Please assist with question 4, case study provided

- 1 Question 4 Before collecting data from the participants, you are required to develop a consent form that participants will sign before participating in Not yet the study. answered ) : ) ) . i ) ; = . With respect to the research documented in the literature review provided, describe the purpose of a consent form. Discuss the different M ut i L 125 IO e aspects that should be included in a consent form. Flag question Your response should not be generic but should be applied to the research documented in the article. Two (2) marks to be awarded for stating the purpose Two (2) marks awarded for discussing each aspect that is included in a consent form = total of 10 marks automation technologies on labour demand is positive due to increasing product demand and spillover 2016). In the manufacturing sector specifically, studies find more robust results for decreased employment as a result of automation adoption (Acemoglu & Restrepo, 2017). However, one of these studies points out that job losses in manufacturing were fully offset by job growth in the services sector (Dauth et al., 2017), and another highlights that the net effect of automation on employment is positive due to job growth in the services sector (Mann & Pittmann, 2018). Moreover, the reduction in employment in most advanced economies is consistent with the overall historical trend of a decline in manufacturing employment since the 1970s and has not accelerated since the introduction of the new automation technologies. This gives us reason to believe that other factors, such as the shift to services, offshoring of manufacturing to developing countries, and increased international competition in manufacturing which some of the above studies highlight have been equally, if not more important, in explaining the decline in manufacturing employment in advanced economies. There are also studies that find an increase in manufacturing employment due to automation, such as Koch et al. (2019). Using a panel dataset of manufacturing firms in Spain over 1990-2016, they found that firms that had adopted robots generated jobs because output were much larger than the share of reduction in the labour costs. On a global scale, UNIDO (2019) calculates that the annual growth in the stock had a positive but small effect on employment growth (mostly in manufacturing activities) from 2000 to 2014. The literature investigating the employment effect of automation in developing countries is scarce. However, in the case of developing countries in Asia, the Asian Development Bank has provided some recent figures. They found that new technologies (not specifically automation technologies) had a net positive impact on employment in 2005-2015, displacing 101 million jobs but creating 134 million jobs (ADB, 2018). Similarly, a study by Dutz et al. (2018) found that manufacturing firms in Latin America that invested in new ICT technologies experienced net job gains. Generally, reports from international organisations (e.g. ADB,_2018; UNCTAD, 2017; UNIDO, 2019; World Bank, 2016) note that most Al-driven automation technologies are developed and used in advanced economies, and have yet to make a significant inroad into developing countries. Ernst et al. (2018) argue that the adoption of Al-driven automation technologies in developing countries has the potential to increase opportunities for productivity growth and hence employment growth, similar to what the above studies have found with respect to digital technologies more broadly. References Acemoglu, D. and Restrepo, P. (2017). \"Robots and Jobs: Evidence from US Labor Markets", National Bureau of Economic Research, vol. 10, pp.23285. Acemoglu, D. and Restrepo, P. (2020). \"The wrong kind of AI? Artificial intelligence and the future of labour demand\Is automation stealing manufacturing jobs? Evidence from South Africa's apparel industry Keywords Automation, Manufacturing, Employment, Africa, South Africa, Apparel industry 1. Introduction There are growing fears that automation will start displacing jobs at a faster rate than they have in the past, leaving less room for human work (Brynjolfsson and McAfee, 2014; Chang et al., 2016). Some studies highlight that jobs in developing countries are at high risk (Chang et al., 2016; Frey & Rahbari, 2016; World Bank, 2016), particularly manufacturing jobs or job creation in the manufacturing sector (Manyika et al., 2017; Schlogl & Sumner, 2020). This trend is especially worrying for developing countries due to the importance of labour-intensive manufacturing for economic development (Chang_et al., 2016; Hauge and Chang, 2019). In fact, the prospect of automation-related unemployment is leading some scholars to suggest that manufacturing-led economic growth may be a less feasible development model (R. Baldwin & R. Forslid, 2020). This paper evaluates the threat of automation to employment, focusing on the manufacturing sector. It does so by critically reviewing the literature on the impact of automation on employment, as well as using industry-specific evidence from the apparel industry in South Africa. Seeing that many recent studies are global in outlook and apply