by-month results could help to understand how Ramadan influences the effects of the lean season on household consumption.

The main variables used to measure the effects of seasonality on welfare come from the EHCVM’s detailed consumption module. The EHCVM questionnaire collects data on up to 138 food items, recording both own-produced food and purchased food consumed over the previous seven days. Information is also recorded on expenditures on food items over the past 30 days. This can be combined with information on spending on education, health, housing, and many other non-food items to produce an overall consumption aggregate that can be used to proxy welfare; however, specific elements of the consumption aggregate may also be considered separately. This consumption aggregate is spatially and temporally deflated, using prices collected within the EHCVM itself. This allows different households to be compared and allows poverty to be calculated using a single national poverty line. The consumption levels are also converted to 2011 USD using Purchasing Power Parities (PPPs) to facilitate comparisons between countries, where necessary.<sup>10</sup> The detailed consumption data are also complemented by additional information on food security, subjective poverty, and employment to assess the impacts of seasonality fully.<sup>11</sup> The comparisons between the lean and non-lean season waves can be enhanced using multivariate regressions. Since the EHCVM data are representative at the wave level, the raw differences in means between the waves for key outcome variables should be sufficient for estimating seasonal variation in welfare across the 2018/19 agricultural and pastoral cycle.<sup>12</sup> To strengthen these results, however, and to ensure that they do not arise due to confounding differences in the sample between the two seasons, it is also be important to test whether any differences in the outcome variables change when controlling for stable household and location characteristics, that is, household and location characteristics that would not be expected to change dramatically season to season.<sup>13</sup> Specifically, for household i , in community j , in region r , in country c , we run a regression of the form:<sup>14</sup> where yijrc is the outcome variable of interest, Sijrc is a dummy variable capturing the season in which the household was interviewed, Xijrc is a vector of household controls, including the age, sex, and education of the household head, and the size and number of dependents in the household, Grc is a series of region fixed effects, and εijrc is the error term. The specific set of household and location controls and

> 10 Leaving consumption in nominal terms does not significantly affect the main results.

> 11 The time horizons for questions on non-monetary poverty indicators, such as access to health and education, are too long in the EHVCM questionnaire to allow for seasonal variation to be captured.

> 12 Section 4 also shows consumption differences across the entire distribution.

> 13 If β remains stable as variables in Xijrc and the fixed effects are added incrementally, this may suggest that unobserved confounding variables are less likely to be biasing the results (Altonji, Elder, & Taber, 2005; Oster, 2019). We therefore report the results with and without controls and region fixed effects.

> 14 This approach follows a wide range of descriptive work looking at the impact of shocks and seasons on household welfare, see for example Dercon, Hoddinott, and Woldehanna (2005), Harrower and Hoddinott (2005), and World Bank (2005).

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