Manufacturing Busts, Housing Booms, and Declining Employment October 2012 Kerwin Kofi Charles...

Manufacturing Busts, Housing Booms, and Declining Employment

October 2012

Kerwin Kofi CharlesUniversity of Chicago

Harris School of Public Policy And NBER

Erik HurstUniversity of Chicago

Booth School of Businessand NBER

Matthew J. NotowidigdoUniversity of Chicago

Booth School of Businessand NBER

This Paper

Try to explain why employment rate changed within the U.S. during the 2000s

Focus on two prominent phenomenon:

o Dramatic decline in manufacturing employment (secular decline)

o Transitory housing boom followed by housing bust.

Assess how those shocks affected employment rates (and other labor market outcomes) during 2000-2007, 2007-2010, and 2000-2010 periods .

Run counterfactuals “shutting off” the labor market effects of each of the changes. Isolate importance of manufacturing declines.

Look at the effects of two phenomenon on human capital attainment.

Total U.S. Manufacturing Employment (in 1,000s)

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

9,000

11,000

13,000

15,000

17,000

19,000

21,000


1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

9,000

11,000

13,000

15,000

17,000

19,000

21,000~1.5 Million Jobs Lost

During 1980s and 1990s


1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

9,000

11,000

13,000

15,000

17,000

19,000



~3.8 Million Jobs LostDuring 2000-2007


1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

9,000

11,000

13,000

15,000

17,000

19,000



~3.8 Million Jobs LostDuring 2000-2007

Even More Jobs LostAfter 2007

7/12

This Paper: Estimate Effects on Employment Rate

• Causally estimate effects using a local labor market strategy.

• Focus on different groups: Primary focus is on non-college men.

2000-2007 2007-2010 2000-2010

ManufacturingDecline

Housing Related“Shock”

Combination of both Phenomenon


2000-2007 2007-2010 2000-2010





2000-2007 2007-2010 2000-2010



~ 0 ( )



• The housing shock “masked” the labor market effects of the manufacturing shock during the 2000-2007 period.

2000-2007 2007-2010 2000-2010



~ 0 ( )


~ 0

Summary of Main Findings

1. Manufacturing decline is important for thinking about changes in non-employment during 2000s.

o About 35-40% of increase in non-employment during 2000s can be attributed to the decline in manufacturing.

2. Labor market was significantly weaker in the 2000-2007 period than we thought.

o Housing boom “masked” deterioration of U.S. labor market.

o 2000-2007 period marked by secular decline in one sector and a temporary boom in another sector.

o Implication: 2007 may not be a good benchmark to evaluate cyclical changes in economic variables of interest.

Summary of Main Findings

3. About one-third of the increase in non-employment during the recession can be attributed to

o manufacturing declines during 2007-2010 period, and

o manufacturing declines during the 2000-2007 period that were masked by housing boom.

4. The net effect of housing booms/busts on labor markets was small over the entire decade.

o The bust reduced employment but the boom raised employed

5. Housing boom deterred college attainment during 2000-2007 period.

A Word on the “Masking” Effect

Masking occurred both across and within individuals.

o Housing booms were not always in places that didn’t experience the manufacturing declines.

o Type of workers affected differed slightly (by age, skill, and nativity).

o However, even for a given individual, evidence that those that were displaced from manufacturing were more likely to find employment in places with a housing boom.

• Both types of masking are interesting.

o Implies that even though the aggregate employment rate may have been relatively stable during 2000-2007 period, there could still have been distributional effects (across people and locations).

Plausibility of “Masking” Effect?

For our empirical work, we are going to identify effects using cross MSA variation.

o Different MSAs received different combinations of manufacturing and “housing” shocks.

o For our aggregate calculations, need to discuss the scaling up of local estimates (migration, etc.)

However, the potential plausibility of masking can be seen from the time series data.

