1. home and garden

What Are Cities Worth? Land Rents, Local Productivity,
and the Total Value of Amenities
David Albouy
University of Illinois and NBER
April 16, 2015
I would like to thank David Agrawal, Bob Barsky, John Bound, Gabriel Ehrlich, Rob Gillezeau,
Michael Greenstone, Andrew Hanson, Andrew Haughwout, Jim Hines, Fabian Lange, Anne Mandich, Peter Mieskowski, John Quigley, Jordan Rappaport, Stuart Rosenthal, Michael Rossi, Nathan Seegert, Bryan
Stuart and the participants of seminars at the Federal Reserve Banks of Kansas City and New York, Aarhus,
Essex, LSE, Rice, Texas A&M, UC Berkeley (Haas), UI-Chicago, Maryland, Michigan, Virginia, and Hebrew. Kevin A. Crosby and Bert Lue provided excellent and diligent research assistance. The Center for
Local, State, and Urban Policy (CLOSUP) at the University of Michigan and the National Science Foundation (Grant SES-0922340) provided valuable support. Any mistakes are my own. Please e-mail any
questions or comments to [email protected].
Abstract
This article examines how to use widely available data on wages and housing costs to infer land
rents, local productivity, and the total value of local amenities in the presence of federal taxes and
locally-produced non-traded goods. I apply the model to U.S. metropolitan areas, with the aid
of visually intuitive graphs, and infer large differences in land rents and improved measures of
productivity. Wage and housing-cost differences across metros are accounted for more by productivity than quality-of-life differences. Regressions using individual amenities reveal that the most
productive and valuable cities are typically coastal, sunny, mild, educated, and large.
Keywords: Land rents, productivity, amenities, hedonic valuation, non-traded goods, federal
taxation.
JEL Codes: H2, H4, J3, Q5, R1
1
Introduction
This article demonstrates both analytically and graphically how to use widely-available data on
wages and housing costs to infer land rents and measure local productivity and the total value of
local amenities. I use an inter-city framework, based on Rosen (1979) and Roback (1982), that features two enhancements: federal taxes, and a production sector for "home" goods, such as housing,
which are not tradable across cities. Albouy (2009) demonstrates that federal taxes increase tax
burdens in places where wage levels are high, creating local fiscal externalities. Roback briefly
models home goods, but ignores local productivity empirically, and uses a simpler model, equating land with housing, to estimate quality-of-life differences. The distinction between land and
housing is important as housing prices are often substituted for difficult-to-find land rents – see
Mills (1998) and Case (2007) – despite being influenced by other costs such as labor.1 Accounting
for home goods distinguishes the local productivity of firms producing traded goods from those
producing housing or other non-traded goods, i.e., "trade-productivity" from "home-productivity."
I demonstrate that without land-rent data, the two productivities cannot be identified separately,
I apply the model to U.S. Census data and assume, as previous researchers did implicitly –
e.g., Beeson and Eberts (1989), Gabriel and Rosenthal (2004), and Shapiro (2006) – that homeproductivity is constant across cities to estimate local land values. Relative to most previous estimates of trade-productivity, the enhanced model puts more weight on high housing costs and less
weight on local wages. Combining the value of trade-productivity to that of quality of life, from
Albouy (2008), I measure each city’s “total amenity value," which goes beyond previous measures by accounting for non-residential land, local labor costs, and federal-tax externalities. The
application ranks cities by their trade-productivity and total amenity value, with San Francisco
topping both lists. A variance decomposition suggests that trade-productivity explains wage and
housing-cost differences across cities more than quality of life.
The illustrative empirical analysis on individual amenities is the first to simultaneously present
1
This paper does not address temporal issues that would make land rents deviate from land values by more than an
interest rate, and so the terms "rents" and "values" are used interchangeably.
1
the value of multiple amenities to both firms and households. A few amenities statistically predict
most of the variation in trade-productivity and total amenity value. Simple cross-sectional hedonic
methods produce estimates of the impact of population and education levels on productivity consistent with more sophisticated analyses. Measuring their value per acre, the most productive and
valuable cities are not only large and educated, but also mild, sunny, and coastal.2
Albouy (2009) derives most of the theoretical results applied here, emphasizing how federal
taxes distort local prices and location decisions. The analysis below actually estimates what amenities are related to tax payments. Albouy (2008) provides the complementary quality of life estimates. Building from this model, Albouy and Stuart (2013) predict population levels in U.S. cities,
while Albouy and Ehrlich (2012) integrate a land-value index using recent market transactions
data, and estimate a cost function for housing together with differences in housing productivity.
2
Prices and the Value of Amenities across U.S. Cities
2.1
An Inter-City Model of Prices and Amenities with Taxes and LocallyProduced Non-Traded Goods
Consider a system of cities, indexed by j, which share a homogenous population of mobile households, N . Households consume a numeraire traded good, x, and a non-traded "home" good, y,
with local price, pj , measured by the flow cost of housing services. Firms produce traded and
home goods out of land, capital, and labor. Within a city, factors receive the same payment in either sector. Land, L, within each city is homogenous and immobile, and is paid a city-specific price
rj . Capital, K, is supplied elastically at the price {. Households, are fully mobile and each supplies
a single unit of labor earning wage wj . Each owns an identical, nationally-diversified, portfolios
of land and capital, providing non-labor income, I; total income, mj
2
I + wj , varies only with
Articles that consider the local productivity of firms with only the traded sector include Rauch (1993), Dekle
and Eaton (1999), Haughwout (2002), Glaeser and Saiz (2004), and Chen and Rosenthal (2008). Rappaport (2008a,
2008b) is the only author that accounts for locally produced goods, although he restricts home-productivity and tradeproductivity to be equal. Tabuchi and Yoshida (2000) use actual data on land rents, although later conflate them with
housing costs.
2
wages. Federal income tax payments of (mj ) are rebated lump-sum. Cities differ in three general
"urban attributes:" (i) quality of life, Qj ; (ii) trade-productivity, AjX ; and (iii) home-productivity,
j
AjY . These attributes depend on a vector of individual urban amenities, Zj = (Z1j ; :::; ZK
).
This model of spatial equilibrium uses duality theory to elegantly map the three prices (rj ; wj ; pj )
one-to-one with the three attributes (Qj ; AjX ; AjY ), by assuming that workers receive the same utility regardless of their location, while all firms make zero profit. The appendix derives the following
first-order approximations of these conditions, describing how prices co-vary with city attributes in
terms of log differences from the national average, using hat notation, x^j = dxj =x. The zero-profit
condition for home-good producers implies that land-rent differences are
r^j =
1
p^j
^
Nw
j
1 ^j
AY
+
L
L
and
N
are the cost shares of land and labor in home production. The formula infers land rents
from housing costs; p^j , subtracting away labor costs
1=
L.
(1)
L
^
Nw
j
, and scaling up the inverse cost share
Using this value to infer land costs for traded-good firms, trade-productivity differences are
inferred from the combined zero-profit condition
A^jX =
L
and
N
L
p^j +
L
N
N
L
w^ j +
L
L
L
A^jY :
(2)
are the cost shares in traded production. I assume traded production is less land intensive
than home production, i.e.,
L= N
<
L= N .
^ j = sy p^j
Q
The mobility condition for households
sw (1
0
)w^ j
(3)
^ j , valued as a fraction of income, is higher in areas that have
states that quality-of-life differences, Q
high prices relative to wages, after adjusting for the marginal tax rate
0
, where sy is the effective
share of income spent on home goods, and sw , the share of income from labor. Combining the
3
value of amenities to households and firms, the total value of amenities is
^j
^ j + sx A^jx + sy A^j = sR p^j +
Q
Y
0
sw
sR
L
where sR = sy
L
+ (1
sy )
L
N
w^ j +
L
sR ^j
d j
AY = sR r^j +
m
L
is the income share of land and d j =m =
0
(4)
sw w^ j is the value of
fiscal externalities from federal taxes. The total value measure reflects those externalities plus the
value of local land. It accounts for non-residential land and adjusts for labor costs, although it, like
land values and trade-productivity, is downward-biased in cities that have high home-productivity.
Researchers applying this model empirically have all used a reduced version that equates land
with housing, i.e., with
N
= 0 and
L
= 1, and ignoring differences in AY . In estimating quality
of life, Roback (1982) uses land values in (3), ignoring other cost factors that affect home goods.
Subsequent analyses, such as Blomquist et al. (1988), Beeson and Eberts (1989), and Gyourko and
Tracy (1989, 1991), use housing costs in lieu of land rents. In estimating trade-productivity, such
measures put too little weight on housing costs and too much weight on wages, double-counting
labor costs in housing. Even with the proper adjustments, low home-productivity, A^jY ,may be
confused for high trade-productivity, A^jX , when data on actual land values are unavailable.
2.2
Estimated Wage and Housing-Cost Differentials
I estimate wage and housing-cost differentials, (w^ j ; p^j ) with the 5-percent sample of Census
data from the 2000 Integrated Public Use Microdata Series (IPUMS) as in Albouy (2009). The
276 cities are defined by 1999 OMB definitions of Metropolitan Statistical Areas (MSAs); nonmetropolitan area of each state are averaged together. The wage differentials are populationdemeaned coefficients of indicator variables, one for each MSA, in a regression of the logarithm
of hourly wages, controlling for worker characteristics. I use an analogous regression for housing
costs, combining gross rents with imputed rents from owner-occupied units. Appendix C provides
more details on the variables and estimation procedure.
4
2.3
Land-Value, Trade-Productivity, and Total-Value Measures
To calculate the differentials of interest, I parameterize equations (1), (2), (3), and (4), according to
values in Albouy (2009) — explained in Appendix B — adjusting slightly for housing deductions
and state taxes.3 The parametrization produces the following formulae:
Differential Full Model
Reduced Model
Land rent r^j = 4:29(^
pj + A^jY )
2:75w^ j ; = p^j
Trade-productivity A^jX = 0:11(^
pj + A^jY ) + 0:79w^ j ; = 0:025^
pj + 0:825w^ j
^ j = 0:32^
Quality of life Q
pj
0:49w^ j ;
= 0:073^
pj
0:75w^ j
Total amenity value ^ j = 0:39(^
pj + A^jY ) + 0:01w^ j ; = 0:10^
pj
Lacking land-rent data, I impose the restriction that unobserved home-productivity differences
are zero, A^jY = 0. This creates biases proportional to the housing-cost coefficients, being large
for land rents, small for trade-productivity, and moderate for total value. I characterize previous
studies by the "Reduced Model," which imposes A^jY = 0,
L
= 1;
N
= 0; and
0
= 0.4
Figure 1 plots the wage and housing-cost differentials across metro areas, together with four
lines, or "curves", describing the (w^ j ; p^j ) combinations where the left-hand sides of equation are
zero, i.e., at their national averages. The iso-rent curve describes cities on (1) where r^ = 0, namely
p^j =
^
Nw
j
. The slope
N
illustrates how home-good prices rise with labor costs. The vertical
distance between this line and a city’s marker, scaled by 1=
L,
provides that city’s inferred land
rent. Figure 2 plots the inferred land-rent differentials against housing costs. It draws a line for
how these rents are inferred from housing costs if wages are held at the national average, w^ j = 0.
The dashed line’s rotation around the origin from the diagonal illustrates the division of p^j by the
Let F0 and S0 be the federal and state marginal tax rates, F and S be the deduction on home-good purchases,
and w
^Sj and p^jS be the wage and housing-cost differentials within state. The tax differential is then
3
d j
= sw (
m
0
^j
Fw
+
0 j
^S )
Sw
sy (
0 j
^
F Fp
+
0 j
^S )
S Sp
(5)
The numbers in the text are regression-based approximations.
4
The simplifications are closest to Beeson and Eberts (1989), although their parameterization imposes r^j =
j ^
^ j = 0:073^
p^ ; AX = 0:028^
pj + 0:927w
^j ; Q
pj 0:73w
^ j ; ^ j = 0:064^
pj
5
cost-share of land,
L.
The vertical distance between the dashed-line and the markers indicates the
adjustments for labor costs.
The zero-profit condition in Figure 1 indicates cities with average trade-productivity, A^jX =
0; using condition (2) with A^jY = 0. The slope,
N
L N = L,
gives the rate at which land
costs, proxied through housing costs, need to fall with wage levels for firms to break even. Cities
above this line have above-average costs, indicating high trade-productivity. The trade-productivity
estimates are graphed in Figure 3 against those from the reduced model. The methodological
refinement of putting more weight on housing costs is not enormous, but nonetheless changes
the relative rankings of many cities, putting Los Angeles in front of Chicago, Boston in front of
Detroit, and Denver in front of Las Vegas.
^j = 0
The mobility condition in Figure 1, indicates cities with average quality of life, i.e., Q
in (3). The positive slope of (1
0
)sw =sy indicates how local costs increase with wage levels to
keep real income levels from rising. Households in cities above this line pay a premium relative to
the wage level, which implies that their quality of life is above average (Albouy 2008).
The iso-value curve, (4) with ^ j = 0, traces out cities with average total amenity values.
The line is quite flat, as properly scaled, housing costs indicate them well. The term for wages,
0
sw
sR
N = L,
reflecting tax externalities net of labor-cost adjustments, is almost zero under the
parameterization.
Figure 4 graphs the quality-of-life and trade-productivity estimates, transforming Figure 1
through a change of coordinate systems. The average mobility condition provides the horizontal axis for trade-productivity, while the average zero-profit condition provides the vertical axis for
quality of life. The axes are scaled so that equidistant attribute differences have equal value.
