Spatial Equilibrium Across CitiesUrban Economics: Week 3
Giacomo A. M. Ponzetto
CREI — UPF — Barcelona GSE
23rd and 24th January 2012
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 1 / 86
Static Spatial Equilibrium Theory
Three Simultaneous Equilibria
1 Individuals are optimally choosing which city to live inI There is a group of homogeneous individualsI Some of them are living in different cities⇒ Their utility level is the same in all those cities
2 Firms earn zero expected profitsI Free entry of firmsI Firm profits are equalized across cities
3 The construction sector operates optimallyI If a city is growing, house prices equal construction costsI If a city is declining, house prices ≤ construction costsI Free entry, zero profit for buildersI Construction profits are equalized across cities
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 2 / 86
Static Spatial Equilibrium Theory
Housing Prices and IncomeM
edia
n H
ousi
ng V
alue
Median Household Income38455 81052.5
77494.6
441903
Akron, OAlbanySAlbuquer Allentow
AtlantaAustinRBaltimor
Baton Ro
Bethesda
Birmingh
BostonQ
Bridgepo
Buffalo
Cambridg
Camden,Charlott
Chicago
CincinnaClevelan
Columbia
ColumbusDallasPDayton,Detroit
Edison,
Essex Co
Fort Lau
Fort WorFresno, Gary, IN
Grand RaGreensbo
Hartford
Honolulu
Houston
IndianapJacksonv Kansas CKnoxvill
Lake Cou
Las Vega
Little R
Los Ange
LouisvilMemphis,
MilwaukeMinneapo
Nashvill
NassauSNewarkU
New Have
New Orle
New York
Oakland
Oklahoma
OmahaCoOrlando,
OxnardT
PhiladelPhoenix
Pittsbur
Portland
ProvidenRaleigh
Richmond
Riversid
Rocheste
Sacramen
St. Loui
Salt Lak
San Anto
San Dieg
San Fran San Jose
Santa An
Sarasota
Scranton
Seattle
Springfi
Syracuse
Tacoma,
TampaSt Toledo,Tucson,
Tulsa, O
Virginia
WarrenF
Washingt
West Pal WilmingtWorceste
Youngsto
Med
ian
Hou
sing
Val
ue
Median Household Income38455 81052.5
77494.6
441903
Akron, OAlbanySAlbuquer Allentow
AtlantaAustinRBaltimor
Baton Ro
Bethesda
Birmingh
BostonQ
Bridgepo
Buffalo
Cambridg
Camden,Charlott
Chicago
CincinnaClevelan
Columbia
ColumbusDallasPDayton,Detroit
Edison,
Essex Co
Fort Lau
Fort WorFresno, Gary, IN
Grand RaGreensbo
Hartford
Honolulu
Houston
IndianapJacksonv Kansas CKnoxvill
Lake Cou
Las Vega
Little R
Los Ange
LouisvilMemphis,
MilwaukeMinneapo
Nashvill
NassauSNewarkU
New Have
New Orle
New York
Oakland
Oklahoma
OmahaCoOrlando,
OxnardT
PhiladelPhoenix
Pittsbur
Portland
ProvidenRaleigh
Richmond
Riversid
Rocheste
Sacramen
St. Loui
Salt Lak
San Anto
San Dieg
San Fran San Jose
Santa An
Sarasota
Scranton
Seattle
Springfi
Syracuse
Tacoma,
TampaSt Toledo,Tucson,
Tulsa, O
Virginia
WarrenF
Washingt
West Pal WilmingtWorceste
Youngsto
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 3 / 86
Static Spatial Equilibrium Theory
Housing Affordability
Starting point: Net income = Wages —Housing CostsI Every household consumes a unit of housing
Measuring affordability by Housing Costs / Wages is a mistakeI A common mistake in policy discussionsI Bias understating the affordability of high-income areas
House prices are strongly positively correlated with income levels
The regression coeffi cient is too low for constant net incomeI The user cost of housing is 7-10% of house valueI The coeffi cient is only 5.2
Measurement errorI The coeffi cient on the reverse regression is an appropriate .092I Income is measured without controlling for human capitalI Current income is a noisy measure of permanent incomeI Mean reversion predicts income declines in richer cities
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 4 / 86
Static Spatial Equilibrium Theory
Hedonics
Cities also differ in their amenities
Rosen (1979): spatial hedonics with varying incomes
Utility u (w − p, a) implies spatial equilibrium
∂p = ∂w +uauc
∂a
Consumption amenities are identified because they are associatedwith lower incomes, controlling for housing prices
Utility u (c, h, a) implies indirect utility v (w , p, a) such that
|vp | ∂p = vw ∂w + va∂a
and by Roy’s lemmah∂p = ∂w +
vavw
∂a
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 5 / 86
Static Spatial Equilibrium Theory
Individuals’Optimal Location Choice
Cobb-Douglas utility with housing share λ ≈ 0.3
u (c , h, a) = (1− λ) logc
1− λ+ λ log
hλ+ log a
Every location must yield the reservation utility u
v (w , p, a) = logw − λ log p + log a = u
Identifying the amenity value of any observable x :
∂ log a∂ log x
= λ∂ log p∂ log x
− ∂ logw∂ log x
Beware again of affordability statistics: p0.3/w , not p/w1 w1 = 40, 000 €, p1 = 10, 000 € ⇒ p1/w1 = 25%2 w2 = 60, 000 €, p2 = 25, 884 € ⇒ p2/w2 = 43%
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 6 / 86
Static Spatial Equilibrium Theory
Income and Housing PricesJournal of Economic Literature, Vol. XLVII (December 2009)992
prediction that d Y t i = − (VP/VY) d P t
i where the ratio VP/VY equals the demand for the nontraded good. High income levels are off-set by high prices.
Again, the Cobb–Douglas utility function is a natural way to empirically use the spatial equilibrium assumption. Under this assump-tion, utility can be written as: θ t
i G T β G N 1−β , which will equal θ t
i W t i ( P t
i )β−1 times a constant. The spatial equilibrium assumption requires this to equal Ut, the reservation utility within the country. This formulation suggests that log( W t
i ) = log(Ut) + (1 − β) log( P t i ) − log( θ t
i ). Figure 3 shows the relationship between the logarithm of median home prices and the
logarithm of median household income across space. The coefficient is 0.34, which is quite close to the average share of expendi-ture on housing, or 1 − β .
The final critical production sector con-cerns the making of nontraded goods, or homes. If we are interested in a truly static model, as in Roback (1982), it is natural to follow her assumption that nontraded goods are produced like traded goods with labor, traded capital, and nontraded capital. In this case, the production function might be H t
i F(K, L), where H t i refers to productiv-
ity in this sector. We will assume that the traded capital here is the same as the traded
10.5
10
9.5
9
Log
inco
me
per
capi
ta, 2
000
–
–
–
–
10.5 11 11.5 12 12.5 13
– – – – – –
Log median housing value, 2000
Figure 3. Housing Prices and Income
Notes: Units of observation are Metropolitan Statistical Areas under the 2006 definitions. Data are from the Census, as described in the Data Appendix.
The regression line is log income = 0.34 [0.02] × log value + 5.97 [0.22].R2 = 0.46 and N = 363.
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 7 / 86
Static Spatial Equilibrium Theory
Production Technology
Three factors of production:1 Labor n2 Tradable capital k3 Non-tradable capital z
Cobb-Douglas production function with constant returns to scale:
y = Anβkγzζ
Constant returns to scale: β+ γ+ ζ = 1
All firms have the same factor proportions
Firm size is indeterminate
Aggregating at the city level
wN = βy , pKK = γy and pZZ = ζy
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 8 / 86
Static Spatial Equilibrium Theory
Labor Demand
City-specific production amenities1 Productivity A2 Non-tradable capital Z
Economy-wide price of capital pK1 Small open economy: pK fixed on international markets; e.g., pK = 12 Closed economy: aggregate capital K is given ⇒ endogenous pK
The competitive wage in each city is
w = β
[(γ
pK
)γ
A(ZN
)ζ] 11−γ
The model is well defined with fewer factors: γ = 0 and/or ζ = 0
The city must have one source of decreasing returns: land suffi ces
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 9 / 86
Static Spatial Equilibrium Theory
Construction
Exogenous amount of land L in each cityI Natural and regulatory constraints
Housing supply is the product of land L and building height f
Height is built with tradable capital at a convex cost ψpK (f /δ)δ forψ > 0 and δ > 1
Free entry of developers
r = maxh
{pH f − ψpK
(fδ
)δ}= (δ− 1)
(pδH
ψpK
) 1δ−1
The maximum profit per unit of land is paid to landowners
Optimal height
f = δ
(pH
ψpK
) 1δ−1
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 10 / 86
Static Spatial Equilibrium Theory
Housing PricesThe user cost of housing is given dynamically by
pt = (1+m) pH ,t −EpH ,t+11+ i
I Maintenance and tax costs mI Constant interest rate i
The pricing equation implies the non-bubble value
pH ,t =∞
∑τ=0
Ept+τ
(1+m)1+τ (1+ i)τ
Let expected rental prices have a constant growth rate gp
Ept+τ = (1+ gp)τ pt
Under these hypotheses the model is stationary: regardless of t,
pH ,tpt
=1+ i
i + im+m− gpGiacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 11 / 86
Static Spatial Equilibrium Theory
Housing Market Equilibrium
The user cost of housing is p = µpH for µ ≈ 0.1Housing demand
H =λwµpH
N
Housing supply
H = δ
(pH
ψpK
) 1δ−1L
Market-clearing price
pH =
[ψpK
(λ
δµ
wNL
)δ−1] 1
δ
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 12 / 86
Static Spatial Equilibrium Theory
Spatial Equilibrium
Three equilibrium conditions
1 Individuals’optimal location choice
logw − λ log pH + log a = u + κ1
2 Firms’labor demand
(1− γ) logw + ζ (logN − log Z )− logA = κ2 − γ log pK
3 Housing market equilibrium
δ log pH − (δ− 1) (logw + logN − log L)− logψ = log pK + κ3
The constants κ1, κ2 and κ3 are functions of exogenous parameters
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 13 / 86
Static Spatial Equilibrium Theory
“Small”CitiesThree endogenous city characteristics
1 Population N2 Wages w3 House prices pH
Three exogenous city characteristics1 Consumption amenities a2 Production amenities A ≡ AZ ζ
3 Construction amenities L ≡ Lψ−1/(δ−1)
Two economy-wide variables:1 Reservation utility u2 Price of tradable capital pK
We need cities to be “small” so each doesn’t affect u and pK1 The entire system of cities is small: u and pK are exogenous2 There is a continuum of cities: u and pK are endogenous,but they depend on aggregates and not on
(a, A, L
)in any single city
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 14 / 86
Static Spatial Equilibrium Theory
Equilibrium Characterization1 Equilibrium wages
logw = κw +(δ− 1) λ
(log A− ζ log L
)− δζ log a
β (δ− 1) λ+ δζ
2 Equilibrium housing prices
log pH = κp +(δ− 1)
(log A+ β log a− ζ log L
)β (δ− 1) λ+ δζ
3 Equilibrium population
logN = κN +[δ (1− λ) + λ] log A+ (β+ ζ)
[δ log a+ (δ− 1) λ log L
]β (δ− 1) λ+ δζ
I Population density reflects the different impacts of ψ and L
log NL = κN +[δ(1−λ)+λ](log A+ζ log L)+(β+ζ)(δ log a−λ log ψ)
β(δ−1)λ+δζ
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 15 / 86
Static Spatial Equilibrium Theory
Congestion in Construction Alone
For ζ = 0 production has constant returns at the city level
⇒ City size N does not influence the marginal product of labor
1 Wages are determined by production amenities only
logw = κw +1βlogA
2 Construction amenities L do not influence prices
log pH = κp +logA+ β log a
βλ
3 Density reflects construction costs ψ but not the supply of land L
logNL= κN +
[δ (1− λ) + λ] log A+ β (δ log a− λ logψ)
β (δ− 1) λ
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 16 / 86
Static Spatial Equilibrium Theory
Congestion in Production Alone
For δ = 1 construction has constant returns
⇒ Height is a perfect substitute for land: pL = 0
1 The price of housing is determined by construction costs only
log pH = κp + logψ
2 Production amenities A do not influence wages
logw = κw + logψ− log a
3 Population reflects construction costs ψ but not the supply of land L
logN = κN +1ζ
[log A+ (β+ ζ) (log a− λ logψ)
]Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 17 / 86
Static Spatial Equilibrium Theory
Comparative StaticsFrom the equilibrium conditions, for any exogenous city characteristic X
1 Individuals’optimal location choice
∂ logw∂X
− λ∂ log pH
∂X+
∂ log a∂X
= 0
2 Firms’labor demand
(1− γ)∂ logw
∂X+ ζ
∂ logN∂X
− ∂ log A∂X
= 0
3 Housing market equilibrium
δ
δ− 1∂ log pH
∂X− ∂ logw
∂X+
∂ logN∂X
− ∂ log L∂X
= 0
To a first order, we can bring these to the data
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 18 / 86
Static Spatial Equilibrium Theory
Linear Model
Assume that for a measurable exogenous city characteristic X
1 log a = κa + ξaX + εa2 log A = κA + ξAX + εA3 log L = κL + ξLX + εL
B Independent homoskedastic errors ε
Log-linearity of these and of the equilibrium conditions implies
1 logw = κw + ξwX + εw for ξw =(δ−1)λ(ξA−ζξL)−δζξa
β(δ−1)λ+δζ
2 log pH = κp + ξpX + εp for ξp =(δ−1)(ξA+βξa−ζξL)
β(δ−1)λ+δζ
3 logN = κN + ξN + εN for ξN =[δ(1−λ)+λ]ξA+(β+ζ)[δξa+(δ−1)λξL ]
β(δ−1)λ+δζ
B Independent homoskedastic errors ε
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 19 / 86
Static Spatial Equilibrium Theory
The Rosen—Roback Approach
Inverting the definitions of ξw , ξp and ξN yields the comparative staticsfrom the equilibrium conditions in global instead of local form
ξa = λξp − ξwξA = ζξN + (1− γ) ξw
ξL = ξN + ξw −δ
δ− 1ξp
ξw , ξp and ξN can be estimated regressing w , pH and N on X
Calibration1 Budget share of housing λ ≈ 0.32 Factor shares β ≈ 0.6 and γ ≈ 0.33 Housing supply elasticity 1/ (δ− 1) ≈ ?
Derived estimates of ξa and ξA, and less credibly of ξL
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 20 / 86
Static Spatial Equilibrium Evidence
Suggestive Empirics
January temperatures
1 A consumption amenity: ξa > 0I Lower wages in warmer U.S. cities
2 Correlated with production disamenities: ξA < 0I Omitted production amenities justify the existence cold cities
3 Housing supply?
1 Glaeser (2008): ξL > 0; more permissive regulation in the South2 Glaeser and Gottlieb (2009): ξL < 0; old houses in declining cold cities
Correlations to understand the dataI No identification of causal relationships
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 21 / 86
Static Spatial Equilibrium Evidence
The Original Rosen—Roback Framework
Roback (1982) classic contribution building upon Rosen’s ideas
1 Endogeneity is not addressed adequately2 Unclustered standard errors are probably meaningless
B Historical paper: you can no longer write empirics like this
Two prices for the largest U.S. cities
1 Wages w from the 1973 Current Population Survey2 House prices p for 1973 from the Federal Housing Administration
I Overrepresents the poor, but gives $/sq. ft.
Estimates of consumption (and potentially production) amenities
Attenuation bias from self-sorting of heterogeneous agents
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 22 / 86
Static Spatial Equilibrium Evidence
City Characteristics
1 Crime levelI At low frequencies it cannot possibly be exogenous
2 Pollution: particulate level3 Unemployment rate
I Harris and Todaro’s (1970) model of rural-urban migrationI High wages compensate for high unemployment ratesI Hall (1972) found some evidence of this across U.S. citiesI Not much since then: possibly related to decline in unionization
4 Population, population density, and population growthI These seem to belong on the left-hand side
5 Climate: heating degree days, snowfall, cloudy days, clear daysI The most plausible exogeneity
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 23 / 86
Static Spatial Equilibrium Evidence
Wage RegressionI .\BI.F. 1
O ' F I I I 0 1 H l 1 S FRO11
Lo(. F..\KYIs(;\ K~( .KTSSION\ I N 98 (:I rlF\
1 2 3 4
T( :RIRl t 13 .94 x 1 0 F . 4 4 x lo-" .74 x 1 0 - 7 8 6 x 10 -j (2.58) (1.17) ( I .93) (2.21)
L-K 73 .36 x 10-' . I 2 x lo-' .32 x lo-' . 2 i x lo-' 11.29) (.4:1) (1.14) ( . 9 i )
P . \RT 73 .24 x .13 x 10r3 .37 x lo-3 .34 x 1 0 ~ (1..5.5) ( 3 6 ) (2.33) 12.15)
P O P 79 ,113 x .15 x lo-' . 1 6 x lo-' . 1 6 x l o 7 (7.97) (7.74) (8.04) (8.11)
DE.SSSSIS.1 .81 x 10-"24 x 10-"20 x lo-;' .38 x I O F ( ,29) (.86) (.73) (1.40)
(;KO\V 6070 . 2 1 ~ 1 0 - ~ . 1 4 x 1 0 - ' . l 5 x I O - ' . 1 7 x 1 0 - ' 17.84) (.5.66) (6.06) (6.47)
HDD .20 x lo-" (8.48)
-I-O.I-SN()\V .72 x 10r3 (3.54)
(:I2F,.1R . 6 4 X 10-' (-4.80)
C;l.OC D l - .72 x lO-' 15.21)
R 2 ,4980 ,4955 ,4960 ,4962
F-~.;~rio 421.2 420.0 420.8 421.1 .\' = 12.001
TvLLges associated with high unemployment. Population size and the population growth rate both have the expected strong positive effects \I hile population density (DENSSMSX) is consistently insignificant.
T h e climate variables in table 1 perform I-emai-kably well. Heating degree days (HDD), total sno~vfall (TOTSNOW), and the number- of cloudy davs (C;LXILTDY) all have strong positive coefficients, which suggests that these indicators of climate are net disamenities. T h e nun~ber-of cleai- days (CLEAR) has a strongly negative coefficient, ~vhich is consistent with the prior notion that clear- days are arnenii- hle.'"
T h e nest question to be addressed is: What is the influence of the tit!- attributes o n the lvell-kno\vn regional differences in earnings! These persistent region efkcts have al~vays been something of a puzzle because a mobile labor force ought to bid auray anv geographic differences in real ear-nings. Because "real earnings" should be defined broadly to mean utility, we test the hypothesis that the ob-
',' P O Iothel- ~ e s u l t s on climate, see Hoch (1977).
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 24 / 86
Static Spatial Equilibrium Evidence
Housing Price Regression
1 ] 2 - ' > ] O L 7 R S . \ l . O F P O I I'I.I(::\L~ F( O \ - O \ l YA
P 4 R I 73
POP 77
1)E \5S>154
(TKO\\ 6070
HDD
high growth rates of population suggests that living in the Ll'est may t)e a proxy for some unmeasured desirable clirnatic o r cultural attri- butes. 'I'hus, the combined evidence seems persuasive that the re- gional differences in earnings can be largely accounted for t)y re-gional differences in local amenities."
2. Implicit PI-ices and Quality of Life Indices
Table 3 presents the results of a series of land price equations compa- ral~le to those in table 1 . The only significant results are the positive coefficients on the unemployment rate, population density, and population gro~vth. 'I'he latter t~vo results are most likely demand effects, ~vliich PI-osy for some unmeasured attributes o f t h e city. 'I'he theory suggests that the land price gradient reveals the rnarginal social value of the amenity to the community. '[he reader may be
" T h i \ result is ~.ot)ust to the inclusion of rnrasul-e\ of cost of living ( \ r e Rohack 1980).
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 25 / 86
Static Spatial Equilibrium Evidence
Consumption Amenities
tempted to inf'ei- from the zero coefficients on crime and pollution that these conditions have no social disutility. T h e limitations of' the data discussed earlier rnake such a conclusion premature. 'locompute the implicit price of each attribute in percentage terms,
\ve need the coefficients horn tables 1 and 3, as well as the budget shai-e of' land. 'I'his budget shai-e Jvas computed from the FHA data by multiplying the fi-action of' income spent on the mortgage (approxi- mately 17.8 percent) by the ratio of' the site pi-ice to the total value of' the house (approximately 19.6percent). 'I'his number was then aver- aged over a11 83 FHA cities to yield an average budget share. 'I'able 4 repoi-ts the irnplicit pi-ices computed from the columns of tables 1 and 3. For example, colurnn 2 reports the pi-ices computed from regres- sions, which include total annual snowfall as the climate variable. .A negative nurnl~ei- indicates a "bad" ~vhile a positive number indicates a good. [Vhile rrlost of the variables perform as expected, looking across the ~ . o ~ v s of' table 4 reveals some sensitivity of' the prices to specifica- t1011.
Each entrv in table 4 tells the marginal price of' the amenity evalu- ated at average annual earnings. Foi- example, the average person
I.(:KI\lE. 73 ~cl . imrsiI00 popul,trion)
YR 73 i f 1-dctiotl U I I C . I I I ~ ~ ~ \ C ~ )
P .IR I' 73 ~ ~ i ~ i c r ~ ~ g ~ ~ , t t ~ ~ \ i c ~ ~ O i cI I I C . ~ C . I )
POP 7:i ( 10.000 pc~l-\oll\)
DENSS\IS.I I 100 per-ion\'\cluarr ~iiile)
C;RO\V 6050 (per( rnt,rgc (hang? in popul;t- tion)
Hr)r) ( 1' I; coltlr.r tot- one t l ,~ \
I - o l - s N o \ V (iticties)
(:Lt:.iK (( l ' l \ \ )
(:I.( )YI)Y ( t I ' t \ \ )
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 26 / 86
Static Spatial Equilibrium Evidence
Government-Related Amenities
Gyourko and Tracy (1991) on local public finance
1 Local government provides important amenities2 Local taxes create tax wedges on p and w3 A rent-seeking public sector might extract the value of amenities
Local government is exogenous for each potential resident or firmI But econometrically exogenous?
