ganong slides - brookings institution

Why Has Regional Income Convergence in the U.S.Declined?

Peter Ganong (UChicago) and Daniel Shoag (Harvard)

July 2016

AL

AZ

AR

CA

CO

CT

DE

FL

GA

ID

IL

IN

IA

KSKY

LA

ME

MDMA

MI

MN

MS

MO

MT

NE

NV

NH

NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VA

WA

WVWI

WY

11

.52

5 6 7Log Income Per Capita, 1940

1940-1960, Coef: -.488 SE: .019

Change in Log Income Per Capita

AL

AZ

AR

CA

CO

CT

DE

FL

GA

ID

IL

IN

IA

KSKY

LA

ME

MDMA

MI

MN

MS

MO

MT

NE

NV

NH

NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VA

WA

WVWI

WY

11

.52

5 6 7Log Income Per Capita, 1940

1940-1960, Coef: -.488 SE: .019

AL

AZ

AR

CA

CO

CT

DE

FLGAID

ILIN

IAKS

KY

LA

MEMD

MA

MI

MN

MS

MO

MTNE

NV

NHNJ

NM

NYNC

ND

OH

OK

ORPA

RI

SC

SD

TN

TXUT

VT

VAWAWV

WI

WY

.4.9

1.4

9.4 10.2Log Income Per Capita, 1990

1990-2010, Coef: -.198 SE: .057

Change in Log Income Per Capita

-.6

-.4

-.2

0

1950 1980 2010

Coef: dInc = b*inc_t-20

Timeseries of Coefficients - Convergence

AL

AZ

AR

CA

CO

CT

DE

FL

GA ID IL

IN

IA

KS

KY

LA

ME

MD

MA

MI

MN

MS

MO

MT

NE

NV

NH

NJ

NM

NYNC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VA

WA

WV

WI

WY

0.5

1

5 6 7Log Income, 1940

1940-1960, Coef: .33 SE: .07

Change in Log Population

AL

AZ

AR

CA

CO

CT

DE

FL

GA ID IL

IN

IA

KS

KY

LA

ME

MD

MA

MI

MN

MS

MO

MT

NE

NV

NH

NJ

NM

NYNC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VA

WA

WV

WI

WY

0.5

1

5 6 7Log Income, 1940

1940-1960, Coef: .33 SE: .07

AL

AZ

AR CA

CO

CT

DE

FLGA

ID

IL

IN

IA

KSKY

LA ME

MD

MAMI

MN

MSMO

MT

NE

NV

NH

NJ

NM

NY

NC

ND OH

OK

OR

PARI

SC

SD

TN

TX

UT

VT

VA

WA

WV

WI

WY

0.5

1C

ha

ng

e in

Lo

g P

op

ula

tio

n,

19

90

-20

10

9.4 10.2Log Income, 1990

1990-2010, Coef: -.09 SE: .12

Change in Log Population

-.6

-.4

-.2

0.2

.4

1950 1980 2010

Coef: dInc = b*inc_t-20 Coef: dPop = b*inc_t-20

Timeseries of Coefficients - Convergence and Pop

Outline

1 Facts About Housing Prices

2 How Does Housing Supply Affect Convergence? New Measure ofSupply Restrictions

Outline

1 Facts About Housing PricesI Differences in Housing Prices Have Grown Relative to Differences in

IncomesI Housing Prices Have Lowered the Returns to Living in Productive

Places For Unskilled WorkersI Migration Flows Respond to Skill-Specific Gains Net of Housing Prices