Employment Trends for Non-College Men (age 21-55)19

77

1979

1981

1983

1985

1987

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

Construction Share

Manufacturing Share

Man + Cons Share

Employment Trends for Non-College Women (age 21-55)

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

0.000

0.020

0.040

0.060

0.080

0.100

0.120

0.140

Construction Share

Manufacturing Share

Manufacturing + Construction Share

Median Real Wage for Non-College Men (age 21-55)

Long-Run Increase in Non-Employment?

Results do not imply a permanent increase in non-employment

o Workers could choose to acquire skills which could increase market wage.

o Workers could choose to move to different labor markets.

We think of this is as more of a medium run increase (as opposed to being just do to cyclical fluctuations) – adjustments take time.

Our force is different from traditional mismatch stories.

o For us, people are just moving up and down labor supply curve in response to labor demand shocks (market wage < reservation wage).

However, our results suggest that temporary government policies to stimulate labor demand will NOT have lasting effects on employment.

o Only implies to the 30-40% of non-employment increase we identify.

Outline

1. Conceptual model

2. Empirical model

3. Main results

4. Counterfactual estimates

5. Examine effects on human capital attainment

6. Conclusion

Conceptual model

Purpose - To provide a simple model which highlights:

o the interplay between shocks in different sectors

o when those shocks will result in changes in nonemployment.

o reasons why the response to nonemployment resulting from a shock may change over time.

Conceptual model

• Mass of workers have skill endowment s and reservation wage r, distributed according to F(s,r).

• Workers can either choose to be “employed” in either sector A or sector B (which pay wA and wB per efficiency unit, respectively), or they can choose to work in “home” sector H.

• Worker of type (s,r) can either supply s efficiency units in A or (1-s) in B.

o Therefore, worker chooses employment in A iff swA > (1-s)wB and

swA > r

• To simplify exposition, assume aggregate production function given by the following:

Y = αLA + βLB

so that wA = α and wB = β

s* given by αs*=β(1-s*)

s

r

αβ

LA

LB

LH

s* s' given by αs*=β(1-s*)

s

r

αβ

LA

LB

LH

A → H

A → B

s

r

αβ

LA

LB

LH

A → H

A → B

A → A → B

H → B

A→H→B

s* s' s'' given by αs*=β(1-s*)

Empirical model

• Changes in Labor Market Outcomes at Local Level

(1) (2) (3) (4)

Definitions:

(1) Effect of Manufacturing Labor Demand Shock (through all channels)

(2) Effect of “Housing Related” Labor Demand Shock

(3) Effect of “Other” Labor Demand Shocks (not proxied by first two)

(4) Effect of Labor Supply Shocks

Note: k denotes a local labor market (e.g., MSA)

Lk could be employment rate, wages, employment in a sector, etc.

( , , , )M H Ok k k k kL f D D D

( )kL

Empirical model

• Changes in Labor Market Outcomes at Local Level

Our Goal:

Estimate:

Problems:

o We do not observe

o We ideally want proxies which are orthogonal to the labor supply shock.

Note: We will estimate a causal channel of manufacturing shock on labor market outcomes (housing will be more of a catch all).

( )kL

( , , , )M H Ok k k k kL f D D D

/ and /M Hk k k kL D L D

and M Hk kD D

Creating a Local Manufacturing Shock

• Instrument for the local declines in manufacturing.

• Construct predicted change in manufacturing employment following Bartik (1991) ( ).

o interact pre-existing cross-sectional variation in industry employment with national industry employment trends.

o Key assumption: initial industry variation across MSAsuncorrelated with (local labor supply changes)

• Instrument is strongly predictive of actual changes in manufacturing employment.

MkD

k

Creating a “Housing Related” Labor Demand Shock

• Use housing price growth in local area ( ) as our measure of housing related demand shock.

• Intuition – We have two direct housing related labor demand channels

o Wealth Effect Channel:

(+)

o Construction Demand Channel:

(?)

• The relationship between construction effect on labor demand and house prices will be positive if variation in house prices is due to variation in housing demand.

