BY CHRISTINA LARSON
Chea Leakhena and Ou Thyda were in their late teens when they began working in Canadia Industrial Park, on the outskirts of the Cambodian capital of Phnom Penh, stitching T-shirts and jeans for global brands including Adidas, Puma, Gap, and H&M. The two women hailed from the same tiny village in rural Prey Veng province, a three-hour bus ride away. Back home, Chea Leakhena’s wages from the factory had funded the installation of a new solar panel, providing enough electricity for the family’s first small TV and two fans. Several other dwellings in the village had similar additions, all paid for the same way. The factory work was hard and could be dangerous, but the women’s relatives in the village praised them as go-getters who had ventured far from home to improve their lives and those of their families.
Such stories have been repeated millions of times over the last century throughout the developing world. As poor countries have transformed the focus of their economies from agriculture to industry, one crucial early step has been the expansion of light manufacturing. In many cases throughout Asia, the process has started with the building of textile industries and the creation of vast numbers of low-skilled, labor-intensive factory jobs, which draw workers from the countryside into cities. In 2016, the Cambodian garment and footwear sector accounted for 78 percent of the country’s merchandise exports, and garments alone accounted for nearly 80 percent of its manufacturing output. Many other countries, of course, have long since moved up the economic ladder into more complicated, higher-value manufacturing, such as electronics and automobiles, and then into services and finance. But virtually all of the recent success stories started the same way.
Today, however, the advent of new technologies has made the ladder a lot shakier. Recent advances in computing power and artificial intelligence are making it possible to automate much of the work of moving, folding, and stitching fabrics. Such automation has advantages — speed, lower prices, and so on. But for poor countries, the automation of garment work now threatens to eliminate a crucial economic opportunity. A 2016 study by the Geneva-based International Labour Organization found that more than half the textile factory jobs in five Southeast Asian countries were “at high risk of automation” — 64 percent of the workforce in Indonesia, 86 percent in Vietnam, 88 percent in Cambodia. The research doesn’t predict when machines will supplant these workers, but in some places the process is already underway. The Mohammadi Fashion Sweaters plant in Dhaka, Bangladesh, for example, has replaced about 500 workers with industrial robots since 2012. In other cases, retailers are coming to rely on high-tech factories closer to their customers: Walmart has worked with U.S.-based producers to churn out robot-made bathmats and towels for sale in U.S. stores, and Adidas is experimenting with 3D printers to make sneaker soles in Germany.
Work in today’s garment production hubs — including the poorer countries of South and Southeast Asia — is often referred to as sweatshop labor. But as Sanchita Saxena, the executive director of the Institute for South Asia Studies (ISAS) at the University of California, Berkeley, explains, the reality is more complicated. “These jobs can be precarious and dangerous. But at the same time, given the choices available in many developing countries, the textile sector is one of the better options,” she said. According to Joe Studwell, an economist and the author of How Asia Works, low-skilled textile work, such as stitching T-shirts and ironing decals on jeans, lays three crucial foundations for industrialization: “basic worker training in the rigors of industrial life; essential foreign exchange for the developing country; and jobs, which tend to be the critical currency at the outset.”
In his book, Studwell traces how Japan, South Korea, and Taiwan each followed a similar path over the past century or so: After improving agricultural efficiency, policymakers created incentives for the development of export-oriented light manufacturing — Japan focused on textiles in the 1880s, and South Korea did the same in the 1960s. After acquiring sufficient management and technical know-how, as well as capital, these economies shifted to focus on industries that required larger investments but yielded higher margins, including steel, ships (in the case of South Korea), electronics, and later automobiles. Their ascent from wartime poverty to become developed nations was swift: Today, Japan’s GDP per capita is ranked just below that of the United Kingdom, and South Korea’s is just above the European Union average. China is in the midst of a similar industrialization process, although on a vastly larger scale; hundreds of millions of people have been leaving their villages and moving to urban factory campuses since the 1980s.
Now, if automation prevents other poor nations from developing the kind of manufacturing that would allow them to increase urbanization, train low-skilled workers, and accumulate capital, the long-term economic impact could be disastrous, said Dani Rodrik, a Harvard University economist. Rodrik has used the phrase “premature deindustrialization” to describe the phenomenon whereby low- and middle-income countries shed manufacturing jobs before they’ve fully industrialized. When countries lose factory jobs before having acquired enough technology and trained workers to shift into post-industrial enterprises, including banking, marketing, and research, their economies can stall.
In place of armies of “factory girls” — a phrase made famous by Leslie Chang’s 2009 book on China’s migrant workers — future textile hubs may be filled with Sewbots, which are produced by the Atlanta-based start-up SoftWear Automation. Sewbots can stitch a complete T-shirt in 22 seconds, twice as fast as a person operating a machine, according to Pete Santora, the company’s chief commercial officer. “Overall computing power has seen such dramatic growth. It just wasn’t possible to do this kind of thing 10 years ago,” he said. Fabric, which is soft and malleable, has traditionally been difficult for robots to manage. But better sensors and artificial intelligence have helped overcome obstacles. “Machine vision maps the fabric, then robotics moves the needles,” Santora said.
The company, which emerged out of labs at Georgia Tech University, started focusing on automating textile production about six years ago, thanks to an initial grant from the Defense Advanced Research Projects Agency, which wanted the ability to manufacture military clothing in the United States. Later, a grant from the Walmart Foundation allowed the team to continue research and expand its commercial horizons. Today, Sewbots produce bathmats and towels for Walmart in the United States, with “Made in the USA” labels. The machines replace human labor and speed up product cycles. Currently, just shipping apparel across the Pacific Ocean from Asian factories takes about three weeks. “Where you’ll start to see rapid change is when apparel designers start to work within the capabilities and limits of automation,” Santora said.
Last year, China’s Tianyuan Garments announced plans to construct a $20 million factory in Little Rock, Arkansas, powered by more than 300 Sewbots. (Santora said the company is not currently selling its technology for use in Asia.) Should more apparel companies adopt Sewbots or other smart machines, the industry could “go from being labor intensive to capital intensive,” said Greg Distelhorst, an assistant professor of global economics and management at the Massachusetts Institute of Technology. That would mean that poor countries’ large populations of low-skilled workers, long an economic asset, could become a liability.
Saxena, the ISAS executive director, argues that it’s the responsibility of governments and companies to manage any transition for the good of their workers: “There is a potential to improve the labor conditions, but it would require a lot of investment and foresight. Will that happen? It’s hard to know. There needs to be a strategy. Change is coming.”
No comments:
Post a Comment