2.4
The Most Trade-Productive and Valuable Cities
Table 1 lists the estimated differentials for select cities, along with averages by Census division
and metro-area size. Table 2 presents the top 20 rankings for trade-productivity, quality of life,
and total amenity value. Appendix Table A1 presents a complete list of metro areas and non-metro
6
areas; Appendix Table A2 lists values by state.
The relationship between trade-productivity and metro population, usually attributed to agglomeration economies, is illustrated in Figure 5. The most trade-productive metro area is San
Francisco, only the fifth largest — New York, the largest, is second — reflecting the exceptional
knowledge spillovers and innovation around Silicon Valley (Saxenian 1994, Florida 2008). The top
ten most productive cities include five other large metros – Los Angeles, Chicago, Boston, Washington, and Detroit – and three small metros – Monterrey, Santa Barbara, and Hartford, which are
very close to much larger metros. The least productive metro, Great Falls, MT, is remote, as are
the two least productive states, South and North Dakota.
Combining quality of life and trade-productivity, the most valuable metropolis is San Francisco. It is followed by six other Pacific cities – Santa Barbara, Honolulu, Monterrey, San Diego,
Los Angeles, and San Luis Obispo – that offer exceptional quality of life and fairly high productivity. Next are large, highly productive, and somewhat amenable metros – New York, Seattle,
Boston, Denver, Chicago, and Portland – and resort-like, yet economically vibrant, metros like
Cape Cod, Santa Fe, Naples, Reno, and Fort Collins. Further down the list are smaller cities in less
crowded areas, such as in Arkansas, Oklahoma, West Virginia, Mississippi, and the Dakotas. The
relationship between total amenity value and city size in Figure 6 reflects the strong relationship
between size and productivity, and the weak relationship between size and quality of life (Albouy
2008). The estimates imply that the highest-value metro area (San Francisco) has land on average
100 times more valuable per acre than the lowest-value land in McAllen, TX.
2.5
Explaining the Variation of Prices across Cities
The theory of spatial equilibrium asserts that price variation across cities reflects differences in
quality of life and productivity. A variance decomposition of the total value of amenities yields:
^ j ) + s2 var(A^j ) + 2sx cov(Q
^ j ; A^j ):
var( ^ j ) = var(Q
x
X
X
7
The relative importance of each attribute is reflected by the two variance terms: if one attribute
is made constant, then the covariance term collapses to zero, and only the variance of the other
attribute remains. The model implies similar decomposition formulae for wages, housing costs,
and land rents. These statistical decompositions provide an interesting accounting of equilibrium
relationships, but must be treated cautiously as attributes may be endogenous. For example, high
quality of life may raise population, leading to endogenous trade-productivity gains from agglomeration.
Table 4 displays the decompositions for all prices. Overall, trade-productivity accounts for a
greater fraction of amenity value than quality of life, which can be seen in Figure 4. Quality of life
has a greater influence on land rents by a slight margin. Variations in nominal wages – as well as
federal-tax burdens – are driven mostly by trade-productivity, contradicting Roback’s (1982) claim
that nominal wages vary more due to quality of life. Housing-cost variation is also driven mainly
by trade-productivity.
3
Individual Predictors of Amenity Value
Researchers commonly use the spatial equilibrium model to estimate the value of individual amenij
ties (Z1j ; ::; ZK
) through regression methods. I illustrate the potential impact of amenities on the
measures derived above using seven, mutually-consistent, linear regressions:
vj =
X
Zkj
kv
+ "jv ;
k
^ A^X ; r^; d =m; ^ g. The amenity coefficients,
where v 2 fw;
^ p^; Q;
kv ;
express the effect of a one-
unit increase in an amenity, and share the same interrelationships as their corresponding regressors,
v. Cross-sectional regressions of this kind are subject to well-known empirical caveats (see Gyourko et al. 1999), including omitted variables, simultaneity, and multi-collinearity. Estimates
should not be interpreted causally.
The regressors, listed in Table 4 and detailed in Appendix C, include both "natural" ameni8
ties, relating to climate and geography, and "artificial" ones, relating to local inhabitants, including
metropolitan population and the share of the adults with college degrees. These are not true amenities, but likely determine amenities that are key to local trade-productivity, engendering agglomeration economies. The Wharton Residential Land-Use Regulatory Index (WRLURI) of Gyourko et
al. (2008) controls for unobserved housing-productivity differences. The coefficients are grouped,
^
with effects on observed data w^ and p^ in columns 1 and 2; amenities to households and firms, Q
and A^X , in columns 3 and 4; the combined value, ^ , in column 5; and their split between land
values and federal tax revenues, sR r^ and d =m, in columns 6 and 7.
The relationships between the natural amenities and trade-productivity, new to the literature,
reveal interesting patterns. Sunshine, coastal proximity, low levels of cold (minus heating degree
days) and low levels of heat (minus cooling degree days) appear to be amenities to firms, as well
as to households. The small but significant positive effect of latitude on productivity evokes findings in Hall and Jones (1999) that social capital is higher in northern areas. The positive estimate
for coastal proximity may reflect savings in transportation costs; the climate effects are more surprising, but could have a physiological basis. Long ago, Montesquieu (1748) hypothesized that
extreme temperatures inhibit the ability of humans to work. Recent studies find that both indoor
and outdoor workers are less productive in warm temperatures (Engineering News Record 2008).
As most modern workplaces are indoors with air conditioning, these estimates need further study.
The elasticity of wages with respect to population is estimated to be 3.8 percent, well inside
the range surveyed by Rosenthal and Strange (2004) and Melo et al. (2009), despite concerns of
endogenous migration. The elasticity of housing-costs to population is 5.6 percent, so that the
elasticity for trade-productivity is 3.6 percent. Doubling a city’s population (an increase of 0.69
log points) increases the total value of its amenities by 1.5 percent of income, of which three-fifths
is captured in local land values, the rest in federal taxes.
The estimates in the second row associate a ten-percentage point increase in college-educated
adults (1.3 standard deviations) with 7-percentage point increases in wages and productivity, and
a 1.8 percentage point increase in quality of life. While these estimates may reflect selective mi-
9
gration, they resemble those of Moretti (2004) and Shapiro (2006), who use instrumental variable
methods. In total, a ten-percent increase in college share is associated with a 7-percent increase in
the total value of amenities, of which federal taxes expropriate one-fifth.
The coefficients of determination (R2 ) reveal that this parsimonious set of amenities explains
88 percent of trade-productivity, and over 90 percent of land rents and total amenity value. Population, education, sunshine, coastal proximity, average slope and mild temperatures are all strongly
associated with total amenity value. The results also imply that households are taxed for living in
cities that are large, flat, cool, coastal, and educated.
Estimates based on the full and reduced models produce different results both in magnitude and
significance. Land rents effects diverge considerably from those for housing costs in the six cases
where the federal tax differential matters. Reduced-model estimates of trade-productivity, based
almost entirely on wages, would find hilliness (average slope) increases firm costs, but would not
find excess heat to do so. Most researchers would also likely ignore impacts on non-residential
land, which the total-value estimates include.
4
Conclusion
The above analysis highlights the importance of considering taxes and housing, separately from
land, when determining the value of local productivity and amenities in general. Wage and housingcost data appear to be largely adequate for inferring local levels of productivity in tradables, and
the resulting measures appear sensible. Statistically, local labor demand factors (productivity) are
more important in determining local wages and housing costs than supply factors (quality of life).
Furthermore, a limited number of variables explain over seven-eighths of the variation in wages,
trade-productivity and total amenity value. Extensions of this model could do more with internal
structure, population heterogeneity, and dynamics. Nevertheless, a clean and intuitive framework
for understanding cross-sectional variation of widely available data sources is informative and an
important stepping stone to future work.
10
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Kelly Hall, Miriam King, and Chad Ronnander (2004) Integrated Public Use Microdata Series:
Version 3.0. Minneapolis: Minnesota Population Center.
Saxenian, Annalee (1994) Regional Advantage: Culture and Competition in Silicon Valley and
Route 128. Cambridge, MA: Harvard University Press.
Shapiro, Jesse M. (2006) "Smart Cities: Quality of Life, Productivity, and the Growth Effects of
Human Capital." The Review of Economics and Statistics, 88, pp. 324-335.
14
Tabuchi, Takatoshi and Atsushi Yoshida (2000) "Separating Urban Agglomeration Economies in
Consumption and Production." Journal of Urban Economics, 48, pp. 70-84.
Thorsnes, Paul (1997) "Consistent Estimates of the Elasticity of Substitution between Land and
Non-Land Inputs in the Production of Housing." Journal of Urban Economics, 42, pp. 98-108.
Valentinyi, Ákos and Berthold Herrendorf (2008) "Measuring Factor Income Shares at the Sectoral
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15
TABLE 1: WAGE, HOUSING COST, LAND RENT, QUALITY-OF-LIFE, PRODUCTIVITY, FEDERAL TAX, AND TOTAL AMENITY VALUE
DIFFERENTIALS, 2000
Adjusted Differentials
Amenity Values
Total
Quality of
Life
Federal Tax
Differential
Amenity
Value
0.29
0.13
0.06
0.14
0.10
0.15
0.21
0.10
0.13
0.07
0.13
0.04
0.12
0.02
0.03
0.11
0.10
0.07
0.06
0.01
0.05
-0.05
-0.01
0.05
-0.05
-0.10
-0.14
-0.23
0.14
0.18
0.20
0.14
0.12
0.08
0.03
0.06
0.03
0.05
0.01
0.05
-0.01
0.04
0.01
-0.05
-0.04
-0.03
-0.03
-0.02
-0.04
0.00
-0.03
-0.07
-0.05
-0.04
-0.02
-0.08
0.05
-0.01
-0.02
0.01
0.00
0.02
0.04
0.01
0.02
0.01
0.03
0.00
0.03
0.00
0.01
0.04
0.03
0.03
0.02
0.01
0.02
-0.01
0.01
0.03
-0.01
-0.02
-0.02
-0.04
0.32
0.26
0.24
0.23
0.19
0.18
0.16
0.12
0.12
0.10
0.09
0.07
0.06
0.05
0.03
0.02
0.02
0.01
0.01
-0.01
-0.01
-0.03
-0.04
-0.04
-0.08
-0.10
-0.11
-0.23
1.42
0.61
0.28
0.16
-0.33
-0.25
-0.83
-0.80
-1.02
0.12
0.07
0.09
-0.04
0.00
-0.04
-0.09
-0.11
-0.13
0.08
0.03
-0.01
0.03
-0.03
-0.01
-0.04
-0.03
-0.04
0.01
0.01
0.02
-0.02
0.01
-0.01
-0.01
-0.02
-0.02
0.16
0.07
0.05
0.00
-0.03
-0.03
-0.10
-0.10
-0.12
0.96
0.08
-0.29
-0.52
-0.91
0.16
0.02
-0.04
-0.10
-0.16
0.03
0.00
-0.01
-0.01
-0.02
0.03
0.01
-0.01
-0.02
-0.04
0.13
0.02
-0.04
-0.07
-0.13
Population Size
Wages
Housing
Costs
7,039,362
399,347
876,156
401,762
2,813,833
16,373,645
21,199,865
3,554,760
5,819,100
2,581,506
9,157,540
2,265,223
7,608,070
3,876,380
3,251,876
5,456,428
6,188,463
2,968,806
4,112,198
2,945,831
5,221,801
2,395,997
2,603,607
4,669,571
2,358,695
1,592,383
1,083,346
569,463
0.26
0.07
-0.01
0.10
0.06
0.13
0.21
0.08
0.12
0.05
0.14
0.02
0.13
0.00
0.03
0.13
0.11
0.08
0.08
0.01
0.06
-0.06
0.01
0.07
-0.04
-0.09
-0.13
-0.21
0.81
0.66
0.61
0.59
0.48
0.45
0.41
0.31
0.29
0.24
0.22
0.17
0.15
0.13
0.08
0.05
0.05
0.02
0.01
-0.03
-0.04
-0.08
-0.10
-0.11
-0.21
-0.25
-0.28
-0.57
2.78
2.65
2.62
2.24
1.89
1.57
1.18
1.10
0.93
0.89
0.59
0.68
0.31
0.54
0.24
-0.13
-0.09
-0.14
-0.15
-0.17
-0.34
-0.20
-0.46
-0.68
-0.77
-0.85
-0.83
-1.86
Pacific
New England
Middle Atlantic
Mountain
East North Central
South Atlantic
West South Central
West North Central
East South Central
45,025,637
13,922,517
39,671,861
18,172,295
45,155,037
51,769,160
31,444,850
19,237,739
17,022,810
0.10
0.07
0.09
-0.06
0.02
-0.04
-0.08
-0.11
-0.12
0.39
0.19
0.13
0.00
-0.07
-0.08
-0.24
-0.25
-0.32
MSA Population
MSA, Pop > 5 Million
MSA, Pop 1.5-4.9 Million
MSA, Pop 0.5-1.4 Million
MSA, Pop < 0.5 Million
Non-MSA areas
84,064,274
57,157,386
42,435,508
42,324,511
55,440,227
0.16
0.03
-0.03
-0.10
-0.16
0.32
0.04
-0.09
-0.19
-0.32
Main city in MSA/CMSA
San Francisco
Santa Barbara
Honolulu
Monterey
San Diego
Los Angeles
New York
Seattle
Boston
Denver
Chicago
Portland
Washington-Baltimore
Miami
Phoenix
Detroit
Philadelphia
Minneapolis
Atlanta
Cleveland
Dallas
Tampa
St. Louis
Houston
Pittsburgh
San Antonio
Oklahoma City
McAllen
CA
CA
HI
CA
CA
CA
NY
WA
MA
CO
IL
OR
DC
FL
AZ
MI
PA
MN
GA
OH
TX
FL
MO
TX
PA
TX
OK
TX
Inferred Land
TradeRent
Productivity
Census Division
United States
281,421,906
0.13
0.30
1.00
0.13
0.05
0.03
0.12
total
standard deviations
Wage and housing price data are taken from the U.S. Census 2000 IPUMS. Wage differentials are based on the average logarithm of hourly wages for fulltime workers ages 25 to 55. Housing-cost differentials are based on the average logarithm of rents and housing prices. Adjusted differentials are the cityfixed effects from individual level regressions on extended sets of worker and housing covariates. See Appendix C.1. for more details. The inferred landrent, quality-of-life, trade-productivity, and total-amenity variables are estimated from the equations in section 2.3, using the calibration in Table A1, with
additional adjustments for housing deductions and state taxes, described in footnote 3.
