As I look out my condo window this early Tuesday morning, I see the city garbage truck enter our village, then stop at the curb. A swing arm emerges, drifts toward a dumpster, hoists it mid-air, and tips it into the dump bed. The robotic arm reverses the sequence, and the truck drives off. My Florida city isn’t alone: many communities have now adopted robotics, often citing the entwined goals of increasing recycling and reducing the cost of hauling away trash. By using automated trucks, the companies collect trash faster with fewer workers, reducing their payroll costs and allowing them to charge communities significantly less for their service. But what happened to those unskilled workers who used to hang off the backs of the trucks and retrieve the trash cans by hand?
Concern about unskilled workers in the age of artificial intelligence (AI), especially in the developing world, is the focus of a recent International Monetary Fund (IMF) working paper. New technologies like AI, machine learning, robotics, big data, and networks are expected to revolutionize production processes, but they could also have a major impact on developing economies. In expectation of these new technologically-driven pressures, a drastic shift to rapidly improve productivity gains and invest in education and skills development is needed to capitalize on the much-anticipated demographic transition associated with AI.
We are at the beginning of a revolution where the scientific community is looking at machines/computers that only process numbers and how to train them to process ideas. AI can be truly remarkable — it can map the location and size of more than 1.8 billion individual tree canopies. The team of engineers at Tesla working on AI continue to roll out new, innovative ways of not only processing and interpreting computer vision but in developing new methods to train its AI toward autonomous driving goals.
Tesla CEO Elon Musk has also spoken about the responsibility that “those who write the algorithms” have. He has underscored the importance of thinking carefully about the labels used during algorithm development. A lot depends on how an individual artificial intelligence system or algorithm was designed, what data helped build it, and how it works.
The AI Revolution & Its Potential For “A Great Divergence”
New technology risks may widen the gap between rich and poor countries by shifting more investment to advanced economies where automation is already established. Alonso, et al., in the IMF working paper titled “Will the AI Revolution Cause a Great Divergence?” describe how such a shift could have negative consequences for jobs in developing countries — it will likely threaten to replace rather than complement their growing labor force, which has traditionally provided an advantage to less developed economies. To prevent this divergence, the paper’s authors say policymakers in developing economies will need to take actions to raise productivity and improve skills among workers.
Their research looks at two countries — one “advanced,” the other “developing” — that both produce goods using 3 factors of production: labor, capital, and robots that substitute for workers. Their findings indicate that imbalances between developing and advanced economies can occur along 3 distinct channels.
- Share-in-production: Advanced economies have higher wages because total factor productivity is higher. These higher wages induce firms in advanced economies to use robots more intensively to begin with, especially when robots easily substitute for workers. Then, when robot productivity rises, the advanced economy will benefit more in the long run. This divergence grows larger the more robots substitute for workers.
- Investment-flows: The increase in productivity of robots fuels strong demand to invest in robots and traditional capital (which is assumed to be complementary to robots and labor). This demand is larger in advanced economies due to robots being used more intensively there. As a result, investment gets diverted from developing countries to finance this capital and robot accumulation in advanced economies, thus resulting in a transitional decline in GDP in the developing country.
- Terms-of-trade: A developing economy will likely specialize in sectors that rely more on unskilled labor, which it has more of compared to an advanced economy. Assuming robots replace unskilled labor but complement skilled workers, a permanent decline in the terms of trade in the developing region may emerge after the robot revolution. This is because robots will disproportionately displace unskilled workers, reducing their relative wages and lowering the price of the good that uses unskilled labor more intensively. The drop in relative price of its main output, in turn, acts as a further negative shock, reducing the incentive to invest and potentially leading to a fall not just in relative but in absolute GDP.
An increase in robot productivity leads to higher GDP in both “advanced” and “developing” regions in the long run as households invest more in robots and in capital (which complements robots). “Advanced” countries tend to benefit from their initially higher robot intensity, driven by their endogenously higher wages and stock of complementary traditional capital. However, which region grows more, and whether the developing economy falls further behind the advanced economy, the authors state, depends crucially on the elasticity of substitution between robots and labor.
Final Thoughts About Unskilled Workers & AI
Improvements in the productivity of robots will tend to increase incomes but also increase income inequality, “at least during the transition and possibly in the long run for some groups of workers, in both advanced and developing economies,” according to Alonso, et al.. Improvements in the productivity of robots drive divergence between advanced and developing countries. Advanced countries will make greater use of such machines, since they will have higher wages, and they will thus differentially benefit from a reduction in their cost.
The authors advise developing countries to invest in raising aggregate productivity and skill levels more urgently than ever before, “so that their labor force is complemented rather than substituted by robots.” Findings also underscore the importance of human capital accumulation to prevent divergence and point to potentially different growth dynamics among developing economies with different skill levels.
The landscape is likely going to be much more challenging for developing countries which have hoped for high dividends from a much-anticipated demographic transition. Policymakers should act to mitigate those risks.