Random effects estimationI Exogeneity of the random effects is rarely easy to acceptI Still no clustering
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 27 / 86
Static Spatial Equilibrium Evidence
Natural and Fiscal Amenities
--
TABLE 1
REGRESSIONRESULTSAND TRAIT PRICES: EFFECTSRANDOM SPECIFICATION pp
pp --ppp-p-
~ ~
--- - - ..- -- ----... . -- . -..
- ~-~~ -~ -- - - ~-- --
ANNUAL HOUSING WEEKLY TRAIT PRICES$ ANNUALIZED
EXPENDITURE WAGE -- ---
HEDONIC* HEDONIC~ Housing Wage Full CITY TKAIT ( 1 ) (2) (3) (4) (5) -- - -~ .. ~- --- -. --~-
Precipitation - ,0139 - ,0027 -$22.82 -$2 1.59 $ 1 . 2 2 (.0030) (.0009) (4.98) (6.83) (8.45)
Cooling degree days (thousands) - ,1344 ,003 1 -7.97 .89 -8.86 (.0562) (.0157) (3.33) (4.49) (5.59)
Heating degree days (thousands) , 0 2 7 7 .0 172 -5.65 16.93 -22.58 (.0248) (.0069) (5.06) (6.82) (8.49)
Relative humidity .0145 ,0033 36.53 40.14 -3.61 (.0053) (.0015) (13.36) (18.67) (22.95)
Sunshine (pert enrage possible) ,0079 - ,0005 21.84 -6.03 27.87 (.0056) (.00 16) (15.38) (2 1.97) (26.82)
Wind speed (mph) - ,0427 -.0192 - 18.18 -39.57 2 1.39 ( .O 179) (.0055) (7.62) (1 1.31) (13.64)
Particulate matter - ,0019 - .0003 -6.36 -4.34 -2.01 (.0013) (.0004) (4.20) (-5.79) (7.15)
Coast ,1345 - .020 1 654.15 -435.70 1,089.86 (.0694) (.0199) (360.15) (429.20) (560.28)
Cost of living ,6496 ,2633 28.89 56.58 -27.70 (1.6197) (.4208) (7 1.94) (90.42) (1 15.55)
Violent crime ,0574 ,0705 2.51 14.91 - 12.40 (.0425) (.0109) (1.86) (2.3 1) (2.97)
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 28 / 86
Static Spatial Equilibrium Evidence
Natural and Fiscal Amenities
Studentlteacher ratio -.0107 -.0010 (.0096) (.0027)
Fire rating .0498 .0156 (.0255) (.0078)
Hospital beds .003 1 - .0036 (.0037) (.0010)
Property tax rate -.lo37 . . . (.0399)
State and local inconie tax rate - ,0287 ,0020 (.0101) (.0029)
State corporate tax rate ,0208 - ,0067 (.0100) (.0029)
Percentage public union organized - ,1646 - ,0041 (. 1302) (.0385)
SMSA population (millions) ,0376 ,0096 (.0223) (.0057)
Percentage working in other SMSA 1.4693 ,1052 (.6325) (. 1969)
Summary statistics:
m i .0434 . . . 4 .I801 . . .
4 . . . .2905
4 . . . ,0023 Number of observations 5,263 38,870
. -- - .. - p~ ~ - -
* The housing hedon~c contains 20 structlrlal trait controls. All results are available on request. Estimated standard el rors are in parentheses 'The wage hedonlc contains 1 1 worker quality variables and controls for 22 major indusrry and occupation groups. All results are availablr on request. Estimated standard errors are in
parentheses. Thc calculations In cols 3-5 are based on a 1 pcrccnt change about the mean of the variables except for the dichotomous coast variable. Its prices arc based on a d~screte change from
noncoast to coastal status. All figures in these threc columns arc annuahzed. We assume 1.5 wage earners per household and 49 work weeks per year. These are the sample averages. Standard errors of the implicit prices are in parentheses. They are calculated via the "delta" method.
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 29 / 86
Static Spatial Equilibrium Evidence
Micro-Geography
Black (1999) on public schools in the Boston area
Tax rates and school spending vary by school district
Average test scores measure quality at the school level
Attendance districts within a school district determine a child’s school
Identification off of discontinuity at administrative boundariesI 39 school districts, 181 attendance district boundaries
Within-city hedonics on p onlyI Transaction prices for 22,679 single-family homes
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 30 / 86
Static Spatial Equilibrium Evidence
Attendance District Boundaries
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 31 / 86
Static Spatial Equilibrium Evidence
House Prices and School Quality
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 32 / 86
Static Spatial Equilibrium Evidence
House Prices and School Quality
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 33 / 86
Static Spatial Equilibrium Evidence
Differences at the Boundary
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 34 / 86
Static Spatial Equilibrium Evidence
Capitalized Prices of Better Test Scores
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 35 / 86
Dynamic Spatial Equilibrium Rosen-Roback
Repeated Static Equilibrium1 Individuals can relocate every period at no cost
logwt − λ log pt + log at = ut
or in first differences
logwt+1wt− λ log
pt+1pt
+ logat+1at
= ut+1 − ut
2 Firms rent tradable capital in every period
(1− γ) logwt + ζ logNt − log At = κ2 − γ log pK ,t
or in first differences
(1− γ) logwt+1wt
+ ζ logNt+1Nt− log At+1
At= −γ log
pK ,t+1pK ,t
The right-hand sides are time- but not city-specificI “Small” cities do not influence ut nor pK ,t
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 36 / 86
Dynamic Spatial Equilibrium Rosen-Roback
Simplified Housing Dynamics
Housing prices are forward-looking and complicate a dynamic model
If Ept+τgrows at a rate gp constant across cities and over time, then
pt = µpH ,t ⇒ logpt+1pt
= logpH ,t+1pH ,t
The housing market equilibrium is
δ log pH ,t − (δ− 1)(logwt + logNt − log Lt
)= log pK ,t + κ3
or in first differences
δ logpt+1pt− (δ− 1)
(log
wt+1wt
+ logNt+1Nt− log Lt+1
Lt
)=log pK ,t+1log pK ,t
We can treat growth rates just as we did log levels
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 37 / 86
Dynamic Spatial Equilibrium Rosen-Roback
Linear Growth Rates for Exogenous Variables
Assume that for a measurable exogenous city characteristic Xt
1 log at+1 − log at = κ∆a + ∆aXt + ε∆a,t
2 log At+1 − log At = κ∆A + ∆AXt + ε∆A,t
3 log Lt+1 − log Lt = κ∆L + ∆LXt + ε∆L,t
B Independent homoskedastic errors ε
Assume that aggregate dynamics satisfy
1 ut+1 − ut = κ∆u + ε∆u,t
2 log pK ,t+1 − log pK ,t = κ∆K + ε∆K ,t
B Independent homoskedastic errors ε
The aggregate assumptions could be relaxedI Time-varying intercept, i.e., time fixed effects
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 38 / 86
Dynamic Spatial Equilibrium Rosen-Roback
Linear Growth Rates for Endogenous Variables
If Ept+τ grows at a rate gp constant across cities and over time, then
1 logwt+1 − logwt = κ∆w + ∆wXt + ε∆w ,t
2 log pH ,t+1 − log pH ,t = κ∆p + ∆pXt + ε∆p,t
3 logNt+1 − logNt = κ∆N + ∆NXt + ε∆N ,t
B Independent homoskedastic errors ε
Given estimates of ∆w , ∆p and ∆N from growth regressions
1 ∆a = λ∆p − ∆w2 ∆A = ζ∆N + (1− γ)∆w3 ∆L = ∆N + ∆w − ∆pδ/ (δ− 1)
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 39 / 86
Dynamic Spatial Equilibrium Rosen-Roback
Consistency Check
We assumedEpt+1 = (1+ gp) pt
We derivedpt+1 = pt exp (κ∆p + ∆pXt + ε∆p,t )
Consistent if E exp (∆pXt ) is constant across cities and over time1 Xt is i.i.d. across cities and over time2 Xt is realized after period t construction
The best exogenous variables are not i.i.d: e.g., climate1 Incomplete modelling of the housing sector2 Simplified microfoundation of construction3 Full-fledged housing dynamics
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 40 / 86
Dynamic Spatial Equilibrium Rosen-Roback
Perfectly Elastic Housing Supply1 Perfectly elastic supply of housing at unit cost ψpK (δ = 1)
log pH ,t − logψt = log pK ,t
2 Growth rate of construction costs independent of Xt
logψt+1 − logψt = κ∆ψ + ε∆ψ,t
B Rental prices
pt = (1+m) pH ,t −EpH ,t+11+ i
= µpH ,t
for
µ = 1+m−exp
(κ∆ψ + ε∆ψ,t
)E exp
(ε∆ψ,t + ε∆K ,t
)1+ i
We could let µ vary across cities and over time: then at = atµ−λt
I Higher expected housing appreciation is appealing to residentsI We need µ to be independent of pH ,t and Xt
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 41 / 86
Dynamic Spatial Equilibrium Rosen-Roback
Perfectly Inelastic Housing SupplyPerfectly inelastic supply of housing Ht = Lt (δ→ ∞)
log pt − logwt + log Lt − logNt = κ3
Equilibrium in terms of rental price pt1 ∆a = λ∆p − ∆w2 ∆A = ζ∆N + (1− γ)∆w3 ∆L = ∆N + ∆w − ∆p
We typically have and prefer data on house prices pH , not rents pIf Xt = X is an invariant city characteristic
pt+τ = pt exp[τ (κ∆p + ∆pX ) +∑τ−1
s=0 ε∆p,t+s
]⇒ pH ,t
pt=
11+m
∞
∑τ=0
[exp (κ∆p + ∆pX )(1+m) (1+ i)
]τ
E exp(∑τ−1s=0 ε∆p,t+s
)⇒ log pH ,t+1 − log pH ,t = log pt+1 − log pt
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 42 / 86
Dynamic Spatial Equilibrium Stylized Facts
Urban and Regional Dynamics
1 Population patterns are very persistent in the long run Figure
2 Population growth rates are persistent in the short run Figure
I Employment growth rates have the same behavior Figure
I Growth rates of housing supply have the same behavior Figure
I Persistence need not apply to the long run Figure
3 Gibrat’s law: growth rates are independent of initial levelsI Often true of population (cf. 1 above) USA France Japan
I Often false of population Table
4 Mean reversion of income Figure
5 Correlation of population growth and initial income Figure
Documented for U.S. cities, counties, and states
Seemingly true in other countries as well
Forward
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 43 / 86
Dynamic Spatial Equilibrium Stylized Facts
Autauga, AL
Baldwin, AL
Barbour, ALBibb, AL
Blount, AL
Butler, AL
Calhoun, AL
Chambers, ALCherokee, AL
Choctaw, AL
Clarke, ALCoffee, AL
Conecuh, ALCoosa, AL
Covington, ALDale, AL Dallas, AL
De Kalb, AL
Fayette, ALFranklin, AL
Greene, ALHenry, AL
Jackson, AL
Jefferson, AL
Lauderdale, AL
Lawrence, AL
Limestone, AL
Lowndes, AL
Macon, AL
Madison, AL
Marengo, ALMarion, AL
Marshall, AL
Mobile, AL
Monroe, AL
Montgomery, AL
Morgan, AL
Perry, AL
Pickens, ALPike, AL
Randolph, AL
Russell, ALSt. Clair, AL
Shelby, AL
Sumter, AL
Talladega, AL
Tallapoosa, AL
Tuscaloosa, AL
Walker, AL
Washington, ALWilcox, AL
Winston, AL
Fairfield, CTHartford, CT
Litchfield, CTMiddlesex, CT
New Haven, CT
New London, CT
Tolland, CTWindham, CTKent, DE
New Castle, DE
Sussex, DE
District of Columbia
Calhoun, FL
Columbia, FL
Duval, FL
Escambia, FL
Gadsden, FL
Hamilton, FLHolmes, FL
Jackson, FL
Jefferson, FL
Leon, FL
Liberty, FL
Madison, FL
Nassau, FL
Santa Rosa, FL
Suwannee, FL
Taylor, FLWakulla, FLWalton, FL
Washington, FLAppling, GA
Baker, GA
Baldwin, GA
Banks, GA
Bartow, GA
Berrien, GA
Bibb, GA
Brooks, GABryan, GA
Bulloch, GA
Burke, GAButts, GA
Calhoun, GA
Camden, GA
Carroll, GACatoosa, GA
Charlton, GA
Chatham, GA
Chattahoochee, GAChattooga, GA
Cherokee, GAClarke, GA
Clay, GA
Clayton, GA
Clinch, GA
Cobb, GA
Coffee, GAColquitt, GA
Columbia, GACoweta, GA
Crawford, GADade, GADawson, GADecatur, GA
De Kalb, GA
Dooly, GA
Dougherty, GA
Early, GA
Echols, GA
Effingham, GA
Elbert, GAEmanuel, GAFannin, GA
Fayette, GAFloyd, GAForsyth, GA
Franklin, GA
Fulton, GA
Gilmer, GA
Glascock, GA
Glynn, GAGordon, GA
Greene, GA
Gwinnett, GA
Habersham, GA
Hall, GA
Hancock, GA
Haralson, GA Harris, GAHart, GA
Heard, GA
Henry, GAHenry, GAHouston, GA
Irwin, GA
Jackson, GA
Jasper, GAJefferson, GA
Johnson, GA
Jones, GA
Laurens, GA
Lee, GA
Liberty, GA
Lincoln, GA
Lowndes, GA
Lumpkin, GA
McIntosh, GAMacon, GA
Madison, GA
Marion, GA
Meriwether, GA
Miller, GA
Mitchell, GA Monroe, GA
Montgomery, GA
Morgan, GA
Murray, GA
Muscogee, GA
Newton, GA
Oglethorpe, GA
Paulding, GA
Pickens, GAPierce, GA Pike, GA
Polk, GA
Pulaski, GA
Putnam, GA
Quitman, GA
Rabun, GA
Randolph, GA
Richmond, GA
Schley, GA
Screven, GA
Spalding, GA
Stewart, GA
Sumter, GA
Talbot, GA
Taliaferro, GA
Tattnall, GA
Taylor, GATelfair, GATerrell, GA
Thomas, GA
Towns, GA
Troup, GA
Twiggs, GAUnion, GA
Upson, GA
Walker, GAWalton, GA
Ware, GA
Warren, GA
Washington, GAWayne, GA
Webster, GA
White, GA
Whitfield, GA
Wilcox, GA Wilkes, GAWilkinson, GA
Worth, GA
Alexander, IL
Bond, IL
Boone, ILBureau, IL
Carroll, IL
Champaign, IL
Christian, IL
Clark, ILClay, IL
Clinton, ILColes, IL
Cook, IL
Crawford, IL
Cumberland, IL
DeKalb, IL
De Witt, ILDouglas, IL
DuPage, IL
Edgar, IL
Edwards, IL
Effingham, ILFayette, IL
Ford, IL
Franklin, IL
Gallatin, IL
Grundy, IL
Hamilton, IL
Hardin, IL
Iroquois, IL
Jackson, IL
Jasper, IL
Jefferson, IL
Johnson, IL
Kane, IL
Kankakee, IL
Kendall, IL
Lake, IL
La Salle, IL
Lawrence, IL
Lee, ILLivingston, ILLogan, IL
McHenry, IL
McLean, ILMacon, IL
Macoupin, IL
Madison, IL
Marion, IL
Marshall, ILMason, ILMassac, ILMenard, IL
Montgomery, IL
Moultrie, IL
Ogle, IL
Peoria, IL
Perry, ILPiatt, IL
Pope, ILPulaski, ILPutnam, IL
Randolph, IL
Richland, IL
St. Clair, IL
Saline, IL
Sangamon, IL
Shelby, IL
Stark, IL
Stephenson, IL
Tazewell, IL
Union, IL
Vermilion, IL
Wabash, ILWashington, ILWayne, ILWhite, IL
Whiteside, IL
Will, IL
Williamson, IL
Winnebago, IL
Woodford, ILAdams, IN
Allen, IN
Bartholomew, IN
Benton, INBlackford, IN
Boone, IN
Brown, INCarroll, IN
Cass, IN
Clark, IN
Clay, INClinton, IN
Crawford, IN
Daviess, INDearborn, IN
Decatur, INDe Kalb, IN
Delaware, IN
Dubois, IN
Elkhart, IN
Fayette, IN
Floyd, IN
Fountain, INFranklin, INFulton, IN
Gibson, IN
Grant, IN
Greene, IN
Hamilton, IN
Hancock, INHarrison, IN
Hendricks, IN
Henry, IN
Howard, IN
Huntington, INJackson, INJasper, IN
Jay, INJefferson, INJennings, IN
Johnson, IN
Knox, IN
Kosciusko, IN
Lagrange, IN
Lake, IN
La Porte, IN
Lawrence, IN
Madison, IN
Marion, IN
Marshall, IN
Martin, IN
Miami, IN
Monroe, IN
Montgomery, IN
Morgan, IN
Newton, IN
Noble, IN
Ohio, IN
Orange, INOwen, INParke, INPerry, IN
Pike, IN
Porter, IN
Posey, IN
Pulaski, IN
Putnam, INRandolph, INRipley, IN
Rush, IN
St. Joseph, IN
Scott, IN
Shelby, IN
Spencer, INStarke, INSteuben, IN
Sullivan, IN
Switzerland, IN
Tippecanoe, IN
Tipton, IN
Union, IN
Vanderburgh, IN
Vermillion, IN
Vigo, IN
Wabash, IN
Warren, IN
Warrick, IN
Washington, IN
Wayne, IN
Wells, INWhite, INWhitley, IN
Adair, KYAllen, KYAnderson, KY
Ballard, KY
Barren, KY
Bath, KY
Boone, KY
Bourbon, KY
Boyd, KY
Boyle, KY
Bracken, KY
Breathitt, KYBreckinridge, KY
Bullitt, KY
Butler, KYCaldwell, KY
Calloway, KY
Campbell, KY
Carroll, KY
Carter, KYCasey, KY
Christian, KY
Clark, KYClay, KY
Clinton, KYCrittenden, KYCumberland, KY
Daviess, KY
Edmonson, KYEstill, KY
Fayette, KY
Fleming, KY
Floyd, KYFranklin, KY
Fulton, KYGallatin, KY
Garrard, KYGrant, KY
Graves, KYGrayson, KY
Green, KY
Greenup, KY
Hancock, KY
Hardin, KY
Harlan, KY
Harrison, KYHart, KY
Henderson, KY
Henry, KY
Hickman, KY
Hopkins, KY
Jackson, KY
Jefferson, KY
Jessamine, KYJohnson, KY
Kenton, KY
Knox, KY
Larue, KY
Laurel, KY
Lawrence, KYLetcher, KY
Lewis, KYLincoln, KY
Livingston, KY
Logan, KY
Lyon, KY
McCracken, KY
McLean, KY
Madison, KY
Magoffin, KYMarion, KY
Marshall, KY
Mason, KYMeade, KY
Mercer, KY
Metcalfe, KYMonroe, KY
Montgomery, KYMorgan, KY
Muhlenberg, KYNelson, KY
Nicholas, KY
Ohio, KY
Oldham, KY
Owen, KY
Owsley, KY
Pendleton, KY
Perry, KY
Pike, KY
Powell, KY
Pulaski, KY
Rockcastle, KYRowan, KY
Russell, KY
Scott, KYShelby, KY
Simpson, KYSpencer, KY
Taylor, KY
Todd, KYTrigg, KYTrimble, KY
Union, KY
Warren, KY
Washington, KY
Wayne, KYWebster, KY
Whitley, KYWoodford, KY
Orleans, LA
St. Tammany, LA
Allegany, MD
Anne Arundel, MDBaltimore, MD
Calvert, MD
Caroline, MD
Carroll, MD
Cecil, MDCharles, MD
Dorchester, MD
Frederick, MDHarford, MDHoward, MD
Kent, MD
Montgomery, MDPrince George's, MD
Queen Anne's, MD
St. Mary's, MD
Somerset, MDTalbot, MD
Washington, MD
Worcester, MD
Barnstable, MABerkshire, MA
Bristol, MA
Dukes, MA
Essex, MA
Franklin, MA
Hampden, MA
Hampshire, MA
Middlesex, MA
Nantucket, MA
Norfolk, MAPlymouth, MA
Suffolk, MAWorcester, MA
Allegan, MI
Barry, MI
Berrien, MI
Branch, MI
Calhoun, MI
Cass, MIClinton, MI
Eaton, MI
Hillsdale, MI
Ingham, MI
Ionia, MI
Jackson, MIKalamazoo, MI
Lenawee, MILivingston, MI
Macomb, MI
Monroe, MI
Oakland, MI
Ottawa, MISt. Clair, MI
St. Joseph, MIShiawassee, MIVan Buren, MI
Washtenaw, MI
Wayne, MI
Attala, MSCalhoun, MS
Carroll, MS
Chickasaw, MS
Choctaw, MS
Clarke, MSCovington, MS
DeSoto, MS
Greene, MS
Hancock, MS
Harrison, MS
Itawamba, MS
Jackson, MS
Jasper, MS
Jones, MS
Kemper, MS
Lafayette, MS
Lauderdale, MS
Leake, MS
Lowndes, MS
Marion, MSMarshall, MSMonroe, MS
Neshoba, MSNewton, MS
Noxubee, MS
Oktibbeha, MSPanola, MS
Perry, MS
Pontotoc, MS
Rankin, MS
Scott, MSSimpson, MSSmith, MS
Tippah, MSTishomingo, MSWayne, MSWinston, MSYalobusha, MS
Cape Girardeau, MO
Mississippi, MONew Madrid, MOPemiscot, MOPerry, MO
Scott, MOStoddard, MO
Cheshire, NH
Hillsborough, NHRockingham, NHAtlantic, NJ
Bergen, NJ
Burlington, NJCamden, NJ
Cape May, NJCumberland, NJ
Essex, NJ
Gloucester, NJ
Hudson, NJ
Hunterdon, NJ
Mercer, NJ
Middlesex, NJMonmouth, NJMorris, NJOcean, NJ Passaic, NJ
Salem, NJ
Somerset, NJ
Sussex, NJ
Union, NJ
Warren, NJ
Albany, NY
Allegany, NY
Broome, NY
Cattaraugus, NYChautauqua, NY
Chemung, NY
Chenango, NYColumbia, NYCortland, NYDelaware, NY
Dutchess, NY
Erie, NY
Greene, NY
Kings, NY
Livingston, NYMadison, NYMontgomery, NY
New York, NY
Ontario, NY
Orange, NY
Otsego, NYPutnam, NY
Queens, NY
Rensselaer, NY
Richmond, NYRockland, NY
Schenectady, NY
Schoharie, NYSchuyler, NY
Seneca, NY
Steuben, NY
Suffolk, NY
Sullivan, NYTioga, NY
Tompkins, NY
Ulster, NY
Westchester, NY
Wyoming, NY
Yates, NY
Alamance, NC
Alexander, NC
Alleghany, NC
