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In a comparison of three human service organisations in which the human body plays a key role, we examine how organisations regulate religious body practices. We concentrate on Muslim norms of dressing and undressing as a potential focal point of cultural and religious diversity. Inspired by Ray’s (2019) idea of racialized organizations, we assume that state-run organizations in Germany are characterized by a strong commitment to religious tolerance and non-discrimination but also marked by anti- Muslim sentiment prevalent among the German population. Our study looks for mechanism that explain how Human Service Organizations accommodate Muslim body practices. It draws on qualitative empirical data collected in state-run hospitals, schools and swimming pools in Germany. Our analyses show that the organizations draw on formal and informal rules at the organizational level to accommodate Islam. We identify five general organizational mechanisms that may hinder Muslim accommodation in human service organizations. In particular, we see a risk of decoupling between the expectation of religious tolerance and processes that lead to informal discrimination, driven mainly by the difficulty of controlling group dynamics among users.
A circulatory loop
(2023)
In the digitalization debate, gender biases in digital technologies play a significant role because of their potential for social exclusion and inequality. It is therefore remarkable that organizations as drivers of digitalization and as places for social integration have been widely overlooked so far. Simultaneously, gender biases and digitalization have structurally immanent connections to organizations. Therefore, a look at the reciprocal relationship between organizations, digitalization, and gender is needed. The article provides answers to the question of whether and how organizations (re)produce, reinforce, or diminish gender‐specific inequalities during their digital transformations. On the one hand, gender inequalities emerge when organizations use post‐bureaucratic concepts through digitalization. On the other hand, gender inequalities are reproduced when organizations either program or implement digital technologies and fail to establish control structures that prevent gender biases. This article shows that digitalization can act as a catalyst for inequality‐producing mechanisms, but also has the potential to mitigate inequalities. We argue that organizations must be considered when discussing the potential of exclusion through digitalization.
In the context of persistent images of self-perpetuated technologies, we discuss the interplay of digital technologies and organisational dynamics against the backdrop of systems theory. Building on the case of an international corporation that, during an agile reorganisation, introduced an AI-based personnel management platform, we show how technical systems produce a form of algorithmic contingency that subsequently leads to the emergence of formal and informal interaction systems. Using the concept of datafication, we explain how these interactions are barriers to the self-perpetuation of data-based decision-making, making it possible to take into consideration further decision factors and complementing the output of the platform. The research was carried out within the scope of the research project ‘Organisational Implications of Digitalisation: The Development of (Post-)Bureaucratic Organisational Structures in the Context of Digital Transformation’ funded by the German Research Foundation (DFG).
The US perennially has a far higher poverty rate than peer-rich democracies.1 This high poverty rate in the US presents an enormous challenge to population health given that considerable research demonstrates that being in poverty is bad for one’s health.2 Despite valuable contributions of prior research on income and mortality, the quantity of mortality associated with poverty in the US remains uknown. In this cohort study, we estimated the association between poverty and mortality and quantified the proportion and number of deaths associated with poverty.
The long term relationship between medicaid expansion and adult life-threatening chronic conditions
(2023)
We test whether the expansions of children's Medicaid eligibility in the 1980s–1990s resulted in long-term health benefits in terms of severe chronic conditions. Still relatively rare in the field, we use prospective individual-level panel data from the Panel Study of Income Dynamics (PSID) along with the higher quality income measures from the Cross-National Equivalent File (adjusting for taxes, transfers and household size). We observe severe chronic conditions (high blood pressure/heart disease, cancer, diabetes, or lung disease) at ages 30–56 (average age 43.1) for 4670 respondents who were also prospectively observed during childhood (i.e., at ages 0–17). Our analysis exploits within-region temporal variation in childhood Medicaid eligibility and adjusts for state- and individual-level controls. We uniquely concentrate attention on adjusting for childhood income. A standard deviation greater childhood Medicaid eligibility significantly reduces the probability of severe chronic conditions in adulthood by 0.05 to 0.12 (16%–37.5% reduction from mean 0.32). Across the range of observed childhood Medicaid eligibility, the probability is approximately cut in half. Greater childhood Medicaid eligibility also substantially reduces childhood income disparities in severe chronic conditions. At higher levels of childhood Medicaid eligibility, we find no significant childhood income disparities in adult severe chronic conditions.
A review of all research papers published in the European Sociological Review in 2016 and 2017 (N = 118) shows that only a minority of papers clearly define the parameter of interest and provide sufficient reasoning for the selected control variables of the statistical analysis. Thus, the vast majority of papers does not reach minimal standards for the selection of control variables. Consequently, a majority of papers interpret biased coefficients, or statistics without proper sociological meaning. We postulate that authors and reviewers should be more careful about control variable selection. We propose graphical causal models in the form of directed acyclic graphs as an example for a parsimonious and powerful means to that end.
