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Dwindling U.S. Internal Migration: - PPT Presentation

Evidence of a Spatial Equilibrium prepared for presentation at the Gosnell Lecture Series Rochester Institute of Technology Department of Economics Rochester NY 27 October 2011 by Mark D Partridge ID: 724127

1990 2000 migration pop 2000 1990 pop migration 2007 growth metro dist county dummy labor industry mix 000 nearest

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Slide1

Dwindling U.S. Internal Migration: Evidence of a Spatial Equilibrium?prepared for presentation at theGosnell Lecture SeriesRochester Institute of TechnologyDepartment of EconomicsRochester, NY27 October, 2011

byMark D. Partridge The Ohio State UniversityCo-authors:Dan S. Rickman, Oklahoma State UniversityM. Rose Olfert, University of SaskatchewanKamar Ali, University of Lethbridge.

1Slide2

IntroductionLong-standing high internal migration rates in USSuch migration reallocates labor to areas with high productivity. The pre 20th settlement of the U.S. and the industrialization pre WW II are the most vivid examples.Rural-urban migration related to farm/manufacturing realignment.Smoothes out regional shocks to labor demand (e.g., from differences in industry composition). Such ‘disequilibrium’ migration is often hailed as why US labor markets are flexible in responding to economic shocks compared to Europe (

Obstfeld and Peri, 1998).2Slide3

Intro—cont.Demands shocks should be random, but US migration is very persistent (Blanchard and Katz, 1992; Partridge and Rickman, 2003). The US also has a long history of ‘equilibrium’ migration that originates on the supply or household side—climate and landscape being the largest factors (Graves) Relates to major debate on jobs vs. people (Greenwood et al., 1991)e.g., amenity demand related to higher income & pop. aging, technological innovations such as air conditioning (Rappaport, 2007; Chen and Rosenthal

JUE (2008).Rappaport (2004) shows how equilibrium migration can persist for decades.Could be man-made amenities—e.g., Florida, 2002; Glaeser et al., 2001; and Adamson et al., 2004.3Slide4

Introduction—cont.Modeling takes place in a spatial equilibrium context. In equilibrium, utility and profits are equalized across space.Migration flows are a positive function of utility differentialsThe question is why does the U.S. has such persistent flows to ‘nice’ places? Would not forward-looking agents and markets eventually capitalize the effects of nice climate, income changes, and demographic shifts in wages & housing prices (Evans 1990)?The US has experienced a long-term downward trend in internal migration Began in late 1980s and really accelerated post-2000. We do not believe this is a housing bubble phenomenon.

4Slide5

Annual Gross Migration Rates, U.S. 1947-2008

Source: U.S. Census Bureau, Current Population Survey.

5Slide6

6Figure 2: Standard Deviation of Annual State Net Migration as a Share of Initial Population Source: U.S. Census Bureau

Intercensal Population Estimates (http://www.census.gov/popest/estimates.html)Slide7

Intro—cont. Does diminished migration imply the U.S. has achieved a spatial equilibrium of roughly equal profits and utility across space?There is still economic migration in the ‘flexible’ U.S labor market, but site-specific factors such as amenities or distance to cities would have a much smaller influence—i.e., the decline of equilibrium migration with capitalization of their effects into wages and housing.7Slide8

Intro ContinuedAn alternative explanation is that there has been a decline in economic migration (only a slight decline in the standard deviation of economic shocks). Superficially, the US labor market would be less flexible.Take on a European flavor of less geographic mobility with local labor demand shocks being satisfied by changes in local labor force participation and unemployment rates.Implications for policy interventions: place-based vs. people-based8Slide9

Theoretical frameworkIn spatial equilibrium, indirect utility of residence (V) should be equal to some level V across locations k (Partridge and Rickman, 1997; Glaeser 2007):V = V(.)k-M

k for all k,V is a function of wages, housing costs, and quality of life. The overall net migration rate from location i to j (Net Migij) during the adjustment in any time period depends on the differences in V over all time pds:Net

Migij

(t)

= f(

∑(

V

j

-V

i

-

M

ij

),

Net

Mig

is a positive function of utility differentials (Douglas, 1997)

9Slide10

Theoretical frameworkIn equilibrium, firm profits are also equalized across space for every region j. π = πjLike movement of households, net movement of firms and establishments from region i to j are a positive function of the respective profit differentialNET

