Recent Employment Growth in Cities, Suburbs, and Rural Communities - - PowerPoint PPT Presentation

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Recent Employment Growth in Cities, Suburbs, and Rural Communities - - PowerPoint PPT Presentation

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Recent Employment Growth in Cities, Suburbs, and Rural Communities Benjamin K. Couillard and Christopher L. Foote Federal Reserve Bank of Boston Research Department October 3, 2019 Couillard & Foote (Boston Fed) Recent Employment Growth

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SLIDE 1

Recent Employment Growth in Cities, Suburbs, and Rural Communities

Benjamin K. Couillard and Christopher L. Foote

Federal Reserve Bank of Boston Research Department

October 3, 2019

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 1 / 22

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SLIDE 2

Disclaimer: We do not speak for:

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 2 / 22

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SLIDE 3

Disclaimer: We do not speak for:

Eric Rosengren, President of Boston Fed

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 2 / 22

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SLIDE 4

Disclaimer: We do not speak for:

Eric Rosengren, President of Boston Fed Jerome Powell, Chair of Federal Reserve

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 2 / 22

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SLIDE 5

Empirical Approach

Many potential dimensions for spatial analysis of labor markets

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 3 / 22

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SLIDE 6

Empirical Approach

Many potential dimensions for spatial analysis of labor markets

Rural vs. urban

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 3 / 22

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SLIDE 7

Empirical Approach

Many potential dimensions for spatial analysis of labor markets

Rural vs. urban Center cities vs. suburbs

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 3 / 22

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SLIDE 8

Empirical Approach

Many potential dimensions for spatial analysis of labor markets

Rural vs. urban Center cities vs. suburbs “Superstar” cities vs. less-successful cities

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 3 / 22

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SLIDE 9

Empirical Approach

Many potential dimensions for spatial analysis of labor markets

Rural vs. urban Center cities vs. suburbs “Superstar” cities vs. less-successful cities

Data: County Business Patterns (yearly, 1964–2016)

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 3 / 22

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SLIDE 10

Empirical Approach

Many potential dimensions for spatial analysis of labor markets

Rural vs. urban Center cities vs. suburbs “Superstar” cities vs. less-successful cities

Data: County Business Patterns (yearly, 1964–2016)

Employment

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 3 / 22

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SLIDE 11

Empirical Approach

Many potential dimensions for spatial analysis of labor markets

Rural vs. urban Center cities vs. suburbs “Superstar” cities vs. less-successful cities

Data: County Business Patterns (yearly, 1964–2016)

Employment

Total (≈ private nonfarm)

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 3 / 22

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SLIDE 12

Empirical Approach

Many potential dimensions for spatial analysis of labor markets

Rural vs. urban Center cities vs. suburbs “Superstar” cities vs. less-successful cities

Data: County Business Patterns (yearly, 1964–2016)

Employment

Total (≈ private nonfarm) Manufacturing (sometimes imputed from county’s establishment-size distribution)

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 3 / 22

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SLIDE 13

Empirical Approach

Many potential dimensions for spatial analysis of labor markets

Rural vs. urban Center cities vs. suburbs “Superstar” cities vs. less-successful cities

Data: County Business Patterns (yearly, 1964–2016)

Employment

Total (≈ private nonfarm) Manufacturing (sometimes imputed from county’s establishment-size distribution)

Payrolls → Avg. earnings per job (total only)

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 3 / 22

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SLIDE 14

Empirical Approach

Many potential dimensions for spatial analysis of labor markets

Rural vs. urban Center cities vs. suburbs “Superstar” cities vs. less-successful cities

Data: County Business Patterns (yearly, 1964–2016)

Employment

Total (≈ private nonfarm) Manufacturing (sometimes imputed from county’s establishment-size distribution)

Payrolls → Avg. earnings per job (total only)

We combine some small counties & Virginia city/counties

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 3 / 22

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SLIDE 15

Empirical Approach

Many potential dimensions for spatial analysis of labor markets

Rural vs. urban Center cities vs. suburbs “Superstar” cities vs. less-successful cities

Data: County Business Patterns (yearly, 1964–2016)

Employment

Total (≈ private nonfarm) Manufacturing (sometimes imputed from county’s establishment-size distribution)

Payrolls → Avg. earnings per job (total only)

We combine some small counties & Virginia city/counties Exclude AK and HI

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 3 / 22

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SLIDE 16

Empirical Approach

Many potential dimensions for spatial analysis of labor markets

Rural vs. urban Center cities vs. suburbs “Superstar” cities vs. less-successful cities

Data: County Business Patterns (yearly, 1964–2016)

Employment

Total (≈ private nonfarm) Manufacturing (sometimes imputed from county’s establishment-size distribution)

Payrolls → Avg. earnings per job (total only)

We combine some small counties & Virginia city/counties Exclude AK and HI ≈ 3,100 US counties → 2,909 sample “counties” in our balanced dataset

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 3 / 22

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SLIDE 17

Empirical Approach

Many potential dimensions for spatial analysis of labor markets

Rural vs. urban Center cities vs. suburbs “Superstar” cities vs. less-successful cities

Data: County Business Patterns (yearly, 1964–2016)

Employment

Total (≈ private nonfarm) Manufacturing (sometimes imputed from county’s establishment-size distribution)

Payrolls → Avg. earnings per job (total only)

We combine some small counties & Virginia city/counties Exclude AK and HI ≈ 3,100 US counties → 2,909 sample “counties” in our balanced dataset We will also use some county-level demographic data from Census

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 3 / 22

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SLIDE 18

Four Population-Density Groups

4 3 2 1 Density Group

1964

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 4 / 22

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Four Population-Density Groups

Density

  • No. of

Share Percentiles Counties

  • f Pop.