Anson, NCAshe, NC
Beaufort, NC
Bertie, NCBladen, NC
Brunswick, NC
Buncombe, NC
Burke, NCCabarrus, NC
Caldwell, NC
Camden, NC
Carteret, NC
Caswell, NC
Catawba, NC
Chatham, NC
Cherokee, NCChowan, NC
Cleveland, NC
Columbus, NCCraven, NC
Cumberland, NC
Currituck, NC
Davidson, NC
Davie, NCDuplin, NCEdgecombe, NC
Forsyth, NC
Franklin, NC
Gaston, NC
Gates, NC
Granville, NC
Greene, NC
Guilford, NC
Halifax, NCHarnett, NC
Haywood, NCHenderson, NC
Hertford, NC
Hyde, NC
Iredell, NC
Jackson, NC
Johnston, NC
Jones, NC
Lenoir, NCLincoln, NCMcDowell, NC
Macon, NCMadison, NC
Martin, NC
Mecklenburg, NC
Montgomery, NC
Moore, NCNash, NC
New Hanover, NC
Northampton, NC
Onslow, NCOrange, NC
Pasquotank, NC
Perquimans, NC
Person, NC
Pitt, NC
Polk, NC
Randolph, NC
Richmond, NC
Robeson, NCRockingham, NC
Rowan, NC
Rutherford, NCSampson, NCStanly, NCStokes, NCSurry, NC
Tyrrell, NC
Union, NC
Wake, NC
Warren, NCWashington, NC
Watauga, NC
Wayne, NCWilkes, NCWilson, NC
Yadkin, NC
Yancey, NCAdams, OH
Allen, OH
Ashland, OH
Ashtabula, OHAthens, OH
Auglaize, OHBelmont, OH
Brown, OH
Butler, OH
Carroll, OHChampaign, OH
Clark, OHClermont, OH
Clinton, OH
Columbiana, OH
Coshocton, OHCrawford, OH
Cuyahoga, OH
Darke, OHDefiance, OH
Delaware, OHErie, OH
Fairfield, OH
Fayette, OH
Franklin, OH
Fulton, OHGallia, OH
Geauga, OHGreene, OH
Guernsey, OH
Hamilton, OH
Hancock, OH
Hardin, OH
Harrison, OH
Henry, OHHighland, OH
Hocking, OHHolmes, OH
Huron, OH
Jackson, OH
Jefferson, OHKnox, OH
Lake, OH
Lawrence, OH
Licking, OH
Logan, OH
Lorain, OHLucas, OH
Madison, OH
Mahoning, OH
Marion, OH
Medina, OH
Meigs, OH
Mercer, OH
Miami, OH
Monroe, OH
Montgomery, OH
Morgan, OH
Morrow, OH
Muskingum, OH
Noble, OH
Ottawa, OH
Paulding, OHPerry, OH
Pickaway, OH
Pike, OH
Portage, OH
Preble, OHPutnam, OH
Richland, OH
Ross, OHSandusky, OHScioto, OH
Seneca, OHShelby, OH
Stark, OHSummit, OH
Trumbull, OH
Tuscarawas, OH
Union, OHVan Wert, OH
Vinton, OH
Warren, OH
Washington, OH
Wayne, OH
Williams, OH
Wood, OH
Wyandot, OH
Adams, PA
Allegheny, PA
Armstrong, PA
Beaver, PA
Bedford, PA
Berks, PA
Blair, PA
Bradford, PA
Bucks, PA
Butler, PACambria, PA
Carbon, PA
Centre, PA
Chester, PA
Clarion, PA
Clearfield, PA
Clinton, PAColumbia, PA
Crawford, PA
Cumberland, PADauphin, PA
Delaware, PA
Elk, PA
Erie, PA
Fayette, PA
Forest, PA
Franklin, PA
Fulton, PA
Greene, PAHuntingdon, PA
Indiana, PA
Jefferson, PA
Juniata, PA
Lancaster, PA
Lawrence, PALebanon, PA
Lehigh, PALuzerne, PA
Lycoming, PA
McKean, PA
Mercer, PA
Mifflin, PA
Monroe, PA
Montgomery, PA
Montour, PA
Northampton, PA
Northumberland, PA
Perry, PA
Philadelphia, PA
Pike, PA
Potter, PA
Schuylkill, PA
Snyder, PA
Somerset, PA
Sullivan, PA
Susquehanna, PATioga, PAUnion, PAVenango, PA
Warren, PA
Washington, PA
Wayne, PA
Westmoreland, PA
Wyoming, PA
York, PA
Bristol, RI
Kent, RI
Newport, RI
Providence, RI
Washington, RI
Abbeville, SC
Anderson, SC
Barnwell, SC
Beaufort, SC
Charleston, SC
Chester, SCChesterfield, SCClarendon, SCColleton, SC
Darlington, SC
Edgefield, SCFairfield, SC
Georgetown, SC
Greenville, SC
Horry, SC
Kershaw, SCLancaster, SCLaurens, SC
Lexington, SC
Marion, SCMarlboro, SCNewberry, SC
Orangeburg, SCPickens, SC
Richland, SCSpartanburg, SC
Sumter, SC
Union, SCWilliamsburg, SC
York, SC
Anderson, TN
Bedford, TN
Benton, TNBledsoe, TN
Blount, TNBradley, TN
Campbell, TN
Cannon, TN
Carroll, TN
Carter, TNCheatham, TNClaiborne, TNCocke, TN
Coffee, TNCumberland, TN
Davidson, TN
Decatur, TNDeKalb, TN
Dickson, TNDyer, TN Fayette, TN
Fentress, TN
Franklin, TNGibson, TNGiles, TN
Grainger, TN
Greene, TN
Grundy, TN
Hamilton, TN
Hancock, TN
Hardeman, TNHardin, TN
Hawkins, TN
Haywood, TNHenderson, TNHenry, TN
Hickman, TNHumphreys, TN
Jackson, TN
Jefferson, TN
Johnson, TN
Knox, TN
Lauderdale, TNLawrence, TN
Lewis, TN
Lincoln, TNMcMinn, TN
McNairy, TNMacon, TN
Madison, TN
Marion, TNMarshall, TN
Maury, TN
Meigs, TN
Monroe, TN
Montgomery, TN
Morgan, TNObion, TNOverton, TN
Perry, TN
Polk, TN
Putnam, TN
Rhea, TN
Roane, TNRobertson, TN
Rutherford, TN
Scott, TN
Sequatchie, TN
Sevier, TN
Shelby, TN
Smith, TNStewart, TN
Sullivan, TNSumner, TN
Tipton, TN
Union, TN
Van Buren, TN
Warren, TN
Washington, TN
Wayne, TN
Weakley, TNWhite, TN
Williamson, TNWilson, TN
Windham, VTAccomack, VA
Albemarle, VA
Alleghany, VAAmelia, VA
Amherst, VA
Appomattox, VA
Arlington, VA
Augusta, VA
Bath, VA
Bedford, VA
Botetourt, VABrunswick, VA
Buchanan, VABuckingham, VA
Campbell, VA
Caroline, VACarroll, VA
Charles City, VA
Charlotte, VA
Chesterfield, VA
Clarke, VA
Craig, VA
Culpeper, VA
Cumberland, VA
Dinwiddie, VA
Essex, VA
Fairfax, VA
Fauquier, VA
Floyd, VAFluvanna, VA
Franklin, VAFrederick, VA
Giles, VA
Gloucester, VA
Goochland, VAGrayson, VAGreene, VAGreensville, VA
Halifax, VA
Hanover, VA
Henrico, VA
Henry, VA
Highland, VA
Isle of Wight, VAJames City, VA
King and Queen, VA
King George, VAKing William, VALancaster, VA
Lee, VA
Loudoun, VA
Louisa, VA
Lunenburg, VAMadison, VAMathews, VA
Mecklenburg, VA
Middlesex, VA
Montgomery, VA
Nelson, VANew Kent, VANorthampton, VANorthumberland, VANottoway, VA
Orange, VAPage, VAPatrick, VA
Pittsylvania, VA
Powhatan, VAPrince Edward, VAPrince George, VA
Prince William, VA
Pulaski, VA
Rappahannock, VARichmond, VA
Roanoke, VA
Rockbridge, VA
Rockingham, VA
Russell, VAScott, VA
Shenandoah, VASmyth, VA
Southampton, VA
Spotsylvania, VAStafford, VA
Surry, VA
Sussex, VA
Tazewell, VAWarren, VA
Washington, VA
Westmoreland, VA
Wise, VAWythe, VA
York, VAGreen, WI
Kenosha, WIRacine, WIRock, WI
Walworth, WI
810
1214
16Lo
g of
Pop
ulat
ion,
200
0
6 8 10 12 14Log of Population, 1860
Figure 1The Stability of Population
logN2000 = 1.268(.32)
+ .996(.03)
logN1860
Back
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 44 / 86
Dynamic Spatial Equilibrium Stylized Facts
Persistence in City Growth Rates
38
Figure 2: Persistence in city growth rates, 1980-2000 lo
g(p
op g
row
th), 1
990-2
000
log(pop growth), 1980-1990-.5 0 .5 1
-.2
0
.2
.4
.6
Sample is all cities with population of 100,000 or more in 1990 and available population data for 1980 (193 observations).
Back
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 45 / 86
Dynamic Spatial Equilibrium Stylized Facts
Persistence of Employment Growth RatesOlivier Jean Blanchard and Lawrence F. Katz 5
Figure 1. Persistence of Employment Growth Rates across U.S. States, 1950-90
Annual employment growth, 1970-90 (percent)
6 NV
AK
4
NHWA TN2UM CO
WY ID S AHI
SM D VT DE MD
0~~~~~~~~0 A
2 MT AI I]
RI M?I '~O',IN "'CT OH
WV IL
/ ~~NY
0 2 4 6 Annual employment growth, 1950-70 (percent)
Source: Authors' calculations using data from Employment anid Earninlgs. See the appendix for more information. Annual employment growth is measured by the average annual change in log employment over the specified time span.
Trends and Fluctuations in Relative Employment
Over the last forty years, U.S. states have experienced large and sus- tained differences in employment growth rates. This experience is illus- trated in figure 1, which plots average nonfarm employment growth from 1950 to 1970 against average nonfarm employment growth from 1970 to 1990. (A few states have a later starting date. The appendix gives exact definitions, sources and coverage for the series used in this paper.) The line is a regression line and has a slope of 0.70 and an R2 of 0.75. Arizona, Florida, and Nevada have consistently grown at 2 percent above the na- tional average. Even leaving these states out, the R2 is still equal to 0.60. Massachusetts, New York, Pennsylvania, Rhode Island, and West Vir- ginia have consistently grown at rates much below the national average.
Back
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 46 / 86
Dynamic Spatial Equilibrium Stylized Facts
Changes in Population and Housing StockLo
g C
hang
e H
ousi
ng U
nits
, 197
020
00
Log Change Population, 19702000
Fitted values
.580574 1.84035
.344024
2.1741
St. LouiJohnstowGary citYoungsto
Detroit
BuffaloClevelanPittsburFlint ciNiagaraWheelingUtica ciSaginawDayton cHuntingtLouisvil
Newark c
BaltimorHarrisbuCincinnaCanton cScrantonBinghamtRochesteSyracuseCharlestParkersbLima citWashingtNorfolk
Hartford
AnnistonWarren c
PhiladelAltoonaAkron ciRichmondMacon ciJamestowJackson
Erie citBirminghWilliamsYork cit
New LondTrentonNew OrleToledo cAlbany cMilwauke
AtlantaAnderson
Atlantic
Terre HaSouth BeRacine cChicagoDuluth cKansas CKansas CLorain cEvansvilGalvesto
BerkeleyMinneapoPeoria cBridgepo
MansfielHamiltonNew HaveMuskegon
KalamazoFitchburDecaturWilmingtLansingPortsmouWaterloo
LivoniaGreenvil
Boston c
Lake Cha
Jersey CNew BedfSt. PaulReadingSpringfiPoughkeeMonroe cPensacolPawtuckeFall RivYonkersProviden
AllentowMuncie cWorcesteLancaste
Topeka cBeaumontPortland
Sioux CiDes MoinWaterburKnoxvill
DavenporGrand RaNew York
IndependRockfordHagersto
Columbia
Torrance
Paterson
RoanokeSalt Lak
MemphisDanvilleBiloxi cMobile cNorwalkPueblo cHouma ciBremertoBrockton
Albany c
Seattle
Florence
Elizabet
Stamford
Lawrence
Denver cMiami ciWichitaSan Fran
Cedar RaFort LauTampa ciShrevepo
Oakland
SavannahLowell cAlexandrOmaha ciState Co
TexarkanAnn ArboHonoluluKenoshaSpokane
Huntsvil
St. PeteAlexandrFort Way
CharlottJoplin c
Odessa c
New Brun
Middleto
Wausau c
Pasadena
Tuscaloo
Tulsa ciElkhartChampaig
Waco cit
AshevillJacksonMadisonLynchburHamptonSpringfiManchestAppletonBurlingt
Lawton cWichita
Inglewoo
Tacoma c
LafayettSpringfiFort SmiJanesvil
Long Bea
AbileneChattanoHollywooNewport
Sarasota
Los AngeSanta Ba
ColumbusLubbock
Garden GJoliet c
Corpus CFort WorAmarilloBattle CBaton RoOklahoma
Little REau ClaiSunnyvalSan AngePortland
Winston
Dallas cDaytonaBoulderDecaturConcordWest Pal
ColumbiaCharlestTyler ciBillings
Fullerto
Glendale
Danbury
Gainesvi
Hayward
LincolnMontgome
Santa FeGreensboLakewoodDothan c
Yakima cHoustonPasadenaMidlandRocheste
Lafayett
SacramenLongview
BloomingBloomingHuntingtJohnsonWilmingt
El Monte
Orange c
Fargo ci
Santa Cr
Bellingh
Pomona c
Sioux FaEl PasoSan AntoSan DiegVallejo
Melbourn
San Bern
Eugene c
Riversid
AlbuquerTucson cOrlandoFayettev
Aurora c
Overland
Bryan ciDurham cLas CrucSimi ValIrving c
Anaheim
GreeleyProvo ciSan JoseSalem ciFremont
Tallahas
Santa An
Hialeah
ChesapeaCharlottMesquiteFayettev
Stockton
Phoenix
Raleigh
Santa MaOxnard c
Killeen
Ontario
Virginia
Reno citBoise Ci
Tempe ci
Chula ViLaredo c
Salinas
Fresno c
Austin cGarlandBrownsviColorado
Fort ColMcAllenSanta Ro
Scottsda
Modesto
ThousandClarksviBakersfi
Escondid
ArlingtoAurora cLas Vega
Oceansid
Glendale
Mesa cit
Log
Cha
nge
Hou
sing
Uni
ts, 1
970
2000
Log Change Population, 19702000
Fitted values
.580574 1.84035
.344024
2.1741
St. LouiJohnstowGary citYoungsto
Detroit
BuffaloClevelanPittsburFlint ciNiagaraWheelingUtica ciSaginawDayton cHuntingtLouisvil
Newark c
BaltimorHarrisbuCincinnaCanton cScrantonBinghamtRochesteSyracuseCharlestParkersbLima citWashingtNorfolk
Hartford
AnnistonWarren c
PhiladelAltoonaAkron ciRichmondMacon ciJamestowJackson
Erie citBirminghWilliamsYork cit
New LondTrentonNew OrleToledo cAlbany cMilwauke
AtlantaAnderson
Atlantic
Terre HaSouth BeRacine cChicagoDuluth cKansas CKansas CLorain cEvansvilGalvesto
BerkeleyMinneapoPeoria cBridgepo
MansfielHamiltonNew HaveMuskegon
KalamazoFitchburDecaturWilmingtLansingPortsmouWaterloo
LivoniaGreenvil
Boston c
Lake Cha
Jersey CNew BedfSt. PaulReadingSpringfiPoughkeeMonroe cPensacolPawtuckeFall RivYonkersProviden
AllentowMuncie cWorcesteLancaste
Topeka cBeaumontPortland
Sioux CiDes MoinWaterburKnoxvill
DavenporGrand RaNew York
IndependRockfordHagersto
Columbia
Torrance
Paterson
RoanokeSalt Lak
MemphisDanvilleBiloxi cMobile cNorwalkPueblo cHouma ciBremertoBrockton
Albany c
Seattle
Florence
Elizabet
Stamford
Lawrence
Denver cMiami ciWichitaSan Fran
Cedar RaFort LauTampa ciShrevepo
Oakland
SavannahLowell cAlexandrOmaha ciState Co
TexarkanAnn ArboHonoluluKenoshaSpokane
Huntsvil
St. PeteAlexandrFort Way
CharlottJoplin c
Odessa c
New Brun
Middleto
Wausau c
Pasadena
Tuscaloo
Tulsa ciElkhartChampaig
Waco cit
AshevillJacksonMadisonLynchburHamptonSpringfiManchestAppletonBurlingt
Lawton c
Log
Cha
nge
Hou
sing
Uni
ts, 1
970
2000
Log Change Population, 19702000
Fitted values
.580574 1.84035
.344024
2.1741
St. LouiJohnstowGary citYoungsto
Detroit
BuffaloClevelanPittsburFlint ciNiagaraWheelingUtica ciSaginawDayton cHuntingtLouisvil
Newark c
BaltimorHarrisbuCincinnaCanton cScrantonBinghamtRochesteSyracuseCharlestParkersbLima citWashingtNorfolk
Hartford
AnnistonWarren c
PhiladelAltoonaAkron ciRichmondMacon ciJamestowJackson
Erie citBirminghWilliamsYork cit
New LondTrentonNew OrleToledo cAlbany cMilwauke
AtlantaAnderson
Atlantic
Terre HaSouth BeRacine cChicagoDuluth cKansas CKansas CLorain cEvansvilGalvesto
BerkeleyMinneapoPeoria cBridgepo
MansfielHamiltonNew HaveMuskegon
KalamazoFitchburDecaturWilmingtLansingPortsmouWaterloo
LivoniaGreenvil
Boston c
Lake Cha
Jersey CNew BedfSt. PaulReadingSpringfiPoughkeeMonroe cPensacolPawtuckeFall RivYonkersProviden
AllentowMuncie cWorcesteLancaste
Topeka cBeaumontPortland
Sioux CiDes MoinWaterburKnoxvill
DavenporGrand RaNew York
IndependRockfordHagersto
Columbia
Torrance
Paterson
RoanokeSalt Lak
MemphisDanvilleBiloxi cMobile cNorwalkPueblo cHouma ciBremertoBrockton
Albany c
Seattle
Florence
Elizabet
Stamford
Lawrence
Denver cMiami ciWichitaSan Fran
Cedar RaFort LauTampa ciShrevepo
Oakland
SavannahLowell cAlexandrOmaha ciState Co
TexarkanAnn ArboHonoluluKenoshaSpokane
Huntsvil
St. PeteAlexandrFort Way
CharlottJoplin c
Odessa c
New Brun
Middleto
Wausau c
Pasadena
Tuscaloo
Tulsa ciElkhartChampaig
Waco cit
AshevillJacksonMadisonLynchburHamptonSpringfiManchestAppletonBurlingt
Lawton cWichita
Inglewoo
Tacoma c
LafayettSpringfiFort SmiJanesvil
Long Bea
AbileneChattanoHollywooNewport
Sarasota
Los AngeSanta Ba
ColumbusLubbock
Garden GJoliet c
Corpus CFort WorAmarilloBattle CBaton RoOklahoma
Little REau ClaiSunnyvalSan AngePortland
Winston
Dallas cDaytonaBoulderDecaturConcordWest Pal
ColumbiaCharlestTyler ciBillings
Fullerto
Glendale
Danbury
Gainesvi
Hayward
LincolnMontgome
Santa FeGreensboLakewoodDothan c
Yakima cHoustonPasadenaMidlandRocheste
Lafayett
SacramenLongview
BloomingBloomingHuntingtJohnsonWilmingt
El Monte
Orange c
Fargo ci
Santa Cr
Bellingh
Pomona c
Sioux FaEl PasoSan AntoSan DiegVallejo
Melbourn
San Bern
Eugene c
Riversid
AlbuquerTucson cOrlandoFayettev
Aurora c
Overland
Bryan ciDurham cLas CrucSimi ValIrving c
Anaheim
GreeleyProvo ciSan JoseSalem ciFremont
Tallahas
Santa An
Hialeah
ChesapeaCharlottMesquiteFayettev
Stockton
Phoenix
Raleigh
Santa MaOxnard c
Killeen
Ontario
Virginia
Reno citBoise Ci
Tempe ci
Chula ViLaredo c
Salinas
Fresno c
Austin cGarlandBrownsviColorado
Fort ColMcAllenSanta Ro
Scottsda
Modesto
ThousandClarksviBakersfi
Escondid
ArlingtoAurora cLas Vega
Oceansid
Glendale
Mesa cit
Wichita
Inglewoo
Tacoma c
LafayettSpringfiFort SmiJanesvil
Long Bea
AbileneChattanoHollywooNewport
Sarasota
Los AngeSanta Ba
ColumbusLubbock
Garden GJoliet c
Corpus CFort WorAmarilloBattle CBaton RoOklahoma
Little REau ClaiSunnyvalSan AngePortland
Winston
Dallas cDaytonaBoulderDecaturConcordWest Pal
ColumbiaCharlestTyler ciBillings
Fullerto
Glendale
Danbury
Gainesvi
Hayward
LincolnMontgome
Santa FeGreensboLakewoodDothan c
Yakima cHoustonPasadenaMidlandRocheste
Lafayett
SacramenLongview
BloomingBloomingHuntingtJohnsonWilmingt
El Monte
Orange c
Fargo ci
Santa Cr
Bellingh
Pomona c
Sioux FaEl PasoSan AntoSan DiegVallejo
Melbourn
San Bern
Eugene c
Riversid
AlbuquerTucson cOrlandoFayettev
Aurora c
Overland
Bryan ciDurham cLas CrucSimi ValIrving c
Anaheim
GreeleyProvo ciSan JoseSalem ciFremont
Tallahas
Santa An
Hialeah
ChesapeaCharlottMesquiteFayettev
Stockton
Phoenix
Raleigh
Santa MaOxnard c
Killeen
Ontario
Virginia
Reno citBoise Ci
Tempe ci
Chula ViLaredo c
Salinas
Fresno c
Austin cGarlandBrownsviColorado
Fort ColMcAllenSanta Ro
Scottsda
Modesto
ThousandClarksviBakersfi
Escondid
ArlingtoAurora cLas Vega
Oceansid
Glendale
Mesa cit
Back
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 47 / 86
Dynamic Spatial Equilibrium Stylized Facts
Fairfield, CTHartford, CTNew Haven, CT
New London, CT
New Castle, DE
District of ColumbiaCook, IL
Jefferson, KY
Orleans, LA
Baltimore, MD
Berkshire, MA
Bristol, MAEssex, MAHampden, MA
Middlesex, MA
Norfolk, MA
Plymouth, MA
Suffolk, MA
Worcester, MA
Wayne, MI
Hillsborough, NH
Rockingham, NH
Essex, NJHudson, NJ
Albany, NY
Chautauqua, NY
Dutchess, NY
Erie, NY
Kings, NYNew York, NY
Orange, NY
Otsego, NY
Queens, NY
Rensselaer, NYSteuben, NY
Ulster, NY
Westchester, NY
Cuyahoga, OH
Franklin, OH
Hamilton, OH
Montgomery, OH
Allegheny, PA
Berks, PA
Bucks, PA
Chester, PA
Lancaster, PA
Luzerne, PA
Montgomery, PA
Philadelphia, PA
Schuylkill, PA
Westmoreland, PA
York, PA
Providence, RI
Charleston, SC
.50
.51
1.5
2Lo
g of
Pop
ulat
ion
Cha
nge,
193
020
00
0 1 2 3 4Log of Population Change, 18601930
Figure 2The Negative Correlation of Population Changes
The figure shows the 54 counties that had more than 50,000 people in 1860
Back
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 48 / 86
Dynamic Spatial Equilibrium Stylized Facts
Gibrat’s Law in the U.S.
