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Kyzyma, Iryna (2020): How Poor Are the Poor? Looking beyond the Binary Measure of Income Poverty. In: Journal of Economic Inequality, Jg. 18, H. 4, S. 525-549. DOI:10.1007/s10888-020-09453-8

Abstract

"This paper contributes to the literature by analysing how poor the income poor are in European countries. Using data from the European Union Statistics on Income and Living Conditions, I go beyond average estimates of the intensity of poverty and analyse the distribution of individual-level poverty gaps in each country of interest. As a next step, I identify which personal and household characteristics predict how far away incomes of the poor fall from the poverty line. The results indicate that, in most European countries, half of the poor have income shortfalls not exceeding 30% of the poverty line whereas only a few percent of the poor have income deficits of 80% and more. The results also suggest that traditional poverty correlates (e.g. age, gender, educational background) are not always significantly associated with the size of normalised poverty gaps at the individual level, or the nature of these associations differs as compared to when the same characteristics are linked to the probability of being poor." (Author's abstract, IAB-Doku, © Springer-Verlag) ((en))

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Moretti, Angelo, Natalie Shlomo & Joseph Sakshaug (2020): Multivariate small area estimation of multidimensional latent economic wellbeing indicators. In: International statistical review, Jg. 88, H. 1, S. 1-28., 2019-04-25. DOI:10.1111/insr.12333

Abstract

"Factor analysis models are used in data dimensionality reduction problems where the variability among observed variables can be described through a smaller number of unobserved latent variables. This approach is often used to estimate the multidimensionality of wellbeing. We employ factor analysis models and use multivariate EBLUP (MEBLUP) under a unit-level small area estimation approach to predict a vector of means of factor scores representing wellbeing for small areas. We compare this approach to the standard approach whereby we use SAE (univariate and multivariate) to estimate a dashboard of EBLUPs of the means of the original variables and then averaged. Our simulation study shows that the use of factor scores provides estimates with lower variability than weighted and simple averages of standardised MEBLUPs and univariate EBLUPs. Moreover, we find that when the correlation in the observed data is taken into account before small area estimates are computed, multivariate modelling does not provide large improvements in the precision of the estimates over the univariate modelling. We close with an application using the European Union Statistics on Income and Living Conditions data." (Author's abstract, Published by arrangement with John Wiley & Sons) ((en))

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Niehues, Judith, Maximilian Stockhausen, Andreas Peichl, Charlotte Bartels, Mario Bossler, Bernd Fitzenberger, Arnim Seidlitz, Moritz Kuhn, Till Baldenius, Sebastian Kohl, Moritz Schularick & Rolf Kleimann (2020): Ungleichheit unter der Lupe - neue politische Antworten auf ein bekanntes Thema. Zur Diskussion gestellt. In: Ifo-Schnelldienst, Jg. 73, H. 2, S. 3-26., 2020-01-27.

Abstract

"Die öffentliche Debatte lässt uns glauben, die Ungleichheit der Einkommen und des Vermögens in Deutschland und in Europa habe in den letzten Jahren stark zugenommen. Daraus wird die Forderung abgeleitet, man müsse politisch umverteilen, damit die Schere zwischen arm und reich nicht weiter auseinandergeht. Aber sind die Daten wirklich so eindeutig? Unterschiedliche Datensätze führen oft zu unterschiedlichen Aussagen über das Ausmaß von Ungleichheit. Deshalb stellt sich einmal mehr die Frage: Wie kann Ungleichheit quantifiziert werden? Ist der Gini-Koeffizient das richtige Maß oder die Armutsrisikoquote? Gibt es ein Problem, weil die Kapitaleinkommen stärker gewachsen sind als die Lohneinkommen? Oder entwickelt sich vor allem die Vermögensverteilung in der Gesellschaft rasant auseinander? Unsere Autoren diskutieren über Antworten auf diese Fragen. Das Dossier enthält folgende Beiträge:
- Judith Niehues und Maximilian Stockhausen, Ungleichheit(en), ein bekanntes Phänomen? - Andreas Peichl, Die Macht der Zahlen: Ein kritischer Blick auf die Quantifizierung von Ungleichheit - Charlotte Bartels: Steigende Polarisierung der Markteinkommen>> - Mario Bossler, Bernd Fitzenberger und Arnim Seidlitz, Neues zur Lohnungleichheit in Deutschland - Moritz Kuhn, Vermögensungleichheit in Deutschland - Till Baldenius, Sebastian Kohl und Moritz Schularick, Die neue Wohnungsfrage. Gewinner und Verlierer des deutschen Immobilienbooms - Rolf Kleimann, Ungleichheit - sehen, was der Fall ist" (Autorenreferat, IAB-Doku)

