AStA Wirtschafts- und Sozialstatistisches Archiv最新文献

{"title":"Measuring the productivity effects of digital capital—a conceptual approach","authors":"Michael Grömling,&nbsp;Thomas Niebel","doi":"10.1007/s11943-025-00350-y","DOIUrl":"10.1007/s11943-025-00350-y","url":null,"abstract":"<div><p>Productivity growth in the advanced economies has been slowing down for some time. This is surprising insofar as large-scale technological impulses are expected as a result of the digital revolution. An analysis for Germany points to significantly weakening productivity impulses from technological progress and in particular from capital formation. In order to understand the weaker capital stock effects, gross fixed capital formation is briefly examined according to different components. Starting from the established types of investment and capital, we discuss how the empirical coverage of capital as a production factor can be advanced. The focus is on a more broadly defined digital capital stock. Comprehensive indicators to describe the extent and development of the general digitization also offer conceptual starting points for defining and measuring digital capital. In this way, it could be possible to estimate what digital capital is actually available for domestic production, how it develops over time and, ultimately, what contributions to growth and productivity it makes. The paper lists a series of requirements necessary for an implementation. The dynamics of the capital stock depend on whether and to what extent current investments exceed retirements of fixed assets. In any case, rising investment per unit of labor does not necessarily indicate an increase in capital deepening. However, modern capital goods in particular are characterized by a high rate of innovation and correspondingly high retirements. This leads us to expect that even comparatively high investments based on a concept that has been comprehensively expanded in terms of digitization will not necessarily trigger correspondingly high capital stock and productivity impulses.</p></div>","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"18 3-4","pages":"319 - 335"},"PeriodicalIF":0.0,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vorwort der Herausgeber","authors":"Jan Pablo Burgard,&nbsp;Markus Zwick","doi":"10.1007/s11943-025-00352-w","DOIUrl":"10.1007/s11943-025-00352-w","url":null,"abstract":"","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"18 3-4","pages":"301 - 303"},"PeriodicalIF":0.0,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11943-025-00352-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143108494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interview mit Helmut Küchenhoff","authors":"Ulrich Rendtel","doi":"10.1007/s11943-024-00349-x","DOIUrl":"10.1007/s11943-024-00349-x","url":null,"abstract":"","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"18 3-4","pages":"337 - 347"},"PeriodicalIF":0.0,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vorwort der Herausgeber","authors":"Jan Pablo Burgard,&nbsp;Markus Zwick,&nbsp;Florian Dumpert,&nbsp;Sebastian Wichert,&nbsp;Thomas Augustin,&nbsp;Nina Storfinger","doi":"10.1007/s11943-024-00347-z","DOIUrl":"10.1007/s11943-024-00347-z","url":null,"abstract":"","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"18 2","pages":"127 - 130"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11943-024-00347-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Connecting algorithmic fairness to quality dimensions in machine learning in official statistics and survey production","authors":"Patrick Oliver Schenk,&nbsp;Christoph Kern","doi":"10.1007/s11943-024-00344-2","DOIUrl":"10.1007/s11943-024-00344-2","url":null,"abstract":"<div><p>National Statistical Organizations (NSOs) increasingly draw on Machine Learning (ML) to improve the timeliness and cost-effectiveness of their products. When introducing ML solutions, NSOs must ensure that high standards with respect to robustness, reproducibility, and accuracy are upheld as codified, e.g., in the Quality Framework for Statistical Algorithms (QF4SA; Yung et al. 2022, <i>Statistical Journal of the IAOS</i>). At the same time, a growing body of research focuses on fairness as a pre-condition of a safe deployment of ML to prevent disparate social impacts in practice. However, fairness has not yet been explicitly discussed as a quality aspect in the context of the application of ML at NSOs. We employ the QF4SA quality framework and present a mapping of its quality dimensions to algorithmic fairness. We thereby extend the QF4SA framework in several ways: First, we investigate the interaction of fairness with each of these quality dimensions. Second, we argue for fairness as its own, additional quality dimension, beyond what is contained in the QF4SA so far. Third, we emphasize and explicitly address data, both on its own and its interaction with applied methodology. In parallel with empirical illustrations, we show how our mapping can contribute to methodology in the domains of official statistics, algorithmic fairness, and trustworthy machine learning.</p><p>Little to no prior knowledge of ML, fairness, and quality dimensions in official statistics is required as we provide introductions to these subjects. These introductions are also targeted to the discussion of quality dimensions and fairness.</p></div>","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"18 2","pages":"131 - 184"},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11943-024-00344-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Bayesian variable selection methods for binary regression models with missing covariate data","authors":"Michael Bergrab,&nbsp;Christian Aßmann","doi":"10.1007/s11943-024-00345-1","DOIUrl":"10.1007/s11943-024-00345-1","url":null,"abstract":"<div><p>Data collection and the availability of large data sets has increased over the last decades. In both statistical and machine learning frameworks, two methodological issues typically arise when performing regression analysis on large data sets. First, variable selection is crucial in regression modeling, as it helps to identify an appropriate model with respect to the considered set of conditioning variables. Second, especially in the context of survey data, handling of missing values is important for estimation, which occur even with state-of-the-art data collection and processing methods. Within this