the most important reason why households fall into poverty is illness of a major income earner in the household, then appropriate primary (and curative) health care can be prioritized; similarly if the principle reason for exiting poverty is obtaining a job, then employment creation can be emphasized. It is therefore important that poverty analysis should attempt to understand the most important reasons for falling into and exiting from poverty. In particular, it is important for us to know the extent to which price reforms are a cause of entries into or exits from poverty.

How might this analysis be done? When there is only one survey available for a country there is little that can be done. This said, it is astonishing that household surveys in addition to collecting large quantities of quantitative data about the _status_ of the household don't also ask the simple qualitative question "what happened?". Households that have fallen into poverty usually know why - analysis of this qualitative information would be extremely valuable and it is important that future surveys conducted should provide for a mix of quantitative and qualitative information about causality.

In some situations we do have more than one survey - usually for different years, but sometimes within year. In this situation there is a lot that can be done. Where the multiple surveys are repeated cross-sections, these are typically used to look at aggregate trends in poverty. However, they also offer the possibility of understanding something about the dynamics of poverty by examining pseudo-panels. For example, one might look at how different cohorts have fared, possibly disaggregated by region, sector or skill (see Deaton (1997) for a discussion and application of the methodology). Although the extent of disaggregation may be limited by the need to maintain reasonable standard errors, such analysis can be very useful in painting a picture of how different groups in society have actually fared as a result of known price shocks. Credible explanations of what has actually happened in previous reform episodes can be more convincing to policymakers than simulations based upon single snapshots in time.

Furthermore, in a handful of cases panel data is available. In these situations much more comprehensive analysis of the impact of economic reforrns is possible. The classic methodology is outlined by Dercon (2000) for Ethiopia. But panel data sets are available and have been analysed for Peru (Glewwe and Hall 1998), Zimbabwe (Alwang, Mills et al.; Hoddinott, Owens et al. 1999), South Africa (Carter and May, 2001), China (Jalan and Ravallion 1998), India (Walker and Ryan 1990), Pakistan (McCulloch and Baulch 2000) and Vietnam (Niimi, Vasudeva-Dutta and Winters, 2002). In other cases intraannual panels may be available (i.e. the same households were recorded at multiple times during the year, but households are different across years). Thus, in addition to exploring how poverty has changed between years, using the intra-annual data to "correct" for seasonality, it may be possible to analyze how seasonality itself moves people into and out of poverty within the year. Seasonality is well documented as one of the most important concerns of the poor (Narayan, Chambers et al. 2000) and since the seasonality of price variation arises precisely because of the lack of suitable behind-the-border trade institutions and policies (warehouse receipting systems, buffer stocks, etc), understanding 26