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Title: Topic 5: Regional Labor Market Dynamics and Housing Markets


1
Topic 5 Regional Labor Market Dynamics and
Housing Markets
2
Part A Housing Data
3
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

4
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).

5
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.

6
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).

7
OFHEO vs. Case Shiller National Index
8
OFHEO vs. Case Shiller L.A. Index
9
OFHEO vs. Case Shiller Denver Index
10
OFHEO vs. Case Shiller Chicago Index
11
OFHEO vs. Case Shiller New York Index
12
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.

13
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.).

14
Part B Housing Cycles (Some Data)
15
Average Annual Real Price Growth By US State
State 1980-2000 2000-2007 2000-10 State 1980-2000 2000-2007 2000-2010
AK -0.001 0.041 0.021 MT 0.003 0.049 0.024
AL 0.000 0.024 0.012 NC 0.008 0.022 0.004
AR -0.009 0.023 0.006 ND -0.010 0.033 0.018
AZ -0.002 0.061 0.008 NE -0.002 0.007 -0.004
CA 0.012 0.066 0.021 NH 0.014 0.041 0.015
CO 0.012 0.012 0.002 NJ 0.015 0.058 0.027
CT 0.012 0.044 0.018 NM -0.002 0.043 0.016
DC 0.010 0.081 0.045 NV -0.005 0.060 -0.006
DE 0.011 0.053 0.022 NY 0.020 0.051 0.024
FL -0.002 0.068 0.016 OH 0.003 -0.001 -0.013
GA 0.008 0.019 -0.003 OK -0.019 0.019 0.007
HI 0.004 0.074 0.036 OR 0.009 0.051 0.016
IA -0.001 0.012 0.001 PA 0.008 0.042 0.018
ID -0.001 0.047 0.012 RI 0.017 0.059 0.027
IL 0.010 0.030 0.004 SC 0.007 0.025 0.014
IN 0.002 0.020 -0.010 SD 0.002 0.025 0.010

Average 0.011 0.036 0.012
16
Typical Country Cycle (US FHFA Data)
U.S. Real House Price Appreciation 1976Q1
2010Q2
17
Typical Local Cycle New York State
18
Typical Local Cycle California
19
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

20
Typical Country Cycle (US OFHEO Data)
U.S. Nominal House Price Appreciation 1976 -
2008
21
Typical Country Cycle (US OFHEO Data)
U.S. Real House Price Appreciation 1976 - 2008
22
Average Annual Real Price Growth By OECD Country
Country 1970-1999 2000-2006 Country 1970-1999 2000-2006

U.S. 0.012 0.055 Netherlands 0.023 0.027
Japan 0.010 -0.045 Belgium 0.019 0.064
Germany 0.001 -0.029 Sweden -0.002 0.059
France 0.010 0.075 Switzerland 0.000 0.019
Great Britain 0.022 0.068 Denmark 0.011 0.065
Italy 0.012 0.051 Norway 0.012 0.047
Canada 0.013 0.060 Finland 0.009 0.040
Spain 0.019 0.081 New Zealand 0.014 0.080
Australia 0.015 0.065 Ireland 0.022 0.059

Average 1970-1999 0.012
2000-2006 0.046
23
Country Cycles The U.S. is Not Alone
24
Country Cycles The U.S. is Not Alone
25
Country Cycles The U.S. is Not Alone
26
(No Transcript)
27
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.

28
Part 2 Some Models of Spatial Equilibrium
29
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
  • Measure of agents who live in i.
  • Size of the house chosen by agents
    living in i.
  • (market clearing condition)
  • (maximum space in i is fixed and
    normalized to 1)

30
Household Preferences
  • Static model

31
Construction
32
Demand Side of Economy
33
Housing and Consumption Demand Functions
34
An Aside Use of Cobb Douglas Preferences?
  • Implication of Cobb Douglas Preferences

35
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) ln(ck) a0 a1 ln(tot. outlays) ß X ?
    (Engle Curve)
  • (2) sharek d0 d1 ln(tot. outlays) ? X ?
    P ? (Demand)
  • Use Individual Level Data
  • Instrument total outlays with current
    income, education, and occupation.
  • Total outlays include spending on durables and
    nondurables.

