Rethinking Everything: Personal Growth through Transactional Analysis

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However, low-wage countries may be affected as well, if companies adopt automation to boost quality, achieve tighter production control, move production closer to end consumers in high-wage countries, or other benefits beyond reducing labor costs.

Transactional Analysis 2: games

Economic growth is essential for job creation; economies that are stagnant or growing slowly create few if any net new jobs. Countries with stronger economic and productivity growth and innovation will therefore be expected to experience more new labor demand. Countries with a shrinking workforce, such as Japan, can expect lower future GDP growth, derived only from productivity growth.

The automation potential for countries reflects the mix of economic sectors and the mix of jobs within each sector. Japan, for example, has a higher automation potential than the United States because the weight of sectors that are highly automatable, such as manufacturing, is higher.

The four factors just described combine to create different outlooks for the future of work in each country see interactive heat map. Japan is rich, but its economy is projected to grow slowly to It faces the combination of slower job creation coming from economic expansion and a large share of work that can be automated as a result of high wages and the structure of its economy. However, Japan will also see its workforce shrink by by four million people. The United States and Germany could also face significant workforce displacement from automation by , but their projected future growth—and hence new job creation—is higher.

The United States has a growing workforce, and in the step-up scenario, with innovations leading to new types of occupations and work, it is roughly in balance.

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At the other extreme is India: a fast-growing developing country with relatively modest potential for automation over the next 15 years, reflecting low wage rates. Our analysis finds that most occupational categories are projected to grow in India, reflecting its potential for strong economic expansion. India could create enough new jobs to offset automation and employ these new entrants by undertaking the investments in our step-up scenario. China and Mexico have higher wages than India and so are likely to see more automation. To model the impact of automation on overall employment and wages, we use a general equilibrium model that takes into account the economic impacts of automation and dynamic interactions.

Automation has at least three distinct economic impacts. Most attention has been devoted to the potential displacement of labor.

But automation also may raise labor productivity: firms adopt automation only when doing so enables them to produce more or higher-quality output with the same or fewer inputs including material, energy, and labor inputs. The third impact is that automation adoption raises investment in the economy, lifting short-term GDP growth. We model all three effects. We also create different scenarios for how quickly displaced workers find new employment, based on historical data. The results reveal that, in nearly all scenarios, the six countries that are the focus of our report China, Germany, India, Japan, Mexico, and the United States could expect to be at or very near full employment by However, the model also illustrates the importance of reemploying displaced workers quickly.

If displaced workers are able to be reemployed within one year, our model shows automation lifting the overall economy: full employment is maintained in both the short and long term, wages grow faster than in the baseline model, and productivity is higher. However, in scenarios in which some displaced workers take years to find new work, unemployment rises in the short to medium term. The labor market adjusts over time and unemployment falls—but with slower average wage growth. In these scenarios, average wages end up lower in than in the baseline model, which could dampen aggregate demand and long-term growth.

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Rethinking Everything: Personal Growth through Transactional Analysis

In general, the current educational requirements of the occupations that may grow are higher than those for the jobs displaced by automation. In advanced economies, occupations that currently require only a secondary education or less see a net decline from automation, while those occupations requiring college degrees and higher grow. In India and other emerging economies, we find higher labor demand for all education levels, with the largest number of new jobs in occupations requiring a secondary education, but the fastest rate of job growth will be for occupations currently requiring a college or advanced degree.

Workers of the future will spend more time on activities that machines are less capable of, such as managing people, applying expertise, and communicating with others. They will spend less time on predictable physical activities and on collecting and processing data, where machines already exceed human performance. The skills and capabilities required will also shift, requiring more social and emotional skills and more advanced cognitive capabilities, such as logical reasoning and creativity.

Wages may stagnate or fall in declining occupations. Although we do not model shifts in relative wages across occupations, the basic economics of labor supply and demand suggests that this should be the case for occupations in which labor demand declines.

Our analysis shows that most job growth in the United States and other advanced economies will be in occupations currently at the high end of the wage distribution. Some occupations that are currently low wage, such as nursing assistants and teaching assistants, will also increase, while a wide range of middle-income occupations will have the largest employment declines. Income polarization could continue.

Policy choices such as increasing investments in infrastructure, buildings, and energy transitions could help create additional demand for middle-wage jobs such as construction workers in advanced economies. The wage-trend picture is quite different in emerging economies such as China and India, where our scenarios show that middle-wage jobs such as retail salespeople and teachers will grow the most as these economies develop.

This implies that their consuming class will continue to grow in the decades ahead.

What is social selling, anyway?

The benefits of artificial intelligence and automation to users and businesses, and the economic growth that could come via their productivity contributions, are compelling. They will not only contribute to dynamic economies that create jobs but also help create the economic surpluses that will enable societies to address the workforce transitions that will likely happen regardless.


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Faced with the scale of worker transitions we have described, one reaction could be to try to slow the pace and scope of adoption in an attempt to preserve the status quo. But this would be a mistake. Although slower adoption might limit the scale of workforce transitions, it would curtail the contributions that these technologies make to business dynamism and economic growth. We should embrace these technologies but also address the workforce transitions and challenges they bring. In many countries, this may require an initiative on the scale of the Marshall Plan, involving sustained investment, new training models, programs to ease worker transitions, income support, and collaboration between the public and private sectors.

Sustaining robust aggregate demand growth is critical to support new job creation, as is support for new business formation and innovation. Fiscal and monetary policies that ensure sufficient aggregate demand, as well as support for business investment and innovation, will be essential. Targeted initiatives in certain sectors could also help, including, for example, increasing investments in infrastructure and energy transitions.

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So if your plan to read any book on personal growth, I highly recommend Rethinking Everything as the one you should read. Arthur S. Weinfeld, Ph.