Author: Siddesh Ghule
MMS – Roll No. 74
Kohinoor Business School
Literature Review.
The Complex Interplay of Job Automation, Demographic Shifts, and Policy Formulation in the Future of Work
The future of work: Meeting the global challenges of demographic change and automation.
The paper discusses the projected need for 305 million jobs between 2020 and 2030, influenced by demographic changes, labour force composition, and the desire to achieve specific unemployment rates, with most jobs expected in low- to middle-income countries. Automation is projected to displace millions of workers, particularly in manufacturing, but this is overshadowed by the job creation needs resulting from demographic and labour force changes. The relationship between job needs and projected job displacement due to automation is highlighted, pointing out that routine, low-skilled jobs needed to accommodate the growth in youth working-age populations in lower-income countries are most susceptible to automation. Investing in high-quality education for youth is essential. For higher-income countries with an aging workforce, the challenge is to maintain the health and productivity of older workers. The paper suggests providing high-quality healthcare, developing legislation to incentivize firms to hire older workers, and fostering collaboration between robots and older workers. Lastly, the paper stresses the multifaceted interplay in job creation and the role of economic policies in facilitating this process by ensuring workers have the required skills and promoting healthy aging in the workforce. It also discusses potential areas for further research, including the differential effects of automation on women and men, the second-order effects of global migration, and the nature of job creation in relation to employee characteristics and available technologies.
Automation and jobs: when technology boosts employment
The paper explores how the elasticity of demand concerning labour productivity significantly influences the impact of productivity-improving technological advancements on industry employment. It highlights that while the pace of productivity growth determines the magnitude of employment change, the elasticity of demand determines whether employment increases or decreases. The authors argue that industries may respond differently to new productivity-improving technologies, which presents a challenge for policymakers as some industries grow while others shrink, potentially leading to disruptive transitions for workers. Additionally, the paper challenges the assumption in much of the literature that demand is inelastic and static by examining historical evidence of demand elasticity in US industries such as cotton textile, primary steel, and motor vehicles. The study finds that demand elasticity changed over time, affecting employment growth. Early on, price reductions from productivity-improving technology spurred strong demand growth, increasing employment. However, as demand became satiated, ongoing automation led to job losses. The authors also address past incorrect predictions of labour demand decline due to automation, emphasizing the depth of human wants and desires and the underestimation of demand behind failed predictions.
While acknowledging the current changes in automation, particularly in white-collar and professional tasks, the paper emphasizes that human needs and desires are not vastly different and are not likely to change rapidly. As a result, the analysis of current demand elasticities and the impacts of productivity-improving technologies are deemed critical for informing policy in the face of new technological advancements, which may have varying effects across industries.
Reinventing Jobs A 4-Step Approach for Applying Automation to Work
It is a scientifically written paper that provides a comprehensive framework for optimizing work output by strategically integrating human-automation combinations. The paper emphasizes the coexistence of human workers and automation to drive overall performance and job evolution, rather than displacing human workers. It explores the deconstruction, redefinition, and optimization of work processes across various industries, making it valuable for business leaders, founders, strategists, business analysts, technology enthusiasts, and management professionals. The paper’s structured approach for crafting optimal human-automation combinations makes it beneficial for management classes and professionals at various career stages.
The geography of job automation in Ireland: what urban areas are most at risk?
This comprehensive paper analyses the impact of future automation and artificial intelligence technologies on Irish towns using two methodologies: Frey and Osborne’s occupational approach (FO) and Nedelkoska and Quintini’s task-based approach (NQ). The study addresses the lack of sub-national geographical analysis of automation risk in employment and its implications for Irish urban policy. Key findings include varying estimates of high-risk jobs. The paper provides a comprehensive understanding of automation’s potential impacts on Irish towns, highlighting regional disparities and factors influencing job exposure. It offers valuable insights for policymakers, urban planners, and researchers to navigate challenges and opportunities in the face of automation’s impact on local and regional labour markets in Ireland.