Digitalisation in industry – also called “Industry 4.0” – is seen by numerous actors as an opportunity to reduce the environmental impact of the industrial sector. The scientific assessments of the effects of digitalisation in industry on environmental sustainability, however, are ambivalent. This cumulative dissertation uses three empirical studies to examine the expected and observed effects of digitalisation in industry on environmental sustainability. The aim of this dissertation is to identify opportunities and risks of digitalisation at different system levels and to derive options for action in politics and industry for a more sustainable design of digitalisation in industry. I use an interdisciplinary, socio-technical approach and look at selected countries of the Global South (Study 1) and the example of China (all studies). In the first study (section 2, joint work with Marcel Matthess), I use qualitative content analysis to examine digital and industrial policies from seven different countries in Africa and Asia for expectations regarding the impact of digitalisation on sustainability and compare these with the potentials of digitalisation for sustainability in the respective country contexts. The analysis reveals that the documents express a wide range of vague expectations that relate more to positive indirect impacts of information and communication technology (ICT) use, such as improved energy efficiency and resource management, and less to negative direct impacts of ICT, such as electricity consumption through ICT. In the second study (section 3, joint work with Marcel Matthess, Grischa Beier and Bing Xue), I conduct and analyse interviews with 18 industry representatives of the electronics industry from Europe, Japan and China on digitalisation measures in supply chains using qualitative content analysis. I find that while there are positive expectations regarding the effects of digital technologies on supply chain sustainability, their actual use and observable effects are still limited. Interview partners can only provide few examples from their own companies which show that sustainability goals have already been pursued through digitalisation of the supply chain or where sustainability effects, such as resource savings, have been demonstrably achieved. In the third study (section 4, joint work with Peter Neuhäusler, Melissa Dachrodt and Marcel Matthess), I conduct an econometric panel data analysis. I examine the relationship between the degree of Industry 4.0, energy consumption and energy intensity in ten manufacturing sectors in China between 2006 and 2019. The results suggest that overall, there is no significant relationship between the degree of Industry 4.0 and energy consumption or energy intensity in manufacturing sectors in China. However, differences can be found in subgroups of sectors. I find a negative correlation of Industry 4.0 and energy intensity in highly digitalised sectors, indicating an efficiency-enhancing effect of Industry 4.0 in these sectors. On the other hand, there is a positive correlation of Industry 4.0 and energy consumption for sectors with low energy consumption, which could be explained by the fact that digitalisation, such as the automation of previously mainly labour-intensive sectors, requires energy and also induces growth effects. In the discussion section (section 6) of this dissertation, I use the classification scheme of the three levels macro, meso and micro, as well as of direct and indirect environmental effects to classify the empirical observations into opportunities and risks, for example, with regard to the probability of rebound effects of digitalisation at the three levels. I link the investigated actor perspectives (policy makers, industry representatives), statistical data and additional literature across the system levels and consider political economy aspects to suggest fields of action for more sustainable (digitalised) industries. The dissertation thus makes two overarching contributions to the academic and societal discourse. First, my three empirical studies expand the limited state of research at the interface between digitalisation in industry and sustainability, especially by considering selected countries in the Global South and the example of China. Secondly, exploring the topic through data and methods from different disciplinary contexts and taking a socio-technical point of view, enables an analysis of (path) dependencies, uncertainties, and interactions in the socio-technical system across different system levels, which have often not been sufficiently considered in previous studies. The dissertation thus aims to create a scientifically and practically relevant knowledge basis for a value-guided, sustainability-oriented design of digitalisation in industry.
Risk perceptions of individuals living in single-parent households during the COVID-19 crisis
(2023)
The COVID-19 crisis had severe social and economic impact on the life of most citizens around the globe. Individuals living in single-parent households were particularly at risk, revealing detrimental labour market outcomes and assessments of future perspectives marked by worries. As it has not been investigated yet, in this paper we study, how their perception about the future and their outlook on how the pandemic will affect them is related to their objective economic resources. Against this background, we examine the subjective risk perception of worsening living standards of individuals living in single-parent households compared to other household types, their objective economic situation based on the logarithmised equivalised disposable household incomes and analyse the relationship between those indicators. Using the German SOEP, including the SOEP-CoV survey from 2020, our findings based on regression modelling reveal that individuals living in single-parent households have been worse off during the pandemic, facing high economic insecurity. Path and interaction models support our assumption that the association between those indicators may not be that straightforward, as there are underlying mechanisms–such as mediation and moderation–of income affecting its direction and strength. With respect to our central hypotheses, our empirical findings point toward (1) a mediation effect, by demonstrating that the subjective risk perception of single-parent households can be partly explained by economic conditions. (2) The moderating effect suggests that the concrete position at the income distribution of households matters as well. While at the lower end of the income distribution, single-parent households reveal particularly worse risk perceptions during the pandemic, at the high end of the income spectrum, risk perceptions are similar for all household types. Thus, individuals living in single-parent households do not perceive higher risks of worsening living standards due to their household situation per se, but rather because they are worse off in terms of their economic situation compared to individuals living in other household types.
Background:
Like most countries, Germany is currently recruiting international nurses due to staff shortages. While these are mostly academic, the academisation of nursing in Germany has only just begun. This allows for a broader look at the participation of migrant nurses: How do care teams deal with the fact that immigrant colleagues are theoretically more highly qualified than long-established colleagues?
