+ The decline of working hours
Welcome to Issue 29!
This week:
The future of work
Challenges and opportunities shaping Work:life.
📕 The race between technology and education
🗒 Sample corporate policy for generative AI use
🔍 The decline of working hours
Trends and signals
Emerging trends in skills, jobs and careers.
🗒 Robots in restaurants
📊 Growth and decline occupations
🔍 AI employment effect: widening inequality
Know-how
Practical technology skills and knowledge to utilise today.
📺 How to learn anything
🗒 Free generative AI courses by AWS
💡Problem solving with 5 Whys
If you have feedback, or future of work interests you’d like me to address in an upcoming issue, get in touch at worklife[at]edaith.com
Tina
📕 The race between technology and education
The United States was a global leader in primary, secondary and then higher education attainment during three waves of progress that spanned the 19th and 20th centuries. However, the quantity of education - years of school and graduation or certification rates - is no longer a sufficient measure in current labour markets. Attending school or undertaking an online course is one thing, but becoming capable or skilful at something is another. A university degree is no longer a ticket to employment success unless it is a certain degree in a certain field.
The current need for ‘digital citizens’ requires unprecedented linkage of education and technology. The authors argue that the competition and cooperation between education and technology need to be reimagined considering the fundamental aims of education, including an emphasis on digital literacy as well as working cooperatively for humanity and the development of society.
🔗 Goldin & Katz 2008. The Race Between Education and Technology (Harvard University Press)
🗒 Sample corporate policy for generative AI use
The sample generative AI corporate policy by the society for innovation, technology and modernisation (which works to support innovation and modernisation in public services) provides a good indication of the realm of issues and terms of use that should be covered.
🔍 The decline of working hours
In 1865 the average British worker worked 124,000 hours over their life, similar to the U.S. and Japan. In 1980, this declined to only 69,000 hours alongside increases in life expectancy. In the decades since then a slower decline has endured - approximately 6%.
Overall, we went from spending 50% of time in our lives working to 20%!
The decline in the amount of work we undertake during our lifetimes has been attributed to factors including public schooling and rising education attainment, as well as innovation raising the value of our labour. These changing conditions have broadened employment involving less hours than historical levels, with higher earnings.
🔗 The times they are not changin’: Days and hours of work in Old and New Worlds, 1870–2000 (Explorations in Economic History Journal)
📊 Working Hours (Our World in Data)
🗒 Robots in restaurants
Amidst worker supply shortages in the restaurant and hospitality industries, ‘quick-serve restaurant’ businesses in the U.S. are experimenting with AI enabled automations as well as robotics to substitute human labour.
📊 Growth and decline occupations
This visualisation was made before the generative AI and chatbot hype of 2023 and highlights that process automation (with data-based work and in manufacturing) has long been impacting jobs and forecasts. Whilst computer and mathematical jobs are highly exposed to AI, at this stage impacted jobs are being transformed e.g. with GitHub Copilot for coding assistance.
U.S. Labour Statistics align with other advanced economies (e.g. Japan, U.K. Australia, Germany) whereby ageing populations and increasing automation are mega trends that will continue to shape jobs growth for the foreseeable future. It’s yet another reference highlighting the projected growth in demand for health-related professionals.
🔗 The 20 Fastest Growing Jobs in the Next Decade (Visual Capitalist)
🔍 AI employment effect: widening inequality
A study of the effect of Artificial Intelligence (AI) on employment across US commuting zones over the period 2000-2020 using a simple model shows that AI can automate jobs or complement workers:
“We find that AI’s impact is different from other capital and technologies, and that it works through services more than manufacturing. Moreover, the employment effect is especially negative for low-skill and production workers, while it turns positive for workers at the top of the wage distribution. These results are consistent with the view that AI has contributed to the automation of jobs and to widen inequality.”
🔗 Artificial Intelligence and Jobs: Evidence from US Commuting Zones (CESifo Working Paper Series)
📺 How to learn anything
Four steps covering key concepts from the book ‘The First 20 Hours: Mastering the Toughest Part of Learning Anything.’
1. Deconstruct the skill
2. Learn enough to self-correct
3. Remove practice barriers (anything that stops you doing the work)
4. Practice for at least 20 hours
🔗 The first 20 hours - how to learn anything | Josh Kaufman | TEDxCSU (YouTube, 19 mins)
🗒 Free generative AI courses by AWS
As part of a broader initiative to remove cost and access barriers to AI training, Amazon Web Services has just released free courses for non-technical and business audiences, as well as courses and scholarships for developers and technical audiences.
💡Problem solving with 5 whys
Have you ever solved an issue only to find that there was something underlying that was the real problem. Root cause analysis frameworks are great problem-solving tools for identifying cause and effect relationships and pinpointing what would best be solved. A simple root cause analysis method is the 5 Whys.*
Originally developed by Sakichi Toyoda in the 1930s for continuous improvement in the Toyota Motor Corporation, 5 Whys is a great tool for problems that need actionable steps uncovered and can sometimes lead to unexpected understandings about what needs to be addressed.
How to Use the 5 Whys method
Define the problem clearly in one sentence or statement.
Ask ‘Why?’ five times. With each answer base the next why on that response until you reach the root cause of the problem. You may need to ask 7 or 8 whys, but usually 5 is enough.
The final why should point to a specific company process, occurrence, action, or activity, not just something generic like ‘not enough time’ or ‘not enough budget’.
Once the root cause has been identified be sure to analyse it and understand how it relates to the problem. This will help in finding effective solutions.
* There are more involved root cause analysis methods more suited to multi-faceted problems that I will cover in future.
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