Industrial automation and intergenerational income mobility in the United States

(2022) - Thor Berger, Per Engzell

Journal: Social Science Research
Link:: https://linkinghub.elsevier.com/retrieve/pii/S0049089X21001630
DOI:: 10.1016/j.ssresearch.2021.102686
Links::
Tags:: #paper #LabourMarket #SocialClass #Mobility
Cite Key:: [@bergerIndustrialAutomationIntergenerational2022]

Abstract

This article examines how the automation of jobs has shaped spatial patterns of intergenerational income mobility in the United States over the past three decades. Using data on the spread of industrial robots across 722 local labor markets, we find significantly lower rates of upward mobility in areas more exposed to automation. The erosion of mobility chances is rooted in childhood environments and is particularly evident among males growing up in low-income households. These findings reveal how recent technological advances have contributed to the unequal patterns of economic opportunity in the United States today.

Notes

"we find significantly lower rates of upward mobility in areas more exposed to automation" (Berger and Engzell 2022:102686)

"erosion of mobility chances is rooted in childhood environments and is particularly evident among males growing up in low-income households. These findings reveal how recent technological advances have contributed to the unequal patterns of economic opportunity in the United States today." (Berger and Engzell 2022:102686)

"community-level exposure to automation erodes chances for upward mobility, perpetuating the transOur findings suggest that" (Berger and Engzell 2022:102687)

"or upward mobility, perpetuating the trans-d mission of economic status across generations. To understand what explains these results, market and early life-course consequences of community job loss" (Berger and Engzell 2022:102687)

"We also show that these effects are largely concentrated among sons rather than daughters, while patterns by race are morh" (Berger and Engzell 2022:102687)

"intergenerational mobility posited that with technological change, allocation to social positions would follow Early work on" (Berger and Engzell 2022:102687)

"ocial positions would follow increasingly meritocratic criteria (Blau and Duncan, 1967; Lipset and Bendix, 1959; Treiman, 1970). Apportunities, reducing the inheritance of economic status" (Berger and Engzell 2022:102687)

"When jobs in declining sectors disappear, there is a What are the theoretical effects of automation for intergenerational mobility? away from following in their parents' footsteps. However, whether that change is for the better depends on how equipped society is to prepare its young for the future." (Berger and Engzell 2022:102687)

"tom of the distribution and therefore focus on upward mobility from the bottom throughout most of our analysis. vantaged children in two ways. The first is by depriving cohorts recently entering the labor market of industrial jobs that used to offer stability and good pay in the past. A second possibility is that children's attainment is harmed by job destruction earlier in the life course, by eroding the ability of families and communities to invest in the young (Ananat et al., 2017; Gassman-Pines et al., 2015)." (Berger and Engzell 2022:102687)

"nd communities to invest in the young (Ananat et al., 2017; Gassman-Pines et al., 2015). We test for the latter mechanism in two ways. heir childhood in a given area (Chetty and Hendren, 2018). If detrimental effects of automation on mobility work solely through opportunities in the labor market, we would expect similar effects for those who live in a given area when entering adulthood, regardless of where they grew up" (Berger and Engzell 2022:102687)

"hildhood experiences, it should be stronger the larger the proportion of childhood spent in a given area. hildhood experiences by looking at educational outcomes. With the erosion of living standards that job loss brings, parents will be worse placed to provide a nurturing environment, access to good neighborhood or schools, or pay for their children's way through college (Ananat et al., 2017; Schneider et al., 2018)." (Berger and Engzell 2022:102687)

"cation is a key potential mediator between automation and mobility. om low-income homes to be especially harmed. The jobs replaced by robots are mostly in routine manual, assembly, and other blue-collar work that is traditionally male-typed." (Berger and Engzell 2022:102687)

"Intergenerational mobility data come from the Equality of Opportunity Project (Chetty et al., 2014), which has estimated a range of mobility metrics using individual federal tax records from the Internal Revenue Service (IRS)." (Berger and Engzell 2022:102688)

"isentangle the separate implications for men and women's labor market prospects. s generated a variety of measures, of which the most common is the intergenerational elasticity of incomes" (Berger and Engzell 2022:102688)

"is usually estimated using ordinary least squares, where the elasticity becomes the regression coefficient (Mitnik et al., 2019).- relation" (Berger and Engzell 2022:102688)

