Cristian Alonso, Mr. Andrew Berg, Siddharth Kothari, Mr. Chris Papageorgiou, and Sidra Rehman
This paper considers the implications for developing countries of a new wave of technological change that substitutes pervasively for labor. It makes simple and plausible assumptions: the AI revolution can be modeled as an increase in productivity of a distinct type of capital that substitutes closely with labor; and the only fundamental difference between the advanced and developing country is the level of TFP. This set-up is minimalist, but the resulting conclusions are powerful: improvements in the productivity of “robots” drive divergence, as advanced countries differentially benefit from their initially higher robot intensity, driven by their endogenously higher wages and stock of complementary traditional capital. In addition, capital—if internationally mobile—is pulled “uphill”, resulting in a transitional GDP decline in the developing country. In an extended model where robots substitute only for unskilled labor, the terms of trade, and hence GDP, may decline permanently for the country relatively well-endowed in unskilled labor.
We leverage insights from machine learning to optimize the tradeoff between bias and
variance when estimating economic models using pooled datasets. Specifically, we develop a
simple algorithm that estimates the similarity of economic structures across countries and
selects the optimal pool of countries to maximize out-of-sample prediction accuracy of a
model. We apply the new alogrithm by nowcasting output growth with a panel of 102
countries and are able to significantly improve forecast accuracy relative to alternative pools.
The algortihm improves nowcast performance for advanced economies, as well as emerging
market and developing economies, suggesting that machine learning techniques using pooled
data could be an important macro tool for many countries.
Mr. Balazs Csonto, Yuxuan Huang, and Mr. Camilo E Tovar Mora
This paper examines the extent to which digitalization—measured by a new proxy based on IP addresses allocations per country—has influenced inflation dynamics in a sample of 36 advanced and emerging economies over 2000-2017. Phillips curve estimates show that digitalization has a statistically significant negative effect on inflation in the short run. Its economic impact is not large but has increased since 2012 and mainly operates through a cost/competition channel. Principal components and cointegration analysis further suggest digitalization is a key driver of lower trend inflation.
International Monetary Fund. Asia and Pacific Dept
Growth in the first half of 2018 was softer than in 2017, especially in advanced economies. In contrast, growth remained robust in emerging market economies and broadly in line with expectations. After rising to 6.9 percent in 2017, growth in China continued to be strong into the first half of 2018 but has likely slowed since, given the latest high-frequency indicators, including weakening investment growth. In Japan, after exceeding potential for two years, growth dropped into negative territory in the first quarter of 2018 before rebounding sharply in the second quarter. In India, growth continues to recover steadily after the disruptions related to demonetization and the rollout of the goods and services tax in the last fiscal year.1 And in ASEAN-4 economies (Indonesia, Malaysia, the Philippines, Thailand), growth generally lost momentum in the first half of 2018, except in Thailand.
IMF Research Perspective (formerly published as IMF Research Bulletin) is a new, redesigned online newsletter covering updates on IMF research. In the inaugural issue of the newsletter, Hites Ahir interviews Valeria Cerra; and they discuss the economic environment 10 years after the global financial crisis. Research Summaries cover the rise of populism; economic reform; labor and technology; big data; and the relationship between happiness and productivity. Sweta C. Saxena was the guest editor for this inaugural issue.
Mr. Federico J Diez, Mr. Daniel Leigh, and Suchanan Tambunlertchai
We estimate the evolution of markups of publicly traded firms in 74 economies from 1980-2016. In advanced economies, markups have increased by an average of 39 percent since 1980. The increase is broad-based across industries and countries, and driven by the highest markup firms in each economic sector. For emerging markets and developing economies, there is less evidence of a rise in markups. We find a positive relation between firm markups and other indicators of market power, such as profits or industry concentration. Focusing on advanced economies, we investigate the relation between markups and investment, innovation, and the labor share at the firm level. We find evidence of a non-monotonic relation, with higher markups being correlated initially with increasing and then with decreasing investment and innovation rates. This non-monotonicity is more pronounced for firms that are closer to the technological frontier. More concentrated industries also feature a more negative relation between markups and investment and innovation. The association between markups and the labor share is generally negative.
Mr. Andrew Berg, Mr. Edward F Buffie, and Luis-Felipe Zanna
We may be on the cusp of a “second industrial revolution” based on advances in artificial
intelligence and robotics. We analyze the implications for inequality and output, using
a model with two assumptions: “robot” capital is distinct from traditional capital in its
degree of substitutability with human labor; and only capitalists and skilled workers save.
We analyze a range of variants that reflect widely different views of how automation may
transform the labor market. Our main results are surprisingly robust: automation is good
for growth and bad for equality; in the benchmark model real wages fall in the short run
and eventually rise, but “eventually” can easily take generations.