36
Engel Curve Results (CEX)
  • Dependent Variable Coefficient S.E.
  • log rent (renters) 0.93 0.014
  • log rent (owners) 0.84 0.001
  • log rent (all) 0.94 0.007
  • Note Rent for owners is self reported rental
    value of home
  • Selection of renting/home ownership appears to
    be important

37
Engel Curve Results (CEX)
  • Dependent Variable Coefficient S.E.
  • log rent (renters) 0.93 0.014
  • log rent (owners) 0.84 0.001
  • log rent (all) 0.94 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) 1.61 0.013
  • log food (all) 0.64 0.005
  • log clothing (all) 1.24
    0.010
  • X controls include year dummies and one year age
    dummies

38
Demand System Results (CEX)
  • Dependent Variable Coefficient S.E.
  • rent share (renters, mean 0.242) -0.030 0.003
  • rent share (owners, mean 0.275) -0.050 0.002
  • rent share (all, mean 0.263) -0.025 0.002
  • Note Rent share for owners is self reported
    rental value of home
  • Selection of renting/home ownership appears to
    be important

39
Demand System Results (CEX)
  • Dependent Variable Coefficient S.E.
  • rent share (renters, mean 0.242) -0.030 0.003
  • rent share (owners, mean 0.275) -0.050 0.002
  • rent share (all, mean 0.263) -0.025 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 0.001
  • food share (all, mean 0.182) -0.073 0.001
  • clothing share (all, mean 0.062) 0.008
    0.001
  • X controls include year dummies and one year age
    dummies

40
Spatial Equilibrium
Households have to be indifferent across
locations
41
Equilibrium
42
Graphical Equilibrium
hD(Y)
ln(P)
ln(?) ln(P)
ln(h)
ln(h)
43
Shock to Income
hD(Y1)
hD(Y)
ln(P)
ln(?) ln(P)
ln(h)
ln(h)
ln(h1)
44
Shock to Income (with adjustment costs to supply)
hD(Y1)
hD(Y)
ln(P)
ln(?) ln(P)
ln(h)
ln(h)
ln(h1)
45
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.

46
Equilibrium with Supply Constraints
  • Suppose city (area broadly) is of fixed size
    (2I). 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))

47
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?
  • With inelastic housing supply (I fixed), a 1
    increase in income leads to a 1 increase in
    prices (given Cobb Douglas preferences)

48
Shock to Income With Supply Constraints
ln(P1)
ln(?) ln(P)
hD(Y1)
hD(Y)
ln(h)
ln(h)ln(h1)
The percentage change in income the percentage
change in price
49
Intermediate Case Upward Sloping Supply
ln(P1)
ln(?) ln(P)
hD(Y1)
hD(Y)
ln(h)
ln(h)ln(h1)
  • Cost of building in the city increases as
    density increases

50
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) lt
    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?

51
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.

52
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).

53
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?

54
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.

55
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 ti.

56
Same Model As Before Except Add in Transport
Costs
  • Static model

57
Demand Side of Economy
58
Housing and Consumption Demand Functions
59
Spatial Equilibrium
Households have to be indifferent across
locations
60
Equilibrium
61
Complete Equilibrium Size of City (Solve for I)
62
Some Algebra (if my algebra is correct)
63
Prices By Distance (Initial Level of Y Y0)
P ? 0
I0 i Linearized only for graphical
illustration Prices fall with distance. Prices
in essentially all locations exceed marginal
cost.
64
Suppose Y increases from Y0 to Y1
P ? 0 I0
I1 i Even when supply is completely
elastic, prices can rise permanently with a
permanent demand shock.
65
From Glaeser (2007) Suburb House Prices and
Distance to Boston
66
From Glaeser (2007) Suburb Density and
Distance to Boston
67
From Glaeser (2007) Cross City Income vs. House
Prices
68
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 t) drives long run price appreciations across
    city.
  • Is it big enough?
  • Fall in t will lead to bigger cities (suburbs)
    and lower prices in center city (i 0).

