survey data collected for both households and businesses within local communities covered by safety nets, as well as comparison non‐covered communities, and empirically‐founded local economy simulations researchers have made predictions on the impact safety net programs have not only on beneficiaries but also non‐beneficiaries. These findings indicate that for each dollar transferred to beneficiaries, non‐beneficiaries also see real income increases:<sup>26</sup> 3 to 16 cents in Kenya CTOVC, 30 cents in Zambia ZCGP, 33 cents in Lesotho LCGP, 36 cents in Zimbabwe HSCTP, 39 cents in Ghana LEAP, 26 to 83 cents in Ethiopia SCTPP.<sup>27</sup> These additional income increases are mainly mediated through increased demand for goods and services from the retail and agriculture sectors of local economies, in which other households are involved.

Together with the impacts on beneficiaries, these additional income effects lead to local economy multipliers estimated at 1.08 to 1.84 in real terms, indicating that each dollar transferred to a poor household is predicted to add more than a dollar to total income in the local economy. These set of findings are especially relevant in a low‐income setting highlighting linkages between social protection and the rural economy. Spillover effects are not typically addressed in impact evaluations, and have received comparatively less attention in more established literature on cash transfers. Going forward, an area of policy debate concerns how and whether these outcomes can be sustained as an intervention is scaled up nationally. Given the model assumptions used in the local economy CGE models, for example, fixed input prices for goods produced outside communities, we may expect much more moderated multiplier effects when programs are scaled up nationally and prices adjust.

An alternative approach to local economy CGE models is to run simple partial equilibrium simulation models. These models do not attempt to model any equilibrium effects that might occur when programs are scaled up and it is common to interpret them as the immediate impact of programs prior to household and producer responses that help translate program findings into aggregate policy outcomes (Coady, 2006). We run these models for three countries: Ghana, Liberia, and Niger, that have recent household survey data and provide contrasting starting points in terms of safety net coverage. At the time of surveys, fewer than 4,000 households were covered in Liberia in 2014 (less than 5% of poor); the coverage was 37,000 households in Niger in 2014 (about 10% of poor households); coverage was 70,000 households in Ghana in 2012 (about 30% of poor households). These countries also show diversity in size, the sources of fragility, livelihood vulnerability, sectoral composition, and level of economic development. To ensure comparability, all simulations are made assuming monthly transfers to households of $50 (2011 PPP), equivalent to the median amount transferred in programs included in the meta‐analysis. Table 1 summarizes information on the value of this transfer in each country.

**_Table 1: The Value of Transfers, Ghana, Liberia, and Niger_**

> 26 The local economy CGE models consider some inflationary price effects of increased demands as land and capital are fixed limiting immediate increased local supply, but labor is assumed perfectly elastic. The retail sector which sees greatest income spillover effects assumes input prices are set outside the local economy, which helps to moderate inflationary pressures. For a nationwide scale‐up of a program this assumption may not be appropriate. 27 Thome et al. 2014a (Zambia’s CGP), Taylor et al. 2014a (Zimbabwe’s HSCT), Taylor et al. 2014b (Lesotho’s CGP), Thome et al. 2014b (Ghana’s LEAP), Kagin et al. 2014 (Ethiopia’s SCTPP), Taylor et al. 2013 (Kenya’s CTOVC).

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