FIRMij = g(πj- πi)We assume this is roughly equalized over the long-run with the exception of responses to economic demand shocks.Bringing labor demand and supply together:Migkt = f(LDSHOCKk0

, INDUSTRY

ko

, AMENITY

k0

, URBAN

k0

, DEMOG

k0

, .),

10Slide11

Empirical ImplementationBase Model Population Growth at the county level:Population growth is measured more accurately than net migration and it includes immigrants who also would be affected by the spatial equilibrium.α, φ, θ, ψ

, and γ are coefficient vectorsExplanatory variables are generally measured in the initial period. Most key variables should be predetermined such as natural amenities.σs represents the state fixed effects; so coefficient vectors reflect average county variation within states;

ε - the residual, assumed to be spatially correlated within BEA areas but not between them.

11Slide12

Explanatory VariablesDEMAND SHOCK/ECON: using total employment growth would be endogenous.Most studies on say ‘jobs versus people’ would need to instrument for total employment using industry mix. In our case, we are not interested in the effects of employment growth per se, but the impact of a demand shock. Industry mix is a demand shock.We use Industry mix employment growth, calculated by multiplying each industry's national job growth (1990-2000 and 2000-2007) by the initial period industry shares in each county (

Bartik, 1991; Blanchard and Katz, 1992; Bound and Holzer, 2000); ∑i(ei/E)*gni,

e

i

represents county employment in industry

i

, E is total county employment, and

gn

i

is the national growth rate in industry

i

.

12Slide13

Explanatory VariablesAMENITIES: a natural amenity scale constructed by U.S. Department of Agriculture ERS: 1 = Low level of amenities, 4 = above average, 7 = High; proximity to Great Lakes, OceansGEOG variables: Distance to nearest Urban Center, Incremental dist. to an MA, Incr. dist. MA> 250K, Incr. dist. to MA> 500K, Incr. dist. to MA>1.5m (representing distances to urban hierarchy)population of actual, surrounding counties & county area

DEMOG incl. 5 ethnicity vars, 4 education vars, % female, % married, and % with a work disability, all measured in 1990 (2000).Migration specialists have long pointed to the importance of demographics—human capital and life cycle. However sorting of the young, in particular, causes us to be very cautious with these variables (especially the age distribution).13Slide14

Data and MethodologyCounty level data dating back to 1980/90 to assess the sources of population growth over two periods, 1990-2000 and 2000-2007 (pre-recession) Four samples, Non-metro, Rural, Small Metro (<250,000) and Large Metro (>250,000)Extensive sensitivity analysis and staged estimations to mitigate estimation problems associated with multicollinearity or the housing bubble.14Slide15

Non-metro

Small MA

Large MA

1990-2000

2000-2007

1990-2000

2000-

2007

1990-

2000

2000-2007

Industry Mix

Empl

. Growth

Amenity2 dummy

 

Amenity3 dummy

 

Amenity4 dummy

 

Amenity5 dummy

 

Amenity6 dummy

 

Amenity7 dummy

 

County pop 1990/2000

 

Pop of nearest or actual UC 1990/2000

4.502***

(3.26)

0.176

(0.90)

0.268

(1.33)

0.489**

(2.34)

0.915***

(3.89)

1.146***

(3.63)

1.499***

(4.40)

-1.7E-06

(-1.10)

4.1E-07

(1.15)

-2.161**

(-2.19)

0.266

(1.18)

0.446**

(2.00)

0.520**

(2.29)

0.764***

(3.12)0.835***(2.79)0.293(0.90)6.0E-06***(4.31)4.3E-07***(2.95)6.881**(2.11)0.745***(3.50)0.789***(3.29)0.715***(2.62)1.062**(2.42)0.915*(1.91)0.644(1.02)3.0E-10(0.00)1.3E-06(1.34)0.377(0.12)0.570***(2.95)0.487**(2.03)0.210(0.75)0.135(0.32)0.034(0.05)-1.547**(-2.11)-1.1E-07(-0.12)2.7E-06***(3.28)8.050***(2.69)0.778**(2.56)0.634**(2.32)0.567**(2.15)0.417*(1.81)0.561***(4.72)(dropped) -8.5E-08(-1.46)4.2E-08*(1.65)1.523(0.47)1.528***(3.35)1.235***(2.73)1.030**(2.48)1.223***(2.99)0.589*(1.67)(dropped)-1.1E-07(-1.13)2.5E-08(0.81)

Table 1: Dep. Variable: Population growth (%/year) U.S. counties

Notes: Robust t-statistics from STATA cluster command ***, **, and * indicate significance at 1%, 5% and 10%.

