|HH Head is Married|0.678|0.467|0.712|0.453|0.702|0.458|0.647|0.478|
|---|---|---|---|---|---|---|---|---|
|HH Wealth and Education Index<sup>a</sup>|0.375|0.234|0.456|0.224|0.363|0.223|0.367|0.243|
|HH Ag Assets and Land Holdings Index<sup>b</sup>|0.388|0.204|0.484|0.207|0.431|0.224|0.325|0.161|
|**Community Characteristics (2010 values)**|||||||||
|Community infrastructure index|‐0.099|1.078|‐0.609|1.006|0.226|1.049|‐0.271|1.034|
|Community median slope (%)|4.564|3.761|5.142|3.915|4.009|2.674|4.931|4.446|
|ln(Access Index)|4.519|1.434|2.439|1.827|5.455|0.957|4.171|0.900|
|District AEDOs per 1000 farming families|0.433|0.135|0.538|0.173|0.472|0.113|0.372|0.112|
|Total N. Projects in District|4.385|3.567|4.298|1.148|6.294|4.457|2.663|1.640|
|Community Land Ownership Gap|1.233|0.640|1.339|0.579|1.489|0.628|0.973|0.557|

_Notes:_ **a** **_._** Calculations account for complex survey design.

**b.** The following variables were used to create a household wealth index with principal component analysis: Furniture dummy capturing whether the household had any of (bed, table, chair, chair/couch, coffee table, drawers, or desk), radio dummy, electronic dummy capturing whether the household had any of (fan, air conditioner, stereo, clock, or solar panel), laundry dummy capturing whether the household had any of (sewing machine, washing machine, clothes iron), kitchen dummy capturing whether the household had any of (kerosene stove, electric/gas stove, refrigerator), a dummy capturing whether the household had any of (TV, VCR, computer, satellite dish, or generator), cell phone dummy, improved walls of dwelling dummy, improved roof of dwelling dummy, improved floor of dwelling dummy, number of rooms per capita in household dwelling, improved lighting fuel usage dummy, improved cooking fuel usage dummy, electrification of dwelling dummy, access to an improved water source dummy, access to an improved latrine dummy, improved rubbish removal usage dummy, use of insecticide treated mosquito nets dummy. The index was then normalized, that is, norm_index = ((index ‐ min(index)) / (max(index)‐min(index))). Household average years of education is the numbers of years of education completed by each individual collapsed to the household mean number of years of education. This variable also was normalized. The wealth and education index displayed in Table 1.4 is the sum of the normalized wealth index and the normalized average number of years of education.

**c.** The following variables were used to create an agricultural asset index with principal component analysis: Hand hoe dummy, watering can dummy, hand tool dummy capturing whether the household had any of (slasher, axe, sprayer, panga, or sickle), a dummy capturing whether the household had any of (tractor, tractor plough, ridger, cultivator, treadle pump, motorized pump, ox cart, or ox plough), livestock facility dummy capturing whether the household had any of (chicken house, livestock kraal, poultry kraal, or pig sty), and grain storage dummy capturing whether the household had any of (storage house, granary, or barn). The index was then normalized, that is, norm_index = ((index ‐ min(index)) / (max(index)‐min(index))). Land holdings are the number of hectares (ha) that the household “holds.” Plots that the household farmed on and had acquired through being granted by local leaders, inheritance, bride price, purchase, or leasehold (about 5% of all plots were acquired by means other than these) are said to be “held.” Any plots that the household did not farm but received rent for also were counted as being held. This variable then was normalized. The agricultural and land index displayed in Table 1.4 was the sum of the normalized agricultural asset index and the normalized household land holdings.

Overall households in Southern faced the greatest rainfall shocks in 2013. They also face relatively high coefficients of variation in rainfall shocks, indicating greater exposure to future rain shocks. Households in Central faced the greatest maize price and malaria shocks in 2013. But, Central has relatively low coefficients of variation for rainfall, maize price, and malaria shocks, though they also face the highest variability in off‐farm employment. Households in Northern faced the lowest weather and price shocks in 2013, but also had the highest employment loss shocks and relatively high incidence of malaria. They do face relatively high risks of price and malaria shocks in the future compared to the other regions. To summarize, different regions face different risks, with Southern being particularly susceptible to rainfall shocks, Central being relatively susceptible to employment loss shocks, and Northern being historically most susceptible to price shocks.

# **_Household and community characteristics_**

In the face of all the perils described above, even if most of our sample households could be regarded as poor, there are varying degrees of deprivation between them and over time. As captured in the literature 11