Estimating income poverty in the presence of measurement error and missing data problems (original) (raw)
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Estimating Income Poverty in the Presence of Missing Data and Measurement Error
Journal of Business & Economic Statistics, 2011
This series presents research findings based either directly on data from the German Socio-Economic Panel Study (SOEP) or using SOEP data as part of an internationally comparable data set (e.g. CNEF, ECHP, LIS, LWS, CHER/PACO). SOEP is a truly multidisciplinary household panel study covering a wide range of social and behavioral sciences: economics, sociology, psychology, survey methodology, econometrics and applied statistics, educational science, political science, public health, behavioral genetics, demography, geography, and sport science.
Poverty Comparisons with Absolute Poverty Lines Estimated from Survey Data
Review of Income and Wealth, 2007
The objective of measuring poverty is usually to make comparisons over time or between two or more groups. Common statistical inference methods are used to determine whether an apparent difference in measured poverty is statistically significant. Studies of relative poverty have long recognized that when the poverty line is calculated from sample survey data, both the variance of the poverty line and the variance of the welfare metric contribute to the variance of the poverty estimate. In contrast, studies using absolute poverty lines have ignored the poverty line variance, even when the poverty lines are estimated from sample survey data. Including the poverty line variance could either reduce or increase the precision of poverty estimates, depending on the specific characteristics of the data. This paper presents a general procedure for estimating the standard error of poverty measures when the poverty line is estimated from survey data. Based on bootstrap methods, the approach can be used for a wide range of poverty measures and methods for estimating poverty lines. The method is applied to recent household survey data from Mozambique. When the sampling variance of the poverty line is taken into account, the estimated standard errors of Foster-Greer-Thorbecke and Watts poverty measures increase by 15 to 30 percent at the national level, with considerable variability at lower levels of aggregation.
Estimation of poverty measures with auxiliary information in sample surveys
Quality & Quantity, 2011
The analysis of poverty measures has been receiving increased attention in recent years. This paper contributes to the literature by developing percentile ratio estimators based on the pseudo empirical likelihood method. In practice, variances of poverty measures could be not expressible by simple formulae and consequently other techniques should be used in the variance estimation stage. Assuming percentile ratios, resampling techniques are investigated in this paper. A numerical example based on data from the Spanish Household Panel Survey is taken up to illustrate how suggested procedures can perform better than existing ones. The effect of a model-misspecification on the proposed estimators is also evaluated by using simulated populations.
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.
The policy relevance of absolute and relative poverty headcounts: What's in a number?
MPRA Paper, 2007
Financial poverty indicators still play an important role in policymaking and evaluation. Countries such as the USA and the EU member states use one or several 'official' poverty indicators on which success of poverty reduction policy is regularly monitored. Whereas the US poverty indicator is based on an absolute concept of poverty, the EU Laeken indicator is based on a relative concept. But the consequences of such a decision are considerable. As absolute and relative poverty indicators reflect related but conceptually distinct approaches to determining insufficient levels of wellbeing; they can yield very different poverty statistics, particularly over time. In this paper, we use the official EU and US poverty indicators to study the policy relevance of using either an absolute or a relative indicator. We find significant differences between the poverty estimates in poverty rates as well as in the poverty profiles. Benefit incidence-and adequacy rates are equally estimated and compared. The paper concludes that the differences between the two poverty concepts is more than important enough to support monitoring poverty and the related social and economic policies, using both relative and absolute poverty indicators.
Journal of Economic Surveys
We offer a review of methods that have been employed to provide poverty estimates of poverty in contexts where household consumption data are unavailable or missing. These contexts range from completely missing and partially missing consumption data in cross sectional household surveys, to missing panel household data. We focus on methods that aim to compare trends and dynamic patterns of poverty outcomes over time. We present the various existing methods under a common framework, with pedagogical discussion on the intuition. Empirical illustrations are provided using several rounds of household survey data from Vietnam. Furthermore, we also offer a practical guide with detailed instructions on computer programs that can be used to implement the reviewed techniques.
The Estimation of Poverty Dynamics Using Different Measurements of Household Income
Review of Income and Wealth, 1998
If surveys offer two different measurements of household income, one can use them simultaneously to identify the potential effects of measurement error on the observed-income mobility of the poor. In this paper we investigate transition tables between subsequent income states. Latent Markov models are used to model incorrect classifications of income states. Misclassifications are interpreted as measurement error or spurious changes that are not consistent with a simple transition table model. The empirical results for the German Socio-Economic Panel (GSOEP) show that the observed transition tables overestimate the mobility between poverty states.
Measuring Child Poverty and Its Uncertainty: A Case Study of 33 European Countries
Sustainability
Over the last few years, there has been increased interest in compiling poverty indicators for children, as well as in providing uncertainty measures that are associated with point estimates. In this paper, we provide point, variance, and interval confidence estimates of the at-risk-of-poverty rate indicator for 33 European countries. Using the 2018 EU-SILC survey, we analysed the spatial distribution of poverty by providing graphical representations at the national level. Our results reveal rates of child poverty that are higher than in the national estimates for most of the countries. By considering the computation of standard errors, we used the bootstrap method thanks to its convenient properties. It is worth noting that, for some countries, such as Finland, Belgium, and Ireland, the confidence intervals do not overlap. These results suggest differences among countries not only in terms of child poverty, but also in terms of social protection and the welfare state.