Our paper thus ties local price effects of anti-poverty cash transfers to welfare outcomes among nonbeneficiaries.<sup>4</sup> We do this using household-level data on health, nutrition, and food prices that we collected, and village-level data from the rollout of the Philippine cash transfer program, _Pantawid Pamilya Pilipino Program_ . Stunting rates are high and there is substantial spatial variation in market integration and poverty levels in the Philippines, creating an environment conducive to these important—and pernicious— spillovers. That said, stunting rates remain high around the world (UNICEF 2019) and the fragmentation of local markets causing persistent price differences is found in many low-income countries.<sup>5</sup> Our identification is as follows. First, we use village-level randomization of the program to estimate its impact on beneficiary households’ food budget shares, and child nutrition and food intake.<sup>6</sup> After establishing a demand increase for nutritious foods among beneficiary households, we compare prices in treated and control villages to find that the program increased the prices of key protein rich foods. We then show that this price effect increases in saturation, where saturation is the proportion of potentially eligible households at baseline in both treated and control villages. Our identification thus leverages the randomized treatment assignment of the program in similarly 4 The unintended consequences of cash transfers have more broadly been studied at three levels: (1) the beneficiary households themselves; (2) participating schools, hospitals and other facilities; and (3) local markets. Within the household, evidence generally suggests the presence of positive spillovers through the information channel on nontargeted siblings’ education and work-for-pay, as well as adult health (Ferreira, Filmer, and Schady 2017; Contreras and Maitra 2013). Evidence also suggests that nontargeted outcomes for beneficiaries may be affected in unexpected ways: Bourguignon, Ferreira, and Leite (2003) and de Hoop et al. (2019) show that conditional cash transfers (CCTs) can increase schooling and work-for-pay by the same children. Within participating facilities, there is robust evidence of peer effects-driven increases in schooling enrollment of nontargeted populations, at least in the case of _PROGRESA/Oportunidades_ (Bobba and Gignoux 2019; Bobonis and Finan 2009; Lalive and Cattaneo 2009). Within affected areas, evidence suggests a variety of positive externalities, including improvements in mental health in Kenya (Haushofer and Shapiro 2016) and reductions in crime and political violence in the Philippines (Crost, Felter, and Johnston 2014). Finally, there is some evidence that _Pantawid_ increased clientelism, which in turn improved political stability (Labonne 2013). In contrast, there is also evidence in other settings of a complete absence of externalities: using data from a Nicaraguan CCT, Macours, Schady, and Vakis (2012) do not find any evidence of externalities on nonbeneficiaries.

> 5 Atkin and Donaldson (2015) find that the effect of log-distance on trade costs within Ethiopia or Nigeria is four to five times larger than in the US, suggesting that transportation costs significantly restrict spatial price convergence in poor countries. Related studies on the construction of new roads find similar market-level impacts in India (Aggarwal 2018) and Vietnam (Mu and van de Walle 2011).

6 As the program is targeted at the household level, eligibility is set through a Proxy Means Test that estimates household resources on the basis of a limited number of observed characteristics. We estimate direct effects on eligible households by comparing across households _below_ the treatment threshold in treated and control villages. Spillovers on nonbeneficiaries are calculated by comparing households _above_ the treatment threshold in treated and control villages. The extent to which a village is defined as a natural market is an open question. Markets in treated and control villages might interact, and we might be missing even more negative spillovers occurring outside the local area. Both channels would mean that the spillovers we capture, if anything, are a lower bound.

3