of this livestock holding.<sup>32,33</sup> Between midline and endline, the total livestock numbers decreased for both groups (40% treatment vs. 56% control); however, since livestock prices increased faster than inflation during this period, we see an increase in livestock value over the same period (control increase = 4%; treatment increase = 21%) . Treatment households own almost 6 times more in livestock value than control, with the majority of this value (70%) in higher-return assets—cows, goats and sheep. In contrast, control households have the majority of their livestock value (70%) in the lowest-return asset, chickens (not shown). Therefore, both the level and types of livestock assets treatment households hold act as a protective investment for the crisis period.

Another highlight from Figure V is the fact that although the control group was able to accumulate livestock from transfer to midline (under relatively good economic conditions), the gains were transitory. As Figure A2 in the Online Appendix shows in detail, most control UP households remain with low livestock holdings from transfer to endline, and only a few manage to accumulate, compared to treatment households. This is consistent with results from Bangladesh that show that only a small group of control households (5.9%) experience similar changes in assets (as the TUP transfer) in the absence of the program. They also suggest these are mostly transitory and conclude that the probability of control group catching up economically to non-UP households is close to zero (Balboni et al., 2021).

Second, on non-livestock economic activities, treatment households own 56% more non-livestock assets for production (out of 30) (control mean = 0.63; effect size = 0.35; p-value = 0.005); twice as many types of crops cultivated (out of 25) (control mean = 0.47; effect size = 0.55; p-value = 0.043), and a higher likelihood of owning a non-agricultural business (control mean = 0.16; effect size = 0.04; p-value = 0.048) (see Table A10 of the Online Appendix).

Moving to income and revenues, Table 6 shows treatment households increase the primary man and woman’s paid labor income by 19% (p-value = 0.036), increase the number of different income sources by 17% (p-value = 0.002) (Table A10 in the Online Appendix), and generate higher income across 3 out of 4 sources (livestock sales, agriculture, and labor income), as reported in Table 6.

These results are consistent with Bandiera et al. (2017) and Balboni et al. (2021) who show that large transfers are essential for ultra-poor households to reach a threshold of initial assets that allows them to accumulate assets (and, in this case, divest less during crises), and take on better occupations that allow

> 32We use the same equivalence between livestock types used by the implementer at the time of the transfer: one cow is equivalent to three sheep and to four goats. As there was no transfer of chickens, for the relative valuation of chickens, we use the average baseline market household self-valuation of chickens and cows to get a ratio of a chicken equal to 0.017 cow, consistent with the ratio used by the Food and Agriculture Organization of the United Nations (FAO) for international comparisons of baskets of livestock (FAO, 2011).

> 33During the implementation, each transfer was done with either pregnant animals or with the same number of offspring. At transfer, we count each pregnant animal as a non-pregnant animal. Offspring are counted as adults. This is consistent with the midline and endline, where we cannot differentiate between pregnant and non-pregnant animals or between offspring and adults, so each animal is counted as a non-pregnant adult.

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