terminological matter, but defining “long-termer” in this way does have unsatisfactory aspects. It leaves as undefined women who have large numbers of spells but a modest level of T, for example, who do not fall into any category. Moreover, the literal interpretation of the verb “to cycle” implies a definition based purely on turnover rates and numbers of spells, not a total-time-on definition; hence it makes more sense in terms of language to let T be an outcome of a turnover definition of cycling, not as a definitional characteristic. One could stick with a T-defined classification scheme by parceling out cyclers to the long-termer and short-termer groups by saying that there are two types of cyclers—those with high T, whom we will call long-termers, and those with modest T, who will be called short-termers. But in this case, the latter group is lumped in with the more conventional short-termers with low turnover rates. The consequence would be that one would move from a definitional scheme that allows cyclers to be a heterogeneous group to one that allows long-termers and short-termers to each be heterogeneous, which would not appear to be a gain in terms of clarity. Alternatively, one could move to a classification scheme that has more than three groups, but then simplicity begins to be lost.
For all these reasons, we will use the three-fold classification based solely on N and L. However, we will examine the heterogeneity of the cycler group by examining their distributions of T and compare the different subgroups of cyclers so defined to short-termers and long-termers.
There is surprisingly little evidence in the literature on the characteristics of individuals with different turnover rates and overall spell patterns, or on how groups of individuals defined by long-termer, short-termer, and cycler status differ by characteristics. The vast majority of studies of welfare dynamics present estimates of the determinants of exit from welfare spells or entry onto welfare or, sometimes, of rates of reentry onto welfare after an exit. These econometric models are not set up to distinguish the determinants of turnover per se from the determinants of total-time-on because they impose a restrictive relationship between the effects of the independent variables on turnover rates, total-time-on, and spell lengths. For example, it would not be possible in these models to find that some variable for labor market potential (e.g., mean potential earnings off welfare) could differ between short-termers and cyclers but not between cyclers and long-termers, to take one case. To distinguish these, a more sophisticated statistical specification would be required. Alternatively, and as a first step, it is more natural to simply examine the characteristics of individual recipients as ranked by their turnover rates, total-time-on, and spell lengths, or by their classification into short-termers, long-termers, or cyclers, as is done in this chapter.
A few recent studies have already attempted this, however. Stevens (2000: Table 4), in a study using administrative data from Maryland, found earn-