Figure 2).<sup>17</sup> Across the programs, food consumption increases by between 0 and 34% relative to the baseline levels of consumption. While there is considerable heterogeneity within this sample, the evidence reinforces a theory of change across most programs, suggesting that the poorest households will prioritize basic food needs and will switch towards a more diversified diet. Furthermore, the majority of the evidence on individual consumption items suggests that households do not see transfers for increased temptation goods such as alcohol or tobacco,<sup>18</sup> and even where findings may be positive there are on a very small scale e.g. Sierra Leone CFW.<sup>19</sup> This is consistent with the global evidence that cash transfers have significant negative impact on expenditures on temptation goods.<sup>20</sup> Rather there is stronger evidence to suggest that households are improving their standards of living through home improvement expenditures, such as purchasing metal or plastic sheeting for roofs and walls (e.g., Sierra Leone CFW, Kenya GIVE, Lesotho CGP).<sup>21</sup> Programs targeting the poorest households tend to see the greatest consumption impacts. Panel B of Figure 2 shows that programs that do particularly well in terms of consumption gain per dollar transferred are those that target very poor households, again such as the Zambia ZCGP and the Malawi SCTP, where households consume about 170 USD 2011 PPP per month or less. The transfer size to these households is modest both in relative (11‐23% baseline total consumption or 14‐30% baseline food consumption) and absolute terms (21‐27 USD 2011 PPP per month). This finding is quite logical ‐ the poorest live under the most stringent household budgets, where the extra dollar is likely to have its greatest impact on standard of living. The GIVE program in Kenya also targets very poor households and realizes robustly positive consumption gains, although at a slightly lower range – about 45% of transfer size. One explanation is that because the GIVE program made transfers ranging from 45 to 160 (mean of 79) USD 2012 PPP per month this encouraged greater spending on durable assets over consumption expenditures. This program also explored delivering transfers lump sum rather than every month and found this increased investment over consumption. A notable outlier in the data is the negative findings from the Ghana LEAP program, with a confidence interval of 12 to ‐185%.<sup>22</sup> The impact of LEAP on household consumption is essentially zero, likely due to low transfer levels and poor payment logistics– two themes that are discussed shortly. However, the LEAP program is not alone in failing to find significant impacts on consumption – the Ethiopia PSNP, the Lesotho LCGP, the Niger NSNP and the Tanzania CB‐CCT, also fail to find statistically significant 17 Sources: Ward et al. 2010 (Kenya CTOVC), Handa and Park 2013 (Ghana LEAP), Evans et al. 2014 (Tanzania CB‐ CCT), Pellerano et al. 2014 (Lesotho LCGP), Seidenfeld et al. 2013 (Zambia ZCGP), Merttens et al. 2013 (Kenya HSNP), Rosas and Sabarwal 2016 (Sierra Leone CFW), Beegle et al. 2015 (Malawi MASAF), Haushofer and Shapiro 2016 (Kenya GIVE), Premand and Del Ninno 2016 (Niger NSNP), Abdoulayi et al. 2015 (Malawi SCTP). Omitted estimates include the following extreme outliers: Malawi SCTP (180% [95%, 265%]) and Ghana LEAP (‐86% [‐282%, 102%].

18 For example, see Handa et al 2016 (Malawi SCTP), Evans et al 2014 (Tanzania CB‐CCT), Hamoudi and Thomas, 2005 (South Africa OAP) and Haushofer and Shapiro 2016 (Kenya GIVE).

19 Rosas and Sabarwal 2016 (Sierra Leone CFW).

20 Evans and Popova 2017 report that spending on temptation goods decreases on average by 0.19 standard deviations across a study on 19 programs in 10 countries.

21 Rosas and Sabarwal 2016 (Sierra Leone CFW), Haushofer and Shapiro 2016 (Kenya GIVE) and Pellerano et al 2014 (Lesotho LCGP).

22 Confidence interval for LEAP omitted from figure given difference in scale for this result.

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