All surveys are nationally representative except Ethiopia, which was only conducted in rural areas (with a few households in “small towns”). In Ethiopia, only one of the two seasons is captured in the surveys. “Rural” areas are defined differently across countries. The sample sizes described above are not weighted, but percentages are. The aggregated sample size across the six countries includes simple summations and unweighted averages.

Many of the inputs we are interested in are best compared per unit of cultivated land, particularly application rates. In all of these surveys, farmer-reported plot sizes are complemented with global positioning system (GPS)-based measures of some plots for comparison. Given evidence that self-reported measures of land size may contain bias and cause the misrepresentation of particular relationships (Carletto, Savastano, Zezza 2013), multiple imputation is used to arrive at a full set of GPS-based plot sizes where self-reported values are used as an instrument following the methodology described by Palacios-Lopez and Djima (2014).<sup>5</sup> Apart from estimating plot size, we clean only the transformed input use per hectare (generally kg/ha) values. Cleaning rules by input variable are described in more detail in the sections that follow and generally involve winsorizing at the 99th percentile under the assumption that all extreme values are due to measurement error.<sup>6</sup> Despite a purposively chosen sample of main season cultivators, we apply household level sampling weights to and account for the complex survey design to construct nationally representative statistics (or, in the case of Ethiopia, representative of rural areas and small towns only). Further, household level weights multiplied by plot size (in hectares) are applied at the plot level so as to not overweight very small units of cultivation. All monetary values are standardized to USD using official yearly-averaged exchange rates from the World Bank. For all countries where the first year of data collection was 2010 (Malawi, Nigeria, Tanzania, Uganda), we use the 2010 average exchange rate. For the remaining two countries (Ethiopia and Niger) the average 2011 rate was applied instead.<sup>7</sup>

> 5 For all owned plots in Uganda, we also subtract off the portion that the household claimed to leave fallow.

> 6 Winsorizing is the process of replacing extreme or outliers beyond a specific percentile with the value observed at that percentile. We prefer this method to trimming, which drops extreme values instead of replacing them.

> 7 We use the following USD conversion factors: 16.90 for Ethiopia, 150.49 for Malawi, 471.87 for Niger, 150.30 for Nigeria, 1409.27 for Tanzania, and 2177.56 for Uganda: http://data.worldbank.org/indicator/PA.NUS.FCRF.

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