1–85 2,473 ≈ 30% 86–95 291 ≈ 25% 96–99 116 ≈ 30% 100 29 ≈ 15% Total 2,909 100%

4 3 2 1 Density Group

1964

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 4 / 22

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Four Population-Density Groups

Density

  • No. of

Share Percentiles Counties

  • f Pop.

1–85 2,473 ≈ 30% 86–95 291 ≈ 25% 96–99 116 ≈ 30% 100 29 ≈ 15% Total 2,909 100%

4 3 2 1 Density Group

1964

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 4 / 22

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Population and Employment Shares

.1 .2 .3 .4

100 96-99 86-95 1-85

2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964

Share of National Population

.1 .2 .3 .4

100 96-99 86-95 1-85

2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964

Share of National Employment

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 5 / 22

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Population and Employment Shares

.1 .2 .3 .4

100 96-99 86-95 1-85

2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964

Share of National Population

.1 .2 .3 .4

100 96-99 86-95 1-85

2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964

Share of National Employment

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 5 / 22

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SLIDE 23

Population and Employment Shares

.1 .2 .3 .4

100 96-99 86-95 1-85

2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964

Share of National Population

.1 .2 .3 .4

100 96-99 86-95 1-85

2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964

Share of National Employment

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 5 / 22

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SLIDE 24

Suburbanization Trends: Empl. & Pop. Shares in Densest County

1964 Classifications

.2 .3 .4 .5 .6 .7 .8 Densest's County Share of Commuting Zone's Population 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 Top 1% 96-99% 86-95% 1-85%

Density Percentile of CZ's Densest County

Note: Commuting zones defined using 1990 data. Densest county of CZ defined using 1964 data.

.2 .3 .4 .5 .6 .7 .8 Densest's County Share of Commuting Zone's Total Employment 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Note: Commuting zones defined using 1990 data. Densest county of CZ defined using 2016 data.

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 6 / 22

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SLIDE 25

Suburbanization Trends: Empl. & Pop. Shares in Densest County

1964 Classifications

Population

.2 .3 .4 .5 .6 .7 .8 Densest's County Share of Commuting Zone's Population 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 Top 1% 96-99% 86-95% 1-85%

Density Percentile of CZ's Densest County

Note: Commuting zones defined using 1990 data. Densest county of CZ defined using 1964 data.

.2 .3 .4 .5 .6 .7 .8 Densest's County Share of Commuting Zone's Total Employment 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Note: Commuting zones defined using 1990 data. Densest county of CZ defined using 2016 data.

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 6 / 22

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Suburbanization Trends: Empl. & Pop. Shares in Densest County

1964 Classifications

Population

.2 .3 .4 .5 .6 .7 .8 Densest's County Share of Commuting Zone's Population 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 Top 1% 96-99% 86-95% 1-85%

Density Percentile of CZ's Densest County

Note: Commuting zones defined using 1990 data. Densest county of CZ defined using 1964 data.

Employment

.2 .3 .4 .5 .6 .7 .8 Densest's County Share of Commuting Zone's Total Employment 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Note: Commuting zones defined using 1990 data. Densest county of CZ defined using 2016 data.

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 6 / 22

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Earnings per Job Averages by Density Percentile

Relative to population-weighted mean across all counties

  • .6 -.5 -.4 -.3 -.2 -.1

.1 .2 .3 .4 .5 Log Average Payroll per Job 20 40 60 80 100 Density Percentile

1964

  • .6 -.5 -.4 -.3 -.2 -.1

.1 .2 .3 .4 .5 Log Average Payroll per Job 20 40 60 80 100 Density Percentile

2016 Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 7 / 22

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Earnings per Job Averages by Density Percentile

Relative to population-weighted mean across all counties

  • .6 -.5 -.4 -.3 -.2 -.1

.1 .2 .3 .4 .5 Log Average Payroll per Job 20 40 60 80 100 Density Percentile

1964

  • .6 -.5 -.4 -.3 -.2 -.1

.1 .2 .3 .4 .5 Log Average Payroll per Job 20 40 60 80 100 Density Percentile

2016 Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 7 / 22

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Earnings per Job Averages by Density Percentile

Relative to population-weighted mean across all counties

  • .6 -.5 -.4 -.3 -.2 -.1

.1 .2 .3 .4 .5 Log Average Payroll per Job 20 40 60 80 100 Density Percentile

1964

  • .6 -.5 -.4 -.3 -.2 -.1

.1 .2 .3 .4 .5 Log Average Payroll per Job 20 40 60 80 100 Density Percentile

2016 Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 7 / 22

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Earnings per Job Dispersion: Inter-Quartile Range

  • .6 -.5 -.4 -.3 -.2 -.1

.1 .2 .3 .4 .5 Log Average Payroll per Job 20 40 60 80 85 95 100 Density Percentile 75th percentile 25th percentile

1964

  • .6 -.5 -.4 -.3 -.2 -.1

.1 .2 .3 .4 .5 Log Average Payroll per Job 20 40 60 80 85 95 100 Density Percentile 75th percentile 25th percentile

2016

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 8 / 22

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Earnings per Job Dispersion: Inter-Quartile Range

  • .6 -.5 -.4 -.3 -.2 -.1

.1 .2 .3 .4 .5 Log Average Payroll per Job 20 40 60 80 85 95 100 Density Percentile 75th percentile 25th percentile

1964

  • .6 -.5 -.4 -.3 -.2 -.1

.1 .2 .3 .4 .5 Log Average Payroll per Job 20 40 60 80 85 95 100 Density Percentile 75th percentile 25th percentile

