Bringing together five consortium partners, the EESDA project, implemented during 2017-2019, advances the current knowledge and expertise on the articulation of social dialogue in Europe and its effectiveness. It studies the ways in which social dialogue at different levels functions and the channels through which EU-level social dialogue - across and within sectors – affects the actors, decisions and outcomes at national and sub-national level, and vice versa. Research conducted within the EESDA project includes an assessment of social dialogue articulation between national and European level across 27 EU member states by means of desk research, an online survey among national social partners and interviews with EU-level social partners as well as other national stakeholders. It then concentrates on the effectiveness of social dialogue in six EU Member States (i.e. Estonia, Ireland, France, Portugal, Slovakia and Sweden – with distinct industrial relations models and traditions) and four sectors (i.e. construction, commerce, education and healthcare, with a focus on a specific occupation in each sector). Findings from interviews, case studies and discourse analysis are completed using network analysis that sets out to visualise and reveal strong and weak ties between different actors and to draw lessons for experiences and best practices in other sectors and countries. The analysis considers efforts that have a direct and indirect impact on social dialogue, such as EU Directives, Autonomous Agreements, Framework of Actions, joint projects, joint statements or programme funding.

Rapid population growth along with increased rates of economic growth around the globe are placing valuable natural resources, water in particular, under unprecedented stress; this in turn drives the pursuit of innovative tools to support integrated Water-Energy-Food (WEF) nexus management. This paper presents a framework for the integrated management of the WEF nexus, which brings together four separate models that address the less well-examined socio-anthropological aspects of the nexus. The proposed framework provides insight into the human element as part of the wider ecosystem in terms of socio-cultural and economic activities, the laws and policies that govern these activities, as well as their potential socio-economic impacts and consequences. This paper outlines each individual model, before going on to present a conceptual framework for the integration of the various models for the purpose of supporting more robust decision-making. The framework, which is grounded in systems thinking, adopts the principles of sustainable development as structural foci in order to position the various models in relation to one another; harmonizing their inputs as well as outputs.

The social-ecological effects of agricultural intensification are complex. We explore farmers perceptions about the impacts of their land management and the impact of social information flows on their management through a case study in a farming community in Navarra, Spain, that is undergoing agricultural intensification due to adoption of large scale irrigation. We found that modern technology adopters are aware that their management practices often have negative social-ecological implications; by contrast, more traditional farmers tend to recognize their positive impacts on non-material benefits such as those linked with traditions and traditional knowledge, and climate regulation. We found that farmers awareness about nature contributions to people co-production and their land management decisions determine, in part, the structure of the social networks among the farming community. Since modern farmers are at the core of the social network, they are better able to control the information flow within the community. This has important implications, such as the fact that the traditional farmers, who are more aware of their impacts on the environment, rely on information controlled by more intensive modern farmers, potentially jeopardizing sustainable practices in this region. We suggest that this might be counteracted by helping traditional farmers obtain information tailored to their practices from outside the social network.

Most choices people make are about 'matters of taste', on which there is no universal, objective truth. Nevertheless, people can learn from the experiences of individuals with similar tastes who have already evaluated the available options-a potential harnessed by recommender systems. We mapped recommender system algorithms to models of human judgement and decision-making about 'matters of fact' and recast the latter as social learning strategies for matters of taste. Using computer simulations on a large-scale, empirical dataset, we studied how people could leverage the experiences of others to make better decisions. Our simulations showed that experienced individuals can benefit from relying mostly on the opinions of seemingly similar people; by contrast, inexperienced individuals cannot reliably estimate similarity and are better off picking the mainstream option despite differences in taste. Crucially, the level of experience beyond which people should switch to similarity-heavy strategies varies substantially across individuals and depends on how mainstream (or alternative) an individual's tastes are and the level of dispersion in taste similarity with the other people in the group.

The purpose of this editorial is to discuss methodological advancements to enhance quantitative theory-testing entrepreneurship research. As the impact of entrepreneurship scholarship accelerates and deepens, our methods must keep pace to continue shaping theory, policy, and practice. Like our sister fields in business, entrepreneurship is coming to terms with the replication and credibility crisis in the social sciences, forcing the field to revisit commonly-held assumptions that limit the promise and prospect of our scholarship. Thus, we provide suggestions for reviewers and editors to identify concerns in empirical work, and to guide authors in improving their analyses and research designs. We hope that our editorial provides useful and actionable guidance for entrepreneurship researchers submitting theory-testing papers to Journal of Business Venturing.

Residential choices and school choices are intimately connected in school systems where school admission relies on proximity rules. In countries with universal school choice systems, however, it remains an open question whether families’ residential mobility is tied to the choice of their children’s school, and with what consequences. Using administrative data on all children approaching primary-school age in Sweden, we study to what extent families’ financial and socio-economic background affects mobility between neighbourhoods and the characteristics of schools chosen by moving families. Our findings show that families do utilise the housing market as an instrument for school choice over the year preceding their firstborn child starting school. However, while families who move do ‘climb the social ladder’ by moving to neighbourhoods with more households of higher socio-economic status, their chosen schools do not appear to be of higher academic quality compared to those their children would otherwise have attended.

Digitalization has arrived.

Digitalization disrupts markets. Changes in power and structures in a fast-paced environment demands strategic and insightful change. A change leaders must act upon.