Differences in Housing Prices

AL

AR

AZ

CA

CO

CT

DEFL

GAIA

ID

IL

IN

KSKY

LA

MA

MD

ME

MIMN

MO

MS

MT

NC

NDNE

NH

NJ

NM

NVNY

OH

OK

ORPA

RI

SC

SDTN

TX

UT

VA

VT

WAWI

WV

WY

10.5

1111

.512

Log

Hous

ing

Valu

e, 1

960

9.2 9.4 9.6 9.8 10Log Income Per Cap, 1960

1960, Coef: .95 SE: .08

AL

AR

AZ

CA

CO

CT

DE

FL

GA

IA

ID

IL

IN KSKY

LA

MA

MD

ME

MI

MN

MO

MS

MT

NC

ND

NE

NH

NJ

NM

NV

NY

OH

OK

OR

PA

RI

SC

SD

TNTX

UT

VA

VT

WA

WI

WV

WY

11.5

1212

.513

Log

Hous

ing

Valu

e, 2

010

10.3 10.5 10.7 10.9 11.1Log Income Per Cap, 2010

2010, Coef: 2.04 SE: .25

Changes in Returns to Migration0

.51

1.5

Coe

f

1940 1960 1970 1980 1990 2000 2010

Unskilled HH Skilled HH

Effect of $1 of Statewide Inc on Skill-Specific Inc Net of Housing

Migration Flows – 2000

-40

48

Net

Mig

ratio

n as

% P

op

10.8 11 11.2 11.4 11.6 11.8Log Nominal Income

Low Skill Coef: -2.17 SE: 1.00

-40

48

Net

Mig

ratio

n as

% P

op

10.8 11 11.2 11.4 11.6 11.8Log Nominal Income

High Skill Coef: 4.07 SE: .69

-40

48

Net

Mig

ratio

n as

% P

op

10.4 10.6 10.8 11 11.2Log (Inc-Housing Cost) for Low Skill

Low Skill Coef: 4.30 SE: 2.00

-40

48

Net

Mig

ratio

n as

% P

op

11 11.2 11.4 11.6 11.8Log (Inc-Housing Cost) for High Skill

High Skill Coef: 4.71 SE: .89

Outline

1 Facts About Housing Prices


A New Regulation Measure

ConstructionI Number of hits per capita from state appeals courts for “land use”I Omnibus measure. Captures many different anti-development tactics.

PropertiesI correlated with cross-sectional measures

F American Institute of Planners, 1975F Wharton Residential Land Use Regulatory Index, 2005

I correlated with prices, conditional on state and year fixed effects

Look at patterns separately for low elasticity and high elasticity states

01

23

Cas

es P

er M

illion

Peo

ple

1940 1950 1960 1970 1980 1990 2000 2010

Land Use Cases Per Million People

How Regulations Affect Convergence

The previous graphs split states at the median within each year.We can instead exploit the regulation measure to fix a cutoff level ofregulation.

Regs,t =Percentile{LandUseCasesstPopst

}

Ys,t = αt +αtRegs,t +β Incs,t +βhigh regInc i ,t−1×Regs,t + εs,t

Dependent variables: log housing prices, population, human capital(growth accounting measure from a Mincerian regression), andincome.


Dep Var lnphouse d lnpop d lnHumanCapital d ln Inc

Log Income 0.77***(0.11)

Log Income 0.83****Reg (0.26)

N 384

Indicators for Year and Year*Reg in all specifications.



Log Income 0.77*** 1.69**(0.11) (0.64)

Log Income 0.83*** -1.88****Reg (0.26) (0.61)

N 384 2,448




Log Income 0.77*** 1.69** -0.043***(0.11) (0.64) (0.007)

Log Income 0.83*** -1.88*** 0.040***Reg (0.26) (0.61) (0.016)

N 384 2,448 288




Log Income 0.77*** 1.69** -0.043*** -2.03***(0.11) (0.64) (0.007) (0.10)

Log Income 0.83*** -1.88*** 0.040** 1.30****Reg (0.26) (0.61) (0.016) (0.39)

N 384 2,448 288 2,448


Regime One


Log Income 0.77*** 1.69** -0.043*** -2.03***(0.11) (0.64) (0.007) (0.10)

Log Income 0.83*** -1.88*** 0.040** 1.30****Reg (0.26) (0.61) (0.016) (0.39)

N 384 2,448 288 2,448


Regime Two


Log Income 0.77*** 1.69** -0.043*** -2.03***(0.11) (0.64) (0.007) (0.10)

Log Income 0.83*** -1.88*** 0.040** 1.30****Reg (0.26) (0.61) (0.016) (0.39)

N 384 2,448 288 2,448


Omitted Variable Bias

More generally, could there be a post-1980 shock that raisedregulation levels and lowered convergence?Use pre-1980 measures of housing supply elasticity

Omitted Variable Bias

Dep Var: Log Income Per Cap t - Log Income Per Cap t-20

Constraint: Regs in 2005 Regs in 1965 Buildable LandPre Post Pre Post Pre Post

Log Income -1.93*** -1.80*** -2.05*** -1.97*** -2.49*** -1.20***(0.11) (0.33) (0.15) (0.47) (0.06) (0.08)

Log Income 0.22 2.01*** 0.20 1.91*** -0.09 0.71****Constraint (0.27) (0.66) (0.27) (0.69) (0.10) (0.17)

N 1,248 1,200 1,248 1,200 8,413 9,194Geo State State State State County County

Indicators for Year and Year*Constraint in all specifications.

Conclusion

Weakening of directed migration and convergence in last 30 yearsDirected migration drove convergence:

I Regional labor markets clear through skill-sorting rather than netmigration. Can be explained by supply shifts.

I Continued convergence in places with unconstrained supply

State-level panel measure for housing regulations.I We are happy to share this with other researchers.

Model Overview

Model with multiple skill types, endogenous migration, andpotentially restricted housing supply.Proposition 1: Directed migration drives convergenceProposition 2: Housing prices affect migration differently by skill level

Substantive Expositional

Regional labor demand is Y = AL1−α

downward sloping

Land is an Inferior Good within a City U = cβ (h−H)1−β

Migration is Costly

Simulation

Income Convergence Rate

Mig Rate from South to North(Skilled & Unskilled)

Reg ↑ Begins(Unanticipated)

Reg ↑ Complete

Mig Rate (Skilled)

Mig Rate (Unskilled)

0R

ate

of C

onve

rgen

ce /

Mig

ratio

n

t0 t1 t2Time

ganong slides - brookings institution

Documents