No abstract

This paper presents empirical evidence of a direct relationship between financial development and poverty. The empirical modeling employs an efficient panel data estimation technique called fixed effect vector decomposition (FEVD) which is applied to a poverty determination model designed to explain poverty in term of financial development and financial instability. This technique can efficiently estimate time-invariant and rarely changing variable which traditional panel data models cannot. Using panel data the study finds that on average financial development is conducive for poverty reduction but the instability accompanying financial development is detrimental to the poor. This result holds for both measures of financial development namely the ratio of money to GDP (M3-GDP) and the ratio credit to GDP.Keywords: Finance-poverty nexus, Fixed effect vector decomposition, Financial development, Poverty determination Current Research on the Linkages between Financial Development and PovertyIn this study we employ a fixed effect (FE) model to predict the impacts of both the level and the instability of financial development on poverty (Note 1). We chose FE model to allow for the fact that unobserved country specific factors not only affect the poverty rate but also are correlated with our explanatory variables namely level of financial development and financial instability. Pure time series or cross-sectional models provides inconsistent and biased parameter estimates in presence of such correlation. FE models consider the unobserved factors affecting the dependent variable as consisting of two types: those that do not change over time but vary across units, and those that vary both over time and units. FE models remove the time invariant effects by applying some simple transformation (e.g. differencing or demeaning) to the data, and then apply OLS to the transformed data in order to minimize the effect of time varying omitted variables. Briefly this is how FE models handle the potentially large number of unobserved explanatory variables (Note 2).However, this apparent superiority of FE models over pure time-series or cross-sectional ones in handling unobserved heterogeneity does not come free of cost. A widely recognized limitation of FE models is their inability in estimating time-invariant variables (see for instance Baltagi 2001, Wooldridge 2002, and Hsiao 2003. Since the FE models use only the within variance for the estimation and disregards the between variance, they do not allow the estimation of time-invariant variables. A second and by far the less recognized drawback of the FE models results from their

No abstract

No abstract

No abstract