27 May 2018

IMF on another robot revolution

'Should We Fear the Robot Revolution? (The Correct Answer is Yes)' (IMF Working Paper 18/116) by Andrew Berg, Edward F. Buffie, and Luis-Felipe Zanna argues 
Advances in artificial intelligence and robotics may be leading to a new industrial revolution. This paper presents a model with the minimum necessary features to analyze the implications for inequality and output. Two assumptions are key: “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.
The authors comment
The factory of the future will have only two employees, a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment. (Warren Bennis, management consultant)
There is a growing perception that advances in artificial intelligence (AI) and robotics will radically transform the workplace in upcoming decades (Brynjolfsson and McAfee, 2014; Ford, 2015). Robots load, unload, retrieve and send out products with minimal human supervision at Symbolic LLC’s automated distribution centers. AI programs have started working as paralegals, accountants, and teaching assistants, and self-driving vehicles may soon eliminate millions of jobs held by truck, bus, and taxicab drivers. Uber aims to be driverless by 2030. Robots staff more assembly lines each year, kiosks are replacing cashiers at fast-food restaurants, and Watson recently co-authored a song. The list goes on, with each week bringing a new or imminent application of smart machines. According to estimates by Frey and Osborne (2017), Chui, Manyika, and Miremadi (2015), and the World Bank (2016), anticipated advances in automation threaten 45-57% of all jobs in the United States. The White House’s Council of Economic Advisors projects that automation will affect 83% of jobs paying $20 an hour or less.
The sense that we are on the cusp of a robot revolution has sparked a lively debate among economists, journalists, and technophiles about the likely impact of automation on growth and the distribution of income. Broadly speaking, there are two camps with starkly different views of what the future holds. Technology pessimists fear that we are headed toward an economic dystopia of extreme inequality and class conflict: "Without ownership stakes, workers will become serfs working on behalf of robots’ overlords [in] a new form of economic feudalism" (Freeman, 2015). Summers (2016) shares Freeman’s vision (if not his colorful language), predicting that the prime-age employment rate for American males will drop below 2 % by mid-century in the absence of an aggressive policy response.
Technology optimists do not deny that automation will prove disruptive in the short run. They point out, however, that historically periods of rapid technological change have created more jobs than they have destroyed and have raised wages and per capita income in rough proportion. The AI revolution may be different, but there are good reasons to believe that a resilient, adaptable economy will again vanquish the specter of technological unemployment: income growth raises the demand for labor in sectors that produce non-automatable goods and for workers that perform manual-intensive tasks; higher productivity stimulates investment throughout the economy in cooperating capital inputs; and while automation renders some jobs obsolete, it complements many others, especially jobs that place a premium on creativity, flexibility, and abstract reasoning. Criticizing technology pessimists for missing the big picture, Autor (2014) argues that "journalists and even expert commentators tend to overstate the extent of machine substitution for human labor and ignore the strong complementarities between automation and labor that increase productivity, raise earnings, and augment the demand for labor . . . Focusing only on what is lost misses a central economic mechanism by which automation affects the demand for labor: raising the value of the tasks that workers supply uniquely." ....
This paper analyzes the short and long-run effects of robots on output and its distribution in a family of dynamic general equilibrium models designed to include the minimum necessary features. The models depart from the existing literature by making two critical additions to the standard neoclassical framework. First, they incorporate investment in both robots and traditional capital. In standard production functions, an increase in the supply of labor stimulates investment by raising the productivity of capital. Since the same logic applies when robots increase the effective supply of labor services, the two types of capital should be gross complements. The positive impact of robot labor services on traditional capital accumulation is missing, however, in existing models. In Acemoglu and Restrepo (2016a), robots are the only type of capital. Both types of capital are present in Sachs, Benzell, and LaGarda (2015), but not as cooperating inputs; robots reside in a separate production function and do not therefore affect the productivity of non-robot capital. At the core of the story here is that robot capital substitutes for human labor as it complements traditional capital, while increases in traditional capital spur demand for robot capital that attenuates the usual effect of capital accumulation on labor demand.
Second, in keeping with the empirical evidence for the U.S. and other developed countries, we allow for two types of agents in the economy: capitalists who save and invest (and in some variants perform skilled labor), and workers who live check to check. Thus we do not assume that everyone saves, as in the representative agent model of Acemoglu and Restrepo (2016a), or that only young wage-earners save, as in the OG models of Sachs and co-authors. We can thus look at inequality along two dimensions: the distribution of income between capitalists and workers, and (in variants with skilled workers) the skilled/unskilled wage differential. Insofar as in reality the number of capitalists is relatively small and that their capital income is generally much greater than wage income, the we interpret the capital share as an index of income inequality. When we allow for skilled as well as unskilled workers in the models, skilled workers are also the capitalists, so that both an increase in the wage premium for skilled workers and the capital labor ratio increase inequality.