TABLE 2: CENSUS METROPOLITAN AREA RANKINGS, 2000
Trade-Productivity Ranking
Quality-of-Life Ranking
Total Value Ranking
1
San Francisco
Honolulu
San Francisco
2
New York
Santa Barbara
Santa Barbara
3
Los Angeles
San Francisco
Honolulu
4
Monterey
Monterey
Monterey
5
Chicago
Santa Fe
San Diego
6
Boston
San Luis Obispo
Los Angeles
7
Santa Barbara
San Diego
San Luis Obispo
8
Hartford
Cape Cod
New York
9
Washington-Baltimore
Grand Junction
Cape Cod
10
Detroit
Missoula
Seattle
11
San Diego
Naples
Boston
12
Philadelphia
Medford
Santa Fe
13
Seattle
Eugene
Naples
14
Stockton
Los Angeles
Denver
15
Anchorage
Corvalis
Chicago
16
San Luis Obispo
Fort Collins
Sacramento
17
Sacramento
Bellingham
Reno
18
Minneapolis
Wilmington
Anchorage
19
Denver
Sarasota
Portland
20
Atlanta
Burlington
Washington-Baltimore
Rankings based off of data in Appendix Table A1, which contains the full MSA/CMSA names. The quality-of-life ranking
is originally from Albouy (2009)
TABLE 3: VARIANCE DECOMPOSTION OF QUALITY-OF-LIFE AND TRADE-PRODUCTIVITY
EFFECTS ON PRICE DIFFERENTIALS ACROSS CITIES
Variance Decomposition
Fraction of variance explained by
Variance
Quality of Life
Productivity
Covariance
(1)
(2)
(3)
(4)
Land Rents
Wages
Housing Costs
Tax Differential
Total Value
1.002
0.019
0.093
0.001
0.015
0.370
0.018
0.184
0.113
0.181
0.287
1.132
0.498
1.276
0.503
0.342
-0.150
0.318
-0.398
0.317
Variances are calculated across 276 metro areas and 49 non-metro areas by state, weighted by population.
Based on the full model described in the text with federal taxes.
TABLE 4: THE RELATIONSHIP BETWEEN INDIVIDUAL AMENITIES AND HOUSING COSTS, WAGES, QUALITY OF LIFE, PRODUCTIVITY, LAND RENTS,
FEDERAL TAXES, AND TOTAL AMENITY VALUES
Standard
Mean Deviation
Observables
Housing
Cost
Wage
(1)
(2)
Amenity Type
Quality
Trade
of Life
Productivity
(3)
(4)
Total
Amenity
Value
(5)
Capitalization Into
Local
Federal
Land Rents
Tax Payment
(6)
(7)
Logarithm of Metro Population
14.63
1.32
0.056***
(0.007)
0.038***
(0.004)
-0.001
(0.002)
0.036***
(0.004)
0.022***
(0.003)
0.013***
(0.002)
0.009***
(0.001)
Percent of Population
College Graduates
0.26
0.07
1.718***
(0.169)
0.714***
(0.069)
0.213***
(0.042)
0.748***
(0.067)
0.692***
(0.067)
0.540***
(0.062)
0.152***
(0.017)
Whartron Residential Land-Use
Regulatory Index (WRLURI)
0.05
0.93
0.008
(0.012)
0.004
(0.007)
0.001
(0.004)
0.004
(0.006)
0.003
(0.005)
0.002
(0.005)
0.001
(0.002)
Minus Heating-Degree Days
(1000s)
-4.38
2.15
0.039***
(0.010)
0.014**
(0.006)
0.006
(0.004)
0.015***
(0.005)
0.016***
(0.004)
0.013***
(0.004)
0.003*
(0.002)
Minus Cooling-Degree Days
(1000s)
-1.28
0.89
0.105***
(0.018)
0.017
(0.012)
0.025***
(0.007)
0.025**
(0.010)
0.041***
(0.007)
0.040***
(0.007)
0.001
(0.003)
Sunshine
(percent possible)
0.60
0.08
1.248***
(0.129)
0.290***
(0.089)
0.260***
(0.044)
0.363***
(0.078)
0.493***
(0.052)
0.455***
(0.048)
0.038
(0.023)
Inverse Distance to Coast
(Ocean or Great Lake)
0.04
0.04
0.078***
(0.008)
0.024***
(0.005)
0.013***
(0.002)
0.027***
(0.004)
0.030***
(0.003)
0.027***
(0.003)
0.003***
(0.001)
Average Slope of Land
(percent)
1.68
1.59
0.023***
(0.005)
-0.006*
(0.003)
0.010***
(0.002)
-0.002
(0.003)
0.009***
(0.002)
0.011***
(0.002)
-0.003***
(0.001)
Latitude
(degrees)
37.76
4.86
0.009**
(0.004)
0.008**
(0.003)
-0.001
(0.002)
0.007***
(0.003)
0.004**
(0.002)
0.002
(0.002)
0.002**
(0.001)
-1.771
(0.168)
-1.020
(0.149)
-0.078
(0.061)
-0.996
(0.129)
-0.716
(0.067)
-0.478
(0.058)
-0.237
(0.038)
0.92
0.84
0.75
0.88
0.92
0.91
0.77
Constant
Coefficient of Determination (R 2 )
282 observations with complete data. Robust standard errors shown in parentheses. * p<0.1, ** p<0.05, *** p<0.01. Regressions weighted by metro population. Amenity
variables are described in Section 3 and Appendix C.2. The coefficients on local land rents are multiplied by the factor s R = 0.10 to be compatible with total amenity value.
Figure 1: Housing Costs versus Wage Levels across Metro Areas, 2000
0.7
0.8
San Francisco
0.6
Santa Barbara
0.5
Honolulu
New York
Cape Cod
HI
0.3
0.2
0.1
0.0
-0.2
-0.1
Log Housing-Cost Differential
0.4
San Diego
San Luis Obispo Los Angeles
Santa Fe
Naples
Seattle Boston
Denver
Chicago
Reno
Sacramento
Portland
Washington-Baltimore
CO Fort Collins
Hartford
Miami
Sarasota
AK
Medford
Grand Junction
Phoenix
Austin
Detroit
Philadelphia
Las Vegas
Wilmington
Atlanta
Albuquerque Springfield Minneapolis
Tucson
Fort
Myers
OR
Nashville NV
Orlando Columbus Dallas
Cincinnati
Norfolk
Tampa
VT
Missoula
NewPierce
Orleans
St. Louis
Punta Gorda Fort
Houston
Fort WaltonMyrtle
Beach Beach
Flagstaff
Bakersfield
Bloomington
AZ
UTYuma
-0.3
MT
Killeen
Jacksonville
-0.4
Pittsburgh
FL Falls
San AntonioSyracuse
Sioux
Oklahoma City
Ocala
Kokomo
Decatur
Beaumont
El Paso
Great Falls
Joplin
SD
-0.5
Monterey
Gadsden
Steubenville
KY
AL
OK
MS
ND
McAllen
-0.2
-0.1
0.0
Log Wage Differential
0.1
0.2
METRO POP
>5.0 Million
Avg Iso-Rent Curve: slope = 0.64
1.5-5.0 Million
0.5-1.5 Million
Avg Zero-Profit Cond: slope = -7.37
<0.5 Million
Non-Metro Areas
Avg Mobility Cond: slope = 1.53
Avg Iso-Value Curve: slope = -0.02
3.0
Figure 2: Housing Costs and Inferred Land Rents
2.5
San Francisco
Santa
Barbara
Honolulu
2.0
1.5
0.0
0.5
1.0
New York
Seattle
Boston
Reno
Sacramento
Sarasota
Chicago
Miami
Washington-Baltimore
Tucson Hartford
-0.5
Jacksonville
-1.5
Orlando
Philadelphia
Detroit
Minneapolis
Columbus
Dallas
St. Louis
Great Falls Bloomington
San Antonio
-1.0
Inferred Land-Rent Differential (r)
Monterey
Diego
San LuisSan
Obispo
Cape Cod
LosHIAngeles
Joplin
El Paso
SD
OK
ND
-2.0
McAllen
-0.5
0.0
0.5
Observed Housing-Cost Differential (p)
Inferred Rent with Avg Wage: slope = 4.29
Diagonal
L os Angele s
Monte re y
Santa Ba rbara
Chi ca go
Boston
Hartford
W a shi ngton-Ba lti more
0.10
Det roi t
San Diego
Sea ttl e Phil adel phia
San L uis Obi spoSac ram e nt o
Denve r Minne apol is
Atla nt a
L as Vegas
0.05
Honol ulu
Cape Cod
Naple s
0.00
Trade-Productivity (Ax) from Full Model
0.15
Figure 3: Trade-P roductivity Estimates Compared
Reno
Port la nd AK
Phoenix
Dal
las
Houston
Kokom o
Bakersfi
Cincinna
ti eld
MiaAustin
mCol
i um bus
NV ingt on
Bloom
St. Loui s
0.00
0.05
0.10
Productivity from Reduced Model
ME TRO SIZE
>5.0 Mil lion
1.5-5. 0 Mi lli on
0.5-1. 5 Mi lli on
<0.5 Mil lion
Non-Met ro Area s
Dia gona l
0.20
Figure 4: Estimated Trade-Productivity and Quality of Life, 2000
Honolulu
0.15
Santa Barbara
Monterey
HI
SantaSan
FeCape
LuisCod
Obispo
San
Diego
San Francisco
0.10
0.05
0.00
-0.10
-0.05
Relative Quality of Life (Q)
Grand Junction CO
Missoula
Naples
Medford
Los Angeles
Fort Collins
VT
WilmingtonSarasota
Fort Walton
OR
Seattle
MT Beach
Denver
Reno
Tucson
Jacksonville Punta Gorda
Albuquerque
Fort
Myers
Portland
Miami
Myrtle Beach
AZ
Great Falls Killeen
Sacramento Boston New York
Flagstaff
Norfolk
Austin
Phoenix
AK
Fort
Pierce
FL New
UT Orleans
Orlando
Chicago
Springfield
YumaTampa
SD
Nashville
Sioux
Falls
Ocala
NV
Joplin
Washington-Baltimore
Oklahoma City
Las Vegas Hartford
Minneapolis
Atlanta
St.Columbus
Louis
OK
Cincinnati
San Antonio
Philadelphia
ND
El Paso
Dallas
Detroit
Pittsburgh
KY
Steubenville
Bloomington
Bakersfield
MS AL
Gadsden
Syracuse
Houston
McAllen
Decatur
Beaumont
-0.234
-0.156
Kokomo
0.078
-0.078
0.000
Relative Trade-Productivity (Ax)
0.156
0.234
METRO POP
>5.0 Million
Iso-Wage Curve: slope = 3.04
1.5-5.0 Million
0.5-1.5 Million
Iso-Housing-Cost Curve: slope = -0.63
<0.5 Million
Non-Metro Areas
Iso-Land-Rent Curve: slope = -0.34
Iso-Value Curve: slope = -0.64
0.3
Figure 5: Trade-Productivity and Population Size
San Francisco
Los Angeles
Monterey
Santa Barbara
0.1
0.0
Kokomo
Naples
Bakersfield
Springfield
Fort Collins
-0.1
Decatur
Sacramento Minneapolis
Denver
Atlanta
Las Vegas
Dallas
Portland Phoenix Houston
Cincinnati
Miami
Austin
Columbus
St. Louis
Nashville
Orlando
Pittsburgh
Tampa
New Orleans
Honolulu
Reno
Bloomington
Santa Fe
Sarasota
Syracuse
Beaumont Albuquerque
Fort Pierce
Fort Myers
Tucson
Medford
Yuma
Wilmington
Flagstaff
Grand Junction
Punta Gorda
Sioux
Falls
Myrtle
Beach
Gadsden
Ocala
Fort Walton Beach
Steubenville
Missoula
Killeen
Chicago
Boston
Washington-Baltimore
Detroit
San DiegoSeattle Philadelphia
Hartford
San Luis Obispo
Cape Cod
-0.2
Relative Trade-Productivity (Ax)
0.2
New York
Norfolk
San Antonio
Oklahoma City
El Paso
McAllen
Joplin
Jacksonville
Great Falls
125000
500000
250000
1000000
2000000
MSA/CMSA Population
4000000
Log-Linear Fit: Slope = = .0683 (.0039)
8000000
16000000
Quadratic Fit
Figure 6: Total Value of Amenities and Population Size
0.3
San Francisco
Santa Barbara
Honolulu
0.2
Monterey
San Diego
0.1
Santa Fe
Seattle
Naples
0.0
Boston
Chicago
Sacramento
Portland
Reno
-0.1
Los Angeles
New York
Denver
Washington-Baltimore
Miami
Sarasota
Phoenix
Grand Junction Medford
Austin
Detroit
Philadelphia
Las Vegas
Minneapolis Atlanta
Albuquerque
Wilmington
Springfield
Fort Myers
Tucson Nashville
Dallas
Orlando
Columbus
Cincinnati
Tampa
Missoula
Norfolk
New Orleans
St.
