Tag: workforce

OM in the News: China’s “996” Culture

Commuters in Beijing

In Chapter 10, “Human Resources, Job Design, and Work Measurement,” we discuss quality of life and work force motivation. But many of our OM students are not cognizant of the cultural differences in workforces outside the U.S. So let’s examine workers in our biggest competitor, China.

To understand work culture in China, start with a number: 996. It’s shorthand for the grueling schedule that has become the norm at many Chinese firms: 9 a.m. to 9 p.m., six days a week. The term originated in the technology sector 5 years ago, writes The New York Times (Aug. 2, 2021), when the country’s nascent internet companies were racing to compete with Silicon Valley. At first, workers were willing to trade their free time for overtime pay and the promise of helping China match the West.

The first major pushback to 996 came in 2019, as China’s economic growth slowed and tech workers began questioning their work conditions. Online protests followed, but the movement faded under government censorship. This year, 996 shot back into the news after two workers died at Pinduoduo, an e-commerce giant. Officials promised to investigate working conditions, although it’s not clear what has come of that. Since then, some companies have taken steps to improve work-life balance. Kuaishou, a video app, just ended a policy requiring its staff to work on weekends twice a month. Tencent began encouraging workers to go home at 6 p.m. — though only on Wednesdays.

Many are willing to endure the working conditions because of the competitiveness of the job market. The number of college graduates in China rose by 73% in the past decade, a stunning achievement for a country that had fewer than 3.5 million university students in 1997. As a result, more people are competing for a limited pool of white-collar jobs.

But it’s also clear that many are sick of the rat race. Some Gen Zers have turned to reading Mao Zedong’s writings on communism to rage against capitalist exploitation. An online craze this year called on young people to “tangping,” or “lie flat” — essentially, to opt out. Still, some in China’s working class dismiss the complaints as elite griping; after all, tech workers are highly paid and educated.
Classroom discussion questions:

  1. How does the “996” culture compare to that in silicon Valley?
  2. What makes the U.S. different from Chinese work patterns?
 

OM in the News: The Changing Workforce

jobsIN 1930, John Maynard Keynes worried of a new disease: “technological unemployment…due to our discovery of means of economizing the use of labor outrunning the pace at which we can find new uses for labor.” Now, 2 Oxford professors are arguing that jobs are at high risk of being automated in 47% of the occupational categories into which work is sorted. That includes accountancy, legal work, technical writing and a lot of other white-collar occupations.

Automation processes have steadily and relentlessly squeezed labor out of the manufacturing sector in most rich economies, writes The Economist (Jan. 18-24, 2014). As we note in Chapter 1, the share of U.S. employment in manufacturing has declined sharply since the 1950s, from almost 30% to less than 10%. At the same time, jobs in services soared, from less than 50% of employment to almost 70% (see chart). It was inevitable that firms would start to apply the same automation to service industries.

jobs2The case for a highly disruptive period of economic growth is made by MIT profs in “The Second Machine Age.” Like the first great era of industrialization, they argue, it should deliver enormous benefits—but not without a period of uncomfortable change. They write that the amount of progress computers will make in the next few years will equal to the progress they have made since their very beginning!

The combination of big data and smart machines will take over some occupations wholesale; in others it will allow firms to do more with fewer workers. Some jobs—especially those currently associated with high levels of education and high wages—will survive (see table). Rich economies seem to be bifurcating into a small successful group of workers with skills highly complementary with machine intelligence, with the rest of workers less successful.

Classroom discussion questions:

1. In what service jobs will automation be a major factor?

2. Will manufacturing reverse its downward slope of employment?