The impact upon organisations is multi-dimensional and profound, affecting both internal and external processes and structures in new and unexpected ways. This book serves as a tool to support managers and other stakeholders in pursuing digital transformation. An inspiring collection of chapters from 27 scholars across various academic disciplines provide several insights, frameworks, and perspectives that will help you leverage and govern organisational change and digital transformation.

This inspiring collection of current research can assist you in facing key challenges in today’s organisations, in the quest to adapt to ever-evolving business environments. This book examines new demands and behaviours, and discusses how businesses need to adapt and re-organise in order to bridge the gap to the digital customer. These visions and actions on digitalization can help corporations and organisations discover new ways of earning money and delivering value. This is just the beginning.

When someone violates a social norm, others may think that some sanction would be appropriate. We examine how the experience of emotions like anger and disgust relate to the judged appropriateness of sanctions, in a pre-registered analysis of data from a large-scale study in 56 societies. Across the world, we find that individuals who experience anger and disgust over a norm violation are more likely to endorse confrontation, ostracism and, to a smaller extent, gossip. Moreover, we find that the experience of anger is consistently the strongest predictor of judgments of confrontation, compared to other emotions. Although the link between state-based emotions and judgments may seem universal, its strength varies across countries. Aligned with theoretical predictions, this link is stronger in societies, and among individuals, that place higher value on individual autonomy. Thus, autonomy values may increase the role that emotions play in guiding judgments of social sanctions.

The emergence of COVID-19 dramatically changed social behavior across societies and contexts. Here we study whether social norms also changed. Specifically, we study this question for cultural tightness (the degree to which societies generally have strong norms), specific social norms (e.g. stealing, hand washing), and norms about enforcement, using survey data from 30,431 respondents in 43 countries recorded before and in the early stages following the emergence of COVID-19. Using variation in disease intensity, we shed light on the mechanisms predicting changes in social norm measures. We find evidence that, after the emergence of the COVID-19 pandemic, hand washing norms increased while tightness and punishing frequency slightly decreased but observe no evidence for a robust change in most other norms. Thus, at least in the short term, our findings suggest that cultures are largely stable to pandemic threats except in those norms, hand washing in this case, that are perceived to be directly relevant to dealing with the collective threat. Tightness-looseness theory predicts that social norms strengthen following threat. Here the authors test this and find that, after the emergence of the COVID-19 pandemic, hand washing norms increased, but no evidence was observed for a robust change in most other norms.

Objectives To evaluate the role of language proficiency and institutional awareness in explaining excess COVID-19 mortality among immigrants. Design Cohort study with follow-up between 12 March 2020 and 23 February 2021. Setting Swedish register-based study on all residents in Sweden. Participants 3 963 356 Swedish residents in co-residential unions who were 30 years of age or older and alive on 12 March 2020 and living in Sweden in December 2019. Outcome measures Cox regression models were conducted to assess the association between different constellations of immigrant-native couples (proxy for language proficiency and institutional awareness) and COVID-19 mortality and all other causes of deaths (2019 and 2020). Models were adjusted for relevant confounders. Results Compared with Swedish-Swedish couples (1.18 deaths per thousand person-years), both immigrants partnered with another immigrant and a native showed excess mortality for COVID-19 (HR 1.43; 95% CI 1.29 to 1.58 and HR 1.24; 95% CI 1.10 to 1.40, respectively), which translates to 1.37 and 1.28 deaths per thousand person-years. Moreover, similar results are found for natives partnered with an immigrant (HR 1.15; 95% CI 1.02 to 1.29), which translates to 1.29 deaths per thousand person-years. Further analysis shows that immigrants from both high-income and low-income and middle-income countries (LMIC) experience excess mortality also when partnered with a Swede. However, having a Swedish-born partner is only partially protective against COVID-19 mortality among immigrants from LMIC origins. Conclusions Language barriers and/or poor institutional awareness are not major drivers for the excess mortality from COVID-19 among immigrants. Rather, our study provides suggestive evidence that excess mortality among immigrants is explained by differential exposure to the virus. 

This study examines how maternal education shapes the life and health of their children. Causal effects are identified from a Danish school reform that increased minimum compulsory schooling from 7 to 9 years in 1972 and estimates are based on large administrative registers. We find that the reform as well as maternal education when instrumented by it, has significant, positive effects on mothers age at first birth and maternal health. Nevertheless, maternal education has no systematic causal effects on child health, neither at birth, during childhood, or in adolescence. This null finding is robust to a wide range of model specifications.

Research on school improvement has accumulated an extensive list of factors that facilitate turnarounds at underperforming schools. Given that context or resource constraints may limit the possibilities of putting all of these factors in place, an important question is what is necessary and sufficient to turn a school around. We use qualitative comparative analysis (QCA) of 77 Swedish schools studied over 12 years to answer this question. Our core finding is that there is no "silver bullet" solution. Instead, there are several distinct combinations of factors that can enable school turnaround. The local school context is essential for which combinations of factors are necessary and sufficient for school turnaround. We discuss implications for research on school improvement and education policy.

Older peoples ability to thrive independently of their adult children is an important feature of a universalistic welfare system. However, population ageing puts this notion under stress. In separate multinomial logistic regression models for older men and women, we examined whether adult childrens gender, number and proximity were associated with older parents relocations into residential care facilities, and whether the effects of these childrens characteristics on older parents institutionalisation vary by parents severe health problems, operationalised as closeness to death - specifically, dying within the two-year observation period. Analyses were based on the Swedish register data between 2014 and 2016 (N = 696,007 person-years). Older parents with at least one co-resident child were less likely to move or become institutionalised than those without a co-resident child. We did not find a relationship between older adults institutionalisation and the closest childs gender. The negative effect of having a non-resident child living nearby on the likelihood of becoming institutionalised was more pronounced for mothers than fathers. Having a child nearby decreased the likelihood of moving to an institution more for mothers who had severe health problems than for those in better health. We found no evidence of a relationship between number of children and likelihood of institutionalisation.

