During Morocco's postcolonial period, from the s through the s, development policy and nationalist ideology supported the formation of a middle class based on the pursuit of education, employment, and material security. Neoliberal reforms adopted by Morocco since the early s have significantly eroded the capacity of the state to nurture the middle class, and unemployment and temporary employment among educated adults has grown.
There is no longer an obvious correlation between the best interests of the state and those of the middle-class worker. As Shana Cohen demonstrates, educated young adults in Morocco do not look toward the state for economic security and fulfillment but toward the diffuse, amorphous global market. Cohen delves into the rupture that has occurred between the middle class, the individual, and the nation in Morocco and elsewhere around the world.
Combining institutional economic analysis with cultural theory and ethnographic observation including interviews with seventy young adults in Casablanca and Rabat, she reveals how young, urban, educated Moroccans conceive of their material, social, and political conditions. She finds that, for the most part, they perceive improvement in their economic and social welfare apart from the types of civic participation commonly connected with nationalism and national identity.
In answering classic sociological questions about how the evolution of capitalism influences identity, Cohen sheds new light on the measurable social and economic consequences of globalization and on its less tangible effects on individuals' perception of their place in society and prospects in life. Morocco Shana Cohen, Larabi Jaidi. Austerity, community action, and the future of citizenship Shana Cohen, Christina Fuhr. Ladda ned. Recensioner i media. For the next three trends, we model both a trendline scenario and a step-up scenario that assumes additional investments in some areas, based on explicit choices by governments, business leaders, and individuals to create additional jobs.
Infrastructure and buildings are two areas of historic underspending that may create significant additional labor demand if action is taken to bridge infrastructure gaps and overcome housing shortages. New demand could be created for up to 80 million jobs in the trendline scenario and, in the event of accelerated investment, up to million more in the step-up scenario. These jobs include architects, engineers, electricians, carpenters, and other skilled tradespeople, as well as construction workers. Investments in renewable energy , such as wind and solar; energy-efficiency technologies; and adaptation and mitigation of climate change may create new demand for workers in a range of occupations, including manufacturing, construction, and installation.
These investments could create up to ten million new jobs in the trendline scenario and up to ten million additional jobs globally in the step-up scenario. The last trend we consider is the potential to pay for services that substitute for currently unpaid and primarily domestic work.
This so-called marketization of previously unpaid work is already prevalent in advanced economies, and rising female workforce participation worldwide could accelerate the trend. We estimate that this could create 50 million to 90 million jobs globally, mainly in occupations such as childcare, early-childhood education, cleaning, cooking, and gardening. When we look at the net changes in job growth across all countries, the categories with the highest percentage job growth net of automation include the following:.
The changes in net occupational growth or decline imply that a very large number of people may need to shift occupational categories and learn new skills in the years ahead. The shift could be on a scale not seen since the transition of the labor force out of agriculture in the early s in the United States and Europe, and more recently in in China.
Seventy-five million to million may need to switch occupational categories and learn new skills. We estimate that between million and million individuals could be displaced by automation and need to find new jobs by around the world, based on our midpoint and earliest that is, the most rapid automation adoption scenarios. New jobs will be available, based on our scenarios of future labor demand and the net impact of automation, as described in the next section.
However, people will need to find their way into these jobs. Of the total displaced, 75 million to million may need to switch occupational categories and learn new skills, under our midpoint and earliest automation adoption scenarios; under our trendline adoption scenario, however, this number would be very small—less than 10 million Exhibit 1. In absolute terms, China faces the largest number of workers needing to switch occupations—up to million if automation is adopted rapidly, or 12 percent of the workforce.
While that may seem like a large number, it is relatively small compared with the tens of millions of Chinese who have moved out of agriculture in the past 25 years. For advanced economies, the share of the workforce that may need to learn new skills and find work in new occupations is much higher: up to one-third of the workforce in the United States and Germany, and nearly half in Japan. Today there is a growing concern about whether there will be enough jobs for workers, given potential automation. History would suggest that such fears may be unfounded: over time, labor markets adjust to changes in demand for workers from technological disruptions, although at times with depressed real wages Exhibit 2.
We address this question about the future of work through two different sets of analyses: one based on modeling of a limited number of catalysts of new labor demand and automation described earlier, and one using a macroeconomic model of the economy that incorporates the dynamic interactions among variables. If history is any guide, we could also expect that 8 to 9 percent of labor demand will be in new types of occupations that have not existed before.
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Both analyses lead us to conclude that, with sufficient economic growth, innovation, and investment, there can be enough new job creation to offset the impact of automation, although in some advanced economies additional investments will be needed as per our step-up scenario to reduce the risk of job shortages. A larger challenge will be ensuring that workers have the skills and support needed to transition to new jobs. Countries that fail to manage this transition could see rising unemployment and depressed wages. The magnitude of future job creation from the trends described previously and the impact of automation on the workforce vary significantly by country, depending on four factors.
Higher wages make the business case for automation adoption stronger. 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. 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.
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.
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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. 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.
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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.
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. 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.