spatial equilibrium across cities - crei1).pdf · logw llogp h +loga = u +k 1 2 firms™labor...

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



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



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



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



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%



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.



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



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



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



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


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



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



“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



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



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



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


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



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




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


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



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



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



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).



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 attributes. 'I'hus, the combined evidence seems persuasive that the regional 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).



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 (approximately 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 regressions, 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 \ \ )



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



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)



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.



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



Attendance District Boundaries



House Prices and School Quality



Differences at the Boundary



Capitalized Prices of Better Test Scores


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


(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



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


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



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


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


The aggregate assumptions could be relaxedI Time-varying intercept, i.e., time fixed effects



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


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)



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



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



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


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



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



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



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 illustrated 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 national 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



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


Fitted values

.580574 1.84035

.344024

2.1741


Detroit


Newark c


Hartford

AnnistonWarren c




AtlantaAnderson

Atlantic





LivoniaGreenvil

Boston c

Lake Cha







Columbia

Torrance

Paterson

RoanokeSalt Lak


Albany c

Seattle

Florence

Elizabet

Stamford

Lawrence



Oakland



Huntsvil


CharlottJoplin c

Odessa c

New Brun

Middleto

Wausau c

Pasadena

Tuscaloo


Waco cit


Lawton c

Log

Cha

nge

Hou

sing

Uni

ts, 1

970

2000


Fitted values

.580574 1.84035

.344024

2.1741


Detroit


Newark c


Hartford

AnnistonWarren c




AtlantaAnderson

Atlantic





LivoniaGreenvil

Boston c

Lake Cha







Columbia

Torrance

Paterson

RoanokeSalt Lak


Albany c

Seattle

Florence

Elizabet

Stamford

Lawrence



Oakland



Huntsvil


CharlottJoplin c

Odessa c

New Brun

Middleto

Wausau c

Pasadena

Tuscaloo


Waco cit


Lawton cWichita

Inglewoo

Tacoma c


Long Bea


Sarasota

Los AngeSanta Ba

ColumbusLubbock

Garden GJoliet c



Winston



Fullerto

Glendale

Danbury

Gainesvi

Hayward

LincolnMontgome



Lafayett

SacramenLongview


El Monte

Orange c

Fargo ci

Santa Cr

Bellingh

Pomona c


Melbourn

San Bern

Eugene c

Riversid


Aurora c

Overland


Anaheim


Tallahas

Santa An

Hialeah


Stockton

Phoenix

Raleigh

Santa MaOxnard c

Killeen

Ontario

Virginia

Reno citBoise Ci

Tempe ci

Chula ViLaredo c

Salinas

Fresno c



Scottsda

Modesto


Escondid


Oceansid

Glendale

Mesa cit

Wichita

Inglewoo

Tacoma c


Long Bea


Sarasota

Los AngeSanta Ba

ColumbusLubbock

Garden GJoliet c



Winston



Fullerto

Glendale

Danbury

Gainesvi

Hayward

LincolnMontgome



Lafayett

SacramenLongview


El Monte

Orange c

Fargo ci

Santa Cr

Bellingh

Pomona c


Melbourn

San Bern

Eugene c

Riversid


Aurora c

Overland


Anaheim


Tallahas

Santa An

Hialeah


Stockton

Phoenix

Raleigh

Santa MaOxnard c

Killeen

Ontario

Virginia

Reno citBoise Ci

Tempe ci

Chula ViLaredo c

Salinas

Fresno c



Scottsda

Modesto


Escondid


Oceansid

Glendale

Mesa cit

Back



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



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



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



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



(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.

Back



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


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


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


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


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


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


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


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



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


Back



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



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



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


Back



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


Tolland, CT

Windham, CT

Kent, DE

New Castle, DESussex, DE


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


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


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


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


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


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


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

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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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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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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


Windham, CT

New Castle, DE

Sussex, DE


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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Back



(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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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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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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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

Back Forward



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

Back


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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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

Back



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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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.

72 � Glaeser, Gyourko, and Saks

at Biblioteca de la U

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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



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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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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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.

Urban growth and housing supply � 77


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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



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



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)



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



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



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



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 identification assumption discussed earlier, namely that unexpected movements in employment within the year primarily reflect movements in labor 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 responses.

While some differences across various state groupings exist (to which we return below), responses are largely similar. The responses of the unemployment rate, the participation rate, and log employment to an adverse 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 employment 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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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 employment 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 after 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


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


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 response, but by relatively little. Absent feedback from wages, employment 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 estimates from estimation using CPS wages, the estimated feedback would be much stronger.

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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


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


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 permanent, 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 decrease 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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