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Abstract
Despite the increasing awareness concerning the importance of distinguishing chronic and transient poverty for policy making, cross-country comparative studies of poverty dynamics in developing countries are virtually non-existent. The small, though increasing, number of existing studies makes it difficult to draw general conclusions because of the methodological differences among them. Crucial questions are still unanswered. Are the factors associated with chronic poverty and vulnerability the same from one country to the other? What are the features that characterize exits from poverty? Based on a large three-wave panel sample of Peruvian and Madagascan urban households (1997–99), the importance of poverty transitions is examined, as well as the characteristics of the temporarily and the chronically poor, with respect to those of non-poor households. Then, we highlight through a multinomial logit model, the specific contributions of household characteristics (demographics, human and physical capital), but also of shocks experienced by these households (related to demographics and the job market) in explaining chronic poverty as well as poverty entries and exits. In this analysis, the impact of location variables linked to neighbourhoods (provision of public goods, income levels, human capital and employment structure, among others) on poverty transitions is also considered. One of the main findings is that the factors associated with permanent poverty amply cover the characteristics generally identified in analyses on static poverty correlates. Nevertheless, these results do not confirm the idea that only shocks are relevant to temporary forms of poverty. The type and quality of entry in the job market, as well as the features of the neighbourhood, turn out to be equally relevant in the analysis of poverty dynamics. These results suggest that the spatial inequality dimension should be added to analyses of income and poverty transition dynamics.
Notes
1. This is an abridged version of a more detailed study on poverty dynamics available at http://www.dial.prd.fr
2. Mitlin stresses this lack of comparability in her review of the main findings on urban chronic poverty in developing countries (Mitlin, Citation2003, p. 1).
3. Both for Peru and Madagascar, poverty levels mentioned in this section agree with official data. They do not compare with those calculated in the rest of the study owing to different data sources and methodological choices (geographic coverage, poverty line, income versus consumption, etc.).
4. Full results of the estimations can be provided from the authors upon request.
5. Therefore avoiding the problem of simultaneous bias found in certain studies in which panels include only two time periods (see Glewwe et al., Citation2000).
6. Chronic poverty is defined as the gap between the poverty line and the average income over the entire period observed, while transient poverty is the remainder of total poverty minus chronic poverty.
7. This is the approach adopted by Glewwe & Hall Citation(1995), and by Grootaert & Kanbur Citation(1995).
8. The term chronic poverty was chosen over permanent poverty owing to the short observation period involved (two years).
9. For obvious reasons of space we will present here only the more complete model including the four sets of variables (model 4). However, the estimates do not change substantially from model 1 to model 4. For more details see Herrera & Roubaud Citation(2003).
10. The only exception is the exit/entry distinction in Madagascar.
11. See McCulloch & Baulch Citation(1999) for a similar argument. In the case of France, Maurin & Chambaz (Citation1996, p. 140) arrived at similar results, which led them to formulate the hypothesis that transitions reflected discontinuities in the working/family life of households, that is, in the “way in which income is created”.
12. Risk factors are highly predictable for non-poverty and chronic poverty, including most traditional variables found in static poverty equations. For a comparison between these two extreme states, see Herrera & Roubaud Citation(2003).
13. This problem is addressed explicitly by Glewwe et al. Citation(2000).
14. When the predicted probability that a household may find itself in one of the four states was higher than 50%, this household was classified in the corresponding state.