Louis
Fort Pierce
Houston
Punta
Gorda
Fort
Walton
Beach
Bakersfield
Flagstaff
Myrtle
Beach
Bloomington
Yuma
Pittsburgh
Kokomo
Sioux Falls
San Antonio
Killeen
Syracuse
Oklahoma City
Jacksonville
Ocala
Decatur
Great Falls
El Paso
Beaumont
Gadsden
Joplin
Steubenville
Fort Collins
-0.2
Total Amenity Value
San Luis Obispo
Cape Cod
Hartford
McAllen
125000
250000
500000
1000000
2000000
MSA/CMSA Population
Log-Linear Fit: Slope = = .056 (.005)
4000000
Quadratic Fit
8000000
16000000
Appendix
A
Derivation of Theoretical Results
The utility function U (x; y; Qj ) ; representing household preferences, is quasi-concave over x and
y, and increases in quality of life, Qj . The dual expenditure function for a household, e(pj ; u; Qj )
minx;y fx + pj y : U (x; y; Qj ) ug , measures the cost of consumption needed to attain utility u,
increases in pj , and decreases in Qj . Because households are fully mobile, all inhabited cities offer
the same utility, u.5 Thus, firms in cities with high prices or low quality of life compensate their
workers with greater after-tax income:
e(pj ; u; Qj ) = wj + R + I
(wj + R + I):
(A.1)
Operating under perfect competition, firms produce traded and home goods according to the
functions X = AjX FX (LX ; NX ; KX ) and Y = AjY FY (LY ; NY ; KY ), where FX and FY are concave and exhibit constant returns to scale, so that any returns to scale are embedded within the
factor-neutral productivities, AjX and AjY :The unit cost of producing a traded good is cX (rj ; wj ; {; AjX ) =
cX (rj ; wj ; {)=AjX where c(r; w; i) c(r; w; i; 1) and c(rj ; wj ; {; Aj ) minL;N;K frj L+wj N +{K :
Aj F (L; N; K) = 1g A symmetric definition holds for the unit cost of a home good, cY . Firms
make zero profits in equilibrium. Therefore, for given output prices, more productive cities pay
higher rents and wages, equal to the marginal revenue products of land and labor. In equilibrium,
the following zero-profit conditions hold:
cX (rj ; wj ; {)=AjX = 1
j
cY (r ; w
j
; {)=AjY
(A.2)
j
=p :
(A.3)
For empirical application, I log-linearize conditions (A.1), (A.2), and (A.3). These conditions
relate each city’s price differentials to its attribute differentials. The differentials are in logarithms:
for any z, z^j = d ln z j = dz j =z = (z j z) =z approximates the percent difference in city j
^j
of z; relative to the national geometric average z. The one exception to this notation is Q
j
j
(@e=@Q) (1=m)dQ , which is the dollar value of a change in Q divided by income.
Log-linearized versions of (A.1), (A.2), and (A.3) describe how prices co-vary with city at5
The use of a single index Qj assumes that amenities are weakly separable from consumption. The model generalizes to one with heterogenous workers that supply different fixed amounts of labor if these workers are perfect
substitutes in production, have identical homothetic preferences, and earn equal shares of income from labor. Additonally, the mobility condition need not apply to all households, but only a sufficiently large subset of mobile marginal
households (Gyourko and Tracy 1989).
i
tributes.6
sw (1
^j
Lr
^j
)w^ j + sy p^j = Q
^j + N w^ j = A^j
Lr
0
+
^
Nw
j
p^j =
(A.4a)
(A.4b)
X
j
^
AY
(A.4c)
These equations are first-order approximations of the equilibrium conditions around a nationallyrepresentative city, and thus use national shares. Equation (1) comes directly from (A.4c); equation
(2) by substituting (1) with (A.4b); (4) follows from applying the defintion provided and simplifying.
B
Parameterization
B.1
Expenditure and Cost-share Parameters
To keep track of the notation, table A3 summarizes the key parameteter values, which refer to
national averages. It presents values for the full model and the reduced model, which is closely
related to Beeson and Eberts (1989), shown here for comparison.
TABLE A3: MODEL PARAMETERS AND POSSIBLE VALUES
Parameter Notation Full Reduced B-E
Home-goods share
sy
0.36
0.077
0.073
Income share to land
sR
0.10
0.10
0.064
Income share to labor
sw
0.75
0.75
0.73
Traded-good cost-share of land
0.025
0.025
0.028
L
Traded-good cost-share of labor
0.825
0.825
0.927
N
Home-good cost-share of land
0.233
1.0
1.0
L
Home-good cost-share of labor
0.617
0.0
0.0
N
0
Average marginal tax rate on labor
0.361
0.0
0.0
Deduction rate for home-goods
0.291
0.0
0.0
"Reduced" model parameterization based on B-E (Beeson and Eberts, 1989) simplifications.
B.2
Economic Parameter Choices
The parameterization used here is the same as in Albouy (2009), although I review it below. Because of accounting identities, only six parameters are free, but choosing these requires reconciling
6
When simply linearized with Shephard’s Lemma, the equations are
(@e=@Q)dQj = y dpj
(1
0
) dwj
dAjX = (LX =X) drj + (NX =X) dwj
p dAjY = (LY =Y ) drj + (NY =Y ) dwj
dpj
The first equation is log-linearized by dividing through by m, and the third, by dividing by p.
ii
slightly conflicting sources.7
Starting with income shares, Krueger (1999) makes the case that the labor share, sw is close
to 75 percent. In practice, this reflects the previous literature that weights the wage differential
by labor income, rather than household income. Albouy (2008) argues this number applies to a
representative household, so higher-income households, with more income from capital, receive
greater weight. It also averages in retirees and others on fixed or transfer income (e.g. students).
The share is also consistent with data in the Survey of Consumer Finances. Theoretically, the use
of sw in (3) implies that if a household moves, its wage may change, but not the flow income from
previous savings.
Poterba (1998) estimates that the share of income from corporate capital is 12 percent, and thus
income from mobile capital, sI should be higher, and is taken as 15 percent. This leaves 10 percent
for land sR . This is the same as in Shapiro (2006) and roughly consistent with estimates in Keiper
et al. (1961) and Case (2007).8
Turning to expenditure shares, Shapiro (2006), Albouy (2008), and Moretti (2013) find that
housing costs can also be used to approximate non-housing cost differences across cities. The
cost-of-living differential is sy p^j , where p^j is equal to the housing-cost differential and sy is the
expenditure share on housing plus an additional term to capture how a one-percent increase in
housing costs predicts a b = 0:26-percent increase in non-housing costs. In the Consumer Expenditure Survey (CEX), the share of income spent on shelter and utilities, shous , is 0.22, although
the share of income spent on other goods, soth , is 0.56, with the remaining 0.22 spent on taxes
or saved (Bureau of Labor Statistics 2002). Thus, the coefficient on housing costs is equal to
sy = shous + soth b = 0:22 + 0:56 0:26 = 36 percent.
I choose the cost-shares to be consistent with the expenditure and income shares above. I use a
value of 2.5 percent for L here. Beeson and Eberts (1989) use a value of 0.027, while Rappaport
(2008a, 2008b) uses a value of 0.016. Valentinyi and Herrendorff (2008) estimate the land share of
tradables at 4 percent, but their definition of tradables makes this an upper bound.
Following Carliner (2003) and Case (2007), I use a cost-share of land in home-goods, taken as
housing, L , at 23.3 percent: this is slightly above values reported in McDonald (1981), Roback
(1982), and Thorsnes (1997), in order to take into account for secular increase in land cost-shares
over time, seen in Davis and Palumbo (2007). Together the cost and expenditure shares imply that
sR is 10 percent, consistent with other income shares. This seems reasonable as the remaining 83
percent of land for home goods includes all residential land, and most land used for commerce,
roads, and government.
The one remaining choice determines the cost-shares of labor and capital in both production
7
Nationally, the parameters obey the following identities: (i) sw + sI + sR = 1; (ii) L + K + N = 1; (iii)
+ K + N = 1; (iv) sw = sx N + sy N ; (v) sI = sx K + sy K ; (vi) sR = sx L + sy L . Furthermore,
the share of land and labor in traded production are (vii) L = sx L =sR , (viii) N = sx N =sw . Other reduced
models with L = 1; N = N = 0 include Shapiro (2006), who proposes values of L = 0:1; N = 0:75, and
sy =sw = 0:32, implying L = 0:20 and N = 1; Gabriel and Rosenthal (2004) use values implying L = 0:5 and
N = 1. Roback (1982, p.1273) considers a case with sy =sw = 0:035, which is too limited to provide other parameter
values. The chosen values here are similar to Rappaport (2008a, 2008b) except that he more narrowly confines home
goods to housing, using a smaller sy = 0:18 and a larger L = 0:35. His implied L = 0:17 is quite similar, whereas
N = 0:87 is smaller.
8
The values Keiper et al. (1961) reports were at a historical low: that total land value was found to be about 1.1
times GDP. A rate of return of 9 percent would justify using sR = 0:10: Case (2007), ignoring agriculture, estimates
the value of land to be $5.6 trillion in 2000 when personal income was $8.35 trillion, implying a smaller share.
L
iii
sectors. As separate information on the cost share of capital, K and K , is unavailable, I set both
of these shares to at 15 percent to be consistent with sI . Accounting identities then determine that
N is 82.5 percent and N is 61.7 percent.
B.3
Choice of Tax Parameters
The federal marginal tax rate on wage income is determined by adding together federal marginal
income tax rate and the effective marginal payroll tax rate. TAXSIM gives an average marginal
federal income tax rate of 25.1 percent in 2000. In 2000, Social Security (OASDI) and Medicare
(HI) tax rates were 12.4 and 2.9 percent on employer and employee combined. Estimates from
Boskin et al. (1987, table 4) show that the marginal benefit from future returns from OASDI taxes
is fairly low, generally no more than 50 percent, although only 85 percent of wage earnings are
subject to the OASDI cap. HI taxes emulate a pure tax (Congressional Budget Office 2005). These
facts suggest adding 37.5 percent of the Social Security tax and all of the Medicare tax to the
federal income tax rate, adding 8.2 percent. The employer-half of the payroll tax (4.1 percent)
has to be added to observed wage levels to produce gross wage levels. Overall, this puts an overall
federal tax rate, 0 , of 33.3-percent on gross wages, although only 29.2 percent on observed wages.
Determining the federal deduction level requires taking into account the fact that many households do not itemize deductions. According to the Statistics on Income, although only 33 percent
of tax returns itemize, they account for 67 percent of reported Adjusted Gross Income (AGI). Since
the income-weighted share is what matters, 67 percent is multiplied by the effective tax reduction
given in TAXSIM in 2000 of 21.6 percent. Thus, on average these deductions reduce the effective
price of eligible goods by 14.5 percent. Since eligible goods only include housing, this deduction
applies to only 59 percent of home goods. Multiplying 14.5 percent by 59 percent gives an effective price reduction of 8.6 percent for home goods. Divided by a federal tax rate of 33.3 percent,
this produces a federal deduction level of 25.7 percent.
State income tax rates from 2000 are taken from TAXSIM, which, per dollar, fall at an average
marginal rate of 4.5 percent. State sales tax data in 2000 are taken from the Tax Policy Center,
originally supplied by the Federation of Tax Administrators. The average state sales tax rate is 5.2
percent. Sales tax rates are reduced by 10 percent to accommodate untaxed goods and services
other than food or housing (Feenberg et al. 1997), and by another 8 percent in states that exempt
food. Overall state taxes raise the marginal tax rate on wage differences within-state by an average
of 5.9 percentage points, from zero points in Alaska to 8.8 points in Minnesota.
State-level deductions for housing expenditures, explicit in income taxes, and implicit in sales
taxes, should also be included. At the state level, deductions for income taxes are calculated in an
equivalent way using TAXSIM data. Furthermore, all housing expenditures are deducted from the
sales tax. Overall this produces an average effective deduction level of = 0:291:
C
C.1
Data and Estimation
Wages and Housing-Cost Differentials
Wage and housing-price data come from the United States Census data from the 2000 Integrated
Public-Use Microdata Series (IPUMS), from Ruggles et al. (2004).
iv
To estimate inter-urban wage differentials, w^ j , I use the logarithm of hourly wages from fulltime workers, ages 25 to 55. These differentials should control for skill differences across workers
to provide an analogue to the representative worker in the model and isolate the effect of a city on
j
)
a worker’s wage. Thus, I regress log wages on city-indicators ( jw ) and on extensive controls (Xwi
j
j
j
j
j
in the equation ln wi = Xwi w + w + "wi , and use the estimates of w for the wage differentials.
Identifying these differentials requires that workers do not sort across cities according to their
unobserved skills. An overstated wage differential for a city biases trade-productivity upwards and
quality of life downwards. Below is a list of the controls used in the wage equation:
12 indicators of educational attainment;
a quartic in potential experience, and potential experience interacted with years of education;
9 indicators of industry at the one-digit level (1950 classification);
9 indicators of employment at the one-digit level (1950 classification);
4 indicators of marital status (married, divorced, widowed, separated);
an indicator for veteran status, and veteran status interacted with age;
5 indicators of minority status (Black, Hispanic, Asian, Native American, and other);
an indicator of immigrant status, years since immigration, and immigrant status interacted
with black, Hispanic, Asian, and other; and
2 indicators for English proficiency (none or poor).
