A1). Moreover, vulnerability is mainly risk-induced and not poverty-induced, with a ratio of 1.1 (see Figure A2). Still, this ratio is considerably lower than in the case without measurement errors (a ratio of 1.7). Additionally, contrary to our previous results, poverty-induced vulnerability outweighs risk-induced vulnerability in rural areas, where this same ratio is 0.9 (compared to 1.4 in the case without measurement errors). Idiosyncratic vulnerability remains more persistent than covariate vulnerability, but the difference is smaller than in the case without measurement error (see Figure A3). In addition, the large variation with respect to vulnerability across departments persists (see Figure A4).

**Assuming that 50 percent of the variation stems from measurement errors is assuming a substantial measurement error.** Therefore, we alternatively assume that 25 percent of the observed variation is due to measurement errors in our income data. The vulnerability rate, in this case, is 0.21 and poverty-induced vulnerability plays a lower role in overall vulnerability than risk-induced vulnerability (see Annex 2 for the detailed results). This applies to rural as well as urban areas. The vulnerability rate is much larger in rural (0.38) than urban areas (0.12), similarly to the case without measurement error. Idiosyncratic vulnerability persists to be relatively more critical than covariate vulnerability. In the case of one-third of variation stemming from measurement errors, the vulnerability rate is 0.20. Risk-induced vulnerability is still the main driver, both for rural and urban areas. Idiosyncratic vulnerability plays a more significant role than covariate vulnerability.

**Given the above results, our main findings are robust to measurement errors.** While the vulnerability rate decreases when accounting for measurement errors, our main messages persist across the different specifications. Thus, risk-induced vulnerability remains the main driver behind vulnerability, and idiosyncratic vulnerability plays a more significant role than covariate vulnerability. Additionally, there is substantial variation in vulnerability across departments. While some of our main messages fall when increasing the measurement error to 50 percent, the occurrence of a measurement error of this scale is highly unlikely.

# 6.3. Relying on 50 Autorepresentative Municipalities

**We take advantage of the fact that 50 of the included municipalities are autorrepresentative and reestimate vulnerability to poverty using this restricted sample.** This robustness test is useful for estimating covariate vulnerability, especially as the estimation might suffer from the lack of representativeness in the rest of the municipalities. Our findings on the relative importance of idiosyncratic and covariate shocks hold (see Annex 4). The ratio of idiosyncratic to covariate vulnerability is very similar to our baseline results (2.5 versus 2.9). Still, covariate vulnerability is slightly lower when only considering the 50 auto-representative municipalities (0.052 versus 0.095). Overall, the vulnerability rate is lower under this model specification than under the full model specification. This is mainly because the 50 auto representative municipalities are not representative of the overall country in many aspects, and the underlying estimation should not be taken as a reference.

# 6.4. Decreasing the Probability Threshold

**To test the validity of our main messages, we decrease the probability threshold, which defines if a person is vulnerable or not, to 25 percent.** This means that a person is vulnerable if their probability of falling into poverty is higher than 25 percent in the next two years, or a yearly probability of 14.5 percent. Similarly to our robustness check on measurement errors, we apply this robustness check to the 2019 crosssection of the underlying household data.

**Under this threshold, the vulnerability rate in El Salvador is much higher and our main messages reinforce themselves.** Vulnerability is high under a 25 percent vulnerability threshold, with an overall rate of 44.2 percent. Moreover, all of our messages reinforce themselves under this vulnerability threshold (for a detailed overview of the results, see Annex 3). First, the rural population is more affected than the urban one (0.672 versus 0.307). Next, vulnerability is more risk- than poverty-induced, with an overall rate of 4.0. Additionally, this holds for both rural and urban areas. In addition, vulnerability is mainly based on idiosyncratic and not covariate shocks. The variation of vulnerability is high in this case. We can therefore conclude that our main results are robust to alternative thresholds.

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