This research examines the relationship between older parents health issues and (i) their relocations closer to their faraway adult children, (ii) their relocations into institutionalised care facilities, or (iii) having distant children move closer. Additionally, we investigate how these relocations are structured by childrens gender and location. We focused on parents aged 80 years and older and their distant children. Multinomial logistic regression models were employed for older men and women based on data from administrative registers of Sweden. Whereas severe health problems were associated with an increased likelihood of parent relocations closer to their children or into institutions, they were not associated with the likelihood of childrens moves towards parents. Mothers were more likely to move towards daughters or towards distant children who had at least one sibling living nearby. Children moved closer to their parents when there was at least one sibling living near the parent or in response to their own life circumstances.

Explaining how properties at the level of individuals translate into properties at the level of collectives is a core objective of sociology. Because the social world is characterized by complex webs of social interdependencies, establishing how micro and macro are related to one another requires a detailed understanding of how individuals are influenced by their social environments and the consequences that such influences have for the dynamics of the social process. However, until very recently, it has been difficult to conduct detailed empirical investigations of micro-macro linkages due to the lack of large-scale data containing information on how individuals interact with one another. In the absence of such data, substantive research has tended to (a) focus its attention elsewhere: studying how social factors influence individual outcomes, rather than how actors in interaction with one another bring about collective outcomes, or (b) propose models of micro-macro linkages that—for reasons of parsimony and tractability—often assume artificially high levels of homogeneity. Against this background, this thesis sets out to investigate, first, how the data and tools that have emerged from the digital and computational revolution can help sociologists construct empirically well-founded mappings from the micro to the macro level, and second, how the conclusions about the role of social interdependencies and networks change when the analysis is informed by real-world heterogeneities.

In the introductory chapter, a conceptual and analytical framework for studying micro-macro processes is proposed that integrates the theoretical principles of analytical sociology with the data and methods of computational social science. This framework constitutes the foundation of the thesis. It is used in Essays I-III, and it is methodologically built upon in Essay IV.

In Essay I, the role of social networks in labor-market segregation processes is examined. Scholarship on labor-market segregation commonly assume that social networks have a segregating effect because of homophilous selection tendencies in network-based recruitment. Using large-scale register data and focusing attention on individuals’ heterogenous opportunities to form same-category ties in different workplaces, Essay I finds that opportunity structures often dominate homophilic preferences. In particular, a mechanism is identified which shows—in contradiction with the main tenet of previous research—that networks often reduce rather than increase segregation by triggering mobility events that counteract the impact of segregating mobility events.

Essay II examines the conditions under which social influence can decouple adoption behaviour from individual preferences and thereby bring about unexpected collective outcomes. Prior research has shown that such decoupling can occur, but conflicting evidence and implicit assumptions of strong homogeneity mean that we still know little about the conditions under which this is likely to occur in the real world. Addressing these limitations, this study uses fine-grained, real-world behavioural data from Spotify to estimate heterogeneous social influence effects conditional on properties of individuals’ social environments, and then examine their macro-implications in empirically calibrated simulations. It is found that partial overlap in preferences and strong social ties between the senders and receivers of social influence is needed for social influence to produce decoupling.

Essay III centers on the phenomenon of urban scaling and examines the relationship between within-city and between-city inequality. Previous urban scaling research has documented how cities’ total outputs increase more than proportionally with city size and has proposed theoretical models which demonstrate impressive predictive accuracy at aggregate levels. However, this research has overlooked the stark inequalities that exist within cities. Using microdata from multiple countries, it is found that between 36–80% of the previously reported scaling effects can be explained by differences in the distributional tails of cities. Providing explanatory depth to these findings, a cumulative advantage mechanism is identified which elucidates one important channel through which differences in the size of cities’ tails emerge.

In Essay IV, a method is proposed for inferring theoretically meaningful dimensions from complex high-dimensional data such as text. The results show that the method captures latent semantic concepts better than or on-par with the current state of the art. For the study of social interactions, the method constitutes a new and potentially important tool for inferring theoretically meaningful dimensions about individuals and their social environments, and in so doing, improves our ability to adjust for specific types of homophily and enables richer and more precise measures of heterogeneity in social interaction processes.

The segregation of labor markets along ethnic and gender lines is socially highly consequential, and the social science literature has long viewed homophily and network-based job recruitments as some of its most crucial drivers. Here, we focus on a previously unidentified mechanism, the Trojan-horse mechanism, which, in contradiction to the main tenet of previous research, suggests that network-based recruitment reduce rather than increase segregation levels. We identify the conditions under which networks are desegregating, and using unique data on all individuals and all workplaces located in the Stockholm region during the years 2000-2017, we find strong empirical evidence for the Trojan-horse mechanism and its role in the gender segregation of labor markets.

