Transcript Leisure Inequality Mark Aguiar and Erik Hurst September 2007

Topic 5:
Regional Labor Market Dynamics
and Housing Markets
Part A:
Housing Data
U.S. Housing Data
•
Housing price movements unconditionally
Census data
Transaction/deed data (provided by government agencies or available
via public records)
Household data (PSID, Survey of Consumer Finances, etc.)
Mortgage data (appraised value of the home)
•
Repeat sales indices
OFHEO
Case-Shiller
Repeat Sales vs. Unconditional Data
•
House prices can increase either because the value of the land under the
home increases or because the value of the structure increases.
*
Is home more expensive because the underlying land is worth more
or because the home has a fancy kitchen.
•
Often want to know the value of the land separate from the value of the
structure.
•
New homes often are of higher quality than existing homes.
•
Repeat sales indices try to difference out “structure” fixed effects –
isolating the effect of changing land prices.
*
Assumes structure remains constant (hard to deal with home
improvements).
OFHEO/FHFA Repeat Sales Index
•
OFHEO – Office of Federal Housing Enterprise Oversight
FHFA – Federal Housing Finance Agency
Government agencies that oversee Fannie Mae and Freddie Mac
•
Uses the stated transaction price from Fannie and Freddie mortgages to
compute a repeat sales index. (The price is the actual transaction price
and comes directly from the mortgage document)
•
Includes all properties which are financed via a conventional mortgage
(single family homes, condos, town homes, etc.)
•
Excludes all properties financed with other types of mortgages (sub
prime, jumbos, etc.)
•
Nationally representative – creates separate indices for all 50 states and
over 150 metro areas.
Case Shiller Repeat Sales Index
•
Developed by Karl Case and Bob Shiller
•
Uses the transaction price from deed records (obtained from public
records)
•
Includes all properties regardless of type of financing (conventional, sub
primes, jumbos, etc.)
•
Includes only single family homes (excludes condos, town homes, etc.)
•
Limited geographic coverage – detailed coverage from only 30 metro
areas. Not nationally representative (no coverage at all from 13 states –
limited coverage from other states)
•
Tries to account for the home improvements when creating repeat sales
index (by down weighting properties that increase by a lot relative to
others within an area).
-5.00%
Jan-92
Aug-92
Mar-93
Oct-93
May-94
Dec-94
Jul-95
Feb-96
Sep-96
Apr-97
Nov-97
Jun-98
Jan-99
Aug-99
Mar-00
Oct-00
May-01
Dec-01
Jul-02
Feb-03
Sep-03
Apr-04
Nov-04
Jun-05
Jan-06
Aug-06
Mar-07
Oct-07
May-08
Dec-08
Jul-09
OFHEO vs. Case Shiller: National Index
20.00%
15.00%
10.00%
5.00%
0.00%
-10.00%
-15.00%
-20.00%
-25.00%
-30.00%
CS Composite 10
CS Composite 20
OFHEO
OFHEO vs. Case Shiller: L.A. Index
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
LA-CS
LA-OFHEO
0.15
OFHEO vs. Case Shiller: Denver Index
0.1
0.05
0
-0.05
-0.1
-0.15
Denver-CS
Denver-OFHEO
0.1
OFHEO vs. Case Shiller: Chicago Index
0.05
0
-0.05
-0.1
-0.15
-0.2
-0.25
Chicago-CS
Chicago-OFHEO
OFHEO vs. Case Shiller: New York Index
0.15
0.1
0.05
0
-0.05
-0.1
-0.15
NY-CS
NY-OFHEO
Conclusion: OFHEO vs. Case - Shiller
• Aggregate indices are very different but MSA indices are nearly identical.
• Does not appear to be the result of different coverage of properties included.
• I think the difference has to do with the geographic coverage.
• If using MSA variation, does not matter much what index is used.
• If calibrating aggregate macro models, I would use OFHEO data instead of
Case-Shiller – I think it is more representative of the U.S.
A Note on Census Data
•
To assess long run trends in house prices (at low frequencies), there is nothing
better than Census data.
•
Very detailed geographic data (national, state, metro area, zip code, census
tract).
•
Goes back at least to the 1940 Census.
•
Have very good details on the structure (age of structure, number of rooms,
etc.).
•
Can link to other Census data (income, demographics, etc.).
Part B:
Housing Cycles (Some Data)
Average Annual Real Price Growth By US State
State
AK
AL
AR
AZ
CA
CO
CT
DC
DE
FL
GA
HI
IA
ID
IL
IN
1980-2000
-0.001
0.000
-0.009
-0.002
0.012
0.012
0.012
0.010
0.011
-0.002
0.008
0.004
-0.001
-0.001
0.010
0.002
2000-2007 2000-10
0.041
0.021
0.024
0.012
0.023
0.006
0.061
0.008
0.066
0.021
0.012
0.002
0.044
0.018
0.081
0.045
0.053
0.022
0.068
0.016
0.019
-0.003
0.074
0.036
0.012
0.001
0.047
0.012
0.030
0.004
0.020
-0.010
State
MT
NC
ND
NE
NH
NJ
NM
NV
NY
OH
OK
OR
PA
RI
SC
SD
1980-2000
0.003
0.008
-0.010
-0.002
0.014
0.015
-0.002
-0.005
0.020
0.003
-0.019
0.009
0.008
0.017
0.007
0.002
2000-2007 2000-2010
0.049
0.024
0.022
0.004
0.033
0.018
0.007
-0.004
0.041
0.015
0.058
0.027
0.043
0.016
0.060
-0.006
0.051
0.024
-0.001
-0.013
0.019
0.007
0.051
0.016
0.042
0.018
0.059
0.027
0.025
0.014
0.025
0.010
15
Average
0.011
0.036
0.012
Typical “Country” Cycle (US – FHFA Data)
10.0%
U.S. Real House Price Appreciation: 1976Q1 – 2010Q2
8.0%
6.0%
4.0%
2.0%
-2.0%
1976
1977
1978
1979
1981
1982
1983
1984
1986
1987
1988
1989
1991
1992
1993
1994
1996
1997
1998
1999
2001
2002
2003
2004
2006
2007
2008
2009
0.0%
-4.0%
-6.0%
-8.0%
-10.0%
-12.0%
16
Typical “Local” Cycle: New York State
New York State: Real Housing Price Growth
0.200
0.150
0.100
0.050
0.000
-0.050
-0.100
-0.150
HPI-Growth-Real
17
Typical “Local” Cycle: California
0.250
California: Real Housing Price Growth
0.200
0.150
0.100
0.050
0.000
-0.050
-0.100
-0.150
HPI-Growth-Real
18
Housing Prices and Housing Cycles (Hurst and Guerrieri
(2009))
• Persistent housing price increases are ALWAYS followed by persistent
housing price declines
Some statistics about U.S. metropolitan areas 1980 – 2000
• 44 MSAs had price appreciations of at least 15% over 3 years during this
period.
• Average price increase over boom (consecutive periods of price increases):
55%
• Average price decline during bust (the following period of price declines):
30%
• Average length of bust: 26 quarters (i.e., 7 years)
• 40% of the price decline occurred in first 2 years of bust
19
1976
1977
1978
1979
1981
1982
1983
1984
1986
1987
1988
1989
1991
1992
1993
1994
1996
1997
1998
1999
2001
2002
2003
2004
2006
2007
2008
OFHEO House Price Index
Typical “Country” Cycle (US – OFHEO Data)
0.20
-0.10
U.S. Nominal House Price Appreciation: 1976 - 2008
0.15
0.10
0.05
0.00
-0.05
20
Typical “Country” Cycle (US – OFHEO Data)
0.12
U.S. Real House Price Appreciation: 1976 - 2008
0.09
0.06
0.03
0.00
-0.03
-0.06
-0.09
-0.12
21
Average Annual Real Price Growth By OECD Country
Country
1970-1999
2000-2006
Country
1970-1999
2000-2006
U.S.
Japan
Germany
France
Great Britain
Italy
Canada
Spain
Australia
0.012
0.010
0.001
0.010
0.022
0.012
0.013
0.019
0.015
0.055
-0.045
-0.029
0.075
0.068
0.051
0.060
0.081
0.065
Netherlands
Belgium
Sweden
Switzerland
Denmark
Norway
Finland
New Zealand
Ireland
0.023
0.019
-0.002
0.000
0.011
0.012
0.009
0.014
0.022
0.027
0.064
0.059
0.019
0.065
0.047
0.040
0.080
0.059
1970-1999
2000-2006
0.012
0.046
Average
22
Country Cycles – The U.S. is Not Alone
Real House Price Growth
UK: 1978 - 2006
0.250
0.200
0.150
0.100
0.050
0.000
-0.050
-0.100
-0.150
23
Country Cycles – The U.S. is Not Alone
Real House Price Growth
Italy: 1978 - 2006
0.250
0.200
0.150
0.100
0.050
0.000
-0.050
-0.100
-0.150
24
Country Cycles – The U.S. is Not Alone
Real House Price Growth
Japan: 1978 - 2006
0.120
0.100
0.080
0.060
0.040
0.020
0.000
-0.020
-0.040
-0.060
-0.080
25
Housing Cycles: Part 2
OECD Country Level Data (1970 - 2000)
Price Changes in Booms vs. Subsequent Busts
0
-0.1
y = -0.6185x + 0.0584
R² = 0.483
Size of Subsequent Bust
-0.2
-0.3
-0.4
-0.5
-0.6
-0.7
0
0.2
0.4
0.6
Size of Boom
0.8
26
1
Summary
• Long run house price appreciation runs from 0-2% real per year.
• Fact is consistent across time, countries, states, metro areas, etc.
• “Large” housing booms that occur over a relatively short period of time at
country, state, and metro area levels almost always lead to substantial
reversals.
• Questions:
-
Why do housing prices cycle?
-
What determines low frequency differences in house price
appreciation across locations.
27
Part 2:
Some Models of Spatial Equilibrium
Model Particulars (Baseline Model): The City
• City is populated by N identical individuals.
• City is represented by the real line such that each point on the line (i) is a
different location:
i  (  ,  )
• nt (i ) :
• ht ( i ) :