All covariates are interacted with gender.
I first run the regression using census-person weights. From the regressions, I calculate a
predicted wage is using the individual characteristics, not the MSAs, to form a new weight equal to
the predicted wage multiplied by the census-person weight. Economically, these income-adjusted
weights are more relevant since workers’ influence on prices is determined by their endowment and
income share. The new weights are then used in a second regression, which is used to calculate
the city-wage differentials from the MSA indicator variables. In practice, this weighting procedure
has only a small effect on the estimated wage differentials.
To estimate housing-cost, p^j , I use both housing values and gross rents, with utilities, to calculate a flow cost. Following previous studies, I calculate comparable imputed rents for owned
units by multiplying reported housing values by a rate of 7.85 percent (Peiser and Smith 1985) and
adding this to utility costs. To avoid measurement error from imperfect recall or rent control, the
sample includes only units that were acquired in the last ten years. I then regress housing costs on
j
j
j
j
flexible controls (Xpi
) – interacted with renter-status – in the equation ln pji = Xpi
p + p + "pi ,
and use the estimates of jp for the housing-cost differentials. Proper identification of housing-cost
differences requires that average unobserved housing quality does not vary systematically across
cities. An overstated housing-cost differential biases both trade-productivity and quality of life
upwards. Below is a list of the controls used in the housing-cost regression:
v
9 indicators for number of units in structure (1-family home, attached; 1-family house, detached; 2-family building; 3-4 family building; 5-9 family building, 10-19 family building;
20-49 family building; 50+ family building; mobile home or trailer);
9 indicators for the number of rooms, 5 indicators for the number of bedrooms, number
of rooms interacted with number of bedrooms, and the number of household members per
room;
2 indicators for lot size;
7 indicators for when the building was built;
2 indicators for complete plumbing and kitchen facilities;
an indicator for commercial use; and
an indicator for condominium status (owned units only).
All of these variables are interacted with tenure, i.e. renter status. Therefore the owner-occupied
user-cost rate of 7.85 percent only matters in how it used to incorporate utility costs.
I first run a regression of housing values on housing characteristics and MSA indicator variables
using only owner-occupied units, weighting by census-housing weights. From this regression, I
calculate a new value-adjusted weight by multiplying the census-housing weights by the predicted
value from this first regression using housing characteristics alone. Economically, these weights
reflect the number of efficiency units of housing each observation provides. I then run a second
regression with these new weights for all units, rented and owner-occupied, on the housing characteristics fully interacted with tenure, along with the MSA indicators, which are not interacted.
I take the house-price differentials from the MSA indicator variables in this second regression.
As with the wage differentials, this adjusted weighting method has only a small impact on the
measured price differentials.
C.2
Amenity Data
All climate and geographic data are calculated at the public-use microdata area (PUMA) level and
averaged up to the metropolitan level, weighting by population. Population density is measured at
the census tract level, and also population-averaged.
Heating and cooling degree days (Annual) Degree day data are used to estimate amounts of energy required to maintain comfortable indoor temperature levels. Daily values are computed
from each day’s mean temperature, (max + min)/2. Daily heating degree days are equal
to maxf0; 65 meantempg and daily cooling degree days are maxf0; meantemp 65g.
Annual degree days are the sum of daily degree days over the year. The data here refer to
averages from 1970 to 2000 (National Climactic Data Center 2008).
Sunshine Average percentage of possible. The total time that sunshine reaches the surface of the
earth is expressed as the percentage of the maximum amount possible from sunrise to sunset
with clear sky conditions. (National Climactic Data Center 2008).
vi
Average slope (percent) The average slope of the land in the metropolitan area. Coded by author
using GIS software.
Coastal proximity Equal to one over the distance in miles to the nearest coastline. Coded by
author using GIS software.
Violent crimes (per capita) These consist of the average of the four z-scores (standard deviations)
for aggravated assaults, robbery, forcible rape, and murder (City and County Data Book
2000).
Property crimes (per capita) These consist of the average of the four z-scores (standard deviations) for aggravated burglary, larceny, motor theft, and arson (City and County Data 2000).
Air quality index (Median) An AQI value is calculated for each pollutant in an area (groundlevel ozone, particle pollution, carbon monoxide, sulfur dioxide, and nitrogen dioxide). The
highest AQI value for the individual pollutants is the AQI value for that day. An AQI over
300 is considered hazardous; under 50, good; values in between correspond to moderate,
unhealthy, and very unhealthy (Environmental Protection Agency 2008).
Bars and restaurants Number of establishments classified as eating and drinking places (NAICS
722) in County Business Patterns 2000.
Arts and Culture Index from Places Rated Almanac (Savageau 1999). Based on a ranking of
cities, it ranges from 100 (New York, NY) to 0 (Houma, LA).
vii
APPENDIX TABLE A1: LIST OF METROPOLITAN AND NON-METROPOLITAN AREAS RANKED BY TOTAL AMENITY VALUE
Full Name of Metropolitan Area
San Francisco-Oakland-San Jose, CA
Santa Barbara-Santa Maria-Lompoc, CA
Honolulu, HI
Salinas (Monterey-Carmel), CA
San Diego, CA
Los Angeles-Riverside-Orange County, CA
San Luis Obispo-Atascadero-Paso Robles, CA
New York, Northern New Jersey, Long Island, NY-NJ-CT-PA
Barnstable-Yarmouth (Cape Cod), MA
Non-metro, HI
Seattle-Tacoma-Bremerton, WA
Boston-Worcester-Lawrence, MA-NH-ME-CT
Santa Fe, NM
Naples, FL
Denver-Boulder-Greeley, CO
Chicago-Gary-Kenosha, IL-IN-WI
Non-metro, RI
Sacramento-Yolo, CA
Reno, NV
Anchorage, AK
Portland-Salem, OR-WA
Washington-Baltimore, DC-MD-VA-WV
Fort Collins-Loveland, CO
Non-metro, CO
Stockton-Lodi, CA
Hartford, CT
Miami-Fort Lauderdale, FL
Bellingham, WA
Non-metro, CA
West Palm Beach-Boca Raton, FL
Non-metro, CT
Madison, WI
New London-Norwich, CT-RI
Sarasota-Bradenton, FL
Non-metro, MA
Non-metro, AK
Eugene-Springfield, OR
Medford-Ashland, OR
Phoenix-Mesa, AZ
Corvalis, OR
Grand Junction, CO
Providence-Fall River-Warwick, RI-MA
Austin-San Marcos, TX
Modesto, CA
Detroit-Ann Arbor-Flint, MI
Raleigh-Durham-Chapel Hill, NC
Philadelphia-Wilmington-Atlantic City, PA-NJ-DE-MD
Population
7,039,362
399,347
876,156
401,762
2,813,833
16,373,645
246,681
21,199,864
162,582
335,381
3,554,760
5,819,100
147,635
251,377
2,581,506
9,157,540
61,968
1,796,857
339,486
260,283
2,265,223
7,608,070
251,494
693,605
563,598
1,183,110
3,876,380
166,814
1,121,254
1,131,184
148,665
426,526
293,566
589,959
247,672
366,649
322,959
181,269
3,251,876
78,153
116,255
1,188,613
1,249,763
446,997
5,456,428
1,187,941
6,188,463
Adjusted Differentials
Housing
Costs
Wages
0.256
0.813
0.068
0.662
-0.010
0.605
0.103
0.590
0.058
0.479
0.129
0.450
0.036
0.452
0.209
0.411
0.005
0.395
-0.029
0.332
0.078
0.308
0.123
0.294
-0.060
0.290
-0.004
0.286
0.051
0.240
0.136
0.224
0.060
0.215
0.067
0.206
0.027
0.210
0.073
0.185
0.024
0.174
0.126
0.154
-0.061
0.150
-0.137
0.137
0.088
0.126
0.134
0.133
0.001
0.126
-0.065
0.127
-0.040
0.134
0.042
0.115
0.083
0.119
-0.038
0.110
0.050
0.110
-0.071
0.094
-0.068
0.108
0.035
0.090
-0.118
0.091
-0.136
0.084
0.028
0.075
-0.113
0.076
-0.180
0.079
0.018
0.073
0.009
0.067
0.056
0.059
0.130
0.053
0.016
0.044
0.114
0.052
Land Rents
Linear
2.780
2.651
2.620
2.244
1.894
1.573
1.840
1.184
1.678
1.504
1.103
0.925
1.408
1.239
0.888
0.585
0.757
0.699
0.826
0.595
0.680
0.314
0.808
0.962
0.296
0.201
0.535
0.726
0.686
0.378
0.285
0.574
0.332
0.595
0.652
0.292
0.716
0.736
0.243
0.634
0.833
0.262
0.264
0.098
-0.130
0.143
-0.090
Quadratic
2.246
2.111
2.069
1.847
1.590
1.369
1.546
1.077
1.422
1.285
0.992
0.851
1.206
1.087
0.812
0.558
0.703
0.653
0.757
0.562
0.632
0.307
0.730
0.843
0.289
0.198
0.503
0.660
0.630
0.364
0.279
0.533
0.321
0.548
0.597
0.283
0.644
0.658
0.237
0.575
0.731
0.255
0.256
0.098
-0.139
0.141
-0.096
Quality of Life
Value
0.138
0.176
0.204
0.137
0.123
0.081
0.124
0.029
0.121
0.126
0.061
0.034
0.127
0.095
0.054
0.005
0.040
0.033
0.053
0.023
0.047
-0.013
0.079
0.112
-0.002
-0.026
0.041
0.074
0.059
0.017
-0.007
0.053
0.006
0.066
0.063
0.012
0.088
0.095
0.012
0.081
0.114
0.014
0.016
-0.008
-0.047
0.011
-0.040
Rank
3
2
1
4
7
14
6
51
8
22
46
5
11
26
80
48
30
59
37
122
16
93
155
39
17
66
28
79
19
13
12
72
15
9
69
67
106
215
74
192
TradeProductivity
Value
0.289
0.125
0.057
0.144
0.098
0.150
0.077
0.209
0.046
0.013
0.095
0.128
-0.017
0.027
0.066
0.131
0.071
0.075
0.043
0.077
0.037
0.116
-0.032
-0.094
0.083
0.120
0.015
-0.038
-0.017