Theories of urban scaling have demonstrated remarkable predictive accuracy at aggregate levels. However, they have overlooked the stark inequalities that exist within cities. Human networking and productivity exhibit heavy-tailed distributions, with some individuals contributing disproportionately to city totals. Here we use micro-level data from Europe and the United States on interconnectivity, productivity and innovation in cities. We find that the tails of within-city distributions and their growth by city size account for 36–80% of previously reported scaling effects, and 56–87% of the variance in scaling between indicators of varying economic complexity. Providing explanatory depth to these findings, we identify a mechanism—city size-dependent cumulative advantage—that constitutes an important channel through which differences in the size of tails emerge. Our findings demonstrate that urban scaling is in large part a story about inequality in cities, implying that the causal processes underlying the heavier tails in larger cities must be considered in explanations of urban scaling. This result also shows that agglomeration effects benefit urban elites the most, with the majority of city dwellers partially excluded from the socio-economic benefits of growing cities.

Research Summary This study explains the conditions under which new venture boards are less or more culturally diverse in terms of their directors country of birth. Longitudinal data on 5,515 Swedish ventures suggest that most directors are recruited from founders proximate social settings-neighborhoods in which they reside and past workplaces-and that diversity levels in these social settings strongly predict the national culture diversity in venture boards. Given the rapid internationalization of workplaces and regions around the world, this paper provides important clues regarding how culturally diverse upper echelons are being incorporated into the organizational design of new ventures. Managerial Summary Most New Venture Boards exhibit limited diversity in terms of their directors country of birth, as they are drawn from the venture founders network. Yet, some new venture boards are indeed born diverse. Our study reveals that founders with prior exposure to culturally diverse workplaces and residential neighborhoods are much more likely to design a culturally diverse board at founding. Given the rapid internationalization of workplaces and regions in most countries around the world, our paper provides important clues regarding how national culture diversity in top management emerges and is being incorporated into the organizational design of new ventures.

Social science theories often postulate causal relationships among a set of variables or events. Although directed acyclic graphs (DAGs) are increasingly used to represent these theories, their full potential has not yet been realized in practice. As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships. Nevertheless, to simplify the task of empirical evaluation, researchers tend to invoke such assumptions anyway, even though they are typically arbitrary and do not reflect any theoretical content or prior knowledge. Moreover, functional form assumptions can engender bias, whenever they fail to accurately capture the complexity of the causal system under investigation. In this article, we introduce causal-graphical normalizing flows (cGNFs), a novel approach to causal inference that leverages deep neural networks to empirically evaluate theories represented as DAGs. Unlike conventional approaches, cGNFs model the full joint distribution of the data according to a DAG supplied by the analyst, without relying on stringent assumptions about functional form. In this way, the method allows for flexible, semi-parametric estimation of any causal estimand that can be identified from the DAG, including total effects, conditional effects, direct and indirect effects, and path-specific effects. We illustrate the method with a reanalysis of Blau and Duncan’s (1967) model of status attainment and Zhou’s (2019) model of conditional versus controlled mobility. To facilitate adoption, we provide open-source software together with a series of online tutorials for implementing cGNFs. The article concludes with a discussion of current limitations and directions for future development.

This work demonstrates the application of a particular branch of causal inference and deep learning models: \emph{causal-Graphical Normalizing Flows (c-GNFs)}. In a recent contribution, scholars showed that normalizing flows carry certain properties, making them particularly suitable for causal and counterfactual analysis. However, c-GNFs have only been tested in a simulated data setting and no contribution to date have evaluated the application of c-GNFs on large-scale real-world data. Focusing on the \emph{AI for social good}, our study provides a counterfactual analysis of the impact of the International Monetary Fund (IMF) program on child poverty using c-GNFs. The analysis relies on a large-scale real-world observational data: 1,941,734 children under the age of 18, cared for by 567,344 families residing in the 67 countries from the Global-South. While the primary objective of the IMF is to support governments in achieving economic stability, our results find that an IMF program reduces child poverty as a positive side-effect by about 1.2±0.24 degree (`0' equals no poverty and `7' is maximum poverty). Thus, our article shows how c-GNFs further the use of deep learning and causal inference in AI for social good. It shows how learning algorithms can be used for addressing the untapped potential for a significant social impact through counterfactual inference at population level (ACE), sub-population level (CACE), and individual level (ICE). In contrast to most works that model ACE or CACE but not ICE, c-GNFs enable personalization using \emph{`The First Law of Causal Inference'}.

Recent research has highlighted the properties that deep-learning inspired causal models such as Deep-Structural Causal Model (Deep-SCM), Causal Autoregressive Flow (CAREFL) and Causal-Graphical Normalizing Flow (c-GNF) should exhibit to guarantee observational and interventional distribution equivalence with the true underlying causal data generating process (DGP), making them suitable for estimating average causal effect (ACE) or conditional ACE (CACE). However, for accurate individual-level causal effect (ICE) estimation and personalized treatment/public-policy formulation, it is crucial to ensure counterfactual equivalence between these models and the DGP. Firstly, we demonstrate that c-GNFs provide counterfactual equivalence under certain monotonicity assumption of the DGP, enabling precise ICE estimation and personalized treatment/public-policy analysis. Secondly, using this counterfactual equivalence of c-GNFs, we perform a counterfactual analysis and personalized public-policy analysis of the impact of International Monetary Fund (IMF) programs on child poverty using large-scale real-world observational data. Our results indicate a reduction in child poverty due to the IMF program at different personalization granularities. Our study also performs sensitivity analyses to assess potential threats to the unconfoundedness assumption and estimates ACE bounds and the E-value. This illustrates the potential of c-GNFs for causal and counterfactual inference in fields such as social, natural, and medical sciences.

