## wp18118

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---

### I. Introduction — demographic context and implications
- Population growth in advanced economies is slowing; dependency ratios are increasing steeply (Figures 1 and 2).
- Dependency ratios are projected to reach "a striking 50–60 percent in most advanced economies by the 2050s."
- United Nations population projections: population growth rates will turn negative in 25 percent of advanced economies in the 2020s and the median advanced economy will confront negative population growth by the late 2050s.
- Life expectancy at birth increased significantly between 1960 and 2016; healthy life expectancy also rose for both men and women (Figure 2, panel 2).
- Definition: labor force participation rate = total labor force as a percentage of total population ages 15 or older.

### II. United States — divergent participation trends and geographic heterogeneity
- The United States experienced a stark decline in participation since the late-1990s (Figure 3).
- Demographic comparisons:
  - The dependency ratio increased less in the United States than in 75 percent of countries and is projected to increase much less in the future as well, expected to stay more than 12 points below the median advanced economy in 2050 and more than 8 points in 2100 (Figure 1, panel 1).
  - Life expectancy at birth was more than 2 years lower in the United States in 2012 compared to the median advanced economy (Figure 2, panel 1).
  - The United States moved from the 75th percentile to the 25th percentile in healthy life expectancy for women and from the middle to the 25th percentile for men between 1990 and 2016.
- Participation declines by subgroup:
  - an especially pronounced decline in participation of prime-age men since at least the 1980s;
  - a decline in participation of women since the late 1990s.
- Geographic heterogeneity:
  - National averages conceal significant heterogeneity across states and roughly 290 metropolitan areas; declines are especially pronounced outside metropolitan areas.
  - Geographic mobility in the United States is at historic lows (Ihrke 2017), tightening the link between individual fates and those of their communities.

### III. Stylized facts on participation and drivers
- Broad-based decline in participation across states and metropolitan areas, with pronounced drops outside metropolitan areas.
- Aging explains part but not all of the decline in participation; trade and technology also played important roles.
- Local labor markets highly exposed to routinization tend to be highly exposed to offshorability.
- Lower participation in metropolitan areas is strongly associated with exposure to:
  - routinization, and
  - offshoring.
- These associations support hypotheses that deteriorating job opportunities from technology and globalization have contributed to increasing detachment from the workforce (consistent with Abraham and Kearney 2018; Acemoglu and Autor 2011; Autor, Dorn and Hanson 2016).

### IV. Candidate explanations from the literature (selected quantified findings)
- Candidate explanations include:
  - aging and cohort effects;
  - cyclical forces and the severity of the Great Recession;
  - role of policies (including less supportive labor market policies, lower spending on active labor market policies);
  - lower labor supply due to incarceration, disability and pain;
  - structurally lower labor demand from trade and technology (especially for low- and middle-skilled).
- Quantified study findings:
  - Council of Economic Advisers (2014): aging can account for around half of the decline since 2007, cyclical effects for about a further sixth.
  - Hall (2014): about one-third of decline between 2007 and 2013 due to aging and baby-boom cohort effects.
  - Balakrishnan and others (2015): aging of the baby boom explains around half of observed decline during 2007–13; cyclical factors about 30–40 percent; remainder from non-demographic structural factors.
  - Blau and Kahn (2013): lack of family-friendly policies in the United States can explain roughly a quarter of the relative decline in women’s participation between 1990 and 2010.
  - Autor, Dorn and Hanson (2016): impacts of China’s emergence concentrated in local labor markets exposed to foreign competition; exposed workers experience greater job churning and reduced lifetime income.
  - Acemoglu and Restrepo (2017): increase in industrial robot usage between 1990 and 2007 has large negative effects on employment and wages across commuting zones.
- Regional/place-based findings:
  - Austin, Glaeser and Summers (2018): diverging long-term inactivity across American regions, arguing for reconsideration of place-based policies.
  - Brooks and others (2017a, b, c): divergence in participation rates across red and blue states appears linked to sectoral composition of employment growth rather than aging.