39
Figure 3: Growth and initial population, 1990-2000
log(p
op g
row
th), 1
990-2
000
log(pop), 199012 14 16
-.2
0
.2
.4
.6
Sample is all cities with population of 100,000 or more in 1990 (195 observations).
France Japan Back
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 49 / 86
Dynamic Spatial Equilibrium Stylized Facts
Gibrat’s Law for 39 French Cities
450 J. Eaton, Z. Eckstein I Regional Science and Urban Economics 27 (1997) 443-474
The stability of the Lorenz curves could be the consequence of any number of dynamic processes driving the population growth of individual cities. The most obvious possibility is that all cities on average grow at the same rate starting at different levels (‘parallel growth’). Two possibilities are ruled out, however. If there were an upper bound on city size that was attained by any city in our sample then we would expect the initial level of population of the city to be negatively correlated with average growth rate (‘convergence of size distribution’). The size distribution of cities would then be getting more equal over time. On the other hand, if the growth rate of a city is positively correlated with its initial size (‘divergence of size distribution’) then we would expect the Lorenz curves to exhibit increased inequality over time.
Fig. 3 displays the French average annual growth rates of the cities from 1876 to 1990 and the initial level of each city in 1876. The regression line implies that there is no correlation between the initial size of the agglomeration and the growth rate during that period. (The slope coefficient is - 1 X 10e6 with a SE. of 1.56 X 10d6.) Fig. 4 presents the equivalent picture for the Japanese cities. Here, as well, there is no obvious correlation between the initial level. The slope is
0.02
0.018
e, O.OlG t;j
5 0.014
5 ,o
c3 0.012
7 2 0.01
s! 0.008
0.006
0.004
1
t
I
I I
t i . 1 . 0’ I r n . I
I
?_-
. I
I
1
J I I , I 1 8 I I
0 500 1000 1500 2000 2500
Population-in 1876 (in Thousands)
R-Squared 0.0247 Coefficient -1.28E-06
No. of Observations 39 Std. Error of Coef. I .3 17E-06
Fig. 3. French cities: Growth rates and 1876 cities size.
USA Japan Back
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 50 / 86
Dynamic Spatial Equilibrium Stylized Facts
Gibrat’s Law for 40 Japanese CitiesJ. Eaton, Z. Eckstein I Regional Science and Urban Economics 27 (1997) 443-474 451
0.04 ,
0.01 -- ;-
0.005 ---Lc--( : I : \ : I I I I f :
0 1000 2030 3000 4000 5000 6000 7000
Population in 1925 (in Thousands)
No. of Observatiolls 40 Std. Error of Coef.
Fig. 4. Japanese cities: Growth rates and 1925 cities size.
positive but not significant (1.3 X lop9 with a S.E. of 0.8 X 10P9). This result is consistent with the stability of the Lorenz curves as well as with parallel growth.17
2.2. The rank-size rule
Given that the distribution of relative city size was so stable over the period, we now ask whether the populations of these urban areas obeyed the ‘rank size rule.’ According to this rule city populations among any group of cities at any time are proportional to the inverse of the ranking of their populations in that group. This result would obtain, for example, if the rank of a city of size N has the expectation:
” The results in Figs. 3 and 4 are similar to Barro (1991) on the relationship between per-capita income growth and the initial level of income per capita using the Summers and Heston (1991) country data. If Paris is omitted from the French data, then the regression has a significant negative slope. If Tokyo is omitted, then, the regression for Japan has a significant positive slope. Given the central role of these cities we think that it is wrong to drop them from the sample.
USA France Back
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 51 / 86
Dynamic Spatial Equilibrium Stylized Facts
(1) (2) (3) (4)
Decades
Correlationwith LaggedPopulation
Change
Correlationwith LaggedPopulation
Change(50,000+)
Correlationwith Initial
LogPopulation
Correlationwith Initial Log
Population(50,000+)
1790s . . 0.4681 0.95051800s 0.3832 0.6462 0.5625 0.13161810s 0.3256 0.4766 0.5674 0.04631820s 0.4423 0.5231 0.5136 0.41781830s 0.4452 0.9261 0.6616 0.2411840s 0.4634 0.8978 0.5122 0.39221850s 0.4715 0.7661 0.319 0.03921860s 0.3985 0.4631 0.0111 0.00651870s 0.1228 0.4865 0.3614 0.02051880s 0.3978 0.4541 0.1252 0.33231890s 0.4935 0.5382 0.1181 0.36911900s 0.4149 0.6454 0.1754 0.29471910s 0.5027 0.5778 0.2747 0.09031920s 0.476 0.4675 0.3381 0.14941930s 0.3005 0.4887 0.0415 0.15851940s 0.4151 0.6752 0.3863 0.06491950s 0.7397 0.7327 0.3985 0.04441960s 0.7225 0.8196 0.2922 0.03111970s 0.3821 0.4349 0.2247 0.44621980s 0.641 0.7096 0.1062 0.06931990s 0.737 0.7863 0.0197 0.157
Table 1:Population Growth Correlations
Source: County level data from ICPSR 2896 Historical, Demographic, Economic, and SocialData: The United States, 17902000.
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Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 52 / 86
Dynamic Spatial Equilibrium Stylized Facts
Autauga, AL
Baldwin, ALBarbour, ALBibb, AL
Blount, ALBullock, AL
Butler, AL
Calhoun, ALChambers, AL
Cherokee, ALChilton, ALChoctaw, AL
Clarke, ALClay, ALCleburne, ALCoffee, AL
Colbert, AL
Conecuh, AL
Coosa, ALCovington, AL
Crenshaw, ALCullman, AL
Dale, AL
Dallas, AL
De Kalb, ALElmore, AL
Escambia, AL
Etowah, AL
Fayette, AL
Franklin, ALGeneva, AL
Greene, ALHale, AL
Henry, ALHouston, AL
Jackson, AL
Jefferson, AL
Lamar, AL
Lauderdale, AL
Lawrence, AL
Lee, AL
Limestone, AL
Lowndes, AL
Macon, ALMadison, AL
Marengo, AL
Marion, ALMarshall, AL
Mobile, AL
Monroe, AL
Montgomery, AL
Morgan, ALPerry, AL
Pickens, AL
Pike, ALRandolph, AL
Russell, ALSt. Clair, AL
Shelby, ALSumter, AL
Talladega, ALTallapoosa, AL
Tuscaloosa, ALWalker, AL
Washington, AL
Wilcox, ALWinston, AL
Fairfield, CT
Hartford, CTLitchfield, CTMiddlesex, CT
New Haven, CTNew London, CT
Tolland, CTWindham, CT
Kent, DENew Castle, DE
Sussex, DE
District of Columbia
Baker, FL
Bay, FL
Calhoun, FL
Columbia, FLDuval, FLEscambia, FL
Gadsden, FLHamilton, FL
Holmes, FLJackson, FL
Jefferson, FL
Leon, FL
Liberty, FLMadison, FL Nassau, FL
Okaloosa, FL
Santa Rosa, FLSuwannee, FL
Taylor, FL
Union, FLWakulla, FLWalton, FLWashington, FLAppling, GAAtkinson, GABacon, GA
Baker, GA
Baldwin, GA
Banks, GA
Barrow, GA
Bartow, GA
Ben Hill, GABerrien, GA
Bibb, GA
Bleckley, GA
Brantley, GABrooks, GA
Bryan, GABulloch, GA
Burke, GA
Butts, GACalhoun, GA
Camden, GACandler, GACarroll, GA
Catoosa, GACharlton, GAChatham, GA
Chattahoochee, GA
Chattooga, GA
Cherokee, GA
Clarke, GA
Clay, GA
Clayton, GA
Clinch, GACobb, GACoffee, GA
Colquitt, GA
Columbia, GA
Cook, GA
Coweta, GACrawford, GA
Crisp, GADade, GA
Dawson, GA
Decatur, GA
De Kalb, GA
Dodge, GA
Dooly, GA
Dougherty, GA
Douglas, GAEarly, GA
Echols, GA
Effingham, GA
Elbert, GA
Emanuel, GAEvans, GA
Fannin, GA
Fayette, GA
Floyd, GA
Forsyth, GA
Franklin, GA
Fulton, GA
Gilmer, GA
Glascock, GA
Glynn, GA
Gordon, GAGrady, GAGreene, GA
Gwinnett, GA
Habersham, GAHall, GA
Hancock, GA
Haralson, GA
Harris, GAHart, GAHeard, GA
Henry, GAHenry, GA
Houston, GA
Irwin, GAJackson, GA
Jasper, GA
Jeff Davis, GA
Jefferson, GAJenkins, GA
Johnson, GA Jones, GA
Lamar, GA
Lanier, GA
Laurens, GA
Lee, GA
Liberty, GALincoln, GALong, GA
Lowndes, GA
Lumpkin, GA
McDuffie, GAMcIntosh, GA
Macon, GA Madison, GAMarion, GA
Meriwether, GAMiller, GAMitchell, GA
Monroe, GAMontgomery, GA
Morgan, GA
Murray, GA
Muscogee, GA
Newton, GA
Oconee, GA
Oglethorpe, GAPaulding, GA
Peach, GAPickens, GAPierce, GA
Pike, GA
Polk, GA
Pulaski, GA
Putnam, GAQuitman, GA
Rabun, GA
Randolph, GA
Richmond, GA
Rockdale, GASchley, GA
Screven, GA
Seminole, GA
Spalding, GAStephens, GA
Stewart, GA
Sumter, GA
Talbot, GA
Taliaferro, GATattnall, GATaylor, GA
Telfair, GA
Terrell, GA
Thomas, GATift, GAToombs, GA
Towns, GA
Treutlen, GA
Troup, GA
Turner, GATwiggs, GA
Union, GA
Upson, GAWalker, GA
Walton, GA
Ware, GA
Warren, GA
Washington, GA
Wayne, GA
Webster, GA
Wheeler, GA
White, GA
Whitfield, GA
Wilcox, GAWilkes, GA
Wilkinson, GA
Worth, GA
Alexander, ILBond, IL
Boone, ILBureau, ILCarroll, ILChampaign, ILChristian, IL
Clark, ILClay, IL
Clinton, IL
Coles, ILCook, ILCrawford, IL
Cumberland, ILDeKalb, IL
De Witt, ILDouglas, IL DuPage, ILEdgar, ILEdwards, IL
Effingham, ILFayette, IL
Ford, ILFranklin, IL
Gallatin, IL Grundy, IL
Hamilton, IL
Hardin, IL Iroquois, ILJackson, IL
Jasper, IL
Jefferson, IL
Johnson, IL
Kane, ILKankakee, IL
Kendall, ILLake, IL
La Salle, ILLawrence, IL
Lee, ILLivingston, ILLogan, IL
McHenry, ILMcLean, IL
Macon, ILMacoupin, ILMadison, ILMarion, ILMarshall, ILMason, IL
Massac, IL
Menard, ILMontgomery, ILMoultrie, ILOgle, IL
Peoria, IL
Perry, ILPiatt, IL
Pope, IL
Pulaski, ILPutnam, IL
Randolph, ILRichland, ILSt. Clair, IL
Saline, IL Sangamon, ILShelby, IL
Stark, ILStephenson, ILTazewell, IL
Union, IL
Vermilion, ILWabash, IL
Washington, ILWayne, IL
White, ILWhiteside, IL
Will, ILWilliamson, IL
Winnebago, IL
Woodford, ILAdams, INAllen, IN
Bartholomew, INBenton, INBlackford, IN
Boone, IN
Brown, IN
Carroll, INCass, INClark, INClay, INClinton, IN
Crawford, IN
Daviess, INDearborn, INDecatur, INDe Kalb, IN
Delaware, IN
Dubois, IN
Elkhart, INFayette, INFloyd, INFountain, IN
Franklin, IN
Fulton, INGibson, INGrant, IN
Greene, IN
Hamilton, IN
Hancock, INHarrison, IN
Hendricks, IN
Henry, INHoward, INHuntington, IN
Jackson, INJasper, IN
Jay, IN
Jefferson, INJennings, INJohnson, IN
Knox, INKosciusko, INLagrange, IN
Lake, INLa Porte, IN
Lawrence, INMadison, INMarion, IN
Marshall, IN
Martin, INMiami, IN
Monroe, INMontgomery, IN
Morgan, IN
Newton, INNoble, INOhio, INOrange, INOwen, INParke, IN
Perry, INPike, INPorter, IN
Posey, IN
Pulaski, IN
Putnam, IN
Randolph, IN
Ripley, IN
Rush, IN
St. Joseph, IN
Scott, IN Shelby, IN
Spencer, IN
Starke, IN
Steuben, INSullivan, IN
Switzerland, IN
Tippecanoe, IN
Tipton, IN
Union, INVanderburgh, INVermillion, IN
Vigo, INWabash, IN
Warren, INWarrick, INWashington, IN
Wayne, IN
Wells, INWhite, INWhitley, IN
Adair, KYAllen, KY
Anderson, KYBallard, KYBarren, KY
Bath, KY
Bell, KY
Boone, KY
Bourbon, KY
Boyd, KY
Boyle, KYBracken, KY
Breathitt, KY Breckinridge, KYBullitt, KY
Butler, KY
Caldwell, KY
Calloway, KY
Campbell, KY
Carlisle, KYCarroll, KYCarter, KY
Casey, KY
Christian, KYClark, KY
Clay, KY
Clinton, KYCrittenden, KY
Cumberland, KY
Daviess, KY
Edmonson, KY
Elliott, KYEstill, KY Fayette, KY
Fleming, KY
Floyd, KY
Franklin, KYFulton, KY
Gallatin, KYGarrard, KYGrant, KY
Graves, KY
Grayson, KYGreen, KY
Greenup, KY
Hancock, KY
Hardin, KY
Harlan, KY
Harrison, KYHart, KY
Henderson, KY
Henry, KYHickman, KY
Hopkins, KY
Jackson, KY
Jefferson, KY
Jessamine, KYJohnson, KY
Kenton, KY
Knott, KYKnox, KY
Larue, KYLaurel, KYLawrence, KY
Lee, KY
Leslie, KYLetcher, KY
Lewis, KY
Lincoln, KY
Livingston, KY
Logan, KYLyon, KY
McCracken, KYMcCreary, KY
McLean, KYMadison, KY
Magoffin, KY
Marion, KY
Marshall, KY
Martin, KY Mason, KY
Meade, KYMenifee, KY Mercer, KY
Metcalfe, KYMonroe, KY
Montgomery, KY
Morgan, KYMuhlenberg, KY
Nelson, KYNicholas, KY
Ohio, KY Oldham, KY
Owen, KYOwsley, KY Pendleton, KY
Perry, KYPike, KY
Powell, KYPulaski, KYRobertson, KY
Rockcastle, KYRowan, KY
Russell, KYScott, KY
Shelby, KY
Simpson, KYSpencer, KY
Taylor, KYTodd, KY
Trigg, KY
Trimble, KY
Union, KY
Warren, KYWashington, KY
Wayne, KY
Webster, KY
Whitley, KYWolfe, KY
Woodford, KY
Orleans, LA
St. Tammany, LA
Allegany, MD
Anne Arundel, MD
Baltimore, MD
Calvert, MD
Caroline, MD
Carroll, MD
Cecil, MD
Charles, MDDorchester, MD
Frederick, MDGarrett, MD Harford, MD
Howard, MD
Kent, MD
Montgomery, MDPrince George's, MD
Queen Anne's, MD
St. Mary's, MDSomerset, MDTalbot, MD
Washington, MDWicomico, MD
Worcester, MD
Barnstable, MA
Berkshire, MABristol, MA
Dukes, MAEssex, MA
Franklin, MA
Hampden, MA
Hampshire, MAMiddlesex, MA
Nantucket, MA
Norfolk, MAPlymouth, MA
Suffolk, MA
Worcester, MAAllegan, MIBarry, MI
Berrien, MIBranch, MI
Calhoun, MI
Cass, MI
Clinton, MIEaton, MIHillsdale, MI
Ingham, MI
Ionia, MI
Jackson, MIKalamazoo, MILenawee, MI
Livingston, MI
Macomb, MIMonroe, MIOakland, MIOttawa, MI
St. Clair, MISt. Joseph, MIShiawassee, MIVan Buren, MI
Washtenaw, MI
Wayne, MI
Alcorn, MSAttala, MS
Benton, MS
Calhoun, MS
Carroll, MS
Chickasaw, MSChoctaw, MS
Clarke, MSClay, MSCovington, MS
DeSoto, MS
Forrest, MS
George, MSGreene, MS
Grenada, MS
Hancock, MS
Harrison, MS
Itawamba, MS
Jackson, MS
Jasper, MSJefferson Davis, MS
Jones, MS
Kemper, MS
Lafayette, MSLamar, MS
Lauderdale, MS
Leake, MS
Lee, MSLowndes, MS
Marion, MS
Marshall, MS
Monroe, MSMontgomery, MSNeshoba, MS
Newton, MS
Noxubee, MS
Oktibbeha, MS
