Changing Identity: Retiring from Unemployment

Hetschko, Clemens; Knabe, Andreas; Schöb, Ronnie

doi:10.1111/ecoj.12046

Cited by 105 publications

(48 citation statements)

References 30 publications

(50 reference statements)

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“…Research on life satisfaction, for instance, documents the importance of non-pecuniary costs of unemployment for subjective well-being and thus demonstrates their importance in principle (Winkelmann andWinkelmann, 1998, Kassenböhmer andHaisken-DeNew, 2009). Further results find identity utility to be important (Hetschko et al, 2013), which from a capability view may indicate an effect on being ashamed or respecting oneself. Some studies also directly examine the effect of unemployment on specific functioning achievements.…”

Section: Discussionmentioning

confidence: 78%

Towards a multidimensional poverty index for Germany

Suppa

2017

Empirica

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Section: Discussionmentioning

confidence: 78%

Towards a multidimensional poverty index for Germany

Suppa

2017

Empirica

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“…In a later paper Easterlin et al (2012) Dynan and Ravina (2007), for example, use OLS regressions to study how peoples' happiness depends on their relative income position within their geographic area. Hetschko et al (2014) apply linear regression models to study the impact of the transition from unemployment to retirement on life satisfaction (after conditioning on different sets of explanatory variables). They also compare average life-satisfaction levels of unemployed at different stages around retirement to support their findings.…”

Section: Literature Reviewmentioning

confidence: 99%

Revisiting the evidence for cardinal treatment of ordinal variables

Schröder

Yitzhaki

2017

European Economic Review

108

101

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in SOEPpapers on Multidisciplinary Panel Data Research at DIW BerlinThis series presents research findings based either directly on data from the German SocioEconomic 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.The decision to publish a submission in SOEPpapers is made by a board of editors chosen by the DIW Berlin to represent the wide range of disciplines covered by SOEP. There is no external referee process and papers are either accepted or rejected without revision. Papers appear in this series as works in progress and may also appear elsewhere. They often represent preliminary studies and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be requested from the author directly.Any opinions expressed in this series are those of the author(s) and not those of DIW Berlin.Research disseminated by DIW Berlin may include views on public policy issues, but the institute itself takes no institutional policy positions. The most common practice-comparing well-being by means of descriptive analysis or linear regressions-ignores that the underlying collected well-being information is ordinal. If the well-being function is ordinal, then monotonic transformations are allowed. We demonstrate that treating ordinal data by methods intended to be used for cardinal data may give an incorrect impression of a robust result. Particularly, we derive the conditions under which the use of cardinal method to an ordinal variable gives an illusionary sense of robustness, while in fact one can reverse the conclusion reached by using an alternative cardinal assumption. The paper provides empirical applications.

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“…History provides many examples of subpopulations not needing to work for economic security, ranging from aristocrats in antiquity to many present-day citizens of Qatar. What societal structures and other factors determine whether such populations flourish?Unemployment is not the same as leisure, and there are deep links between unemployment and unhappiness, self-doubt, and isolation (Hetschko, Knabe, and Schöb 2014;Clark and Oswald 1994); understanding what policies and norms can break these links could significantly improve the median quality of life. Empirical and theoretical research on topics such as the basic income proposal could clarify our options (Van Parijs 1992;Widerquist et al 2013).…”

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confidence: 99%

Research Priorities for Robust and Beneficial Artificial Intelligence

2015

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Articles WINTER 2015 105A rtificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agentssystems that perceive and act in some environment. In this context, the criterion for intelligence is related to statistical and economic notions of rationality -colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic representations and statistical learning methods has led to a large degree of integration and crossfertilization between AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems. Copyright AI MAGAZINEAs capabilities in these areas and others cross the threshold from laboratory research to economically valuable technologies, a virtuous cycle takes hold whereby even small improvements in performance have significant economic value, prompting greater investments in research. There is now a broad consensus that AI research is progressing steadily, and that its impact on society is likely to increase. The potential benefits are huge, since everything that civilization has to offer is a product of human intelligence; we cannot predict what we might achieve when this intelligence is magnified by the tools AI may provide, but the eradication of disease and poverty is not unfathomable. Because of the great potential of AI, it is valuable to investigate how to reap its benefits while avoiding potential pitfalls.Progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI. Such considerations motivated the AAAI 2008-09 Presidential Panel on Long-Term AI Futures (Horvitz and Selman 2009) and other projects and community efforts on AI's future impacts. These constitute a significant expansion of the field of AI itself, which up to now has focused largely on techniques that are neutral with respect to purpose. The present document can be viewed as a natural continuation of these efforts, focusing on identifying research directions that can help maximize the societal benefit of AI. This research is by necessity interdisciplinary, because it involves both society and AI. It ranges from economics, law, and philosophy to computer security, formal methods, and, of course, various branches of AI itself. The focus is on delivering AI that is beneficial to society and robust in the sense that the benefits are guaranteed: our AI systems must do what we want them to do.This article was drafted with input from the attendees of the 2015 conference The Future of AI: Opportunities and Challenges (see Acknowledgements),...

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Changing Identity: Retiring from Unemployment

Cited by 105 publications

References 30 publications

Towards a multidimensional poverty index for Germany

Towards a multidimensional poverty index for Germany

Revisiting the evidence for cardinal treatment of ordinal variables

Research Priorities for Robust and Beneficial Artificial Intelligence

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