### V. Data, measures, and empirical strategy (metropolitan focus)
- Geography and sample:
  - Analysis uses data for the 50 states and roughly 290 metropolitan areas.
  - The roughly 290 metropolitan areas analyzed accounted for 79 percent of the total US population in 2016.
  - District of Columbia treated as a state.
- Time coverage:
  - Labor market indicators available from 1976 at the state level and 1990 at the metropolitan area level.
- Key proxies and constructions:
  - Aging and cyclical conditions proxied by old-age dependency ratios (population age 65 and older as a percentage of population age 15 to 64) from the US Census Bureau’s American Community Survey, and real GDP growth rates from the US Bureau of Economic Analysis.
  - Routinization scores based on Autor and Dorn (2013), measuring "routine task intensity" for 330 occupations using the US Department of Labor’s Dictionary of Occupational Titles.
  - Offshorability scores from Goos, Manning, and Salomons (2014), converting Blinder and Krueger (2014) assessments into ISCO occupational categories.
  - Regional exposures are employment-weighted averages of occupational scores for routinizability and offshorability; because exposures are highly correlated, they are included in regressions one at a time.
  - Education proxied by share of population enrolled in tertiary education from the American Community Survey.
- Regression objective:
  - Examine effects of aging, cyclical conditions, trade and technology on participation at metropolitan level over long changes (2000–2016).
  - Dependent variable: ∆y_i = change in labor force participation between 2000 and 2016 in metropolitan area i.
  - Main regressors include ∆depratio_i, growth_i (average real GDP growth 2000–2016), ∆educ_i, rout_i0, offsh_i0, and state fixed effects α_j.

### VI. Stylized temporal and geographic patterns (1990–2016)
- State-level trends:
  - Participation rates rose in 32 out of 51 states between 1990 and 2000.
  - Between 2000 and 2016, participation rates declined in almost all states, with the exception of the District of Columbia and North Dakota.
  - Between 2000 and 2008 participation rates rose in 15 states.
  - Between 2008 and 2016 participation rates declined in all states except the District of Columbia.
  - Declines most pronounced in the Southeast: Alabama, Georgia, Kentucky, Mississippi, South Carolina; and in parts of the Midwest and West: Alaska, Michigan, Nevada, Oregon.
  - Decline much smaller in the Mid-Atlantic: Maryland, New Jersey, Pennsylvania; and New England: Connecticut, Massachusetts.
- Metropolitan-area trends:
  - Participation rates rose in 86 percent of metropolitan areas between 1990 and 2000.
  - Participation rates declined in 78 percent of metropolitan areas between 2000 and 2016.
  - Between 2000 and 2008 participation rates increased in 54 percent of metropolitan areas.
  - Between 2008 and 2016 participation rates decreased in 81 percent of metropolitan areas.
  - Declines typically larger in states as a whole than in their metropolitan areas, exacerbating urban-rural differences.
  - Atlanta, Dallas, and Phoenix are exceptions where metropolitan participation declines exceeded state declines; extraordinary population growth noted: Phoenix 50 percent, Atlanta 42 percent, Dallas 42 percent.
- Convergence/divergence:
  - Metropolitan areas show convergence in participation rates; state-level evidence for convergence is very weak, suggesting non-metropolitan (rural) areas may be diverging from metropolitan areas.
  - Higher mobility between metropolitan areas relative to between states may contribute to these patterns.

### VII. Key regression results (Table 1) — estimated coefficients and fit (2000–2016)
- Sample sizes and fit:
  - Observations: 381 in columns (1) and (2); 346 in columns (3), (4), and (5).
  - R2 values: 0.284, 0.313, 0.358, 0.367, 0.412 for columns (1)–(5) respectively.
- Average Real GDP Growth coefficients:
  - Column (1): 0.0362 (standard error 0.0227)
  - Column (2): 0.0396* (standard error 0.0227)
  - Column (3): 0.0540** (standard error 0.0221)
- Change in Old-Age-Dependency Ratio coefficients:
  - Column (1): -0.149*** (standard error 0.0393)
  - Column (2): -0.137*** (standard error 0.0397)
  - Column (3): -0.159*** (standard error 0.0378)
- Change in Postsecondary Share coefficients:
  - Column (1): 0.441*** (standard error 0.144)
  - Column (2): 0.443*** (standard error 0.143)
  - Column (3): 0.370*** (standard error 0.139)
- Initial Exposure to Routinization:
  - Column (4): -2.617** (standard error 1.133)
  - Column (5): -2.342* (standard error 1.194)
- Initial Exposure to Offshoring:
  - Column (4): -4.026*** (standard error 0.914)
  - Column (5): -4.825*** (standard error 0.937)
- Significance notation: * p < 0.1; ** p < 0.05; *** p < 0.01.
- Note: Standard errors reported in parentheses. Dependent variable is change in labor force participation rate.