15Slide16

Non-metro

Small MA

Large MA

1990-2000

2000-2007

1990-2000

2000-2007

1990-

2000

2000-

2007

Dist.

nearest

UC

Incr

. dist. to a MA

Incr

.

dist.

MA>

250K

Incr

.

dist.

to MA> 500K

Incr

.

dist.

to MA>1.5m

Amenities

State fixed effects

Demog

.

vars

1990/2000

-0.010***

(-8.22)

-0.004***

(-5.60)

-0.003***

(-5.60)

-0.002***

(-2.85)

-0.001**

(-2.56)

Y

Y

Y

-0.008***

(-8.40)

-0.003***

(-5.73)

-0.002***

(-4.62)

-0.002***

(-3.83)

-0.001**

(-2.31)YYY-0.0002(-0.08)n.a.-0.004***(-5.70)-0.002**(-2.04)-0.002*(-1.83)YYY-0.001(-0.22)n.a. -0.003***(-3.02)-0.003***(-2.62)-0.002*(-1.95)YYY0.007**(2.36)n.a. n.a. -0.002(-1.54)0.00008(0.16)YYY0.001(0.24)n.a. n.a. -0.004**(-2.41)-0.002*(-1.66)YYYNR-squared1,9700.5221,9700.5234160.6044160.5166410.6426410.483

F-stats: All dist=0Inc dist to

MA=0Amenity vars

=

017.92***12.55***8.17***18.04***11.48***8.76***8.63***11.43***2.39**3.27***4.36***5.67***4.79***1.785.46***4.33***5.61***3.49***

Table 1: Dep. Variable: Population growth (%/year) U.S. counties

Notes: Robust t-statistics from STATA cluster command ***, **, and * indicate significance at 1%, 5% and 10%.

16Slide17

Interpretation Industry Mix Employment growth effect (local growth potential based on industry composition, relative to sources of national growth)—a priori expectation positive, i.e., demand driven migration evident in the 1990-00 period. This effect is not statistically significant in the latter period for all FOUR subsamples! This appears to be a structural change in economic migration.

17Slide18

Table 2: Pop. Growth in U.S. counties: Impact at Mean ValuesNon-Metro

Small MA

Large MA

1990-2000

2000-2007

1990-2000

2000-2007

1990-2000

2000-2007

Average

pop growth (%/year)

0.595

-0.092

1.266

0.747

1.544

1.094

Ind

mix

emp

gr

1990-00/2000-07

Distance

to nearest or actual UC

Incremental

dist to a metro

Inc

. dist to metro > 250,000 pop

Inc

. dist to metro > 500,000 pop

Inc

. dist to metro >

1.5 mil.

pop

County

pop 1990/2000

Pop

of nearest or actual

MA1990/2000

County

area (sq miles)

Amenity2

dummy

Amenity3

dummy

Amenity4

dummy

Amenity5

dummy

Amenity6

dummy

Amenity7

dummy

Great

lakes Pacific ocean Atlantic ocean0.934-0.410-0.225-0.218-0.073-0.079-0.0380.0270.0430.0280.1090.1510.0760.0350.013-0.001-0.007-0.001-0.127-0.326-0.182-0.133-0.074-0.0640.1470.0310.0380.0420.1820.1610.0640.0250.003-0.007-0.0010.0071.445-0.004n.a.-0.389-0.074-0.1200.0000.1800.0380.1130.2830.2530.0690.0400.008-0.007-0.0070.0230.021-0.016n.a.-0.235-0.108-0.141-0.0090.4200.0230.0860.1740.0740.0090.001-0.019-0.006-0.007-0.0321.7300.198n.a.n.a.-0.0580.009-0.0230.071-0.0140.0700.2680.1890.0330.030n.a.-0.009-0.005-0.1260.0850.029n.a.n.a.-0.127-0.177-0.0330.048

-0.0490.1380.5220.3440.0950.031n.a.

