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Gartner, Hermann (2021): Amartya Sen und die Idee der Gerechtigkeit. In: F. Schulze (Hrsg.) (2021): Humanistik und Philosophie, Bd. 2. Jahresband der Humanistischen Akademie 2021, S. 1-8.

Abstract

"Der Aufsatz stellt die Grundideen des Wirtschaftsnobelpreisträgers Amartya Sen zu Fragen der Gerechtigkeit dar. Dabei wird auch der Kontext zu anderen Gerechtigkeitstheorien hergestellt, wie dem Utilitarismus oder den Vorstellungen von John Rawls. Im Zentrum von Amartya Sens Vorstellungen zu Gerechtigkeit steht der Befähigungsansatz. Befähigungen umfassen dabei substantielle Freiheiten, das Leben so zu führen, wie wir es mit guten Gründen wollen." (Autorenreferat, IAB-Doku)

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Kuypers, Sarah & Ive Marx (2021): Poverty in the EU using augmented measures of financial resources: The role of assets and debt. In: Journal of European Social Policy, Jg. 31, H. 5, S. 496-516. DOI:10.1177/09589287211040421

Abstract

"Despite clear limitations, poverty research in the rich world overwhelmingly relies on income-based measures. Households may have significant savings and assets that they can draw on to boost their living standards, but may also have debts that depress the living standard they can actually achieve with their disposable income. Using data from the Eurosystem Household Finance and Consumption Survey (HFCS), this article offers a picture of poverty in 17 EU countries that takes into account assets and debt, using various approaches. While earlier studies have found that poverty rates tend to be lower when wealth is accounted for, this study highlights the situation of those who become or remain poor even when savings and assets are included. It focuses both on within-country patterns of joint income–wealth poverty and on cross-country differences. It is shown that the elderly are generally less prone to being poor once assets are accounted for. However, for renter households with a young, female, low educated, unemployed or inactive and single head, the risk of being poor when assets and debt are accounted for remains high and in some cases even increases. That is generally the case because they have few assets, rather than because of high debts. The substantial variation in poverty rates observed across countries can to some extent be accounted for by socio-demographic factors, but a lot of variation still remains unaccounted for." (Author's abstract, IAB-Doku) ((en))

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Moretti, Angelo, Natalie Shlomo & Joseph Sakshaug (2021): Small area estimation of latent economic well-being. In: Sociological methods & research, Jg. 50, H. 4, S. 1660-1693., 2018-09-17. DOI:10.1177/0049124119826160

Abstract

"Small area estimation (SAE) plays a crucial role in the social sciences due to the growing need for reliable and accurate estimates for small domains. In the study of well-being, for example, policy makers need detailed information about the geographical distribution of a range of social indicators. We investigate data dimensionality reduction using factor analysis models and implement SAE on the factor scores under the empirical best linear unbiased prediction approach. We contrast this approach with the standard approach of providing a dashboard of indicators or a weighted average of indicators at the local level. We demonstrate the approach in a simulation study and a real data application based on the European Union Statistics for Income and Living Conditions for the municipalities of Tuscany." (Author's abstract, IAB-Doku) ((en))

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Bedük, Selçuk (2020): Missing Dimensions of Poverty? Calibrating Deprivation Scales Using Perceived Financial Situation. In: European Sociological Review, Jg. 36, H. 4, S. 562-579. DOI:10.1093/esr/jcaa004

Abstract

"Deprivation scales usually cover some but not all aspects of poverty. Missing dimensions could affect who is and is not identified as poor. Despite its importance, whether missing dimensions affect the measurement of poverty has not been empirically examined in the EU context. Such an examination requires data on missing dimensions that existing surveys do not usually collect. In this article, I get around this problem with an innovative design and using the rich content of the British Household Panel Survey (1999–2008). I use perceived financial inadequacy as a proxy for poverty and show that, independent of the deprivation status, having a need in healthcare, childcare, social care, or education increases the risk of reporting financial inadequacy. The main explanations for these effects are extra spending and reduced earnings of the families (as a response to having extra needs), and not other biases that might arise from using a self-assessed proxy measure such as scale heterogeneity, personality traits, state dependence, anticipations, or psychological negativity. These findings demonstrate the need for more comprehensive measures. Unless relevant indicators of missing dimensions (e.g. cost-related unmet needs in healthcare) are included in the analysis, deprivation scales might fail to identify some people experiencing poverty." (Author's abstract, IAB-Doku) ((en))

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Butterwegge, Christoph (2020): Die zerrissene Republik: wirtschaftliche, soziale und politische Ungleichheit in Deutschland. Weinheim: Beltz Juventa, 414 S.

Abstract

"Seit geraumer Zeit ist das Problem wachsender Ungleichheit das Kardinalproblem unserer Gesellschaft, wenn nicht der gesamten Menschheit. Während daraus im globalen Maßstab ökonomische Krisen, Kriege und Bürgerkriege resultieren, die wiederum größere Migrationsbewegungen nach sich ziehen, sind in Deutschland der soziale Zusammenhalt und die repräsentative Demokratie bedroht. Daher wird nicht bloß thematisiert, wie soziale Ungleichheit entsteht und warum sie zugenommen hat, sondern auch, weshalb die politisch Verantwortlichen darauf kaum reagieren und was getan werden muss, um sie einzudämmen." (Autorenreferat, IAB-Doku, © Beltz Juventa)

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Eren, Gürer & Alfons Weichenrieder (2020): Pro-rich inflation in Europe: Implications for the measurement of inequality. In: German Economic Review, Jg. 21, H. 1, S. 107-138. DOI:10.1515/ger-2018-0146

Abstract

"This paper studies the distributional consequences of a systematic variation in expenditure shares and prices. Using European Union Household Budget Surveys and Harmonized Index of Consumer Prices data, we construct household-specific price indices and reveal the existence of a pro-rich inflation in Europe. Over the period 2001–15, the consumption bundles of the poorest deciles in 25 European countries have, on average, become 11.2 percentage points more expensive than those of the richest deciles. We find that ignoring the differential inflation across the distribution underestimates the change in the Gini (based on consumption expenditure) by almost up to 0.04 points. Cross-country heterogeneity in this change is large enough to alter the inequality ranking of numerous countries. The average inflation effect we detect is almost as large as the change in the standard Gini measure over the period of interest." (Author's abstract, IAB-Doku, Published by arrangement with De Gruyter) ((en))

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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))