and services it also incorporates a basic ―basket of insurance‖ against ―unacceptable risks‖. The main argument is that by embedding risk in the measurement of poverty, such a measure becomes a wider concept of vulnerability that captures the welfare consequences of exposure to risk, not only that of having been subject to shocks.

Longitudinal data for Chile, Mexico and Peru –described in detail in section 4— allow us to observe the incidence of events that could potentially affect welfare. In Mexico, table 1 shows the incidence of several shocks between 2002 and 2005: death or illness of a household member; economic shock defined as business bankruptcy or unemployment; and loss of dwelling, crops and livestock due to climate-related events. While a higher incidence of loss of crops and livestock is evident for poorest households (1st quintile) –probably due to these activities are mostly rural and where poor population could be more exposed to shocks— an important result is that the occurrence of deaths (8.8 percent), health shocks for which the individual was hospitalized (12 percent), economic shocks (6.6 percent) and, in general, any kind of shock (25 percent), is a somewhat stable through the income distribution suggesting thus that the entire population is prone to negative shocks.

**Table 1: Incidence of shocks by income quintiles; Mexico 2002-2005**

_Percentage of households_

|**Income**<br>**quintiles**|**Any shock**|**Death**|**Health**<br>**shock**|**Economic**<br>**shock**|**Loss of**<br>**dwelling**|**Loss of**<br>**crops**|**Loss of**<br>**livestock**|**Dwelling,**<br>**crops and**<br>**livestock**|
|---|---|---|---|---|---|---|---|---|
|1|29.0|10.8|11.5|6.1|1.6|5.7|2.3|8.0|
|2|24.9|8.8|12.0|6.5|1.3|3.7|1.0|5.4|
|3|25.8|8.7|12.2|7.4|0.4|1.9|0.5|2.5|
|4|24.2|8.2|12.1|7.1|0.5|1.2|0.7|2.2|
|5|21.5|7.4|11.0|6.0|0.5|1.5|1.5|3.1|
|**Total**|**25.1**|**8.8**|**11.8**|**6.6**|**0.9**|**2.8**|**1.2**|**4.2**|

_Source_ : Author‘s calculations based on data from MxFLS panel database.

In the cases of Chile and Peru the data shows only the occurrence of health shocks, so that a cross-country comparison is only possible for those shocks requiring hospitalization. While the occurrence of health shocks is stable for all five quintiles in Mexico, in Chile and Peru it shows a growing pattern through the income distribution. Although this pattern suggests a higher use of hospitalization for upper quintiles according to the capacity to pay, these results evidence a generalized exposure to risks in all three countries (see table 2).

Given that insurance markets are actually imperfect or inexistent, households insure against risks through less efficient strategies (e.g. investing resources in activities with low returns, but which assure a minimum level of consumption), making the efficient cost of insuring against risk both variable and problematic to realistically define. We argue that it is possible to find the income level associated with a set of assets and socioeconomic characteristics that would allow the households to be less vulnerable to fall into poverty due to idiosyncratic and asymmetric shocks, and interpret it as an ―augmented‖ poverty line in the spirit of Cafiero and Vakis. From that perspective, and based on the notion discussed in Goldthorpe and McKnight, we explore the link between income and vulnerability to poverty to define the lower threshold of

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