-0.017-0.022-0.134

N

19701970416416641641Notes: The table reports the regression coefficient reported in Table 1 multiplied by the variable mean.Slide19

Further Investigation: LF Part Dep. Var.: (Empl/Pop18+)00/07 - (Empl/Pop18+)90/00 Does positive (negative) local demand shocks (decline) generate a higher (lower) employment rate?

The industry mix variable and emp/pop are measured in the same units.Industry Mix Growth variable is positive significant in both periods for all samples, i.e., we observe the expected supply response; But MUCH larger in latter periodSince this supply response did not come from in-migration, we infer it must have been in the form of increased local (internal) labor force participation response19Slide20

Non-metro

Small MA

Large MA

1990-2000

2000-2007

1990-2000

2000-

2007

1990-

2000

2000-2007

Industry Mix

Empl

. Growth

 

0.200***

(4.41)

0.725***

(9.65)

0.222***

(3.03)

0.547***

(5.10)

0.269***

(2.95)

0.513***

(4.00)

Table 3: Dep. Var.: Diff. in employment-pop (18+year) ratio U.S. counties

Notes: Robust t-statistics from STATA cluster command ***, **, and * indicate significance at 1%, 5% and 10%.

20Slide21

What about slack labor markets post-2000?Perhaps in slack labor markets, more of the labor demand shock could be satisfied by local labor supply. The 1990s had strong labor markets, but weaker post 2000. Does this explain our employment/pop results—i.e., it is a cyclical phenomenon when measured at the mean.We estimate a series of pop growth quartile regressions at 10, 25, 75, 90th percentiles. In both decades, the slack labor market hypothesis suggests industry mix would have a stronger impact in areas that are growing faster. We show 75/25 difference in quantile regression results.

21Slide22

Non-metro

Small MALarge MA

1990-2000

2000-2007

1990-2000

2000-2007

1990-2000

2000-2007

Ind

mix

emp

growth

rt. 1990-00/2000-07

.

522

(0.38)

1.22

(1.05)

5.73

(1.29)

0.92

(0.15)

5.78

(1.37)

7.70*

(1.69)

N

1972

1972

416

416

641

641

.

75

Pseudo

R

2

0.3471

0.3441

0.4506

0.4118

0.4663

0.4067

.

25

Pseudo

R

2

0.3215

0.3560

0.3917

0.3267

0.4132

0.2719

Notes: The coefficients are the difference between the industry mix regression coefficients in a 75th percentile quantile regression model and the corresponding coefficient in the 25th percentile regression model. In parentheses are the bootstrapped t-statistics for the statistical significance between the two estimates using 250 repetitions. Table 4: Diff. in 75-25 interquantile regression results22Slide23

Other hypothesesDemographics and super reduced form.Due to possible self-sorting we remove the demog variables.Not shown, the results are basically the same.Home ownership and housing bubble. In the bubble, home ownership rates rose. Did this impede migration? Partridge and Rickman (1997) and Oswald (1997) argue that home ownership reduces out-migration due to selling ‘costs’. We also argue that it reduces in-migration due to a lower housing supply.When adding home ownership share to the model, it supports these migration expectations, though our key results are unchanged.

23Slide24

Other hypothesesArmed forces. Pingle (2007) argues that a decline in the size of the military and resulting decline in transfers reduced gross migration. Did this affect net migration?We add the armed forces employment share variable to the model.While it has the expected negative coefficient, the industry mix results are basically unchanged.24Slide25

Table 5: Selected sensitivity analysis regression resultsPanel A

Non-metro

Small MA

Large

MA

1990-2000

2000-2007

1990-2000

2000-2007

1990-2000

2000-2007

Ind

mix

emp

growth rt

.

1990-00/2000-07

4.89***

(3.58)

-

2.08**

(-2.11)

8.06***

(2.60)

2.27

(0.72)

8.89***

(3.13)

3.90

(1.24)

Armed Forces Emp Share 1990/2000

-0.045***

(-2.72)

-0.024

(-1.51)

-0.072***

(-3.84)

-0.073***

(-2.93)

-0.086***

(-5.95)

-0.121***

(-5.01)

N

1970

1970

416

416

641

641

R

2

0.526

0.524

0.630

0.536

0.668

0.52025Slide26

Other hypothesesInitial housing and labor market disequilibrium.Housing bubble.Regress for the initial period 1990 and 2000 log median housing price and median wage on quality and other site-specific characteristics (Clark et al., 2003; Partridge et al., 2010).Ln(median housing price) = βhXh + e

h Ln(median wage) = βwXw + ew Use the residuals eh

& e

w

as measures of initial ‘disequilibrium’ and place them in the base regression model. This allows us to assess if a frothy housing market affects our results.