2016

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 8 / 22

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SLIDE 32

Earnings per Job Dispersion: Inter-Quartile Range

  • .6 -.5 -.4 -.3 -.2 -.1

.1 .2 .3 .4 .5 Log Average Payroll per Job 20 40 60 80 85 95 100 Density Percentile 75th percentile 25th percentile

1964

  • .6 -.5 -.4 -.3 -.2 -.1

.1 .2 .3 .4 .5 Log Average Payroll per Job 20 40 60 80 85 95 100 Density Percentile 75th percentile 25th percentile

2016

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 8 / 22

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All Counties Bottom 85% of Counties

.1 .15 .2 .25 .3 .35 Interquartile Range: Unweighted 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 .1 .15 .2 .25 .3 .35 Interquartile Range: Unweighted 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Percentiles 86–95 Densest 5%

.1 .15 .2 .25 .3 .35 Interquartile Range: Unweighted 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 .1 .15 .2 .25 .3 .35 Interquartile Range: Unweighted 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 9 / 22

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SLIDE 34

County-Level Regression Model

Timing:

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 10 / 22

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SLIDE 35

County-Level Regression Model

Timing:

Regressions run separately year-by-year (not panel)

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 10 / 22

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SLIDE 36

County-Level Regression Model

Timing:

Regressions run separately year-by-year (not panel)

Dependent Variable:

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 10 / 22

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SLIDE 37

County-Level Regression Model

Timing:

Regressions run separately year-by-year (not panel)

Dependent Variable:

Average earnings per job

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 10 / 22

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SLIDE 38

County-Level Regression Model

Timing:

Regressions run separately year-by-year (not panel)

Dependent Variable:

Average earnings per job

Regressors:

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 10 / 22

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SLIDE 39

County-Level Regression Model

Timing:

Regressions run separately year-by-year (not panel)

Dependent Variable:

Average earnings per job

Regressors:

Average January temperature

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 10 / 22

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SLIDE 40

County-Level Regression Model

Timing:

Regressions run separately year-by-year (not panel)

Dependent Variable:

Average earnings per job

Regressors:

Average January temperature Log population density and indicator for densest 5% of counties

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 10 / 22

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SLIDE 41

County-Level Regression Model

Timing:

Regressions run separately year-by-year (not panel)

Dependent Variable:

Average earnings per job

Regressors:

Average January temperature Log population density and indicator for densest 5% of counties Manufacturing share of employment

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 10 / 22

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SLIDE 42

County-Level Regression Model

Timing:

Regressions run separately year-by-year (not panel)

Dependent Variable:

Average earnings per job

Regressors:

Average January temperature Log population density and indicator for densest 5% of counties Manufacturing share of employment Log share of persons with bachelor’s degrees (interpolated from Census and ACS)

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 10 / 22

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SLIDE 43

Spatial Specification

Yt = Xtβt + WXtγt + εt Yt = Xtβt + WXtγt + (λtWεt + νt)

  • error=εt

Weighting matrix W is “second-order contiguity matrix”

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 11 / 22

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SLIDE 44

Spatial Specification

Yt = Xtβt + WXtγt + εt Yt = Xtβt + WXtγt + (λtWεt + νt)

  • error=εt

Weighting matrix W is “second-order contiguity matrix” Neighboring variables will be weighted averages of X’s in surrounding counties

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 11 / 22

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SLIDE 45

Spatial Specification

Yt = Xtβt + WXtγt + εt Yt = Xtβt + WXtγt + (λtWεt + νt)

  • error=εt

Weighting matrix W is “second-order contiguity matrix” Neighboring variables will be weighted averages of X’s in surrounding counties Errors can also be spatially correlated

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 11 / 22

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SLIDE 46

Spatial Specification

Yt = Xtβt + WXtγt + εt Yt = Xtβt + WXtγt + (λtWεt + νt)

  • error=εt

Weighting matrix W is “second-order contiguity matrix” Neighboring variables will be weighted averages of X’s in surrounding counties Errors can also be spatially correlated λt measures strength of error correlation (“clumpiness” of residuals)

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 11 / 22

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SLIDE 47

Spatial Specification

Yt = Xtβt + WXtγt + εt Yt = Xtβt + WXtγt + (λtWεt + νt)

  • error=εt

Weighting matrix W is “second-order contiguity matrix” Neighboring variables will be weighted averages of X’s in surrounding counties Errors can also be spatially correlated λt measures strength of error correlation (“clumpiness” of residuals) Estimate via GMM

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 11 / 22

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SLIDE 48

Manufacturing Shares in 1980

(.5,1] (449) (.4,.5] (447) (.3,.4] (505) (.2,.3] (544) (.1,.2] (525) [0,.1] (439)

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 12 / 22

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SLIDE 49

Neighboring Manufacturing Shares in 1980

(.5,1] (207) (.4,.5] (521) (.3,.4] (772) (.2,.3] (666) (.1,.2] (544) [0,.1] (199)

