Notes: The full list of regression estimates of equation (1) is presented in Annex 2. Additional explanatory variables included but not reported here: family size, head is female, age of head, marital status of head, education level of head, head has a job, building type (single house vs, apartment etc.), type of material for roof, type of material for wall, floor area, type of toilet, water source, and separate binary variables for semester, year, and region, along with a complete set of interactions among these binary variables. Standard errors are corrected for clustering at the barangay level.

It is hypothesized that the direct effect of the typhoon on PCE summarized by the estimate of the parameter 2 is negative ( 2 < 0) . The estimate of 2 in col. 1 of Table 1 reveals that exposure of a barangay to a typhoon in the 6 months before the month of interview is associated with a statistically significant decline in PCE. The coefficient -0.157 in col.1 of Table ββ ββ ββ 1 implies that when the index (denoted by f203_hy) is 1, which implies that there is 100% damage of the building/infrastructure, then PCE decline by 15.7 percent.<sup>19</sup> Along similar lines, PCE on food decline by 23.5 percent (see col. 2) while PCE on non-food are not significantly ff affected. Also, PCE on protein seem to be negatively affected (decline by 48.3%) whereas PCE on fruits and vegetables, cereals, education and medical services appear to be unaffected.

Table 2 below reports estimates of equation (1) including an interaction term of the windspeed variable with the household being a beneficiary of the 4Ps conditional cash transfer program (i.e., the binary variable 4Ps and the interaction term 3 b ∗4P ) are included in equation (1)). This is useful for assessing whether access to the 4Ps program is bb associated with some protection to household welfare (as measured by PCE) from the ββ (WW negative effects of typhoons (at least among the current beneficiaries in the typhoon affected barangays).<sup>20</sup> Given that the 4Ps program was established in 2007, expanding coverage gradually over the years, the estimates in Table 2 are based on the reduced sample of households surveyed in the two most recent years of FIES in 2012 and 2015, when coverage was more extensive.

The coefficient of the binary variable 4Ps is negative and statistically significant which attests to the targeting of the 4Ps program towards the poor (i.e., households benefitting from the 4Ps program have lower PCE than those not in the program). The negative and statistically significant coefficient of the wind speed variable (f203_hy) implies that the shock has a negative effect on the PCE of nonparticipants in the 4Ps program (the reference group). The <mark>interaction of the shock with 4Ps is not statistically significant from zero but, it is positive, which suggests that the (negative) impact of the shock is smaller for 4Ps beneficiaries. In addition, the coefficient of the interaction term of the shock with 4Ps is smaller than the</mark>

> 19 Alternatively, the -0.157 coefficient implies a 11.4% decline in PCE when _f_ = _0_ .729, the value of the _f_ index at the historically maximum windspeed recorded during the period 1950-2016 (0.729).  Using the sample average of the non-zero maximum value of the _f_ index ( _f_ = 0.42), total PCE declines by 6.6%, food PCE decline by 9.87% and protein PCE decline by 20.3%.

> 20 As mentioned above, the present targeting system of the 4Ps is designed towards identifying the chronically poor and not necessarily those “vulnerable to poverty” from a typhoon or other natural disasters.

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