We find that our results are unchanged for industry mix and that a initial local housing bubble is positively associated with net migration. (also controlling for 1980 wages and housing prices)

26Slide27

Panel B

Non-metroSmall MA

Large MA

1990-2000

2000-2007

1990-2000

2000-2007

1990-2000

2000-2007

Ind

mix

emp

growth rt.

1990-00/2000-07

5.28***

(3.47)

-2.48**

(-2.24)

7.72**

(2.18)

0.61

(0.18)

9.28***

(2.88)

1.79

(0.46)

Resids

from log(avg. wage) 1990/2000

-

0.078

(-0.25)

0.40

(1.19)

-

0.17

(-0.18)

1.22

(1.08)

0.55

(0.94)

1.88**

(2.29)

Resids

from log(avg. rent) 1990/2000

1.40***

(4.55)

1.61***

(5.49)

1.51**

(2.55)

1.16

(1.22)

1.08**

(2.38)

0.32

(0.36)

N

19701970416416641641R20.53260.53670.61070.52150.64700.4899Table 5: Selected sensitivity analysis regression results27Slide28

Other hypothesesDemographicsAn aging population may alter migration patternsYet, this is in the constant term and in the state fixed effects. Moreover, in terms of life cycle effects, it would alter how the amenity variables affect migration.Directly controlling for the age composition has endogeneity issues—i.e., the young move to growing places.When we control for age composition, the results are fairly similar. We take the results cautiously because of the self-sorting makes age composition endogenous.28Slide29

Panel C

Non-metroSmall MA

Large MA

1990-2000

2000-2007

1990-2000

2000-2007

1990-2000

2000-2007

Ind

mix

emp

growth rt.

1990-00/2000-07

6.86***

(5.38)

-

0.77

(-0.66)

8.37***

(3.08)

2.35

(0.77)

11.44***

(

4.40)

8.16**

(2.34)

Pop share 7-17

-0.163***

(-4.62)

-

0.16***

(-4.78)

-0.206*

(-1.85)

-

0.30***

(-2.69)

-0.103

(-0.96)

-

0.50

***

(-4.00)

Pop share 18-24

-0.070**

(-2.28)

-

0.09***

(-3.05)

-0.092

(-1.14)

-

0.117

(-1.34)

0.017

(0.25)-0.296***(-3.17)Pop share 25-54-0.034(-1.12)-0.12***(-3.95)-0.102(-1.18)-0.1448(-1.46)0.093(1.29)-0.240**(-2.06)Pop share 55-59

-0.060(-1.19)

-

0.08*

(-1.82)-0.292**(-2.04)-0.308**(-2.02)-0.41***(-3.57)-0.583***(-5.70)Pop share 60-640.071(1.40)

-0.054

(-1.19)

-0.041

(0.29)

-0.2051*

(-1.65)

-0.177

(-1.12)

-0.485***

(-3.80)

Pop share 65+

-0.141***

(-4.82)

-

0.184***

(-6.86)

-0.190**

(-2.13)

-

0.24**

(-2.60)

-0.028

(-0.40)

-0.354***

(-3.62)

N

1970

1970

416

416

641

641

R

2

0.5636

0.5739

0.6402

0.5876

0.7014

0.5625

Table

5:

Selected sensitivity analysis regression results

29Slide30

Further InvestigationAdd occupation mix variable to assess whether mobility became more linked with skill/occupation shocks, implying increased mobility across industries while staying in the same occupation.Statistically significant results suggest weak evidence of this being true post-2000, but not during the 1990s. Net-Migration—much like Pop. Chg.Industry Mix variable strongly positive in initial period, insignificant in the latter period all samplesModest reduction in Amenity influence between periods for Small and Large MetrosNet migration models with international migration are also not qualitatively differentUsing in- and out-migration flows do not alter our conclusions.

