## 2. Effect of Robots on Poverty: ## Source details **Canonical URL:** [2. Effect of Robots on Poverty:](https://www.imf.org/-/media/files/publications/wp/2024/english/wpiea2024095-print-pdf.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2024/english/wpiea2024095-print-pdf.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2024/english/wpiea2024095-print-pdf.pdf.json) --- ### Scope and data - Coverage: U.S. commuting zones, period 2000-2007 (long differences). - Exposure measure: Bartik-style adjusted measure of robots per worker (Acemoglu and Restrepo 2020); instrumented using industry-level robot penetration in European countries ahead of the U.S. in robotics technology. - Robot data source: International Federation of Robotics (IFR) combined with EU KLEMS industry employment and output. - Social protection measures: - Unemployment insurance (UI) generosity: product of maximum weekly benefit amount and maximum benefit duration (weeks) in 2000; "High UI generosity" = dummy = 1 if > median across U.S. states, 0 otherwise. - Social assistance (SA) generosity (TANF): maximum monthly benefit for a family of three with no income in 1999; "High SA generosity" = dummy = 1 if > median across U.S. states, 0 otherwise. - Controls: census division dummies, baseline commuting-zone covariates (log population; share female; share over 65; education shares: no college, some college, college/professional degree, masters/doctoral degree; race shares: Whites, Blacks, Hispanics, Asians), manufacturing employment in 2000, exposure to Chinese imports, share of employment in routine jobs. ### Key empirical estimates (IV, commuting-zone level, 2000–2007) - Robot exposure effects: - One additional robot per thousand workers reduces employment-to-population ratio by approximately 0.6 percentage points (roughly 1.7 percent decline). - One additional robot per thousand workers reduces average hourly wages by approximately 0.8 percent. - The increase in robots during the period analyzed is around 1 robot per thousand workers. - Poverty: - One additional robot per thousand workers increased the poverty rate by 0.3 percentage points (3 percent increase). - U.S. poverty rate in 2000 was 11.3 percent (U.S. Census statistic cited in the source). - Role of unemployment insurance (UI): - UI generosity does not produce differential long-term effects on employment (no attenuation of robot effects on employment observed). - In states with relatively more generous UI, about two thirds of the negative effects of robotization on wages are attenuated. - Wage attenuation effect concentrated among workers without a college degree. - Role of social assistance (SA): - Temporary UI benefits do not eliminate the small long-term increase in poverty associated with robotization. - Higher SA generosity offsets the robot-induced increase in poverty (interaction effects reported in Table 2 and Figure 3 indicate the negative poverty effect is offset in areas with relatively higher social assistance generosity). ### Interpretation and mechanisms - UI mechanism for wages: - More generous UI likely allows displaced workers time to search for better job matches and acquire new skills, contributing to more efficient labor allocation and mitigating wage declines. - Effect stronger for workers without a college degree who may rely relatively more on UI when unemployed. - Employment non-effect: - UI benefits are temporary; therefore they are unlikely to generate persistent demand effects that would alter long-run employment levels. - Poverty mitigation: - Cash social assistance (TANF generosity) can reduce the small long-term increase in poverty associated with robot adoption; UI alone does not suffice to eliminate these poverty effects. ### Robustness and identification - Instrument: U.S. exposure to robots instrumented using changes in robot penetration in European countries (focus on variation from industries with concurrent robot adoption across advanced economies). - Interaction term for social protection generosity is instrumented using the analogous European-based measure interacted with the social protection dummy. - Results robust to inclusion of state fixed effects and alternative specifications at the commuting-zone and demographic-group levels. - First-stage statistics, F-statistics and p-values reported in the paper for IV specifications; standard errors robust to heteroskedasticity and within-state correlation. ### Policy implications and recommendations - Strengthen UI design and coverage: - Well-designed UI with adequate benefit generosity can smooth transitions and improve job matching, thereby attenuating wage losses from automation. - Eligibility rules should be more flexible and include self-employment and non-standard employment (NSE) contracts to address growing NSE and platform work. - Integrate UI with active labor market policies (ALMPs): - Complement UI with ALMPs focused on human capital development and retraining rather than only job-search assistance. - Engage private sector in training design to align skills with demand. - Reinforce social assistance for poverty protection: - Generous social assistance (cash transfers) helps offset robot-induced increases in poverty and protects households facing prolonged income losses. - Policy design considerations: - Coverage, design, and generosity of social protection systems determine effectiveness in cushioning technological shocks. - Portability and universal coverage are important as labor market structures shift toward NSE and platform-based work. *Source: IMF Working Paper — Strengthening Social Protection to Pave the Way for Technological Innovation: Evidence from the U.S., Section 2.* ### References ............................................................................................................. ### References ......................................................................................................................................................... 16 ### Figures - 1. Effect of Robots on Employment and Wages in the U.S. Local Labor Market: The Role of ........................... 11 - 2. Effect of Robots on Wages in the U.S. Local Labor Market: The Role of Unemployment .............................. 12 - 3. Effect of Robots on Poverty: The Role of Social Assistance ........................................................................... 13 ### Tables - 1. Effect of Robots on Employment and Wages in the U.S. Local Labor Market: ............................................... 12 *Source: https://www.imf.org/-/media/files/publications/wp/2024/english/wpiea2024095-print-pdf.pdf* ### 2. Effect of Robots on Poverty: ........................................................................................ ### 2. Effect of Robots on Poverty: ### Scope and data - Coverage: U.S. commuting zones, period 2000-2007 (long differences). - Exposure measure: Bartik-style adjusted measure of robots per worker (Acemoglu and Restrepo 2020); instrumented using industry-level robot penetration in European countries ahead of the U.S. in robotics technology. - Robot data source: International Federation of Robotics (IFR) combined with EU KLEMS industry employment and output. - Social protection measures: - Unemployment insurance (UI) generosity: product of maximum weekly benefit amount and maximum benefit duration (weeks) in 2000; "High UI generosity" = dummy = 1 if > median across U.S. states, 0 otherwise. - Social assistance (SA) generosity (TANF): maximum monthly benefit for a family of three with no income in 1999; "High SA generosity" = dummy = 1 if > median across U.S. states, 0 otherwise. - Controls: census division dummies, baseline commuting-zone covariates (log population; share female; share over 65; education shares: no college, some college, college/professional degree, masters/doctoral degree; race shares: Whites, Blacks, Hispanics, Asians), manufacturing employment in 2000, exposure to Chinese imports, share of employment in routine jobs. ### Key empirical estimates (IV, commuting-zone level, 2000–2007) - Robot exposure effects: - One additional robot per thousand workers reduces employment-to-population ratio by approximately 0.6 percentage points (roughly 1.7 percent decline). - One additional robot per thousand workers reduces average hourly wages by approximately 0.8 percent. - The increase in robots during the period analyzed is around 1 robot per thousand workers. - Poverty: - One additional robot per thousand workers increased the poverty rate by 0.3 percentage points (3 percent increase). - U.S. poverty rate in 2000 was 11.3 percent (U.S. Census statistic cited in the source). - Role of unemployment insurance (UI): - UI generosity does not produce differential long-term effects on employment (no attenuation of robot effects on employment observed). - In states with relatively more generous UI, about two thirds of the negative effects of robotization on wages are attenuated. - Wage attenuation effect concentrated among workers without a college degree. - Role of social assistance (SA): - Temporary UI benefits do not eliminate the small long-term increase in poverty associated with robotization. - Higher SA generosity offsets the robot-induced increase in poverty (interaction effects reported in Table 2 and Figure 3 indicate the negative poverty effect is offset in areas with relatively higher social assistance generosity). ### Interpretation and mechanisms - UI mechanism for wages: - More generous UI likely allows displaced workers time to search for better job matches and acquire new skills, contributing to more efficient labor allocation and mitigating wage declines. - Effect stronger for workers without a college degree who may rely relatively more on UI when unemployed. - Employment non-effect: - UI benefits are temporary; therefore they are unlikely to generate persistent demand effects that would alter long-run employment levels. - Poverty mitigation: - Cash social assistance (TANF generosity) can reduce the small long-term increase in poverty associated with robot adoption; UI alone does not suffice to eliminate these poverty effects. ### Robustness and identification - Instrument: U.S. exposure to robots instrumented using changes in robot penetration in European countries (focus on variation from industries with concurrent robot adoption across advanced economies). - Interaction term for social protection generosity is instrumented using the analogous European-based measure interacted with the social protection dummy. - Results robust to inclusion of state fixed effects and alternative specifications at the commuting-zone and demographic-group levels. - First-stage statistics, F-statistics and p-values reported in the paper for IV specifications; standard errors robust to heteroskedasticity and within-state correlation. ### Policy implications and recommendations (from analysis) - Strengthen UI design and coverage: - Well-designed UI with adequate benefit generosity can smooth transitions and improve job matching, thereby attenuating wage losses from automation. - Eligibility rules should be more flexible and include self-employment and non-standard employment (NSE) contracts to address growing NSE and platform work. - Integrate UI with active labor market policies (ALMPs): - Complement UI with ALMPs focused on human capital development and retraining rather than only job-search assistance. - Engage private sector in training design to align skills with demand. - Reinforce social assistance for poverty protection: - Generous social assistance (cash transfers) helps offset robot-induced increases in poverty and protects households facing prolonged income losses. - Policy design considerations: - Coverage, design, and generosity of social protection systems determine effectiveness in cushioning technological shocks. - Portability and universal coverage are important as labor market structures shift toward NSE and platform-based work. *Source: IMF Working Paper — Strengthening Social Protection to Pave the Way for Technological Innovation: Evidence from the U.S., Section 2.* ### References ### wpiea2024095-print-pdf - References ### Technology, Automation, AI, and Labor Markets - Acemoglu D., and P. 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(2019) “Good Economics for Hard Times: Better Answers to Our Biggest Problems”. Penguin Books Limited. - Brollo F., Ibarra G. L., and Vale R. C. (2024) “Strengthening Income Stabilization through Social Protection in Emerging and Developing Economies: The Brazilian Experience” IMF Working Paper. - Card D., Kluve J., Weber A. (2017) “What Works? A Meta Analysis of Recent Active Labor Market Program Evaluations”. Journal of the European Economic Association, Volume 16, Issue 3, June 2018, Pages 894–931. - Chetty Raj (2008) “Moral Hazard versus Liquidity and Optimal Unemployment Insurance”. Journal of Political Economy. Volume 116, Number 2. - Di Maggio M., and Kermani A. (2016) “The Importance of Unemployment Insurance as an Automatic Stabilizer”. NBER Working Paper 22625. - Hsu, Joanne W., David A. Matsa, and Brian T. Melzer. 2018. "Unemployment Insurance as a Housing Market Stabilizer." 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Volume109, Issue 455. *Strengthening Social Protection to Pave the Way for Technological Innovation: Evidence from the U.S. — Working Paper No. WP/2024/095* --- _Source: https://www.imf.org/-/media/files/publications/wp/2024/english/wpiea2024095-print-pdf.pdf_