from their processes. If one only counts with cross-section data, under some heavy assumptions, intertemporal shocks (such as falling into poverty) are predicted from crosssectional variability. The estimation of such propensity, then, imposes many assumptions on the underlying distribution of welfare and is affected by household and time specific unobserved shocks. Dercon (2001), for example, criticized the cross-sectional approach arguing that the consumption distribution observed in a period of time is contaminated by unobserved components that could be idiosyncratic to the household (individual) or the period when the survey was carried out.

The availability of panel data in developing countries has enabled researchers to examine the dynamic of observable dimensions of welfare, particularly consumption or income. Since households are observed over time, household specific components are controlled for. In addition, surveys implemented during crisis periods have allowed the identification of household behavioral responses to specific shocks (such as currency crises in Russia and Indonesia, or hurricanes in Central America). Distributional assumptions are less restrictive and the welfare distribution can be estimated from actual information about welfare and shocks experienced by households.<sup>3</sup> An example of this approach is the series of papers exploiting the Indonesian panel data that assess the effects of the devaluation of the Indonesian currency on household outcomes. This paper follows this latter approach to examine the consumption/income vulnerability and other dimensions.

# _4._ Evdennce _Ifrom_ MoIdova

The data in this paper is from the Moldova Household Budget Survey. This household survey has been conducted monthly by the Moldova Department of Statistics since 1997. The sampling frame is based on the pooling lists for the December 1996 presidential elections and is intended to be representative at the national level by quarter. It contains information similar to other household budget surveys (like the LSMS, e.g.), with some basic information at the person level, and much more detailed information at the household level.

This paper exploits both the cross section and the panel components of the survey. Out of a quarterly sample of about 1,600 households, a sub-sample is followed with different patterns.<sup>4</sup> For our analysis of consumption and expenditures in health, we form a panel using all households with two yearly observations, one before and one after the Russian Crisis (i.e., the third quarter of 1998). More specifically, we exclude the observations at 1998 Q3, and take as the pre-crisis data that for those households interviewed in 1997Q4, 1998Q1 and 1998Q2. Then the post-crisis data correspond to the second yearly

> 3 Also, some recent studies are starting to place nsk "structurally" in their model by conditioning on available nsk information in their prediction model A more detailed review of this sort of approach is in Dercon (2001).

4 There are three rotation schemes, one quarterly and two yearly. In the quarterly rotation, some panel households are surveyed twice in 2 consecutive quarters (i.e., they are interview for a second time after 3 months). In the yearly rotations, some others are surveyed once a year (during the same month) for 2 years, while some others are surveyed once a year (durmg the same month) for 4 years.

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