Automation, Job Security and Teacher Employment in the United States.
The passage outlines the argument that automation presents an opportunity to enhance the teaching labour process rather than replace teachers with robots. It highlights historical government support for technology integration in education, noting past introduction of microcomputers in public schools and current strategies by governments, such as the UK, to introduce robots to improve student learning and reduce teacher workload. While acknowledging the potential benefits of automation in the education sector, the passage also emphasizes the challenges it presents. It calls for policymakers to augment existing education technology plans to address the limitations of organizational policies, particularly in addressing technology-related work issues like privacy and potential deskilling of teachers. In summary, the passage underscores the historical and ongoing government support for technology integration in education, the potential of automation to enhance teaching processes, and the need for policymakers to address associated challenges through comprehensive education technology plans.
Training Requirements, Automation, and Job Polarisation.
The article explores the interaction between job training requirements, engineering complexity, and firms’ automation decisions. It predicts that employment polarisation should occur based on initial occupational training requirements, shifting towards more-complex occupations. The relationship between complexity and employment growth is expected to be weakest among occupations with low training requirements. These predictions are supported by US data from 1980–2008, with training requirements and complexity jointly accounting for much of the job polarisation during this period. The article suggests a future research avenue to explore whether the theoretical mechanism can explain earlier episodes of job polarisation following advances in automation. Historical examples include the replacement of middle-wage workers with the adoption of steam power and electricity, and a wider economy-wide job polarisation coinciding with the introduction of numerical control and computer-aided manufacturing prior to 1980.
The article raises an important question about whether job polarisation will persist in the future, and if the next wave of automation will threaten high- or low-skill workers. The model suggests that automation may lead to a continuing displacement of workers in the middle of the distribution, with the growth in low-skill jobs increasingly dominating that of high-skill jobs. This implies that the term “middle” may refer to increasingly skilled workers over time, as machines take over more complex, non-innate tasks.
Automation and Job Polarization: On the Decline of Middling Occupations in Europe.
The research investigates the impact of falling information technology (IT) prices on employment distribution across various wage occupations in 10 Western European countries during 1993–2007. The study found that decreased IT prices were associated with a decline in middle-wage occupations and an increase in high-wage occupations in industries with heavier dependence on IT compared to less dependent industries. The results also highlighted differences in employment patterns among gender and age groups, indicating that the effect of falling IT prices on employment distribution varied significantly. Furthermore, the study offered valuable insights into the nuanced effects of recent technological changes on labour markets, shedding light on the polarization hypothesis. The study utilized a robust empirical methodology with rigorous analysis and comprehensive data sources, including the EU Labour Force Survey (ELFS), EU KLEMS database, and ISCO-88 coding. It employed a difference-in-differences framework to identify the differential effect of falling IT prices on employment shares across industries with varying levels of IT dependence. The findings were well-supported and consistent across various robustness checks, instrumental variables, and additional variables, underscoring the reliability of the results. The implications of the study are significant in understanding the impact of technological changes and providing critical insights into labour market dynamics. The research contributes to the broader literature on job polarization, highlighting the need for a more nuanced perspective on the labour market effects of recent technological advancements, especially concerning gender and age group differences. The study offers a strong foundation for further investigation into the intricate relationship between technology, employment, and skill distribution, providing valuable implications for policymakers and labour market stakeholders.
Automation, Computerization and Future Employment in Singapore.
In the research on Singapore’s Automation, Computerization, and Future Employment, it is revealed that around 25% of the workforce, approximately 502,200 jobs in the country, are susceptible to automation and computerization over the next ten to fifteen years. The study indicates that the majority of high-risk jobs are in the services industry, particularly in the sub-industries of Wholesale & Retail Trade and Public Administration & Education. Moreover, there is a significant proportion of workers aged 50 and above in this high-risk category, as well as a majority with non-tertiary educational qualifications, suggesting challenges in finding new employment. The paper recommends retraining and education initiatives to prepare for the disruptive impact of new technology on employment, along with reevaluating the wealth redistribution system and societal values while acknowledging the potential transition to more leisure time for individuals due to technological innovation. Additionally, an international comparison shows Singapore having one of the lowest proportions of workers in high-risk jobs compared to other countries
Potential Risk of Automation for Jobs in Slovakia: A District- and Industry-Level Analysis.