Panola, MS
Pearl River, MS
Perry, MSPontotoc, MS
Prentiss, MS
Rankin, MS
Scott, MSSimpson, MS
Smith, MS
Stone, MS
Tate, MS
Tippah, MSTishomingo, MSUnion, MS
Wayne, MS
Webster, MSWinston, MSYalobusha, MS
Cape Girardeau, MOMississippi, MO
New Madrid, MO
Pemiscot, MOPerry, MO
Scott, MO
Stoddard, MO
Cheshire, NHHillsborough, NH
Rockingham, NH
Atlantic, NJ Bergen, NJBurlington, NJ
Camden, NJ
Cape May, NJ
Cumberland, NJEssex, NJ
Gloucester, NJ
Hudson, NJ
Hunterdon, NJ
Mercer, NJMiddlesex, NJ
Monmouth, NJMorris, NJOcean, NJ
Passaic, NJSalem, NJ
Somerset, NJSussex, NJ
Union, NJ
Warren, NJ
Albany, NYAllegany, NY
Bronx, NY
Broome, NYCattaraugus, NYChautauqua, NYChemung, NYChenango, NY
Columbia, NYCortland, NYDelaware, NY
Dutchess, NY
Erie, NY
Greene, NY
Kings, NY
Livingston, NYMadison, NY
Montgomery, NY
Nassau, NYNew York, NY
Ontario, NYOrange, NY
Otsego, NY
Putnam, NY
Queens, NY
Rensselaer, NYRichmond, NY
Rockland, NY
Schenectady, NYSchoharie, NYSchuyler, NY
Seneca, NYSteuben, NY
Suffolk, NY
Sullivan, NYTioga, NYTompkins, NYUlster, NY Westchester, NY
Wyoming, NYYates, NYAlamance, NC
Alexander, NC
Alleghany, NC
Anson, NC
Ashe, NCAvery, NC
Beaufort, NC
Bertie, NCBladen, NC
Brunswick, NC
Buncombe, NCBurke, NCCabarrus, NCCaldwell, NC
Camden, NC
Carteret, NC
Caswell, NC
Catawba, NC
Chatham, NCCherokee, NCChowan, NC
Clay, NC
Cleveland, NCColumbus, NC
Craven, NCCumberland, NC
Currituck, NCDare, NC
Davidson, NC
Davie, NC
Duplin, NC
Durham, NCEdgecombe, NCForsyth, NC
Franklin, NC
Gaston, NC
Gates, NC
Graham, NC Granville, NCGreene, NC
Guilford, NCHalifax, NC
Harnett, NC
Haywood, NC
Henderson, NCHertford, NC
Hoke, NCHyde, NC
Iredell, NC
Jackson, NCJohnston, NC
Jones, NC
Lee, NCLenoir, NC
Lincoln, NC
McDowell, NC
Macon, NCMadison, NC
Martin, NC
Mecklenburg, NC
Mitchell, NCMontgomery, NCMoore, NCNash, NCNew Hanover, NC
Northampton, NC
Onslow, NC
Orange, NC
Pamlico, NC
Pasquotank, NC
Pender, NC
Perquimans, NCPerson, NC
Pitt, NCPolk, NC
Randolph, NC
Richmond, NC
Robeson, NC
Rockingham, NCRowan, NC
Rutherford, NC
Sampson, NC
Scotland, NC
Stanly, NC
Stokes, NC
Surry, NC
Swain, NCTransylvania, NC
Tyrrell, NCUnion, NC
Vance, NC
Wake, NCWarren, NC
Washington, NC
Watauga, NC
Wayne, NCWilkes, NC
Wilson, NCYadkin, NC
Yancey, NC
Adams, OH
Allen, OHAshland, OH
Ashtabula, OH
Athens, OHAuglaize, OH
Belmont, OH
Brown, OH
Butler, OHCarroll, OHChampaign, OH
Clark, OH
Clermont, OHClinton, OH
Columbiana, OHCoshocton, OH
Crawford, OHCuyahoga, OH
Darke, OHDefiance, OH
Delaware, OH
Erie, OH
Fairfield, OHFayette, OH
Franklin, OHFulton, OH
Gallia, OH Geauga, OHGreene, OHGuernsey, OH Hamilton, OHHancock, OHHardin, OH
Harrison, OH
Henry, OHHighland, OH
Hocking, OHHolmes, OHHuron, OH
Jackson, OH
Jefferson, OH
Knox, OHLake, OHLawrence, OH
Licking, OHLogan, OH
Lorain, OH
Lucas, OH
Madison, OH
Mahoning, OHMarion, OH
Medina, OHMeigs, OH Mercer, OHMiami, OH
Monroe, OH
Montgomery, OH
Morgan, OHMorrow, OH
Muskingum, OH
Noble, OH
Ottawa, OHPaulding, OHPerry, OH
Pickaway, OH
Pike, OH
Portage, OHPreble, OHPutnam, OH
Richland, OH
Ross, OHSandusky, OH
Scioto, OHSeneca, OH
Shelby, OH
Stark, OHSummit, OHTrumbull, OHTuscarawas, OH
Union, OH
Van Wert, OH
Vinton, OH Warren, OHWashington, OH
Wayne, OHWilliams, OHWood, OHWyandot, OH
Adams, PA
Allegheny, PAArmstrong, PABeaver, PA
Bedford, PA
Berks, PABlair, PABradford, PA
Bucks, PAButler, PA
Cambria, PACameron, PACarbon, PA
Centre, PAChester, PA
Clarion, PAClearfield, PAClinton, PAColumbia, PA
Crawford, PA
Cumberland, PADauphin, PADelaware, PAElk, PAErie, PAFayette, PA
Forest, PAFranklin, PA
Fulton, PA
Greene, PA
Huntingdon, PAIndiana, PAJefferson, PA
Juniata, PALackawanna, PALancaster, PA
Lawrence, PA
Lebanon, PALehigh, PALuzerne, PALycoming, PAMcKean, PAMercer, PA
Mifflin, PA
Monroe, PA Montgomery, PAMontour, PANorthampton, PANorthumberland, PA
Perry, PA
Philadelphia, PA
Pike, PAPotter, PA
Schuylkill, PASnyder, PA
Somerset, PASullivan, PASusquehanna, PATioga, PA
Union, PA
Venango, PAWarren, PAWashington, PAWayne, PA
Westmoreland, PA
Wyoming, PAYork, PA
Bristol, RIKent, RI
Newport, RI
Providence, RI
Washington, RIAbbeville, SC
Aiken, SC
Allendale, SC
Anderson, SC
Bamberg, SC
Barnwell, SCBeaufort, SCBerkeley, SC
Calhoun, SC
Charleston, SCCherokee, SCChester, SC
Chesterfield, SCClarendon, SCColleton, SC
Darlington, SCDillon, SC
Dorchester, SC
Edgefield, SC
Fairfield, SCFlorence, SC
Georgetown, SC
Greenville, SCGreenwood, SC
Hampton, SCHorry, SC
Jasper, SCKershaw, SC
Lancaster, SCLaurens, SC
Lee, SC
Lexington, SC
McCormick, SC
Marion, SC
Marlboro, SC
Newberry, SC
Oconee, SC
Orangeburg, SC
Pickens, SCRichland, SC
Saluda, SC
Spartanburg, SC
Sumter, SC
Union, SC
Williamsburg, SC
York, SC
Anderson, TN
Bedford, TNBenton, TN
Bledsoe, TN
Blount, TNBradley, TN
Campbell, TN
Cannon, TNCarroll, TN
Carter, TN
Cheatham, TNChester, TN
Claiborne, TN
Clay, TN
Cocke, TN Coffee, TNCrockett, TN
Cumberland, TN
Davidson, TN
Decatur, TNDeKalb, TN
Dickson, TN
Dyer, TN
Fayette, TN
Fentress, TN Franklin, TNGibson, TN
Giles, TNGrainger, TN
Greene, TNGrundy, TN
Hamblen, TNHamilton, TN
Hancock, TNHardeman, TN
Hardin, TNHawkins, TN
Haywood, TN
Henderson, TN
Henry, TN
Hickman, TNHouston, TN
Humphreys, TNJackson, TN
Jefferson, TNJohnson, TN
Knox, TN
Lake, TNLauderdale, TNLawrence, TNLewis, TNLincoln, TNLoudon, TN
McMinn, TN
McNairy, TN
Macon, TN
Madison, TNMarion, TNMarshall, TNMaury, TN
Meigs, TNMonroe, TN
Montgomery, TN
Moore, TN
Morgan, TNObion, TN
Overton, TNPerry, TNPickett, TN
Polk, TN
Putnam, TN
Rhea, TNRoane, TN
Robertson, TNRutherford, TN
Scott, TNSequatchie, TNSevier, TN
Shelby, TN
Smith, TN
Stewart, TN
Sullivan, TN
Sumner, TNTipton, TNTrousdale, TN
Unicoi, TN
Union, TN
Van Buren, TN
Warren, TN
Washington, TN
Wayne, TNWeakley, TNWhite, TN
Williamson, TN
Wilson, TN
Windham, VTAccomack, VA
Albemarle, VA
Alleghany, VA
Amelia, VA
Amherst, VA
Appomattox, VA
Arlington, VA
Augusta, VABath, VABedford, VABland, VA Botetourt, VABrunswick, VA
Buchanan, VA
Buckingham, VA
Campbell, VA
Caroline, VACarroll, VA
Charles City, VACharlotte, VA
Chesterfield, VA
Clarke, VACraig, VA
Culpeper, VA
Cumberland, VA
Dickenson, VA
Dinwiddie, VAEssex, VA
Fairfax, VA
Fauquier, VAFloyd, VA
Fluvanna, VA
Franklin, VAFrederick, VA
Giles, VA
Gloucester, VAGoochland, VA
Grayson, VA
Greene, VA
Greensville, VAHalifax, VA
Hanover, VA
Henrico, VAHenry, VA
Highland, VA
Isle of Wight, VA
James City, VA
King and Queen, VA
King George, VA
King William, VALancaster, VA
Lee, VA
Loudoun, VA
Louisa, VA
Lunenburg, VA
Madison, VAMathews, VA
Mecklenburg, VA
Middlesex, VA
Montgomery, VA
Nelson, VANew Kent, VA
Northampton, VANorthumberland, VA
Nottoway, VA
Orange, VAPage, VA
Patrick, VA
Pittsylvania, VA
Powhatan, VA
Prince Edward, VA
Prince George, VA
Prince William, VA
Pulaski, VA
Rappahannock, VA
Richmond, VA
Roanoke, VARockbridge, VARockingham, VARussell, VAScott, VA
Shenandoah, VA
Smyth, VA
Southampton, VASpotsylvania, VAStafford, VASurry, VA
Sussex, VA
Tazewell, VAWarren, VA
Washington, VAWestmoreland, VA
Wise, VA
Wythe, VAYork, VA
Alexandria, VABristol, VABuena Vista, VA
Charlottesville, VA
Clifton Forge, VA
Colonial Heights, VADanville, VA
Falls Church, VA
Fredericksburg, VAHampton, VAHarrisonburg, VA
Hopewell, VALynchburg, VA
Martinsville, VA
Newport News, VA
Norfolk, VAPetersburg, VAPortsmouth, VARadford, VA
Richmond, VARoanoke, VA
Staunton, VASuffolk, VA
Waynesboro, VAWilliamsburg, VA
Winchester, VABarbour, WV Berkeley, WV
Boone, WV
Braxton, WV
Brooke, WVCabell, WV
Calhoun, WV
Clay, WV
Doddridge, WV
Fayette, WV
Gilmer, WV
Grant, WV
Greenbrier, WV
Hampshire, WV
Hancock, WV
Hardy, WV
Harrison, WV
Jackson, WVJefferson, WV
Kanawha, WV
Lewis, WVLincoln, WV
Logan, WV
McDowell, WV
Marion, WVMarshall, WV
Mason, WV
Mercer, WV
Mineral, WV
Mingo, WV
Monongalia, WV
Monroe, WVMorgan, WV
Nicholas, WVOhio, WV
Pendleton, WV
Pleasants, WVPocahontas, WVPreston, WV
Putnam, WV
Raleigh, WVRandolph, WV
Ritchie, WVRoane, WV
Summers, WVTaylor, WV
Tucker, WVTyler, WVUpshur, WV
Wayne, WV
Webster, WV
Wetzel, WV
Wirt, WV
Wood, WVWyoming, WV
Green, WIKenosha, WI
Racine, WIRock, WIWalworth, WI
23
45
Log
Cha
nge
in M
edia
n In
com
e, 1
950
2000
6 6.5 7 7.5 8 8.5Log of Median Income, 1950
Figure 4The Convergence of Median Incomes
Back
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 53 / 86
Dynamic Spatial Equilibrium Stylized Facts
Growth and Poverty
41
Figure 5: Growth and poverty, 1990-2000
log(p
op g
row
th), 1
990-2
000
% poor, 19890 10 20 30 40
-.2
0
.2
.4
.6
Sample is all cities with population of 100,000 or more in 1990 (195 observations).
Back
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 54 / 86
Dynamic Spatial Equilibrium Stylized Facts
Post-War U.S. Urban and Regional Growth1 Dispersion of manufacturing
I Manufacturing predicts the decline of cities Figure
F Specialization in health services even more so Figure
I Manufacturing does not predict the decline of counties Figure
2 Education predicts population Figure and income Table growthI Also predicts growth in education itself Figure
I Greater impact in larger cities (Glaeser and Resseger 2010)I Seemingly true in the past as well (Simon and Nardinelli 2002)
3 Small firm size predicts employment Figure and income growth Table
I Effect at the city-industry level (Glaeser, Kerr and Ponzetto 2010)
4 Good weather predicts population Figure and income Figure growth
Productivity channel: population and income co-move
Back Forward
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 55 / 86
Dynamic Spatial Equilibrium Stylized Facts
Manufacturing and Urban Decline
capita income in 1980. These taxes are meant to include city-level taxes from all sources.The graph shows a significant negative relationship, and also shows that Boston is amongthe highest taxed cities in the sample. The line in the graph tells us that as taxes (relative toincome) rise by 1%, the expected growth rate during the 1920–1980 period declines by 6%.
Manufacturing Employment Share
Population Growth 20-80 .
0 20 40
-1
0
1
2
3
SAN DIEG
LOS ANGE
DULUTH,
SIOUX CI
NEW ORLE SALT LAKDES MOIN
MANCHEST
TACOMA, SPOKANE,
KNOXVILL
FLINT, M
WATERBUR
FALL RIV
BIRMINGH
WICHITA,
HOUSTON,
SPRINGFI
PORTLAND
DENVER,
ST. PAUL
SAN ANTO
SOUTH BEOAKLAND,
ERIE, PA
WORCESTE
YONKERS,OMAHA, N
YOUNGSTO
OKLAHOMA
SEATTLE,
FORT WAY
KANSAS C
CINCINNAUTICA, N
ST. JOSE
JACKSONV
ALBANY, NEW BEDF
KANSAS C
EL PASO,
FORT WORNASHVILL
TROY, NY
MEMPHIS,
DALLAS,
CANTON,
SCRANTON
RICHMOND
INDIANAP
WASHINGT ALLENTOW
ATLANTA,
MINNEAPO
GRAND RA
PEORIA, TOLEDO,
LOWELL, HARTFORD
NEW HAVE
AKRON, O
TULSA, O
BALTIMORSYRACUSE
DAYTON, EVANSVIL
BRIDGEPOELIZABET
ROCHESTE
LOUISVIL
COLUMBUS
READING,SCHENECT
SAVANNAH
SAN FRAN
ST. LOUI
DETROIT,
HARRISBU BUFFALO, PROVIDEN
CHICAGO,
LAWRENCECLEVELANWILKES-B
PHILADEL
PITTSBUR CAMDEN,
NORFOLK,
WILMINGT
PATERSON
TRENTON,BOSTON, CAMBRIDG
NEWARK,
MILWAUKENEW YORK
BAYONNE,JERSEY C
SOMERVIL
Figure 9. Manufacturing and urbane decline.
Source: Population numbers are from theUSCensus. The observations are cities.Manufacturing EmploymentShare refers to the number of employees residing in the city who are in manufacturing industries.
CityTaxes/Income 1980
Population Growth 20-80 .
0 .05 .1 .15 .2
-.60854
2.46159 SAN DIEG
LOS ANGE
DULUTH,
SIOUX CI
NEW ORLESALT LAKDES MOIN
MANCHEST
TACOMA, SPOKANE,
KNOXVILL
FLINT, M
WATERBUR
FALL RIV
BIRMINGH
WICHITA,
HOUSTON,
SPRINGFI
PORTLAND
DENVER,
ST. PAUL
SAN ANTO
SOUTH BEOAKLAND,
ERIE, PA
WORCESTE
YONKERS,
OMAHA, N
YOUNGSTO
OKLAHOMA
SEATTLE,
FORT WAY
KANSAS C
CINCINNA
UTICA, N
ST. JOSE
JACKSONV
ALBANY, NEW BEDF
KANSAS C
EL PASO,
FORT WORNASHVILL
TROY, NY
MEMPHIS,
DALLAS,
CANTON,
SCRANTON
RICHMOND
INDIANAP
WASHINGTALLENTOW
ATLANTA,
MINNEAPO
GRAND RA
PEORIA, TOLEDO,
LOWELL,
HARTFORD
NEW HAVE
AKRON, O
TULSA, O
BALTIMORSYRACUSE
DAYTON, EVANSVIL
BRIDGEPOELIZABET
ROCHESTE
LOUISVIL
COLUMBUS
READING,SCHENECT
SAVANNAH
SAN FRAN
ST. LOUI
DETROIT,
HARRISBU BUFFALO,PROVIDEN
CHICAGO,
LAWRENCECLEVELANWILKES-B
PHILADEL
PITTSBURCAMDEN,
NORFOLK,
WILMINGT
PATERSON
TRENTON, BOSTON,
CAMBRIDGNEWARK,
MILWAUKENEW YORK
BAYONNE,JERSEY C
SOMERVIL
Figure 10. Taxes and city growth, 1920–1980.
Source: Population data are from theUSCensus. City taxes and income are fromCity andCountyData Booksand refer to the end period of 1980.
142 ! Glaeser
Back
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 56 / 86
Dynamic Spatial Equilibrium Stylized Facts
Growth and Health Services
40
Figure 4: Growth and health services, 1990-2000
log(p
op g
row
th), 1
990-2
000
% health services, 19905 10 15 20
-.2
0
.2
.4
.6
Sample is all cities with population of 100,000 or more in 1990 (195 observations).
