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Despite widespread interest in the gig economy, academic research on the topic has lagged behind. The present chapter applies organizational theory and research to compose a working model for understanding participation in the gig economy and how gig work may impact worker health and well-being. Drawing from past research this chapter defines the gig economy in all its diversity and advances a framework for understanding why individuals enter into gig economy. Next, the authors discuss how various characteristics of the gig economy and gig workers can be understood as both demands and resources that influence how gig work is likely to be experienced by the individual. To understand how these characteristics are likely to influence worker health and well-being, we draw from past research on alternative work arrangements and entrepreneurship, as well as the limited extant research on the gig economy. Finally, a research agenda is proposed to spur much needed research on the gig economy and its workers.

Prejudice against Jews was part of the landscape in the Union of South Africa long before Nazism made inroads into the country during the 1930s, at which stage Jews constituted approximately 4.6% of the country’s white (or European) population. Aggressive Afrikaner nationalism was marked by fervent attempts to proscribe Jewish immigration. By 1939, Jewish immigration was included as an official plank in the political platform of the opposition Purified National Party led by Dr D.F. Malan, along with a ban on party membership for Jews residents in the Transvaal province. Racial discrimination, in a country with diversified ethnic elements and intense political complexities, was synonymous with life in the Union long before the Apartheid system, with its official policy of enforced legal, political and economic segregation, became law in May 1948 under Dr Malan’s prime ministership. Although the Jews, while maintaining their own subcultural identity, were classified within South Africa’s racial hierarchy as part of the privileged white minority, the emergence of recurrent anti-Jewish stereotypes and themes became manifest in a country permeated by the ideology of race and white superiority. This was exacerbated by the growth of a powerful Afrikaner nationalist movement, underpinned by conservative Calvinist theology. This chapter focusses on measures taken in South Africa by organisational structures within the political sphere to restrict Jewish immigration between 1930 and 1939 and to do so on ethnic grounds. These measures were underscored by radical Afrikaner nationalism, which flew in the face of the principles of ethics and moral judgement.

Drawing on Dewey’s accounts of learning the Alexander Technique (AT), this chapter explores why he found the process so powerful. As AT teachers, we explain how the technique enables practitioners to become aware of fixed, unconscious habits and to bring them under conscious control. With a new student, work begins with physical habits. However, because physical, cognitive, emotional and social functionings are interdependent, AT lessons typically enable flexibility in each of these spheres. Dewey’s writings show his strong theoretical commitment to the idea of learning as practical and experiential. His AT lessons were truly revelatory in providing him with both direct, embodied experience of the power of habit to drive human behaviour and a practical means of becoming aware of, and resisting, his own habits of thought and action.

Perceptions are shaped by habit in such a way that the senses can be unreliable in working out how to respond in a given situation. Dewey’s practice of the AT revealed to him the dissonance between his habitual self in activity and his conscious view of himself. Dewey was challenged by his AT lessons, which required an open, enquiring attitude and sense of humility. In the AT, Dewey found a means of pursuing an active, critical, self-directed process of discovery and adaptation akin to childhood learning. AT begins with the self, our ‘tool of tools’. Through fundamentally modifying the self, the AT supports the openness and flexible response to the physical and social world that characterize productive experiential learning.

Many jurisdictions fine illegal cartels using penalty guidelines that presume an arbitrary 10% overcharge. This article surveys more than 700 published economic studies and judicial decisions that contain 2,041 quantitative estimates of overcharges of hard-core cartels. The primary findings are: (1) the median average long-run overcharge for all types of cartels over all time periods is 23.0%; (2) the mean average is at least 49%; (3) overcharges reached their zenith in 1891–1945 and have trended downward ever since; (4) 6% of the cartel episodes are zero; (5) median overcharges of international-membership cartels are 38% higher than those of domestic cartels; (6) convicted cartels are on average 19% more effective at raising prices as unpunished cartels; (7) bid-rigging conduct displays 25% lower markups than price-fixing cartels; (8) contemporary cartels targeted by class actions have higher overcharges; and (9) when cartels operate at peak effectiveness, price changes are 60–80% higher than the whole episode. Historical penalty guidelines aimed at optimally deterring cartels are likely to be too low.

While it has long been established that social factors, such as housing, transportation, and income, influence health and health care outcomes, over the last decade, attention to this topic has grown dramatically. Reforms that promote high-quality care as well as responsibility for total cost of care have shifted focus among health care providers toward upstream determinants of health care outcomes. As a result, there has been a proliferation of activity focused on integrating and aligning social and medical care, many of which depend critically on cross-sector alliances. Despite considerable activity in this area, cross-sector alliances in health care remain largely undertheorized. Both literatures stand to gain from more attention to carefully knitting together the theoretical and management literature on alliances with the empirical, health policy and health services literature on cross-sector alliances in health care. In this chapter, we lay out what exists in the current scientific literature as well as a framework for considering much needed work in this area. We organize the literature and our commentary around the lifecycle of alliances: alliance formation, including factors prompting alliance formation, partner selection, and alliance goals; alliance maturity, including the work of these cross-sector alliances, governance, finance and contracts, staffing structure, and rewards; and critical crossroads, including alliance timelines, definitions of success, and dissolution. We also lay out critical areas for future inquiry, including better theorizing on cross-sector alliances, developing typologies of these cross-sector health care alliances, and the role of policy in cross-sector alliances.

This paper posits that legal avoidance – employers’ search for forms of employment to which labor and employment laws do not apply – is an important driver of the restructuring of work. It examines three examples of restructuring that enable employers to avoid legal liability and compliance costs: the classification of workers as independent contractors; the use of part-time and variable-schedule work; and employers’ deskilling of jobs and reliance on vulnerable workers. None of these strategies is itself unlawful, but their impact is to limit workers’ legal protections and weaken the law itself. Employers may also experience unintended consequences of restructuring.

This paper develops a cross-sectionally augmented distributed lag (CS-DL) approach to the estimation of long-run effects in large dynamic heterogeneous panel data models with cross-sectionally dependent errors. The asymptotic distribution of the CS-DL estimator is derived under coefficient heterogeneity in the case where the time dimension (T ) and the cross-section dimension (N ) are both large. The CS-DL approach is compared with more standard panel data estimators that are based on autoregressive distributed lag (ARDL) specifications. It is shown that unlike the ARDL-type estimator, the CS-DL estimator is robust to misspecification of dynamics and error serial correlation. The theoretical results are illustrated with small sample evidence obtained by means of Monte Carlo simulations, which suggest that the performance of the CS-DL approach is often superior to the alternative panel ARDL estimates, particularly when T is not too large and lies in the range of 30–50.