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 13 / 22

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SLIDE 50

Log Earnings Per Job: Average January Temperature

  • .06
  • .04
  • .02

.02 1965 1975 1985 1995 2005 2015

January Temperature

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 14 / 22

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SLIDE 51

Log Earnings Per Job: Density Regressors

  • .05

.05 .1 .15 1965 1975 1985 1995 2005 2015

Log Population Density

  • .05

.05 .1 .15 .2 1965 1975 1985 1995 2005 2015

Top 5% Densest

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 15 / 22

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SLIDE 52

Log Earnings Per Job: Density Regressors

  • .05

.05 .1 .15 1965 1975 1985 1995 2005 2015

Log Population Density

  • .05

.05 .1 .15 .2 1965 1975 1985 1995 2005 2015

Top 5% Densest

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 15 / 22

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SLIDE 53

Log Earnings Per Job: Density Regressors

  • .05

.05 .1 .15 1965 1975 1985 1995 2005 2015

Log Population Density

  • .05

.05 .1 .15 .2 1965 1975 1985 1995 2005 2015

Top 5% Densest

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 15 / 22

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SLIDE 54

Log Earnings Per Job: Education and Manufacturing Shares

.01 .02 .03 .04 .05 .06 1965 1975 1985 1995 2005 2015

Log Share Bachelor Degrees

  • .15
  • .1
  • .05

.05 .1 1965 1975 1985 1995 2005 2015

Share Manufacturing

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 16 / 22

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SLIDE 55

Log Earnings Per Job: Education and Manufacturing Shares

.01 .02 .03 .04 .05 .06 1965 1975 1985 1995 2005 2015

Log Share Bachelor Degrees

  • .15
  • .1
  • .05

.05 .1 1965 1975 1985 1995 2005 2015

Share Manufacturing

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 16 / 22

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SLIDE 56

Log Earnings Per Job: Education and Manufacturing Shares

.01 .02 .03 .04 .05 .06 1965 1975 1985 1995 2005 2015

Log Share Bachelor Degrees

  • .15
  • .1
  • .05

.05 .1 1965 1975 1985 1995 2005 2015

Share Manufacturing

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 16 / 22

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SLIDE 57

Log Earnings Per Job: Spatial Error Term

.5 .6 .7 .8 .9 1965 1975 1985 1995 2005 2015

Spatial Error Term

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 17 / 22

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SLIDE 58

Log Earnings Per Job: Spatial Error Term

.5 .6 .7 .8 .9 1965 1975 1985 1995 2005 2015

Spatial Error Term

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 17 / 22

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SLIDE 59

Log Earnings Per Job: Spatial Error Term

.5 .6 .7 .8 .9 1965 1975 1985 1995 2005 2015

Spatial Error Term

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 17 / 22

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SLIDE 60

Log Earnings Per Job: Spatial Error Term

.5 .6 .7 .8 .9 1965 1975 1985 1995 2005 2015

Spatial Error Term

Hypothesis: Starting in mid-1990s, superstar cities pull away from other cities

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 17 / 22

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SLIDE 61

Log Earnings Per Job: Spatial Error Term

.5 .6 .7 .8 .9 1965 1975 1985 1995 2005 2015

Spatial Error Term

Hypothesis: Starting in mid-1990s, superstar cities pull away from other cities IQR of earnings rises at top end of density distribution (seen earlier)

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 17 / 22

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SLIDE 62

Log Earnings Per Job: Spatial Error Term

.5 .6 .7 .8 .9 1965 1975 1985 1995 2005 2015

Spatial Error Term

Hypothesis: Starting in mid-1990s, superstar cities pull away from other cities IQR of earnings rises at top end of density distribution (seen earlier) Similar movements in superstar cities constituent counties increase “clumpiness” of regression residuals (λt)

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 17 / 22

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SLIDE 63

Review of Main Results

Rural vs. urban

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 18 / 22

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SLIDE 64

Review of Main Results

Rural vs. urban

Relative earnings-per-job in densest 5% counties picks up in 1980s, even controlling for county’s college share and its manufacturing-employment share

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 18 / 22

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SLIDE 65

Review of Main Results

Rural vs. urban

Relative earnings-per-job in densest 5% counties picks up in 1980s, even controlling for county’s college share and its manufacturing-employment share Low employment and population growth in 2000s

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 18 / 22

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SLIDE 66

Review of Main Results

Rural vs. urban

Relative earnings-per-job in densest 5% counties picks up in 1980s, even controlling for county’s college share and its manufacturing-employment share Low employment and population growth in 2000s

“Superstar” cities vs. less-successful cities

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 18 / 22

slide-67
SLIDE 67

Review of Main Results

Rural vs. urban

Relative earnings-per-job in densest 5% counties picks up in 1980s, even controlling for county’s college share and its manufacturing-employment share Low employment and population growth in 2000s

“Superstar” cities vs. less-successful cities

Conditional on county-level density, earnings-per-job dispersion generally declines for most of the sample period.

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 18 / 22

slide-68
SLIDE 68

Review of Main Results

Rural vs. urban

Relative earnings-per-job in densest 5% counties picks up in 1980s, even controlling for county’s college share and its manufacturing-employment share Low employment and population growth in 2000s

“Superstar” cities vs. less-successful cities

Conditional on county-level density, earnings-per-job dispersion generally declines for most of the sample period. But dispersion starts rising in 1990s, probably b/c of superstar cities

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 18 / 22

slide-69
SLIDE 69

Review of Main Results

Rural vs. urban

Relative earnings-per-job in densest 5% counties picks up in 1980s, even controlling for county’s college share and its manufacturing-employment share Low employment and population growth in 2000s

“Superstar” cities vs. less-successful cities

Conditional on county-level density, earnings-per-job dispersion generally declines for most of the sample period. But dispersion starts rising in 1990s, probably b/c of superstar cities

Center cities vs. suburbs

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 18 / 22

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SLIDE 70

Review of Main Results

Rural vs. urban

Relative earnings-per-job in densest 5% counties picks up in 1980s, even controlling for county’s college share and its manufacturing-employment share Low employment and population growth in 2000s

“Superstar” cities vs. less-successful cities

Conditional on county-level density, earnings-per-job dispersion generally declines for most of the sample period. But dispersion starts rising in 1990s, probably b/c of superstar cities