Structural Equation/Causal Models (SEMs/SCMs) are widely used in epidemiology and social sciences to identify and analyze the average causal effect (ACE) and conditional ACE (CACE). Traditional causal effect estimation methods such as Inverse Probability Weighting (IPW) and more recently Regression-With-Residuals (RWR) are widely used - as they avoid the challenging task of identifying the SCM parameters - to estimate ACE and CACE. However, much work remains before traditional estimation methods can be used for counterfactual inference, and for the benefit of Personalized Public Policy Analysis (P3A) in the social sciences. While doctors rely on personalized medicine to tailor treatments to patients in laboratory settings (relatively closed systems), P3A draws inspiration from such tailoring but adapts it for open social systems. In this article, we develop a method for counterfactual inference that we name causal-Graphical Normalizing Flow (c-GNF), facilitating P3A. A major advantage of c-GNF is that it suits the open system in which P3A is conducted. First, we show how c-GNF captures the underlying SCM without making any assumption about functional forms. This capturing capability is enabled by the deep neural networks that model the underlying SCM via observational data likelihood maximization using gradient descent. Second, we propose a novel dequantization trick to deal with discrete variables, which is a limitation of normalizing flows in general. Third, we demonstrate in experiments that c-GNF performs on-par with IPW and RWR in terms of bias and variance for estimating the ACE, when the true functional forms are known, and better when they are unknown. Fourth and most importantly, we conduct counterfactual inference with c-GNFs, demonstrating promising empirical performance. Because IPW and RWR, like other traditional methods, lack the capability of counterfactual inference, c-GNFs will likely play a major role in tailoring personalized treatment, facilitating P3A, optimizing social interventions - in contrast to the current `one-size-fits-all' approach of existing methods.

We propose a novel sensitivity analysis to unobserved confounding in observational studies using copulas and normalizing flows. Using the idea of interventional equivalence of structural causal models, we develop ρρ-GNF (ρρ-graphical normalizing flow), where ρ∈[−1,+1]ρ∈[−1,+1] is a bounded sensitivity parameter. This parameter represents the back-door non-causal association due to unobserved confounding, and which is encoded with a Gaussian copula. In other words, the ρρ-GNF enables scholars to estimate the average causal effect (ACE) as a function of ρρ, while accounting for various assumed strengths of the unobserved confounding. The output of the ρρ-GNF is what we denote as the ρcurveρcurve that provides the bounds for the ACE given an interval of assumed ρρ values. In particular, the ρcurveρcurve enables scholars to identify the confounding strength required to nullify the ACE, similar to other sensitivity analysis methods (e.g., the E-value). Leveraging on experiments from simulated and real-world data, we show the benefits of ρρ-GNF. One benefit is that the ρρ-GNF uses a Gaussian copula to encode the distribution of the unobserved causes, which is commonly used in many applied settings. This distributional assumption produces narrower ACE bounds compared to other popular sensitivity analysis methods.

We propose a new sensitivity analysis model that combines copulas and normalizing flows for causal inference under unobserved confounding. We refer to the new model as ρ-GNF (ρ-Graphical Normalizing Flow), where ρ∈[−1,+1] is a bounded sensitivity parameter representing the backdoor non-causal association due to unobserved confounding modeled using the most well studied and widely popular Gaussian copula. Specifically, ρ-GNF enables us to estimate and analyse the frontdoor causal effect or average causal effect (ACE) as a function of ρ. We call this the ρcurve. The ρcurve enables us to specify the confounding strength required to nullify the ACE. We call this the ρvalue. Further, the ρcurve also enables us to provide bounds for the ACE given an interval of ρ values. We illustrate the benefits of ρ-GNF with experiments on simulated and real-world data in terms of our empirical ACE bounds being narrower than other popular ACE bounds.

In social-ecological systems (SES), where social and ecological processes are intertwined, phenomena are usually complex and involve multiple interdependent causes. Figuring out causal relationships is thus challenging but needed to better understand and then affect or manage such systems. One important and widely used tool to identify and communicate causal relationships is visualization. Here, we present several common visualization types: diagrams of objects and arrows, X-Y plots, and X-Y-Z plots, and discuss them in view of the particular challenges of visualizing causation in complex systems such as SES. We use a simple demonstration model to create and compare exemplary visualizations and add more elaborate examples from the literature. This highlights implicit strengths and limitations of widely used visualization types and facilitates adequate choices when visualizing causation in SES. Thereupon, we recommend further suitable ways to account for complex causation, such as figures with multiple panels, or merging different visualization types in one figure. This provides caveats against oversimplifications. Yet, any single figure can rarely capture all relevant causal relationships in an SES. We therefore need to focus on specific questions, phenomena, or subsystems, and often also on specific causes and effects that shall be visualized. Our recommendations allow for selecting and combining visualizations such that they complement each other, support comprehensive understanding, and do justice to the existing complexity in SES. This lets visualizations realize their potential and play an important role in identifying and communicating causation.

Models are widely used for investigating cause-effect relationships in complex systems. However, often different models yield diverging causal claims about specific phenomena. Therefore, critical reflection is needed on causal insights derived from modeling. As an example, we here compare ecological models dealing with the dynamics and collapse of cod in the Baltic Sea. The models addressed different specific questions, but also vary widely in system conceptualization and complexity. With each model, certain ecological factors and mechanisms were analyzed in detail, while others were included but remained unchanged, or were excluded. Model-based causal analyses of the same system are thus inherently constrained by diverse implicit assumptions about possible determinants of causation. In developing recommendations for human action, awareness is needed of this strong context dependence of causal claims, which is often not entirely clear. Model comparisons can be supplemented by integrating findings from multiple models and confronting models with multiple observed patterns.

