Manufacturing Busts, Housing Booms, and Declining Employment

Kerwin Kofi Charles University of Chicago Harris School of Public Policy And NBER Erik Hurst University of Chicago Booth School of Business and NBER Matthew J. Notowidigdo University of Chicago Booth School of Business and NBER October 2012

This Paper



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



Focus on two prominent phenomenon

: o o Dramatic decline in

manufacturing employment

(secular decline) 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.

21 000

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

19 000 17 000 15 000 13 000 11 000 9 000

21 000

Total U.S. Manufacturing Employment (in 1,000s) ~1.5 Million Jobs Lost During 1980s and 1990s

19 000 17 000 15 000 13 000 11 000 9 000

21 000

Total U.S. Manufacturing Employment (in 1,000s) ~1.5 Million Jobs Lost During 1980s and 1990s

19 000 17 000 15 000 13 000 11 000 9 000

~3.8 Million Jobs Lost During 2000-2007

21 000

Total U.S. Manufacturing Employment (in 1,000s) ~1.5 Million Jobs Lost During 1980s and 1990s

19 000 17 000 15 000 13 000 11 000 9 000

~3.8 Million Jobs Lost During 2000-2007 Even More Jobs Lost After 2007

7/12

This Paper: Estimate Effects on Employment Rate 2000-2007 2007-2010 2000-2010

Manufacturing Decline Housing Related “Shock” Combination of both Phenomenon • •

Causally estimate effects using a local labor market strategy.

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

This Paper: Estimate Effects on Employment Rate 2000-2007 2007-2010 2000-2010

Manufacturing Decline Housing Related “Shock” Combination of both Phenomenon

This Paper: Estimate Effects on Employment Rate 2000-2007 2007-2010 2000-2010

Manufacturing Decline Housing Related “Shock” Combination of both Phenomenon

This Paper: Estimate Effects on Employment Rate 2000-2007 2007-2010 2000-2010

Manufacturing Decline Housing Related “Shock” Combination of both Phenomenon

~ 0 ( )

This Paper: Estimate Effects on Employment Rate 2000-2007 2007-2010 2000-2010

Manufacturing Decline Housing Related “Shock” Combination of both Phenomenon

~ 0 ~ 0 ( )

•

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

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

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

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 o manufacturing declines during 2007-2010 period, and 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 o Different MSAs received different combinations of manufacturing and “housing” shocks.

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

.

0,40

Employment Trends for Non-College Men (age 21-55) Man + Cons Share

0,35 0,30 0,25 0,20 0,15 0,10 0,05 0,00

Manufacturing Share Construction Share

0,040 0,020 0,000 0,100 0,080 0,060 0,140

Employment Trends for Non-College Women (age 21-55) Manufacturing + Construction Share

0,120

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

w A

and

w B

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

sw A

> r

A

iff

sw A

> (1-s)w

B

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

Y = αL

A

+ βL

B

so that

w A

= α

and

w B

= β

r β L B L H L A α s*

given by

αs*=β(1-s*) s

r β L B L H A → H L A α A → B s* s'

given by

αs*=β(1-s*) s

r β L B H → B L H A→H→B A → H L A A → A → A → B B α s s* s' s''

given by

αs*=β(1-s*)

•

Empirical model Changes in Labor Market Outcomes at Local Level

( 

L k

)  

k f

( 

D M

, 

D H

, 

D O

,  

k k k

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

k

)

Definitions

: (1) (2) (3) (4) Effect of Manufacturing Labor Demand Shock (through all channels) Effect of “Housing Related” Labor Demand Shock Effect of “Other” Labor Demand Shocks (not proxied by first two) Effect of Labor Supply Shocks

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

L k

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

•

Empirical model Changes in Labor Market Outcomes at Local Level

( 

L k

)  

k f

( 

D k M

, 

D k H

, 

D k O

,  

k

)

Our Goal

: Estimate: 

L k

/ 

D k M

and 

L k

/ 

D k H

Problems

: o o We do not observe

D k M

and

D k H

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).

Creating a Local Manufacturing Shock

•

Instrument for the local declines in manufacturing.

•

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

k

o interact

pre-existing cross-sectional variation

in industry employment with

national industry employment trends

.

o Key assumption: initial industry variation across MSAs



k

•

Instrument is strongly predictive of actual changes in manufacturing employment.

•

Creating a “Housing Related” Labor Demand Shock



P k

housing related demand shock.

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

o Wealth Effect Channel:



W k

( 

P k H

)

(+) o Construction Demand Channel:



C k

( 

P k H

)

(?)

• 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

.