0.046
0.078
-0.018
0.051
-0.046
-0.042
0.037
-0.084
-0.099
0.030
-0.081
-0.134
0.022
0.014
0.050
0.108
0.018
0.096
Federal Tax
Rank Differential
1
0.045
7
-0.010
22
-0.022
4
0.005
11
-0.004
3
0.020
16
-0.011
2
0.044
26
-0.017
-0.016
13
0.011
6
0.024
60
-0.025
34
-0.011
19
0.008
5
0.030
0.009
17
0.011
29
-0.002
15
0.013
30
0.003
9
0.030
72
-0.022
-0.044
14
0.021
8
0.030
39
-0.003
82
-0.023
-0.021
27
0.009
0.015
63
-0.016
23
0.006
85
-0.023
-0.029
0.006
127
-0.037
147
-0.042
32
0.007
122
-0.035
200
-0.055
35
0.002
40
-0.001
24
0.014
10
0.035
38
0.008
12
0.030
Total Amenity
Values
Value
0.323
0.255
0.240
0.229
0.185
0.177
0.173
0.163
0.151
0.135
0.121
0.116
0.116
0.113
0.097
0.089
0.085
0.081
0.081
0.073
0.071
0.062
0.059
0.052
0.051
0.050
0.050
0.050
0.048
0.047
0.043
0.042
0.039
0.037
0.036
0.035
0.035
0.031
0.031
0.029
0.029
0.028
0.026
0.024
0.022
0.022
0.021
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
APPENDIX TABLE A1: LIST OF METROPOLITAN AND NON-METROPOLITAN AREAS RANKED BY TOTAL AMENITY VALUE
Full Name of Metropolitan Area
Salt Lake City-Ogden, UT
Milwaukee-Racine, WI
Colorado Springs, CO
Portland, ME
Burlington, VT
Las Vegas, NV-AZ
Minneapolis-St. Paul, MN-WI
Chico-Paradise, CA
Albuquerque, NM
Atlanta, GA
Wilmington, NC
Springfield, MA
Charlottesville, VA
Fort Myers-Cape Coral, FL
Non-metro, WA
Redding, CA
Tucson, AZ
Non-metro, NV
Charlotte-Gastonia-Rock Hill, NC-SC
Non-metro, NH
Non-metro, OR
Nashville, TN
Provo-Orem, UT
Iowa City, IA
Cleveland-Akron, OH
Dallas-Fort Worth, TX
Fresno, CA
Orlando, FL
Pittsfield, MA
Columbus, OH
Merced, CA
Lancaster, PA
Green Bay, WI
Cincinnati-Hamilton, OH-KY-IN
Allentown-Bethlehem-Easton, PA
Asheville, NC
Yakima, WA
Charleston-North Charleston, SC
Non-metro, VT
Tampa-St. Petersburg-Clearwater, FL
Missoula, MT
Yuba City, CA
Norfolk-Virginia Beach-Newport News, VANon-metro, DE
New Orleans, LA
Indianapolis, IN
Richmond-Petersburg, VA
Population
1,333,914
1,689,572
516,929
243,537
169,391
1,563,282
2,968,806
203,171
712,738
4,112,198
233,450
591,932
159,576
440,888
994,967
163,256
843,746
250,521
1,499,293
496,087
919,033
1,231,311
368,536
111,006
2,945,831
5,221,801
922,516
1,644,561
84,699
1,540,157
210,554
470,658
226,778
1,979,202
637,958
225,965
222,581
549,033
439,436
2,395,997
95,802
139,149
1,569,541
156,638
1,337,726
1,607,486
996,512
Adjusted Differentials
Housing
Costs
Wages
-0.024
0.038
0.043
0.032
-0.088
0.033
-0.078
0.019
-0.107
0.021
0.068
0.029
0.082
0.020
-0.090
0.043
-0.082
0.013
0.078
0.014
-0.134
0.017
-0.006
0.009
-0.113
-0.003
-0.105
-0.014
-0.083
-0.014
-0.094
0.001
-0.114
-0.010
0.008
-0.017
0.013
-0.033
-0.100
-0.030
-0.140
-0.024
-0.016
-0.030
-0.056
-0.030
-0.087
-0.032
0.012
-0.032
0.064
-0.037
-0.012
-0.039
-0.040
-0.046
-0.058
-0.033
0.023
-0.054
-0.010
-0.048
-0.015
-0.053
-0.019
-0.064
0.035
-0.070
0.002
-0.064
-0.159
-0.060
-0.027
-0.072
-0.095
-0.069
-0.197
-0.086
-0.057
-0.084
-0.251
-0.090
-0.073
-0.074
-0.109
-0.081
-0.081
-0.088
-0.070
-0.097
0.017
-0.096
0.006
-0.098
Land Rents
Linear
0.226
0.018
0.384
0.299
0.386
-0.065
-0.142
0.432
0.282
-0.154
0.441
0.055
0.300
0.228
0.170
0.263
0.268
-0.097
-0.177
0.147
0.284
-0.086
0.024
0.105
-0.171
-0.338
-0.133
-0.088
0.021
-0.296
-0.180
-0.188
-0.223
-0.394
-0.280
0.181
-0.236
-0.036
0.173
-0.204
0.306
-0.115
-0.045
-0.155
-0.224
-0.459
-0.437
Quadratic
0.219
0.017
0.360
0.283
0.359
-0.067
-0.149
0.402
0.267
-0.161
0.405
0.055
0.281
0.215
0.162
0.248
0.252
-0.099
-0.182
0.140
0.264
-0.086
0.024
0.101
-0.176
-0.360
-0.135
-0.088
0.020
-0.310
-0.184
-0.192
-0.229
-0.420
-0.291
0.167
-0.242
-0.037
0.157
-0.208
0.271
-0.116
-0.047
-0.157
-0.228
-0.492
-0.466
Quality of Life
Value
0.026
-0.009
0.055
0.051
0.065
-0.025
-0.032
0.053
0.049
-0.032
0.071
0.002
0.054
0.049
0.037
0.041
0.052
-0.011
-0.013
0.042
0.062
-0.001
0.019
0.034
-0.016
-0.044
-0.008
0.006
0.014
-0.028
-0.012
-0.011
-0.011
-0.038
-0.022
0.058
-0.009
0.025
0.073
0.003
0.101
0.009
0.027
0.010
0.005
-0.039
-0.033
Rank
55
107
25
33
20
150
171
29
34
174
18
87
27
36
38
31
123
90
64
45
127
205
104
78
70
159
121
117
116
186
141
23
108
57
86
10
76
53
81
189
178
TradeProductivity
Value
-0.015
0.037
-0.066
-0.060
-0.082
0.057
0.067
-0.067
-0.064
0.063
-0.104
-0.003
-0.090
-0.084
-0.067
-0.074
-0.091
0.005
0.007
-0.082
-0.113
-0.016
-0.048
-0.072
0.006
0.047
-0.014
-0.037
-0.050
0.013
-0.013
-0.017
-0.022
0.020
-0.005
-0.132
-0.029
-0.082
-0.165
-0.054
-0.208
-0.066
-0.095
-0.073
-0.065
0.003
-0.006
Federal Tax
Rank Differential
57
-0.006
31
0.013
103
-0.025
97
-0.017
125
-0.026
21
0.018
18
0.026
104
-0.033
100
-0.020
20
0.024
155
-0.039
48
-0.005
136
-0.034
129
-0.028
-0.022
110
-0.032
139
-0.033
0.002
43
0.009
-0.025
-0.039
59
-0.003
87
-0.014
109
-0.022
44
0.005
25
0.019
56
-0.004
79
-0.008
89
-0.020
41
0.009
55
-0.002
61
-0.003
66
-0.002
37
0.014
49
0.002
197
-0.044
70
-0.004
123
-0.024
-0.050
94
-0.012
260
-0.063
102
-0.022
141
-0.029
-0.021
101
-0.015
45
0.009
50
0.006
Total Amenity
Values
Value
0.017
0.015
0.013
0.013
0.013
0.011
0.011
0.010
0.008
0.008
0.005
0.000
-0.004
-0.005
-0.005
-0.006
-0.006
-0.008
-0.008
-0.010
-0.011
-0.011
-0.011
-0.012
-0.012
-0.014
-0.017
-0.017
-0.018
-0.020
-0.020
-0.022
-0.025
-0.025
-0.026
-0.026
-0.027
-0.028
-0.032
-0.032
-0.033
-0.034
-0.034
-0.037
-0.037
-0.037
-0.037
Rank
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
APPENDIX TABLE A1: LIST OF METROPOLITAN AND NON-METROPOLITAN AREAS RANKED BY TOTAL AMENITY VALUE
Full Name of Metropolitan Area
St. Louis, MO-IL
Bloomington, IN
Fort Pierce-Port St. Lucie, FL
Boise City, ID
Visalia-Tulare-Porterville, CA
State College, PA
Tallahassee, FL
Harrisburg-Lebanon-Carlisle, PA
Jacksonville, FL
Punta Gorda, FL
Houston-Galveston-Brazoria, TX
Richland-Kennewick-Pasco, WA
Lawrence, KS
Des Moines, IA
Kansas City, MO-KS
Non-metro, MD
Greensboro--Winston Salem--High Point, NC
Dayton-Springfield, OH
Bakersfield, CA
Fort Walton Beach, FL
Lafayette, IN
Spokane, WA
Grand Rapids-Muskegon-Holland, MI
Bryan-College Station, TX
Lansing-East Lansing, MI
Cedar Rapids, IA
Flagstaff, AZ-UT
Louisville, KY-IN
Appleton-Oshkosh-Neenah, WI
York, PA
Columbia, SC
Lincoln, NE
Myrtle Beach, SC
Reading, PA
Albany-Schenectady-Troy, NY
Sheboygan, WI
Rochester, NY
Bloomington-Normal, IL
Champaign-Urbana, IL
Gainesville, FL
Savannah, GA
Panama City, FL
Janesville-Beloit, WI
Yuma, AZ
Rochester, MN
Athens, GA
Dover, DE
Population
2,603,607
120,563
319,426
432,345
368,021
135,758
284,539
629,401
1,100,491
141,627
4,669,571
191,822
99,962
456,022
1,776,062
385,446
1,251,509
950,558
661,645
170,498
182,821
417,939
1,088,514
152,415
447,728
191,701
122,366
1,025,598
358,365
381,751
536,691
250,291
196,629
373,638
875,583
112,646
1,098,201
150,433
179,669
217,955
293,000
148,217
152,307
160,026
124,277
153,444
126,697
Adjusted Differentials
Housing
Costs
Wages
0.005
-0.104
-0.127
-0.090
-0.085
-0.100
-0.083
-0.109
-0.032
-0.094
-0.139
-0.092
-0.110
-0.106
-0.011
-0.105
-0.050
-0.110
-0.167
-0.108
0.073
-0.111
0.029
-0.117
-0.148
-0.112
-0.030
-0.123
-0.001
-0.129
-0.033
-0.105
-0.046
-0.126
-0.021
-0.124
0.044
-0.132
-0.204
-0.125
-0.070
-0.123
-0.097
-0.128
0.004
-0.122
-0.138
-0.126
0.006
-0.126
-0.081
-0.137
-0.146
-0.128
-0.040
-0.138
-0.047
-0.138
-0.026
-0.138
-0.076
-0.145
-0.134
-0.150
-0.169
-0.135
-0.002
-0.146
-0.014
-0.132
-0.058
-0.146
-0.018
-0.136
0.024
-0.149
-0.082
-0.142
-0.148
-0.156
-0.081
-0.151
-0.153
-0.159
-0.002
-0.164
-0.106
-0.158
0.018
-0.164
-0.138
-0.153
-0.087
-0.158
Land Rents
Linear
-0.458
-0.036
-0.197
-0.239
-0.315
-0.010
-0.150
-0.420
-0.333
-0.006
-0.675
-0.584
-0.070
-0.444
-0.547
-0.360
-0.414
-0.475
-0.684
0.025
-0.336
-0.281
-0.532
-0.162
-0.557
-0.365
-0.145
-0.480
-0.463
-0.518
-0.410
-0.272
-0.116
-0.618
-0.526
-0.465
-0.532
-0.705
-0.385
-0.262
-0.426
-0.263
-0.699
-0.387
-0.753
-0.275
-0.439
Quadratic
-0.489
-0.038
-0.200
-0.243
-0.326
-0.014
-0.152
-0.444
-0.345
-0.011
-0.762
-0.640
-0.073
-0.469
-0.592
-0.375
-0.435
-0.506
-0.767
0.017
-0.347
-0.287
-0.575
-0.164
-0.605
-0.378
-0.147
-0.510
-0.489
-0.555
-0.427
-0.277
-0.119
-0.676
-0.565
-0.490
-0.572
-0.788
-0.400
-0.267
-0.445
-0.267
-0.775
-0.401
-0.848
-0.281
-0.458
Quality of Life
Value
-0.034
0.032
0.011
0.010
-0.016
0.036
0.022
-0.029
-0.009
0.049
-0.072
-0.051
0.038
-0.022
-0.037
-0.022
-0.016
-0.030
-0.063
0.062
-0.006
0.008
-0.044
0.027
-0.046
-0.002
0.030
-0.023
-0.021
-0.032
-0.007
0.022
0.038
-0.046
-0.041
-0.019
-0.041
-0.061
-0.009
0.024
-0.011
0.026
-0.050
0.002
-0.061
0.016
-0.009
Rank
180
49
73
75
130
43
61
162
110
35
267
225
41
140
184
126
164
252
21
98
77
204
54
211
92
50
144
138
175
99
62
42
210
198
133
195
248
112
58
115
56
224
88
246
68
111
TradeProductivity
Value
-0.007
-0.110
-0.078
-0.077
-0.036
-0.120
-0.098
-0.020
-0.051
-0.143
0.045
0.011
-0.129
-0.037
-0.015
-0.037
-0.049
-0.030
0.020
-0.174
-0.069
-0.090
-0.010
-0.122
-0.008
-0.078
-0.129
-0.047
-0.052
-0.036
-0.076
-0.122
-0.148
-0.017
-0.026
-0.062
-0.029
0.003
-0.080
-0.134
-0.080
-0.138
-0.019
-0.100
-0.003
-0.125
-0.086
Federal Tax
Rank Differential
51
0.008
166
-0.035
113
-0.019
112
-0.015
77
-0.008
173
-0.039
145
-0.026
65
0.000
90
-0.009
212
-0.042
28
0.025
42
0.014
190
-0.037
78
-0.001
58
0.009
-0.010
88
-0.006
71
-0.002
36
0.019
241
-0.052
106
-0.016
138
-0.022
53
0.003
176
-0.035
52
0.004
115