opaque methodologies, which are bound to ignore many country-specific and industry-specific conditions/barriers to implementing automation technologies, we investigate a particular case: the apparel industry in South Africa. We focus on evaluating the employment impact of automation, barriers to adopting it, and major technical challenges to further automation. We chose to study the apparel industry because of the importance of apparel manufacturing to the industrialisation of developing countries, and the high likelihood of technical automation predicted for this industry in forecasts. We chose to focus on the South African apparel industry because it has recently shifted somewhat from a traditional labour-intensive approach to using more automation, allowing insight into the problem. The limited research on this topic carried out in developing countries also motivated us to look at a specific developing country. We find that the overall impact of automation on unemployment in South Africa''s apparel industry has been negligible and is predicted to continue to be negligible for the foreseeable future. But in some instances, increased automation has and is predicted to increase employment by improving productivity at the firm level. We also argue that numerous barriers specific to developing countries as well as the apparel industry will slow the development and adoption of automation. et al. (2015). Moreover, the restructuring of the labour force in the second half of the twentieth century | Time left 2:38:46 | Hide than many people think. For example, James Bessen found that among the 270 occupations in the 1950 US census, 232 of them (86%) still exist today, 37 of these disappeared due to changes in consumer demand or technological obsolescence. Only one occupation disappeared due to automation: elevator operators (Bessen, 2016). Additionally, evidence on the impact of automation on within-sector employment is mixed. For example, during the first industrial revolution, machines were able to take over 98% of human work in the textile industry, but the decrease in prices resulting from automation adoption and technological progress rapidly increased product demand and caused an overall increase in labour demand for weavers (Bessen, 2016). In fact, Bessen (2017) shows that as productivity increases and prices fall, many manufacturing sectors initially increase employment. The adoption of automation technologies also has the potential to create new jobs within the sector (e.g., people to operate new machinery). 2.2. Why is this time supposed to be different? Despite the lack of historical precedent for widespread unemployment due to automation, fears that new technologies may disrupt these patterns should not be dismissed out of hand. Arguments for this view expect that new technologies, especially in the digital realm, will impact more jobs across industries, will allow for more full automation and will occur more quickly than before (Brynjolfsson & McAfee, 2014; Schwab, 2017; Susskind and Susskind, 2015). Previously, computer-based automation was largely confined to manual and cognitive routine tasks involving explicit rule-based activities. This made it difficult for automation to be used in applications which involve abstract thinking, manual adaptability and/or situational awareness often referred to as non-routine tasks in the literature (Autor et al., 2015; Frey and Osborne, 2017). Examples of non-routine tasks are found in both high-skilled jobs (e.g. creative design) and low-skilled manual jobs (e.g. housework). Following recent technological advances, especially within artificial intelligence (Al), automation is now spreading to domains commonly defined as non-routine (Frey and Osborne, 2017). The scale of applicability of Al in the economy is intensified by the miniaturisation of computers, increased computing power, cloud computing and continuous data collection through the so-called Internet-of-Things. Through the use of sensors, the Internet-of-Things allows data transfer over the internet between objects without requiring human-to-human or human-to-computer interaction (see UNIDO (2019) for a good taxonomy of these advanced digital technologies). Moreover, technologies no longer exist in silos and can feed off one another to a greater extent. The most recent and ongoing technological advancements leverage the interactive possibilities between digital technologies to achieve desired outcomes (Banga et al., 2018). Increased flexibility of technological systems means that changes could happen in a short time frame, increasing the shock to the labour market. Even new work that arises due to technological advances could be automated. 