Back
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 57 / 86
Dynamic Spatial Equilibrium Stylized Facts
Autauga, AL
Baldwin, AL
Barbour, ALBibb, AL
Blount, AL
Bullock, AL Butler, AL
Calhoun, AL
Chambers, AL
Cherokee, ALChilton, AL
Choctaw, ALClarke, ALClay, ALCleburne, AL
Coffee, ALColbert, AL
Conecuh, AL
Coosa, ALCovington, ALCrenshaw, AL
Cullman, AL
Dale, AL
Dallas, AL
De Kalb, AL
Elmore, AL
Escambia, AL Etowah, ALFayette, AL
Franklin, ALGeneva, AL
Greene, AL
Hale, ALHenry, AL
Houston, AL
Jackson, ALJefferson, AL
Lamar, AL
Lauderdale, ALLawrence, AL
Lee, AL
Limestone, AL
Lowndes, ALMacon, AL
Madison, AL
Marengo, AL
Marion, AL
Marshall, ALMobile, AL
Monroe, AL
Montgomery, ALMorgan, AL
Perry, AL
Pickens, ALPike, AL Randolph, ALRussell, AL
St. Clair, AL
Shelby, AL
Sumter, AL
Talladega, ALTallapoosa, AL
Tuscaloosa, AL
Walker, AL Washington, AL
Wilcox, AL
Winston, ALFairfield, CTHartford, CT
Litchfield, CTMiddlesex, CT
New Haven, CTNew London, CT
Tolland, CT
Windham, CT
Kent, DE
New Castle, DESussex, DE
District of Columbia
Baker, FLBay, FL
Calhoun, FL
Columbia, FLDuval, FLEscambia, FL
Gadsden, FLHamilton, FLHolmes, FLJackson, FLJefferson, FL
Leon, FL
Liberty, FL
Madison, FL
Nassau, FL
Okaloosa, FLSanta Rosa, FL
Suwannee, FLTaylor, FL
Union, FL
Wakulla, FL
Walton, FL
Washington, FL
Appling, GAAtkinson, GABacon, GA
Baker, GA
Baldwin, GA
Banks, GA
Barrow, GABartow, GA
Ben Hill, GABerrien, GABibb, GABleckley, GA
Brantley, GA
Brooks, GA
Bryan, GA
Bulloch, GA
Burke, GA
Butts, GA
Calhoun, GA
Camden, GA
Candler, GA
Carroll, GA
Catoosa, GA
Charlton, GA
Chatham, GAChattahoochee, GA Chattooga, GA
Cherokee, GA
Clarke, GA
Clay, GA
Clayton, GA
Clinch, GA
Cobb, GA
Coffee, GAColquitt, GA
Columbia, GA
Cook, GA
Coweta, GA
Crawford, GA
Crisp, GA
Dade, GA
Dawson, GA
Decatur, GA
De Kalb, GA
Dodge, GADooly, GA
Dougherty, GA
Douglas, GA
Early, GA
Echols, GA
Effingham, GA
Elbert, GAEmanuel, GA
Evans, GAFannin, GA
Fayette, GA
Floyd, GA
Forsyth, GA
Franklin, GAFulton, GA
Gilmer, GA
Glascock, GA
Glynn, GA Gordon, GA
Grady, GA Greene, GA
Gwinnett, GA
Habersham, GA
Hall, GA
Hancock, GA
Haralson, GAHarris, GA
Hart, GA Heard, GA
Henry, GAHenry, GA
Houston, GA
Irwin, GA
Jackson, GA
Jasper, GAJeff Davis, GA
Jefferson, GAJenkins, GAJohnson, GA
Jones, GA
Lamar, GALanier, GALaurens, GA
Lee, GA
Liberty, GA
Lincoln, GA
Long, GALowndes, GALumpkin, GA
McDuffie, GAMcIntosh, GA
Macon, GA
Madison, GA
Marion, GAMeriwether, GA
Miller, GA
Mitchell, GA
Monroe, GA
Montgomery, GAMorgan, GA
Murray, GA
Muscogee, GA
Newton, GAOconee, GA
Oglethorpe, GA
Paulding, GA
Peach, GA
Pickens, GA
Pierce, GAPike, GA
Polk, GAPulaski, GA
Putnam, GA
Quitman, GA
Rabun, GA
Randolph, GA
Richmond, GA
Rockdale, GA
Schley, GAScreven, GA
Seminole, GA
Spalding, GAStephens, GA
Stewart, GA
Sumter, GA
Talbot, GA
Taliaferro, GA
Tattnall, GA
Taylor, GATelfair, GATerrell, GA
Thomas, GA
Tift, GAToombs, GA
Towns, GA
Treutlen, GATroup, GA
Turner, GA
Twiggs, GA
Union, GA
Upson, GA
Walker, GA
Walton, GA
Ware, GA
Warren, GA
Washington, GA
Wayne, GA
Webster, GA
Wheeler, GA
White, GA
Whitfield, GA
Wilcox, GA Wilkes, GAWilkinson, GAWorth, GA
Alexander, IL
Bond, IL
Boone, IL
Bureau, ILCarroll, IL
Champaign, IL
Christian, ILClark, ILClay, IL
Clinton, ILColes, IL Cook, IL
Crawford, ILCumberland, IL
DeKalb, IL
De Witt, ILDouglas, IL
DuPage, IL
Edgar, ILEdwards, IL
Effingham, IL
Fayette, ILFord, ILFranklin, ILGallatin, IL
Grundy, IL
Hamilton, ILHardin, IL
Iroquois, IL
Jackson, IL
Jasper, IL
Jefferson, IL
Johnson, IL
Kane, IL
Kankakee, IL
Kendall, ILLake, IL
La Salle, IL
Lawrence, ILLee, ILLivingston, ILLogan, IL
McHenry, IL
McLean, IL
Macon, ILMacoupin, ILMadison, IL
Marion, ILMarshall, ILMason, IL Massac, ILMenard, IL
Montgomery, ILMoultrie, IL
Ogle, IL
Peoria, ILPerry, ILPiatt, IL
Pope, IL
Pulaski, IL
Putnam, ILRandolph, IL
Richland, ILSt. Clair, IL
Saline, IL
Sangamon, IL
Shelby, ILStark, IL
Stephenson, IL
Tazewell, IL
Union, IL Vermilion, ILWabash, ILWashington, IL
Wayne, ILWhite, IL
Whiteside, IL
Will, IL
Williamson, IL
Winnebago, ILWoodford, ILAdams, IN
Allen, INBartholomew, IN
Benton, INBlackford, IN
Boone, INBrown, IN
Carroll, INCass, IN
Clark, IN
Clay, INClinton, INCrawford, INDaviess, IN
Dearborn, IN
Decatur, INDe Kalb, IN
Delaware, INDubois, IN
Elkhart, IN
Fayette, IN
Floyd, IN
Fountain, IN
Franklin, INFulton, INGibson, IN Grant, INGreene, IN
Hamilton, IN
Hancock, IN
Harrison, IN
Hendricks, IN
Henry, IN
Howard, INHuntington, IN
Jackson, INJasper, IN
Jay, IN
Jefferson, INJennings, IN
Johnson, IN
Knox, IN
Kosciusko, INLagrange, IN
Lake, INLa Porte, INLawrence, IN Madison, INMarion, INMarshall, IN
Martin, INMiami, IN
Monroe, IN
Montgomery, IN
Morgan, IN
Newton, IN
Noble, IN
Ohio, INOrange, IN
Owen, IN
Parke, IN Perry, INPike, IN
Porter, IN
Posey, INPulaski, IN
Putnam, IN
Randolph, IN
Ripley, IN
Rush, IN
St. Joseph, IN
Scott, INShelby, IN
Spencer, INStarke, IN
Steuben, IN
Sullivan, INSwitzerland, IN
Tippecanoe, IN
Tipton, INUnion, IN Vanderburgh, INVermillion, IN
Vigo, INWabash, IN
Warren, IN
Warrick, IN
Washington, IN
Wayne, IN
Wells, INWhite, INWhitley, IN
Adair, KYAllen, KY
Anderson, KY
Ballard, KY
Barren, KYBath, KY
Bell, KY
Boone, KY
Bourbon, KY Boyd, KY
Boyle, KY
Bracken, KYBreathitt, KY
Breckinridge, KY
Bullitt, KY
Butler, KYCaldwell, KY
Calloway, KY
Campbell, KY
Carlisle, KY
Carroll, KYCarter, KYCasey, KY
Christian, KYClark, KY
Clay, KYClinton, KYCrittenden, KYCumberland, KY
Daviess, KYEdmonson, KY
Elliott, KY Estill, KY
Fayette, KY
Fleming, KY
Floyd, KY
Franklin, KY
Fulton, KY
Gallatin, KY
Garrard, KY
Grant, KY
Graves, KYGrayson, KY
Green, KY
Greenup, KYHancock, KY
Hardin, KY
Harlan, KY
Harrison, KYHart, KY
Henderson, KYHenry, KY
Hickman, KY
Hopkins, KYJackson, KY
Jefferson, KY
Jessamine, KY
Johnson, KY
Kenton, KY
Knott, KYKnox, KY
Larue, KY
Laurel, KY
Lawrence, KYLee, KY
Leslie, KYLetcher, KY
Lewis, KYLincoln, KYLivingston, KY
Logan, KYLyon, KY McCracken, KYMcCreary, KYMcLean, KY
Madison, KY
Magoffin, KY Marion, KY
Marshall, KY
Martin, KYMason, KY
Meade, KY
Menifee, KYMercer, KY
Metcalfe, KYMonroe, KY
Montgomery, KY
Morgan, KYMuhlenberg, KY
Nelson, KY
Nicholas, KYOhio, KY
Oldham, KY
Owen, KY
Owsley, KY
Pendleton, KY
Perry, KYPike, KY
Powell, KYPulaski, KY
Robertson, KY
Rockcastle, KY
Rowan, KY
Russell, KY
Scott, KYShelby, KY
Simpson, KY
Spencer, KYTaylor, KY
Todd, KY
Trigg, KYTrimble, KY
Union, KY
Warren, KY
Washington, KY
Wayne, KYWebster, KYWhitley, KY
Wolfe, KY
Woodford, KY
Orleans, LA
St. Tammany, LA
Allegany, MD
Anne Arundel, MD
Baltimore, MD
Calvert, MD
Caroline, MD
Carroll, MDCecil, MD
Charles, MD
Dorchester, MD
Frederick, MD
Garrett, MD
Harford, MD
Howard, MD
Kent, MD
Montgomery, MDPrince George's, MD
Queen Anne's, MDSt. Mary's, MD
Somerset, MD
Talbot, MD Washington, MDWicomico, MDWorcester, MD
Barnstable, MA
Berkshire, MA
Bristol, MA
Dukes, MA
Essex, MAFranklin, MAHampden, MA
Hampshire, MAMiddlesex, MA
Nantucket, MA
Norfolk, MA
Plymouth, MA
Suffolk, MA
Worcester, MA
Allegan, MIBarry, MI
Berrien, MIBranch, MICalhoun, MI
Cass, MIClinton, MI
Eaton, MI
Hillsdale, MIIngham, MIIonia, MIJackson, MIKalamazoo, MILenawee, MI
Livingston, MI
Macomb, MI
Monroe, MI
Oakland, MIOttawa, MI
St. Clair, MISt. Joseph, MIShiawassee, MI
Van Buren, MIWashtenaw, MI
Wayne, MI
Alcorn, MS
Attala, MSBenton, MSCalhoun, MS
Carroll, MS
Chickasaw, MSChoctaw, MS Clarke, MS
Clay, MSCovington, MS
DeSoto, MS
Forrest, MSGeorge, MS
Greene, MSGrenada, MS
Hancock, MS
Harrison, MS
Itawamba, MS
Jackson, MS
Jasper, MSJefferson Davis, MSJones, MS
Kemper, MS
Lafayette, MS
Lamar, MS
Lauderdale, MSLeake, MS
Lee, MSLowndes, MS
Marion, MSMarshall, MS
Monroe, MSMontgomery, MS
Neshoba, MSNewton, MS
Noxubee, MS
Oktibbeha, MS
Panola, MS
Pearl River, MS
Perry, MSPontotoc, MSPrentiss, MS
Rankin, MS
Scott, MSSimpson, MSSmith, MS
Stone, MS
Tate, MSTippah, MSTishomingo, MSUnion, MSWayne, MS
Webster, MSWinston, MSYalobusha, MS
Cape Girardeau, MO
Mississippi, MONew Madrid, MOPemiscot, MO
Perry, MOScott, MO
Stoddard, MO
Cheshire, NHHillsborough, NH
Rockingham, NH
Atlantic, NJBergen, NJ
Burlington, NJ
Camden, NJ
Cape May, NJ
Cumberland, NJ
Essex, NJ
Gloucester, NJ
Hudson, NJ
Hunterdon, NJ
Mercer, NJ
Middlesex, NJMonmouth, NJMorris, NJ
Ocean, NJ
Passaic, NJSalem, NJ
Somerset, NJ
Sussex, NJ
Union, NJ
Warren, NJ
Albany, NYAllegany, NYBronx, NY
Broome, NYCattaraugus, NYChautauqua, NYChemung, NYChenango, NYColumbia, NYCortland, NY
Delaware, NY
Dutchess, NY
Erie, NY
Greene, NY
Kings, NY
Livingston, NYMadison, NY
Montgomery, NY
Nassau, NY
New York, NY
Ontario, NY
Orange, NY
Otsego, NY
Putnam, NY
Queens, NYRensselaer, NY
Richmond, NY
Rockland, NY
Schenectady, NY
Schoharie, NYSchuyler, NYSeneca, NYSteuben, NY
Suffolk, NY
Sullivan, NY Tioga, NYTompkins, NYUlster, NY
Westchester, NYWyoming, NYYates, NY
Alamance, NCAlexander, NC
Alleghany, NC
Anson, NCAshe, NC
Avery, NCBeaufort, NC
Bertie, NC
Bladen, NC
Brunswick, NC
Buncombe, NCBurke, NCCabarrus, NC
Caldwell, NCCamden, NC
Carteret, NC
Caswell, NC
Catawba, NCChatham, NC
Cherokee, NCChowan, NC
Clay, NC Cleveland, NC
Columbus, NC
Craven, NC
Cumberland, NCCurrituck, NC
Dare, NC
Davidson, NCDavie, NC
Duplin, NC
Durham, NC
Edgecombe, NC
Forsyth, NC
Franklin, NC Gaston, NC
Gates, NCGraham, NC
Granville, NC
Greene, NC
Guilford, NC
Halifax, NC
Harnett, NC
Haywood, NC
Henderson, NC
Hertford, NC
Hoke, NC
Hyde, NC
Iredell, NCJackson, NCJohnston, NC
Jones, NC
Lee, NC
Lenoir, NC
Lincoln, NC
McDowell, NCMacon, NC
Madison, NCMartin, NC
Mecklenburg, NC
Mitchell, NC
Montgomery, NC
Moore, NC
Nash, NC
New Hanover, NC
Northampton, NC
Onslow, NC Orange, NC
Pamlico, NCPasquotank, NC
Pender, NC
Perquimans, NCPerson, NC
Pitt, NCPolk, NC
Randolph, NC
Richmond, NCRobeson, NC Rockingham, NC
Rowan, NCRutherford, NCSampson, NC Scotland, NC
Stanly, NCStokes, NC
Surry, NCSwain, NC
Transylvania, NC
Tyrrell, NC
Union, NC
Vance, NC
Wake, NC
Warren, NCWashington, NC
Watauga, NCWayne, NC
Wilkes, NCWilson, NCYadkin, NC
Yancey, NCAdams, OH Allen, OH
Ashland, OHAshtabula, OHAthens, OH
Auglaize, OH
Belmont, OH
Brown, OHButler, OH
Carroll, OHChampaign, OHClark, OH
Clermont, OH
Clinton, OH
Columbiana, OHCoshocton, OHCrawford, OHCuyahoga, OH
Darke, OHDefiance, OH
Delaware, OH
Erie, OH
Fairfield, OH
Fayette, OH
Franklin, OHFulton, OH
Gallia, OH
Geauga, OH
Greene, OH
Guernsey, OHHamilton, OH
Hancock, OH
Hardin, OHHarrison, OH
Henry, OHHighland, OHHocking, OH
Holmes, OH
Huron, OHJackson, OH
Jefferson, OH
Knox, OH
Lake, OH
Lawrence, OH
Licking, OH
Logan, OHLorain, OH
Lucas, OH
Madison, OH
Mahoning, OHMarion, OH
Medina, OH
Meigs, OH
Mercer, OHMiami, OH
Monroe, OH
Montgomery, OHMorgan, OH
Morrow, OH
Muskingum, OHNoble, OHOttawa, OHPaulding, OH
Perry, OH
Pickaway, OHPike, OHPortage, OH
Preble, OHPutnam, OH Richland, OHRoss, OH Sandusky, OH
Scioto, OHSeneca, OH
Shelby, OHStark, OHSummit, OHTrumbull, OHTuscarawas, OH
Union, OH
Van Wert, OHVinton, OH
Warren, OH
Washington, OH
Wayne, OHWilliams, OH
Wood, OH
Wyandot, OH
Adams, PA
Allegheny, PAArmstrong, PABeaver, PA
Bedford, PABerks, PA
Blair, PABradford, PA
Bucks, PA
Butler, PA
Cambria, PACameron, PA
Carbon, PA
Centre, PA
Chester, PA
Clarion, PAClearfield, PA Clinton, PAColumbia, PACrawford, PA
Cumberland, PA
Dauphin, PA Delaware, PAElk, PA
Erie, PA
Fayette, PAForest, PA
Franklin, PAFulton, PA
Greene, PAHuntingdon, PAIndiana, PA
Jefferson, PA
Juniata, PA
Lackawanna, PA
Lancaster, PA
Lawrence, PA
Lebanon, PALehigh, PA
Luzerne, PA
Lycoming, PA
McKean, PAMercer, PAMifflin, PA
Monroe, PA
Montgomery, PA
Montour, PANorthampton, PA
Northumberland, PA
Perry, PA
Philadelphia, PA
Pike, PA
Potter, PA
Schuylkill, PA
Snyder, PA
Somerset, PA Sullivan, PA
Susquehanna, PATioga, PA
Union, PA
Venango, PAWarren, PAWashington, PA
Wayne, PA
Westmoreland, PA
Wyoming, PAYork, PABristol, RIKent, RI
Newport, RIProvidence, RI
Washington, RI
Abbeville, SC
Aiken, SC
Allendale, SC
Anderson, SC
Bamberg, SC
Barnwell, SC
Beaufort, SCBerkeley, SC
Calhoun, SC
Charleston, SCCherokee, SC
Chester, SCChesterfield, SC
Clarendon, SCColleton, SCDarlington, SC
Dillon, SC
Dorchester, SC
Edgefield, SC
Fairfield, SC
Florence, SCGeorgetown, SC
Greenville, SC
Greenwood, SC
Hampton, SC
Horry, SC
Jasper, SCKershaw, SC Lancaster, SCLaurens, SC
Lee, SC
Lexington, SC
McCormick, SCMarion, SCMarlboro, SC
Newberry, SC
Oconee, SCOrangeburg, SC
Pickens, SCRichland, SC
Saluda, SC
Spartanburg, SCSumter, SC
Union, SCWilliamsburg, SC
York, SC
Anderson, TN
Bedford, TNBenton, TNBledsoe, TN
Blount, TN
Bradley, TN
Campbell, TNCannon, TN
Carroll, TNCarter, TN
Cheatham, TN
Chester, TNClaiborne, TN
Clay, TN
Cocke, TN
Coffee, TN
Crockett, TN
Cumberland, TN
Davidson, TN
Decatur, TNDeKalb, TN
Dickson, TN
Dyer, TNFayette, TN Fentress, TN
Franklin, TN
Gibson, TNGiles, TN
Grainger, TNGreene, TN
Grundy, TN
Hamblen, TN
Hamilton, TN
Hancock, TN
Hardeman, TNHardin, TN
Hawkins, TN
Haywood, TN
Henderson, TNHenry, TN
Hickman, TNHouston, TNHumphreys, TN
Jackson, TN
Jefferson, TN
Johnson, TNKnox, TN
Lake, TN
Lauderdale, TNLawrence, TN
Lewis, TN
Lincoln, TN
Loudon, TNMcMinn, TNMcNairy, TN
Macon, TNMadison, TNMarion, TN
Marshall, TNMaury, TNMeigs, TNMonroe, TN
Montgomery, TN
Moore, TNMorgan, TN
Obion, TNOverton, TNPerry, TNPickett, TN
Polk, TN
Putnam, TNRhea, TNRoane, TN
Robertson, TN
Rutherford, TN
Scott, TN
Sequatchie, TN
Sevier, TN
Shelby, TN
Smith, TNStewart, TNSullivan, TN
Sumner, TN
Tipton, TNTrousdale, TN
Unicoi, TN
Union, TN
Van Buren, TNWarren, TNWashington, TN
Wayne, TNWeakley, TNWhite, TN
Williamson, TN
Wilson, TN
Windham, VT
Accomack, VA
Albemarle, VA
Alleghany, VA
Amelia, VAAmherst, VAAppomattox, VAArlington, VA
Augusta, VA
Bath, VA
Bedford, VA
Bland, VA
Botetourt, VA
Brunswick, VABuchanan, VA
Buckingham, VA
Campbell, VACaroline, VA
Carroll, VA
Charles City, VA
Charlotte, VA
Chesterfield, VA
Clarke, VACraig, VA
Culpeper, VA
Cumberland, VA
Dickenson, VA
Dinwiddie, VAEssex, VA
Fairfax, VA
Fauquier, VA
Floyd, VA
Fluvanna, VA
Franklin, VA
Frederick, VA
Giles, VA
Gloucester, VA
Goochland, VA
Grayson, VA
Greene, VA
Greensville, VAHalifax, VA
Hanover, VAHenrico, VA
Henry, VA
Highland, VA
Isle of Wight, VA
James City, VA
King and Queen, VA
King George, VA
King William, VALancaster, VA
Lee, VA
Loudoun, VA
Louisa, VA
Lunenburg, VA
Madison, VAMathews, VA
Mecklenburg, VA
Middlesex, VA
Montgomery, VA
Nelson, VA
New Kent, VA
Northampton, VA
Northumberland, VANottoway, VA
Orange, VA
Page, VAPatrick, VA
Pittsylvania, VA
Powhatan, VA
Prince Edward, VAPrince George, VA
Prince William, VA
Pulaski, VARappahannock, VARichmond, VA
Roanoke, VA
Rockbridge, VA
Rockingham, VA
Russell, VAScott, VA
Shenandoah, VA
Smyth, VA
Southampton, VA
Spotsylvania, VAStafford, VA
Surry, VASussex, VATazewell, VA
Warren, VA
Washington, VAWestmoreland, VA
Wise, VA
Wythe, VA
York, VA
Alexandria, VA
Bristol, VA Buena Vista, VA
Charlottesville, VA
Clifton Forge, VA
Colonial Heights, VA
Danville, VAFalls Church, VAFredericksburg, VA
Hampton, VA
Harrisonburg, VA
Hopewell, VA
Lynchburg, VA
Martinsville, VA
Newport News, VA
Norfolk, VAPetersburg, VA
Portsmouth, VA
Radford, VA
Richmond, VARoanoke, VA
Staunton, VA
Suffolk, VA
Waynesboro, VAWilliamsburg, VA Winchester, VA
Barbour, WV
Berkeley, WV
Boone, WVBraxton, WVBrooke, WVCabell, WV
Calhoun, WVClay, WVDoddridge, WV
Fayette, WVGilmer, WV
Grant, WV
Greenbrier, WV
Hampshire, WV
Hancock, WV
Hardy, WV
Harrison, WV
Jackson, WV
Jefferson, WV
Kanawha, WVLewis, WVLincoln, WV
Logan, WV
McDowell, WV
Marion, WVMarshall, WV
Mason, WVMercer, WV
Mineral, WV
Mingo, WV
Monongalia, WVMonroe, WV
Morgan, WV
Nicholas, WV
Ohio, WVPendleton, WV
Pleasants, WV
Pocahontas, WVPreston, WV
Putnam, WV
Raleigh, WVRandolph, WV
Ritchie, WVRoane, WVSummers, WV
Taylor, WVTucker, WV
Tyler, WV
Upshur, WV Wayne, WV
Webster, WV
Wetzel, WV
Wirt, WVWood, WV
Wyoming, WV
Green, WI
Kenosha, WIRacine, WIRock, WI
Walworth, WI
10
12
3Lo
g C
hang
e in
Pop
ulat
ion,
195
020
00
0 .2 .4 .6 .8Share of Workers in Manufacturing, 1950
Figure 5Population Growth and Share in Manufacturing in 1950
Back
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 58 / 86
Dynamic Spatial Equilibrium Stylized Facts
City Growth and Education
Boston had a number of features which drove its decline during the middle decades ofthe 20th century. It was built at density levels too high for the automobile to functioneffectively and it was located in a cold state. The city had concentrated in manufacturing(although this was over by 1980) and had extremely high local tax levels. Together thesefactors drove Boston’s decline. By 1980, Boston was just another of America’s formerlygreat declining cities. Its real estate values were low and it had lost population steadilysince 1950. The citywas beginning to suffer from the social problems, such as high povertyand unemployment, that generally accompany urban decline. Indeed, an urban observerlooking at Boston in 1980 would have every reason to believe that it would go the way ofDetroit and Syracuse and continue along its sad path towards urban irrelevance.
5. 1980–2000: America’s Athens turns commercial
But that did not happen. During the past 20 years, the Boston metropolitan area hasgained population steadily. The city ofBostonhasnot grown significantlymore populous,but at least the population drain has stopped. Most dramatically, there has been anexplosion of housing values. These values create urban problems of their own, butthey are a strong indication of demand for that particular city. While Detroit andSyracuse are still places marked by extremely low housing demand, the Bostonmarket has generally been extremely hot. Moreover, Boston has been linked to anumber of the most important developments in the US economy over the past 20years. In this section, I explore the reasons for Boston’s success in the 1980–2000 period.
One of the most persistent predictors of urban growth over the last century is the skilllevel of a city (Simon and Nardinelli, 2002, Glaeser et al., 1995). Figure 11 shows therelationship between percent college educated in 1980 and the population growth overthe next 20 years among the 147 metropolitan areas with more than 100,000 residentsin 1980 with mean January temperatures below 40 degrees. Among these colder cities,
Share w ith BA's
Population Grow th 1980-2000 .
10 20 30 40
-.2
0
.2
.4
.6
Danvil le
Steubenv
Altoona,
Johnstow
Cumberla
Hickory-
Wheeling
MansfielLima, OH
Fort Smi
HuntingtScrantonYoungsto
Williams
Joplin,
Kokomo, St. JoseCanton--
Johnson Sheboyga
York, PA
Yakima,
Reading,
Parkersb
Florence
Wausau,
Sharon, Jamestow
Clarksvi
EvansvilJackson,
Elkhart-
Janesvil
Saginaw-Utica--R
Chattano
AllentowRockfordFort Way
Decatur,
Lancaste
Lynchbur
Pueblo,
Erie, PA
Benton H
Glens Fa
Louisvil
Terre Ha
Appleton
Pittsbur
Sioux Ci
Springfi
St. ClouAshevill
Bangor,
Duluth--
Grand Ra
Peoria--
Roanoke,
Eau ClaiHarrisbu
Memphis,
Toledo,
Buffalo-
Green Ba
Davenpor
South Be
Charlest
Providen
Greensbo
Detroit-
Muncie,
ClevelanLawton,
Fayettev
Cincinna
Dayton--
Medford-
Indianap
WaterlooBinghamt
St. Loui
Norfolk-
Knoxvil l
Sioux Fa
Amarillo
Pittsfie
Springfi
Little R
La Cross
PhiladelCedar Ra
Milwauke
Nashvill
Syracuse
New Lond
Tulsa, O
Kalamazo
Chicago-
Kansas CSpokane,Wichita,
Bellingh
Albany--
Des Moin
Omaha, N
Springfi
Grand Fo
RichmondColumbus
Rocheste
Oklahoma
Boise Ci
Portland
New York
Portland
Reno, NV
Billings
Topeka,
Lubbock,
Richland
Boston--
Hartford
Lexingto
Salt Lak
Fargo--M
Colorado
Albuquer
Lafayett
Minneapo
Lansing-New Have
BurlingtBlooming
Provo--O
Anchorag
Raleigh-
Barnstab
Lincoln,Washingt
Denver--
State Co
Charlott
Fort Col
Champaig
Madison,Columbia
Figure 11. MSA growth and education, 1980–2000.
Source: Data are from the US Census and the City and County Data Books (various years).
