can be tracked across seasons, unlike plots. This is not the case for the plot-crop-level dataset which is at the _plot_ and _crop_ level.

- In both Mali and Niger, perennial crops were provided at the household and crop level. In order to retain this information to some degree, and for merging purposes, each household-crop pair was associated with a bespoke ID. These lines can be identified by cases where the plot link identifier begins with “missing_”. To limit confusion in the plot dataset, however, these observations were not taken into account.

_Construction of the harmonized household and individual datasets_ This section describes the HP household and individual datasets, which mostly contain variables deriving from the household questionnaires (see Figure 2). <mark>An important aspect of these datasets is that they include</mark> _<mark>all</mark>_ <mark>households that are listed in the household questionnaire cover module, regardless of their agricultural activities. They therefore include close to 30,000 households in the seven countries which are not in the plot-crop or plot-level datasets.</mark> The household-level dataset <mark>consists of variables from the household questionnaire that are key to analyzing livelihood and welfare outcomes. Included are access to electricity, household dependency ratios – defined as the ratio of individuals younger than 15 or older than 65 to the other members of the household, whether the household operates a nonfarm enterprise, and household size. Critically, the dataset also contains information on household consumption. The variables included are the total consumption value of the household in LCU and in USD and the consumption quintile of the household (where the quintiles are defined within country-waves). The consumption aggregates are provided in LCU by the data producers are part of the publicly available survey data. More information on the steps taken to calculate the consumption aggregate can be found in the surveys’ online documentation (see Table 1). We also compute a household asset index using a principal component analysis (PCA), quantifying asset ownership in single dimensions drawn from inventories of household assets. A regression method is used to predict factor scores (</mark> Rencher, 2002) <mark>. We further compute the household dietary diversity index, following guidelines from the Food and Agriculture Organization (FAO). It consists of the sum of distinct food groups consumed by the household over the past 7 days (</mark> Kennedy et al., 2011) <mark>. Finally, we include information on whether the household suffered any shocks in the last 12 months prior to the interview. These variables are all drawn from self-reported data. In addition, a geospatial variable capturing population density is also included in this dataset.</mark> <mark>Finally, the individual-level dataset includes information about the household members, which is derived from the household questionnaire. A first set of demographic variables includes age, sex, educational attainment including indicators for any formal and primary education. A variable which identifies the members’ relationship to the household head has been included in text format, due to the absence of a unified set of codes. On time use and work/employment, we have indicators capturing any work done during the reference period, the number of hours worked overall, on a farm, in a small enterprise, or in a wage job over a reference seven-day period (either the last seven days, or a typical seven days in the year). Additional variables were added to identify the industry of members’ wage jobs, if applicable. The</mark> 13