### VIII. Interpretation and magnitudes — aging, cyclical conditions, trade, and technology
- Aging:
  - Change in dependency ratios can account for about half of the observed decline in participation rates at the metropolitan-area level.
- Cyclical conditions:
  - Slower real GDP growth is associated with larger declines in participation.
  - Over the 2000–2016 horizon cyclical conditions would tend to increase participation, but their positive effect is more than offset by trade and technology effects.
- Trade and technology:
  - Metropolitan areas with higher initial exposures to routinization and offshoring experienced larger subsequent declines in participation rates.
  - The exposure measures can explain an additional 60 percent (routinization) and 40 percent (offshoring) of the decline in participation rates (decompositions based on Table 1 columns 4 and 5).
  - State fixed effects contribute an additional 30 percent (column 4) and 39 percent (column 5) drag in the decompositions.
- Regional illustrations:
  - Highly routinization-exposed metropolitan areas (above the 75th percentile) including Grand Rapids, MI; Buffalo, NY; Los Angeles, CA; Miami, FL experienced larger declines in participation.
  - Low routinization-exposed metropolitan areas (below the 25th percentile) including Washington, DC; Boston, MA; some California central valley areas experienced declines about half the size on average, with some increases.
  - Highly offshoring-exposed metropolitan areas (above the 75th percentile) including Detroit, MI; Cleveland, OH; Akron, OH saw declines roughly double those in areas less exposed; some successful metros (San Francisco, CA; Minneapolis, MN; Dallas, TX) are also highly exposed.
- Local example:
  - Detroit, MI: exposure to routinization alone could account for a 1.2-percentage-point decline in participation (illustrative result discussed in text).

### IX. Changes in exposure to routinization and offshoring (2000–2016)
- Selected metropolitan examples with (labor force participation decline, exposure to offshoring decline):
  - Grand Rapids, MI (1.5, 1.9)
  - Fort Wayne, IN (1.5, 1.9)
  - Lancaster, PA (1.5, 1.6)
  - Akron, OH (1.4, 1.4)
  - Cleveland, OH (1.3, 1.4)
  - Dayton, OH (1.3, 1.5)
  - Youngstown, OH (1.3, 1.2)
  - Toledo, OH (1.3, 1.2)
  - Allentown, PA (1.3, 1.1)
- Correlation between initial exposure and subsequent change (regression lines and fit statistics from Figure 15):
  - Exposure to Routinization, States: y = -0.4051x + 0.1014; R² = 0.177
  - Exposure to Offshoring, States: y = -0.239x - 0.0346; R² = 0.4491
  - Exposure to Routinization, Metropolitan Areas: y = -0.4578x + 0.1368; R² = 0.3272
  - Exposure to Offshoring, Metropolitan Areas: y = -0.3013x - 0.0266; R² = 0.344
- Occupational structure associations:
  - Larger increases in participation associated with rising shares of ‘old economy’ occupations such as construction and production.
  - Larger increases in participation also associated with some new/booming occupations such as computer/mathematical, science, or legal services.
  - Declines in participation generally larger in areas with a rising share of service sector jobs, especially education, health care, sales, food service, and cleaning and maintenance.
- Geographic pattern:
  - Exposures to routinization and offshoring were especially high in the Southeast and Midwest, which also exhibited the largest declines in participation.
- Quantitative summary:
  - At the metropolitan area level since 2000:
    - Aging accounts for about half of the observed decline in participation rates.
    - Trade and technology, captured by initial local exposures to routinization and offshoring, can explain a further 40–60 percent.