Reinventing Boston ! 143
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Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 59 / 86
Dynamic Spatial Equilibrium Stylized Facts
Income GrowthPopulation
GrowthShare of Workers in Manufacturing, 1950 0.3025 0.5597
(0.05) (0.1369)Log of Population, 1950 0.0868 0.2817
(0.0139) (0.0381)Mean January Temperature 0.0003 0.0198
(0.0008) (0.0022)Longitude 0.0048 0.0107
(0.0012) (0.0032)Distance to Center of Nearest Great Lake 0.0009 0.0007
(0.0002) (0.0006)Share with Bachelor Degrees, 1950 2.5141 4.3104
(0.3098) (0.8479)Log of Population/Bachelor Degree Interaction, 1950 1.1749 2.7005
(0.2127) (0.5822)Log of Median Income, 1950 0.7392 0.4600
(0.0221) (0.0605)Constant 8.8912 3.2321
(0.2083) (0.57)
Observations 1328 1328Rsquared 0.7476 0.1833
Table 6:Income and Population Growth Regressions, 19502000
Sources: County level data from ICPSR 2896 Historical, Demographic, Economic, and Social Data:The United States, 17902000. Geographical information from ESRI GIS data.
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Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 60 / 86
Dynamic Spatial Equilibrium Stylized Facts
Calhoun, ALDallas, ALEtowah, AL
Jefferson, AL
Madison, AL
Mobile, AL
Montgomery, AL
Talladega, AL
Tuscaloosa, AL
Walker, AL
Fairfield, CT
Hartford, CTLitchfield, CT
Middlesex, CT
New Haven, CTNew London, CT
Windham, CT
New Castle, DE
Sussex, DE
District of Columbia
Duval, FLEscambia, FLBibb, GA
Chatham, GA
De Kalb, GA
Floyd, GA
Fulton, GA
Muscogee, GARichmond, GA
Champaign, IL
Cook, IL
DuPage, IL
Franklin, IL
Kane, IL
Kankakee, IL
Lake, IL
La Salle, IL
McLean, IL
Macon, ILMadison, IL
Peoria, ILSt. Clair, IL
Sangamon, IL
Tazewell, IL
Vermilion, IL
Will, IL
Williamson, ILWinnebago, ILAllen, INDelaware, IN
Elkhart, INGrant, INLake, IN
La Porte, INMadison, IN
Marion, INSt. Joseph, IN
Tippecanoe, IN
Vanderburgh, INVigo, IN
Wayne, IN
Campbell, KYDaviess, KY
Fayette, KY
Floyd, KYHarlan, KY
Jefferson, KYKenton, KY
Pike, KY
Orleans, LA
Allegany, MD
Anne Arundel, MDBaltimore, MDFrederick, MD
Montgomery, MD
Prince George's, MD
Washington, MD
Berkshire, MA
Bristol, MA
Essex, MA
Hampden, MA
Hampshire, MA
Middlesex, MANorfolk, MA
Plymouth, MA
Suffolk, MA
Worcester, MA
Berrien, MI
Calhoun, MI
Ingham, MI
Jackson, MI
Kalamazoo, MI
Lenawee, MIMacomb, MIMonroe, MI
Oakland, MI
Ottawa, MI
St. Clair, MI
Washtenaw, MI
Wayne, MIHarrison, MSLauderdale, MS
Hillsborough, NHRockingham, NH
Atlantic, NJ
Bergen, NJ
Burlington, NJCamden, NJ
Cumberland, NJ
Essex, NJGloucester, NJ
Hudson, NJ
Mercer, NJMiddlesex, NJMonmouth, NJ
Morris, NJ
Passaic, NJ
Somerset, NJ
Union, NJWarren, NJ
Albany, NY
Bronx, NY
Broome, NY
Cattaraugus, NYChautauqua, NYChemung, NY
Dutchess, NYErie, NYKings, NY
Montgomery, NY
Nassau, NY
New York, NY
Ontario, NYOrange, NYQueens, NYRensselaer, NYRichmond, NY
Rockland, NY
Schenectady, NY
Steuben, NY
Suffolk, NYUlster, NY
Westchester, NY
Alamance, NCBuncombe, NC
Cabarrus, NCCatawba, NC
Cleveland, NC
Cumberland, NC
Davidson, NC
Durham, NC
Forsyth, NC
Gaston, NC
Guilford, NC
Halifax, NC
Iredell, NCJohnston, NC
Mecklenburg, NC
Nash, NC
Pitt, NC
Robeson, NCRockingham, NCRowan, NC
Wake, NC
Wayne, NCWilson, NCAllen, OHAshtabula, OHBelmont, OH
Butler, OH
Clark, OHColumbiana, OH
Cuyahoga, OH
Franklin, OHHamilton, OH
Jefferson, OH
Lake, OHLicking, OH
Lorain, OH
Lucas, OHMahoning, OHMiami, OH
Montgomery, OH
Muskingum, OHRichland, OHRoss, OHScioto, OH
Stark, OH
Summit, OH
Trumbull, OHTuscarawas, OHWayne, OH
Wood, OHAllegheny, PA
Armstrong, PA
Beaver, PABerks, PA
Blair, PABradford, PA
Bucks, PA
Butler, PA
Cambria, PACarbon, PA
Centre, PA
Chester, PA
Clearfield, PAColumbia, PA
Crawford, PA
Cumberland, PADauphin, PA
Delaware, PA
Erie, PA
Fayette, PAFranklin, PAIndiana, PA
Jefferson, PA
Lackawanna, PALancaster, PA
Lawrence, PALebanon, PA
Lehigh, PA
Luzerne, PALycoming, PAMcKean, PA
Mercer, PA
Montgomery, PA
Northampton, PA
Northumberland, PA
Philadelphia, PA
Schuylkill, PASomerset, PAVenango, PA
Washington, PAWestmoreland, PAYork, PA
Kent, RI
Providence, RI
Anderson, SC
Charleston, SC
Florence, SC
Greenville, SC
Horry, SC
Orangeburg, SC
Richland, SC
Spartanburg, SCSumter, SC
York, SC
Davidson, TN
Hamilton, TN
Knox, TN
Madison, TNShelby, TN
Sullivan, TN
Washington, TN
Arlington, VA
Pittsylvania, VAWise, VA
Norfolk, VAPortsmouth, VA
Richmond, VA
Roanoke, VACabell, WV
Fayette, WVHarrison, WV
Kanawha, WV
Logan, WVMcDowell, WV
Marion, WVMercer, WV
Monongalia, WV
Ohio, WV
Raleigh, WVWood, WV
Kenosha, WIRacine, WI
Rock, WI
0.2
.4.6
Cha
nge
in S
hare
with
Bac
helo
r's D
egre
e, 1
940
2000
0 .05 .1 .15 .2Share with Bachelor's Degree, 1940
Figure 6Growth in Share of Population with a Bachelor Degree
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Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 61 / 86
Dynamic Spatial Equilibrium Stylized Facts
Back
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 62 / 86
Dynamic Spatial Equilibrium Stylized Facts
(1) (2) (3) (4)
Full SampleCounties with
50,000+ Full SampleCounties with
50,000+Share of Workers in Manufacturing, 1980 0.338 0.600 0.390 0.434
(0.063)** (0.117)** (0.031)** (0.052)**Log of Population, 1980 0.017 0.039 0.001 0.008
(0.007)* (0.013)** (0.003) (0.006)Share with Bachelor's Degree, 1980 0.493 0.830 0.966 0.846
(0.145)** (0.188)** (0.071)** (0.084)**Distance to Center of Nearest Great Lake 0.000 0.000 0.000 0.000
(0.000)* (0.000)** (0.000)** (0.000)**Average Establishment Size, 1977 0.016 0.022 0.011 0.012
(0.002)** (0.003)** (0.001)** (0.001)**Log of Median Income, 1980 0.519 0.646 0.065 0.062
(0.039)** (0.071)** (0.019)** (0.032)Longitude 0.005 0.001 0.006 0.007
(0.002)** (0.002) (0.001)** (0.001)**Mean January Temperature 0.010 0.009 0.003 0.004
(0.002)** (0.002)** (0.001)** (0.001)**Constant 4.629 6.027 1.982 0.737
(0.382)** (0.663)** (0.187)** (0.297)*Observations 1336 444 1336 444Rsquared 0.28 0.45 0.31 0.52
Sources: County level data from ICPSR 2896 Historical, Demographic, Economic, and Social Data: The UnitedStates, 17902000. Geographical information from ESRI GIS data. Average establishment size in 1977 from CountyBusiness Patterns.
Log Change in Population,19802000
Log Change in MedianIncome, 19802000
Table 8:Income and Population Growth Regressions, 19802000
Note: Standard Errors in parenthesis (* significant at 5%; ** significant at 1%).
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Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 63 / 86
Dynamic Spatial Equilibrium Stylized Facts
Population Growth and January Temperature
39
Notes: For the top 10% of counties based on 1950 population. Population data from historical U.S. Census County Data Books, found in Haines, Michael R.; Inter-university Consortium for Political and Social Research, 2005-02-25, "Historical, Demographic, Economic, and Social Data: The United States, 1790-2000", hdl:1902.2/02896 http://id.thedata.org/hdl%3A1902.2%2F02896 Inter-university Consortium for Political and Social Research [distributor(DDI)]. Temperature data from cdo.ncdc.noaa.gov.
Fig. 2: Population Growth 1950-2000 and January TemperatureMean January Temperature
Population Growth 1950-2000 Fitted values
-50 0 50 100
-2
0
2
4
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Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 64 / 86
Dynamic Spatial Equilibrium Stylized Facts
Income Growth and January Temperature
40
Notes: For the top 10% of counties based on 1950 population. Population and income data from historical U.S. Census County Data Books, found in Haines, Michael R.; Inter-university Consortium for Political and Social Research, 2005-02-25, "Historical, Demographic, Economic, and Social Data: The United States, 1790-2000", hdl:1902.2/02896 http://id.thedata.org/hdl%3A1902.2%2F02896 Inter-university Consortium for Political and Social Research [distributor(DDI)]. Temperature data from cdo.ncdc.noaa.gov.
Fig. 3: Income Growth 1950-2000 and January TemperatureMean January Temperature
Income Growth 1950-2000 Fitted values
-50 0 50 100
0
.5
1
1.5
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Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 65 / 86
Dynamic Spatial Equilibrium Stylized Facts
Unemployment1 Unemployment is hardly persistent Figure
2 Unemployment predicts lower population and income growth Table
3 Education predicts lower unemployment in the Great Recession Figure
Education predicts lower unemployment at the individual levelCompositional effect at the city level
Predicted Unemployment = ∑Groups
ShareMSAGroup · UUSAGroup
I ShareMSAGroup = share of city’s adult labor force in each group in 2000I UUSAGroup = national unemployment rate for each group
= 5.1% for college graduates= 10.25% for high school graduates= 17.6% for high school dropouts
Actual city-level effect > predicted compositional effectI The regression line has a slope of 1.78 Figure
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Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 66 / 86
Dynamic Spatial Equilibrium Stylized Facts
Persistence of Unemployment RatesOlivier Jean Blanchard and Lawrence F. Katz 11
Figure 3. Persistence of Unemployment Rates across U.S. States, 1975-85 Unemployment rate, 1985 (percent)
WV
12 - LA
MS
10 _ Ml AK KY
IL AL OH NMOR AR
DC PA WA
8 - IA ID *IN NV MT TN
WY WI OK CA
MO GA NY AZ
6 AD MN UT CO FL VA HI NJ M
SD NE NC I*-ME KS CT DE RI
MD VT
4 NH MA I IIII
4 6 8 10 12
Unemployment rate, 1975 (percent) Source: Authors' calculations using data from the Current Population Survey (CPS) and from the Bureau of Labor
Statistics unemployment rates for Labor Market Areas.
bility of mean growth rates across time, by allowing for different inter- cepts for 1950-70 and 1970-90 for each state. In only two states-Maine and Washington-are the mean growth rates significantly different over the two subperiods.
To summarize, the correct image of employment evolutions is one of states growing at different rates, with shocks having largely permanent effects. In response to an adverse shock, employment eventually ends up growing at the same underlying rate, but at a lower level.
The Low Persistence of Relative Unemployment Rates
In contrast to employment, relative unemployment rates exhibit no trend and do not exhibit high persistence. Reasonably consistent mea- sures of state unemployment rates are available or can be constructed back only to 1970 (see the appendix for details). Figure 3 plots relative
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Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 67 / 86
Dynamic Spatial Equilibrium Stylized Facts132 E.L. Glaeser et al. / Journal o f Monetary Economics 36 (1995) 117-143
Table 5 C i t y growth and unemployment, dependent variable: growth in log of variable ( 1 9 6 0 - 1 9 9 0 )
Variable
(1) (2) (3) (4) (5)
C i ty C i t y C i t y S M S A a C i ty
population unemployed employed population income
Intercept 1.777
Log (population 1960) - 0 .050
(0.024)
Per capita income 1960 - 0 .116
($1000) (0.074)
Unemployment rate - 0 .057
1960 (0.016)
Manufacturing share - 0.631
1960 (0.250)
Geographical dummies
South
Central
Northeast
4.544 3.787 0.963 15.944
- 0 .172 - 0 .166 0 .030 - 0 .015
( - 0 .048) (0.045) (0.025) (0.009)
- 0 .354 - 0 .278 - 0 .050 - 0 .084
(0.149) (0.141) (0.111) (0.0297)
- O. 164 - 0 .086 - 0 .047 - 0 .022
(0.031) (0.030) (0.017) .(0.006)
- 0.623 - 1.381 - 0.888 - 0 .225
(0.490) (0.463) (0.227) (0.095)
- 0 .370 -- 0.121 - 0.243 -- 0 .172 0 .116
(0.085) (0.168) (0.159) (0.079) (0.033)
- 0.521 - 0 .067 - 0 .269 - 0 .356 0 .006
(0.084) (0.165) (0.156) (0.079) (0.032)
- 0 .570 - 0 .462 - 0.321 - 0 .266 - 0 .018
(0.086) (0.169) (0.160) (0.160) (0.032)
N 203 201 201 133 20 I
Adj . R 2 0 .364 0.268 0.203 0 .387 0.426
Numbers in parentheses are standard errors.
~ S M S A regression excludes Las Vegas S M S A .
shows that the percentage of the population with 12 to 15 years (high-school graduates or some college) is more important than the percentage of the population with over 16 years (college graduates). This result suggests the importance of a well-educated labor force, not just of the top of the education distribution. Interestingly, median years of schooling are less significant if we look at the SMSA population, suggesting that schooling may have contributed to suburbanization and not just city growth.
One interesting question in the theory of economic growth is whether the average or the total quantity of human capital speeds up economic growth. One can build human capital spillover models in the spirit of Lucas (1988) in which growth is proportional to either total or average education. As a rough test of
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Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 68 / 86
Dynamic Spatial Equilibrium Stylized Facts
Williamsport
Lancaster
State College
AltoonaElmira New York
Providence
Albany
Springfield
BuffaloAllentown
YorkErie
Vineland
Barnstable Town
Bangor
Hartford
TrentonHarrisburgGlens Falls
Johnstown
SyracuseBinghamton
BurlingtonPortland
New HavenRochesterPittsburgh Boston
Utica
Reading
Canton
Evansville
Decatur
Lansing
ColumbiaSioux CityRapid City
Grand Rapids
Elkhart
Cedar Rapids
Youngstown
MadisonDubuque Minneapolis
Bismarck
Cleveland
Topeka
Lawrence
Jackson
Fargo
Kokomo
Duluth
Kankakee
Terre Haute
Springfield
Rochester
Cincinnati
Iowa City
WichitaEau Claire
Lincoln
Sheboygan Milwaukee
Flint
Saginaw
Lafayette
La Crosse
Champaign
DaytonRacine
Columbus
Joplin
Kalamazoo
Sioux Falls
Davenport
Detroit
Waterloo
Grand Forks
Green BayBloomington
Appleton
South Bend
Springfield
Rockford
JanesvilleToledo
Omaha
Mansfield
Chicago
WausauFort Wayne
Muncie
Ann Arbor
Lima Akron
Kansas City Bloomington
Houma
Tuscaloosa
San AngeloLawton
Punta Gorda
GreenvilleCumberlandFayetteville
Longview
Florence
Jacksonville
Rocky Mount
Charleston
Pine Bluff
Tallahassee
Dothan
Naples
BaltimoreEl Paso
McAllen
AshevilleGoldsboro
Richmond
Memphis
LaredoCorpus Christi Athens
AbileneJonesboro
ShermanShreveport
BeaumontChattanoogaColumbus
Lafayette
Roanoke
Ocala
DoverTulsaSan Antonio
Victoria
JacksonvilleJacksonMacon
Hickory
Augusta
Baton RougeLynchburg
Washington
Clarksville
LakelandGreensboro
Birmingham
Lake Charles
DeltonaMobileBrownsville
Fayetteville
Pensacola
Wichita Falls
Danville
Huntington
Sumter
KilleenAlexandria
MontgomeryGreenvilleCharlotte
Jackson
Decatur
Johnson City
KnoxvilleOdessaColumbia
Waco
Hagerstown
Charlottesville
Savannah
Wheeling
Huntsville
Amarillo
Wilmington
DallasTyler Lexington GainesvilleTexarkana
HattiesburgFort Smith
Lubbock
OwensboroGadsdenAnnistonAlbany
Auburn
Monroe
Atlanta
New Orleans
Raleigh
Tampa
Oklahoma CityAustin
Redding
Boise City
Santa Cruz
Tucson
Yakima
Greeley
Billings
Albuquerque
San Francisco
Phoenix
Vallejo
Santa Rosa
Reno
Fort Collins
Grand Junction
Salt Lake City
Colorado Springs
Boulder
Salem
Missoula
CheyennePueblo
Provo
StocktonModesto
CorvallisLas Cruces
Santa Fe
SeattlePocatello
Bremerton
Portland
Chico
Great Falls
Riverside
San DiegoEugene
Salinas
Bellingham
Medford
Fresno
Flagstaff
Los Angeles
Merced
San Jose
Yuba City
Las Vegas
Santa Barbara
Honolulu
San Luis Obispo
CasperAnchorage
Bakersfield
Spokane
Visalia
Olympia
Yuma
.05
.1.1
5.2
.25
Une
mpl
oym
ent R
ate,
Jan
201
0
.1 .2 .3 .4 .5College completion among population 25 or above, 2000
Figure 7Unemployment in January 2010 and Education in 2000
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Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 69 / 86
Dynamic Spatial Equilibrium Stylized Facts
Williamsport
Lancaster
State College
AltoonaElmiraNew York
Providence
Albany
Springfield
BuffaloAllentown
YorkErie
Vineland
Barnstable Town
Bangor
Hartford
Trenton HarrisburgGlens Falls
Johnstown
SyracuseBinghamton
BurlingtonPortland
New HavenRochesterPittsburghBoston
Utica
Reading
Canton
Evansville
Decatur
Lansing
Columbia Sioux CityRapid City
Grand Rapids
Elkhart
Cedar Rapids
Youngstown
MadisonDubuqueMinneapolis
Bismarck
Cleveland
Topeka
Lawrence
Jackson
Fargo
Kokomo
Duluth
Kankakee
Terre Haute
Springfield
Rochester
Cincinnati
Iowa City
WichitaEau Claire
Lincoln
SheboyganMilwaukee
Flint
Saginaw
Lafayette
La Crosse
Champaign
DaytonRacine
Columbus
Joplin
Kalamazoo
Sioux Falls
Davenport
Detroit
Waterloo
Grand Forks
Green BayBloomington
Appleton
South Bend
Springfield
Rockford
JanesvilleToledo
Omaha
Mansfield
Chicago
WausauFort Wayne
Muncie
Ann Arbor
LimaAkron
Kansas CityBloomington
Houma
Tuscaloosa
San AngeloLawton
Punta Gorda
Greenville CumberlandFayetteville
Longview
Florence
Jacksonville
Rocky Mount
Charleston
Pine Bluff
Tallahassee
Dothan
Naples
BaltimoreEl Paso
McAllen
Asheville Goldsboro
Richmond
Memphis
LaredoCorpus ChristiAthens
AbileneJonesboroShermanShreveport
BeaumontChattanoogaColumbus
Lafayette
Roanoke
Ocala
DoverTulsa San Antonio
Victoria
JacksonvilleJacksonMacon
Hickory
Augusta
Baton RougeLynchburg
Washington
Clarksville
LakelandGreensboro
Birmingham
Lake Charles
DeltonaMobileBrownsville
Fayetteville
Pensacola
Wichita Falls
Danville
Huntington
Sumter
Killeen Alexandria
MontgomeryGreenvilleCharlotte
Jackson
Decatur
Johnson City
Knoxville OdessaColumbia
Waco
Hagerstown
Charlottesville
Savannah
Wheeling
Huntsville
Amarillo
Wilmington
DallasTylerLexingtonGainesvilleTexarkana
HattiesburgFort Smith
Lubbock
OwensboroGadsdenAnnistonAlbany
Auburn
Monroe
Atlanta
New Orleans
Raleigh
Tampa
Oklahoma CityAustin
Redding
Boise City
Santa Cruz
Tucson
Yakima
Greeley
Billings
Albuquerque
San Francisco
Phoenix
Vallejo
Santa Rosa
Reno
Fort Collins
Grand Junction
Salt Lake City
Colorado Springs
Boulder
Salem
Missoula
Cheyenne Pueblo
Provo
StocktonModesto
CorvallisLas Cruces
Santa Fe
Seattle Pocatello
Bremerton
Portland
Chico
Great Falls
Riverside
San DiegoEugene
Salinas
Bellingham
Medford
Fresno
Flagstaff
Los Angeles
Merced
San Jose
Yuba City
Las Vegas
Santa Barbara
Honolulu
San Luis Obispo
CasperAnchorage
Bakersfield
Spokane
Visalia
Olympia
Yuma
.05
.1.1
5.2
.25
Act
ual U
nem
ploy
men
t Rat
e
.08 .09 .1 .11 .12 .13Predicted Unemployment Rate
Figure 8Actual Unemployment and Unemployment Predicted by Education
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Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 70 / 86
Dynamic Spatial Equilibrium Housing Supply
Housing Supply and the Impact of Productivity Shocks
In this article we re-unite the topics of urban and real estate by exploring the role thathousing supply has played in mediating urban dynamics over recent decades. Our par-
ticular interest is how the nature of supply impacts the form that urban success takes.
