Germany also provides a lesson in the perils of buying existing chains. The Wiesbaden outlet is worlds away from a squeaky-clean American Wal-Mart: nearby are a couple of sex shops. View all New York Times newsletters. Compounding the problem, Wal-Mart shut down the headquarters of one of the chains, infuriating employees who opted to quit rather than move.
Such a decision would have been routine in the United States, where Ms. In South Korea, Wal-Mart had only 16 stores — a small presence that contributed to its decision in May to sell out to a Korean discount chain. Many Koreans have never heard of Wal-Mart. In Seoul, a sprawling area of 10 million, there is only a single store. This lack of scale causes another problem that has afflicted Wal-Mart in several countries: its inability to compete with established discounters, like the Aldi chain in Germany and E-Mart in Korea.
The obvious lesson is to try to bulk up. In Brazil, Wal-Mart opened only 25 stores in its first decade there and struggled to compete against bigger local rivals. Wal-Mart did not change the names of the stores, which range from neighborhood grocers to large American-style hypermarkets.
Size has given Wal-Mart increased leverage with suppliers there, though analysts say the company needs even more stores to be in a position to undercut local discounters on the prices it offers customers. At a Wal-Mart store in suburban Rio de Janeiro the other day, Ana Paula Cunha de Almeida, a year-old housewife, had loaded her shopping cart with rice, beans and flour. But she was also carrying a bag from a smaller grocery store, where she had bought meat, cheese and cold cuts.
The grocery business has proven the most difficult for Wal-Mart to crack. Aldi, with 4, stores in Germany, undercuts Wal-Mart on price, while still offering high-quality food.
Even in Canada, where Wal-Mart steamrolled local department store chains when it entered the country as a nonfood retailer in , the grocery trade looms as a challenge. Wal-Mart recently announced plans to build supercenters that will also sell groceries.
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Bernie Skelding, a vacationer shopping at a Wal-Mart in Huntsville, Ontario, north of Toronto, said he liked going to the store when he had a varied shopping list. The company initially installed American executives, who had little feel for what German consumers wanted. But even more subtle differences in shopping habits have tripped up the company. They prefer daily outings to a variety of local stores that specialize in groceries, drugs or household goods, rather than shopping once a week at Wal-Mart.
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Newsletter Sign Up Continue reading the main story Please verify you're not a robot by clicking the box. Invalid email address. Please re-enter. You must select a newsletter to subscribe to. It is important to note, however, that even when some tasks are automated, employment in those occupations may not decline but rather workers may perform new tasks. Automation will have a lesser effect on jobs that involve managing people, applying expertise, and social interactions, where machines are unable to match human performance for now.
Jobs in unpredictable environments—occupations such as gardeners, plumbers, or providers of child- and eldercare—will also generally see less automation by , because they are technically difficult to automate and often command relatively lower wages, which makes automation a less attractive business proposition. Workers displaced by automation are easily identified, while new jobs that are created indirectly from technology are less visible and spread across different sectors and geographies.
We model some potential sources of new labor demand that may spur job creation to , even net of automation. For the first three trends, we model only a trendline scenario based on current spending and investment trends observed across countries. The effects of these new consumers will be felt not just in the countries where the income is generated but also in economies that export to these countries. Globally, we estimate that million to million new jobs could be created from the impact of rising incomes on consumer goods alone, with up to an additional 50 million to 85 million jobs generated from higher health and education spending.
By , there will be at least million more people aged 65 years and older than there were in As people age, their spending patterns shift, with a pronounced increase in spending on healthcare and other personal services. This will create significant new demand for a range of occupations, including doctors, nurses, and health technicians but also home-health aides, personal-care aides, and nursing assistants in many countries.
Globally, we estimate that healthcare and related jobs from aging could grow by 50 million to 85 million by Jobs related to developing and deploying new technologies may also grow. Overall spending on technology could increase by more than 50 percent between and About half would be on information-technology services. The number of people employed in these occupations is small compared to those in healthcare or construction, but they are high-wage occupations.
By , we estimate that this trend could create 20 million to 50 million jobs globally. 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.
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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.
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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. 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.
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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.