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

Empirical Model

 

k

 0  1

D k M

 2

P k H

 

X k

 

D k O

•

Note: Housing prices are endogenous



P k H

  1

D k M

 

g

( 

D k H

;

Z k

)  

X k

 

D k O



k



k



k Z k

 

k

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

H

Where is some national change in housing demand.

Note:

 2  

k



k H

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

  

L k dW k



k



k H

, 

L k



C k dC k



k H

, 

k



k O

 

H k k

, 

L d k

 

k

 

k



k H

 

What We Estimate

 

k

 0  1

D k M

 2

P k H

 

X k

 

D k O



k



k

Comment 1:

dL k

/ 

k M

  1    2 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.

What We Estimate

 

k

 0  1

D k M

 2

P k H

 

X k

 

D k O



k



k

Comment 2:

o o We estimate the above via OLS 

P

isolate a more

k

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 o

Not necessarily important for our purposes to estimate a causal relationship

. Want to isolate variation orthogonal to

θ k

.

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

Data For Main Results

•

2000 Census and 2005-2007 and 2009-2010 ACS

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

All of our analysis starts in 2000 (as a result) 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 o o Start in 2000 because of data limitations.

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

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

Change in average wage 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 construction employment share, 2000-2007

Change in manufacturing employment share, 2000-2007

Change in manufacturing employment share, 2000-2007

Estimates from the Empirical Model: Formal Estimates

Dependent variable: Specification:

Table 4: Baseline Results, Non-College Men Change in Non employment Rate, 2000-2007

OLS (1)

Change in Average Wage, 2000-2007

OLS (1) Change in Housing Prices [Housing Boom] -0.034

(0.011) 0.059

(0.010) Predicted Change in Share of Non-College Men Empl. in Manuf.

[Manufacturing Bust]

Housing price effect (1σ) Manufacturing effect (1σ)

-0.724

(0.245)

-0.011

-0.010

1.545

(0.369)

0.018

0.021

N R 2 Include baseline controls 235 0.741

y 235 0.444

y

Dependent variable: Specification:

Table 4: Baseline Results, Non-College Men Change in Construction Share, 2000-2007

OLS (5)

Change in Manufacturing Share 2000-2007

OLS (7) Change in Housing Prices [Housing Boom] 0.024

(0.006) 0.001

(0.004) 0.450

(0.178) 1.025

(0.074) Predicted Change in Share of Non-College Men Empl. in Manuf.

[Manufacturing Bust]

Housing price effect (1σ) Manufacturing effect (1σ)

N R 2 Include baseline controls

0.007

0.006

235 0.492

y

0.000

0.014

235 0.532

y

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

Non College Men Non College Women College Men College Women All

Non-Employment Change

Bartik Housing -0.010

-0.007

-0.004

-0.003

-0.007

-0.011

-0.007

-0.003

-0.002

-0.008

Wage Growth

Bartik Housing 0.021

0.012

0.004

0.007

0.011

0.018

0.012

0.008

0.008

0.014

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

Non College Men Non College Women College Men College Women All

Non-Employment Change

Bartik Housing -0.010

-0.007

-0.004

-0.003

-0.007

-0.011

-0.007

-0.003

-0.002

-0.008

Wage Growth

Bartik Housing 0.021

0.012

0.004

0.007

0.011

0.018

0.012

0.008

0.008

0.014

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

Non College Men Non College Women College Men College Women All

Non-Employment Change

Bartik Housing -0.010

-0.007

-0.004

-0.003

-0.007

-0.011

-0.007

-0.003

-0.002

-0.008

Wage Growth

Bartik Housing 0.021

0.012

0.004

0.007

0.011

0.018

0.012

0.008

0.008

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

Alternate Housing Instrument Identification

House Price Growth and Alternate Housing Instrument

Estimates from the Empirical Model: Formal Estimates

Table 4: Baseline Results, With Housing Instruments

Dependent variable: Specification:

Change in Non-employment Rate,

OLS

2000-2007

IV-Saiz IV-Alt (1) (2) (3) Change in Housing Prices [Housing Boom] -0.034

(0.011) -0.035

(0.015) -0.022

(0.010) Predicted Change in Share of Non-College Men Empl. in Manuf.