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Pattaro, Serena, Nick Bailey & Chris Dibben (2020): Using Linked Longitudinal Administrative Data to Identify Social Disadvantage. In: Social indicators research, Jg. 147, H. 3, S. 865-895. DOI:10.1007/s11205-019-02173-1

Abstract

"Administrative data are widely used to construct indicators of social disadvantage, such as Free School Meals eligibility and Indices of Multiple Deprivation, for policy purposes. For research these indicators are often a compromise between accuracy and simplicity, because they rely on cross-sectional data. The growing availability of longitudinal administrative data may aid construction of more accurate indicators for research. To illustrate this potential, we use administrative data on welfare benefits from DWP’s National Benefits Database and annual earnings from employment from HMRC’s P14/P60 data to reconstruct individual labour market histories over a 5-year period. These administrative datasets were linked to survey data from the Poverty and Social Exclusion UK 2012. Results from descriptive and logistic regression analyses show that longitudinal measures correlate highly with survey responses on the same topic and are stronger predictors of poverty risks than measures based on cross-sectional data. These results suggest that longitudinal administrative measures would have potentially wide-ranging applications in policy as well as poverty research." (Author's abstract, IAB-Doku, © Springer-Verlag) ((en))

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Cremer, Georg (2019): Fallstricke der Armutsdebatte. In: Ifo-Schnelldienst, Jg. 72, H. 10, S. 27-33.

Abstract

"Von den Vorstellungen über Armut hängt ab, wie Maßnahmen zur materiellen Besserstellung armer Personen oder zur Erhöhung ihrer Teilhabechancen bewertet werden. Georg Cremer, ehemaliger Generalsekretär des Deutschen Caritasverbandes e. V., zeigt, dass einige Armutsindikatoren, beispielsweise die Verwendung der Zahl der Grundsicherungsbezieher, problematisch sind. Eine Debatte zu geeigneten Armutsindikatoren ist notwendig." (Autorenreferat, IAB-Doku)

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Dang, Hai-Anh, Dean Jolliffe & Calogero Carletto (2019): Data gaps, data incomparability, and data imputation: A review of poverty measurement methods for data-scarce environments. In: Journal of Economic Surveys, Jg. 33, H. 3, S. 757-797. DOI:10.1111/joes.12307

Abstract

"Questions that often come up in contexts where household consumption data are unavailable or missing include: what are the best existing methods to obtain poverty estimates at a single snapshot in time? and over time? and what are the best available methods to study poverty dynamics? A variety of different techniques have been developed to tackle these questions, but unfortunately, they are presented in different forms and lack unified terminology. We offer a review of poverty imputation methods that address contexts ranging from completely missing and partially missing consumption data in cross-sectional household surveys, to missing panel household data. We present the various existing methods under a common framework, with pedagogical discussion on their 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." (Author's abstract, Published by arrangement with John Wiley & Sons) ((en))

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Dütsch, Matthias, Ralf Himmelreicher & Clemens Ohlert (2019): Calculating gross hourly wages: The (structure of) earnings survey and the German Socio-Economic Panel in comparison. In: Jahrbücher für Nationalökonomie und Statistik, Jg. 239, H. 2, S. 243-276. DOI:10.1515/jbnst-2017-0121

Abstract

"The statutory minimum wage in Germany was set as an hourly wage. Thus, valid information on gross hourly wages must be calculated from monthly wages and weekly working hours. This paper compares the German Socio-Economic Panel (GSOEP) and the (Structure of) Earnings Survey (SES/ES). The sampling and collection of data on employees in the household survey GSOEP, and on jobs in the administrative surveys SES/ES exhibit fundamental conceptual differences. Accordingly, there is variation in the definition of types of employment and in the distribution of the observed units regarding central characteristics. Monthly wages, weekly working hours and gross hourly wages differ especially in the lower range of the respective distribution. Against this backdrop specific implications can be derived for minimum wage research." (Author's abstract, © De Gruyter) ((en))

