From a new NBER working paper, "Artificial Intelligence, Automation and Work," by Daron Acemoglu and Pascual Restrepo.
Abstract:
We summarize a framework for the study of the implications of automation and AI on the demand for labor, wages, and employment. Our task-based framework emphasizes the displacement effect that automation creates as machines and AI replace labor in tasks that it used to perform. This displacement effect tends to reduce the demand for labor and wages. But it is counteracted by a productivity effect, resulting from the cost savings generated by automation, which increase the demand for labor in non-automated tasks. The productivity effect is complemented by additional capital accumulation and the deepening of automation (improvements of existing machinery), both of which further increase the demand for labor. These countervailing effects are incomplete. Even when they are strong, automation increases output per worker more than wages and reduce the share of labor in national income. The more powerful countervailing force against automation is the creation of new labor-intensive tasks, which reinstates labor in new activities and tends to increase the labor share to counterbalance the impact of automation. Our framework also highlights the constraints and imperfections that slow down the adjustment of the economy and the labor market to automation and weaken the resulting productivity gains from this transformation: a mismatch between the skill requirements of new technologies, and the possibility that automation is being introduced at an excessive rate, possibly at the expense of other productivity-enhancing technologies.
Another formidable paper by the noted economist David Autor, David Dorn and Gordon Hanson: "When Work Disappears: Manufacturing Decline and the Falling Marriage Market Value of Young Men."
Abstract:
We exploit the gender-specific components of large-scale labor demand shocks stemming from rising international manufacturing competition to test how shifts in the relative economic stature of young men versus young women affected marriage, fertility and children’s living circumstances during 1990-2014. On average, trade shocks differentially reduce employment and earnings, raise the prevalence of idleness, and elevate premature mortality among young males. Consistent with Becker’s model of household specialization, shocks to male relative stature reduce marriage and fertility. Consistent with sociological accounts, these shocks raise the share of mothers who are unwed and share of children living in below-poverty, single-headed households.
Hat tip to David Warsh over at Economic Principals.
Extract:
The figure seems to indicate a positive correlation between these
variables. For instance, the Seattle, Denver, Anchorage, and San Diego
MSAs, which had relatively higher real incomes in 1970, experienced high
population growth over the period. On the other hand, the Detroit,
Philadelphia, and Pittsburgh MSAs, which had lower real incomes in 1970,
seem to have experienced lower (negative) population growth. Not all
cities exhibit the positive correlation, however. The Atlanta, Houston,
and Dallas MSAs had low average real incomes in 1970 yet experienced
high population growth over the period. Incomes in these MSAs, however,
have grown rapidly since 1970, which could be a main reason for the
population growth.
Read the entire synopsis here. Complete analysis in PDF.
From the new NBER working paper by Anton Korinek, Joseph E. Stiglitz titled, "Artificial Intelligence and Its Implications for Income Distribution and Unemployment."
Abstract:
Inequality is one of the main challenges posed by the proliferation of artificial intelligence (AI) and other forms of worker-replacing technological progress. This paper provides a taxonomy of the associated economic issues: First, we discuss the general conditions under which new technologies such as AI may lead to a Pareto improvement. Secondly, we delineate the two main channels through which inequality is affected - the surplus arising to innovators and redistributions arising from factor price changes. Third, we provide several simple economic models to describe how policy can counter these effects, even in the case of a "singularity" where machines come to dominate human labor. Under plausible conditions, non-distortionary taxation can be levied to compensate those who otherwise might lose. Fourth, we describe the two main channels through which technological progress may lead to technological unemployment via efficiency wage effects and as a transitional phenomenon. Lastly, we speculate on how technologies to create super-human levels of intelligence may affect inequality and on how to save humanity from the Malthusian destiny that may ensue.
Read the whole working paper here. (Gated)
Perhaps 2018 will be the year productivity finally begins to pick up. Technologies such as speech recognition, online chatbots and machine learning are being quickly adopted, capital spending is picking up and tight labor markets give companies an incentive to find better ways of working.
But productivity defies forecasters, and the lesson of the past is to be humble. This is a story of how little anyone really understands about what moves productivity, even though it’s the key to long-run prosperity—and to what happens to inflation and share and bond prices.
Income inequality has been increasing but so has consumption according to a working paper by Bruce Sacerdote of Dartmouth College.
Extract:
Despite the large increase in U.S. income inequality, consumption for families at the 25th and 50th percentiles of income has grown steadily over the time period 1960-2015. The number of cars per household with below median income has doubled since 1980 and the number of bedrooms per household has grown 10 percent despite decreases in household size. The finding of zero growth in American real wages since the 1970s is driven in part by the choice of the CPI-U as the price deflator (Broda and Weinstein 2008). Small biases in any price deflator compound over long periods of time. Using a different deflator such as the Personal Consumption Expenditures index (PCE) yields modest growth in real wages and in median household incomes throughout the time period. Accounting for the Hamilton (1998) and Costa (2001) estimates of CPI bias yields estimated wage growth of 1 percent per year during 1975-2015. Meaningful growth in consumption for below median income families has occurred even in a prolonged period of increasing income inequality, increasing consumption inequality and a decreasing share of national income accruing to labor.
Link: Fifty Years Of Growth In American Consumption, Income, And Wages - 61497-w23292.pdf
Or, is the debate shifting to one about semantics?
From Fatih Guvenen and Greg Kaplan in a new NBER paper.
We revisit recent empirical evidence about the rise in top income inequality in the United States, drawing attention to four key issues that we believe are critical for an informed discussion about changing inequality since 1980. Our goal is to inform researchers, policy makers, and journalists who are interested in top income inequality. Our analysis is based on a reexamination of publicly available detailed statistics from two administrative data sources: (i) Internal Revenue Service (IRS) data on total incomes (labor income plus capital income), reported in Saez (2012), and (ii) individual-level micro data on labor income (wage plus self-employment income) from the U.S. Social Security Administration (SSA) reported in Guvenen et al. (2014).
One key take-away:
Put simply, so far in the 21st century, all the action in top income shares has been S-corporation income at very, very high income levels.