69
Part C Gentrification and House Price
Dynamics (Some Within City Dynamics)
70
Endogenous Gentrification and Housing Price
Dynamics September 2011 Veronica Guerrieri,
Daniel Hartley and Erik Hurst
71
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
    Greenwich Village, Soho, Tribecca
  • 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
  • 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.

72
Within City House Price Growth Appreciation
  • Midtown All
  • Manhattan Harlem NYC
  • 2000 2006 45 130 80
  • Lincoln Hyde All
  • Park Park Chicago
  • 2000 2006 20 95
    40
  • Zip Zips All
  • 28277 28203-7
    Charlotte
  • 2000 2006 8 40 8

73
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.

74
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 ones 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

75
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.).

76
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.

77
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.

78
Caveat 1 Other Stories For Within City
Differences
  • 1. Commuting costs (production agglomeration)
  • o Classic Urban Story Muth (1967), Mills
    (1969), Alonzo (1962))
  • o Recent Work Van Nieuwerburgh and Weill
    (2009), Moretti (2009)).
  • People pay a cost to commute to jobs.
  • Different fixed amenities
  • o Classic Urban Story Rosen (1979), Roback
    (1982)
  • o 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.

79
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.

80
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.

81
Organization of the Talk
  • Some background data on within city house price
    movements
  • Introduce dynamic model of spatial equilibrium
    with neighborhood externalities.
  • o Highlight the endogenous gentrification
    mechanism that arises during city wide housing
    demand shocks.
  • 3. Empirically Evaluate Model With Respect to
    House Prices
  • o Descriptive relationship between border
    neighborhoods and house price dynamics.
  • o Use Bartik Variation
  • 4. Empirically Evaluate Model with Respect to
    Gentrification
  • o Descriptive relationship between border
    neighborhoods and gentrification
  • o Use Bartik Variation

82
Part 1 Background Facts
83
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.

84
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).

85
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.

86
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.

87
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

88
Regression of Case-Shiller Growth Rates on
Zillow or Census Growth Rates
2000-2006 2000-2006 1990-2000 1990-2000
Independent Var. Zillow Zillow Census Census

Coefficient 1.06 1.02 0.96 1.02
(0.01) (0.02) (0.03) (0.06)

Constant 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

89
Fact 1 Within City Dispersion
Between MSA Between MSA Cross Zip Code Within MSA or City Cross Zip Code Within MSA or City Cross Zip Code Within MSA or City Cross Zip Code Within MSA or City Cross Tract (Within City) Cross Tract (Within City)
Time Period FHFA Case-Shiller Case-Shiller (MSA) Case-Shiller (City) Zillow (City) Census Median (City) Census Median (CS Cities) Census Median (30 Tracts 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 - 0.15 0.33 0.54
obs 348 17 1,498 496 496 9,684 16,161

1980-1990 0.31 0.24 0.44
obs 158 4,640 8,729
90
Fact 1 Within City Dispersion
Between MSA Between MSA Cross Zip Code Within MSA or City Cross Zip Code Within MSA or City Cross Zip Code Within MSA or City Cross Zip Code Within MSA or City Cross Tract (Within City) Cross Tract (Within City)
Time Period FHFA Case-Shiller Case-Shiller (MSA) Case-Shiller (City) Zillow (City) Census Median (City) Census Median (CS Cities) Census Median (30 Tracts 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 - 0.15 0.33 0.54
obs 348 17 1,498 496 496 9,684 16,161

1980-1990 0.31 0.24 0.44
obs 158 4,640 8,729
91
Fact 1 Within City Dispersion
Between MSA Between MSA Cross Zip Code Within MSA or City Cross Zip Code Within MSA or City Cross Zip Code Within MSA or City Cross Zip Code Within MSA or City Cross Tract (Within City) Cross Tract (Within City)
Time Period FHFA Case-Shiller Case-Shiller (MSA) Case-Shiller (City) Zillow (City) Census Median (City) Census Median (CS Cities) Census Median (30 Tracts 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 - 0.15 0.33 0.54
obs 348 17 1,498 496 496 9,684 16,161