The digitization of music has changed how we consume, produce, and distribute music. In this paper, we explore the effects of digitization and streaming on the globalization of popular music. While some argue that digitization has led to more diverse cultural markets, others consider that the increasing accessibility to international music would result in a globalized market where a few artists garner all the attention. We tackle this debate by looking at how cross-country diversity in music charts has evolved over 4 years in 39 countries. We analyze two large-scale datasets from Spotify, the most popular streaming platform at the moment, and iTunes, one of the pioneers in digital music distribution. Our analysis reveals an upward trend in music consumption diversity that started in 2017 and spans across platforms. There are now significantly more songs, artists, and record labels populating the top charts than just a few years ago, making national charts more diverse from a global perspective. Furthermore, this process started at the peaks of countries charts, where diversity increased at a faster pace than at their bases. We characterize these changes as a process of Cultural Divergence, in which countries are increasingly distinct in terms of the music populating their music charts.

The market-inspired reforms of New Public Management have been particularly pronounced in Swedish local government. Notably, municipally owned enterprises (MOEs) have rapidly grown in numbers. Principal-agent theory gives rise to the hypothesis that the massive introduction of MOEs has impacted negatively on the conditions for accountability in Swedish local government. To study this, social network analysis was employed in mapping networks for 223 MOEs in 11 strategically chosen municipalities, covering a total of 732 politicians. The analysis reveals substantial overlaps between principals (representatives of the ultimate stakeholders, citizens) and agents (the boards of the MOEs). Hence, corporatization of public services seems to imply worrisome entanglements between the politicians who are set to steer, govern, and oversee MOEs on the one hand, and the board members of MOEs on the other. The increasing numbers of MOEs may therefore have adverse effects on accountability in important and growing parts of Swedish local government.

Local governments are increasingly relying on municipally owned corporations (MOCs) to provide public services. Some describe this development as a rational response to austerity challenges and emphasise the cost-efficiency of MOCs (‘the optimistic view’). Others identify complications and associate MOCs with weak supervision, lack of accountability, and corruption risks (‘the sceptical view’). Hitherto, no studies have analysed these opposing claims on MOCs in the one and same inquiry. We address this gap by focusing on Sweden, which has experienced a dramatic growth in the number of MOCs. We examine the association between the number of MOCs, the business climate, satisfaction with local government, local tax rates, and a corruption index for all 290 Swedish municipalities. Putting the ‘optimistic view’ into doubt, results indicate that municipalities relying heavily on MOCs are associated with more perceived corruption and higher taxes but do not have more satisfied citizens nor a better business climate.

This chapter focuses on a masculine and entrepreneurial identity. Eschewing this gendered and 'white' Western construction the author strives instead for more reflexive and nuanced considerations of entrepreneurship. The chapter highlights particular excerpts taken from the identity narratives of Muslim business women of Turkish descent living in the Netherlands to show how these stories describe quite different ways of experiencing, interpreting and responding to their marginalization. These narrative excerpts focus on some of the underlying relations of power that shapes the entrepreneurial identities of these women. The chapter takes issues with the often taken-for-granted universal subjectivity of 'the entrepreneur' by including the identity categories of ethnicity, gender and religion. An analysis of the women's own stories is used to illustrate the competing tensions in the fashioning of hybrid identities, both for and against assimilation and acceptance in the Dutch socio-political context.

Objectives This is the first population-level study to examine inequalities in COVID-19 mortality according to working-age individuals occupations and the indirect occupational effects on COVID-19 mortality of older individuals who live with them. Methods We used early-release data for the entire population of Sweden of all recorded COVID-19 deaths from 12 March 2020 to 23 February 2021, which we linked to administrative registers and occupational measures. Cox proportional hazard models assessed relative risks of COVID-19 mortality for the working-aged population registered in an occupation in December 2018 and the older population who lived with them. Results Among working aged-adults, taxi/bus drivers had the highest relative risk of COVID-19 mortality: over four times that of skilled workers in IT, economics, or administration when adjusted only for basic demographic characteristics. After adjusting for socioeconomic factors (education, income and country of birth), there are no occupational groups with clearly elevated (statistically significant) COVID-19 mortality. Neither a measure of exposure within occupations nor the share that generally can work from home were related to working-aged adults risk of COVID-19 mortality. Instead of occupational factors, traditional socioeconomic risk factors best explained variation in COVID-19 mortality. Elderly individuals, however, faced higher COVID-19 mortality risk both when living with a delivery or postal worker or worker(s) in occupations that generally work from home less, even when their socioeconomic factors are taken into account. Conclusions Inequalities in COVID-19 mortality of working-aged adults were mostly based on traditional risk factors and not on occupational divisions or characteristics in Sweden. However, older individuals living with those who likely cannot work from home or work in delivery or postal services were a vulnerable group.

While several models for analysing longitudinal network data have been proposed, their main differ-ences, especially regarding the treatment of time, have not been discussed extensively in the literature.However, differences in treatment of time strongly impact the conclusions that can be drawn from data.In this article we compare auto-regressive network models using the example of TERGMs – a temporalextensions of ERGMs – and process-based models using SAOMs as an example. We conclude that theTERGM has, in contrast to the ERGM, no consistent interpretation on tie-level probabilities, as well as noconsistent interpretation on processes of network change. Further, parameters in the TERGM are stronglydependent on the interval length between two time-points. Neither limitation is true for process-basednetwork models such as the SAOM. Finally, both compared models perform poorly in out-of-sampleprediction compared to trivial predictive models.

