# **4. Risk, vulnerability to poverty and social policy**

# **4.1. Risk insurance and vulnerability to poverty**

A large and growing literature shows that in some contexts uninsured risk increases or leads to poverty, through ex-ante behavioral responses, affecting activities, assets and technology choices, as well as through possibly permanent effects from transitory shocks via asset loss, malnutrition, child labor, and withdrawal from schooling.

Based on the MxFLS, one can explore the magnitude of the relative contribution of (at least) the lack of health insurance and the occurrence of the abovementioned shocks to the probability of falling into poverty. Figure 4a shows the changes in probabilities of falling into poverty between 2002 and 2005—calculated from a probit model<sup>18</sup> — which results from changing the insurance status and the occurrence of the shocks shown at the bottom of Table 2 _vis-à-vis_ a baseline that characterizes the i<sup>th</sup> household as follows: “it is located in an urban area in central Mexico; no household member has faced shocks; the household head is male, he has secondary education, he is married, he is a skilled manual worker in the formal sector, and he has health insurance”. The purple bar shows the estimated probability of falling into poverty for the baseline, while gray (having health insurance) and blue (no health insurance) bars show the estimated probabilities when changing the occurrence of shocks, each at a time, holding the rest constant.

The bottom line is that having health insurance reduces dramatically the probability of falling into poverty due to shocks. For instance, if the baseline household faces the death, illness or bankruptcy or unemployment of an economically active household member, its probability of being poor would rise in 4 percentage points (from 8.4 to 12.5 percent). However, if the baseline household faces the same shocks, with the difference of not having access to health insurance, then its probability of being poor would increase 13 percentage points (from 8.4 to 21.4 percent). These results are consistent, though in greater magnitude, for a rural baseline household characterized as follows: “it is located in a rural area in central Mexico; no household member has faced shocks; the household head is male, he has secondary education, he is married, he is a farmer, and he has health insurance” (Figure 4b). Overall, the magnitude of the effect of being covered by insurance schemes is thus relatively large. It is important to note, however, that the longitudinal data used here only covers the 2002-2005 period, while the _Seguro Popular_ program (designed to protect those without social security against financial risks linked to ill health), although it started in 2003, expanded significantly its coverage since 2010 reaching a large percentage of the uninsured (both poor and vulnerable

> 18 The model uses the transition to poverty between 2002 and 2005 as the dependent variable —based on the transition matrices estimated by Lopez-Calva and Ortiz-Juarez (2014)—, and indicators on demography, education, geographic location, labor market resources, self-reported shocks affecting the household, and dwelling characteristics as explanatory variables.

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