**Table 10. Difference in total monetary consumption per capita between the lean season and non-lean season waves, split by the household head’s main sector of work**

||Not w|orking|Agricu|lture|Livestoc|k/fishing|Industry/c|onstruction|Servic|es/other|
|---|---|---|---|---|---|---|---|---|---|---|
||No<br>controls|Full<br>controls|No controls|Full<br>controls|No controls|Full controls|No<br>controls|Full<br>controls|No<br>controls|Full controls|
|Lean season<br>wave|-0.0812|-0.0532**|-0.1365***|-0.1429***|-0.1767**|-0.1314***|-0.0787|-0.0698**|-0.0713*|-0.0568***|
||(0.0510)|(0.0261)|(0.0246)|(0.0176)|(0.0712)|(0.0447)|(0.0510)|(0.0284)|(0.0392)|(0.0203)|
|N|2720|2639|7848|7773|1114|1090|2023|2017|6485|6443|
|R-squared|0.0037<br>Note: * p<0.1<br>comprises Bu<br>capita. Individ<br>and education<br>household ro<br>access to imp<br>Bank estimate|0.6208<br>0, ** p<0.0<br>rkina Faso,<br>ual-level sa<br>level of th<br>of material,<br>roved sanit<br>s.|0.0187<br>5, *** p<0.01.<br>Niger, and Sen<br>mpling weigh<br>e household h<br>household flo<br>ation as well a|0.3612<br>Standard er<br>egal. Depen<br>ts applied. ‘F<br>ead, househ<br>or material,<br>s an urban-r|0.0184<br>rors clustered a<br>dent variable is<br>ull controls’ sp<br>old size, the de<br>access to electr<br>ural dummy an|0.5938<br>t the ‘grappe’<br>the log of tot<br>ecifications inc<br>pendency rati<br>icity, access to<br>d region fixed|0.0043<br>level in pare<br>al monetary<br>lude contro<br>o, househol<br>improved<br>effects. Sou|0.6019<br>ntheses. Sa<br>consumptio<br>ls for the ge<br>d walls mate<br>drinking wa<br>rce: EHCVM|0.0030<br>mple<br>n per<br>nder, age,<br>rial,<br>ter, and<br>and World|0.5724<br>|

# Section 6. Conclusion and policy discussion

This paper demonstrates the dramatic effects that seasonality has on Sahelian households’ welfare, especially in rural areas. Household consumption is approximately 10.5 percent lower in the lean season than the non-lean season, as captured by the two waves of the EHCVM sample. Since households across the entire consumption distribution are affected by these welfare losses, this leaves poverty – measured at each country’s national line – 13.7 percentage points higher in Burkina Faso, 6.6 percentage points higher in Niger, and 8.1 percentage points higher in Senegal in the lean season. Moreover, the types of consumption losses that households incur appear to be consistent with particularly extreme forms of deprivation, as the quantities of cereals, or key staple goods, that are consumed are reduced. In-keeping with these results, subjective poverty appears to be 4.4 percentage points higher in the lean season too.

The large seasonal swings in welfare observed in the EHCVM data are unsurprising given the limited strategies that Sahelian households have to manage and cope with seasonality. Households’ income sources are concentrated in agriculture and raising livestock and are not especially well diversified. There is a shift towards work in non-farm household enterprises when the lean season arrives, but this option may not be available to all households, especially in rural areas. Additionally, financial penetration is low, so smoothing consumption through saving or borrowing may be difficult. Finally, social protection is rare compared with the extent of poverty and vulnerability, with only 10.5 percent of Sahelians living in a household that received food assistance during the previous 12 months, and 3.6 percent living in a household receiving cash transfers.

The results in this paper have three clear policy implications; first, expanding the coverage of _regular_ safety net programs – which aim to ensure that even the poorest and most vulnerable households reach a minimum level of consumption and can cover their basic needs – could help tackle seasonal deprivation. Extensive evidence demonstrates that such programs can improve resilience and reduce poverty (Bastagli, et al., 2016; Premand & Stoeffler, 2020). Yet in most Sahelian countries, regular safety nets’ coverage of poor households is extremely limited. In turn, considerable effort is dedicated to trying to track unpredictable shocks and estimate the number of households that will be food insecure when they hit. This includes cases where shocks compound seasonal variation. Every year, annual response plans from 22