Measure of agents who live in i.
Size of the house chosen by agents living in i.
•

•
n t ( i ) ht ( i )  1

nt (i ) d i  N
(market clearing condition)
(maximum space in i is fixed and
normalized to 1)
29
Household Preferences
Static model:

m ax c ( i ) h ( i )

 > 0 an d  > 0
c t , ht , i
c (i )  R (i ) h (i )  Y
n o rm alize p rice o f co n su m p tio n to 1
A rb itrag e im p lies:
Pt ( i )  R ( i ) 
1
1 r
Pt  1 ( i )
Construction
A continuum of com petitive builders can alw ays build a unit of housing
at constant m arginal cost  .
P rofit m axim ization im plies builders w ill build a unit of housing anytim e:
Pt  
Demand Side of Economy


m ax c ( i ) h ( i )   [Y  c ( i )  R ( i ) h ( i )]

 c (i )
 1
h (i )


c (i ) h (i )


(F .O .C . w rt c)
  R (i )
(F .O .C . w rt h )
c (i )


 c (i ) h (i )
 1
 
c (i ) h (i )

h (i )
 h (i )
 c (i )


h (i )
 (Y  R ( i ) h ( i ))

1
R (i )
Housing and Consumption Demand Functions

 1 
h (i ) 
Y 

(   )  R ( i ) 
c (i ) 

(   )
Y
An Aside: Use of Cobb Douglas Preferences?
•
Implication of Cobb Douglas Preferences:



h  
 Y
   
 1 
 
 R 



Rh  
 Y






(ex p en d itu re o n h o u sin g )
Im p licatio n :
C o n stan t ex p en d itu re sh ar e o n h o u sin g
Im p licatio n :
H o u sin g ex p en d itu re in co m e elasticity = 1
ln (R h ) =  0   1 l n ( Y )  
E stim ated  1 sh o u ld b e 1
Use CEX To Estimate Housing Income Elasticity
•
Use individual level data from CEX to estimate “housing service” Engel
curves and to estimate “housing service” (pseudo) demand systems.
Sample:
NBER CEX files 1980 - 2003
Use extracts put together for “Deconstructing Lifecycle
Expenditure” and “Conspicuous Consumption and Race”
Restrict sample to 25 to 55 year olds
Estimate:
(1)
(2)
*
*
*
ln(ck) = α0 + α1 ln(tot. outlays) + β X + η
(Engle Curve)
sharek = δ0 + δ1 ln(tot. outlays) + γ X + λ P + ν (Demand)
Use Individual Level Data
Instrument total outlays with current income, education, and occupation.
Total outlays include spending on durables and nondurables.
35
Engel Curve Results (CEX)
Dependent Variable
log rent (renters)
log rent (owners)
log rent (all)
Coefficient
S.E.
0.93
0.84
0.94
0.014
0.001
0.007
* Note: Rent for owners is “self reported” rental value of home
Selection of renting/home ownership appears to be important
36
Engel Curve Results (CEX)
Dependent Variable
log rent (renters)
log rent (owners)
log rent (all)
Coefficient
S.E.
0.93
0.84
0.94
0.014
0.001
0.007
* Note: Rent for owners is “self reported” rental value of home
Selection of renting/home ownership appears to be important
Other Expenditure Categories
log entertainment (all)
log food (all)
log clothing (all)
1.61
0.64
1.24
0.013
0.005
0.010
X controls include year dummies and one year age dummies
37
Demand System Results (CEX)
Dependent Variable
rent share (renters, mean = 0.242)
rent share (owners, mean = 0.275)
rent share (all, mean = 0.263)
Coefficient
S.E.
-0.030
-0.050
-0.025
0.003
0.002
0.002
* Note: Rent share for owners is “self reported” rental value of home
Selection of renting/home ownership appears to be important
38
Demand System Results (CEX)
Dependent Variable
rent share (renters, mean = 0.242)
rent share (owners, mean = 0.275)
rent share (all, mean = 0.263)
Coefficient
S.E.
-0.030
-0.050
-0.025
0.003
0.002
0.002
* Note: Rent share for owners is “self reported” rental value of home
Selection of renting/home ownership appears to be important
Other Expenditure Categories
entertainment share (all, mean = 0.033) 0.012
food share (all, mean = 0.182)
-0.073
clothing share (all, mean = 0.062)
0.008
0.001
0.001
0.001
X controls include year dummies and one year age dummies
39
Spatial Equilibrium
Households have to be indifferent across locations:
C o n sid er tw o lo catio n s i an d %
i.
S p atial in d ifferen ce im p lies th at:




 c (%
i ) h (%
i)


c (i ) h (i )
 

 
 
Y



 Y
   
   
R (i )  R (%
i)

 1 


R
(
i
)


fo r all i an d %
i



 

 
 

Y


 Y
   
   

 1 


%
R
(
i
)



Equilibrium
R (i ) 
r
(1  r )
P (i )
H o u s in g D e m a n d C u rv e :

 1 r  1 

h (i ) = h = 


Y 



r
P



 
H o u s in g S u p p ly C u rv e :
P = 
Graphical Equilibrium
ln(P)
hD(Y)
ln(κ) =
ln(P*)
ln(h*)
ln(h)
Shock to Income
hD(Y1)
ln(P)
hD(Y)
ln(κ) =
ln(P*)
ln(h*)
ln(h*1) ln(h)
Shock to Income (with adjustment costs to supply)
hD(Y1)
ln(P)
hD(Y)
ln(κ) =
ln(P*)
ln(h*)
ln(h*1) ln(h)
Some Conclusions (Base Model)
•
If supply is perfectly elastic in the long run (land is available and
construction costs are fixed), then:
Prices will be fixed in the long run
Demand shocks will have no effect on prices in the long run.
Short run amplification of prices could be do to adjustment costs.
Model has “static” optimization. Similar results with dynamic
optimization (and expectations – with some caveats)
•
Notice – location – per se – is not important in this analysis. All locations
are the same.
Equilibrium with Supply Constraints
Suppose city (area broadly) is of fixed size (2*I). For illustration, lets index
the middle of the city as (0).
-I
0
I
Lets pick I such that all space is filled in the city with Y = Y and r = r.
2I = N (h(i)*)
  
1 r  1 
2I  N 
 
 Y  



r

 P 


 N   
1 r 
P 
Y


 
2
I





 r 

Comparative Statics
What happens to equilibrium prices when there is a housing demand shock (Y
increases or r falls).
Focus on income shock. Suppose Y increases from Y to Y1. What happens to
prices?
 N   
1 r 
P 


 Y  
 2I     
 r 
 N   1 r 
ln( P )  ln  

   ln(Y )

