Abstract
This paper focuses on the persistency of poverty in rural and urban households in Ethiopia by estimating dynamic probit models. Unobserved heterogeneity, first order state dependence and serially correlated error component are allowed for The dynamic probit model of poverty that controlled for household heterogeneity and serial correlation performed better in explaining the dynamics of poverty in Ethiopia.. In rural areas, the effect of controlling for heterogeneity and serial correlation was typically in increasing the coefficient of the true state dependence by almost one fold. The statistical significance of some of the observed determinants of poverty remained unchanged. In urban areas controlling for transitory shocks brought out more strongly the effects of differences in towns of residence on the incidence of poverty, while it reduced the importance of such exogenous household attributes as ethnicity, age and family- background. Transitory shocks also contributed to poverty persistence in two additional ways. First, the persistence of urban poverty increased dramatically once we controlled for transitory shocks. Secondly, intrinsic risk of falling into poverty also declined substantially. That is, if not for transitory shocks, only a tiny fraction of the urban population would be at risk of falling into poverty.
Introduction
Existing studies (see Bane and Ellwood, 1986; Stevens, 1994) on the dynamics of poverty commonly use a spell approach to compute the underlying probabilities as functions of the number of durations in a particular spell. This approach, although powerful in capturing the effects of duration in poverty or out of poverty, it does not provide explicitly the magnitude of previous states on the risk of being poor in the present state, which provides an opportunity to estimate state dependency of the motion of poverty. That is, if the risk of entering into poverty is dependent on being in poverty in the previous period, after controlling for unobserved individual effects and serially correlated error components, then, it implies that there is much to be gained from policy interventions that reduce poverty in the current period on the evolution of poverty in subsequent periods. This suggests for the need to actually quantify the true state dependency of the poverty evolution and its contribution to the risk of being in poverty or not. This paper contributes to the literature on poverty dynamics by estimating an econometric model of poverty dynamics that explicitly takes into account the effect of the lag dependent variable, unobserved heterogeneity and serially correlated error components.
The rest of the paper is organized as follows: section 2 describes the data and variables, section 3 provides the methodological framework, discusses the underlying econometric model and methods of estimation, section 4 discuss the results, and Section 5 draws conclusion.
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