includes the educational variable. We draw from the subnational INFORM Risk Index to measure a municipality’s exposure to natural hazards. We rely on administrative data on homicides to proxy for human hazards. The estimates are very similar to the ones relying on self-reported risk exposure.

**Lastly, column 5 reports results for a model specification which pools several survey waves.** More specifically, we pool data for the years 2016-2019 and estimate our model relying on the pooled data set. We control for year fixed effects and exclude the educational variable. This specification demonstrates that vulnerability is much higher when pooling several survey waves. The higher vulnerability rate is not surprising, given our findings on decreasing vulnerability over time. Importantly, it is not possible to compare vulnerability estimates from pooled data to the one relying on one single year.

_Table 4: Diffrent model specifications and estimates of the total vulnerability rate_

|**Model Specification**|**Baseline**|**Robustness**<br>**Test 1**|**Robustness**<br>**Test 2**|**Robustness**<br>**Test 3**|**Robustness**<br>**Test 4**|
|---|---|---|---|---|---|
|All|0.237|0.295|0.294|0.234|0.410|
|Rural|0.412|0.492|0.498|0.413|0.627|
|Urban|0.135|0.164|0.172|0.139|0.265|
|_Educational variable_|_Yes_|_No_|_No_|_Yes_|_No_|
|_Self-reported hazard exposure_|_Yes_|_Yes_|_No_|_No_|_Yes_|
|_Survey rounds_|_2019_|_2019_|_2019_|_2019_|_2016-2019_|

_Note: The table presents empricial estimates of the total vulnerability rate relying on different model specifications. The first colum presents the baseline estimate. Column 2 refers to a model specification which excludes the educational variable. Column 3 presents results for a model specification which relies on external data sources to measure risk exposure and excludes the educational variable. Column 4 relies on external data sources to measure risk exposure and includes the educational variable. Lastly, Column 5 considers pooled data from survey years 2016 to 2019. Source: World Bank estimates based on EHPM (2019), INFORM (2019)._

# 6.2. Accounting for Potential Measurement Errors

**The income-based measure of welfare employed in this paper is subject to important empirical limitations.** There are two main approaches to measure material well-being of the poor. One approach relies on income while a second approach draws from consumption data. Both approaches have important advantages and disadvantages (Meyer and Sullivan, 2003). While income is easier to measure, it might be inaccurately measured for the poor due to measurement error and under-reporting as well as high levels of informality among those with lower income levels. In addition, consumption might more accurately reflect material well-being. These limitations are an important caveat and could influence the results presented in this paper.

**We test if our results are robust to the inclusion of measurement errors.** One of the weaknesses of the vulnerability tool is that it assumes no measurement errors. Especially in the case of income, measurement errors are not uncommon. As the vulnerability rate draws from the idiosyncratic errors, measurement errors could inflate our vulnerability rate (measurement errors form part of the idiosyncratic error). Therefore, we assume that 25-50 percent of the overall volatility, which we observe across households when looking at their income, stems from measurement errors. We apply this exercise to our tool using the 2019 crosssection of the household data for El Salvador.

**When assuming that half of the total variance is due to measurement errors of income, the vulnerability rate decreases to 18.4 percent.** Therefore, it is lower than when we assume no measurement error. This means that the vulnerability rate is lower than the poverty rate, with a 50 percent measurement error. Still, several of our main findings hold. First of all, rural vulnerability is much higher than the urban one. The vulnerability rate of the rural population is 0.336 compared to 0.095 in urban areas (see Figure

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