  2I      r  
With inelastic housing supply (I fixed), a 1% increase in income leads to a 1%
increase in prices (given Cobb Douglas preferences)
Shock to Income With Supply Constraints
ln(P1)
ln(κ) =
ln(P)
hD(Y1)
hD(Y)
ln(h)=ln(h1)
ln(h)
The percentage change in income = the percentage change in price
Intermediate Case: Upward Sloping Supply
ln(P1)
ln(κ) =
ln(P)
hD(Y1)
hD(Y)
ln(h)=ln(h1)
ln(h)
Cost of building in the city increases as “density” increases
Implication of Supply Constraints (base model)?
•
The correlation between income changes and house price changes should
be smaller (potentially zero) in places where density is low (N h(i)* < 2I).
•
The correlation between income changes and house price changes should
be higher (potentially one) in places where density is high.
•
Similar for any demand shocks (i.e., decline in real interest rates).
Question:
Can supply constraints explain the cross city differences
in prices?
Topel and Rosen (1988)
“Housing Investment in the United States” (JPE)
•
First paper to formally approach housing price dynamics.
•
Uses aggregate data
•
Finds that housing supply is relatively elastic in the long run
Long run elasticity is much higher than short run elasticity.
Long run was about “one year”
•
Implication:
Long run annual aggregate home price appreciation
for the U.S. is small.
Siaz (2010)
“On Local Housing Supply Elasticity (QJE 2010)
• Estimates housing supply elasticities by city.
• Uses a measure of “developable” land in the city.
• What makes land “undevelopable”?
Gradient
Coverage of water
• Differences across cities changes the potential supply responsiveness across
cities to a demand shock (some places are more supply elastic in the short
run).
Are Housing Markets Efficient?
• Evidence is mixed
• Thing to read:
“The Efficiency of the Market for Single-Family Homes” (Case and
Shiller, AER 1989)
“There is a profitable trading rule for persons who are free to time
the purchase of their homes. Still, overall, individual housing price
changes are not very forecastable.”
Subsequent papers find mixed evidence:
Transaction costs?
Can Supply Constraints Explain Cycles?
“Housing Dynamics” (working paper 2007) by Glaeser and Gyrouko
Calibrated spatial equilibrium model
Match data on construction (building permits) and housing prices using time
series and cross MSA variation.
Find that supply constraints cannot explain housing price cycles.
Their explanation:
Negatively serially correlated demand shocks.
What Could Be Missing From Simple Model?
• Add in reasons for agglomeration.
• Long literature looking at housing prices across areas with agglomeration.
• Most of these focus on “production” agglomerations.
• We will lay out one of the simplest models – Muth (1969), Alonzo (1964),
Mills (1967)
• Locations are no longer identical. There is a center business district in the
area where people work (indexed as point (0) for our analysis).
• Households who live (i) distance from center business district must pay
additional transportation cost of τi.
Same Model As Before – Except Add in Transport Costs
Static model:
m ax

c (i ) h (i )

 > 0 an d  > 0
c t , ht , i
c (i )  R (i ) h (i )  Y   i
S till n o su p p ly co n strain ts (u n lim ited a reas)
Demand Side of Economy


m ax c ( i ) h ( i )   [Y   i  c ( i )  R ( i ) h ( i )]

 c (i )
 1
h (i )


c (i ) h (i )


(F .O .C . w rt c)
  R (i )
(F .O .C . w rt h )
c (i )


 c (i ) h (i )
 1
 
c (i ) h (i )

h (i )
   h (i )   
h (i )
1
 

 
R (i )
   c ( i )    (Y   i  R ( i ) h ( i ))
Housing and Consumption Demand Functions

 1 
h (i ) 
(Y   i ) 

(   )
 R (i ) 
c (i ) 

(   )
(Y   i )
Spatial Equilibrium
Households have to be indifferent across locations:
C o n sid er tw o lo catio n s i an d %
i.
S p atial in d ifferen ce im p lies th at:

c (i ) h (i )
Y
  i



 c (%
i ) h (%
i)
 

 
 Y   %
i 
R (%
i )  R (i )

W h en i > %
i, R (i) < R ( %
i)
Equilibrium
E q u ilib riu m R esu lt:
A ll o ccu p p ied n eig h b o rh o o d s i w ill b e co n tain ed in [-I,I].
D efin e R (I) an d P (I) as th e ren t an d p ri ce, resp ectively,
at th e b o u n d ary o f th e city.
G iven arb itrag e, w e k n o w th at:
R (I) =
r
(1  r )

Y
Y
  i
 
 I

r
 
(1  r )


 R (i)
Complete Equilibrium: Size of City (Solve for I)

R em em b er:
h (i)n (i) = 1

an d
n (i ) d i  N
i  
 1
2  
h (i )
i0 
I

d i  N

 

h (i )  






1  r   1 
 r     Y   I

 

 


(Y   i )

Some Algebra (if my algebra is correct…)

I 
1
2  
  
 1  r   1 
i0
 
Y  I
      r    
 

 


N  
 (Y   i ) d i  2     
i0
I


 


(Y   i )
1  r   1 
 r     Y   I

 

1 
I    (Y )
 

 N 1  r   1 
 
 1
1





2  r   



 N 1  r   1 
 
 1





2  r   





d i  N




 