HkP

( )Hk kW P

( )Hk kC P

Relationship Between Housing Price Growth and Change in Construction Share (Non-College Men, 25-55)

Empirical Model

• Note: Housing prices are endogenous

• Where Z is some measure of housing supply across locations.

• Where is some national change in housing demand.

Note: We do not want to take a stand on why house prices changed during the 2000s.

0 1 2

M H Ok k k k k k kL D P X D

2 , , ,O

k k k k k k k k kH H H O H H

k k k k k k k k k

d L L dW L dC L d D L dh

d P W d P C d P D d P d P

1 ( ; )H M H O

k k k k k k k k kP D g D Z X D Z

HkD

What We Estimate

Comment 1:

o Effect of manufacturing shock on labor market outcomes includes the direct effect and the indirect effect through house prices

- In essence, the house price measure is residualized of manufacturing shock.

0 1 2


11 2/ M

k kdL d D

What We Estimate

Comment 2:

o We estimate the above via OLS

o We also estimate the above instrumenting for to isolate a more causal channel of house prices on labor market outcomes.

- Use variation in Z across places (Saiz developable land measure).

- Use temporal variation in house price movements within a city (a new instrument).

o Not necessarily important for our purposes to estimate a causal relationship. Want to isolate variation orthogonal to θk.

o OLS results and IV results are very similar in most specifications.

0 1 2


HkP

Data For Main Results

• 2000 Census and 2005-2007 and 2009-2010 ACS

o Most of our analysis comes using Census/ACS data.

o All of our analysis starts in 2000 (as a result)

o Focus on individuals aged 21-55.

• FHFA metro house price indexes

• Index of Available Land (Saiz 2010)

o Identical results if we use his housing supply elasticity measure.

Time Periods

• Base estimation: 2000 – 2007 period

o Start in 2000 because of data limitations.

o Want to focus on pre-recessionary period to get estimated responses.

o Interesting to focus on the boom period (highlights masking).

• Follow up with estimation during the 2007-2010

o Can see if the responses change in different periods.

• Discuss long changes in outcomes: 2000-2010

o Highlights the role of the temporary effects of housing booms.

A Little More on the Bartik Instruments

Bartik Shock vs. Actual Change in Manufacturing

Bartik Shock and House Price Growth, 2000-2007

Estimates from the Empirical Model: Some Graphical Results

Change in non-employment rate for non-college men, 2000-2007

Change in average wage for non-college men, 2000-2007

Change in construction employment share, 2000-2007

Change in manufacturing employment share, 2000-2007

Estimates from the Empirical Model:Formal Estimates

Dependent variable:Change in Non-

employment Rate,2000-2007

Change in Average Wage,

2000-2007Specification: OLS OLS (1) (1) Change in Housing Prices -0.034 0.059 [Housing Boom] (0.011) (0.010)

Predicted Change in Share of -0.724 1.545 Non-College Men Empl. in Manuf. (0.245) (0.369) [Manufacturing Bust]

Housing price effect (1σ) -0.011 0.018Manufacturing effect (1σ) -0.010 0.021

N 235 235R2 0.741 0.444Include baseline controls y y

Table 4: Baseline Results, Non-College Men

Dependent variable:Change in

Construction Share,2000-2007

Change in Manufacturing Share

2000-2007Specification: OLS OLS (5) (7) Change in Housing Prices 0.024 0.001 [Housing Boom] (0.006) (0.004)

Predicted Change in Share of 0.450 1.025 Non-College Men Empl. in Manuf. (0.178) (0.074) [Manufacturing Bust]

Housing price effect (1σ) 0.007 0.000Manufacturing effect (1σ) 0.006 0.014

N 235 235R2 0.492 0.532Include baseline controls y y

Table 4: Baseline Results, Non-College Men

Non-employment Effects for Other Groups(One Standard Deviation Effect – IV Saiz Specification)

