Kernel Density Estimation Based on Grouped Data: The Case of Poverty Assessment, Issues 2008-2183
The countries of Eastern Europe achieved two remarkable transitions in the short period of the last two decades: from plan to market and, then, in the run-up to and entry into the European Union, they rode a wave of global trade and financial market integration. Focusing on the second transition, this paper reaches three conclusions. First, by several metrics, East European and East Asian growth performances were about on par from the mid-1990s; both regions far surpassed Latin American growth. Second, the mechanisms of growth in East Europe and East Asia were, however, very different. East Europe relied on a distinctive-often discredited-model, embracing financial integration with structural change to compensate for appreciating real exchange rates. In contrast, East Asia contained further financial integration and maintained steady or depreciating real exchange rates. Third, the ongoing financial turbulence has, thus far, not had an obviously differential impact on emerging market regions: rather, the hot spots in each region reflect individual country vulnerabilities. If the East European growth model is distinctive, is it sustainable and replicable? The paper speculates on the possibilities.
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Bias in the poverty headcount ratio versus location of poverty line
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$1/day poverty line $2/day poverty amount of smoothing ascending order average income biases associated boundary bias canonical bandwidths countries Dagum distribution datapoints datasets decile means density biases Epanechnikov kernel estimate poverty estimated density estimates from grouped Gaussian kernel give rise global poverty estimates grouped data higher poverty lines hybrid bandwidth income averages income distributions income levels Input data kernel density estimation ln(Income Log-normal distribution Lorenz curve median million Minoiu Monte Carlo simulations multimodal distribution nationally representative household Nicaragua nonparametric normal distribution number of quantile optimal bandwidth order statistics oversmoothed bandwidth Panel parametric estimation percent percentage points population quantiles poverty analysis poverty gap poverty headcount ratio poverty indicators poverty measures poverty rates quintile means ratio is overestimated Reddy and Pogge representative household surveys rule-of-thumb bandwidths S3 bandwidth Sala-i-Martin sample small number survey-based Tanzania trimmed means True Density underlying distribution unimodal distributions unit data Weibull distribution weighting function world poverty