Center cities vs. suburbs

Evidence that employment suburbanization stalled for ≈ 20 commuting zones with densest core counties starting in 2000s

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 18 / 22

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SLIDE 71

Review of Main Results

Rural vs. urban

Relative earnings-per-job in densest 5% counties picks up in 1980s, even controlling for county’s college share and its manufacturing-employment share Low employment and population growth in 2000s

“Superstar” cities vs. less-successful cities

Conditional on county-level density, earnings-per-job dispersion generally declines for most of the sample period. But dispersion starts rising in 1990s, probably b/c of superstar cities

Center cities vs. suburbs

Evidence that employment suburbanization stalled for ≈ 20 commuting zones with densest core counties starting in 2000s Less evidence for ≈ 50 cities with less-dense cores

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 18 / 22

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SLIDE 72

Review of Main Results

Rural vs. urban

Relative earnings-per-job in densest 5% counties picks up in 1980s, even controlling for county’s college share and its manufacturing-employment share Low employment and population growth in 2000s

“Superstar” cities vs. less-successful cities

Conditional on county-level density, earnings-per-job dispersion generally declines for most of the sample period. But dispersion starts rising in 1990s, probably b/c of superstar cities

Center cities vs. suburbs

Evidence that employment suburbanization stalled for ≈ 20 commuting zones with densest core counties starting in 2000s Less evidence for ≈ 50 cities with less-dense cores Population suburbanization slows in 2000s for both groups

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 18 / 22

slide-73
SLIDE 73

Manufacturing Employment Growth by Density Group

  • 4
  • 2

2 4 Annualized Percentage Growth Rate

100 96-99 86-95 1-85

2000 1989 1973 1964 2000 1989 1973 1964 2000 1989 1973 1964 2000 1989 1973 1964

By Initial Density Group and Year

Within-Group Manufacturing Employment Change

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 19 / 22

slide-74
SLIDE 74

Manufacturing Employment Growth by Density Group

  • 4
  • 2

2 4 Annualized Percentage Growth Rate

100 96-99 86-95 1-85

2000 1989 1973 1964 2000 1989 1973 1964 2000 1989 1973 1964 2000 1989 1973 1964

By Initial Density Group and Year

Within-Group Manufacturing Employment Change

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 19 / 22

slide-75
SLIDE 75

Manufacturing Employment Growth by Density Group

  • 4
  • 2

2 4 Annualized Percentage Growth Rate

100 96-99 86-95 1-85

2000 1989 1973 1964 2000 1989 1973 1964 2000 1989 1973 1964 2000 1989 1973 1964

By Initial Density Group and Year

Within-Group Manufacturing Employment Change

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 19 / 22

slide-76
SLIDE 76

Manufacturing Employment Growth by Density Group

  • 4
  • 2

2 4 Annualized Percentage Growth Rate

100 96-99 86-95 1-85

2000 1989 1973 1964 2000 1989 1973 1964 2000 1989 1973 1964 2000 1989 1973 1964

By Initial Density Group and Year

Within-Group Manufacturing Employment Change

Growth in factory jobs in less-dense areas early in the sample period

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 19 / 22

slide-77
SLIDE 77

Manufacturing Employment Growth by Density Group

  • 4
  • 2

2 4 Annualized Percentage Growth Rate

100 96-99 86-95 1-85

2000 1989 1973 1964 2000 1989 1973 1964 2000 1989 1973 1964 2000 1989 1973 1964

By Initial Density Group and Year

Within-Group Manufacturing Employment Change

Growth in factory jobs in less-dense areas early in the sample period Big losses everywhere post-2000

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 19 / 22

slide-78
SLIDE 78

Manufacturing Employment Growth by Density Group

  • 4
  • 2

2 4 Annualized Percentage Growth Rate

100 96-99 86-95 1-85

2000 1989 1973 1964 2000 1989 1973 1964 2000 1989 1973 1964 2000 1989 1973 1964

By Initial Density Group and Year

Within-Group Manufacturing Employment Change

Growth in factory jobs in less-dense areas early in the sample period Big losses everywhere post-2000 For manufacturers, 21st century has been an equal opportunity disemployer

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 19 / 22

slide-79
SLIDE 79

Additional Questions Related to Manufacturing Share

1 Why have recent declines in the US manufacturing share had such large effects on

local communities? (Charles, Hurst and Schwartz 2018)

.55 .65 .75 .85 1965 1975 1985 1995 2005 2015

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 20 / 22

slide-80
SLIDE 80

Additional Questions Related to Manufacturing Share

1 Why have recent declines in the US manufacturing share had such large effects on

local communities? (Charles, Hurst and Schwartz 2018)

Human capital differences are likely important (Russ and Shambaugh 2019)

.55 .65 .75 .85 1965 1975 1985 1995 2005 2015

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 20 / 22

slide-81
SLIDE 81

Additional Questions Related to Manufacturing Share

1 Why have recent declines in the US manufacturing share had such large effects on

local communities? (Charles, Hurst and Schwartz 2018)

Human capital differences are likely important (Russ and Shambaugh 2019) Paper develops a potential spatial angle:

.55 .65 .75 .85 1965 1975 1985 1995 2005 2015

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 20 / 22

slide-82
SLIDE 82

Additional Questions Related to Manufacturing Share

1 Why have recent declines in the US manufacturing share had such large effects on

local communities? (Charles, Hurst and Schwartz 2018)

Human capital differences are likely important (Russ and Shambaugh 2019) Paper develops a potential spatial angle:

Being surrounded by other manufacturing counties partially “insulated” a county from factory-job losses—until 1995