-0.016
191
-0.038
86
-0.005
91
-0.008
76
-0.003
111
-0.015
175
-0.029
217
-0.045
62
0.004
68
-0.005
98
-0.011
69
-0.006
46
0.011
119
-0.022
201
-0.035
120
-0.019
207
-0.036
64
0.007
151
-0.024
47
0.012
185
-0.037
133
-0.020
Total Amenity
Values
Value
-0.038
-0.038
-0.039
-0.039
-0.039
-0.040
-0.041
-0.042
-0.042
-0.043
-0.043
-0.044
-0.045
-0.045
-0.046
-0.046
-0.047
-0.049
-0.050
-0.050
-0.050
-0.050
-0.050
-0.051
-0.052
-0.052
-0.052
-0.053
-0.054
-0.055
-0.056
-0.056
-0.057
-0.057
-0.058
-0.058
-0.059
-0.060
-0.060
-0.061
-0.062
-0.062
-0.063
-0.063
-0.063
-0.064
-0.064
Rank
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
APPENDIX TABLE A1: LIST OF METROPOLITAN AND NON-METROPOLITAN AREAS RANKED BY TOTAL AMENITY VALUE
Full Name of Metropolitan Area
Baton Rouge, LA
Toledo, OH
Melbourne-Titusville-Palm Bay, FL
Non-metro, AZ
Memphis, TN-AR-MS
Birmingham, AL
Non-metro, UT
Omaha, NE-IA
Daytona Beach, FL
Little Rock-North Little Rock, AR
Greenville, NC
Tuscaloosa, AL
Canton-Massillon, OH
Fayetteville-Springdale-Rogers, AR
Kalamazoo-Battle Creek, MI
Greenville-Spartanburg-Anderson, SC
Elkhart-Goshen, IN
Buffalo-Niagara Falls, NY
Benton Harbor, MI
Columbia, MO
Pittsburgh, PA
Cheyenne, WY
Montgomery, AL
Rockford, IL
Roanoke, VA
Fayetteville, NC
Jackson, MI
Lewiston-Auburn, ME
Hickory-Morganton-Lenoir, NC
Peoria-Pekin, IL
Glens Falls, NY
Non-metro, ME
Pensacola, FL
Kokomo, IN
Knoxville, TN
Springfield, IL
Non-metro, MT
Tyler, TX
South Bend, IN
Lexington, KY
Huntsville, AL
Jackson, MS
Billings, MT
Scranton--Wilkes-Barre--Hazleton, PA
Saginaw-Bay City-Midland, MI
Non-metro, FL
Rocky Mount, NC
Population
602,894
618,203
476,230
603,632
1,135,614
921,106
524,673
716,998
493,175
583,845
133,798
164,875
406,934
311,121
452,851
962,441
182,791
1,170,111
162,453
135,454
2,358,695
81,607
333,055
371,236
235,932
302,963
158,422
90,830
341,851
347,387
124,345
808,317
412,153
101,541
687,249
201,437
596,684
174,706
265,559
479,198
342,376
440,801
129,352
624,776
403,070
1,144,881
143,026
Adjusted Differentials
Housing
Costs
Wages
-0.045
-0.169
-0.025
-0.164
-0.108
-0.171
-0.184
-0.163
0.008
-0.178
-0.019
-0.179
-0.134
-0.171
-0.080
-0.195
-0.157
-0.185
-0.099
-0.197
-0.081
-0.195
-0.098
-0.195
-0.079
-0.191
-0.139
-0.206
-0.020
-0.196
-0.071
-0.210
-0.047
-0.204
-0.027
-0.190
-0.076
-0.194
-0.180
-0.204
-0.041
-0.207
-0.246
-0.214
-0.129
-0.209
-0.002
-0.211
-0.106
-0.212
-0.198
-0.209
-0.014
-0.212
-0.125
-0.229
-0.127
-0.220
-0.022
-0.217
-0.110
-0.204
-0.201
-0.229
-0.154
-0.232
0.069
-0.237
-0.127
-0.231
-0.074
-0.222
-0.266
-0.239
-0.102
-0.234
-0.060
-0.235
-0.088
-0.241
-0.045
-0.244
-0.092
-0.246
-0.180
-0.252
-0.103
-0.236
-0.012
-0.239
-0.178
-0.247
-0.111
-0.246
Land Rents
Linear
-0.601
-0.636
-0.434
-0.194
-0.784
-0.716
-0.366
-0.617
-0.362
-0.572
-0.613
-0.564
-0.602
-0.503
-0.783
-0.706
-0.744
-0.742
-0.623
-0.380
-0.773
-0.240
-0.542
-0.897
-0.616
-0.351
-0.870
-0.639
-0.592
-0.869
-0.573
-0.426
-0.573
-1.208
-0.642
-0.749
-0.294
-0.722
-0.842
-0.790
-0.921
-0.801
-0.582
-0.728
-0.990
-0.569
-0.750
Quadratic
-0.649
-0.694
-0.452
-0.197
-0.886
-0.794
-0.376
-0.663
-0.372
-0.609
-0.658
-0.599
-0.646
-0.526
-0.878
-0.771
-0.822
-0.824
-0.671
-0.390
-0.860
-0.245
-0.570
-1.033
-0.658
-0.359
-0.993
-0.683
-0.629
-0.989
-0.608
-0.439
-0.604
-1.531
-0.686
-0.823
-0.301
-0.786
-0.945
-0.872
-1.053
-0.886
-0.612
-0.793
-1.158
-0.597
-0.819
Quality of Life
Value
-0.031
-0.041
0.000
0.037
-0.060
-0.047
0.010
-0.019
0.019
-0.011
-0.022
-0.013
-0.024
0.005
-0.056
-0.031
-0.043
-0.054
-0.029
0.023
-0.047
0.056
-0.003
-0.069
-0.017
0.028
-0.064
-0.008
-0.008
-0.061
-0.020
0.027
0.003
-0.110
-0.011
-0.039
0.059
-0.025
-0.047
-0.033
-0.055
-0.031
0.013
-0.027
-0.074
0.010
-0.024
Rank
166
196
89
242
212
134
63
119
139
124
148
82
234
165
202
231
163
60
217
24
95
262
131
52
259
101
105
247
136
85
276
120
187
151
214
177
232
170
71
157
270
145
TradeProductivity
Value
-0.053
-0.037
-0.104
-0.163
-0.013
-0.034
-0.124
-0.084
-0.144
-0.100
-0.085
-0.099
-0.083
-0.132
-0.037
-0.078
-0.059
-0.042
-0.081
-0.164
-0.054
-0.217
-0.124
-0.024
-0.107
-0.178
-0.034
-0.123
-0.124
-0.041
-0.109
-0.184
-0.146
0.029
-0.125
-0.082
-0.236
-0.106
-0.072
-0.095
-0.062
-0.099
-0.169
-0.106
-0.035
-0.167
-0.114
Federal Tax
Rank Differential
92
-0.005
81
-0.001
154
-0.023
-0.048
54
0.011
73
0.003
-0.033
128
-0.011
213
-0.036
150
-0.017
131
-0.014
146
-0.020
126
-0.017
195
-0.029
80
-0.002
114
-0.010
96
-0.006
84
-0.007
121
-0.019
230
-0.044
93
-0.005
263
-0.059
183
-0.029
67
0.005
162
-0.024
246
-0.051
74
0.001
180
-0.023
181
-0.028
83
-0.001
163
-0.033
-0.048
216
-0.034
33
0.030
184
-0.027
124
-0.016
-0.062
160
-0.021
108
-0.009
142
-0.015
99
-0.002
149
-0.015
236
-0.037
161
-0.022
75
0.003
-0.040
168
-0.022
Total Amenity
Values
Value
-0.065
-0.065
-0.066
-0.067
-0.068
-0.069
-0.069
-0.072
-0.073
-0.075
-0.076
-0.077
-0.077
-0.080
-0.080
-0.081
-0.081
-0.081
-0.081
-0.082
-0.082
-0.083
-0.083
-0.084
-0.085
-0.086
-0.086
-0.087
-0.087
-0.088
-0.090
-0.090
-0.091
-0.091
-0.091
-0.091
-0.091
-0.093
-0.093
-0.094
-0.094
-0.095
-0.095
-0.095
-0.096
-0.097
-0.097
Rank
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
APPENDIX TABLE A1: LIST OF METROPOLITAN AND NON-METROPOLITAN AREAS RANKED BY TOTAL AMENITY VALUE
Full Name of Metropolitan Area
Davenport-Moline-Rock Island, IA-IL
Wichita, KS
Tulsa, OK
Mobile, AL
Non-metro, WY
Lakeland-Winter Haven, FL
Non-metro, ID
Sioux Falls, SD
Auburn-Opelika, AL
San Antonio, TX
Killeen-Temple, TX
La Crosse, WI-MN
Amarillo, TX
Corpus Christi, TX
Chattanooga, TN-GA
Las Cruces, NM
Rapid City, SD
Eau Claire, WI
Wausau, WI
Non-metro, WI
Syracuse, NY
Waterloo-Cedar Falls, IA
Fort Wayne, IN
Pueblo, CO
Oklahoma City, OK
Non-metro, MI
Non-metro, NC
Erie, PA
Springfield, MO
Youngstown-Warren, OH
Jacksonville, NC
Topeka, KS
Lubbock, TX
Biloxi-Gulfport-Pascagoula, MS
Evansville-Henderson, IN-KY
Williamsport, PA
Sherman-Denison, TX
Augusta-Aiken, GA-SC
Ocala, FL
Lake Charles, LA
Mansfield, OH
St. Cloud, MN
Macon, GA
Goldsboro, NC
Dubuque, IA
Monroe, LA
Lynchburg, VA
Population
359,062
545,220
803,235
540,258
345,642
483,924
786,043
172,412
115,092
1,592,383
312,952
126,838
217,858
380,783
465,161
174,682
88,565
148,337
125,834
1,723,367
732,117
128,012
502,141
141,472
1,083,346
1,768,978
2,612,257
280,843
325,721
594,746
150,355
169,871
242,628
363,988
296,195
120,044
110,595
477,441
258,916
183,577
175,818
167,392
322,549
113,329
89,143
147,250
214,911
Adjusted Differentials
Housing
Costs
Wages
-0.078
-0.245
-0.065
-0.257
-0.096
-0.260
-0.129
-0.248
-0.174
-0.256
-0.116
-0.254
-0.186
-0.251
-0.149
-0.258
-0.132
-0.252
-0.088
-0.254
-0.245
-0.249
-0.126
-0.247
-0.146
-0.253
-0.099
-0.255
-0.098
-0.258
-0.205
-0.261
-0.232
-0.266
-0.118
-0.256
-0.074
-0.265
-0.116
-0.260
-0.037
-0.251
-0.127
-0.271
-0.049
-0.268
-0.168
-0.269
-0.133
-0.278
-0.102
-0.258
-0.150
-0.268
-0.108
-0.268
-0.185
-0.272
-0.077
-0.276
-0.286
-0.264
-0.135
-0.286
-0.166
-0.282
-0.132
-0.289
-0.093
-0.286
-0.126
-0.282
-0.134
-0.291
-0.078
-0.294
-0.170
-0.298
-0.066
-0.303
-0.099
-0.294
-0.100
-0.290
-0.059
-0.299
-0.183
-0.297
-0.148
-0.307
-0.126
-0.307
-0.137
-0.300
Land Rents
Linear
-0.838
-0.925
-0.849
-0.709
-0.619
-0.770
-0.565
-0.694
-0.716
-0.846
-0.393
-0.713
-0.684
-0.820
-0.837
-0.554
-0.501
-0.772
-0.934
-0.795
-0.973
-0.813
-1.014
-0.689
-0.826
-0.826
-0.736
-0.854
-0.658
-0.970
-0.344
-0.854
-0.750
-0.875
-0.970
-0.861
-0.879
-1.046
-0.810
-1.118
-0.988
-0.969
-1.117
-0.771
-0.909
-0.966
-0.911
Quadratic
-0.936
-1.052
-0.945
-0.766
-0.655
-0.842
-0.592
-0.745
-0.773
-0.943
-0.404
-0.771
-0.734
-0.908
-0.930
-0.579
-0.520
-0.845
-1.063
-0.873
-1.127
-0.894
-1.180
-0.737
-0.909
-0.915
-0.796
-0.949
-0.699
-1.111
-0.353
-0.944
-0.810
-0.971
-1.105
-0.955
-0.976
-1.216
-0.883
-1.324
-1.129
-1.103
-1.326
-0.833
-1.012
-1.092
-1.017
Quality of Life
Value
-0.041
-0.048
-0.032
-0.016
0.007
-0.023
0.012
-0.006
-0.015
-0.039
0.040
-0.020
-0.010
-0.034
-0.035
0.019
0.033
-0.026
-0.049
-0.028
-0.069
-0.023
-0.063
-0.003
-0.020
-0.038
-0.013
-0.035
0.003
-0.052
0.051
-0.024
-0.009
-0.026
-0.047
-0.031
-0.028
-0.057
-0.010
-0.064
-0.048
-0.048
-0.068
-0.007
-0.024
-0.036
-0.031
Rank
199
218
172
128
142
97
125
188
40
135
113
179
181
65
47
154
222
264
143
254
94
137
182
84
227
32
147
109
156
213
168
158
235
114
258
219
220
261
100
146
183
169
TradeProductivity
Value
-0.088
-0.079
-0.104
-0.128
-0.165
-0.119
-0.174
-0.146
-0.132
-0.097
-0.220
-0.126
-0.142
-0.105
-0.105
-0.190
-0.212
-0.120
-0.086
-0.120
-0.056
-0.129
-0.067
-0.162
-0.135
-0.108
-0.148
-0.114
-0.175
-0.090
-0.254
-0.137
-0.161
-0.135
-0.104
-0.130
-0.137
-0.093
-0.166
-0.085
-0.110
-0.110
-0.079
-0.176
-0.150
-0.133
-0.140
Federal Tax
Rank Differential
135
-0.014
116
-0.005
153
-0.013
188
-0.027
-0.037
172
-0.022
-0.043
215
-0.030
196
-0.028
143
-0.016
266
-0.061
187
-0.029
211
-0.033
159
-0.019
158
-0.018
254
-0.047
262
-0.052
174
-0.026
134
-0.011
-0.025
95
-0.008
189
-0.024
105
-0.004
228
-0.038
202
-0.024
-0.024
-0.034
167
-0.023
242
-0.043
137
-0.013
275
-0.077
206
-0.027
227
-0.037
203
-0.025
157
-0.017
192
-0.028
205
-0.028
140
-0.012
232
-0.036
130
-0.006
164
-0.019
165
-0.022
117
-0.007
243
-0.043
220
-0.029
199
-0.024
209
-0.030
Total Amenity
Values
Value
-0.098
-0.098
-0.098
-0.098
