maximum observed saturation of 94.7 percent. In panel C of Table 3, we see that the interaction effect indicates a price increase of approximately six percentage points in above-median saturated treated villages in comparison to the controls. Although not precisely estimated, the interaction effect of being in a top quartile saturated treated village (panel D in Table 3) is associated with a price increase of 5.4 percent. The price changes in high saturation villages for the “tradable” goods, rice and sugar, are small in magnitude and not precisely estimated. As eggs are the most perishable good in this three-good comparison, the price changes are consistent with the predictions discussed above—we observe a price rise in saturation in program villages, but only for the non-tradable good.

We continue the exploration of price changes and program exposure with official price data. First, Appendix Table 8 explores the covariation in food prices and program saturation using a monthly time series of item-specific food prices collected in each of the Philippines’ 81 provinces by the Philippine Statistics Authority from 2006 to 2014.<sup>22</sup> We present prices for three largely nontradable goods – fresh eggs, fish, and chicken – and three tradable goods – rice, snacks, and sugar. The three largely “non-tradable” goods all exhibit price increases correlated with changes in program saturation at the provincial level, although the estimated coefficients don’t reach the standard levels of precision. If we take the point estimates for the coefficients at face value, maximum observed program saturation at the province level data is 0.40, suggesting that provincial prices for eggs can rise as much as 7.7 percent (0.192*0.40) because of price effects from the _Pantawid_ program. Maximum price increases are on the order of 5 to 6 percent for fresh fish and chicken. In contrast, the coefficients for the three tradable goods show much smaller price covariation with program saturation. This pattern of results is also apparent in the first differenced specifications, with covariation between price changes and program saturation changes for the

> 22 In each province, price enumerators visit six markets, four rural and two urban; however, the locations of these markets are unknown to the researchers. Since these price data serve as an input to the Consumer Price Index of the Philippines, rigorous field controls are used to ensure the quality and comparability of goods assessed (Philippine Statistics Authority 2015). In the analysis, the average annual provincial price for each good, _Pipy_ , is related to program expansion according to the following specification: Pipy = γ0 + γ1Spy + Fp + Fy + εipy, where _S_ is the provinceyear-specific saturation measure and _i_ , _p_ , and _y_ index good, province, and year. The specification also includes province and year fixed effects, _Fp_ and _Fy_ respectively. Standard errors are clustered at the province level. The coefficient of interest, γ1, captures the good-specific price deviation from its provincial mean level, net of common year effects, as a function of the (mean-differenced) changes in provincial program exposure. We also explore an alternative specification that regresses the year-on-year difference in a food good price on the contemporaneous change in program exposure, also controlling for common year fixed effects.

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