In 2019, a study found that 20% to 47% of Slovak employment was at high risk of automation, which is relatively high compared to other countries. This poses potential risks to income inequality, particularly for lower-income employees. Key at-risk industries include manufacturing and wholesale/retail trade, impacting roles such as sales assistants and machinery assemblers. These findings can guide policymakers in allocating resources to protect workers from job transitions and prepare them for higher-skilled jobs, with a focus on investing in education, particularly vocational skills. However, there is a low participation rate in education and training among adults in Slovakia, necessitating government interventions to increase participation. Additionally, while automation has the potential to disrupt the labour market, policymakers have tools to promote training, lifelong learning, and focus on productivity-increasing technologies to mitigate job losses. It is crucial to consider variations among districts and industries in terms of their exposure to automation when creating or saving jobs.
Worrying about automation and jobs.
The two books delve into the concerns surrounding the impact of technological advancements on employment prospects. While acknowledging fears of job loss due to automation and artificial intelligence, the books also emphasize historical trends demonstrating that technological innovation often leads to the creation of new job opportunities. Furthermore, they examine various policy responses, such as the proposal for a robot tax and the implementation of a universal basic income, highlighting their complexities and potential downsides. In conclusion, the books stress the need for a balanced approach to addressing potential challenges without sacrificing the overall benefits of technological progress.
Conclusion
In navigating the intricate landscape of job automation, demographic shifts, and policy formulation in the future of work, it is evident that a holistic and nuanced approach is imperative. The projected need for 305 million jobs by 2030, coupled with the impending impact of automation, underscores the pivotal role of proactive policy measures. Striking a delicate balance between addressing job displacement due to automation, especially in routine and low-skilled sectors, and aligning these efforts with the dynamic demands of an evolving workforce, is crucial. Investing in high-quality education, particularly for the youth in low- to middle-income countries, becomes a cornerstone for sustainable employment. Simultaneously, recognizing the challenges posed by an aging workforce in higher-income countries calls for innovative solutions, from healthcare initiatives to incentivizing firms to hire older workers. The interplay of automation, demand elasticity, and historical patterns emphasizes the need for policies that adapt to evolving industry landscapes and prioritize skill development. The comprehensive exploration of specific geographical risks in Ireland and Slovakia further underscores the importance of localized strategies in job creation and protection. Furthermore, the reassessment of the teaching labour process, as outlined in the context of the United States, signifies the potential for technology to enhance rather than replace certain occupations. The proposed 4-step approach in “Reinventing Jobs” encapsulates a strategic framework that promotes the coexistence of human workers and automation, ensuring optimized performance and sustainable job evolution. However, the research also raises questions about the persistence of job polarization, particularly the displacement of middle-skilled workers in the face of advancing automation. The European study on declining middling occupations due to falling IT prices provides valuable insights into the nuanced effects of technological changes on labour markets, emphasizing the need for a refined perspective that considers gender and age group differences. In Singapore, the susceptibility of approximately 25% of the workforce to automation prompts a call for robust retraining and education initiatives, coupled with a re-evaluation of societal values and wealth redistribution. Ultimately, while concerns about the impact of technological advancements on employment are acknowledged, the overarching conclusion is a call for a balanced and forward-thinking approach to policy-making. Acknowledging historical trends of job creation through technological innovation, the emphasis is on navigating challenges without forsaking the immense benefits that technological progress brings to the evolving landscape of work.
References
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Acesso em: 23 fev. 2024.
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