Who or What Works
Why Is Productivity Slowing Down?
Ian Goldin et al.
Journal of Economic Literature, March 2024, Pages 196-268
Abstract:
We examine the contribution of different explanations to the slowdown of labor productivity. Comparing the post-2005 period with the preceding decade for five advanced economies, we seek to explain a slowdown of 0.8 to 1.8 pp. No single explanation accounts for the slowdown, but we have identified a combination of factors that, taken together, account for much of what has been observed. In the countries we have studied, these are mismeasurement, a decline in the contribution of capital per worker, lower spillovers from the growth of intangible capital, the slowdown in trade, and a lower growth of allocative efficiency. Sectoral reallocation and a lower contribution of human capital may also have played a role in some countries. In addition to our quantitative assessment of explanations for the slowdown, we qualitatively assess other explanations, including whether productivity growth may be declining due to innovation slowing down.
Why Are Older Men Working More? The Role of Social Security
Zhixiu Yu
Journal of Public Economics, March 2024
Abstract:
This paper investigates the role of Social Security reforms in explaining the increase in labor supply of older men across cohorts and evaluates the labor response by health status. I develop and estimate a rich dynamic life-cycle model of labor supply, savings, and Social Security application that captures the key structure of Social Security retirement benefits, disability insurance, and pension systems, while accounting for uncertainties in health, survival, wages, and medical expenditures. The model matches well the observed life-cycle profiles of employment, hours per worker, and savings for men in the 1930s cohort from the Panel Study of Income Dynamics. I find that Social Security reforms account for over 77% of the observed rises in employment and hours worked by the 1950s cohort, with the retirement earnings test reforms being the most important. The labor response is smaller for unhealthy individuals due to the work disincentives provided by disability benefits.
Size Matters: Matching Externalities and the Advantages of Large Labor Markets
Enrico Moretti & Moises Yi
NBER Working Paper, March 2024
Abstract:
Economists have long hypothesized that large and thick labor markets facilitate the matching between workers and firms. We use administrative data from the LEHD to compare the job search outcomes of workers originally in large and small markets who lost their jobs due to a firm closure. We define a labor market as the Commuting Zone×industry pair in the quarter before the closure. To account for the possible sorting of high-quality workers into larger markets, the effect of market size is identified by comparing workers in large and small markets within the same CZ, conditional on workers fixed effects. In the six quarters before their firm’s closure, workers in small and large markets have a similar probability of employment and quarterly earnings. Following the closure, workers in larger markets experience significantly shorter non-employment spells and smaller earning losses than workers in smaller markets, indicating that larger markets partially insure workers against idiosyncratic employment shocks. A 1 percent increase in market size results in a 0.014 and 0.023 percentage points increase in the 1-year re-employment probability of high school and college graduates, respectively. Displaced workers in larger markets also experience a significantly lower need for relocation to a different CZ. Conditional on finding a new job, the quality of the new worker-firm match is higher in larger markets, as proxied by a higher probability that the new match lasts more than one year; the new industry is the same as the old one; and the new industry is a “good fit” for the worker’s college major. Consistent with the notion that market size should be particularly consequential for more specialized workers, we find that the effects are larger in industries where human capital is more specialized and less portable. Our findings may help explain the geographical agglomeration of industries -- especially those that make intensive use of highly specialized workers -- and validate one of the mechanisms that urban economists have proposed for the existence of agglomeration economies.
Capital and Wages
Daron Acemoglu
NBER Working Paper, March 2024
Abstract:
Does capital accumulation increase labor demand and wages? Neoclassical production functions, where capital and labor are q-complements, ensure that the answer is yes, so long as labor markets are competitive. This result critically depends on the assumption that capital accumulation does not change the technologies being developed and used. I adapt the theory of endogenous technological change to investigate this question when technology also responds to capital accumulation. I show that there are strong parallels between the relationship between capital and wages and existing results on the conditions under which equilibrium factor demands are upward-sloping (e.g., Acemoglu, 2007). Extending this framework, I provide intuitive conditions and simple examples where a greater capital stock leads to lower wages, because it triggers more automation. I then offer an endogenous growth model with a menu of technologies where equilibrium involves choices over both the extent of automation and the rate of growth of labor-augmenting productivity. In this framework, capital accumulation and technological change in the long run are associated with wage growth, but an increase in the saving rate increases the extent of automation, and at first reduces the wage rate and subsequently depresses its long-run growth rate.
Scenarios for the Transition to AGI
Anton Korinek & Donghyun Suh
NBER Working Paper, March 2024
Abstract:
We analyze how output and wages behave under different scenarios for technological progress that may culminate in Artificial General Intelligence (AGI), defined as
the ability of AI systems to perform all tasks that humans can perform. We assume that human work can be decomposed into atomistic tasks that differ in their complexity. Advances in technology make ever more complex tasks amenable to automation. The effects on wages depend on a race between automation and capital accumulation. If automation proceeds sufficiently slowly, then there is always enough work for humans, and wages may rise forever. By contrast, if the complexity of tasks that humans can perform is bounded and full automation is reached, then wages collapse. But declines may occur even before if large-scale automation outpaces capital accumulation and makes labor too abundant. Automating productivity growth may lead to broad-based gains in the returns to all factors. By contrast, bottlenecks to growth from irreproducible scarce factors may exacerbate the decline in wages.
The Simple Macroeconomics of AI
Daron Acemoglu
MIT Working Paper, April 2024
Abstract:
This paper evaluates claims about the large macroeconomic implications of new advances in AI. It starts from a task-based model of AI’s effects, working through automation and task complementarities. It establishes that, so long as AI’s microeconomic effects are driven by cost savings/productivity improvements at the task level, its macroeconomic consequences will be given by a version of Hulten’s theorem: GDP and aggregate productivity gains can be estimated by what fraction of tasks are impacted and average task-level cost savings. Using existing estimates on exposure to AI and productivity improvements at the task level, these macroeconomic effects appear nontrivial but modest -- no more than a 0.71% increase in total factor productivity over 10 years. The paper then argues that even these estimates could be exaggerated, because early evidence is from easy-to-learn tasks, whereas some of the future effects will come from hard-to-learn tasks, where there are many context-dependent factors affecting decision-making and no objective outcome measures from which to learn successful performance. Consequently, predicted TFP gains over the next 10 years are even more modest and are predicted to be less than 0.55%. I also explore AI’s wage and inequality effects. I show theoretically that even when AI improves the productivity of low-skill workers in certain tasks (without creating new tasks for them), this may increase rather than reduce inequality. Empirically, I find that AI advances are unlikely to increase inequality as much as previous automation technologies because their impact is more equally distributed across demographic groups, but there is also no evidence that AI will reduce labor income inequality. AI is also predicted to widen the gap between capital and labor income. Finally, some of the new tasks created by AI may have negative social value (such as design of algorithms for online manipulation), and I discuss how to incorporate the macroeconomic effects of new tasks that may have negative social value.
Correlated Labor Market Risk and Housing Investment
Daniel Ladd
University of California Working Paper, January 2024
Abstract:
This paper shows that households have lower levels of housing investment when they live in areas with labor markets that are more correlated with their industry of employment. In other words, if a household lives in an area where many other households work in the same or similar industries, then housing may be a riskier asset as it is more correlated with labor market income. Thus households decrease their investment in housing. Using US microdata from 2007-2017 a one-standard deviation increase in a household’s correlated labor market risk is associated with a decline in housing investment by around $6,750. This decline is driven by concentrations and riskiness of other correlated industries, suggesting agglomeration in one industry can have negative spillovers to workers of other related industries.