1980-1990 0.31 0.24 0.44
obs 158 4,640 8,729
92
Fact 2 Some of the Dispersion is Systematic
Chicago Main City Community Areas 2000-2006
93
Fact 2 Poor Neighborhoods Appreciate More
New York Metro Area Zip Codes 2000-2006
94
Fact 2 Poor Neighborhoods Appreciate More
Boston, L.A., San Francisco, and Washington ß
-0.22 to -0.49
95
Fact 2 Patterns are Robust Over Time/Space
MSA/Time Period Top Quartile Initial House Price Bottom Quartile Initial House Price

2000-2006 (Case Shiller) 2000-2006 (Case Shiller) 2000-2006 (Case Shiller)
Washington, D.C. 1.29 1.61
L.A. 1.21 1.76
San Francisco 0.35 0.61

1990-1997 (Case Shiller) 1990-1997 (Case Shiller) 1990-1997 (Case Shiller)
Portland 0.41 0.69
Denver 0.51 0.89

1984-1989 (Furman/Case Shiller) 1984-1989 (Furman/Case Shiller) 1984-1989 (Furman/Case Shiller)
New York City 0.33 1.06
Boston 0.65 0.84
96
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.
  • ?1 is always negative and statistically different
    from zero.
  • ?1 -0.23 (standard error 0.05) for Case Shiller
    data during 2000-2006.
  • ?1 is more negative the larger the city wide
    house price boom.

97
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.

98
Fact 3 More Variability Among Poor Neighborhoods
  • Variability difference increases with the size of
    the city wide property price boom.

99
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.

100
Part 2 A Spatial Equilibrium Model of Within
City Gentrification and House Price Dynamics
101
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
  • Measure of agents of type s who live in
    i.
  • Size of the house chosen by agents of
    type s living in i.
  • (market clearing condition)
  • (maximum space in i is fixed and
    normalized to 1)

102
Model Particulars Preferences
  • Utility
  • Neighborhood Externality
  • Preference Assumptions
  • Static budget constraint
  • Income (Exogenous)

103
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.
  • No bounds on the city (or mechanisms to bound the
    city like transport costs or location specific
    amenities).
  • Only two types of income (rich and poor).
  • Only one dimension of preference externality.
  • Neighborhoods are of fixed size (do not allow
    building up).
  • Externality is over space occupied by rich people
    (not amount of rich people).
  • No uncertainty (more on this later if time
    allows).

104
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.
  • 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

105
Equilibrium
  • An equilibrium is a sequence of
  • rent and price schedules
  • allocations
  • feasible locations
  • Such that
  • households maximize utility
  • representative firm maximizes profits
  • intermediaries maximize profits
  • markets clear

106
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 from each other).

107
Model Predictions Neighborhoods, Externality,
and Prices
108
Response to Increasing N keeping NR/NP
constant (similar to lower r or increasing yR)
109
Response to Increasing N keeping NR/NP
constant (similar to lower r or increasing yR)
Poor Neighborhoods That Appreciate Substantially
110
Response to Increasing N keeping NR/NP
constant (similar to lower r or increasing yR)
Poor Neighborhoods That Do Not Appreciate
111
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).

112
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 ps.
  • The stronger the externality (d), the larger the
    price growth at the city level (P), all else
    equal.

113
Part 3 House Price Dynamics Among Poor
Neighborhoods
114
Part 3a Some Descriptive Results
115
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).
  • 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
    citys 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.

116
Proximity to Rich and House Price Changes
  • Estimate the following
  • Estimate this for different periods (t, tk
    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).

117
Distance to Rich and House Price Growth
118
Distance to Rich and House Price Growth
119
Distance to Rich and House Price Growth
120
Part 3b Within City House Price Variation in
Response to Exogenous Demand Shock
121
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 tk?
  • Bartik-type instrument Predicted change in
    income (between t and tk) within the MSA based
    on the MSAs 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).