What can social sciences do with machine learning, and what can the latter do to them? A contribution to the emerging debate on the role of machine learning for the social sciences, this article offers an introduction to this class of statistical techniques. It details its premises, logic, and the challenges it faces. This is done by comparing machine learning to more classical approaches to quantification - most notably parametric regression - both at a general level and in practice. The article is thus an intervention in the contentious debates about the role and possible consequences of adopting statistical learning in science. We claim that the revolution announced by many and feared by others will not happen any time soon, at least not in the terms that both proponents and critics of the technique have spelled out. The growing use of machine learning is not so much ushering in a radically new quantitative era as it is fostering an increased competition between the newly termed classic method and the learning approach. This, in turn, results in more uncertainty with respect to quantified results. Surprisingly enough, this may be good news for knowledge overall.

To further research how organizations influence workforce wage inequality, the authors replicate and extend Sorensen and Sorenson's study on organizational demography and wage inequality in region-industries by (1) replicating original results from Danish regions from 1992 to 1998 using a close-to-identical dataset in Sweden during the same time period, (2) using multiverse analysis to gauge the effect of analytical choices on research results, and (3) expanding the scope of Sorensen and Sorenson's model by two new measures of organizational diversity. The findings suggest strong to fair test-retest validity of the original model, but model extensions with nuanced measures of organization form diversity do not enhance the model's explanatory power. The authors analyze and discuss replication anomalies and show how multiverse analysis can be gainfully used more generally in comparative organizational sociology.

A large number of studies have demonstrated that the proportion of home-owners in a region tend to be positively associated with the unemployment levels in that region. In this paper, we introduce a missing piece of explaining this commonly found pattern. By analysing individual-level population register data on Sweden, we jointly examine the effects of micro- and macro-level home-ownership on individuals’ unemployment. The findings indicate that even though home-owners have a lower probability of being unemployed, there is a penalty for both renters and home-owners on unemployment in regions with high home-ownership rates. Differences in mobility patterns cannot explain this pattern. However, when labour market size is considered, the higher probability of unemployment in high home-owning regions is drastically reduced. This suggests that high home-ownership regions tend to coincide with small labour markets, affecting the job matching process negatively.

Research Summary Innovative entrepreneurship, defined as the creation of new products, services, production methods, or business models, is critical for firm, industry, and economic growth and a key determinant of societal well-being. This special issue explores the roles of institutions and government policies in promoting or impeding innovative entrepreneurship. In this introductory editorial, we review theory and evidence on entrepreneurship at the macro-institutional and micro-policy levels, highlighting costs and benefits of alternative institutional environments and targeted policy interventions, as well as interactions within and across levels. We summarize the six papers in the special issue, discuss their contributions to the literature, and suggest how future work can build upon these and other papers to advance our understanding of the conditions and mechanisms underlying successful entrepreneurial innovation. Managerial Summary Innovation and entrepreneurship bring new products and services to market, help firms and industries to grow, and generate improvements in social and economic life. The papers in this special issue explore the background conditions-laws, political processes, regulations, tax policy, subsidies, training programs, and more-that make entrepreneurship and innovation successful. Both "macro" and "micro" policies can stimulate successful entrepreneurial and innovative outcomes, but can also become politicized, be ineffective, and generate unintended consequences. The papers offer lessons to researchers, policymakers, and practitioners about making entrepreneurship and innovation more successful.

Vi har undersökt hur äldre stockholmares risk att avlida av covid-19 mellan 12 mars och 7 maj 2020 var associerad med boendesituation och bostadsområde. Individer som bodde med någon iarbetsför ålder löpte högre risk att dö av covid-19 jämfört med äldre som endast bodde medandra äldre, och detsamma gällde individer som bodde på äldreboenden och individer boende itätt befolkade grannskap eller stadsdelar med utbredd smittspridning. 

Sex gånger så många som har dött i covid-19 har förlorat en familjemedlem i sjukdomen, och i Stockholm är låginkomsttagare, lågutbildade och individer med invandrarbakgrund överrepresenterade i denna grupp. Stadsdelar med hög andel låginkomsttagare eller utrikesfödda är särskilt drabbade. Att förlora en familjemedlem kan innebära en förlust av socialt och ekonomiskt stöd och att förlora en familjemedlem i just covid-19 antas vara extra traumatiskt. Individer som drabbats av detta kan därför vara i behov av olika former av samhälleligt stöd.

Människor i Sverige lever segregerat med avseende på socioekonomisk status och invandrarbakgrund. Samtidigt varierar bland annat arbetslöshets- och utbildningsnivå samt grad av ohälsa mellan olika bostadsområden. Vilken roll spelar bostads- eller uppväxtområdet för en individs livschanser? Bidrar boendesegregationen i sig till att öka ojämlikheten? Denna forskningsöversikt visar att även om de flesta studier tyder på att grannskapet bidrar i att forma individers livsutfall, så skiljer sig resultaten ofta beroende på vilken metod eller mått som har använts.