While we are confident that the right model features complementary capital inputs and households too poor to save, a number of potentially important design decisions are less clearcut. The basic problem is that nobody knows what the world will look like in say 2035. There is considerable disagreement among and between economists and technology experts about whether automation will (i) destroy jobs for just low-skill labor or labor at all skill levels; (ii) penetrate most or only a small subset of sectors; (iii) reduce the demand for labor in all tasks or decrease it in some and increase it in others. The lack of consensus coupled with the uncertainty inherent in predicting a "future that ain’t what it used to be" (Yogi Berra) makes it hard to justify choosing one set of answers over any other.
Our solution to this problem is to start with a benchmark model and then examine the implications of plausible variations that reflect widely different views about how automation may transform the labor market. In Model 1, robots compete against all labor in all tasks. Subsequent extensions of the model assume that robots (i) compete only for some tasks (Model 2); substitute only for unskilled labor while complementing skilled labor (Model 3); and contribute to production only in one sector — elsewhere, production requires only labor and traditional capital (Model 4). We examine the implications of an increase in the level of robot productivity on the level of output and its distribution, both in the long run and during the transition. Our main results, previewed below, are surprisingly robust. Automation is very good for growth and very bad for equality in all variants, including those reputed to be conducive to technological optimism:
• Real per capita income increases 30 - 240% in the long run. The large positive impact on growth does not require dramatic advances in robot technology. Small improvements suffice when robots and human labor are very close substitutes. In runs for this scenario, the direct gains from more investment in more productive robots account for only 6 - 16% of the increase in GDP. The remaining 84 - 94% reflects the strong positive feedback effects between robot and non-robot capital accumulation. 
• In the benchmark model, the real wage decreases in the short run under weak conditions. In the long run, however, growth in the non-robot capital stock raises the demand for labor and the real wage. Both the long-run increase in the real wage and the depth and duration of the low-wage phase are greater the higher the elasticity of substitution between robots and labor. The intertemporal trade-off for labor is thus sharply defined: more short-run pain for a larger long-run gain. 
• Following up on the hint in the last statement, the transition path is difficult for labor. When multiple parameters happen to fall within specific narrow ranges, it takes as little as twelve years for positive real wage growth to materialize. In other scenarios, the low wage phase lasts 20 - 50+ years. (The "short run" can consume an entire working life.) 
• Although the real wage increases in the long run, labor’s share in income decreases most when real output increases most. The bigger the increase in the GDP pie, the less equitable the distribution of the pie. 
• In the limiting case of perfect substitutability between robots and labor, the long run never comes. There is a dramatic "singularity": the increase in the level of robot productivity sends the economy on a trajectory that converges to endogenous growth, in which the accumulation of robot and traditional capital continues forever, wages fall and stay below initial levels forever, and the labor share of income converges to zero. 
• The distributional outcome is much worse when robots substitute only for low-skill labor (Model 3). While skilled labor enjoys continuous large gains, the wage for lowskill labor decreases in the short/medium run under conditions much weaker than in the benchmark model. Nor is there any assurance that growth eventually raises the lowskill wage. Quite the contrary: there is a strong presumption the real wage decreases more in the long run than in the short run. And the magnitude of the worsening in inequality is horrific. In our base case calibration, the skilled wage increases 56 - 157% in the long run while the wage paid to low-skill labor drops 26 - 56% and the group’s share in national income decreases from 31% to 8 - 18%. 
• The most common arguments for technology optimism do not stand up to scrutiny. Neither the assumption that robots complement labor in some production tasks (Model 2) or that a non-automatable sector co-exists alongside the automation-vulnerable sector (Model 4) delivers optimistic results. Rather, they tend to underscore an underlying trade-off: variations in which inequality worsens by less also tend to deliver less output growth and lower wage growth.
The rest of the paper is organized into five sections. Section II lays out the benchmark model in which robots compete with homogeneous human labor in a single production task. Following this, Sections III – V develop models with low- and high-skill labor, automatable and non-automatable sectors, and human-specific production tasks. Section VI revisits the debate between optimists and pessimists, concluding with an appeal for research aimed at finding policy measures that promote a more equitable distribution of the gains from automation-led growth.