their functional form is described in Annex 3. The coefficient β can be interpreted as the effect of seasonality.<sup>15</sup> To examine heterogeneity in the impacts of seasonality, the regressions can be augmented with interaction terms. Simply splitting the sample into different segments of the population provides a first check on heterogeneity in the results. However, to formally test whether the effects of seasonality differ for certain sub-populations, it is also helpful to run regressions of the form: where all variables are defined as above, Zijc gives the ‘cutting variable’ (such as urban-rural or different livelihoods), and δ can be interpreted as the differential impact of seasonality on certain types of households.<sup>16</sup>

# Section 3. Strategies for managing and coping with seasonality in the Sahel

Building on the literature described above, descriptive statistics from the EHCVM data immediately reveal three key characteristics of Sahelian households, which could limit their strategies for managing and coping with seasonality. First, their livelihoods are concentrated in agriculture and rearing livestock and are not well diversified. Second, financial inclusion appears to be low; holdings savings and taking out loans are relatively rare phenomena, even accounting for mobile money. Third, social assistance that could help households weather both seasonality and shocks does not appear to be widespread.

## Section 3.1. Livelihoods in the Sahel

Sahelian households, especially those in rural areas, depend mainly on rainfed agriculture, which is especially exposed to the effects of seasonality. In the absence of widespread irrigation systems, agriculture in the Sahel is mainly rainfed, so yearly swings in temperature and rainfall are crucial for determining output (see Annex 2 for further details). Pooling data from the EHCVM countries, 50.5 percent of the population live in a household where the head’s primary sector of work was agriculture and a further 5.0 percent of the population live in a household where the head worked primarily in raising animals or fishing (Figure 1).<sup>17</sup> Nevertheless, there is substantial variation between the five countries, with work in agriculture and livestock being relatively more common in Burkina Faso, Mali,

> 15 When yijrc is a binary variable, the regression will still be estimated using Ordinary Least Squares, yielding a Linear Probability Model. This means the coefficients can directly be read as marginal effects. The standard errors will be robust to the inherent heteroskedasticity introduced into the model from this approach, as they will be clustered at the ‘grappe’ (or cluster) level for each regression. The results are qualitatively similar if a probit model is estimated and the marginal effects at the mean are calculated.

16 The regressions with interaction terms focus only on continuous outcome variables, principally deflated consumption per capita. This avoids the challenges associated with estimating interaction terms with a binary outcome variable, which may arise even in Linear Probability Models (Holm, Ejrnæs, & Karlson, 2015).

17 The statistics shown in Figure 1 focus on the household head’s primary activity over the last 12 months. Similarly large shares of household members work in agriculture, if looking at the activities in which they engaged during the last seven days. Across the five countries, 52.8 percent of households had a head engaged in farming in the previous seven days and 58.2 percent of all working-age individuals were engaged in farming in the previous seven days.

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