"Fig. 1. Automation and measurement of parent and child income in our data." (Berger and Engzell 2022:102688)

"-specific effects that we describe next. esis that the link between automation and income attainment is rooted in childhood events rather than mere labor market prospects, we use estimates from a specification comparing children who move at different ages (Chetty and Hendren, 2018)." (Berger and Engzell 2022:102689)

"of those children will have lived their whole life there, others have moved there with their families at any time between agen 0 and 16. have moved to the area, depending on when they arrived" (Berger and Engzell 2022:102689)

"of permanent residents and compares only the outcomes of children who have moved to the area, depending on when they arrived. before the age of 16, since local labor market prospects are the same for all children regardless of when they arrived. These estimates are scaled to reflect the expected percentage decrease (or increase) in adult income from spending one additional year of childhood in a given commuting zone. Because of the later birth of these cohorts (1980-1986), income is here measured at age 26." (Berger and Engzell 2022:102689)

"A central challenge in identifying the impacts of automation is a lack of data on the diffusion of technology. Most studies have consequently adopted an indirect occupationor task-based approach" (Berger and Engzell 2022:102689)

"we study the spread of industrial robots that provide a rare opportunity to directly observe the spread of an automation technology.dustrial robots in 13 manufacturing industries" (Berger and Engzell 2022:102689)

"es and that the highest levels of exposure are heavily concentrated to the Rust Belt, as we would expect. e of industrial robots is limited throughout the 1980s (see Fig. 1). However, we know that the number of robots by 1982 was as low as 6300 nationwide (Office of Technology Assessment, 1984), which allows us to establish that robot use in the early 1980s was negligible" (Berger and Engzell 2022:102690)

"nationwide (Office of Technology Assessment, 1984), which allows us tos establish that robot use in the early 19" (Berger and Engzell 2022:102690)

"related with our baseline measure. is that the spread of industrial robots may partly be driven by local factors also shaping mobility prospects. The main empirical strategy partly alleviates such concerns by focusing on differences in the exposure to automation, rather than the actual adoption of industrial robots that is likely more endogenous. However, to further address such concerns we deploy the instrumental variable strategy from Acemoglu and Restrepo (2020). Their strategy uses variation in the adoption of robots in five European countries (Denmark, Finland, France, Italy, and Sweden) that are ahead of the U.S. in robotics. Importantly, the rate of robot adoption in European industry is unlikely to be driven by factors that shape mobility outcomes across local labor markets in the United States" (Berger and Engzell 2022:102690)

"e of the major shocks that have hit local labor markets and an important question is to what extent it is conflated with other variables. zone controls, we include the log of population size, the log of average household income, and whether the commuting zone intersects a metropolitan statistical area." (Berger and Engzell 2022:102690)

"share of Black residents, the share of population in four different age bins (below 15, age 15-24, 25-64, and 65 or above), and the share female" (Berger and Engzell 2022:102691)

"in four different age bins (b" (Berger and Engzell 2022:102691)

"5-24, 25-64, and 65 or above), and the sharet female. In a further step we add a control for the share of college educated. Thereafter we rs employed in manufacturing in 1980. Finally, in a last step we include controls for Census region and division as a set of fixed effects (U.S. Census Bureau, n.d.). To avoid conditioning on posttreatment variables, all these measures are observed at baseline in the 1980 Census." (Berger and Engzell 2022:102691)

"ercussions. aper uses data on local labor markets in the United States to document that automation significantly has reduced the chances for upward mobility among children born in low-income families in the early 1980s. Mobility differs markedly across areas more and less exposed to industrial automation: a standard deviation higher exposure to industrial robots is associated with a 0.9-1.5-point reduction in upward mobility, the percentile rank that a child from the bottom half of the income distribution can expect to attain in adulthood. This difference corresponds to one tenth of the distance that separates a place like Charlotte from one like San Jose, representing the very bottom and top of the urban mobility hierarchy (Chetty et al., 2014)" (Berger and Engzell 2022:102696)

"to regain their sense of collective identity and purpose (Goldstein, 2017). cular automation The analysis is not free of li." (Berger and Engzell 2022:102696)

"places. ond, the variation we use is cross-sectional in nature and the observation window for parent and child incomes is limited" (Berger and Engzell 2022:102697)

"l into the 21st century. analysis leverages variation across local labor markets within the U.S. that exhibit significant variation in economic and social makeup." (Berger and Engzell 2022:102697)