relations typically do not take into account that incomes fluctuate greatly from year to year. A oneyear income measure gives a snapshot and static picture of the households’ economic status, and fails to take into account the dynamics of poverty (e.g., Hulme and Shepherd 2003). This has made some propose to take asset holdings into account when assessing a household’s poverty status. Carter and May (2001), among others, highlight the importance of assets in poverty analysis, and distinguish between structural (permanent or chronic) and stochastic (temporary or transient) poverty.

In the current paper we extend this approach. Using the method outlined in Dokken and Angelsen (2015), we predict household income based on a range of assets and other household and context characteristics to distinguish between structural and stochastic poverty. We categorize households into the four different categories, based on their observed and predicted income being below/above the poverty line, in a way that resembles the categorization done with panel data.<sup>6</sup> Our approach has several advantages compared to a more conventional approach of using the overall value of assets or an asset index. We can include a broader range of variables that are important in determining household income. Also, we avoid the problem of converting all assets into a monetary value. The regression analysis provides an estimate of the marginal returns to various assets and characteristics.

Households are then classified to belong to one of four categories, as shown in Table 1: We refer to these as the: ( _i) income & asset poor, (ii) income rich & asset poor, (iii) income poor & asset rich,_ and _(iv) income & asset rich._ We use the term “asset poor” in the meaning of “low predicted income”; while assets are critical to predict income, a key point above is that we include other characteristics in an “augmented asset approach” in the regressions to predict income.

**Table 1: Household poverty categories, based on low (< median) and high (> median) observed and** **<u>predicted income</u>**

||**Predicted inco**<br>**Low**|**me(incl. assets)**<br>**High **|
|---|---|---|
|**Observed**<br>**Low**|Income & asset poor<br>(structurally poor)|Income poor & asset rich<br>(stochastically poor)|
|**income**<br>**High**|Income rich & asset poor<br>(stochasticallynon-poor)|Income & asset rich<br>(structurallynon-poor)|

Our distinction between stochastic and structural poverty is relevant for the vulnerability discussion. In their study of long term asset accumulation and income poverty in Ecuador, Moser and Felton (2007) find that stochastically non-poor ( _income rich & asset poor_ ) are very vulnerable. Relatedly, Dercon (2002) propose that “vulnerable households” could be defined as those that will fall below a pre-set poverty line with a certain probability. From a vulnerability perspective, one might therefore argue that _predicted_ income based on a range of assets and other household and context characteristics is a more useful variable than observed one-year income. Predicted income should complement the conventional approach (when using cros s-sectional data) of simply splitting the sample into poor and non-poor based on observed income. The definition of poverty groups matters for policy makers because it can improve the targeting of households and identify

> 6 Our approach extends the work by Nielsen et al. (2012). They use both income and households’ liquid asset holdings to define poverty groups. We include a wider range of household assets and characteristics to predict income.

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