### X. Policy implications and recommendations
- Near- and medium-term: provide support to workers displaced as a result of automation and globalization to dampen negative effects of labor market shocks that are highly concentrated in some sectors, occupations, or geographic areas.
- Further research recommended to examine channels linking occupational and industry shares to participation in greater detail.

*Source: wp18118 - References (PDF).*

### References .............................................................................................................

### wp18118 - References

### I. Introduction: demographic context and implications
- Population growth in advanced economies is slowing; dependency ratios are increasing steeply (Figures 1 and 2).
- Dependency ratios are projected to reach "a striking 50–60 percent in most advanced economies by the 2050s."
- United Nations population projections: population growth rates will turn negative in 25 percent of advanced economies in the 2020s and the median advanced economy will confront negative population growth by the late 2050s.
- Life expectancy at birth increased significantly between 1960 and 2016; healthy life expectancy also rose for both men and women (Figure 2, panel 2).
- The labor force participation rate is defined as the total labor force as a percentage of total population ages 15 or older.

### II. United States: divergent participation trends and geographic heterogeneity
- The United States stands out with a stark decline in participation since the late-1990s (Figure 3).
- Demographic shifts in the United States were less pronounced than in many other advanced economies:
  - The dependency ratio increased less in the United States than in 75 percent of countries and is projected to increase much less in the future as well, expected to stay more than 12 points below the median advanced economy in 2050 and more than 8 points in 2100 (Figure 1, panel 1).
  - Life expectancy at birth was more than 2 years lower in the United States in 2012 compared to the median advanced economy (Figure 2, panel 1).
  - The United States moved from the 75th percentile to the 25th percentile in healthy life expectancy for women and from the middle to the 25th percentile for men between 1990 and 2016.
- The U.S. experienced both:
  - an especially pronounced decline in participation of prime-age men since at least the 1980s, and
  - a decline in participation of women since the late 1990s.
- National averages conceal significant heterogeneity across states and roughly 290 metropolitan areas; declines are especially pronounced outside metropolitan areas.
- Geographic mobility in the United States is at historic lows (Ihrke 2017), tightening the link between individual fates and those of their communities.

### III. Stylized facts on participation and drivers
- Broad-based decline in participation across states and metropolitan areas, with pronounced drops outside metropolitan areas.
- Aging explains part but not all of the decline in participation; trade and technology also played important roles.
- Local labor markets highly exposed to routinization tend to be highly exposed to offshorability; empirical approach examines each in turn.
- Lower participation in metropolitan areas is strongly associated with exposure to:
  - routinization, and
  - offshoring.
- These associations support hypotheses that deteriorating job opportunities from technology and globalization have contributed to increasing detachment from the workforce (consistent with Abraham and Kearney 2018; Acemoglu and Autor 2011; Autor, Dorn and Hanson 2016).

### IV. Literature: proposed explanations and empirical findings
- Candidate explanations for U.S. participation decline include:
  - aging and cohort effects,
  - cyclical forces and the severity of the Great Recession,
  - the role of policies (including less supportive labor market policies, lower spending on active labor market policies),
  - lower labor supply due to incarceration, disability and pain,
  - structurally lower labor demand from trade and technology (especially for low- and middle-skilled).
- Quantified findings from cited studies:
  - Council of Economic Advisers (2014): aging can account for around half of the decline since 2007, cyclical effects for about a further sixth.
  - Hall (2014): about one-third of decline between 2007 and 2013 due to aging and baby-boom cohort effects.
  - Balakrishnan and others (2015): aging of the baby boom explains around half of observed decline during 2007–13; cyclical factors about 30–40 percent; remainder from non-demographic structural factors.
  - Blau and Kahn (2013): lack of family-friendly policies in the United States can explain roughly a quarter of the relative decline in women’s participation between 1990 and 2010.
  - Autor, Dorn and Hanson (2016): impacts of China’s emergence concentrated in local labor markets exposed to foreign competition; exposed workers experience greater job churning and reduced lifetime income.
  - Acemoglu and Restrepo (2017): increase in industrial robot usage between 1990 and 2007 has large negative effects on employment and wages across commuting zones.
- Regional and place-based perspectives:
  - Diverging long-term inactivity across American regions documented by Austin, Glaeser and Summers (2018), arguing for reconsideration of place-based policies.
  - Brooks and others (2017a, b, c): divergence in participation rates across red and blue states appears linked to sectoral composition of employment growth rather than aging.