Non-Employment Change Wage Growth

Bartik Housing Bartik Housing

Non College Men -0.010 -0.011 0.021 0.018

Non College Women -0.007 -0.007 0.012 0.012

College Men -0.004 -0.003 0.004 0.008

College Women -0.003 -0.002 0.007 0.008

All -0.007 -0.008 0.011 0.014

Instrumenting for Housing Price Changes

Temporary Nature of The Housing Boom: Booms vs. Busts

House Price Growth and Saiz Instrument

Saiz Instrument and Construction Employment, 2000-2007

Alternate Housing Instrument Identification

House Price Growth and Alternate Housing Instrument

Estimates from the Empirical Model:Formal Estimates

Dependent variable: Change in Non-employment Rate,2000-2007

Specification: OLS IV-Saiz IV-Alt (1) (2) (3) Change in Housing Prices -0.034 -0.035 -0.022 [Housing Boom] (0.011) (0.015) (0.010)

Predicted Change in Share of -0.724 -0.694 -0.661 Non-College Men Empl. in Manuf. (0.245) (0.220) (0.205) [Manufacturing Bust]

Housing price effect (1σ) -0.011 -0.011 -0.008Manufacturing effect (1σ) -0.010 -0.009 -0.009

First stage F-statistic 14.290 16.90N 235 235 235R2 0.741 0.740 0.737Include baseline controls y y y

Table 4: Baseline Results, With Housing Instruments

Dependent variable: Change in Wages,2000-2007

Specification: OLS IV-Saiz IV-Alt (1) (2) (3) Change in Housing Prices 0.059 0.048 0.060 [Housing Boom] (0.010) (0.013) (0.011)

Predicted Change in Share of 1.545 1.504 1.375 Non-College Men Empl. in Manuf. (0.369) (0.304) (0.337) [Manufacturing Bust]

Housing price effect (1σ) 0.018 0.015 0.021Manufacturing effect (1σ) 0.021 0.020 0.019

First stage F-statistic 14.290 16.90N 235 235 235R2 0.444 0.439 0.432Include baseline controls y y y

Table 4: Baseline Results, With Housing Instruments

Implied labor supply elasticity ~ -0.5 to -0.7.

Results are Robust To Many Alternate Specifications

• Controlling for Census regions

• Using sub-measures of the land availability index

• Including interactions between manufacturing shocks and housing shocks

o None of the interaction terms were significant

Population change, non-college men, 2000-2007

o One standard deviation decline in Bartik manufacturing shock decreases population growth by about 3 percent (from our main empirical specification)

Long Run Results

Change in non-employment rate for non-college men, 2000-2010

Long Run Results: Change in Non-employment, Standardized Effects

2007-2010 Change in Non-emp



Saiz-IV Alt-IV Saiz-IV Alt-IV Saiz-IV Alt-IV

House Change, 2007-2010

-0.017 -0.016 -0.004 -0.010


0.003 0.011

Manufacturing Change,Relevant Period

-0.007 -0.009 -0.018 -0.020 -0.018 -0.020

Long Run Results: Change in Non-employment, Standardized Effects




Saiz-IV Alt-IV Saiz-IV Alt-IV Saiz-IV Alt-IV


-0.017 -0.016 -0.004 -0.010


0.003 0.011

Manufacturing Change,Relevant Period

-0.007 -0.009 -0.018 -0.020 -0.018 -0.020

• OLS coefficients on house price were = -0.016, -0.010, and 0.000 (respectively)

• Long run house price change on long run employment changes was close to zero withSaiz measure and negative in the OLS

Within and Between Masking

How Much of the Masking Comes from Within Individuals?

• Spatial correlation of shocks

o Shocks were in Different Places

Bartik Shock and House Price Growth, 2000-2007

Manufacturing “Instrument” vs. Saiz Housing “Instrument”




• Sub-groups of the populations

o Look at masking across broad demographic groups.

o Focus on age and nativity.




• Sub-groups of the populations

o Look at masking across broad demographic groups.

o Focus on age and nativity.

