Estimating Income Poverty in the Presence of Missing Data and Measurement Error (original) (raw)

Estimating income poverty in the presence of measurement error and missing data problems

2007

Reliable measures of poverty are an essential statistical tool to evaluate public policies aimed at reducing poverty. In this paper we consider the reliability of income poverty measures based on survey data which are typically plagued by measurement error and missing data problems. Neglecting these problems can bias the estimated poverty rates. We show how to derive upper and lower bounds for the population poverty rate using only the sample evidence and an upper limit on the probability of misclassifying people into poor and non-poor. By using the European Community Household Panel, we compute bounds for the poverty rate in eleven European countries and study the sensitivity of poverty comparisons across countries to measurement errors and missing data problems.

Multidimensional approaches to poverty measurement: an empirical analysis of poverty in Belgium, France, Germany, Italy and Spain, based on the European panel

Applied Economics, 2011

This paper has three goals. First, we wish to compare three multidimensional approaches to poverty and check to what extent they identify the same households as poor. Second, we aim at better understanding the determinants of poverty by estimating Logit regressions with five categories of explanatory variables: size of the household, age of the head of the household, her gender, marital status and status at work. Third, we introduce a decomposition procedure proposed recently in the literature, the so-called Shapley decomposition, in order to determine the exact marginal impact of each of the categories of explanatory variables. Our empirical analysis is based on data made available by the European Community Household Panel (ECHP). We used its third wave and selected five countries:

Poverty in Europe and the USA: Exchanging official measurement methods

MPRA Paper, 2007

Official poverty methodologies differ from other poverty measurement methods in the sense that the official ones are more often used as a benchmark to develop new policies as well as to evaluate the performance of existing programs. Europe has the tradition and the practice to use relative poverty as "official" poverty estimates (Common Laeken indicators); the USA use an objective method to estimate official poverty (Orshansky indicator). Although related, each approach portrays different dimensions of poverty. In this study we compare the official poverty methodologies of the USA and EU by applying them on datasets of both countries. Using the harmonized European Community Household Panel (ECHP) for the EU and the Panel Study on Income Dynamics (PSID) for the USA, we compare poverty trends in the USA and EU in relative and absolute terms on a national level as well as for various subgroups of the populations. Additionally, we use the panel dimension of the data to analyze individual poverty dynamics. We find considerable differences between the estimates based on Laeken indicators and the estimates based on an Orshansky type of technology. It was expected that in general Orshansky generates lower poverty estimates than the Laeken indicators. However, it is puzzling to find that a.) these differences are less systematic than expected and b.) these differences are not constant over time and in some cases even have the reverse sign. These findings point to the desirability of involving both poverty concepts into (official) poverty assessments.

Poverty across Europe: the latest evidence using the EU-SILC dataset

the results presented are part of a long-term research project completed within the European observatory on the social situation, financed by the European Commission 1 . the rate of poverty varies between 10% and 23% in the countries of the European union. low levels of poverty characterize the scandinavian countries, the so-called Corporatist countries (austria, Germany), and the Czech republic, slovakia and slovenia among the ex-socialist countries. in contract, the risk of poverty tends to be relatively high in the mediterranean and the Baltic states. this policy Brief discusses the sensitivity of these estimates to the measures used and explores the underlying patterns across the vulnerable groups and the likely causes of poverty in these countries.

Income and Poverty in Households in Selected European Countries

Acta Universitatis Lodziensis. Folia Oeconomica, 2019

Incomes of population and poverty are key elements of the EU cohesion policy which aims at reducing disparities between the levels of development of individual regions. The traditionally appropriate study to evaluate the convergence of the Member States is the European Union Statistics on Income and Living Conditions (EU‑SILC). However, this is not the only source of information on income distribution and social inclusion in the European Union. In this article, the basis for calculations are the results of the fourth European Quality of Life Surveys (EQLS), whose purpose is to measure both objective and subjective indicators of the standard of living of citizens and their households. The aim of the paper is to assess the diversity of distributions of household incomes and the level of income poverty due to the selected socio‑demographic characteristics of the respondent or household in selected European countries in two periods: 2007 and 2016. Countries of the Visegrad Group (Poland...

The Definition and Measurement of Poverty

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. ABSTRACT Both poverty research and social policy employ a variety of poverty definitions. The choice of one specific definition has major consequences for the resulting poverty population. This paper uses eight different definitions of poverty to determine who is poor, using a 1983 Dutch sample of more than 12,000 households. Poverty according to each of these definitions is compared over different subgroups. The relevance of the choice between definitions for social policy is shown by the presentation of poverty percentages according to the various definitions, which vary widely.