### V. Data, measures and empirical strategy
- Geography and sample:
  - Analysis uses data for the 50 states and roughly 290 metropolitan areas.
  - Metropolitan statistical areas are defined by OMB; metropolitan analysis accounts for commuting ties and contiguous high population density.
  - The roughly 290 metropolitan areas analyzed accounted for 79 percent of the total US population in 2016.
  - District of Columbia treated as a state.
- Time coverage:
  - Labor market indicators available from 1976 at the state level and 1990 at the metropolitan area level.
- Key variables and proxies:
  - Aging and cyclical conditions proxied by old-age dependency ratios (population age 65 and older as a percentage of population age 15 to 64) from the US Census Bureau’s American Community Survey, and real GDP growth rates from the US Bureau of Economic Analysis.
  - Technology and trade proxied by regions’ initial exposures to routinization and offshoring:
    - Routinization scores based on Autor and Dorn (2013), measuring "routine task intensity" for 330 occupations using the US Department of Labor’s Dictionary of Occupational Titles.
    - Offshorability scores from Goos, Manning, and Salomons (2014), converting Blinder and Krueger (2014) assessments into ISCO occupational categories.
    - Regional exposures are employment-weighted averages of occupational scores for routinizability and offshorability.
    - Because exposures to routinization and offshoring are highly correlated, they are included in regressions one at a time.
  - Education proxied by share of population enrolled in tertiary education from the American Community Survey.
- Regression aims:
  - Examine effects of aging, cyclical conditions, trade and technology on participation at metropolitan level.
  - Objective is not to precisely disentangle trade vs. technology but to assess impact of each in turn and illustrate relative contributions of different factors at the metropolitan area level.

### VI. Stylized temporal patterns
- State-level participation evolution shows marked breaks:
  - increases among a majority of states before 2000,
  - declines in most states in the 2000–08 period,
  - near-universal declines after the global financial crisis.

*Source: wp18118 - References (PDF). Canonical source: https://www.imf.org/-/media/files/publications/wp/2018/wp18118.pdf*

### 2008. Labor force participation rates rose in

### 2008. Labor force participation rates rose in

### State-level trends (1990–2016)
- Labor force participation rates rose in 32 out of 51 states between 1990 and 2000.
- Between 2000 and 2016, participation rates declined in almost all states, with the exception of the District of Columbia and North Dakota.
- Declines were most pronounced in the Southeast: Alabama, Georgia, Kentucky, Mississippi, South Carolina.
- Declines were also pronounced in parts of the Midwest and West: Alaska, Michigan, Nevada, Oregon.
- The decline was much smaller in the Mid-Atlantic: Maryland, New Jersey, Pennsylvania; and New England: Connecticut, Massachusetts.
- Between 2000 and 2008 participation rates rose in 15 states.
- Between 2008 and 2016 participation rates declined in all states except the District of Columbia.

### Metropolitan-area trends (1990–2016)
- Participation rates rose in 86 percent of metropolitan areas between 1990 and 2000 (Figure 5, panel 1).
- Participation rates declined in 78 percent of metropolitan areas between 2000 and 2016 (Figure 5, panel 2).
- Between 2000 and 2008 participation rates increased in 54 percent of metropolitan areas (Figure 5, panel 3).
- Between 2008 and 2016 participation rates decreased in 81 percent of metropolitan areas (Figure 5, panel 4).
- Declines were typically larger in states as a whole than in their metropolitan areas, exacerbating urban-rural differences.
- Atlanta, Dallas, and Phoenix are notable exceptions where metropolitan participation declines exceeded state declines; extraordinary population growth is noted: Phoenix 50 percent, Atlanta 42 percent, Dallas 42 percent.

