comparable non‐beneficiary households once included in a program. Two, most economic models predict that means‐tested social assistance programs lead to lower precautionary savings, for example, if beneficiaries expect that safety nets will respond to unanticipated shocks with greater benefits, reducing the need to self‐insure (see Aiyagari 1994) or even the incentive (see Hubbard et al. 1995). However, where studies report the level of savings, such as in the Kenya GIVE program, the IDRC and the Sierra Leone CFW, the value of savings is seen to increase between 9% (CFW) to 92% (GIVE).<sup>43</sup> Furthermore, where heterogeneity analysis is carried out in the TASAF evaluation, the authors indicate that it is the poorest households that are most likely to start saving under the program.<sup>44</sup> This may be taken as evidence that safety‐nets are not perceived by individuals as sufficient to reduce their need to self‐insure, rather through consistent support they are better able to build their own buffer stock savings.

In crises situations, however, these new savings may be quickly exhausted, as was the situation among drought‐affected households under the TASAF program between the midline and endline surveys.<sup>45</sup> Evaluations suggest that households are also using transfers to reduce borrowing and indebtedness, as evidenced under Ghana’s LEAP and the IDRC programs when one off additional or lump sum cash payments are transferred, and under the Malawi SCTS where beneficiaries report less need to make purchases on credit.<sup>46</sup> The evidence on credit access is less clear: evaluations reflect on the increased credit‐worthiness of transfer recipient households (e.g. Ghana LEAP and Kenya HSNP<sup>47</sup> ), but there is little evidence that credit extended has increased. Overall the policy implication is that safety nets can boost savings for improved risk management, but they are not sufficient for households to completely buffer against shocks independently. At the same time, safety net programs are not significantly crowding out private transfers and should not adversely impact other risk management strategies **.** An important sign of resilience is the reduced reliance on temporary low wage work and the use of child labor for household income. Poor, rural households will often sell more than the optimal amount of labor off their farms to obtain an immediate income source. In Malawi, this type of work is known as _ganyu_ and is generally low‐wage and casual, and can lead to poverty traps (Devereaux, 1997). From a household production perspective, selling family labor off‐farm can be optimal as long as the marginal product of labor off‐farm is higher than the marginal product of labor on the farm. However, a growing literature suggests that off‐farm labor reflects a coping strategy rather than income maximization (Kerr 2005; Bryceson 2006; Orr et al. 2009; Michaelowa et al. 2010; Cole and Hoon 2013; Fink et al. 2014). Relatedly, the dependence of income from child labor is suboptimal, particularly when it prevents school attendance and can negatively impact their future earnings potential.

Figure 5 highlights the impacts of safety nets on these two outcomes. The mean effect on the wage labor is a decrease of 16% relative to baseline levels [95% CI: ‐41 to 10%]<sup>48</sup> and the mean effect on child labor

> 43 Aker, 2013 (DRC IDRC), Rosas and Sabarwal 2016 (Sierra Leone CFW), Haushofer and Shapiro 2016 (Kenya GIVE).

> 44 Evans et al. 2014 (Tanzania CB‐CCT).

> 45 Evans et al. 2014 (Tanzania CB‐CCT).

> 46 Aker, 2013 (DRC IDRC), Handa and Park 2013 (Ghana LEAP), Abdoulayi et al. 2015 (Malawi SCTP).

> 47 Handa and Park 2013 (Ghana LEAP), Merttens et al. 2013 (Kenya HSNP).

> 48 Sources: Gilligan et al. 2008 (Ethiopia PSNP), Ardington et al., 2009 (South Africa OAP), Handa and Park 2013

> (Ghana LEAP), Boone et al., 2013 (Malawi SCTP), Pellerano et al. 2014 (Lesotho LCGP), Seidenfeld et al. 2013

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