• Within Individual Results (Displaced Worker Survey)

o Construction does not absorb lots of displaced manufacturing workers.

o Increased some in the 2000-2007 period.

o Exploit variation in housing market conditions.

Document Within Worker Effects: Displaced Worker Survey

• Focus on non-college men displaced from manufacturing and look at:

o Fraction who remained non-employed at time of survey and

o Fraction who were re-employed in construction

• Divide sample into “Housing Boom MSAs” and “All Other MSAs” based on sharpness of house price change between 2000-2007 (alternative IV)

Document Within Worker Effects: Displaced Worker Survey

Counterfactuals

Extrapolating Local Estimates to National Labor Market

• We try to address to several concerns with this exercise:

o Migration

o Housing Boom → Manufacturing demand

o Construction Boom

o Other National GE effects (e.g., interest rates)

• To the extent we can address these concerns, they seem to indicate our results are conservative.

Model Predictions: Manufacturing Counterfactuals

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20110.00

0.02

0.04

0.06

0.08

0.10

0.12

Total Increase(Raw Data)


2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20110.00

0.02

0.04

0.06

0.08

0.10

0.12


Predicted Effect From Manufacturing


2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20110.00

0.02

0.04

0.06

0.08

0.10

0.12



~40% of Increase


2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20110.00

0.02

0.04

0.06

0.08

0.10

0.12



Predicted Effect From Manufacturing Plus Housing


2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 20110.00

0.02

0.04

0.06

0.08

0.10

0.12



Predicted Effect From Manufacturing Plus Housing

~ 35% during Recession

Education

Propensity to Have At Least One Year of College (Age: 18-29)

Did Housing Boom Delay College Attendance?

Use same local labor market design to answer this question.

The answer is YES!

Places that had large housing booms had a large reduction in the propensity to attend at least one year of college.

o Nearly all the action was on two year colleges (community colleges, technical schools, trade schools, etc.).

o Found effects for both men and women.

During the bust, this trend reversed (but, not completely).

Estimates can explain about 80% of the time series change.

Other Results

Other Results

• Counterfactual analysis for wages for less-skilled men implies “missing” 3.3% decline, coming primarily from lack of downward wage adjustment during bust period.

Other Results


• Decomposing non-employment results into unemployment and non-participation.

o Bartik instrument primarily affects non-participation

o Suggests much of the medium run forces we are identifying are on non-participation.

o Rethink earlier work on sector shifts on labor markets (Lilien (1982), Abraham and Katz (1986)). All such tests were on unemployment – not non-employment!

Other Results


• Decomposing non-employment results into unemployment and non-participation.

o Bartik instrument primarily affects non-participation

o Suggests much of the medium run forces we are identifying are on non-participation.

o Rethink earlier work on sector shifts on labor markets (Lilien (1982), Abraham and Katz (1986)). All such tests were on unemployment – not non-employment!

• Other boom/bust cycle: 1980s housing boom

• Preliminary results indicate broadly similar results

Conclusions

1. Manufacturing decline is important for thinking about changes in non-employment during 2000s (including recession).

o About 35-40% of increase in non-employment during 2000s can be attributed to the decline in manufacturing.

2. Labor market was significantly weaker in the 2000-2007 period than we thought.

o Housing boom “masked” deterioration of U.S. labor market.

o 2000-2007 period marked by secular decline in one sector and a temporary boom in another sector.

o Implication: 2007 may not be a good benchmark to evaluate cyclical changes in economic variables of interest.

Conclusions

3. About one-third of the increase in non-employment during the recession can be attributed to

o manufacturing declines during 2007-2010 period, and

o manufacturing declines during the 2000-2007 period that were masked by housing boom.

4. The non-employment from the manufacturing declines will likely persist in the medium run (i.e., it is not driven by cyclical forces).

5. Housing boom deterred college attainment during 2000-2007 period.

Manufacturing Busts, Housing Booms, and Declining Employment October 2012 Kerwin Kofi Charles...

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