paper, we provide an Bayesian approach based on a spike-and-slab prior for the regression coefficients, which allows for simultaneous handling of variable selection and estimation in combination with handling of missing values in covariate data. The paper also discusses the implementation of the approach using Markov chain Monte Carlo techniques and provides results for simulated data sets and an empirical illustration based on data from the German National Educational Panel Study. The suggested Bayesian approach is compared to other statistical and machine learning frameworks such as Lasso, ridge regression, and Elastic net, and is shown to perform well in terms of estimation performance and variable selection accuracy. The simulation results demonstrate that ignoring the handling of missing values in data sets can lead to the generation of biased selection results. Overall, the proposed Bayesian method offers a holistic, flexible, and powerful framework for variable selection in the presence of missing covariate data.</p></div>","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"18 2","pages":"203 - 244"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11943-024-00345-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Die Volkswirtschaftlichen Gesamtrechnungen in Zeiten der Pandemie – wurden alle Herausforderungen gemeistert?","authors":"Josef Richter","doi":"10.1007/s11943-024-00348-y","DOIUrl":"10.1007/s11943-024-00348-y","url":null,"abstract":"<p>Die Corona-Pandemie hat die amtliche Statistik und insbesondere die Volkswirtschaftlichen Gesamtrechnungen vor gewaltige Aufgaben gestellt. Es galt neue, vorher nie beobachtete Phänomene in das System zu integrieren und es musste mit fehlenden und geänderten Datengrundlagen operiert werden. Gleichzeitig waren unter erschwerten Bedingungen die legitimen Informationsbedürfnisse der Allgemeinheit und der Entscheidungsträger zu befriedigen. Zudem wurden auch grundlegende Konzeptfragen, die üblicherweise vernachlässigt werden können, virulent. So war die Frage zu beantworten, ob es Preise geben kann, wenn keine Transaktionen stattfinden und es galt sich darüber klar zu werden, welcher Produktionsbegriff eigentlich operationalisiert werden soll.</p><p>Im System der Volkswirtschaftlichen Gesamtrechnungen ist sowohl ein technischer Produktionsbegriff als auch ein ökonomischer Produktionsbegriff präsent, wie an Hand ausgewählter Bestimmungen näher gezeigt wird. Unter Normalbedingungen werden sich bei beiden Ansätzen ähnliche Ergebnisse ergeben. Wie in dem Beitrag illustriert wird, können unter den besonderen Umständen der Pandemie freilich größere Unterschiede resultieren.</p><p>Unter dem Druck der Ereignisse wurde in der Pandemie ein sehr pragmatisches Vorgehen gewählt und die Nichtbefassung mit zentralen Konzeptfragen auch damit entschuldigt, dass die Auswirkungen auf die großen Aggregate gering sind. Für die Erfüllung der Aufgabe des Monitoring und für die dominierenden operationalen Funktionen der Daten trifft dies sicher zu. Die Gesamtrechnungen haben aber auch eine wichtige Rolle als empirische Grundlage der Wirtschaftsforschung zu spielen. In diesem Zusammenhang hätten die Konzeptfragen mehr Aufmerksamkeit verdient. Ungenügend wurde auch die Herausforderung bewältigt, die Nutzer adäquat zu informieren. In der Präsentation der Ergebnisse wurden die durch die spezifisch Situation bedingten unterschiedlichen Charakteristika der Resultate für die Pandemieperioden meist ausgeblendet.</p>","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"18 3-4","pages":"305 - 318"},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11943-024-00348-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143108247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fairness als Qualitätskriterium im Maschinellen Lernen – Rekonstruktion des philosophischen Konzepts und Implikationen für die Nutzung außergesetzlicher Merkmale bei qualifizierten Mietspiegeln","authors":"Ludwig Bothmann,&nbsp;Kristina Peters","doi":"10.1007/s11943-024-00346-0","DOIUrl":"10.1007/s11943-024-00346-0","url":null,"abstract":"<p>Mit der verstärkten Nutzung von Modellen des Maschinellen Lernens (ML) innerhalb von Systemen der automatisierten Entscheidungsfindung wachsen die Anforderungen an die Qualität von ML-Modellen. Die reine Prognosegüte ist nicht länger das alleinige Qualitätskriterium; insbesondere wird vermehrt gefordert, dass Fairnessaspekte berücksichtigt werden. Dieser Beitrag verfolgt zwei Ziele. Zum einen werden die aktuelle Fairnessdiskussion im Bereich ML (fairML) zusammengefasst und die aktuellsten Entwicklungen, insbesondere in Bezug auf die philosophischen Grundlagen des Fairnessbegriffs innerhalb ML, beschrieben. Zum anderen wird die Frage behandelt, inwiefern sogenannte „außergesetzliche“ Merkmale bei der Erstellung qualifizierter Mietspiegel genutzt werden dürfen. Ein aktueller Vorschlag von Kauermann und Windmann (AStA Wirtschafts- und Sozialstatistisches Archiv, Band 17, 2023) zur Nutzung außergesetzlicher Merkmale in qualifizierten Mietspiegeln beinhaltet eine modellbasierte Imputationsmethode, welche wir den gesetzlichen Anforderungen gegenüberstellen. Schließlich zeigen wir auf, welche Alternativen aus dem Bereich fairML genutzt werden könnten und legen dar, welche unterschiedlichen philosophischen Grundannahmen hinter den verschiedenen Verfahren stehen.</p>","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"18 2","pages":"185 - 201"},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11943-024-00346-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interview mit Walter Krämer","authors":"Ulrich Rendtel","doi":"10.1007/s11943-024-00343-3","DOIUrl":"10.1007/s11943-024-00343-3","url":null,"abstract":"","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"18 2","pages":"289 - 295"},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141825356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"„Mister SOEP et al.“ – ein Nachruf auf Gert G. Wagner","authors":"C. Katharina Spieß","doi":"10.1007/s11943-024-00342-4","DOIUrl":"10.1007/s11943-024-00342-4","url":null,"abstract":"","PeriodicalId":100134,"journal":{"name":"AStA Wirtschafts- und Sozialstatistisches Archiv","volume":"18 2","pages":"297 - 300"},"PeriodicalIF":0.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11943-024-00342-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141663187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}