.55 .65 .75 .85 1965 1975 1985 1995 2005 2015

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 20 / 22

slide-83
SLIDE 83

Additional Questions Related to Manufacturing Share

1 Why have recent declines in the US manufacturing share had such large effects on

local communities? (Charles, Hurst and Schwartz 2018)

Human capital differences are likely important (Russ and Shambaugh 2019) Paper develops a potential spatial angle:

Being surrounded by other manufacturing counties partially “insulated” a county from factory-job losses—until 1995 Spatial error term from manufacturing-share regression:

.55 .65 .75 .85 1965 1975 1985 1995 2005 2015

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 20 / 22

slide-84
SLIDE 84

Additional Questions Related to Manufacturing Share

1 Why have recent declines in the US manufacturing share had such large effects on

local communities? (Charles, Hurst and Schwartz 2018)

Human capital differences are likely important (Russ and Shambaugh 2019) Paper develops a potential spatial angle:

Being surrounded by other manufacturing counties partially “insulated” a county from factory-job losses—until 1995 Spatial error term from manufacturing-share regression:

.55 .65 .75 .85 1965 1975 1985 1995 2005 2015

Implication: Current job-losers less able to diversify than earlier job losers

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 20 / 22

slide-85
SLIDE 85

Additional Questions Related to Manufacturing Share

2 How does the timing of manufacturing declines in various counties relate to rising

within-city earnings inequality (Baum-Snow et al. 2018, Autor 2019)?

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 21 / 22

slide-86
SLIDE 86

Additional Questions Related to Manufacturing Share

2 How does the timing of manufacturing declines in various counties relate to rising

within-city earnings inequality (Baum-Snow et al. 2018, Autor 2019)?

CEA analysis (2015): Occupations with largest job losses from 1989–2014:

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 21 / 22

slide-87
SLIDE 87

Additional Questions Related to Manufacturing Share

2 How does the timing of manufacturing declines in various counties relate to rising

within-city earnings inequality (Baum-Snow et al. 2018, Autor 2019)?

CEA analysis (2015): Occupations with largest job losses from 1989–2014:

1

Machine operators (“routine manual”)

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 21 / 22

slide-88
SLIDE 88

Additional Questions Related to Manufacturing Share

2 How does the timing of manufacturing declines in various counties relate to rising

within-city earnings inequality (Baum-Snow et al. 2018, Autor 2019)?

CEA analysis (2015): Occupations with largest job losses from 1989–2014:

1

Machine operators (“routine manual”)

2

Secretaries, stenographers, and typists (“routine cognitive”)

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 21 / 22

slide-89
SLIDE 89

Additional Questions Related to Manufacturing Share

2 How does the timing of manufacturing declines in various counties relate to rising

within-city earnings inequality (Baum-Snow et al. 2018, Autor 2019)?

CEA analysis (2015): Occupations with largest job losses from 1989–2014:

1

Machine operators (“routine manual”)

2

Secretaries, stenographers, and typists (“routine cognitive”)

Foote and Ryan (2014): Routine-cognitive share of employment rises until 1990s—then declines

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 21 / 22

slide-90
SLIDE 90

Additional Questions Related to Manufacturing Share

2 How does the timing of manufacturing declines in various counties relate to rising

within-city earnings inequality (Baum-Snow et al. 2018, Autor 2019)?

CEA analysis (2015): Occupations with largest job losses from 1989–2014:

1

Machine operators (“routine manual”)

2

Secretaries, stenographers, and typists (“routine cognitive”)

Foote and Ryan (2014): Routine-cognitive share of employment rises until 1990s—then declines Autor (2019): Decline in urban premium for non-college workers comes mostly after 2000

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 21 / 22

slide-91
SLIDE 91

Additional Questions Related to Manufacturing Share

2 How does the timing of manufacturing declines in various counties relate to rising

within-city earnings inequality (Baum-Snow et al. 2018, Autor 2019)?

CEA analysis (2015): Occupations with largest job losses from 1989–2014:

1

Machine operators (“routine manual”)

2

Secretaries, stenographers, and typists (“routine cognitive”)

Foote and Ryan (2014): Routine-cognitive share of employment rises until 1990s—then declines Autor (2019): Decline in urban premium for non-college workers comes mostly after 2000 Suggests that technological obsolescence of routine-cognitive workers is to blame

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 21 / 22

slide-92
SLIDE 92

Additional Questions Related to Manufacturing Share

2 How does the timing of manufacturing declines in various counties relate to rising

within-city earnings inequality (Baum-Snow et al. 2018, Autor 2019)?

CEA analysis (2015): Occupations with largest job losses from 1989–2014:

1

Machine operators (“routine manual”)

2

Secretaries, stenographers, and typists (“routine cognitive”)

Foote and Ryan (2014): Routine-cognitive share of employment rises until 1990s—then declines Autor (2019): Decline in urban premium for non-college workers comes mostly after 2000 Suggests that technological obsolescence of routine-cognitive workers is to blame A technological reallocation of traditional office tasks away from non-college workers would have large implications for urban/housing policy

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 21 / 22

slide-93
SLIDE 93

The End

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-94
SLIDE 94

Commuting Zones: Population Shares

.1 .2 .3 .4 .5 Share of National Population in Density Group 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Top 1% 96-99% 86-95% 1-85% Density Percentile of CZ's Densest County

Note: Commuting zones defined using 1990 data. Densest county of CZ defined using 1964 data.

.1 .2 .3 .4 .5 Share of National Population in Density Group 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Top 1% 96-99% 86-95% 1-85% Density Percentile of CZ's Densest County

Note: Commuting zones defined using 1990 data. Densest county of CZ defined using 2016 data.