### Changes in labor market dynamics and margins of adjustment
- Before 2000: employment increased on average, matched by declines in unemployment and increases in participation.
- After 2000: employment declined, matched by (mostly) increasing unemployment and falling participation.
- Most pronounced declines in participation concentrated in the states of Nevada, Michigan, Georgia, and South Carolina.
- Most pronounced declines in metropolitan areas concentrated in Atlanta, Phoenix, Dallas, and Detroit.
- Figure 8 displays changes in employment rate, unemployment rate, and inactivity rate as population-weighted averages across states and metropolitan areas for intervals: 1990-2000, 2000-2016, 2000-2008, 2008-2016.

### Convergence and divergence patterns
- Metropolitan areas show convergence in participation rates; state-level evidence for convergence is very weak (Figure 7).
- Weak state-level convergence suggests non-metropolitan (rural) areas may be diverging from metropolitan areas.
- Higher mobility between metropolitan areas relative to between states may contribute to these patterns.

### Empirical strategy (metropolitan-area level, 2000–2016)
- Dependent variable: ∆y_i, change in labor force participation between 2000 and 2016 in metropolitan area i.
- Main regressors:
  - ∆depratio_i: change in the old-age-dependency ratio.
  - growth_i: average real GDP growth (2000–2016).
  - ∆educ_i: change in the share of population enrolled in tertiary education.
  - rout_i0: initial exposure to routinization (index, 2000).
  - offsh_i0: initial exposure to offshoring (index, 2000).
- Specification includes state fixed effects α_j and controls for educational attainment changes.
- Regressions focus on long changes (2000–2016) to capture long-term effects of shocks such as automation and trade.

### Key regression results (Table 1)
- Sample sizes and fit:
  - Observations: 381 in columns (1) and (2); 346 in columns (3), (4), and (5).
  - R2 values: 0.284, 0.313, 0.358, 0.367, 0.412 for columns (1)–(5) respectively.
- Average Real GDP Growth coefficients:
  - Column (1): 0.0362 (standard error 0.0227)
  - Column (2): 0.0396* (standard error 0.0227)
  - Column (3): 0.0540** (standard error 0.0221)
- Change in Old-Age-Dependency Ratio coefficients:
  - Column (1): -0.149*** (standard error 0.0393)
  - Column (2): -0.137*** (standard error 0.0397)
  - Column (3): -0.159*** (standard error 0.0378)
- Change in Postsecondary Share coefficients:
  - Column (1): 0.441*** (standard error 0.144)
  - Column (2): 0.443*** (standard error 0.143)
  - Column (3): 0.370*** (standard error 0.139)
- Initial Exposure to Routinization:
  - Column (4): -2.617** (standard error 1.133)
  - Column (5): -2.342* (standard error 1.194)
- Initial Exposure to Offshoring:
  - Column (4): -4.026*** (standard error 0.914)
  - Column (5): -4.825*** (standard error 0.937)
- Significance notation: * p < 0.1; ** p < 0.05; *** p < 0.01.
- Note: Standard errors reported in parentheses. Dependent variable is change in labor force participation rate.

### Interpretation and magnitudes: aging, cyclical conditions, trade, and technology
- Aging:
  - Change in dependency ratios can account for about half of the observed decline in participation rates at the metropolitan-area level.
- Cyclical conditions:
  - Slower real GDP growth is associated with larger declines in participation.
  - Over the 2000–2016 horizon cyclical conditions would tend to increase participation, but their positive effect is more than offset by trade and technology effects.
- Trade and technology:
  - Metropolitan areas with higher initial exposures to routinization and offshoring experienced larger subsequent declines in participation rates.
  - The novel exposure measures can explain an additional 60 percent (routinization) and 40 percent (offshoring) of the decline in participation rates (decompositions based on Table 1 columns 4 and 5).
  - State fixed effects contribute an additional 30 percent (column 4) and 39 percent (column 5) drag in the decompositions.
- Regional illustrations:
  - Highly routinization-exposed metropolitan areas (above the 75th percentile), including Grand Rapids, MI; Buffalo, NY; Los Angeles, CA; Miami, FL, experienced larger declines in participation.
  - Low routinization-exposed metropolitan areas (below the 25th percentile), including Washington, DC; Boston, MA; some California central valley areas, experienced declines about half the size on average, with some increases.
  - Highly offshoring-exposed metropolitan areas (above the 75th percentile), including Detroit, MI; Cleveland, OH; Akron, OH, saw declines roughly double those in areas less exposed; some successful metros (San Francisco, CA; Minneapolis, MN; Dallas, TX) are also highly exposed.