122
Some Preliminary Statistics (90 MSAs)
  • Large Variation Across Industries (1980 1990)
  • o Security, Commodity Brokerage, and Investment
    Company 59
  • o Trucking Services 3
  • Some Variation Across Cities
  • o Income Shock Median 0.20
  • Mean 0.19
  • Standard Deviation 0.015
  • 5th Percentile 0.17
  • 95th Percentile 0.22
  • Predictive Power of Instrument
  • Actual Income Growth on Predicted Income
    Growth 1.95 (0.58)
  • F-Stat of Instrument 11.0

123
Bartik Instrument House Price Growth
  • Estimate the following
  • Broad Census Tract Sample
  • o 1980 1990 sample as before (109 cities with
    at least 30 census tracts in 1980).
  • o Again, focus only on those census tracts in
    the bottom half of the initial house price
    distribution (i.e., variation among poor
    neighborhoods).
  • o Controls are same as above.
  • Coefficient of interest ß2 (interaction term)

124
Bartik Instrument Distance to Rich and House
Price Growth
Key Independent Variable Specification (1) Specification (2)

Log Distance to Nearest Rich -2.27
MSA Income Shock (ß2) (0.53)

0 1 Miles to Nearest Rich 0.061
1 SD MSA Income Shock (0.019)

1 3 Miles to Nearest Rich 0.015
1 SD MSA Income Shock (0.009)

Observations 4,251 4,251

1 SD Bartik Shock ?dist from 1 to 4 miles 0.068
Mean Dependent Variable 0.238
125
Bartik Instrument Distance to Rich and House
Price Growth
Key Independent Variable Specification (1) Specification (2)

Log Distance to Nearest Rich -2.27
MSA Income Shock (ß2) (0.53)

0 1 Miles to Nearest Rich 0.061
1 SD MSA Income Shock (0.019)

1 3 Miles to Nearest Rich 0.015
1 SD MSA Income Shock (0.009)

Observations 4,251 4,251

1 SD Bartik Shock ?dist from 1 to 4 miles 0.068
Mean Dependent Variable 0.238
126
Part 4 House Price Dynamics Among Poor
Neighborhoods and Gentrification
127
Part 4a Some Descriptive Results
128
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.

129
Correlation of House Price and Income Growth
130
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

131
Gentrification and Proximity to Rich Neighborhoods
Ex-post Gentrification Measure (Income Growth) Ex-post Gentrification Measure (Income Growth) Ex-post Gentrification Measure (Income Growth) Ex-post Gentrification Measure (Income Growth)
50 50 25 25
Time Period 80-90 90-00 80-90 90-00
Distance to Nearest Rich Neighborhood
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)
City FE 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
132
Gentrification and Proximity to Rich Neighborhoods
Ex-post Gentrification Measure (Income Growth) Ex-post Gentrification Measure (Income Growth) Ex-post Gentrification Measure (Income Growth) Ex-post Gentrification Measure (Income Growth)
50 50 25 25
Time Period 80-90 90-00 80-90 90-00
Distance to Nearest Rich Neighborhood
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)
City FE 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
133
Part 4b Within City Gentrification in Response
to Exogenous Demand Shock
134
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.

135
Bartik Instrument Distance to Rich and Income
Growth
Sample 1980-1990 109 Cities, 30 Tracts or more 1980-1990 109 Cities, 30 Tracts or more 1980-1990 109 Cities, 30 Tracts or more
Dependent Var. Census Median HH Income Growth Change in Poverty Rate Change in Fraction with BS Degree

Log Distance to Nearest Rich -0.57 0.23 -0.24
MSA Income Shock (0.27) (0.12) (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 1980-1990 109 Cities, 30 Tracts or more 1980-1990 109 Cities, 30 Tracts or more 1980-1990 109 Cities, 30 Tracts or more
Dependent Var. Census Median HH Income Growth Change in Poverty Rate Change in Fraction with BS Degree

Log Distance to Nearest Rich -0.57 0.23 -0.24
MSA Income Shock (0.27) (0.12) (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
Bartik Instrument Distance to Rich and Income
Growth
Sample 1980-1990 109 Cities, 30 Tracts or more 1980-1990 109 Cities, 30 Tracts or more 1980-1990 109 Cities, 30 Tracts or more
Dependent Var. Census Median HH Income Growth Change in Poverty Rate Change in Fraction with BS Degree

Log Distance to Nearest Rich -0.57 0.23 -0.24
MSA Income Shock (0.27) (0.12) (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
138
Other Thoughts
  • Expectations and Gentrification
  • o Bubble-like behavior
  • o Busts are unfulfilled expectations of
    gentrifications
  • o Some antidotal evidence in Chicago
  • o Something we are working on
  • Cross city variation?
  • Subprime behavior or expectations?
  • Rental prices vs. house prices?