Older individuals are overrepresented among COVID-19 deaths,raising questions of how to best mitigate patterns of social contact as the pandemic progresses.Researchers have underlined the importance of living arrangements and household composition, such as care homes, crowded housing, and mixed-age households, as well as social contacts outside the household for understanding the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Living arrangements shape the contact that older adults have with individuals from within or outside the household. In addition, their risk of infection can be shaped by their broader neighbourhood environment. Residential clustering of infections has received considerable attention in previous studies. In attempting to understand the structural features responsible for differences in the spread of the virus across neighbourhoods, early hypotheses pointed to population density as an important contributor, but the evidence has been mixed. A major limitation of previous research on the associations between living arrangements, neighbourhood characteristics, and COVID-19 mortality has been the reliance on aggregated data, unadjusted for differences by age and other individual-level risk factors. Because how and where older people live is partly determined by their sociodemographic characteristics and health, drawing individual-level conclusions on the basis of aggregated data can lead to biased conclusions on the importance of living arrangements and neighbourhoods in COVID-19 mortality.

This study examines how similarity in work-family related attitudes matter for relationship satisfaction and union dissolution among Swedish couples. It utilizes a data set from 2009 (the Young Adult Panel Study) containing information on 1055 opposite-sex couples (married or co-residential), and registered union dissolutions up to 2014. Results indicate that couples who have similar notions on the importance of being successful at work; on the importance of having children; or on the importance of having enough time for leisure activities are more likely to be satisfied with their partner relationship than couples who have dissimilar attitudes. However, there are no effects of similarity in attitudes regarding the importance of living in a good partner relationship or doing well economically on relationship satisfaction, and we do not find any impact of similarity in attitudes of any kind on actual breakups. We find no support for specialization theory, which would predict that dissimilarity in work orientation would increase relationship quality. The study concludes that having similar priorities regarding work, career, and family does seem to matter for relationship quality, at least when it comes to the partners satisfaction with the relationship.

We examine the impact of ethnic school segregation on the educational outcomes of students, using Swedish population register data. Through a school fixed effects, family fixed effects, and a two-way school- and family fixed effects design, we adjust for selection effects related to variation in the student composition across schools. The analyses show that students’ grades are relatively unaffected by the proportion of immigrant schoolmates.  However, it has a small negative effect on levels of eligibility for upper secondary school. Furthermore, immigrants’ educational outcomes are weakly positively affected by the proportion of peers with the same national background as themselves.

We examine the impact of ethnic school composition on students’ educational outcomes using Swedish population register data. We add to the literature on the consequences of ethnic school segregation for native and immigrant students by distinguishing social interaction effects from selection and environmental effects through one- and two-way fixed effects models. Our findings demonstrate that native and immigrant students’ grades are relatively unaffected by social interaction effects stemming from the proportion of immigrant schoolmates. However, we find nontrivial effects on their eligibility for upper secondary school. Immigrants’ educational outcomes are weakly positively affected by the proportion of co-ethnics in school.

The aim of this study is to investigate how school choice opportunities affect school segregation. Theoretically, free school choice may affect school segregation in different directions, depending on its design, baseline residential segregation, and underlying preferences for separation. Our test case is the Swedish voucher-based free school choice system, and we utilize Swedish population register data that include 13 cohorts of ninth graders, with geocoded information on schools, their socioeconomic and ethnic composition, and the composition of the neighborhoods surrounding the schools. To identify causal effects of school choice opportunities, we treat fixed school areas as the unit of analysis, and we follow these over time to net out time-invariant area-level confounders. Within-area panel estimates indicate segregation based on both ethnic and educational background to be strongly affected by school choice opportunities. Increased choice opportunities lead to increased school segregation, to a large extent because of a higher propensity among native children and children with well-educated parents to attend newly established (non-public) independent schools. The segregating impact of school choice opportunities is uniform across school areas with different socioeconomic and ethnic profiles, but school segregation increases much more in residentially integrated areas as a consequence of an increase in school choice options. The lesson to be learned from the Swedish case is that large scale school voucher systems need to be designed to include mechanisms that counteract the strong segregating forces that such systems appear to produce.

It is a matter of debate whether free school choice should lead to higher or lower levels of school segregation. We investigate how school choice opportunities affect school segregation utilizing geocoded Swedish population register data with information on 13 cohorts of ninth graders. We find that local school choice opportunities strongly affect the sorting of students across schools based on the parents country of birth and level of education. An increase in the number of local schools leads to higher levels of local segregation net of stable area characteristics, and time-varying controls for population structure and local residential segregation. In particular, the local presence of private voucher schools pushes school segregation upwards. The segregating impact of school choice opportunities is notably stronger in native areas with high portions of highly educated parents, and in areas with low residential segregation. Our results point to the importance of embedding individual actors in relevant opportunity structures for understanding segregation processes.

It is well known that couples tend to relocate for the sake of the mans career rather than the womans, also known as the trailing spouse phenomenon. The role of employer choices in this process is unknown however. If employers are hesitant to make job offers to women who live a long way from the workplace (e.g., because of work-family balance concerns or a perceived risk that they will not follow through on their applications, or stay hired if employed), this tendency might constitute an underlying mechanism behind the moving premium of partnered men. Ours is the first study to empirically test whether employers prefer geographically distant men over geographically distant women. We sent applications for 1,410 job openings in the Swedish labour market, randomly assigning gender and parental status to otherwise equivalent applications from cohabiting or married women and men and recorded employer callbacks to these. The results indicate that employers in general tend to disfavour job applicants who live a long way from the employers workplace. This tendency is stronger for women, both for mothers and for women with no children. Our estimated effects are imprecise but clearly suggest that employer recruitment choices contribute to the trailing spouse phenomenon by offering men a larger pool of geographically distant jobs. We call for more research on this hitherto ignored mechanism behind the trailing spouse phenomenon.