debt compared to 47 percent of households in the highest quintile. Finally, only 4.6 to 7.6 percent of all households are at risk before a shock (i.e., the last dark blue column in each panel of Figure 4). The low levels of vulnerable households, as well as the low levels of debt incidence, suggest that household debt is unlikely to become a drag on aggregate economic activity.

<!-- Start of picture text --> Figure 4. Distribution of Household Debt and Household Vulnerability by Income Quintile<br>Panel A. Arbitrary Threshold Panel B. Financial Margin Threshold<br>50<br>40<br>30<br>20<br>10<br>0<br>I II III IV V TOTAL I II III IV V TOTAL<br>Indebted Households Households at Risk<br><!-- End of picture text --> Source: Croatia HBS and authors’ calculations.

It should be noted that the financial margin threshold makes most households in low income quintiles vulnerable. Do banks know something about their borrowers that is not captured in HBS? One can speculate as to possible explanations. A non comprehensive list includes banks’ knowledge about informal sources of income and assets. Kraft (2007) highlights other factors that make household lending less risky, including informal incomes originating in family ties with Croatians living abroad the traditionally stringent banking lending conditions (e.g., codebtor or guarantor requirements). This also points to weaknesses in Croatia’s HBS that are thoroughly discussed in World Bank (2010). In particular, the original HBS sample was framed following the 2001 population census, but consistency checks carried out in recent years using macro data point to an underestimation over time in incomes and consumption expenditures. As a result, household vulnerability in the financial margin threshold calculations would be biased upwards as subsistence consumption levels are imposed on a sample of underestimated incomes. To a lesser degree, this upward bias is also present when arbitrary thresholds are used to tag household as vulnerable. But the problems with the HBS represent a bias only for the initial level of vulnerability; this is less of a concern once, as is the case in this paper, the focus is on changes in vulnerability as a result of economic shocks among households with debt.

The characteristics of indebted and vulnerable household groups are presented in Appendix Table. The table looks at mean values of several household-level indicators while distinguishing according to the vulnerability status of households—vulnerable versus non-vulnerable. Figure 4 already suggests that household vulnerability is more evenly distributed across income quintiles than is the case when the financial margin threshold is used to tag households as vulnerable. With the arbitrary threshold, where the distinction between vulnerable and non-vulnerable (i.e., simply indebted) households is less stark, _t_ -tests suggest that only the mean values on household income (and its sources) and savings—and marginally the household responses on living standards—are not equivalent across vulnerability status. Characteristics on head of household and on the possession of durable goods do not show a statistically significant difference between vulnerable and non-vulnerable households. By contrast, with the financial margin threshold, almost all the mean value indicators of vulnerable households are statistically lower than those of 8