30Slide31

Summary and ConclusionsPreliminary results suggest that one culprit of the decline in migration may be a modest decline in amenity migration. Rural/urban migration trends continue as well.The major factor, however, appears to be a decline in economic migration due to differential local economic growth Until this decade, a major factor underlying migration was job related, but local labor force demand appears to be increasingly satisfied with local and nearby workers rather than new migrantsWe conclude that diminishing migration is not reflective of the U.S. reaching a spatial equilibrium. Rather there is a structural change in economic migration.

31Slide32

Conclusions U.S. labor markets took on more of a European flavor with local labor demand being satisfied with local labor supply.Does this imply that the U.S. labor market is less flexible due to less geographic mobility?Perhaps not….Technological change facilitates more information about other labor markets but it also helps one find out about information about local job opportunities (much like unexpected NEG and transport results or internet is a complement to face-to-face interaction). There is evidence of more industry mobility (Kambourov

and Manovskii, 2008).There is also more temporary and contingent workers.Together, it may be less necessary to relocate for economic reasons. Geographical mobility is being replaced with industry mobility. This needs to be addressed with micro data.32Slide33

Thank-you33Slide34

Appendix slides34Slide35

Annual Gross Migration Rates, U.S. 1947-2008

Source: U.S. Census Bureau, Current Population Survey.Slide36

361990-2000 period

2000-2007 period

Non-metro

Rural

Sm. MA

Lg. MA

Non-metro

Rural

Sm. MA

Lg. MA

Pop growth 1990-00 and 2000-07

0.5950

(1.08)

0.4776

(1.10)

1.2661

(1.12)

1.5442

(1.36)

-0.0919

(0.96)

-0.2795

(0.95)

0.7472

(1.07)

1.0945

(1.52)

Chg in

emp

/pop. (18+) ratio 1990-00/2000-07

0.0165

(0.05)

0.0173

(0.05)

0.0191

(0.03)

0.0153

(0.03)

-0.0027

(0.05)

-0.0003

(0.06)

-0.0121

(0.03)

-0.0257

(0.03)

Ind

mix

emp

gr

1990-00/2000-07

0.2075

(0.04

)

0.2095

(0.04)

0.2100(0.03)0.2149(0.02)0.0588(0.03)0.0617(0.03)0.0564(0.03)0.0561(0.02)Distance to nearest or actual UC41.07(36.52)59.91(30.56)16.85(17.00)26.27(16.77)41.07(36.52)

59.91(30.56)

16.85(17.00)

26.27

(16.77)Incremental dist to a metro55.40(51.67)43.47(49.93)0.00(0.00)2.33(7.37)55.40(51.67)

43.47

(49.93)

0.00

(0.00)

2.33

(7.37)

Inc. dist to metro > 250,000 pop

66.80

(106.20)

76.02

(115.19)

93.23

(93.26)

0.00

(0.00)

66.80

(106.20)

76.02

(115.19)

93.23

(93.26)

0.00

(0.00)

Inc. dist to metro > 500,000 pop

42.89

(66.07)

45.32

(68.95)

36.89

(59.07)

36.29

(73.34)

42.89

(66.07)

45.32

(68.95)

36.89

(59.07)

36.29

(73.34)

Inc. dist to metro > 1,500,000 pop

89.03(111.10)

83.45(106.24)

78.54

(115.44)

99.37

(139.88)

89.03

(111.10)

83.45

(106.24)

78.54

(115.44)

99.37

(139.88)

County pop 1990/2000

22442

(20585)

13770

(10401)

72161

(64892)

270700

(539956)

