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Finite sample distributions of studentized inequality measures differ substantially from their asymptotic normal distribution in terms of location and skewness. We study these aspects formally by deriving the second-order expansion of the first and third cumulant of the studentized inequality measure. We state distribution-free expressions for the bias and skewness coefficients. In the second part we improve over first-order theory by deriving Edgeworth expansions and normalizing transforms. These normalizing transforms are designed to eliminate the second-order term in the distributional expansion of the studentized transform and converge to the Gaussian limit at rate O(n-1). This leads to improved confidence intervals and applying a subsequent bootstrap leads to a further improvement to order O(n-3/2). We illustrate our procedure' with an application to regional inequality measurement in Côte d'Ivoire.
Heavy-tailed distributions, such as the distribution of stock returns, are prone to generate large values. This renders difficult the detection of outliers. We propose a new outward testing procedure to identify multiple outliers in these distributions. A major virtue of the test is its simplicity. The performance of the test is investigated in several simulation studies. As a substantive empirical contribution we apply the test to Dow Jones Industrial Average return data and find that the Black Monday market crash was not a structurally unusual event.
Mobility indices are popular tools designed to quantify the extent of income changes by aggregating “local” distributional change into a “global” scalar according to some rule. For some mobility measures, this aggregation rule is only implicit in their standard definition. We derive an insightful approximation to the (statistical) aggregation rule for the important class of mobility indices introduced by Shorrocks (Journal of Economic Theory 19 (1978), 376–93) and further generalized by Maasoumi and Zandvakili (Economic Letters 22 (1986), 97–102), which enables us to characterize their normative properties. We also develop methods for estimation and inference. A substantive empirical contribution emerges from the comparison of mobility between the United States and Germany. Our methods reveal why income mobility is higher in Germany than in the United States: Higher German mobility in the bottom of the distribution is combined with an implicitly higher weighting by the mobility index at the bottom.
We analyze why child poverty rates were much higher in Britain than in Western Germany during the 1990s, using a framework focusing on poverty transition rates. Child poverty exit rates were significantly lower, and poverty entry rates significantly higher, in Britain. We decompose these cross-national differences into differences in the prevalence of “trigger events” (changes in household composition, household labor market attachment, and labor earnings), and differences in the chances of making a poverty transition conditional on experiencing a trigger event. The latter are the most important in accounting for the cross-national differences in poverty exit and entry rates.
No abstract is available for this item.
The approximate effects of measurement error on a variety of measures of inequality and poverty are derived. They are shown to depend on the measurement error variance and functionals of the error-contaminated income distribution, but not on the form of the measurement error distribution, and to be accurate within a rich class of error-free income distributions and measurement error distributions. The functionals of the error-contaminated income distribution that approximate the measurement error induced distortions can be estimated. So it is possible to investigate the sensitivity of welfare measures to alternative amounts of measurement error and, when an estimate of the measurement error variance is available, to calculate corrected welfare measures. The methods are illustrated in an application using Indonesian household expenditure data. Copyright 2002 by The Review of Economic Studies Limited
This article is about statistical inference for inequality and poverty measures when income data exhibit contemporaneous dependence across members of the same household. While much empirical research is based on household survey data such as the PSID, standard methods assume that income is an independent and identically distributed random variable. Applying them to contemporaneously dependent data produces biased results, and Monte Carlo experiments reveal that their confidence intervals are too narrow. By contrast, our proposed distribution-free estimators perform well. Copyright Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association
No abstract is available for this item.
This paper analyzes the performance of unemployment benefit systems in a search theoretic framework. The criteria of evaluation comprise the alleviation of poverty and the reduction in income inequality, while the diversity of opinions about these is taken in account. Also, the trade-off between the attainment of social objectives and work incentives is examined. Copyright 1997 by Royal Economic Society.