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Furceri, Davide & Jonathan D. Ostry (2019): Robust determinants of income inequality. In: Oxford review of economic policy, Jg. 35, H. Nol. 3, S. 490-517. DOI:10.1093/oxrep/grz014

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Furman, Edward, Yisub Kye & Jianxi Su (2019): Computing the Gini index: A note. In: Economics Letters, Jg. 185. DOI:10.1016/j.econlet.2019.108753

Abstract

The Gini index of inequality has been extensively studied by economists in a variety of contexts with the notions of wealth and income distribution serving as two primary examples. Nevertheless, the Gini index is by far less popular outside of the economics literature, and even in economics it is not uncommon to replace Gini with other measures of inequality. A reason for this lies in the critics associated with the computability of the Gini index. In this note, we reveal convenient ways to compute the Gini index explicitly and in some cases effortlessly. The thrust of our approach is the herein discovered link between the Gini index and the notion of statistical sample size-bias. Not only the just-mentioned link opens up advantageous computational routes for the Gini index, but also yields an alternative interpretation for it.

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Hochgürtel, Tim (2019): Einkommensanalysen mit dem Mikrozensus. In: Wirtschaft und Statistik H. 3, S. 53-64.

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Hérault, Nicolas & Stephen P. Jenkins (2019): How valid are synthetic panel estimates of poverty dynamics? In: Journal of Economic Inequality, Jg. 17, H. 1, S. 51-76. DOI:10.1007/s10888-019-09408-8

Abstract

"A growing literature uses repeated cross-section surveys to derive 'synthetic panel' data estimates of poverty dynamics statistics. It builds on the pioneering study by Dang et al. ('DLLM', Journal of Development Economics, 2014) providing bounds estimates and the innovative refinement proposed by Dang and Lanjouw ('DL', World Bank Policy Research Working Paper 6504, 2013) providing point estimates of the statistics of interest. We provide new evidence about the accuracy of synthetic panel estimates relative to benchmarks based on estimates derived from genuine household panel data, employing high quality data from Australia and Britain, while also examining the sensitivity of results to a number of analytical choices. For these two high-income countries we show that DL-method point estimates are distinctly less accurate than estimates derived in earlier validity studies, all of which focus on low- and middle-income countries. We also demonstrate that estimate validity depends on choices such as the age of the household head (defining the sample), the poverty line level, and the years analyzed. DLLM parametric bounds estimates virtually always include the true panel estimates, though the bounds can be wide." (Author's abstract, IAB-Doku, © Springer-Verlag) ((en))

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McGregor, Thomas, Brock Smith & Samuel Wills (2019): Measuring inequality. In: Oxford review of economic policy, Jg. 35, H. Nol. 3, S. 368-395. DOI:10.1093/oxrep/grz015

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Meyer, Bruce D., Derek Wu, Victoria R. Moores & Carla Medalia (2019): The use and misuse of income data and extreme poverty in the United States. (NBER working paper 25907), Cambrige, Mass., 60 S. DOI:10.3386/w25907

Abstract

"Recent research suggests that rates of extreme poverty, commonly defined as living on less than $2/person/day, are high and rising in the United States. We re-examine the rate of extreme poverty by linking 2011 data from the Survey of Income and Program Participation and Current Population Survey, the sources of recent extreme poverty estimates, to administrative tax and program data. Of the 3.6 million non-homeless households with survey-reported cash income below $2/person/day, we find that more than 90% are not in extreme poverty once we include in-kind transfers, replace survey reports of earnings and transfer receipt with administrative records, and account for the ownership of substantial assets. More than half of all misclassified households have incomes from the administrative data above the poverty line, and several of the largest misclassified groups appear to be at least middle class based on measures of material well-being. In contrast, the households kept from extreme poverty by in-kind transfers appear to be among the most materially deprived Americans. Nearly 80% of all misclassified households are initially categorized as extreme poor due to errors or omissions in reports of cash income. Of the households remaining in extreme poverty, 90% consist of a single individual. An implication of the low recent extreme poverty rate is that it cannot be substantially higher now due to welfare reform, as many commentators have claimed." (Author's abstract, IAB-Doku) ((en))

