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The effect of automation levels on US interstate migration

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This study investigates the extent to which job process automation, which has resulted in wage inequality and job polarization in the USA and has affected US interstate migration over the past two decades. The level of automation in each state is calculated using data on the degree of automation of each occupation. In particular, this study examines how the difference in the levels among states explains the movement of migrants. The results show that people move to states with more automation in skilled occupations and less automation in unskilled occupations. This finding implies that automation has a complementary (substitution) effect on skilled (unskilled) occupations. The results also show that the former effect is larger and more robust than the latter one. Further analyses use migration flow data classified into several subgroups and find that both skilled and unskilled workers in most occupations move to states with more automation in skilled occupations and less automation in unskilled occupations.

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  1. Autor and Dorn (2013) also examine the effect of computerization on the difference in education shares between migrant and non-migrant workers, but do not focus on the direction of migration flows.

  2. Autor and Dorn (2013) assume that only high-skilled workers can migrate across regions, whereas low-skilled workers cannot. However, they state that the similar result holds when low-skilled workers can also migrate.

  3. The previous title of the database was the Dictionary of Occupational Titles, which has been used in the literature of automation such as Autor et al. (2003), Autor and Dorn (2013), and Berger and Frey (2016).

  4. Handel (2016) reports the detailed data collection method of O*NET.

  5. Here, the mean, standard deviation, max, and min of the degree of automation are calculated for the standard occupational classification (SOC)-level occupations in O*NET-SOC, especially the occupations used for the analyses in this study.

  6. The education level of each automated occupation can be identified using the “Typical education needed for entry” from the Occupational Projections and Training Data (Employment Projections: 2010-2020) published by the Bureau of Labor Statistics and complied/distributed by the National Crosswalk Service Center.

  7. Again, the values are calculated for the SOC-level occupations in O*NET-SOC, especially those used for the analyses in this study.

  8. The version of the O*NET for each year is following: O*NET 5.0 (based on O*NET-SOC2000) for 2003, O*NET 6.0 (based on O*NET-SOC2000) for 2004, O*NET 8.0 (based on O*NET-SOC2000) for 2005, O*NET10.0 (based on O*NET-SOC2006) for 2006, O*NET12.0 (based on O*NET-SOC2006) for 2007, O*NET13.0 (based on O*NET-SOC2006) for 2008, O*NET14.0 (based on O*NET-SOC2009) for 2009, O*NET15.0 (based on O*NET-SOC2009) for 2010, O*NET16.0 (based on O*NET-SOC2010) for 2011, O*NET17.0 (based on O*NET-SOC2010) for 2012, O*NET18.0 (based on O*NET-SOC2010) for 2013, O*NET19.0 (based on O*NET-SOC2010) for 2014, and O*NET20.0 (based on O*NET-SOC2010) for 2015.

  9. We estimate the same specification model with AES, \(AES_{\mathrm{skilled}}\), and \(AES_{\mathrm{unskilled}}\) constructed using all occupations. Skill level is defined using the “required level of education” in O*NET for each year. This implementation does not change the sign or significance of the estimated coefficients in terms of automation, especially in the specification model in Sect. 6.1.

  10. For example, “State-to-State Migration Flows: 2007” reports migration flows from 2006 to 2007.

  11. We use “Migration status, 1 year (whether the person had changed residence since a reference point a year ago),” “State or country of residence 1 year ago”, and “Person weight,” which indicates how many people in the US population are represented by a given person to calculate the migration flows between each states and non-migrants for each state.

  12. The Occupational Employment Statistics is a semiannual survey that estimates the number of jobs for SOC occupations in each state.

  13. The occupations with missing values in the degree of automation are excluded from K.

  14. This study uses the weekly average wage.

  15. The data sources of the control variables are as follows. Population: 2000–2010 Intercensal Estimates and State Population Totals Tables 2010–2016 (the Bureau of the Census). Wage: Quarterly Census of Employment and Wages (the Bureau of Labor Statistics). Unemployment rate and employment growth: Local Area Unemployment Statistics (the Bureau of Labor Statistics). House price index: House Price Index Datasets (Federal Housing Finance Agency). Share of high-skilled workers: Occupational Employment Statistics (the Bureau of Labor Statistics) and Occupational Projections and Training Data (the Bureau of Labor Statistics, the National Crosswalk Service Center). Employment share by industry: Gross Domestic Product by State (the Bureau of Economic Analysis).

  16. We also estimate the same specification model with zero flows. The dependent variable is redefined by \(\log ((P_{ij}+1)/(P_{ii}+1))\). This implementation does not change the conclusions of the analyses.

  17. Tables 5 and 6 do not show all the decimal places used in the calculation for ease of viewing.


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The author would like to thank Takatoshi Tabuchi, Marcus Berliant, Michael Pflüger, Yasuhiro Sato, and Atsushi Yamagishi. I am also indebted to an anonymous referee and the editor-in-chief, Martin Andersson, for their helpful comments and suggestions. The author also thanks the participants of the JEA meeting at Ritsumeikan University, the Asian Seminar in Regional Science at National Taiwan University, and of seminars at Tohoku University, Kyushu Sangyo University, Kyoto University, the University of Tokyo, and Kagawa University.

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Correspondence to Chigusa Okamoto.

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Okamoto, C. The effect of automation levels on US interstate migration. Ann Reg Sci 63, 519–539 (2019).

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