139
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

140
Part D Some Data on Recent Regional Variation
in Labor Markets
141
SD of Unemployment By State (Blue) and SD of
Unemployment Change (1-yr) By State (Red)
142
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 5.9 S. Carolina 5.4
  • Ohio 7.2 Mississippi 5.5
  • Michigan 6.8 Alabama 7.3
  • West Virginia 8.7 Tennessee 5.8
  • Wisconsin 5.7 Arizona 5.4
  • Bottom 10 States Increase in Unemployment Averag
    e 1.7
  • New York 1.8 Maryland 2.0
  • New Jersey 2.5 Delaware 0.8
  • Connecticut 1.7 Hawaii 0.9
  • Maine 1.7 Alaska 1.6
  • Vermont 2.5 S. Dakota 2.0

143
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 3.9 MA 2.5
  • NY 3.7 WV 2.8
  • RI 2.7 PA 2.3
  • FL 2.6 OK 2.0
  • NJ 3.8 LA 2.8
  • Bottom 10 States Increase in Unemployment Averag
    e 0.3
  • MO 0.3 UT 0.6
  • MT 0.3 AR 0.4
  • KS 0.3 MT 0.5
  • NE 0.6 SD -0.2
  • IA 0.0 ND 0.6

144
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 1.9 MA 2.5
  • NY 2.1 OR 2.0
  • TX 2.1 CT 2.6
  • OH 2.0 OK 2.0
  • NJ 2.2 CO 2.9
  • Bottom 10 States Increase in Unemployment Averag
    e 0.5
  • MD 0.8 HI -0.2
  • LA 0.9 RI 0.9
  • NV 0.5 MT 0.0
  • NE 0.8 SD 0.3
  • ID 0.8 ID 0.8

145
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 5.1 SC 5.7
  • FL 4.8 AL 5.2
  • MI 5.6 OR 6.7
  • NC 5.8
  • ID 5.4 NV 5.4
  • Bottom 10 States Increase in Unemployment Averag
    e 1.8
  • NE 1.7 WY 1.6
  • IA 1.3 AK 1.7
  • UT 2.2 MT 2.2
  • AR 1.6 SD 2.1
  • NM 2.2 ND 0.9

146
Current Unemployment Rate (March 2011)
147
House Price Growth (00-06) and Change in
Construction Labor Share (00-06)
  • Construction Share from ACS Prime Age Men (Out
    of All Men in Labor Force)
  • (R-squared0.44)

148
House Price Growth and Change in Construction
Labor Share
  • Construction Share from ACS Prime Age Men (Out
    of All Men in Labor Force)
  • (R-squared0.44)

149
House Price Growth (006-06) and Change in
Construction Labor Share (01-06)
  • Construction Share from BEA Employment Data
    (R-squared0.52)

150
House Price Growth (00-06) vs Total Employment
Growth (01-06)
  • Employment Data from BEA Employment Data
    (R-squared0.11)

151
Change in Construction Share (01-06) vs. Total
Employment Growth (01-06)
  • All Data from BEA Employment Data (R-squared0.46)

152
Change in Construction Share (01-06) vs Total
Employment Growth (08-10)
  • All Data from BEA Employment Data (R-squared0.45)

153
Change in Construction Share (01-06) vs
Population Growth (00-06)
  • Construction Share Data from BEA Employment Data
    (R-squared0.40)

154
Change in Construction Share (00-06) vs
Population Growth (00-06)
  • Construction Share Data from ACS (R-squared0.60)