Prices By Distance (Initial Level of Y = Y0)
P
κ
0
I0
i
Linearized only for graphical illustration
Prices fall with distance. Prices in essentially all locations exceed marginal cost.
Suppose Y increases from Y0 to Y1
P
κ
0
I0
I1
i
Even when supply is completely elastic, prices can rise permanently with a
permanent demand shock.
From Glaeser (2007): Suburb House Prices and
Distance to Boston
From Glaeser (2007): Suburb Density and
Distance to Boston
From Glaeser (2007): Cross City Income vs. House
Prices
A Quick Review of Spatial Equilibrium Models
• Cross city differences?
Long run price differences across cities with no differential
supply constraints.
Strength of the center business district (size of τ) drives long
run price appreciations across city.
• Is it big enough?
• Fall in τ will lead to bigger cities (suburbs) and lower prices in
center city (i = 0).
Part C:
Gentrification and House Price Dynamics
(Some Within City Dynamics)
Endogenous Gentrification and
Housing Price Dynamics
September 2011
Veronica Guerrieri, Daniel Hartley
and Erik Hurst
70
Background
• NY Times (Jan 2010):
Harlem got more expensive and richer
during the last decade.
• Similar phenomenon occurred within many major cities:
o
New York during late 1980s and 1990s:
o
Chicago during the late 1980s and early 1990s (Lakeview) and during the
2000s (Hyde Park, Wicker Park, South Loop)
o
San Francisco during the 1980s and 1990s
Greenwich Village, Soho, Tribecca
• What is the relationship between gentrification and land price appreciation
within cites? Moreover, how do we interpret cross city differences in
housing price dynamics in light of the gentrification process.
71
Within City House Price Growth Appreciation
2000 – 2006
2000 – 2006
2000 – 2006
Midtown
Manhattan
All
NYC
Harlem
45%
130%
~80%
Lincoln
Park
Hyde
Park
All
Chicago
20%
95%
Zip
28277
Zips
28203-7
8%
40%
~40%
All
Charlotte
~8%
72
Within City House Price Growth Appreciation
Between MSA vs. Within MSA Variation in
House Price Appreciation
Mean
Between S.D.
Within S.D.
2000 – 2006
0.81
0.42
0.18 *
1990 – 1997
-0.07
0.21
0.17
• Data from Case Shiller Zip Code Data
• * Within city variation is 2-3 times larger for cities that experienced
non-trivial property price appreciation.
73
What We Do In This Paper
• Present and empirical evaluate a model of within city house price growth
heterogeneity during city wide housing price booms (and busts).
• Formalize the link between neighborhood gentrification and housing price
dynamics in response to city wide housing demand shocks.
• Key ingredient of our model:
o
Assume individual utility is increasing in the income of one’s
neighbors (e.g., a spatial neighborhood externality).
o
Such preferences have been empirically documented by:
Bayer et al. (2007) ; Rossi-Hansberg et al. (2010)
o
Neighborhood amenities are endogenous
74
Where Do the Preferences Come From
• Our preference structure is a catch all for many potential stories.
• As a result, we do not take a stand on what – in particular – people like
about “rich” neighborhoods.
-
Lower crime (dislike poor neighborhoods)
-
Quality and extent of public goods (like schools) – could be through
expenditures or peer effects.
-
Increasing returns to scale in the provision of local service
amenities (restaurants, entertainment options, etc.).
75
Mechanism for Within City Price Movements
• With the externality, any land occupied by rich people will be of higher
value than land occupied by non-rich people.
– Can explain the within city differences in prices such that rich
neighborhoods have higher land prices (Becker and Murphy (2003)).
• Anything that increases the demand for housing of rich people (i.e., an
influx of new rich people) increases the value of the land onto which they
move.
o
New/expanding rich will migrate to the poor neighborhoods that
directly border the existing rich neighborhoods (to maximize value of
the externality)
o
The poor will get priced out of these border neighborhoods.
o
We refer to this process as “endogenous” gentrification.
76
Document Empirical Support for the Model
• Use variation from Bartik-type shocks across cities (cities that get an
exogenous labor demand shock based on initial industry mix).
• For cities that get larger Bartik shocks:
1.
House prices in the city as a whole appreciate more.
2.
Poor neighborhoods that directly abut rich neighborhoods appreciate
the most (both relative to rich neighborhoods and poor neighborhoods
that are far from rich neighborhoods).
3.
Poor neighborhoods that directly abut rich neighborhoods show much
more signs of gentrification (income growth of residents) relative to
other poor neighborhoods.
4.
These patterns occur in the 1980s, 1990s, and 2000s.
77
Caveat 1: Other Stories For Within City Differences
1.
Commuting costs (production agglomeration)
o
o
Classic Urban Story: Muth (1967), Mills (1969), Alonzo (1962))
Recent Work: Van Nieuwerburgh and Weill (2009), Moretti (2009)).
People pay a cost to commute to jobs.
2.
Different fixed amenities
o
o
Classic Urban Story: Rosen (1979), Roback (1982)
Recent Work: Gyrouko et al. (2009)).
Fixed amenities include weather, beautiful vistas, ocean front property, etc.
Note:
The mechanism we highlight could still go through in the presence of
these other stories (even if neighborhood externality is zero).
Note:
We attempt to distinguish among potential mechanisms in our
empirical work.
78
Caveat 2: Booms vs. Busts
•
Our data on within city house prices only extends through 2008.
o
Do not have a lot of data on the recent bust.
o
Have some data on housing price busts during the 1990s (New York,
San Francisco, Boston).
o
Working on getting more recent data (particularly 2010 data – not a lot
of transactions in 2009).
Implication:
Most of our empirical work today will focus on within city
house price dynamics during city-wide housing booms.
79
Why We Care?
•
Understand the nature of housing price movements within and across
cities.
•
Welfare implications of local demand shocks (e.g., Moretti 2010)
•
Think about gentrification more broadly.
80
Organization of the Talk
1.
Some background data on within city house price movements
2.
Introduce dynamic model of spatial equilibrium with neighborhood
externalities.
o
3.
4.
Highlight the endogenous gentrification mechanism that arises during
city wide housing demand shocks.
Empirically Evaluate Model With Respect to House Prices
o
Descriptive relationship between border neighborhoods and house price
dynamics.
o
Use Bartik Variation
Empirically Evaluate Model with Respect to Gentrification
o
o
Descriptive relationship between border neighborhoods and gentrification
Use Bartik Variation
81
Part 1:
Background Facts
82
Main Data Sources
• We utilize three data sources for within city house prices:
– Case Shiller Zip Code Level Price Index: Repeat sales index
– Zillow Zip Code Level Price Index: Hedonic price index
– Census Median Neighborhood Price: Computed by us (simple
hedonics).
• All the data have different plusses and minuses.
• Good news: Results are remarkably robust across the data sets.
83
Case-Shiller Data
• Zip code level price indices (quarterly) for roughly 30 cities.
• Repeat sale price index (get deed records and compute constant quality
price indices within the zip code).
• Not publically available (provided to us by Fiserv – up through 2008)
• Data extends back to the late 1980s/early 1990s for most cities.
• Focuses exclusively on single family homes
• Does not cover all zip codes within the city
• Tries to account for remodeling/renovations
o
Down-weights outliers in price movements, excludes houses held for
less than 6 months, and down-weights properties that were held for a
long time).
84
Zillow Data
• Zip code level price indices (monthly) for most zip codes in
metropolitan areas.
• Uses same underlying deed records as Case Shiller.
• Data extends back only to about 2000.
• Uses hedonics to value characteristics from recent transactions then takes
median vales of all units in the zip code.
• Gets control variables (characteristics) from a variety of places (assessor
records, MLS, etc.)
• Has bigger samples than Case Shiller (does not rely on repeat sales).
• Identifies zip codes with not enough transactions to make a reliable
index.
85
Census Data
• Median of reported home value for either zip code or census tract (finer
geography).
• Available for 1980, 1990 and 2000.
• Self reported from owner-occupiers.
• Adjust for simple hedonics (based on neighborhood housing characteristics)
• Create measures at the zip code AND census tract level
• Has bigger sample than Case Shiller and Zillow.
• When we use it, we weight by number of owner occupied households.
86
Correlation Across Growth Rates of Price Indices
House Price Index Measure
Correlation
2000 – 2006: Case-Shiller Index vs. Zillow Index
(All Case-Shiller Zip Codes, # observations = 3,404)
0.95
2000 – 2006: Case-Shiller Index vs. Zillow Index
(All “Main City” Case Shiller Zip Codes, # observations = 472)
0.96
1990 – 2000: Case-Shiller Index vs. Census Median
(All Case-Shiller Zip Codes, # observations = 3,280)
0.78
1990 – 2000: Case-Shiller Index vs. Census Median
(All “Main City” Case Shiller Zip Codes, # observations = 496)
0.82
87
Regression of Case-Shiller Growth Rates on
Zillow or Census Growth Rates
2000-2006
Independent Var.
1990-2000
Zillow
Zillow
Census
Census
1.06
1.02
0.96
1.02
(0.01)
(0.02)
(0.03)
(0.06)
0.04
0.09
0.02
0.07
(0.01)
(0.01)
(0.01)
(0.03)
R-squared
0.92
0.92
0.66
0.71
Sample
MSA
Main City
MSA
Main City
Coefficient
Constant
88
Fact 1: Within City Dispersion
Between MSA
FHFA
CaseShiller
Time
Period
Cross Zip Code
Cross Tract
Within MSA or City
(Within City)
CaseCaseZillow Census Census Census
Shiller Shiller
Median Median Median
(30+
(CS
Tracts
(MSA) (City)
(City)
(City) Cities) Cities)
2000-2006
0.33
0.42
0.18
0.18
0.24
obs
384
20
1,602
472
472
1990-2000
0.17
0.21
0.16
0.17
-
obs
348
17
1,498
496
1980-1990
obs
-
0.15
0.33
0.54
496
9,684
16,161
0.31
0.24
0.44
158
4,640
8,729
89
Fact 1: Within City Dispersion
Between MSA
FHFA
CaseShiller
Time
Period
Cross Zip Code
Cross Tract
Within MSA or City
(Within City)
CaseCaseZillow Census Census Census
Shiller Shiller
Median Median Median
(30+
(CS
Tracts
(MSA) (City)
(City)
(City) Cities) Cities)
2000-2006
0.33
0.42
0.18
0.18
0.24
obs
384
20
1,602
472
472
1990-2000
0.17
0.21
0.16
0.17
-
obs
348
17
1,498
496
1980-1990
obs
-
0.15
0.33
0.54
496
9,684
16,161
0.31
0.24
0.44
158
4,640
8,729
90
Fact 1: Within City Dispersion
Between MSA
FHFA
CaseShiller
Time
Period
Cross Zip Code
Cross Tract
Within MSA or City
(Within City)
CaseCaseZillow Census Census Census
Shiller Shiller
Median Median Median
(30+
(CS
Tracts
(MSA) (City)
(City)
(City) Cities) Cities)
2000-2006
0.33
0.42
0.18
0.18
0.24
obs
384
20
1,602
472
472
1990-2000
0.17
0.21
0.16
0.17
-
obs
348
17
1,498
496
1980-1990
obs
-
0.15
0.33
0.54
496
9,684
16,161
0.31
0.24
0.44
158
4,640
8,729
91
Fact 2: Some of the Dispersion is Systematic
Chicago Main City “Community Areas”: 2000-2006
92
Fact 2: “Poor” Neighborhoods Appreciate More
New York Metro Area Zip Codes: 2000-2006
93
Fact 2: “Poor” Neighborhoods Appreciate More
Boston, L.A., San Francisco, and Washington: β: -0.22 to -0.49
94
Fact 2: Patterns are Robust Over Time/Space
Top Quartile
Initial House Price
Bottom Quartile
Initial House Price
Washington, D.C.
1.29
1.61
L.A.
1.21
1.76
San Francisco
0.35
0.61
Portland
0.41
0.69
Denver
0.51
0.89
New York City
0.33
1.06
Boston
0.65
0.84
MSA/Time Period
2000-2006 (Case Shiller)
1990-1997 (Case Shiller)
1984-1989 (Furman/Case Shiller)
95
Fact 2: “Poor” Neighborhoods Appreciate More
• Estimate:
• Run this during the 80s, 90s, and 00-06 periods.
• Do this for Case-Shiller, Census, and Zillow indices.
• ω1 is always negative and statistically different from zero.
• ω1 = -0.23 (standard error 0.05) for Case Shiller data during 2000-2006.
• ω1 is more negative the larger the city wide house price boom.
96
Fact 3: More Variability Among Poor Neighborhoods
•
Variability among neighborhoods in bottom quartile of 2000 house price
distribution was 0.29.
•
Variability among neighborhoods in bottom quartile of 2000 house price
distribution was 0.05.
97
Fact 3: More Variability Among Poor Neighborhoods
•
Variability difference increases with the size of the city wide property price boom.
98
Summary
• Tremendous amount of within city house price variation.
• Variation across zip codes/census tracts within a city is of similar magnitude
as the well studied cross city variation.
• Poor neighborhoods within a city appreciate most during city wide housing
booms. The more the city as a whole appreciates, the bigger the differential
between rich and poor neighborhoods within a city.
• There is much greater variation in house price appreciation rates among poor
neighborhoods. The variation increases with the size of the city wide
housing boom.
• All the facts are interesting and should be explored more fully in subsequent
theoretical and empirical work.
• Our subsequent theory and empirical work only focuses on trying to
explain the variation among the poor neighborhoods.
99
Part 2:
A Spatial Equilibrium Model of Within City
Gentrification and House Price Dynamics
100
Model Particulars (Baseline Model): The City
• City is populated by two types (indexed by s) of infinitely lived households;
NR and NP (rich and poor, respectively)
• City is represented by the real line such that each point on the line (i) is a
different location:
i  (  ,  )
• n ts ( i ) :
• hts ( i ) :
•



