TY - GEN A1 - Class, Fabian A1 - Kohler, Ulrich A1 - Krawietz, Marian T1 - The Potsdam Grievance Statistics File BT - new data on quality of life and political participation for the German Democratic Republic 1970–1989 T2 - Historical Methods: A Journal of Quantitative and Interdisciplinary History N2 - The newly collected “Potsdam Grievance Statistics File” (PGSF) holds data on the number and topics of grievances (“Eingaben”) that were addressed to local authorities of the German Democratic Republic (GDR) in the years 1970 to 1989. The PGSF allows quantitative analyses on topics such as participation, quality of life, and value change in the German Democratic Republic. This paper introduces the concepts of the data set and discusses the validity of its contents. T3 - Zweitveröffentlichungen der Universität Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe - 97 KW - Potsdam Grievance Statistics File (PGSF) KW - German Democratic Republic (GDR) KW - Eingaben KW - Participation KW - Quality of Life Y1 - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-412843 ER - TY - JOUR A1 - Class, Fabian A1 - Köhler, Ulrich A1 - Krawietz, Marian T1 - The Potsdam Grievance Statistics File BT - New data on quality of life and political participation for the German Democratic Republic 1970-1989 JF - Historical Methods N2 - The newly collected Potsdam Grievance Statistics File (PGSF) holds data on the number and topics of grievances (Eingaben) that were addressed to local authorities of the German Democratic Republic (GDR) in the years 1970 to 1989. The PGSF allows quantitative analyses on topics such as participation, quality of life, and value change in the German Democratic Republic. This paper introduces the concepts of the data set and discusses the validity of its contents. KW - Potsdam Grievance Statistics File (PGSF) KW - German Democratic Republic (GDR) KW - Eingaben KW - Participation KW - Quality of Life Y1 - 2018 U6 - https://doi.org/10.1080/01615440.2018.1429970 SN - 0161-5440 SN - 1940-1906 VL - 51 IS - 2 SP - 92 EP - 114 PB - Routledge, Taylor & Francis Group CY - Abingdon ER - TY - JOUR A1 - Brady, David A1 - Giesselmann, Marco A1 - Kohler, Ulrich A1 - Radenacker, Anke T1 - How to measure and proxy permanent income BT - evidence from Germany and the US JF - The Journal of Economic Inequality N2 - Permanent income (PI) is an enduring concept in the social sciences and is highly relevant to the study of inequality. Nevertheless, there has been insufficient progress in measuring PI. We calculate a novel measure of PI with the German Socio-Economic Panel (SOEP) and U.S. Panel Study of Income Dynamics (PSID). Advancing beyond prior approaches, we define PI as the logged average of 20+ years of post-tax and post-transfer ("post-fisc") real equivalized household income. We then assess how well various household- and individual-based measures of economic resources proxy PI. In both datasets, post-fisc household income is the best proxy. One random year of post-fisc household income explains about half of the variation in PI, and 2-5 years explain the vast majority of the variation. One year of post-fisc HH income even predicts PI better than 20+ years of individual labor market earnings or long-term net worth. By contrast, earnings, wealth, occupation, and class are weaker and less cross-nationally reliable proxies for PI. We also present strategies for proxying PI when HH post-fisc income data are unavailable, and show how post-fisc HH income proxies PI over the life cycle. In sum, we develop a novel approach to PI, systematically assess proxies for PI, and inform the measurement of economic resources more generally. KW - Income KW - Permanent income KW - Lifetime income KW - Measurement KW - Longitudinal and panel data KW - Social class Y1 - 2018 U6 - https://doi.org/10.1007/s10888-017-9363-9 SN - 1569-1721 SN - 1573-8701 VL - 16 IS - 3 SP - 321 EP - 345 PB - Springer CY - Dordrecht ER - TY - JOUR A1 - Kohler, Ulrich A1 - Kreuter, Frauke A1 - Stuart, Elizabeth A. T1 - Nonprobability Sampling and Causal Analysis JF - Annual review of statistics and its application N2 - The long-standing approach of using probability samples in social science research has come under pressure through eroding survey response rates, advanced methodology, and easier access to large amounts of data. These factors, along with an increased awareness of the pitfalls of the nonequivalent comparison group design for the estimation of causal effects, have moved the attention of applied researchers away from issues of sampling and toward issues of identification. This article discusses the usability of samples with unknown selection probabilities for various research questions. In doing so, we review assumptions necessary for descriptive and causal inference and discuss research strategies developed to overcome sampling limitations. KW - causal inference KW - generalizability KW - self-selection KW - nonprobability sampling KW - validity KW - measurement error KW - heterogeneous treatment effects KW - big data Y1 - 2018 U6 - https://doi.org/10.1146/annurev-statistics-030718-104951 SN - 2326-8298 SN - 2326-831X VL - 6 SP - 149 EP - 172 PB - Annual Reviews CY - Palo Alto ER -