### Local examples and counterfactual contributions
- Detroit, MI: exposure to routinization alone could account for a 1.2-percentage-point decline in participation (illustrative result discussed in text).
- Figure 12 panels show contributions of exposure to routinization and offshoring comparing 25th percentile vs 75th percentile metropolitan areas for each exposure measure.

*Sources: US Bureau of Labor Statistics; US Bureau of Economic Analysis; US Census Bureau; and authors' calculations.*

### 1. Change in Exposure to Routinization (index)

### 1. Change in Exposure to Routinization (index)

### Changes in exposures to routinization and offshoring (2000–2016)
- Figure 14 and accompanying text document metropolitan-area changes in exposures to routinization and offshoring between 2000 and 2016.
- Selected metropolitan examples of declines in labor force participation and exposure to offshoring (labor force participation decline, exposure to offshoring decline):
  - Grand Rapids, MI (1.5, 1.9)
  - Fort Wayne, IN (1.5, 1.9)
  - Lancaster, PA (1.5, 1.6)
  - Akron, OH (1.4, 1.4)
  - Cleveland, OH (1.3, 1.4)
  - Dayton, OH (1.3, 1.5)
  - Youngstown, OH (1.3, 1.2)
  - Toledo, OH (1.3, 1.2)
  - Allentown, PA (1.3, 1.1)

### Correlation between initial exposure and subsequent change
- States and metropolitan areas with higher exposures to routinization and offshoring in 2000 experienced larger subsequent declines in these exposures (Figures 14 and 15).
- Regression lines and fit statistics reported in Figure 15:
  - Exposure to Routinization, States: y = -0.4051x + 0.1014; R² = 0.177
  - Exposure to Offshoring, States: y = -0.239x - 0.0346; R² = 0.4491
  - Exposure to Routinization, Metropolitan Areas: y = -0.4578x + 0.1368; R² = 0.3272
  - Exposure to Offshoring, Metropolitan Areas: y = -0.3013x - 0.0266; R² = 0.344

### Occupational structure, employment shares, and participation rates
- Measures of routinization and offshorability are composite proxies of the initial occupational mix of a metropolitan area (vulnerability to, rather than the actual number of, jobs lost).
- Strong link between changes in occupational employment shares and changes in labor force participation across metropolitan areas (Figure 16).
- Associations between occupational shares and changes in participation:
  - Larger increases in participation rates associated with rising shares of ‘old economy’ occupations such as construction and production (despite their vulnerability to automation).
  - Larger increases in participation rates also associated with some new/booming occupations such as computer/mathematical, science, or legal services.
  - Declines in participation generally larger in areas with a rising share of service sector jobs, especially in education, health care, sales, food service, and cleaning and maintenance.
- These associations could point to difficulties in occupational mobility.

### Geographic patterns
- Exposures to routinization and offshoring were especially high in the Southeast and Midwest, which also exhibited the largest declines in participation as jobs became automated or offshored.
- Evidence suggests convergence in participation rates among metropolitan areas, but not among states, raising concerns that urban and rural areas may be diverging.

### Quantitative contributions to participation declines
- At the metropolitan area level since 2000:
  - Aging accounts for about half of the observed decline in participation rates.
  - Trade and technology, captured by initial local exposures to routinization and offshoring, can explain a further 40–60 percent.

### Policy implications and recommendations
- In the near and medium term, provide support to workers displaced as a result of automation and globalization to dampen negative effects of labor market shocks that are highly concentrated in some sectors, occupations, or geographic areas.
- Further research recommended to examine channels linking occupational and industry shares to participation in greater detail.

*Source: Authors' calculations.*

---


_Source: https://www.imf.org/-/media/files/publications/wp/2018/wp18118.pdf_