Measure of agents of type s who live in i.
Size of the house chosen by agents of type s living in i.
nt (i ) d i  N
s
s
(market clearing condition)
• n t ( i ) ht ( i )  n t ( i ) ht ( i )  1
R
R
P
P
(maximum space in i is fixed and
normalized to 1)
101
Model Particulars: Preferences

• Utility

m ax c h ( A  H t ( i ))

s
c , h ,i
 ,  ,  0

i
• Neighborhood Externality:
H (i ) 
• Preference Assumptions:

• Static budget constraint:
c (i ) + h (i ) R (i ) £ y
• Income (Exogenous)
R
i
R
R
h ( j ) n ( j ) dj
  ; can assum e ( 
P
s
s
s
R
 )
P
s
y  y  y  y
R
P
102
Comments on the Model
1.
No distinction between poor people and farm land (nothing interesting
about the poor except they are not rich).
-
Could include a negative externality from living near the poor. We have not
done that at this time.
2.
No bounds on the city (or mechanisms to bound the city – like transport
costs or location specific amenities).
3.
Only two types of income (rich and poor).
4.
Only one dimension of preference externality.
5.
Neighborhoods are of fixed size (do not allow building up).
6.
Externality is over space occupied by rich people (not amount of rich
people).
7.
No uncertainty (more on this later if time allows).
103
Housing Supply/Intermediaries
• Representative builder who builds poor houses in any location at marginal
cost CP and who builds rich houses in any location at marginal cost CR.
•
s
pt (i )
the price (per unit) of housing in location i at time t for household type
s.
• Assume houses are owned by risk-neutral intermediaries
• Absence of arbitrage implies:
104
Equilibrium
An equilibrium is a sequence of:
•
rent and price schedules:
•
allocations:
•
feasible locations:
Such that:
1.
2.
3.
4.
households maximize utility
representative firm maximizes profits
intermediaries maximize profits
markets clear
105
Full Segregation
•
Many equilibria (with full segregation)
•
Focus on one of the equilibria.
•
Rich live together at center of line (normalize i = 0 to be center of line).
•
Symmetric city – restrict attention to positive side of line.
•
Implications in other equilibria similar (as long as centers are far enough
106
from each other).
Model Predictions:
Neighborhoods, Externality, and Prices
107
Response to Increasing N keeping NR/NP constant
(similar to lower r or increasing yR)
108
Response to Increasing N keeping NR/NP constant
(similar to lower r or increasing yR)
Poor Neighborhoods
That Appreciate Substantially
109
Response to Increasing N keeping NR/NP constant
(similar to lower r or increasing yR)
Poor Neighborhoods
That Do Not Appreciate
110
Implications of Model: Within City
• Lower priced neighborhoods are more price responsive than high priced
neighborhoods to positive demand shocks.
• It is the low priced neighborhoods in close proximity to the high priced
neighborhoods that appreciate the most when there is a positive
housing demand shock.
• The low priced neighborhoods in close proximity to the high priced
neighborhoods that appreciate the most do so because they gentrify
(rich people move into those neighborhoods).
111
Implications of Model: Cross City
• Mechanism is relevant in that it can also explain differences in price
appreciation across cities.
• Higher income growth (NR increase) within a city leads to higher house
price appreciation (P) at the city level, all else equal.
-
Define P as the weighted average of prices within the city.
The city P just reflects the aggregation of the neighborhood p’s.
• The stronger the externality (δ), the larger the price growth at the city level
(P), all else equal.
112
Part 3: House Price Dynamics Among Poor
Neighborhoods
113
Part 3a: Some Descriptive Results
114
Proximity to Rich and House Price Changes
• Estimate the following:
•
is distance for neighborhood i in city j to the nearest “rich
neighborhood” (those in the top quarter of the period t house price
distribution).
i, j
D ist t
• X controls include initial house prices, initial income, initial fraction
African-American, and initial fraction Hispanic.
• Z variables include controls for other prominent stories – average
commuting times and distance to city’s center business district, distance
to lake (if applicable), distance to ocean (if applicable), distance to river (if
applicable), and initial age of housing stock.
• When dependent variable is Census Median Home Value Growth controls
for changes in the area housing stock are included.
115
Proximity to Rich and House Price Changes
• Estimate the following:
• Estimate this for different periods (t, t+k = 2000 – 2006, 1990-2000, or
1980 – 1990).
• Estimate this for different measures of house prices growth (Case-Shiller,
Zillow, or Census).
• Focus on only variation among poor neighborhoods (i.e., we restrict the
sample to only include those neighborhoods that had period t median house
prices within the bottom half of the city).
• Focus only on variation within the main city (not the whole MSA).
116
Distance to Rich and House Price Growth
117
Distance to Rich and House Price Growth
118
Distance to Rich and House Price Growth
119
Part 3b: Within City House Price Variation
in Response to Exogenous Demand Shock
120
What We Do
• “Shock” the income of a given MSA.
• Look at spatial pattern of house price increases.
• What is the shock to income in MSA i between t and t+k?
Bartik-type instrument: Predicted change in income (between t and
t+k) within the MSA based on the MSA’s industry mix in t.
Use census IPUMS data between 1980 and 1990, compute the average real
growth in household income by 2 digit industry.
Impute predicted income growth for each MSA between 1980 and 1990 by
multiplying the employment mix (by industry) of the MSA in 1980 and the
national growth rate of per-worker, industry earnings.
• Similar to Blanchard and Katz (1992).
121
Some Preliminary Statistics
(90 MSAs)
Large Variation Across Industries (1980 – 1990):
o
o
Security, Commodity Brokerage, and Investment Company:
Trucking Services:
59%
3%
Some Variation Across Cities:
o
Income Shock:
Median
Mean
Standard Deviation
5th Percentile
95th Percentile
0.20
0.19
0.015
0.17
0.22
Predictive Power of “Instrument”
Actual Income Growth on Predicted Income Growth:
F-Stat of “Instrument”:
1.95 (0.58)
~11.0
122
Bartik Instrument: House Price Growth
• Estimate the following:
• Broad Census Tract Sample:
o
o
o
1980 – 1990 sample as before (109 cities with at least 30 census tracts
in 1980).
Again, focus only on those census tracts in the bottom half of the
initial house price distribution (i.e., variation among poor
neighborhoods).
Controls are same as above.
• Coefficient of interest: β2 (interaction term)
123
Bartik Instrument: Distance to Rich and House
Price Growth
Key Independent Variable
Log Distance to Nearest Rich
* MSA Income Shock (β2)
Specification
(1)
Specification
(2)
-2.27
(0.53)
0 – 1 Miles to Nearest Rich
* 1 SD MSA Income Shock
0.061
(0.019)
1 – 3 Miles to Nearest Rich
* 1 SD MSA Income Shock
0.015
(0.009)
Observations
4,251