2.1. How has automation impacted employment in the past? Seeing that most recent studies focus on automation driven by digital technologies, it is easy to lose sight of the fact that automation has been around for centuries. The first completely automated industrial process was developed in 1785 by Oliver Evans in the form of an automatic flour mill (Andreoni and Anzolin, 2019). The fear of job losses due to automation dates back almost as far: to the early 19th century Luddite protests, when textile workers in Great Britain destroyed machines in fear of being replaced by them. In some ways, their anger was justified the history of industrialisation shows that the introduction of automation technologies have caused disruptions to labour demand and short-term spikes of unemployment (Allen, 2009). However, in the long run, evidence strongly supports that automation creates a multitude of jobs and unleashes demand for existing ones, mare than offsetting the number of jobs it displaces (Muro et al., 2019). For example, while the introduction of the personal computer (PC) displaced jobs, it also created new ones. According to (Manyika et al, 2017), the PC has enabled the creation of 15.8 million net new jobs in the United States since 1980, even after accounting for jobs displaced. It should, therefore, come as no surprise that a vast body of literature shows that increased productivity: has been associated with increased overall employment in both advanced economies and developing countries. We should highlight though that automation, and technological progress more generally, has historically disrupted within-sector employment, even as overall employment has grown. In the United States, the agricultural sector's share of total employment declined from 60% in 1850 to less than 5% in 1970, and the manufacturing sector's share of total employment fell from 26% in 1960 to below 10% today (Manyika et al., 2017). But these structural workforce shifts are not only due to technological advances. For example, while declining employment in manufacturing in advanced economies between the 1950s and 1990s has been associated with technological advances and productivity growth within the manufacturing sector (Bessen, 2017), offshoring of production to developing countries and increased international competition has become a more important factor after 1990 Autor et al. (2015). Moreover, the restructuring of the labour force in the second half of the twentieth century has been more modest than many people think. For example, James Bessen found that among the 270 occupations in the 1950 US census, 232 of them (86%) still exist today, 37 of these disappeared due to changes in consumer demand or technological obsolescence. Only one (Peck, 2017) highlights a large number of business process tasks some of which have not existed for that long that are immediately available for automation, including supply chain management, employee data management, invoicing, customer support, litigation support, and employee data management. However, the adoption of Al-driven technologies and other advanced digital technologies is still slow. Surveys suggest that while businesses are interested in Al and intelligent machine technology, not many are actually rolling them out for commercial use. In developing countries, less than 5% of manufacturing firms use advanced digital production technologies, and in some developing countries, more than 70% of manufacturing firms only use analogue production technologies (UNIDO, 2019). According to Willcocks (2020), the global market for Al, worth US$4.1 billion in 2018, constitutes a very small fraction of the global market for information technology as a whole, worth roughly US$5 frillion in 2018. These figures should caution the claims that Al-driven technologies will have a massive and rapid economic impact. The following sections examine the evidence, particularly the impact on and predictions about employment. 2.3. Is new automation technology already causing unemployment? Despite the limited adoption of advanced digital automation technologies, some of them are already in use, for example, in robots, so it may be useful to investigate the recent impact of automation technologies on employment. It is not straightforward, however, to measure this impact. A particular challenge and source of variation in the results between studies is the choice of a proxy for automation. Some studies look at robot implementation, some look at capital formation, some look at productivity growth, some look at R&D spending, and some look at adoption of digital technologies. Beyond the challenge of finding an appropriate measure, most of these proxies will have both job-creating and job-displacing impacts. However, a big-picture look at studies can still provide us some useful insights. Let us first look at advanced economies. In general, the evidence of the impact of new automation technology on employment is mixed. Although some studies find that automation has displaced labour (Acemoglu & Restrepo, 2019; Chiacchio et al., 2018), other studies attribute job losses to different factors (particularly in the case of the United States), such as increased international competition (Autor et al., 2015), a productivity slowdown (Miller and Atkinson, 2013), or demographic factors, such as a peak in women's participation in the labour force (Miller & Atkinson, 2013). Some studies actually find that the net effect of new automation technologies on labour demand is positive due to increasing product demand and spillover effects (Gregory et al., 2016). In the manufacturing sector specifically, studies find more robust results for decreased employment as a result of