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Meyer, Bruce D. & Nikolas Mittag (2019): Using linked survey and administrative data to better measure income. Implications for poverty, program effectiveness and holes in the safety net. In: American Economic Journal. Applied Economics, Jg. 11, H. 2, S. 176-204. DOI:10.1257/app.20170478

Abstract

"We examine the consequences of survey underreporting of transfer programs for prototypical analyses of low-income populations. We link administrative data for four transfer programs to the CPS to correct its severe understatement of transfer dollars received. Using survey data sharply understates the income of poor households, distorts our understanding of program targeting, and greatly understates the effects of anti-poverty programs. Using the combined data, the poverty-reducing effect of all programs together is nearly doubled. The effect of housing assistance is tripled. Correcting survey error often reduces the share of single mothers falling through the safety net by one-half or more." (Author's abstract, IAB-Doku) ((en))

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Bedük, Selçuk (2018): Understanding material deprivation for 25 EU countries. Risk and level perspectives, and distinctiveness of zeros. In: European Sociological Review, Jg. 34, H. 2, S. 121-137. DOI:10.1093/esr/jcx089

Abstract

"Existing deprivation scales identify a majority of the population in each European Union (EU) country with zero deprivation. In this article, I hypothesize and test whether scoring zero on a material deprivation scale is a qualitatively different phenomenon to scoring at least one by applying and comparing multiple count models. I then examine how neglecting the distinctiveness of zeros, as the case in conventional models, influences our understanding of deprivation risk (deprived vs. non-deprived) and deprivation level (high vs. low deprivation), specifically regarding their relationship to social class. Consistently across 25 EU countries, the findings show that those with zero deprivation have significantly distinct profiles to those who have at least one deprivation. These results are robust to different weighting and index specifications. I then demonstrate how neglecting the distinctiveness of zeros results in significant underestimation of the strong social class gradient in risk of deprivation, and significant overestimation of the rather weak social class gradient in level of deprivation. Moreover, accounting for the distinctiveness of zeros reveals the conceptual difference between the risk and the level of deprivation given their different determinants, while conventional models identify the same determinants for both. These latter findings are also broadly consistent across 25 EU countries, with some exceptions in countries with very low level of zeros, such as Hungary, Bulgaria, and Romania. Relevant scales with a zero threshold can be used to study deprivation or to measure poverty in the EU yet either with some reconsiderations of conceptual and data problems or using a consistent poverty approach." (Author's abstract, IAB-Doku) ((en))

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Beste, Jonas, Markus M. Grabka & Jan Göbel (2018): Armut in Deutschland. Ein Vergleich zwischen den beiden Haushaltspanelstudien SOEP und PASS. In: Wirtschafts- und Sozialstatistisches Archiv, Jg. 12, H. 1, S. 27-62., 2018-02-19. DOI:10.1007/s11943-018-0221-4

Abstract

"Die Ergebnisse von Armutsanalysen auf Basis von Befragungsdaten unterliegen statistischen Unsicherheiten und möglichen systematischen Verzerrungen, deren Ursachen sowohl in der Pre-Data-Collection-Phase (z.B. bei der Stichprobenziehung), der Data-Collection-Phase (Unit- bzw. Item-Non-Response), als auch in der Post-Data-Collection-Phase (Gewichtung, Datengenerierung) liegen können. Um diese studienspezifischen Einflüsse bewerten zu können, kann ein Vergleich der Ergebnisse auf Basis von mehreren Datenquellen hilfreich sein. In dieser Untersuchung werden die Einkommensverteilungen, mehrere Armutsmaße, die identifizierten Armutsrisikoquoten von Subpopulationen, Armutsfaktoren, die Betroffenheit von dauerhafter Armut sowie Auf- und Abstiege mit den beiden Haushaltspanelstudien Panel 'Arbeitsmarkt und soziale Sicherung' (PASS) und soziooekonomisches Panel (SOEP) berechnet und miteinander verglichen. Als zusätzliche Referenz nutzen wir Ergebnisse zu Armut basierend auf dem Mikrozensus. Ziel dieses Vorhabens ist es, die Aussagekraft der Ergebnisse von Armutsanalysen besser einschätzen zu können. Zwischen den beiden Studien können signifikante Unterschiede in den Armutsmaßen festgestellt werden, welche sich allerding teilweise über den Beobachtungszeitraum auflösen. Eine Annäherung der in PASS bestimmten Armut an die Werte des SOEP kann entweder durch einen Ausfallsprozess in den ersten Erhebungsjahren der PASS-Studie oder durch eine Verbesserung der Datenqualität der Einkommensinformation bedingt sein. Die Resultate einer multivariaten Analyse auf die Wahrscheinlichkeit ein Einkommen unterhalb der Armutsschwelle aufzuweisen weichen zwischen den beiden Studien kaum voneinander ab. In der beobachteten Armutsdynamik weisen die beiden Panelstudien neben großen Gemeinsamkeiten auch klare Unterschiede auf. Insgesamt zeigt sich ein in vielen Bereichen vergleichbares Bild von relativer materieller Armut in den beiden Haushaltspanelstudien, das jedoch in einigen Punkten voneinander abweicht. Hieraus lässt sich die Relevanz für die Analyse von Armut anhand mehrerer verschiedener Datenquellen ableiten." (Autorenreferat, © Springer-Verlag)

