“Masking and Aggregate Employment Changes: Housing
Booms and Manufacturing Decline During the 2000s”
“Housing Booms, Labor Market Outcomes
and Educational Attainment”
Kerwin Charles
Erik Hurst
Matt Notowidigdo
Research Design
•
•
A local labor market approach
o
Identify a “manufacturing” labor demand shifter
o
Identify a “housing boom” labor demand shifter
Some towns experienced larger manufacturing declines than others
o
•
Some towns experienced larger “housing” demand shocks than others
o
•
Detroit vs. Orlando
Las Vegas vs. Dallas
Adjust for migration responses
Simple Labor Market Model
N     ln wkt
D
kt
D
kt
D
l
N     ln wkt
S
kt
S
kt
S
l
 ktD   M ktM   H ktH   O ktO   X X kt
Estimating Equation
N kt   0N  1N ktM   2N ktH   N X kt   ktN
1N 
 2N 

N
kt

lS
M

S
lD  l
lS
H

S
lD  l

S
l
  
S
lD  l
O
O

D
l
lD  lS
 ktS
The Manufacturing “Instrument”: Shift Share (Bartik)
ktM   sikt (mi ,~k ,t 1  mi ,~k ,t )
i
•
Identifying Assumption:
o
•
Manufacturing composition in location k in period t is orthogonal to
local supply shocks and local changes in demand of other sectors.
Highly Predictive “First Stage”:
o
Shift share measure strongly predicts actual manufacturing
employment changes within the MSA.
Predicted vs. Actual Change in Manufacturing
Inferring Housing Demand Changes
log  H kD   kH  kD , H log  Pk 
log  H kS   kH  kS , H log  Pk 
• Assuming no local housing supply shocks
kH  kD, H Pk  H kS  kD, H  kS , H  Pk
• Housing demand changes are potentially correlated with other labor demand
changes and labor supply changes.
• Need an instrument.
Estimating Equations
N kt   0N  1N ktM   2N ktH   N X kt   ktN
(1)
ktH   0  1ktM  f ( Z kt )   H X kt  DktO  kt  kt
(2)
Effects of interest:
o
β1 + δ1β2
(Total effect of predicted manufacturing decline)
o
β2
(Effect of predicted housing demand change)
Key Assumption: Housing demand change does not affect predicted
manufacturing decline in location
(Data strongly support this assumption)
Estimating Equations
•
•
N kt   0N  1N ktM   2N ktH   N X kt   ktN
(1)
ktH   0  1ktM  f ( Z kt )   H X kt  DktO  kt  kt
(2)
Motivation for using an instrument for housing demand change:
o
Housing demand change measured with error (e.g., housing supply
shocks are possible, measurement error in supply elasticity estimate).
o
Housing demand change may be result of other labor demand shocks
or labor supply shocks (omitted variables bias)
Instrument using sharp, structural break in quarterly house price
series that occurred in some MSAs during mid-2000s.
o
o
Isolate the “Bubble” component of housing demand change (wish test)
Look for “structural breaks” in housing demand series.
Identifying Assumptions

Trying to capture housing markets during the 2000s.

Evidence that national/local house prices changed in part because of
speculative behavior and changes in lending technology
o
As opposed to traditional housing demand factors (e.g., income
growth, population growth, etc.)
o
Speculative behavior may differ spatially.
o
Lending technology changes may not differ spatially.

Our structural break measure is uncorrelated with all traditional labor
market variables (lagged population growth, lagged employment
growth, composition of workforce, etc.).

Our structural break measure is highly correlated with changes in
Price-to-Rent ratios and share of out-of-town home owners in MSA.
Our New Housing “Instrument”: Structural Breaks
Relationship Between Instrument and Housing Demand Change
Relationship Between Instrument and Lagged Housing Change
Relationship Between Instrument and Supply Elasticity
Relationship Between Instrument and Lagged Non-Employment
Relationship Between Instrument and Lagged Wages
Instrument vs. “Out of Town” Buyers (21 MSAs)
Instrument vs. “Out of Town” Buyers (21 MSAs)
Effects on Employment: Manufacturing Decline

Manufacturing declines depress employment
o
A one standard deviation manufacturing decline reduced employment
by 0.7 percentage points between 2000 and 2007.
o
A one standard deviation manufacturing decline between 2000 and
2007 reduced employment by 1.1 percentage points between 2000 and
2011 (suggesting persistence in manufacturing declines).