24441

(22808)14832(11427)82750(76200)308194(595249)Pop of nearest or actual MA 1990/200065459(93944)73970(113164)133332(49192)1486906(2726624)72664(110160)82460(132956)151186(59500)1681592(2997111)% 1990(00) Pop. African American7.7560(14.74)7.1534(14.77)8.9211(12.25)11.0011(13.98)7.8995(14.89)7.3009(14.87)8.8893(12.24)11.2831(14.18)% 1990(00) Pop. Native America1.8192(6.72)2.0982(7.66)0.8659(2.67)0.6514(1.64)1.9445(7.06)2.2666(8.09)0.8963(2.63)0.6623(1.49)% 1990(00) Pop. Hispanic4.3354(11.64)4.2174(11.46)3.7863(9.17)4.8435(9.82)5.9260(12.55)5.5656(12.21)5.7645(10.66)7.0098(11.25)% 1990(00) Pop. Asian0.3159(0.43)0.2164(0.27)0.7911(1.16)1.3257(2.30)0.4241(0.46)0.3173(0.31)1.0180(1.28)1.8556(2.93)% 1990(00) Pop. Other origin1.7779(4.84)1.7162(4.85)1.6347(3.95)1.9858(4.06)2.4443(4.88)2.2584(4.71)2.5950(4.98)2.9430(4.72)% 1990(00) High School Grad. 35.00(5.96)35.25(5.82)34.26(6.32)32.47(6.07)35.97(5.89)36.40(5.59)34.27(6.76)31.49(7.00)% 1990(00) Pop. 25+ Some College15.65(4.38)15.28(4.32)17.10(4.41)18.19(4.34)20.04(4.52)19.98(4.62)20.86(3.99)21.22(3.82)% 1990(00) Pop. 25+ Assoc. Degree5.15(2.20)5.01(2.26)5.56(1.96)5.79(1.77)5.47(2.05)5.29(2.02)5.97(1.86)6.23(1.68)% 1990(00) Pop. 25+ College Degr11.75(4.73)10.98(4.12)14.83(6.98)17.68(8.27)14.32(5.64)13.51(4.99)18.01(8.04)21.90(9.64)% 1990(00) Pop. that is female51.02(1.63)50.95(1.65)50.97(1.52)51.05(1.60)50.37(2.07)50.25(2.18)50.63(1.55)50.80(1.45)% 1990(00) Pop. married59.92(5.91)60.79(5.77)58.03(5.91)57.36(6.89)58.14(5.15)58.83(5.05)56.95(5.29)56.83(6.09)% 1990(00) Pop. with a disability10.05(3.05)10.29(3.28)9.11(2.30)8.28(2.20)12.25(3.33)12.39(3.43)11.50(3.01)11.33(2.75)N1972130041664119721300416641

Appendix Table 1: Descriptive Statistics, Mean and (Standard Deviation) for

U.S. Counties, Selected VariablesSlide37

Impact at one std. dev. Pop. Change Model

Non-metroRural

1990-2000

2000-2007

1990-2000

2000-2007

Average pop growth (%/year)

0.595

-0.092

0.478

-0.279

Ind

mix

emp

gr

1990-00/2000-07

Distance to nearest or actual UC

Incremental dist to a metro

Inc. dist to metro > 250,000 pop

Inc. dist to metro > 500,000 pop

Inc. dist to metro > 1,500,000 pop

County pop 1990/2000

Pop of nearest or actual UC 1990/2000

County area (sq miles)

Amenity2 dummy

Amenity3 dummy

Amenity4 dummy

Amenity5 dummy

Amenity6 dummy

Amenity7 dummy

Great lakes

Pacific ocean

Atlantic ocean

0.159

-0.365

-0.210

-0.346

-0.112

-0.099

-0.035

0.038

0.054

0.064

0.132

0.226

0.253

0.197

0.139

-0.009

-0.074

-0.004

-0.069

-0.290

-0.170

-0.211

-0.114

-0.0790.1370.0470.0470.0970.2190.2400.2110.1430.027-0.040-0.0080.0360.869-0.523-0.178-0.247-0.084-0.0960.1030.012-0.0260.0000.0170.0860.0790.0260.0090.000-0.004-0.002-0.148-0.410-0.131-0.174-0.081-0.0760.3060.020-0.005-0.0060.0570.0620.0510.0140.002-0.002-0.0010.003N197019701300130037Slide38

Impact at one std. dev. Pop. Change Model

Small MALarge MA

1990-2000

2000-2007

1990-2000

2000-2007

Average pop growth (%/year)

1.266

0.747

1.544

1.094

Ind

mix

emp

gr

1990-00/2000-07

Distance to nearest or actual UC

Incremental dist to a metro

Inc. dist to metro > 250,000 pop

Inc. dist to metro > 500,000 pop

Inc. dist to metro > 1,500,000 pop

County pop 1990/2000

Pop of nearest or actual UC 1990/2000

County area (sq miles)

Amenity2 dummy

Amenity3 dummy

Amenity4 dummy

Amenity5 dummy

Amenity6 dummy

Amenity7 dummy

Great lakes

Pacific ocean

Atlantic ocean

0.190

-0.004

n.a

.

