@misc{FliesserDeWittHubertsWippert2018,
  author    = {Fliesser, Michael and De Witt Huberts, Jessie and Wippert, Pia-Maria},
  title     = {The choice that matters: the relative influence of socioeconomic status indicators on chronic back pain},
  series = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Humanwissenschaftliche Reihe},
  number    = {377},
  issn      = {1866-8364},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-407422},
  year      = {2018},
  abstract  = {Background In health research, indicators of socioeconomic status (SES) are often used interchangeably and often lack theoretical foundation. This makes it difficult to compare results from different studies and to explore the relationship between SES and health outcomes. To aid researchers in choosing appropriate indicators of SES, this article proposes and tests a theory-based selection of SES indicators using chronic back pain as a health outcome. Methods Strength of relationship predictions were made using Brunner \& Marmot's model of 'social determinants of health'. Subsequently, a longitudinal study was conducted with 66 patients receiving in-patient treatment for chronic back pain. Sociodemographic variables, four SES indicators (education, job position, income, multidimensional index) and back pain intensity and disability were obtained at baseline. Both pain dimensions were assessed again 6 months later. Using linear regression, the predictive strength of each SES indicator on pain intensity and disability was estimated and compared to the theory based prediction. Results Chronic back pain intensity was best predicted by the multidimensional index (beta = 0.31, p < 0.05), followed by job position (beta = 0.29, p < 0.05) and education (beta = -0.29, p < 0.05); whereas, income exerted no significant influence. Back pain disability was predicted strongest by education (beta = -0.30, p < 0.05) and job position (beta = 0.29, p < 0.05). Here, multidimensional index and income had no significant influence. Conclusions The choice of SES indicators influences predictive power on both back pain dimensions, suggesting SES predictors cannot be used interchangeably. Therefore, researchers should carefully consider prior to each study which SES indicator to use. The introduced framework can be valuable in supporting this decision because it allows for a stable prediction of SES indicator influence and their hierarchy on a specific health outcomes.},
  language  = {en}
}
@article{BradyGiesselmannKohleretal.2018,
  author    = {Brady, David and Giesselmann, Marco and Kohler, Ulrich and Radenacker, Anke},
  title     = {How to measure and proxy permanent income},
  series = {The Journal of Economic Inequality},
  volume    = {16},
  journal   = {The Journal of Economic Inequality},
  number    = {3},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {1569-1721},
  doi       = {10.1007/s10888-017-9363-9},
  pages     = {321 -- 345},
  year      = {2018},
  abstract  = {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.},
  language  = {en}
}
@article{FliesserDeWittHubertsWippert2017,
  author    = {Fliesser, Michael and De Witt Huberts, Jessie and Wippert, Pia-Maria},
  title     = {The choice that matters: the relative influence of socioeconomic status indicators on chronic back pain},
  series = {BMC health services research},
  volume    = {17},
  journal   = {BMC health services research},
  publisher = {BioMed Central},
  address   = {London},
  issn      = {1472-6963},
  doi       = {10.1186/s12913-017-2735-9},
  year      = {2017},
  abstract  = {Background In health research, indicators of socioeconomic status (SES) are often used interchangeably and often lack theoretical foundation. This makes it difficult to compare results from different studies and to explore the relationship between SES and health outcomes. To aid researchers in choosing appropriate indicators of SES, this article proposes and tests a theory-based selection of SES indicators using chronic back pain as a health outcome. Methods Strength of relationship predictions were made using Brunner \& Marmot's model of 'social determinants of health'. Subsequently, a longitudinal study was conducted with 66 patients receiving in-patient treatment for chronic back pain. Sociodemographic variables, four SES indicators (education, job position, income, multidimensional index) and back pain intensity and disability were obtained at baseline. Both pain dimensions were assessed again 6 months later. Using linear regression, the predictive strength of each SES indicator on pain intensity and disability was estimated and compared to the theory based prediction. Results Chronic back pain intensity was best predicted by the multidimensional index (beta = 0.31, p < 0.05), followed by job position (beta = 0.29, p < 0.05) and education (beta = -0.29, p < 0.05); whereas, income exerted no significant influence. Back pain disability was predicted strongest by education (beta = -0.30, p < 0.05) and job position (beta = 0.29, p < 0.05). Here, multidimensional index and income had no significant influence. Conclusions The choice of SES indicators influences predictive power on both back pain dimensions, suggesting SES predictors cannot be used interchangeably. Therefore, researchers should carefully consider prior to each study which SES indicator to use. The introduced framework can be valuable in supporting this decision because it allows for a stable prediction of SES indicator influence and their hierarchy on a specific health outcomes.},
  language  = {en}
}
@article{SorgnerFritschKritikos2017,
  author    = {Sorgner, Alina and Fritsch, Michael and Kritikos, Alexander},
  title     = {Do entrepreneurs really earn less?},
  series = {Small business economics : an international journal},
  volume    = {49},
  journal   = {Small business economics : an international journal},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {0921-898X},
  doi       = {10.1007/s11187-017-9874-6},
  pages     = {251 -- 272},
  year      = {2017},
  abstract  = {Based on large representative German household survey data, we compare incomes of the self-employed with those of paid employees. We find that the entrepreneurial income gap is largest for those holding a tertiary degree, but in two directions: positive for employers (self-employed with further employees) and negative for solo entrepreneurs. Entrepreneurs holding a tertiary degree also face the greatest income variation. However, some solo self-employed earn more than their employed counterparts, in particular those with a university entrance degree as the highest level of education.},
  language  = {en}
}