In addition, the analysis incorporates data from other sources on market prices of agricultural products, weather and conflict. These data provide objective measures of shocks that households experienced during this time. Objective measures are preferred to subjective measures, in which the households themselves report whether they face one type of shock or another (Thomas et al. 2013, Anttila-Hughes and Hsiang 2014, Hill and Porter 2016). Households are more likely to recall shocks that resulted in large losses of income and not report those that did not. As such, there is likely to be significant reporting bias in the occurrence of shocks, which will bias the estimate of the impact of shocks on income and welfare. However, the cost of using objective measures of shocks is that our analysis is restricted to covariate shocks. That is, shocks experienced by everyone in a given area (weather, prices, conflict) that can be observed in data other than the household survey. This means that we are unable to say much about the idiosyncratic shocks that affect welfare in Uganda. In particular, we are unable to look at the impact of health shocks on welfare despite a literature that suggests un-insured health shocks have a substantial impact on welfare in Uganda (Bridges and Lawson 2008, Helbert et al. 2013, Aliga 2013).

More specifically, the following sources of secondary data were included:

- Monthly price data collected at eight wholesale markets across Uganda as part of the Uganda Bureau of Statistic’s Consumer Price Index (CPI) data collection exercise. A 12-month moving average was assigned to each household according to the date in which they were interviewed. All prices are deflated by the national CPI before being used in the analysis such that the prices used capture relative price changes, i.e. the degree to which the maize price increased or decreased more than the price of the basket of consumption goods represented in the CPI. Each household is assigned the prices of the closest market, measured by geographical distance.

- Water Requirement Satisfaction Index (WRSI) calculated from satellite rainfall data for each pixel using a maize crop model calibrated to the growing seasons across Uganda. Specifically, the geoWRSI v 3.0 was used with the global PET and RFE<sup>5</sup> v2 (2001-2014) time series.  The WRSI is an indicator of crop performance based on the availability of water for the crop during the growing season. The index ranges from 0 to 100, where 100 means there was no deficit in the water needed, and each household was assigned the average between the main and short seasons for the period for which crop income data were collected.<sup>6</sup> These data have been merged into the UNPS using the Geographical Information System (GIS) coordinates of households.

- As a proxy of the incidence of violence and conflict, we use the number of fatalities per year in a 25-kilometer vicinity of each household, obtained from the Armed Conflict Location Event Dataset (ACLED).

Table 3 provides a description of the type of crops grown by the households for the latter two years of the analysis. Maize, beans, matooke and cassava are the four most important crops as a share of total crop income. Maize and beans are universally important—comprising 10 percent or more of crop incomes in all regions in all years. Matooke is important in all regions except the Northern region,

> 5 Potential evapotranspiration (PTE) and satellite gauge rainfall estimate (RFE).

> 6 For those households in Uganda living below the equator, the main season runs from February to July and the short season runs from August to January. For households living in bimodal areas above the equator, the main season runs from August to January and the short season from February to July. For households in the north of the country, one season is present. The static soil WHC and average LGP inputs for Uganda that come with geoWRSI were used.

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