-0.389

-0.118

-0.176

0.000

0.059

0.052

0.267

0.379

0.342

0.262

0.186

0.070

-0.033

-0.048

0.070

0.010

-0.016

n.a

.-0.235-0.173-0.208-0.0080.1470.0320.2040.2340.1000.0330.007-0.169-0.026-0.044-0.0961.7300.198n.a.n.a.-0.0580.009-0.0230.071-0.0140.0700.2680.1890.0330.030n.a.-0.009-0.005-0.1260.0850.029n.a.n.a.-0.127-0.177-0.0330.048-0.0490.1380.5220.3440.0950.031n.a.-0.017-0.022-0.134N41641664164138Slide39

Background cont’d.“Slump Creates Lack of Mobility for Americans,”New York Times by Sam Roberts, April 23, 2009 “the number of people who changed residences declined to 35.2 million from March 2007 to March 2008, the lowest number since 1962, when the nation had 120 million fewer people.”Refers to concern that “if job-related moves are getting suppressed and workers are not getting re-sorted to the jobs that best use their skills,” long term negative consequences.Reasons for greater immobility: greater home ownership, two earner households, ageing (LF).39Slide40

Theoretical Model cont’d.Households in region i derive utility from consumption (X), housing (H), and site-specific amenities (S). HH provide labour to the market for which they receive wages (w) with some probability of employment (e), and pay rents (

r) for housingHHs in region i are located in a spatial environment such that they can access employment opportunities, built amenities and consumption goods in n other regions j by traversing Distance (Dij)40Slide41

Theoretical Model cont’d.HH and Firm expected conditions location iIndirect Utility Function: Vi(wi, r

iH, ei, Si H, DISTij, ·)Indirect profit function:

Πi

(

w

i

,

r

F

i

, m

i

,

S

i

F

DIST

ij

,

·

)

LR equilibrium

requires HH and Firm re-locations, at rate

α

,

to equalize expected utility and profits

HHNM

i

=

α

H

i

(

V

j

-V

i

)

for any

j

; HH net migration

FNM

i

=

α

F

i

(

COST

j

-COST

i

)

for any

j

; firm net migrationPop. Chg.i = f(initial conditions in i incl. DISTij)41Slide42

Descriptive Statistics Average Annual Population Growth (%)Average Chg in Empl/Pop 18+ ratio1990-2000

2000-20071990-20002000-2007Non-Metro0.595-0.0920.0165

-0.0027Rural

0.478

-0.279

0.0173

-0.0003

Small

Metro

1.266

0.747

0.0191

-0.0121

Large Metro

1.544

1.094

0.0153

-0.0257

42Slide43

Garfield County, a rural Utah county, ≈ 4,000 residents (1990). The nearest urban area is Cedar City (a MICRO) located 88kms away. The nearest MA

is St. George (≈ 90,000 pop.), 146kms away, an incremental distance of 58kms (146-88). Nearest larger MA > 250K, which is Provo-Orem, UT (pop. of 377,000), is 278kms from Garfield County, incremental distance versus St. George is 132kms (278-146). The nearest MA > 500K, the next higher tier,

Salt Lake City, UT (969,000 people). Salt Lake is 321kms from Garfield County,

incremental distance of 43kms

(321-278).

Nearest MA > 1.5 million

people, the next higher tier above Salt Lake, is

Phoenix, AZ

(3.25 million 1990 pop.). Phoenix is 477kms away from Garfield County, an

incremental distance of 156kms

(477-321).

Distance calculations

43Slide44

Representing the Urban HierarchyFor Carbon County, a rural Wyoming county (pop. 8,500) nearest urban area is Albany County (a MICRO), containing the city of Laramie, 129kms away. The nearest MA is Casper (67,000pop.), located 143kms away, an incremental distance of 14kms (143-129). Because Casper is a small MA, Carbon County will also be influenced by its remoteness from larger MA of at least 250K. Nearest of >250 is Ft. Collins, Colorado, (pop. 250,000) 210kms from Carbon County, but the incremental distance beyond Casper is 67kms (210-143). The nearest MA of at least 500K, the next tier, is Denver, Colorado (2.5 pop). Denver is 282kms away from Carbon County, an incremental distance of 72kms (282-210). Because Denver is already over 1.5 million population, no incremental distance to reach a MA of at least 1.5 million. 44