Meituan Currently Surpassing Average Local Earnings for Food Delivery Workers
Meituan's Earnings Report for Food Delivery Workers
New data released by Meituan, China's largest on-demand services provider, demonstrates that its food delivery crews in major cities like Beijing, Shanghai, and Shenzhen are earning more on average than the local population. In June, those meeting the company's minimum work time made an average monthly pay of 11,000 yuan (US$1,550), while those working less earned 7,354 yuan, both figures exceeding Beijing's per-capita monthly disposable income of 7,180 yuan.
Flexible Employment Amid Economic Challenges
As China faces ongoing economic difficulties, many have turned to flexible employment in the food delivery sector. The year saw a 50% increase in delivery workers in Beijing, reaching 17,000, while the unemployment rate slightly decreased to 4.1%. It is noted that around 50% of Meituan's 7.45 million delivery workers operated for fewer than 30 days last year, suggesting many view these gigs as transitional positions.
Government Measures for Gig Workers
Under the rising trend of gig employment, the Chinese government has implemented numerous measures to safeguard the rights of flexible workers, who often lack formal labor protections. Currently, gig workers constitute approximately 23% of China's working population, totaling about 200 million individuals. Importantly, 60% of Meituan's riders were covered by occupational injury insurance, as part of a pilot program initiated in 2022.
Financial Performance and Expansion
Meituan recently reported stronger-than-expected financial results, with revenue rising more than 21% year on year to 82.3 billion yuan, and profits more than doubling to 11 billion yuan. Furthermore, the company is expanding its international service to regions like Al-Kharj in Saudi Arabia after a successful launch in Hong Kong.
This article was prepared using information from open sources in accordance with the principles of Ethical Policy. The editorial team is not responsible for absolute accuracy, as it relies on data from the sources referenced.