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(2018): Armut stört. Schattenbericht der Nationalen Armutskonferenz. (Armut in Deutschland : Schattenbericht der Nationalen Armutskonferenz), Berlin, 38 S.

Abstract

"Am 17. Oktober 2018, dem Internationalen Tag zur Beseitigung der Armut, veröffentlicht die Nationale Armutskonferenz ihren dritten Schattenbericht zur Armut in Deutschland. Der Bericht gibt einen Überblick über den armutspolitischen Handlungsbedarf und lässt Betroffene zu Wort kommen." (Autorenreferat, IAB-Doku)

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Bossert, Walter & Bhaskar Dutta (2018): The measurement of welfare change. (Warwick economic research papers 1151), Coventry, 13 S.

Abstract

"We propose and characterize a class of measures of welfare change that are based on the generalized Gini social welfare functions. In addition, we analyze these measures in the context of a second-order dominance property that is akin to generalized Lorenz dominance as introduced by Shorrocks (1983) and Kakwani (1984). Because we consider welfare differences rather than welfare levels, the requisite equivalence result involves linear welfare functions (that is, those associated with the generalized Ginis) only, as opposed to the entire class of strictly increasing and S-concave welfare indicators." (Author's abstract, IAB-Doku) ((en))

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Desmond, Matthew & Bruce Western (2018): Poverty in America: New directions and debates. In: Annual review of sociology, Jg. 44, S. 305-318. DOI:10.1146/annurev-soc-060116-053411

Abstract

"Reviewing recent research on poverty in the United States, we derive a conceptual framework with three main characteristics. First, poverty is multidimensional, compounding material hardship with human frailty, generational trauma, family and neighborhood violence, and broken institutions. Second, poverty is relational, produced through connections between the truly advantaged and the truly disadvantaged. Third, a component of this conceptual framework is transparently normative, applying empirical research to analyze poverty as a matter of justice, not just economics. Throughout, we discuss conceptual, methodological, and policy-relevant implications of this perspective on the study of extreme disadvantage in America." (Author's abstract, IAB-Doku) ((en))

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Gallardo, Mauricio (2018): Identifying vulnerability to poverty. A critical survey. In: Journal of Economic Surveys, Jg. 32, H. 4, S. 1074-1105. DOI:10.1111/joes.12216

Abstract

"In the economic literature on poverty, various methods have been proposed for measuring a phenomenon known as 'vulnerability'. However, after more than a quarter century of research, no consensus has been reached on how to identify such vulnerable individuals within a given population. Some misunderstandings have also arisen from the overlapping of other closely related concepts, such as the expectation of being poor, expected poverty, multi-period poverty and risk exposure. This paper offers a detailed conceptual discussion on vulnerability to poverty and its related elements, reviewing a wide range of identifying criteria provided in the literature. It is found that according to the state of the art in this ?eld of research, two key elements stand out in identifying vulnerable individuals: an expected well-being below the poverty line and a relevant risk of falling into poverty due to downside deviation from a reference level of well-being. The traditional classi?cation of vulnerability approaches has been updated into four groups: (i) those that stress the element of exposure to risk; (ii) those that emphasize the element of expected poverty; (iii) those that de?ne vulnerability through a utility gap and (iv) those that are supported by a mean-risk dominance criterion." (Author's abstract, Published by arrangement with John Wiley & Sons) ((en))