Manufacturing declines also reduced wage growth 2000-2007 (but not
between 2007 and 2011).

Manufacturing declines caused an in migration of workers (but
employment propensities of the migrants were similar to natives).

Manufacturing declines hit older workers harder than younger
workers (and also resulted in higher disability take ups).
Effects on Employment: Housing Boom

Housing boom lifted employment
o
A one standard deviation housing demand change increased
employment by about 1 percentage points between 2000 and 2007.
o
A one standard deviation housing boom between 2000 and 2007 had
essentially no effect on employment between 2000 and 2011 (the
booms were followed by busts – different interpretation of Mian and
Sufi results.)

Housing booms increased wage growth between 2000-2007 and 20002011 (wags declines during bust didn’t offset the boom).

Housing boom caused an in migration of workers (but employment
propensities of the migrants were similar to natives).

For men, employment response concentrated in construction (90%);
For women concentrated in FIRE (about 50%). Housing boom hit
younger workers more than older workers.
Within Individual Masking: Re-Employment Rate
A Simple Counterfactual
Estimated Effect of Manufacturing Decline on Non-Employment
0.080
0.070
0.060
0.050
0.040
0.030
0.020
0.010
0.000
-0.010
-0.020
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Estimated Effect of Manufacturing Decline on Non-Employment
0.080
0.070
~42%
Explained
0.060
0.050
0.040
0.030
0.020
0.010
0.000
-0.010
-0.020
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Estimated Effect of Housing Cycle on Non-Employment
0.080
0.070
0.060
0.050
0.040
0.030
Manufacturing Decline
0.020
0.010
0.000
-0.010
Housing Cycle (Construction and Other)
-0.020
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
The Housing Boom Masked The Manufacturing Decline in 2000s
0.080
0.070
Data
0.060
0.050
0.040
0.030
Manufacturing
0.020
0.010
Housing + Manufacturing
0.000
-0.010
-0.020
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
The Housing Boom Masked The Manufacturing Decline in 2000s
0.080
0.070
Data
0.060
0.050
34% During
Recession
0.040
0.030
Manufacturing
0.020
0.010
Housing + Manufacturing
0.000
-0.010
-0.020
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
Housing Boom and Human Capital Attainment
Propensity to Have At Least One Year of College (Age: 18-29)
0.65
0.60
0.55
0.50
0.45
0.40
1979
1984
1989
1994
1999
2004
2009
Did Housing Boom Delay College Attendance?

Use same local labor market design to answer this question.

The answer is YES – in both survey and administrative data

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.
o
Effect only present among “lost generation”; those who were young in
the early 2000s in boom markets.

For this “lost generation”, the effect was persistent through 2013.

Estimates can explain about 40% of the time series change.
Summary: Our Interpretation
Interpretation


Housing boom “masked” some of the labor market effects of declining
manufacturing during the early 2000s.
o
Cross-MSA masking (Detroit vs. Las Vegas)
o
Cross individual masking (Old hurt by manufacturing decline while
young lifted by housing boom)
o
Within individual masking (Displaced manufacturing workers are
more likely to be reemployed in a MSA that experienced a housing
boom).
Is the 2007 labor market the right benchmark to assess cyclical
fluctuations?
o
Our results suggest no
o
Large temporary housing boom lifted labor markets during early 2000s
and then brought them back to trend (particularly for low skilled).
Interpretation


We are predicting a period of a “medium run” decline in employment
to population decline (relative to pre-recession period)
o
Some displaced middle age and older workers in manufacturing
decline MSAs have taken up disability (Sloane, 2014).
o
Younger workers will slowly adjust to new labor market conditions
(process was delayed because of housing boom).
Is this transition from manufacturing (routine) jobs to non-routine
services different from the transition from agriculture to
manufacturing?
o
We think so. We are working on estimating the transition rate across
sectors for different types of workers.
Policy Thoughts

Temporary policy stimulus (either monetary or fiscal) will:
o
Only have temporary effects on labor market outcomes
o
Potentially slow down the human capital accumulation process

For example, another temporary housing boom could temporarily
improve labor markets and again deter schooling choices.

How do we train workers displaced by manufacturing (routing jobs) to
move to non-routine services?.
O
O
o
Are those workers willing/able to work at service job wages?
Will those policies only work for younger workers – or can they lift
the employment propensities of older workers.
Not likely something influenced by Fed policy.