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-95
SLIDE 95

Commuting Zones: Population Shares

1964 County Classifications

.1 .2 .3 .4 .5 Share of National Population in Density Group 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Top 1% 96-99% 86-95% 1-85% Density Percentile of CZ's Densest County

Note: Commuting zones defined using 1990 data. Densest county of CZ defined using 1964 data.

.1 .2 .3 .4 .5 Share of National Population in Density Group 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Top 1% 96-99% 86-95% 1-85% Density Percentile of CZ's Densest County

Note: Commuting zones defined using 1990 data. Densest county of CZ defined using 2016 data.

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-96
SLIDE 96

Commuting Zones: Population Shares

1964 County Classifications

.1 .2 .3 .4 .5 Share of National Population in Density Group 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Top 1% 96-99% 86-95% 1-85% Density Percentile of CZ's Densest County

Note: Commuting zones defined using 1990 data. Densest county of CZ defined using 1964 data.

2016 County Classifications

.1 .2 .3 .4 .5 Share of National Population in Density Group 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Top 1% 96-99% 86-95% 1-85% Density Percentile of CZ's Densest County

Note: Commuting zones defined using 1990 data. Densest county of CZ defined using 2016 data.

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-97
SLIDE 97

Commuting Zones: Population Shares

1964 County Classifications

.1 .2 .3 .4 .5 Share of National Population in Density Group 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Top 1% 96-99% 86-95% 1-85% Density Percentile of CZ's Densest County

Note: Commuting zones defined using 1990 data. Densest county of CZ defined using 1964 data.

2016 County Classifications

.1 .2 .3 .4 .5 Share of National Population in Density Group 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Top 1% 96-99% 86-95% 1-85% Density Percentile of CZ's Densest County

Note: Commuting zones defined using 1990 data. Densest county of CZ defined using 2016 data.

Evidence for population growth in middle two tiers in both panels (lt. blue and tan)

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-98
SLIDE 98

Commuting Zones: Population Shares

1964 County Classifications

.1 .2 .3 .4 .5 Share of National Population in Density Group 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Top 1% 96-99% 86-95% 1-85% Density Percentile of CZ's Densest County

Note: Commuting zones defined using 1990 data. Densest county of CZ defined using 1964 data.

2016 County Classifications

.1 .2 .3 .4 .5 Share of National Population in Density Group 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Top 1% 96-99% 86-95% 1-85% Density Percentile of CZ's Densest County

Note: Commuting zones defined using 1990 data. Densest county of CZ defined using 2016 data.

Evidence for population growth in middle two tiers in both panels (lt. blue and tan) Rappaport (2018): Faster growth of “larger, less-crowded locations”

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-99
SLIDE 99

Theories of Earnings Inequality

Canonical Model

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-100
SLIDE 100

Theories of Earnings Inequality

Canonical Model

Technical progress has been biased toward skilled workers throughout the 20th/21st centuries

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-101
SLIDE 101

Theories of Earnings Inequality

Canonical Model

Technical progress has been biased toward skilled workers throughout the 20th/21st centuries Changes in the relative supply and demand for skill determine the college premium

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-102
SLIDE 102

Theories of Earnings Inequality

Canonical Model

Technical progress has been biased toward skilled workers throughout the 20th/21st centuries Changes in the relative supply and demand for skill determine the college premium Tinbergen (1974, 1975), Goldin & Katz (2008): “Race” between education and technology

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-103
SLIDE 103

Theories of Earnings Inequality

Canonical Model

Technical progress has been biased toward skilled workers throughout the 20th/21st centuries Changes in the relative supply and demand for skill determine the college premium Tinbergen (1974, 1975), Goldin & Katz (2008): “Race” between education and technology

Alternative Models

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-104
SLIDE 104

Theories of Earnings Inequality

Canonical Model

Technical progress has been biased toward skilled workers throughout the 20th/21st centuries Changes in the relative supply and demand for skill determine the college premium Tinbergen (1974, 1975), Goldin & Katz (2008): “Race” between education and technology

Alternative Models

Technical change and/or capital can reduce the demand for certain types of labor

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-105
SLIDE 105

Theories of Earnings Inequality

Canonical Model

Technical progress has been biased toward skilled workers throughout the 20th/21st centuries Changes in the relative supply and demand for skill determine the college premium Tinbergen (1974, 1975), Goldin & Katz (2008): “Race” between education and technology

Alternative Models

Technical change and/or capital can reduce the demand for certain types of labor

Summers’ 2013 Feldstein Lecture at NBER

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-106
SLIDE 106

Theories of Earnings Inequality

Canonical Model

Technical progress has been biased toward skilled workers throughout the 20th/21st centuries Changes in the relative supply and demand for skill determine the college premium Tinbergen (1974, 1975), Goldin & Katz (2008): “Race” between education and technology

Alternative Models

Technical change and/or capital can reduce the demand for certain types of labor

Summers’ 2013 Feldstein Lecture at NBER Acemoglu/Autor’s Ricardian model (2011): Skills → Tasks → Output

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-107
SLIDE 107

Theories of Earnings Inequality

Canonical Model

Technical progress has been biased toward skilled workers throughout the 20th/21st centuries Changes in the relative supply and demand for skill determine the college premium Tinbergen (1974, 1975), Goldin & Katz (2008): “Race” between education and technology

Alternative Models

Technical change and/or capital can reduce the demand for certain types of labor

Summers’ 2013 Feldstein Lecture at NBER Acemoglu/Autor’s Ricardian model (2011): Skills → Tasks → Output

Robots/foreign workers perform tasks previously done by factory workers

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-108
SLIDE 108

Theories of Earnings Inequality

Canonical Model

Technical progress has been biased toward skilled workers throughout the 20th/21st centuries Changes in the relative supply and demand for skill determine the college premium Tinbergen (1974, 1975), Goldin & Katz (2008): “Race” between education and technology