1 SD Bartik Shock * Δdist from 1 to 4 miles
Mean Dependent Variable
0.068
0.238
4,251
124
Bartik Instrument: Distance to Rich and House
Price Growth
Key Independent Variable
Log Distance to Nearest Rich
* MSA Income Shock (β2)
Specification
(1)
Specification
(2)
-2.27
(0.53)
0 – 1 Miles to Nearest Rich
* 1 SD MSA Income Shock
0.061
(0.019)
1 – 3 Miles to Nearest Rich
* 1 SD MSA Income Shock
0.015
(0.009)
Observations
4,251
1 SD Bartik Shock * Δdist from 1 to 4 miles
Mean Dependent Variable
0.068
0.238
4,251
125
Part 4: House Price Dynamics Among Poor
Neighborhoods and Gentrification
126
Part 4a: Some Descriptive Results
127
Proximity to Rich and Neighborhood Income Changes
• Focus on poorer neighborhoods (those in the bottom half of the house price
distribution within a city at the initial period).
• Estimate the following:
• Y is median household income.
• Same samples as used for house price growth.
• Can add all X and Z controls and results do not change.
128
Correlation of House Price and Income Growth
129
Another Descriptive Result
• Our model emphasizes a spatial dimension to gentrification.
• When faced with positive local demand shocks, poor neighborhoods
abutting the wealthy neighborhoods will start to convert from poor to rich.
• Question:
How many neighborhoods that are identified ex-post to
have gentrified were in close proximity to rich
neighborhoods?
• Empirical Approach:
-
Use all cities with at least 30 census tracts in initial year (same as
before).
-
~170 cities for 1990 – 2000; ~100 cities for 1980 – 1990
-
Look at all census tracts within the city that were in the bottom half
of the house price distribution in initial year.
-
Define “ex-post gentrification” as actual income growth among poor
neighborhoods of (1) at least 50% or (2) at least 25%
130
Gentrification and Proximity to Rich Neighborhoods
Ex-post Gentrification Measure (Income Growth)
50%
Time Period
25%
80-90
90-00
80-90
90-00
0.0 - 0.5 miles
0.069
(0.017)
0.057
(0.027)
0.082
(0.035)
0.109
(0.040)
0.5 - 1.0 miles
0.015
(0.007)
0.017
(0.009)
0.092
(0.020)
0.062
(0.020)
1.0 - 2.0 miles
0.006
(0.008)
0.018
(0.007)
0.076
(0.020)
0.029
(0.014)
2.0 - 3.0 miles
-0.005
(0.007)
0.002
(0.005)
0.024
(0.019)
0.018
(0.014)
Yes
Yes
Yes
Yes
Sample Size
4,251
7,981
4,251
7,981
Mean of Dependent Variable
0.110
0.059
0.302
0.197
Distance to Nearest Rich Neighborhood
City FE
131
Gentrification and Proximity to Rich Neighborhoods
Ex-post Gentrification Measure (Income Growth)
50%
Time Period
25%
80-90
90-00
80-90
90-00
0.0 - 0.5 miles
0.069
(0.017)
0.057
(0.027)
0.082
(0.035)
0.109
(0.040)
0.5 - 1.0 miles
0.015
(0.007)
0.017
(0.009)
0.092
(0.020)
0.062
(0.020)
1.0 - 2.0 miles
0.006
(0.008)
0.018
(0.007)
0.076
(0.020)
0.029
(0.014)
2.0 - 3.0 miles
-0.005
(0.007)
0.002
(0.005)
0.024
(0.019)
0.018
(0.014)
Yes
Yes
Yes
Yes
Sample Size
4,251
7,981
4,251
7,981
Mean of Dependent Variable
0.110
0.059
0.302
0.197
Distance to Nearest Rich Neighborhood
City FE
132
Part 4b: Within City Gentrification in
Response to Exogenous Demand Shock
133
“Bartik” Instrument: Income Growth
• Estimate the following:
o Same sample and specification as above (poor neighborhoods in all cities
with at least 30 census tracts in 1980; look at changes 1980 – 1990, etc.)
o Same Bartik shock and same controls.
o Measure of gentrification (G) takes one of the following:
-
Percent growth in neighborhood income
Percentage point change in poverty rate in neighborhoods
Percentage point change in fraction of population with bachelors
degree or higher.
134
“Bartik” Instrument: Distance to Rich and Income
Growth
Sample
Dependent Var.
1980-1990
109 Cities, 30 Tracts or more
Census Median
Change in
Change in
HH Income
Poverty Rate
Fraction with
Growth
BS Degree
Log Distance to Nearest Rich
* MSA Income Shock
-0.57
(0.27)
0.23
(0.12)
-0.24
(0.08)
Observations
4,251
4,251
4,251
1 SD Shock * Delta from 4 to 1 Miles
0.021
-0.0069
0.0072
Mean Dependent Variable
0.149
0.029
0.028
Response to 1 SD Shock (1 to 4 miles)
14%
-24%
26%
135
“Bartik” Instrument: Distance to Rich and Income
Growth
Sample
Dependent Var.
1980-1990
109 Cities, 30 Tracts or more
Census Median
Change in
Change in
HH Income
Poverty Rate
Fraction with
Growth
BS Degree
Log Distance to Nearest Rich
* MSA Income Shock
-0.57
(0.27)
0.23
(0.12)
-0.24
(0.08)
Observations
4,251
4,251
4,251
1 SD Shock * Delta from 4 to 1 Miles
0.021
-0.0069
0.0072
Mean Dependent Variable
0.149
0.029
0.028
Response to 1 SD Shock (1 to 4 miles)
14%
-24%
26%
136
“Bartik” Instrument: Distance to Rich and Income
Growth
Sample
Dependent Var.
1980-1990
109 Cities, 30 Tracts or more
Census Median
Change in
Change in
HH Income
Poverty Rate
Fraction with
Growth
BS Degree
Log Distance to Nearest Rich
* MSA Income Shock
-0.57
(0.27)
0.23
(0.12)
-0.24
(0.08)
Observations
4,251
4,251
4,251
1 SD Shock * Delta from 4 to 1 Miles
0.021
-0.0069
0.0072
Mean Dependent Variable
0.149
0.029
0.028
Response to 1 SD Shock (1 to 4 miles)
14%
-24%
26%
137
Other Thoughts
• Expectations and Gentrification
o
o
o
o
Bubble-like behavior
Busts are unfulfilled expectations of gentrifications
Some antidotal evidence in Chicago
Something we are working on
• Cross city variation?
• Subprime behavior or expectations?
• Rental prices vs. house prices?
138
Conclusions
• Endogenous gentrification is a first order explanation for within city
housing price dynamics during city wide housing price booms.
• Data supports the existence of neighborhood externalities
• Important for welfare calculations of local demand shocks (amenities are
endogenously changing).
• Use MSA industry shocks to see how neighborhood prices respond.
New facts about within city price movements:
1.
Poorer neighborhoods are much more price responsive than richer
neighborhoods during housing price booms and busts.
2.
The poor neighborhoods that appreciate most during booms are
spatially close to the rich neighborhoods.
Note:
Future research can exploit within city dynamics of housing prices
139
Part D:
Some Data on Recent Regional Variation in
Labor Markets
1977-01
1977-10
1978-07
1979-04
1980-01
1980-10
1981-07
1982-04
1983-01
1983-10
1984-07
1985-04
1986-01
1986-10
1987-07
1988-04
1989-01
1989-10
1990-07
1991-04
1992-01
1992-10
1993-07
1994-04
1995-01
1995-10
1996-07
1997-04
1998-01
1998-10
1999-07
2000-04
2001-01
2001-10
2002-07
2003-04
2004-01
2004-10
2005-07
2006-04
2007-01
2007-10
2008-07
2009-04
SD of Unemployment By State (Blue) and SD of
Unemployment Change (1-yr) By State (Red)
3.0
2.5
2.0
1.5
1.0
0.5
0.0
Variation By Recession: 1980-1983
Total Increase in Unemployment U.S. As Whole:
4.5%
Top 10 States Increase in Unemployment:
Average 6.4%
Illinois:
Ohio:
Michigan:
West Virginia
Wisconsin:
5.9%
7.2%
6.8%
8.7%
5.7%
Bottom 10 States Increase in Unemployment:
New York:
New Jersey:
Connecticut:
Maine:
Vermont:
1.8%
2.5%
1.7%
1.7%
2.5%
S. Carolina
Mississippi:
Alabama:
Tennessee
Arizona:
5.4%
5.5%
7.3%
5.8%
5.4%
Average 1.7%
Maryland:
Delaware:
Hawaii:
Alaska:
S. Dakota:
2.0%
0.8%
0.9%
1.6%
2.0%
142
Variation By Recession: 1990-1993
Total Increase in Unemployment U.S. As Whole:
2.2%
Top 10 States Increase in Unemployment:
Average 3.0%
CA:
NY:
RI:
FL:
NJ:
3.9%
3.7%
2.7%
2.6%
3.8%
MA:
WV:
PA:
OK
LA:
Bottom 10 States Increase in Unemployment:
MO:
MT:
KS:
NE:
IA:
0.3%
0.3%
0.3%
0.6%
0.0%
2.5%
2.8%
2.3%
2.0%
2.8%
Average 0.3%
UT:
AR:
MT:
SD:
ND:
0.6%
0.4%
0.5%
-0.2%
0.6%
143
Variation By Recession: 2000-2003
Total Increase in Unemployment U.S. As Whole:
1.7%
Top 10 States Increase in Unemployment:
Average 2.2%
CA:
NY:
TX:
OH:
NJ:
1.9%
2.1%
2.1%
2.0%
2.2%
MA:
OR:
CT:
OK
CO:
Bottom 10 States Increase in Unemployment:
MD:
LA:
NV:
NE:
ID:
0.8%
0.9%
0.5%
0.8%
0.8%
2.5%
2.0%
2.6%
2.0%
2.9%
Average 0.5%
HI:
RI:
MT:
SD:
ID:
-0.2%
0.9%
0.0%
0.3%
0.8%
144
Variation By Recession: 2007-2009 (Update)
Total Increase in Unemployment U.S. As Whole:
4.0%
Top 10 States Increase in Unemployment:
Average 5.5%
CA:
FL:
MI:
NC:
ID:
5.1%
4.8%
5.6%
5.8%
5.4%
SC:
AL:
OR:
5.7%
5.2%
6.7%
NV:
5.4%
Bottom 10 States Increase in Unemployment:
NE:
IA:
UT:
AR:
NM:
1.7%
1.3%
2.2%
1.6%
2.2%
Average 1.8%
WY:
AK:
MT:
SD:
ND:
1.6%
1.7%
2.2%
2.1%
0.9%
145
0
Nevada
California
Florida
Rhode Island
Kentucky
Michigan
Georgia
Mississippi
Oregon
South Carolina
Idaho
North Carolina
Arizona
Tennessee
District of Columbia
Missouri
West Virginia
Alabama
Colorado
New Jersey
Ohio
Washington
Connecticut
Illinois
Indiana
United States (national)[5]
New Mexico
Delaware
Massachusetts
New York
Texas
Pennsylvania
Louisiana
Arkansas
Utah
Alaska
Maine
Montana
Wisconsin
Maryland
Kansas
Minnesota
Oklahoma
Virginia
Hawaii
Wyoming
Iowa
Vermont
New Hampshire
South Dakota
Nebraska
North Dakota
Current Unemployment Rate (March 2011)
16
14
12
10
8
6
4
2
146
House Price Growth (00-06) and Change in Construction Labor
Share (00-06)
.05
Construction Share from ACS – Prime Age Men (Out of All Men in Labor Force)
(R-squared=0.44)
NV
AZ
.04
FL
HI
.03
MT
CA
ID
NM
AK
TX
.02
GA
NC
CO
LA
.01
WV
IN
0
OH
ARSC UT
AL
IA
OK
TN MO
WIND
KY
MS
KS
SD
IL
MN
MA ME
VT
NY
CT
NH
DE
WA
OR
VANJ
RI
MD
PA
WY
MINE
0
.2
.4
hp_growth_00_06
delta_const_real_share_00_06
Fitted values
.6
.8
delta_const_real_share_00_06
147
House Price Growth and Change in Construction Labor Share
Construction Share from ACS – Prime Age Men (Out of All Men in Labor Force)
(R-squared=0.44)
148
House Price Growth (006-06) and Change in Construction
Labor Share (01-06)
.03
Construction Share from BEA Employment Data (R-squared=0.52)
NV
.02
FL
IDMT
UT
AZ
WA
.01
NM
SD
WV
MS SCND
GA
OK
NC
AR
TN
MO
IL
KS COAL WI
LA
TX
IA
NE
0
IN
OH
VANJ
AK
ME
NH
MA
CT
CA
RI
MD
NY
KY
-.01
MI
DE
WYOR
VT
PA
MN
HI
0
.2
.4
hp_growth_00_06
delta_bea_house_share_01_06
Fitted values
.6
.8
delta_bea_house_share_01_06
149
House Price Growth (00-06) vs Total Employment
Growth (01-06)
Employment Data from BEA Employment Data (R-squared=0.11)
.2
NV
WY
ID
UT
AZ
MT
.1
NM
AK
HI FL
ND
0
TX
IN
WA
OR
SD
SC
AL
GA
IA
NC
TN
NE KY OK ARWV
WI
KS CO MO
MS
VA
MN
PA
LA
IL
NH
ME
CTVT
RI
DE
NY
MD
CA
NJ
MA
OH
-.1
MI
0
.2
.4
hp_growth_00_06
bea_totemp_gr_01_07
Fitted values
.6
.8
bea_totemp_gr_01_07
150
Change in Construction Share (01-06) vs. Total Employment
Growth (01-06)
All Data from BEA Employment Data (R-squared=0.46)
.2
NV
WY
.15
AZ
UT
NM
.1
AK
ID
MT
HI
FL
ND
.05
TX
-.05
0
KY
-.01
SDOR
AL
GA
NC
AR
OK
NE
MN TN
WI
CO
KSMO NH
PA
ME
LA
NY
CT
IN
IL
MA
OH
0
VA
MD
SC
IA WV RI
MS
NJ
WA
CA
DE
VT
.01
.02
delta_bea_house_share_01_06
bea_totemp_gr_01_07
Fitted values
.03
bea_totemp_gr_01_07
151
Change in Construction Share (01-06) vs Total Employment
Growth (08-10)
.05
All Data from BEA Employment Data (R-squared=0.45)
ND
0
AK
-.05
TX
LA
-.1
KY
SD
NYMA
NE
PA
OK
ME
AR
KS
NH
MN
CT
CO
WI MO
IL
IN TN
NC
OH
AL
GA
IA WV
VT
VA
WA
MDWY
NJ
NM
MS
DE
RI
CA
SC
OR
MT
UT
HI
ID
-.15
AZ
FL
NV
-.01
0
.01
.02
delta_bea_house_share_01_06
bea_totemp_gr_08_10
Fitted values
.03
bea_totemp_gr_08_10
152
Change in Construction Share (01-06) vs Population Growth
(00-06)
Construction Share Data from BEA Employment Data (R-squared=0.40)
.3
NV
AZ
FL
.2
UT
TX
ID
GA
CO
.1
NC
KY
MI
MN
NH
ME
IL CTTN
WI
KS
OK
NY
AR
IN
NE
MO
MA
PA
OH AL
VA OR
AK
MD
NJ
SC
WY
VT
RI
SD
IA
MS
CA
NMWA
DE
HI
MT
0
ND WV
LA
-.01
0
.01
.02
delta_bea_house_share_01_06
delta_total_num_00_06
Fitted values
.03
delta_total_num_00_06
153
Change in Construction Share (00-06) vs Population Growth
(00-06)
Construction Share Data from ACS (R-squared=0.60)
.3
NV
AZ
FL
.2
UT
GA TX
.1
WA
OR VA
DE
MD
MN
NJ
SC
NH
WY
IL
KS
WI TN
RI
OK IN
KY MO
AR
SD
IA
PA AL
MS OH
NE
MI
CO
NC
AK
NM
ID
CA
HI
MT
ME
CT
VT
NY
MA
WV
0
ND
LA
0
.01
.02
.03
delta_const_real_share_00_06
delta_total_num_00_06
Fitted values
.04
.05
delta_total_num_00_06
154
Change in Construction Share (00-06) vs Change in LFP (00-06)
.06
Construction Share Data from ACS (R-squared=0.50)
NV
.04
ND
.02
TX
UT
IL
WV
-.02
-.04
0
KS
NE IA
OK GA
CO
AR
MS
KY
AL
MO
NC SC
WI
MI
FL
NY
SD
IN
OH
WY
NM
CA
HI
AZ
TN
LA
AK
MD
VANJ
ID
RI
MT
PA
MN
NH
CTME
OR
MA
WA
VT
DE
0
.2
.4
hp_growth_00_06
delta_labor_force_share_00_06
Fitted values
.6
.8
delta_labor_force_share_00_06
155
Construction Labor Share (00-06 vs. 06-09)
.02
Construction Share from ACS Data (R-squared=0.45)
WY
NE
0
SD
KS
MI
OK
IA
PA
LA
AR
MD
VTTX
NY
AK
VA
NJ
KY
MOAL
WI TN
WV
ME
MN DENH
OH
NM
RI
IN
IL
CT
WA
GA
OR UT
NC
SC
MA
CO
HI
CA
ID
-.04
-.02
MS
ND
MT
FL
-.06
AZ
0
.01
.02
.03
delta_const_real_share_00_06
delta_const_real_share_06_09
Fitted values
.04
NV
.05
delta_const_real_share_06_09
156
Construction Labor Share (01-06 vs. 06-09)
Construction Share from BEA Employment Data (R-squared=0.64)
0
LA
-.02
KY
MI
NY AR
NE
OK
WY
SD
KS
PA
AL TN
ND
IANJ
OHWI
WV
ILINCT
AK
MO
MA
ME
RI
NH
VT
GA
MN
NC
MDOR NMWA
VA
CO
SC
CA
DE
MS
TX
HI
MT
UT
-.04
ID
FL
-.06
AZ
-.08
NV
-.01
0
.01
.02
delta_bea_house_share_01_06
delta_bea_house_share_06_09
Fitted values
.03
delta_bea_house_share_06_09
157
House Price Growth and Change in Construction Labor Share
Unemployment Rate: BLS Statistics
158
Change in Construction Labor Share (01-06) vs. Change in
Unemployment (06-10)
10
Construction Share from BEA Employment Data (R-squared=0.47)
NV
8
FL
CA
RI
6
ID
MI
4
KY
AL
GA
IL
IN
NC
MO
TN
OHCO CT
LA
PA
NY
TX
MA
AR
OK
ME
VA
MDWY
MS
IA
2
WI
MN
KS
OR
WV
NJ
SC
AZ
UT
DE
NM
WA
HI
MT
AK VT
SD
NE
NH
0
ND
-.01
0
.01
.02
delta_bea_house_share_01_06
delta_bls_unemp_06_10
Fitted values
.03
delta_bls_unemp_06_10
159
Change in Construction Labor Share (01-06) vs. Share of
Unemployment Coming From Construction (09)
.4
Unemployment Share from ACS (R-squared=0.34)
.35
HI
NV
MT
.3
NH
AK
VA
ME
WY
VT
SD
MD
SC
WV
.25
LA
.2
KY
MI
CT
CO MONE
MN
AL MANC
WI
GA
IL
TX
PA
AR
OK
TN
OH
KS
IN
NY
-.01
0
MS
IA
NJ OR
RI
WA
AZ
ID
FL
DE
UT
CA
NM
ND
.01
.02
delta_bea_house_share_01_06
share_unemp_const_real_09
Fitted values
.03
share_unemp_const_real_09
160
Change in Construction Labor Share (01-06) vs. Change in
Share of Unemployment Coming From Construction (Out of
Labor Force (06-09)
.03
Unemployment Share from ACS (R-squared=0.50)