That is, how do differences in supply-side conditions across areas help determine
whether demand shocks translate primarily into changing prices or quantities? While
our empirical analysis at the metropolitan area level leads us away from the micro-level
issues such as segregation and bid-rent patterns that were discussed by the authors cited
above, our intellectual debt to them is clear.
Figure 1 illustrates the key issues. If a city’s housing supply is relatively elastic, weshould expect an outward shift in demand to result in an increase in population, while
the corresponding increase in housing prices should be relatively modest (see point A).
Even in the face of a major positive demand shock, unfettered new supply should
prevent the price of housing from rising much above construction costs. Moreover,
new construction helps ensure that an increase in labor demand will not lead to large
increases in wages because an elastic supply of homes helps create an elastic supply of
labor. However, if housing supply is inelastic, then positive shocks to urban productiv-
ity will have little impact on new construction or the urban population. Because thenumber of homes does not increase significantly, housing prices must rise (point B).
Wages should rise as well, both because firms have become more productive and
because workers must be compensated for rising housing prices. In contrast, if an
amenity level rises in a city with an inelastic housing supply, then nominal wages
(which are determined by labor demand) will be unchanged and housing prices will
rise, implying a decline in real wages.
Our analysis begins in Section 2 with a review of the extraordinarily tight link
between population and the stock of housing across areas within the United States.The number of people in an area can be thought of as a multiple of the number of
housing units. Moreover, this relationship is strong in term of growth rates, not just in
the levels. To break the tight link between the number of homes and the number of
Hou
sing
Pri
ce a
nd W
ages
Old DemandNew Demand
InelasticSupply
ElasticSupply
Number of Homes and Population
B
A
Figure 1. The nature of housing supply and the impacts of demand shocks.
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Housing and Urban DynamicsPopulation and the stock of housing units co-move almost perfectly
1 Variation in vacancy rates is modestF 10th percentile: 4.9% —90th percentile: 14.8%F Small effect of population growth on vacancies Figure
2 Variation in household size is modestF Overall decline: 1970 average: 3.15 — 1980 average: 2.75F The R2 of household size on population is 0.06 Figure
Housing is extremely durableI Permament loss of housing units below 1% per yearI The downward elasticity of housing supply is very lowI Cities grow faster than they decline Table
The upward elasticity of housing supply is variableI Differences in zoning and regulation (Glaeser, Gyourko, and Saks 2005)I Differences in topography (Saiz 2010)
Results
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Dynamic Spatial Equilibrium Housing Supply
Changes in Population and Vacancy Rates
Figure 2 provides additional evidence that changes in city populations are unlikely
to have been achieved through changes in vacancy rates. While there is a negative
relationship between the growth rate of population from 1990 to 2000 and the change
in the vacancy rate over the same time period, a 0.1 log point increase in population is
associated with only a 0.8 percentage point decrease in vacancy rate. This magnitudeimplies that as a city grows by 10 percent, that city’s vacancy rate would decline by
eight-tenths of one percent. Furthermore, the R2 of the reverse regression reveals that
only 13% of changes in population can be explained by changes in vacancy rates during
this decade.1 Thus, population growth in cities is not likely to be achieved solely or even
largely through changes in the fraction of occupied housing units.
Even if the amount of occupied housing remains the same, changes in the local
population could occur if the number of people per household changed. For example,
rising housing prices might cause larger households to crowd into smaller homes, thusweakening the link between the total population and the size of the housing supply.2
Over the past 30 years, the average number of people per housing unit has declined as
families have become smaller. The average number of people in each occupied housing
unit in our sample of metropolitan areas fell from 3.15 in 1970 to 2.56 in 2000, with the
bulk of that decline occurring in the first ten years (there were 2.75 people per house-
hold in 1980).3 These changes help to explain how the population in certain locations
such as Detroit was able to decline more quickly than the housing stock during the
Figure 2. Changes in population and vacancy rates, 1990–2000.
1 Further evidence suggesting that vacancy rates do not play a large role in urban dynamics can be found inHwang and Quigley (2004), who find that local macroeconomic conditions such as income andunemployment do not have a significant impact on vacancy rates.
2 Although in a very different context, the issue of ‘crowding’ was central to some of the early work inmodern urban economics. In addition to the works cited above, see Kain and Quigley (1975) for anexamination of crowding in the context of race and discrimination.
3 Household size is defined as the number of people living in households divided by the number of occupiedhousing units. The actual decline in household size is likely to have been larger than reported between1970 and 1980 due to a change in the definition of the population living in group quarters (Ruggles andBrower, 2003).
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Dynamic Spatial Equilibrium Housing Supply
Population and Household Size
1970s. If the typical household size in an area shrank by the national average and if nonew homes were built, the population in a metropolitan area could have fallen by as
much as 13% in the 1970s. In contrast, declines in household size were substantially
smaller in the 1980s and 1990s. Therefore, changes in household size cannot account for
the changes in population we observe during these later decades.
The weak connection between household size and metropolitan area population is
illustrated in Figure 3, which plots the relationship between the logarithms of these two
variables in 2000. The relationship is statistically significant, but it is not economically
important. The R2 from the underlying regression is only 0.06, and the dispersiondepicted indicates that there are many other factors driving population differences
across cities besides differences in household size. In other words, relatively little growth
or decline of city populations has occurred in recent decades that can be attributed to
changes in household size. Because changes in the number of occupied housing units
also cannot explain differences in city growth rates, growth must have been accommod-
ated through changes in the total number of housing units.
2.3. Urban decline and durable housing
Previous research by Glaeser and Gyourko (2005) contends that many features of
declining cities can be understood only as the result of the durable nature of housing.
Because housing is very slow to deteriorate, the elasticity of housing supply is very low
in response to a decline in housing demand. One of the key predictions of the model in
their article is that cities grow more quickly than they decline.In this article, we provide additional evidence pertaining to the durability of housing
from the American Housing Survey (AHS) and decennial Census. One useful gauge of
the durability of homes is how rarely they disappear from the housing stock. Using the
panel structure of the AHS, we calculated the permanent loss rates of housing units
using central city data from the national core files over the four two-year periods
between 1985 and 1993. Every two years, between 1.3 and 1.8% of housing units either
permanently exit the housing stock or suffer such severe damage as to render the units
Figure 3. Metropolitan area population and household size, 2000.
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Dynamic Spatial Equilibrium Housing Supply
Housing Unit Growth in Cities with 100,000+ Residents
uninhabitable. This suggests that the housing stock of a city is unlikely to decline bymore than 1% per year or 10% per decade.
Low rates of housing destruction imply that the distribution of growth rates of the
housing stock will be skewed to the left. In Table 2, we report data on the fastest and
slowest growing cities during the 1970s, 1980s and 1990s. The sample includes all cities
with more than 100,000 residents at the beginning of each decade. We look at cities here
instead of metropolitan areas because only central cities have experienced declines in
demand during the past several decades. Even in metropolitan areas like Detroit, the
demand for housing in the suburbs has increased over time.Some cities obviously have had the ability to expand their housing stock extremely
rapidly. Housing units in Las Vegas grew by approximately 50% in both the 1980s and
the 1990s. The stock of units in Colorado Springs grew by more than 60% during the
1970s. While there seems almost no upper bound on growth, declines rarely exceed 10%
of the beginning of decade stock. Over the entire 30-year period, in only five cases did a
city lose 11% or more of its housing stock in a single decade: St. Louis in the 1970s
(–16.5%), Detroit in both the 1970s and 1980s (–11.5 and –14%, respectively), and Gary
and Newark in the 1980s (–14.5% and –16.9%, respectively). This lower bound on thedecline in housing units is consistent with the annual rate of housing loss of no more
than 1% calculated just above using the AHS. In addition, the biggest population
declines have only been modestly more severe than the corresponding changes in the
housing stock (see Glaeser and Gyourko, 2005).
In summary, many of America’s declining cities have housing prices that are too
low to justify new construction. To understand the population and employment
dynamics of this group, it is particularly important to recognize the key role played
Table 2. Housing unit growth cities with 100,000þ residents
Bottom five Top five
1970–1980
St. Louis �16.5% Colorado Springs 64.1%
Detroit �11.5% Austin 53.7%
Cleveland �9.8% Albuquerque 52.2%
Buffalo �6.0% Stockton 48.4%
Pittsburgh �5.8% San Jose 46.4%
1980–1990
Newark �16.9% Las Vegas 49.1%
Gary �14.5% Raleigh 47.1%
Detroit �14.0% Virginia Beach 46.9%
Youngstown �10.0% Austin 39.3%
Dayton �7.7% Fresno 37.7%
1990–2000
Gary �10.5% Las Vegas 53.5%
Hartford �10.3% Charlotte 28.8%
St. Louis �10.2% Raleigh 25.0%
Youngstown �9.6% Austin 22.3%
Detroit �9.3% Winston-Salem 21.3%
Data source: Decennial censuses for 1970, 1980, 1990, 2000. Housing units defined to include
owner-occupied and rental units.
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Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 75 / 86
Dynamic Spatial Equilibrium Housing Supply
Housing Supply and the Impact of Productivity Shocks
using income per capita in each metropolitan area as published by the Bureau ofEconomic Analysis.
The top panel of Table 4 reports estimates from specifications in which industrial
labor demand is used as a proxy for productivity. The coefficients in the first row
reflect the impact of the labor demand variable in the typical low-regulation metropol-
itan area, while those in the second row report the differential effect of this variable
in an area with a highly-regulated housing supply. Stronger labor demand is associated
with higher population and income growth (significant at the 5 and 10% levels, respect-
ively), but not higher house price appreciation. The coefficient of 1.28 indicates thatthis labor demand proxy strongly predicts population growth in low-regulation
metropolitan areas. We cannot reject the null hypothesis that population rises pro-
portionately with predicted labor demand. A 1% increase in labor demand is also
associated with $92 higher real wage growth. This effect is equivalent to about
one-third of a standard deviation of changes in income, so the size of this effect
is not large economically. Finally, stronger labor demand is not associated with higher
house price appreciation in the typical market. The point estimate is negative,
but it is both small in economic magnitude and not close to being statisticallysignificant.
Table 4. Effects of productivity shocks on changes in income/capita, housing prices and population,
1980–2000
DLn(Population) D Income/capita D Housing prices
Labor demand
Labor demand 1.28** (.40) 9240* (5148) �19294 (74217)
Labor demand · high regulation �0.63* (0.37) �244 (6903) 190597* (97074)
Share of population with Bachelors Degreea
Pop. share 0.091** (0.035) 2223** (425) 17286 (11271)
Pop. Share · high regulation �0.063 (0.043) 3161** (1434) 23764** (8167)
Both productivity shocks
Both shocks (normalized) 0.0040** (0.0012) 45** (12) 195 (197)
Productivity · high regulation �0.0023* (0.0013) 66 (48) 851** (293)
Notes: aShare of population with a BA degree in initial year (1980 for 1980–1990 changes and 1990 for 1990–2000 changes).
Each cell shows results from a separate regression with 118 observations. All regressions include a dummy variable for high
regulation and a dummy variable for the 1990–2000 period. Standard errors are in parentheses and are clustered by state.
*Significant at the 10% level; significant at the 5% level.
housing price is lower in the outlying regions of a metropolitan area, the overall change in the medianhousing value will be biased downward. Another problem with using the Census data is that a portion ofthe change in housing values will reflect changes in the quality of housing, which could differ system-atically across metropolitan areas. Using the OFHEO price indexes solves both of these problemsbecause these indexes maintain constant definitions of metropolitan areas over time. Also, housingprice changes are calculated from repeated sales of the same house, which avoids the bulk of the concernover changes in quality. Nevertheless, the results we present below are not materially different from thoseobtained when using median housing values instead of the OFHEO price indexes.
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Dynamic Spatial Equilibrium Reduced-Form Macroeconomics
Regional Productivity Fluctuations
All variables are log deviations from national averagesI Relative wage witI Relative employment nit
Labor demand wit = −dnit + zitI d > 0 reflects decreasing demand for a state’s product bundle
Labor demand shocks zi ,t+1 − zit = xdi − awit + εdi ,t+1
I State-specific trend xdi captures productive amenitiesI a = 0 if each state keeps the same product bundle over timeI a > 0 if a state with low wages attracts new industriesI εdi ,t+1 is white noise
Labor supply ni ,t+1 − nit = x si + bwit + εsi ,t+1I State-specific trend xsi captures consumption amenitiesI b > 0 because a state with higher wages attracts migrationI εsi ,t+1 is white noise
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 77 / 86
Dynamic Spatial Equilibrium Reduced-Form Macroeconomics
Full Employment EquilibriumRelative wage dynamics
wi ,t+1 = (1− a− bd)wit + xdi − dx si + εdi ,t+1 − dεsi ,t+1
Stationary levels
Ewi =(xdi − dx si
)/ (a+ bd)
Mean reversion in wages
Relative employment growth
∆ni ,t+1 = (1− a− bd)∆nit + ax si + bxdi + bεdit − (1− a) εsit + εsi ,t+1
Stationary growth rates
E∆ni =(ax si + bx
di
)/ (a+ bd)
Unit root of employment
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 78 / 86
Dynamic Spatial Equilibrium Reduced-Form Macroeconomics
Impulse ResponsesA negative labor demand shock εdi ,0 = −1Deviation of wages from its base path
(εdit = εsit = 0∀t
)wit = − (1− a− bd)t → 0
1 Decrease on impact2 Gradual recovery to the initial equilibrium
F Job creation (a) and worker out-migration (b)
Deviation of employment from its base path(εdit = εsit = 0∀t
)∆ni ,t = −b (1− a− bd)t−1
ni ,t =t
∑τ=1
∆ni ,τ = −b
a+ bd
[1− (1− a− bd)t
]→ − b
a+ bd
1 No change on impact2 Long-run decline depending on short-run elasticities
F Firms moving in faster (a) or workers moving out faster (b)
Giacomo Ponzetto (CREI) Urban Economics 23 — 24 January 2012 79 / 86
Dynamic Spatial Equilibrium Reduced-Form Macroeconomics
Unemployment
Labor force n∗it , unemployment rate uit⇒ Employment nit ≈ n∗it − uit⇒ Labor demand wit = −d (n∗it − uit ) + zit
Labor demand shocks zi ,t+1 − zit = xdi − awit + εdi ,t+1Wage curve cwit = −uitLabor force n∗i ,t+1 − n∗it = x si + bwit − guit + εsi ,t+1
I g > 0 because a state with lower unemployment attracts migration
1 uit and wit move in opposite directions: ∂Eui/∂x si > 0 > ∂Eui/∂xdi2 Falls in wit attract firms, increases in uit only repel workers
I By assumption: ∂∆zi ,t+1/∂uit = 0I If uit reacts more than wit in the short-run, nit reacts more in thelong-run
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Dynamic Spatial Equilibrium Reduced-Form Macroeconomics
Identification
How can we separate εdit from εsit?
1 Simply assume all high-frequency fluctuations are εditI Intuitively plausibleI In practice, wages and employment co-move
2 Construct observable labor demand shocksI The Bartik (1991) instrument: ∑j
nj ,i ,tni ,t
Nj ,t+1Nj ,t
F nj ,i ,t is employment in industry j in region i at time tF ni ,t = ∑j nj ,i ,t is total employment in region i at time tF Nj ,t = ∑i nj ,i ,t is national employment in sector j at time t
I Growth predicted by industry mix and national industry growth
F Valid if national growth rates are uncorrelated with regional shocksF Broad industries and fine regions to avoid geographic concentration
I You can also predict Nj ,t+1/Nj ,t with exchange rate fluctuations
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Dynamic Spatial Equilibrium Reduced-Form Macroeconomics
Regional Evolutions
1 Short-run employment fluctuations are mostly nation-wideI Most procyclical in manufacturing statesI More idiosyncratic in farm states and oil states
2 The importance of aggregate fluctuations declines with the horizon3 Negative employment shocks increase uit on impact4 States recover from employment shocks by out-migration Figure
5 Nominal wages decline, then recover in about ten years Figure
I Insuffi cient decline to cushion the employment shock Figure
6 Real wages decline modestly, as housing prices fall sharply Figure
I Migration is driven by unemployment
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Dynamic Spatial Equilibrium Reduced-Form Macroeconomics
Unemployment Response to an Employment ShockOlivier Jean Blanchard and Lawrence F. Katz 33
Figure 7. Response of Employment, Unemployment, and Labor Force Participation to an Employment Shock
Effect of shock (percent)
0.50 - Unemployment rate
0.25 -
0 M
-025 - "
-0.50 - Participation tate
-0.75 -
-1.00
-1.25
-1.50 - \ ~~~~~~~~~~~Emplovnieztt
-1.75 -
-2.00 -
-2.25 F l l l l l l l l l l
0 1 2 3 4 5 6 7 8 9 10 I 1 12
Year Source: Authors' calculations based on the system of equations described in the text, using data described in the
appendix. All 51 states are used in the estimation. The shock is a - I percent shock to employment. Bands of one standard error are shown around each line.
interpretation Of E,e as an innovation to labor demand reflect the identifi- cation assumption discussed earlier, namely that unexpected move- ments in employment within the year primarily reflect movements in la- bor demand. Under this assumption, tracing the effects of Eie gives us the dynamic effects of an innovation in labor demand on employment, unemployment, and the labor force. We now report these impulse re- sponses.
While some differences across various state groupings exist (to which we return below), responses are largely similar. The responses of the un- employment rate, the participation rate, and log employment to an ad- verse shock-a negative unit shock to log relative employment-using all 51 states are plotted in figure 7. In the first year, a decrease in employ- ment of 1 percent is reflected in an increase in the unemployment rate of 0.32 percentage points and a decrease in the participation rate of 0.17 percentage points. Over time, the effect on employment builds up, to
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Dynamic Spatial Equilibrium Reduced-Form Macroeconomics
Wage Response to an Employment Shock40 Brookings Papers on Economic Activity, 1:1992
Figure 11. Response of Employment and Manufacturing Wages to an Employment Shock Effect of shock (percent)
0
-0.25
-0.50 - Manzufactut-ing wage
-0.75 -
-1.00 \ ~~~~~~~~~Employment
-1.25 -
-1.50
-1.75-
-2.00 -
0 2 4 6 8 10 12 14 16 18 20
Year Source: Authors' calculations using data described in the appendix. The shock is a - I percent shock to
employment. Bands of one standard error are shown around each line.
ment in state i at time t minus its national counterpart; wit is the differ- ence between the logarithm of average hourly earnings in manufacturing in state i at time t and its national counterpart. We use four lags for each of the variables and estimate the system over the period 1952-90. We leave out unemployment and participation, not on theoretical grounds, but because including them would reduce the size of the sample and in- troduce additional right-hand-side variables, leading to too few degrees of freedom. Figure 11 gives the joint response of employment and wages to a negative innovation in employment, from pooled estimation using all states and allowing for state fixed effects. The picture is clear. First, the employment response is close to those obtained earlier, with em- ployment decreasing to 1.7 times its initial response, to eventually pla- teau at - 1.2 percent. Wages decrease, reaching a minimum after six years and then returning to zero slowly over time. Putting together the results from figures 7 and 11, an initial negative shock of 1 percent to employment increases the unemployment rate by up to 0.37 percent af- ter two years and decreases wages by up to 0.4 percent after about six
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Wage Adjustment Dampening the Employment Response44 Brookings Papers on Economic Activity, 1:1992
Figure 14. Response of Employment to an Employment Shock, with and without Wage Feedback
Effect of shock (percent)
0 -0.25 -
-0.50 -
-0.75 -
-1.00 -
\ ~~~~~~~~~With wagefeedback -1.25 -
-1.50 -
-1.75 -
-2.00 - I I
0 2 4 6 8 10 12 14 16 18 20
Year Source: Authors' calculations using data described in the appendix. The shock is a - I percent shock to
employment. Bands of one standard error are shown around each line.
the first place, is that labor demand does not respond to current wages. The second, subject to an obvious and probably relevant Lucas critique, is that the labor demand function would remain unchanged were wages not to adjust. With those caveats in mind, the message from figure 14 is fairly clear: the adjustment of wages dampens the employment re- sponse, but by relatively little. Absent feedback from wages, employ- ment would end at 1.6 times its initial change, compared to 1.2 with wage feedback. This evidence is suggestive of a weak effect of wages on job creation and job in-migration.46
Nominal versus Consumption Wages
We now focus on relative consumption wages, rather than nominal wages. The incentives of workers to migrate in or out of a state depend
46. One caveat is needed here, following up on earlier results. Had we used our esti- mates from estimation using CPS wages, the estimated feedback would be much stronger.
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Dynamic Spatial Equilibrium Reduced-Form Macroeconomics
House Price Response to an Employment Shock46 Br-ookings Paper-s on Economic Activity, 1:1992
Figure 15. Response of Employment and City House Prices to an Employment Shock
Effect of shock (percent)
O)7 - Median house price *
-1.25 -e
0
-1.50 -
emplymen. Bads o on stadarderro areshon Eruploachente
-1.75rs to-sell their house when they move and the size of transaction
-2.25-
plausible.~ ~ ~ ~~~Mdinhosep'-c
-2.50 -
Secod, wile he ffecs ofthe hoc on mplomentareloymelyn er
0 2 4 6 8 10 12
Year
Source: Authors' calculations using data described in the appendix. The shock is a - I percent shock to
employment. Bands of one standard error are shown around each line.
workers to sell their house when they move and the size of transaction costs in the housing market, these predictable excess returns are not im- plausible.
Second, while the effects of the shock on employment are largely per- manent, the long-run effects of relative housing prices appear not to be. This implicitly requires a flat long-run supply of land in each MSA. Be- cause of the quality of the data and the short span of the time series we use, we do not want to emphasize this result too much. We nevertheless want to flag it for further work.4
Finally, putting our results on wages and housing prices together, in response to a decrease in employment of 1 percent, nominal wages de- crease to a trough of about - 0.5 percent, while housing prices decrease to a trough at about - 2 percent. Thus assuming a share of housing ser-
48. Bartik (1991) finds a similar hump shape pattern of the response of MSA housing prices to employment shocks. Bartik concludes from his analysis that some evidence ex- ists that employment shocks have small permanent effects on house prices.
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