155
Change in Construction Share (00-06) vs Change in
LFP (00-06)
  • Construction Share Data from ACS (R-squared0.50)

156
Construction Labor Share (00-06 vs. 06-09)
  • Construction Share from ACS Data (R-squared0.45)

157
Construction Labor Share (01-06 vs. 06-09)
  • Construction Share from BEA Employment Data
    (R-squared0.64)

158
House Price Growth and Change in Construction
Labor Share
  • Unemployment Rate BLS Statistics

159
Change in Construction Labor Share (01-06) vs.
Change in Unemployment (06-10)
  • Construction Share from BEA Employment Data
    (R-squared0.47)

160
Change in Construction Labor Share (01-06) vs.
Share of Unemployment Coming From Construction
(09)
  • Unemployment Share from ACS (R-squared0.34)

161
Change in Construction Labor Share (01-06) vs.
Change in Share of Unemployment Coming From
Construction (Out of Labor Force (06-09)
  • Unemployment Share from ACS (R-squared0.50)

162
Change in Construction Labor Share (01-06) vs.
Change in Vacancies (07-10)
  • Vacancies From Conference Boards HWOL Index
    (R-squared0.31)

163
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?

164
Even More Data
165
Change in Construction Labor Share (79-82avg -
89) vs. Change in Construction Share (89-92)
166
Change in Construction Labor Share (79-82avg -
89) vs. Change in Unemployment Rate (89-93)
167
Change in Construction Labor Share (79-82avg -
89) vs. Change in Share of Unemployed From
Construction (91)
168
Part E Local Labor Market Adjustment (Blanchard
and Katz)
169
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 Mobility.
  • What implications do mobility have on the
    response of labor supply, wages, and unemployment
    to local economic shocks?
  • Some work
  • Blanchard/Katz Regional Evolutions (Brookings,
    1992)
  • Topel Local Labor Markets (JPE, 1986)

170
Consider the Following Labor Market (Inelastic
Labor Supply)
Labor Supply
Labor Demand
171
Consider the Following Labor Market (Inelastic
Labor Supply)
Labor Supply
Labor Demand
In short run, adjustment takes place on wages
(labor supply is less elastic in short run)
172
Consider the Following Labor Market (Inelastic
Labor Supply)
Labor Supply
Labor Demand
In long run, adjustment takes place on N (labor
supply is more elastic in long run)
173
What is the Mechanism?
  • In/out migration of workers..

174
Blanchard/Katz Facts Persistence of Growth Rates
175
Blanchard/Katz Facts Cumulative Declines
(relative to trend)
176
Blanchard/Katz Facts Cumulative Declines
(relative to trend)
177
Blanchard/Katz Facts Cumulative Declines
(relative to trend)
178
Blanchard/Katz Facts Cumulative Declines
(relative to trend)
179
Blanchard/Katz Facts Cumulative Declines
(relative to trend)
180
Blanchard/Katz Facts Persistence of
Unemployment Rate?
181
Blanchard/Katz Facts Convergence of Wages
182
Blanchard/Katz Facts Unemployment vs. Growth
183
Blanchard/Katz Facts Growth vs. Wages
184
Blanchard/Katz Facts Unemployment vs. Wages
185
Blanchard/Katz Facts VAR of Negative Regional
Shock
186
Blanchard/Katz Facts VAR of Negative Regional
Shock
187
Blanchard/Katz Facts VAR of Negative Regional
Shock
188
Blanchard/Katz Facts VAR of Negative Regional
Shock
189
Blanchard/Katz Facts VAR of Negative Regional
Shock
190
Blanchard/Katz Facts VAR of Negative Regional
Shock
191
Blanchard/Katz Facts VAR of Negative Regional
Shock
192
Blanchard/Katz Facts VAR of Negative Regional
Shock
193
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.

194
Part F Regional Convergence (Barro and
Sali-Martin)
195
Cross-State Convergence in Y/N (R-squared 0.91)
196
Cross-State Convergence in Y/N (R-squared 0.88)
197
Cross-State Convergence in Y/N (R-squared 0.6)
198
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)

199
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.

200
Part G Effect of Chinese Imports on U.S.
Cities (Autor et al. 2011)
201
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!
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