Methods:
Case studies were conducted in four inpatient care teams of two hospitals in 2022. Qualitative data include 26 observation protocols, 4 group discussions and 17 guided interviews. These were analysed using the documentary method and validated intersubjectively.
Results:
Due to current academisation efforts in Germany and the immigration of academised nursing staff from abroad, the areas of activity and responsibility of nursing in Germany are under negotiating pressure. This concerns basic care for example, which in Germany is provided by skilled workers, but in other countries is mostly provided by assistants or relatives. The question of who should provide basic care, whether all nurses or only nursing assistants, documents the struggle between an established and a new understanding of care. In this context, the knowledge and skills of migrant and academicised care workers become a crucial aspect in the struggle for a new professional identity for care in Germany.
Conclusions:
The specific situation in Germany makes it possible to show the potential for change that international care migration can constitute for destination countries. The far-reaching process of change of German nursing is given a further dimension not only by its academization, but by the immigration of international and academically trained nursing staff, where inclusive or exclusive effects can already be observed.
Key messages: The increasing proportion of migrant nurses accelerates the current discussion on nursing in Germany. Conflict areas show up in everyday work of care teams and must be addressed there.
Background: Following the rapid increase of asylum seekers arriving in the European Union in 2015/16, policymakers have invested heavily in improving their foresight and forecasting capabilities. A common method to elicit expert predictions are Delphi surveys. This approach has attracted concern in the literature, given the high uncertainty in experts’ predictions. However, there exists limited guidance on specific design choices for future-related Delphi surveys.
Objective: We test whether or not small adjustments to the Delphi survey can increase certainty (i.e., reduce variation) in expert predictions on immigration to the EU in 2030.
Methods: Based on a two-round Delphi survey with 178 migration experts, we compare variation and subjective confidence in expert predictions and assess whether additional context information (type of migration flow, sociopolitical context) promotes convergence among experts (i.e., less variation) and confidence in their own estimates.
Results: We find that additional context information does not reduce variation and does not increase confidence in expert predictions on migration.
Conclusions: The results reaffirm recent concerns regarding the limited scope for reducing uncertainty by manipulating the survey setup. Persistent uncertainty may be a result of the complexity of migration processes and limited agreement among migration experts regarding key drivers.
Contribution: We caution policymakers and academics on the use of Delphi surveys for eliciting expert predictions on immigration, even when conducted based on a large pool of experts and using specific scenarios. The potential of alternative approaches such as prediction markets should be further explored.
Web scraping, a technique for extracting data from web pages, has been in use for decades, yet its utilization in the field of migration, mobility, and migrant integration studies has been limited. The field faces notorious limitations regarding data access and availability, particularly in low-income settings. Web scraping has the potential to provide new datasets for further qualitative and quantitative analysis. Web scraping requires no financial resources, is agnostic to epistemic divides in the field, reduces researcher bias, and increases transparency and replicability of data collection. As large providers of digital data such as Facebook or Twitter increasingly restrict access to their data for researchers, web scraping will become more important in the future and deserves its place in the toolbox of migration and mobility scholars. This short and nontechnical methods note introduces the fundamental concepts of web scraping, provides guidance on how to learn the technique, showcases practical applications of web scraping in the study of migrant populations, and discusses potential future use cases.
A growing number of studies have recently postulated a so-called local turn in the study of immigrant and refugee integration policy. A fundamental, yet untested, assumption of this body of research is that local (sub-national) policies and administrations shape how migrants and refugees integrate into society. We develop and apply an analytical model using multilevel modeling techniques based on large-N, longitudinal survey data (N > 9000) with refugees (2012–2018) in a highly decentralized country (Germany) to estimate the scope for local policy effects net of individual-level and state- and district-level characteristics. We show that region and district-level variation in integration outcomes across multiple dimensions (employment, education, language, housing, social) is limited (∼5%) within 4–8 years after immigration. We find modest variation in policy indicators (∼10%), which do not appear to directly translate into outcomes. We discuss implications for the study of local policies and the potential for greater convergence between administrative and political science, interested in governance structures and policy variation, and sociology and economics, interested primarily in integration outcomes.
The digitization process has triggered a profound transformation of modern societies. It encompasses a broad spectrum of technical, social, political, cultural and economic developments related to the mass use of computer- and internet-based technologies. It is now becoming increasingly clear that digitization is also changing existing structures of social inequality and that new structures of digital inequality are emerging. This is shown by a growing number of recent individual studies. In this paper, we set ourselves the task of systematizing this new research within the framework of an empirically supported literature review. To do so, we use the PRISMA model for literature reviews and focus on three central dimensions of inequality - ethnicity, gender, and age - and their relevance within the discourse on digitization and inequality. The empirical basis consists of journal articles published between 2000 and 2020 and listed on the Web of Science, as well as an additional Google Scholar search, through which we attempt to include important monographs and contributions to edited volumes in our analyses. Our text corpus thus comprises a total of 281 articles. Empirically, our literature review shows that unequal access to digital resources largely reproduces existing structures of inequality; in some cases, studies report a reduction in social inequalities as a result of the digitization process.