Alternative Models

Technical change and/or capital can reduce the demand for certain types of labor

Summers’ 2013 Feldstein Lecture at NBER Acemoglu/Autor’s Ricardian model (2011): Skills → Tasks → Output

Robots/foreign workers perform tasks previously done by factory workers New technologies allow high-skill workers to perform tasks previously performed by

  • ffice workers

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-109
SLIDE 109

Theories of Earnings Inequality

Canonical Model

Technical progress has been biased toward skilled workers throughout the 20th/21st centuries Changes in the relative supply and demand for skill determine the college premium Tinbergen (1974, 1975), Goldin & Katz (2008): “Race” between education and technology

Alternative Models

Technical change and/or capital can reduce the demand for certain types of labor

Summers’ 2013 Feldstein Lecture at NBER Acemoglu/Autor’s Ricardian model (2011): Skills → Tasks → Output

Robots/foreign workers perform tasks previously done by factory workers New technologies allow high-skill workers to perform tasks previously performed by

  • ffice workers

Executives making their own travel arrangements using Orbitz/Travelocity/Priceline

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-110
SLIDE 110

Theories of Earnings Inequality

Canonical Model

Technical progress has been biased toward skilled workers throughout the 20th/21st centuries Changes in the relative supply and demand for skill determine the college premium Tinbergen (1974, 1975), Goldin & Katz (2008): “Race” between education and technology

Alternative Models

Technical change and/or capital can reduce the demand for certain types of labor

Summers’ 2013 Feldstein Lecture at NBER Acemoglu/Autor’s Ricardian model (2011): Skills → Tasks → Output

Robots/foreign workers perform tasks previously done by factory workers New technologies allow high-skill workers to perform tasks previously performed by

  • ffice workers

Executives making their own travel arrangements using Orbitz/Travelocity/Priceline Researchers typing their own papers using L

A

T EX or Scientific Word

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-111
SLIDE 111

National Employment by Occupational Group

Source: CPS

.1 .2 .3 .4 .5 Share of Total Nonagricultural Employment 1950q1 1960q1 1970q1 1980q1 1990q1 2000q1 2010q1 Routine Cognitive Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-112
SLIDE 112

National Employment by Occupational Group

Source: CPS

.1 .2 .3 .4 .5 Share of Total Nonagricultural Employment 1950q1 1960q1 1970q1 1980q1 1990q1 2000q1 2010q1 Routine Cognitive Nonroutine Cognitive Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-113
SLIDE 113

National Employment by Occupational Group

Source: CPS

.1 .2 .3 .4 .5 Share of Total Nonagricultural Employment 1950q1 1960q1 1970q1 1980q1 1990q1 2000q1 2010q1 Routine Cognitive Nonroutine Cognitive Routine Manual Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-114
SLIDE 114

National Employment by Occupational Group

Source: CPS

.1 .2 .3 .4 .5 Share of Total Nonagricultural Employment 1950q1 1960q1 1970q1 1980q1 1990q1 2000q1 2010q1 Routine Cognitive Nonroutine Cognitive Routine Manual Nonroutine Manual Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-115
SLIDE 115

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-116
SLIDE 116

Growth of Clerical/Office-Support Empl, by Density Group

Sources: Decennial Censuses and American Community Survey

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-117
SLIDE 117

New York City in the 1970s

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-118
SLIDE 118

New York City in the 1970s

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-119
SLIDE 119

New York City in the 1970s

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-120
SLIDE 120

New York City in the 1970s

As recently as the 1970s, pretty much every older industrial city seemed simultaneously doomed. Both New York and Detroit were reeling from the decline of their core industries, and if anything, New York seemed worse off because the car industry seemed more tightly tied to Motown than the garment sector did to

  • Gotham. In 1977, workers in Wayne

County, Michigan, which includes Detroit, were paid more than workers in Manhattan. Edward L. Glaeser Triumph of the City

  • p. 56

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-121
SLIDE 121

Change in Employment by Detailed Occupation: 1989–2014

Source: Council of Economic Advisers (2015 Economic Report of the President)

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-122
SLIDE 122

Second-Order Contiguity Matrix

Apache County, AZ

0.5 1

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-123
SLIDE 123

Second-Order Contiguity Matrix

Autauga County, AL

0.5 1

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-124
SLIDE 124

Manufacturing Employment Share by Density Group

.1 .2 .3 .4

100 96-99 86-95 1-85

2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964

Share of National Manufacturing Employment

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-125
SLIDE 125

Manufacturing Employment Share by Density Group

.1 .2 .3 .4

100 96-99 86-95 1-85

2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964

Share of National Manufacturing Employment

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-126
SLIDE 126

Manufacturing Employment Share by Density Group

.1 .2 .3 .4

100 96-99 86-95 1-85

2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964

Share of National Manufacturing Employment

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-127
SLIDE 127

Manufacturing Employment Share by Density Group

.1 .2 .3 .4

100 96-99 86-95 1-85

2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964

Share of National Manufacturing Employment

Striking fact: In 1964, more factory jobs in densest 1% of counties than in the least-dense 85%

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22

slide-128
SLIDE 128

Manufacturing Employment Share by Density Group

.1 .2 .3 .4

100 96-99 86-95 1-85

2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964 2016 2000 1989 1973 1964

Share of National Manufacturing Employment

Striking fact: In 1964, more factory jobs in densest 1% of counties than in the least-dense 85% Dramatic reversal in importance of dense vs. rural counties during sample period

Couillard & Foote (Boston Fed) Recent Employment Growth October 3, 2019 22 / 22