[Manufacturing Bust] -0.724

(0.245) -0.694

(0.220) -0.661

(0.205)

Housing price effect (1σ) Manufacturing effect (1σ)

First stage F-statistic N R 2 Include baseline controls

-0.011

-0.010

235 0.741

y

-0.011

-0.009

14.290

235 0.740

y

-0.008

-0.009

16.90

235 0.737

y

Table 4: Baseline Results, With Housing Instruments

Dependent variable: Specification: OLS (1)

Change in Wages, 2000-2007

IV-Saiz (2) IV-Alt (3) Change in Housing Prices [Housing Boom] 0.059

(0.010) 0.048

(0.013) 0.060

(0.011) 1.545

(0.369) 1.504

(0.304) 1.375

(0.337) Predicted Change in Share of Non-College Men Empl. in Manuf.

[Manufacturing Bust]

Housing price effect (1σ) Manufacturing effect (1σ)

First stage F-statistic N R 2 Include baseline controls

0.018

0.021

235 0.444

y

0.015

0.020

14.290

235 0.439

y

0.021

0.019

16.90

235 0.432

y

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

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 2000-2010 Change in Non-emp Saiz-IV Alt-IV 2000-2010 Change in Non-emp Saiz-IV Alt-IV House Change, 2007-2010 House Change, 2000-2007

-0.017

Manufacturing Change, Relevant Period

-0.007

-0.016

-0.009

-0.004

-0.018

-0.010

-0.020

0.003

-0.018

0.011

-0.020

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

2007-2010 Change in Non-emp Saiz-IV Alt-IV 2000-2010 Change in Non-emp Saiz-IV Alt-IV 2000-2010 Change in Non-emp Saiz-IV Alt-IV House Change, 2007-2010 House Change, 2000-2007

-0.017

Manufacturing Change, Relevant Period

-0.007

-0.016

-0.009

-0.004

-0.018

-0.010

-0.020

0.003

-0.018

0.011

-0.020

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

2007-2010 Change in Non-emp Saiz-IV Alt-IV 2000-2010 Change in Non-emp Saiz-IV Alt-IV 2000-2010 Change in Non-emp Saiz-IV Alt-IV House Change, 2007-2010 House Change, 2000-2007

-0.017

Manufacturing Change, Relevant Period

-0.007

-0.016

-0.009

-0.004

-0.018

-0.010

-0.020

0.003

-0.018

0.011

-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 with Saiz 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”

•

How Much of the Masking Comes from Within Individuals?

Spatial correlation of shocks

o Shocks were in Different Places •

Sub-groups of the populations

o o Look at masking across broad demographic groups.

Focus on age and nativity.

•

How Much of the Masking Comes from Within Individuals?

Spatial correlation of shocks

o Shocks were in Different Places •

Sub-groups of the populations

o o Look at masking across broad demographic groups.

Focus on age and nativity.

•

Within Individual Results (Displaced Worker Survey)

o o o Construction does not absorb lots of displaced manufacturing workers.

Increased some in the 2000-2007 period.

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 o Fraction who

remained non-employed

at time of survey and 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

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 o o o Migration Housing Boom → Manufacturing demand Construction Boom Other National GE effects (e.g., interest rates) •

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

0,12

Model Predictions: Manufacturing Counterfactuals Total Increase (Raw Data)

0,10 0,08 0,06 0,04 0,02 0,00 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

0,12

Model Predictions: Manufacturing Counterfactuals Total Increase (Raw Data)

0,10 0,08 0,06 0,04 0,02

Predicted Effect From Manufacturing

0,00 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

0,12

Model Predictions: Manufacturing Counterfactuals Total Increase (Raw Data)

0,10 0,08 0,06

~40% of Increase

0,04 0,02

Predicted Effect From Manufacturing

0,00 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

0,12

Model Predictions: Manufacturing Counterfactuals Total Increase (Raw Data)

0,10 0,08 0,06 0,04 0,02

Predicted Effect From Manufacturing Predicted Effect From Manufacturing Plus Housing

0,00 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

0,12

Model Predictions: Manufacturing Counterfactuals Total Increase (Raw Data)

0,10 0,08 0,06 0,04

Predicted Effect From Manufacturing Predicted Effect From Manufacturing Plus Housing

0,02 0,00

~ 35% during Recession

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

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

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

• Counterfactual analysis for wages for less-skilled men implies “missing” 

3.3% decline

, coming primarily from lack of downward wage adjustment during bust period.

• o o

Decomposing non-employment results into unemployment and non participation

.

Bartik instrument primarily affects non-participation 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

• Counterfactual analysis for wages for less-skilled men implies “missing” 

3.3% decline

, coming primarily from lack of downward wage adjustment during bust period.

• o o

Decomposing non-employment results into unemployment and non participation

.

Bartik instrument primarily affects non-participation 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 o o Housing boom “masked” deterioration of U.S. labor market.

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

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 o manufacturing declines during 2007-2010 period, and 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.