NV
FL
.02
AZ
HI
NC
NH
GA
CT
CO
MA ME
IL MO
OH
WIINPATN
MN AR
LA
TX
KSNY NE
OK
WA
SC
AL
.01
MI
KY
ID
OR
CA
UT
MT
MD
VA
RI
MS
NJ
WV VT
DE
NM
SD WY
0
IA
AK
-.01
ND
-.01
0
.01
.02
delta_bea_house_share_01_06
delta_share_unemp_lab_06_09
Fitted values
.03
delta_share_unemp_lab_06_09
161
Change in Construction Labor Share (01-06) vs. Change in
Vacancies (07-10)
1
Vacancies From Conference Board’s HWOL Index (R-squared=0.31)
NH
MI
.5
OH
MO
OK
SD
MN
AR
KY
NE
AL
0
KS
IA
IN
TN
ND
WI PA
MD
RI
DE
NJ
IL
VA
SC
MS
VT
AK
WV
LANY
NC ME
MA
GA
WY
HI
MT
FL
TX
-.5
CT
WA
CO
OR
AZ
CA
ID
NM
-1
UT
NV
0
.01
.02
.03
delta_const_real_share_00_06
gr_vac_47_07_10
Fitted values
.04
.05
gr_vac_47_07_10
162
Some Quick Conclusions
1.
Large amount of regional variation during recent boom and bust
2.
Strong relationship between size of employment boom and subsequent
employment bust.
3.
The boom/bust relationship seems correlated with share of workforce in
housing. Does not identify causality!
4.
Much of the unemployed in these booming construction states are coming
from the construction sector.
5.
Is there a structural component to current unemployment?
163
Even More Data
Change in Construction Labor Share (79-82avg - 89) vs.
Change in Construction Share (89-92)
.02
UT
MT
OR
0
ID
OK
NE
CO
ND
WY
TX
KS
IA
WA
IN
NM
SDMS
MI WI
MO
LA
VA
AL
AZ
MNGAIL OH
AR
-.02
WV
KY
DE
TN
SC
NVNJ CA
MA
MD
-.04
FL
RI
NY
NC
PA
VT
-.06
ME
CT
NH
-.04
-.02
0
.02
delta_const_real_share_79avg_89
delta_const_real_share_89_92
.04
.06
Fitted values
165
.06
Change in Construction Labor Share (79-82avg - 89) vs.
Change in Unemployment Rate (89-93)
.04
CA
ME
RI
CT
NH
NJ
WV
.02
AZ
0
WY
UT
KS
IN
SD
AL
OH
MO NMGA
NV
WI
KY
MI
IA
NC
TN
AR
MS
LA
-.02
NE
DE
IL
FLVA
MN
ID
CO MT
OK
ND
VT
MD
NY
PA
SC
WA
OR
TX
MA
-.04
-.02
0
.02
delta_const_real_share_79avg_89
delta_unemp_rate_89_93
.04
.06
Fitted values
166
Change in Construction Labor Share (79-82avg - 89) vs.
Change in Share of Unemployed From Construction (91)
.35
ME
VT
ND
.3
RI
NV
MN
MD
CT
DE
WY
MA
VA
WI
.25
SD
NE
MT
ID
CO
.2
TX
IA
AZ
IN
SC
LA
OK
WA
MS
KS
MO
UT
NH
NY
NM
FL
NJ
WV AL TN
IL OH
GA
MI
KY
AR
CA
PA
NC
.15
OR
-.04
-.02
0
.02
delta_const_real_share_79avg_89
share_unemp_const_real_91
.04
.06
Fitted values
167
Part E:
Local Labor Market Adjustment
(Blanchard and Katz)
How Do Locations Respond to Local Shocks?
•
Continue our theme about thinking about regional economics (house prices
are one part of that).
•
The direct mechanism:
•
What implications do mobility have on the response of labor supply, wages,
and unemployment to local economic shocks?
•
Some work:
Mobility.
Blanchard/Katz “Regional Evolutions” (Brookings, 1992)
Topel “Local Labor Markets” (JPE, 1986)
Consider the Following Labor Market (Inelastic Labor Supply)
Labor Supply
W0  W
i
Labor Demand
i
N0
Consider the Following Labor Market (Inelastic Labor Supply)
Labor Supply
W0  W
i
W1
i
Labor Demand
i
N0
In short run, adjustment takes place on wages (labor supply is less elastic in short run)
Consider the Following Labor Market (Inelastic Labor Supply)
Labor Supply
W0  W
i
Labor Demand
i
N2
i
N0
In long run, adjustment takes place on N (labor supply is more elastic in long run)
What is the Mechanism?
•
In/out migration of workers…..
Blanchard/Katz Facts: Persistence of Growth Rates
Blanchard/Katz Facts: Cumulative Declines (relative to trend)
Blanchard/Katz Facts: : Cumulative Declines (relative to trend)
Blanchard/Katz Facts: : Cumulative Declines (relative to trend)
Blanchard/Katz Facts: : Cumulative Declines (relative to trend)
Blanchard/Katz Facts: : Cumulative Declines (relative to trend)
Blanchard/Katz Facts: Persistence of Unemployment Rate?
Blanchard/Katz Facts: Convergence of Wages
Blanchard/Katz Facts: Unemployment vs. Growth
Blanchard/Katz Facts: Growth vs. Wages
Blanchard/Katz Facts: Unemployment vs. Wages
Blanchard/Katz Facts: VAR of Negative Regional Shock
Blanchard/Katz Facts: VAR of Negative Regional Shock
Blanchard/Katz Facts: VAR of Negative Regional Shock
Blanchard/Katz Facts: VAR of Negative Regional Shock
Blanchard/Katz Facts: VAR of Negative Regional Shock
Blanchard/Katz Facts VAR of Negative Regional Shock
Blanchard/Katz Facts: VAR of Negative Regional Shock
Blanchard/Katz Facts: VAR of Negative Regional Shock
Conclusions of Blanchard/Katz
•
Regional Adjustments Take Place
•
In short run, response occurs on unemployment and wage margins.
•
In long run, it occurs on labor supply margin (via migration).
•
Spatial equilibrium model has to make individuals indifferent to move
across regions.
Part F:
Regional Convergence
(Barro and Sali-Martin)
Cross-State Convergence in Y/N (R-squared ~ 0.91)
Historical Trends in Convergence
2
Unadjusted 1940-1980
MS
1.5
AR
AL
1
KY
OK
NC
SC
GA
TNLA
KS
SD
ND
TX
NM
VA
NE
WV
MNCO WY
ID IA
AZFLWI
MO
UT
VT IN NHOR WA
ME MT
MD
PA
OH
MI
IL
RI
MA
NJCA
CT
NYNV
.5
DE
2000
4000
6000
8000
Per Capita Income 1940
Fitted values
10000
gr_ipc_40_80
12000
Cross-State Convergence in Y/N (R-squared ~ 0.88)
Historical Trends in Convergence
Unadjusted 1940-1960
1
MS
.8
ARAL
ND
SD
OK
KY
NC
GA NM KS
NE
TN
SC LA
TX
.6
UT
MOCO
IA
ID
WV VA AZMNWI
IN
WY
FL
NHOR
VT MT
WA
ME
OH
PA MI
IL
.4
MD
CA
NJ
NYNV
MA
CT
RI
.2
DE
2000
4000
6000
8000
Per Capita Income 1940
Fitted values
10000
gr_ipc_40_60
12000
Cross-State Convergence in Y/N (R-squared ~ 0.6)
Historical Trends in Convergence
.7
Unadjusted 1960-1980
MS
SC
AR
VA
.6
LA
AL KY
TN
NC
GA
WV
WY
TX
OK FL
.5
MN
.4
ID
VT
NM
ME
SD
ND
MD
IA
AZ KS
CO
NHWI
WA
OR
PA
MT
IN RI
MO
NE
OHMI MA
UT
NJ
CT
CA
IL
NV
.3
NY
DE
8000
10000
12000
14000
Per Capita Income 1960
Fitted values
16000
gr_ipc_60_80
18000
Cross-State Convergence
•
Why did cross-state convergence decline. (I am looking for someone to work
on this paper with me – there is low hanging fruit here – it is with Chang-Tai
Hseih).
•
Precursor: Why was there convergence?
Some Literature
o
Barro/Sala-i-Martin: Document Some Facts (Brookings, 1991)
o
Barro/Mankiw/Sala-i-Martin: Capital Mobility (AER, 1995)
Cross-State Convergence
More Literature
o
Caselli and Coleman (JPE, 2001): U.S. Structural Transformation
-
South had comparative advantage in producing unskilled labor intensive
goods (agriculture).
-
Declining education costs induce individuals to leave unskilled sector and
move into the skilled sector.
-
Ag wages increase AND composition shift – both increase income per
capital of south relative to the north.
Part G:
Effect of Chinese Imports on U.S. Cities
(Autor et al. 2011)
Read Autor, Dorn, and Hanson (2011)
o
Look at the rise of imports to China on U.S. regional activity (wages,
employment, population movements, transfer program response, etc.)
o
Use a “Bartik”-like instrument. Use the initial share of manufacturing
employment in specific industries in which China has grown.
-
Identify within manufacturing variation
o
Find it reduces local manufacturing employment
o
Local unemployment and non-participation rise.
o
Wage reductions in local non-manufacturing
o
Large effect on local transfers!