Incidence of outbreak-associated COVID-19 cases by industry in Ontario, Canada, 1 April 2020–31 March 2021

ObjectivesThe objective of our study was to estimate the rate of workplace outbreak-associated cases of COVID-19 by industry in labour market participants aged 15–69 years who reported working the majority of hours outside the home in Ontario, Canada.MethodsWe conducted a population-based cross-sectional study of COVID-19 workplace outbreaks and associated cases reported in Ontario between 1 April 2020 and 31 March 2021. All outbreaks were manually classified into two-digit North American Industry Classification System codes. We obtained monthly denominator estimates from the Statistics Canada Labour Force Survey to estimate the incidence of outbreak-associated cases per 100 000 000 hours among individuals who reported the majority of hours were worked outside the home. We performed this analysis across industries and in three distinct time periods.ResultsOverall, 12% of cases were attributed to workplace outbreaks among working-age adults across our study period. While incidence varied across the time periods, the five industries with the highest incidence rates across our study period were agriculture, healthcare and social assistance, food manufacturing, educational services, and transportation and warehousing.ConclusionsCertain industries have consistently increased the incidence of COVID-19 over the course of the pandemic. These results may assist in ongoing efforts to reduce transmission of COVID-19 by prioritising resources, as well as industry-specific guidance, vaccination and public health messaging.

The Birth of a Consumer Democracy

This chapter surveys housing and suburbanization, autos, and television as three vital engines that drove economic expansion and mass consumerism. It opens with a discussion of Chester Bowles, wartime head of the OPA, and his emphasis on housing and homebuilding as a key to future prosperity. Technological innovation, the productivity of American industry, and the prosperity that followed brought all the former privileges of the wealthy classes within reach of the rapidly expanding American middle class. These factors help explain why so many Americans look back with nostalgia on the postwar decades as “Happy Days.”

Incidence of outbreak-associated COVID-19 cases by industry in Ontario, Canada, April 1, 2020- March 31, 2021

Objectives The objective of our study was to estimate the rate of workplace outbreak-associated cases of COVID-19 by industry in labour market participants aged 15-69 years who reported working the majority of hours outside the home in Ontario, Canada. Methods We conducted a population based cross-sectional study of COVID-19 workplace outbreaks and associated-cases reported in Ontario between April 1, 2020 and March 31, 2021. All outbreaks were manually classified into two digit North American Industry Classification System (NAICS) codes. We obtained denominator data from the Statistics Canada Labour Force Survey in order to estimate the incidence of outbreak-associated cases per 100,000,000 hours amongst individuals who reported the majority of hours were worked outside the home. We performed this analysis across industries and in three distinct time periods. Results Overall, 12% of cases were attributed to workplace outbreaks among working age adults across our study period. While incidence varied across the time periods, the five industries with the highest incidence rates across our study period were agriculture; healthcare and social assistance; food manufacturing; educational services; and, transportation and warehousing. Conclusions Certain industries have consistently increased incidence of COVID-19 over the course of the pandemic. These results may assist in ongoing efforts to reduce transmission of COVID-19, by prioritizing resources, as well as industry-specific guidance, vaccination, and public health messaging.

An artificial intelligence-enabled industry classification and its interpretation

PurposeTo overcome the shortcomings of traditional industry classification systems such as the Standard Industrial Classification Standard Industrial Classification, North American Industry Classification System North American Industry Classification System, and Global Industry Classification Standard Global Industry Classification Standard, the authors explore industry classifications using machine learning methods as an application of interpretable artificial intelligence (AI).Design/methodology/approachThe authors propose a text-based industry classification combined with a machine learning technique by extracting distinguishable features from business descriptions in financial reports. The proposed method can reduce the dimensions of word vectors to avoid the curse of dimensionality when measuring the similarities of firms.FindingsUsing the proposed method, the sample firms form clusters of distinctive industries, thus overcoming the limitations of existing classifications. The method also clarifies industry boundaries based on lower-dimensional information. The graphical closeness between industries can reflect the industry-level relationship as well as the closeness between individual firms.Originality/valueThe authors’ work contributes to the industry classification literature by empirically investigating the effectiveness of machine learning methods. The text mining method resolves issues concerning the timeliness of traditional industry classifications by capturing new information in annual reports. In addition, the authors’ approach can solve the computing concerns of high dimensionality.

The platform economy matures: measuring pervasiveness and exploring power

Online platforms are pervasive and powerful in today’s economy. We explore the increased centrality of platforms in two ways. First, we measure the extent to which platforms are insinuating themselves into the economy. We accomplish this by analyzing the presence of platforms as intermediating organizations across all US service industries at the six-digit North American Industry Classification System code level. Our results show that 70% of service industries, representing over 5.2 million establishments, are potentially affected by one or more platforms. Secondly, we undertake a detailed firm-level case study of the mega-platform, Amazon, which demonstrates the ways that the aforementioned macro-level data is expressed by a single platform firm. This case study shows that Amazon’s growth trajectory has resulted in it entering and transforming existing industries and sectors. We conclude by reflecting upon the limitations and implications for future research.

Engineering Hollywood

Engineering Hollywood tells the story of the formation of the Hollywood studio system not as the product of a genius producer, but as an industry that brought together creative practices and myriad cutting-edge technologies in ways that had never been seen before. Using extensive archival research, this book examines the role of technicians, engineers, and trade organizations in creating a stable technological infrastructure on which the studio system rested for decades. Here the studio system is seen as a technology-dependent business with connections to the larger American industrial world. By focusing on the role played by technology, we see a new map of the studio system beyond the backlots of Los Angeles and the front offices in New York. In this study, Hollywood includes the labs of industrial manufacturers, the sales routes of independent firms, the garages of tinkerers, and the clubhouses of technicians’ societies. Rather than focusing on the technical improvements in any particular motion picture tool, this book centers on the larger systems and infrastructures for dealing with technology in this creative industry. Engineering Hollywood argues that the American industry was stabilized and able to dominate the motion picture field for decades through collaboration over technologies of everyday use. Hollywood’s relationship to its essential technology was fundamentally one of interdependence and cooperation—with manufacturers, trade organizations, and the competing studios. Accordingly, Hollywood could be defined as an industry by participation in a closed system of cooperation that allowed a select group of producers and manufacturers to dominate the motion picture business for decades.

Setting the Scene

Part I examines the world music industry from the points of view of European and American industry personnel (e.g., booking agents, record labels personnel, tour managers). Chapter 1 contextualizes the world music industry in the larger music and culture industries. Since its birth in 1987, world music has been a vague category. It has encompassed an enormous variety of music: traditional and folk musics, newly composed traditional musics, and vintage and contemporary popular musics. What, then, is “world music”? Where did it come from? After providing a historical overview of world music through its emergence as a genre category in the 1980s and its growth in the ’90s, the chapter examines how culture industries have, in collaboration with consumers, developed a market for, and expectations of, ethnic “others” in Europe and North America.