-0.099
-0.099
-0.099
-0.099
-0.100
-0.100
-0.101
-0.101
-0.101
-0.101
-0.102
-0.102
-0.102
-0.103
-0.105
-0.105
-0.105
-0.106
-0.106
-0.106
-0.107
-0.107
-0.108
-0.108
-0.109
-0.110
-0.111
-0.112
-0.112
-0.113
-0.114
-0.114
-0.115
-0.116
-0.117
-0.118
-0.118
-0.119
-0.119
-0.120
-0.120
-0.121
-0.121
Rank
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
APPENDIX TABLE A1: LIST OF METROPOLITAN AND NON-METROPOLITAN AREAS RANKED BY TOTAL AMENITY VALUE
Full Name of Metropolitan Area
Shreveport-Bossier City, LA
Muncie, IN
Non-metro, IN
Non-metro, SC
Non-metro, OH
Waco, TX
Jackson, TN
Bangor, ME
Decatur, AL
Albany, GA
Charleston, WV
Non-metro, NM
Lima, OH
Sharon, PA
Non-metro, NY
Laredo, TX
Binghamton, NY
Houma, LA
Owensboro, KY
St. Joseph, MO
Florence, SC
Non-metro, GA
Non-metro, VA
Clarksville-Hopkinsville, TN-KY
Florence, AL
San Angelo, TX
Abilene, TX
Decatur, IL
Lafayette, LA
Victoria, TX
Alexandria, LA
Casper, WY
Duluth-Superior, MN-WI
Johnson City-Kingsport-Bristol, TN-VA
Hattiesburg, MS
Utica-Rome, NY
Great Falls, MT
Elmira, NY
Altoona, PA
Non-metro, PA
El Paso, TX
Terre Haute, IN
Cumberland, MD-WV
Non-metro, IA
Non-metro, IL
Odessa-Midland, TX
Fargo-Moorhead, ND-MN
Population
392,302
118,769
1,690,582
1,205,050
2,139,364
213,517
107,377
90,864
145,867
120,822
251,662
783,991
155,084
120,293
1,503,399
193,117
252,320
194,477
91,545
102,490
125,761
2,519,789
1,550,447
207,033
142,950
104,010
126,555
114,706
385,647
84,088
126,337
66,533
243,815
480,091
111,674
299,896
80,357
91,070
129,144
1,889,525
679,622
149,192
102,008
1,600,191
1,877,585
237,132
174,367
Adjusted Differentials
Housing
Costs
Wages
-0.115
-0.308
-0.114
-0.304
-0.101
-0.306
-0.135
-0.311
-0.099
-0.306
-0.108
-0.311
-0.080
-0.322
-0.170
-0.324
-0.064
-0.326
-0.082
-0.316
-0.103
-0.331
-0.212
-0.324
-0.087
-0.322
-0.147
-0.319
-0.115
-0.304
-0.200
-0.332
-0.114
-0.313
-0.110
-0.338
-0.136
-0.338
-0.167
-0.335
-0.120
-0.341
-0.140
-0.330
-0.160
-0.334
-0.214
-0.342
-0.142
-0.348
-0.177
-0.348
-0.235
-0.349
-0.053
-0.349
-0.116
-0.356
-0.083
-0.356
-0.171
-0.358
-0.228
-0.366
-0.098
-0.356
-0.179
-0.363
-0.178
-0.364
-0.112
-0.342
-0.307
-0.373
-0.120
-0.345
-0.150
-0.363
-0.135
-0.364
-0.158
-0.369
-0.125
-0.372
-0.167
-0.365
-0.192
-0.381
-0.145
-0.369
-0.121
-0.382
-0.168
-0.382
Land Rents
Linear
-1.004
-0.989
-1.033
-0.960
-1.041
-1.038
-1.157
-0.921
-1.220
-1.129
-1.133
-0.806
-1.139
-0.963
-0.985
-0.870
-1.028
-1.146
-1.074
-0.976
-1.131
-1.029
-0.992
-0.877
-1.102
-1.006
-0.848
-1.349
-1.207
-1.299
-1.064
-0.941
-1.254
-1.064
-1.071
-1.159
-0.755
-1.148
-1.142
-1.190
-1.148
-1.251
-1.102
-1.105
-1.182
-1.304
-1.174
Quadratic
-1.146
-1.126
-1.190
-1.082
-1.202
-1.195
-1.377
-1.023
-1.480
-1.334
-1.332
-0.872
-1.347
-1.083
-1.121
-0.952
-1.179
-1.350
-1.236
-1.096
-1.324
-1.173
-1.118
-0.959
-1.275
-1.132
-0.920
-1.699
-1.438
-1.598
-1.213
-1.038
-1.519
-1.211
-1.221
-1.367
-0.801
-1.348
-1.330
-1.405
-1.336
-1.502
-1.267
-1.264
-1.389
-1.588
-1.370
Quality of Life
Value
-0.042
-0.043
-0.050
-0.033
-0.052
-0.047
-0.063
-0.018
-0.072
-0.063
-0.052
0.002
-0.062
-0.033
-0.050
-0.008
-0.054
-0.054
-0.041
-0.026
-0.049
-0.040
-0.031
-0.004
-0.042
-0.025
0.004
-0.089
-0.057
-0.074
-0.031
-0.002
-0.069
-0.028
-0.029
-0.064
0.036
-0.061
-0.045
-0.053
-0.041
-0.060
-0.040
-0.027
-0.052
-0.063
-0.039
Rank
201
203
216
253
132
266
255
226
251
176
103
229
230
194
152
223
96
200
149
83
274
236
269
167
91
265
160
161
257
44
245
207
197
244
191
256
190
TradeProductivity
Value
-0.124
-0.122
-0.113
-0.140
-0.111
-0.118
-0.098
-0.169
-0.085
-0.099
-0.117
-0.202
-0.103
-0.151
-0.123
-0.194
-0.123
-0.123
-0.144
-0.168
-0.131
-0.146
-0.162
-0.206
-0.149
-0.177
-0.223
-0.080
-0.130
-0.104
-0.173
-0.219
-0.116
-0.180
-0.180
-0.125
-0.283
-0.132
-0.158
-0.145
-0.164
-0.139
-0.171
-0.192
-0.154
-0.136
-0.174
Federal Tax
Rank Differential
182
-0.021
177
-0.023
-0.019
-0.026
-0.019
171
-0.019
144
-0.010
235
-0.034
132
-0.005
148
-0.014
170
-0.014
-0.046
152
-0.015
222
-0.033
-0.031
256
-0.045
179
-0.030
178
-0.018
214
-0.026
234
-0.036
194
-0.020
-0.031
-0.036
259
-0.049
218
-0.027
244
-0.038
268
-0.055
118
-0.005
193
-0.019
156
-0.010
239
-0.035
265
-0.048
169
-0.018
248
-0.037
247
-0.037
186
-0.028
276
-0.069
198
-0.031
225
-0.032
-0.027
231
-0.031
208
-0.023
238
-0.039
-0.039
-0.032
204
-0.020
240
-0.033
Total Amenity
Values
Value
-0.121
-0.121
-0.122
-0.122
-0.123
-0.123
-0.125
-0.126
-0.126
-0.127
-0.127
-0.127
-0.129
-0.129
-0.129
-0.132
-0.133
-0.133
-0.133
-0.133
-0.133
-0.134
-0.135
-0.136
-0.137
-0.138
-0.139
-0.140
-0.140
-0.140
-0.142
-0.142
-0.143
-0.144
-0.144
-0.144
-0.145
-0.146
-0.146
-0.146
-0.146
-0.148
-0.150
-0.150
-0.150
-0.151
-0.151
Rank
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
APPENDIX TABLE A1: LIST OF METROPOLITAN AND NON-METROPOLITAN AREAS RANKED BY TOTAL AMENITY VALUE
Full Name of Metropolitan Area
Non-metro, MN
Pocatello, ID
Columbus, GA-AL
Wichita Falls, TX
Longview-Marshall, TX
Beaumont-Port Arthur, TX
Sumter, SC
Non-metro, TN
Dothan, AL
Pine Bluff, AR
Danville, VA
Sioux City, IA-NE
Gadsden, AL
Anniston, AL
Joplin, MO
Fort Smith, AR-OK
Enid, OK
Non-metro, LA
Non-metro, TX
Non-metro, WV
Non-metro, SD
Jonesboro, AR
Lawton, OK
Wheeling, WV-OH
Steubenville-Weirton, OH-WV
Jamestown, NY
Non-metro, AR
Non-metro, KY
Grand Forks, ND-MN
Parkersburg-Marietta, WV-OH
Non-metro, MO
Non-metro, NE
Huntington-Ashland, WV-KY-OH
Non-metro, KS
Non-metro, AL
Johnstown, PA
Texarkana, TX-Texarkana, AR
Non-metro, OK
Brownsville-Harlingen-San Benito, TX
Non-metro, MS
Bismarck, ND
Non-metro, ND
McAllen-Edinburg-Mission, TX
Population
1,456,119
75,565
274,624
140,518
208,780
385,090
104,646
1,827,139
137,916
84,278
110,156
124,130
103,459
112,249
157,322
207,290
57,813
1,098,766
3,159,940
1,042,776
493,867
82,148
114,996
153,172
132,008
139,750
1,352,381
2,068,667
97,478
151,237
1,800,410
811,425
315,538
1,167,355
1,338,141
232,621
129,749
1,352,292
335,227
1,820,996
94,719
358,234
569,463
Adjusted Differentials
Housing
Costs
Wages
-0.157
-0.367
-0.125
-0.396
-0.140
-0.379
-0.234
-0.383
-0.136
-0.386
-0.035
-0.390
-0.178
-0.388
-0.185
-0.403
-0.181
-0.404
-0.156
-0.416
-0.151
-0.403
-0.147
-0.417
-0.133
-0.421
-0.183
-0.424
-0.254
-0.417
-0.187
-0.433
-0.218
-0.435
-0.167
-0.435
-0.200
-0.442
-0.205
-0.445
-0.291
-0.457
-0.240
-0.452
-0.260
-0.448
-0.178
-0.448
-0.178
-0.448
-0.140
-0.430
-0.238
-0.457
-0.182
-0.456
-0.204
-0.455
-0.153
-0.457
-0.256
-0.456
-0.261
-0.464
-0.160
-0.477
-0.240
-0.469
-0.174
-0.477
-0.190
-0.476
-0.185
-0.498
-0.255
-0.496
-0.211
-0.502
-0.202
-0.517
-0.244
-0.532
-0.260
-0.532
-0.212
-0.570
Land Rents
Linear
-1.142
-1.355
-1.237
-0.995
-1.280
-1.574
-1.170
-1.219
-1.232
-1.353
-1.312
-1.385
-1.440
-1.314
-1.086
-1.343
-1.267
-1.406
-1.341
-1.345
-1.157
-1.277
-1.204
-1.430
-1.429
-1.459
-1.306
-1.456
-1.387
-1.537
-1.248
-1.271
-1.603
-1.351
-1.568
-1.519
-1.625
-1.424
-1.570
-1.660
-1.610
-1.565
-1.861
Quadratic
-1.327
-1.669
-1.473
-1.106
-1.542
-2.147
-1.361
-1.430
-1.450
-1.651
-1.586
-1.708
-1.810
-1.576
-1.223
-1.620
-1.490
-1.733
-1.611
-1.614
-1.310
-1.498
-1.384
-1.767
-1.766
-1.840
-1.541
-1.810
-1.683
-1.975
-1.449
-1.481
-2.098
-1.611
-2.019
-1.917
-2.124
-1.720
-1.998
-2.180
-2.052
-1.959
-2.641
Quality of Life
Value
-0.047
-0.061
-0.055
-0.008
-0.057
-0.108
-0.037
-0.038
-0.04
-0.053
-0.057
-0.06
-0.069
-0.046
-0.011
-0.045
-0.032
-0.058
-0.043
-0.042
0.001
-0.026
-0.016
-0.058
-0.058
-0.079
-0.028
-0.057
-0.046
-0.072
-0.023
-0.021
-0.074
-0.035
-0.067
-0.062
-0.068
-0.034
-0.057
-0.066
-0.048
-0.041
-0.079
Rank
249
233
102
237
275
185
193
228
239
243
263
209
118
206
173
153
129
240
241
273
208
268
271
250
260
238
221
272
TradeProductivity
Value
-0.163
-0.141
-0.152
-0.226
-0.149
-0.070
-0.182
-0.189
-0.186
-0.168
-0.163
-0.161
-0.15
-0.19
-0.246
-0.194
-0.219
-0.178
-0.206
-0.21
-0.279
-0.238
-0.253
-0.189
-0.189
-0.157
-0.237
-0.193
-0.21
-0.17
-0.251
-0.256
-0.177
-0.24
-0.189
-0.201
-0.2
-0.255
-0.221
-0.215
-0.25
-0.262
-0.228
Federal Tax
Rank Differential
-0.037
210
-0.016
223
-0.029
269
-0.053
219
-0.025
107
0.005
249
-0.037
-0.037
250
-0.037
233
-0.025
229
-0.030
226
-0.024
221
-0.021
253
-0.036
272
-0.059
255
-0.034
264
-0.045
-0.031
-0.041
-0.042
-0.062
271
-0.050
274
-0.058
251
-0.035
252
-0.036
224
-0.034
-0.049
-0.035
261
-0.042
237
-0.027
-0.059
-0.057
245
-0.027
-0.053
-0.031
258
-0.039
257
-0.033
-0.054
267
-0.041
-0.037
273
-0.047
-0.052
270
-0.039
Total Amenity
Values
Value
-0.151
-0.152
-0.152
-0.152
-0.152
-0.153
-0.154
-0.159
-0.160
-0.161
-0.162
-0.162
-0.165
-0.168
-0.168
-0.169
-0.172
-0.172
-0.175
-0.176
-0.178
-0.178
-0.178
-0.178
-0.179
-0.180
-0.180
-0.180
-0.180
-0.180
-0.183
-0.184
-0.188
-0.188
-0.188
-0.191
-0.196
-0.197
-0.198
-0.203
-0.208
-0.208
-0.225
Rank
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
State Name
APPENDIX TABLE A2: LIST OF STATES RANKED BY TOTAL AMENITY VALUE
TradeFederal
Adjusted Differentials
Land Rents
Quality of Life
Productivity
Tax
Housing
Differentia
Population
Wages
Linear Quadratic
Value
Rank
Value
Rank
l
Costs
Hawaii
California
New Jersey
Connecticut
Massachusetts
New York
Washington
Colorado
New Hampshire
District of Columbia
Alaska
Maryland
Oregon
Rhode Island
Illinois
Nevada
Arizona
Delaware
Utah
Florida
Vermont
Michigan
Virginia
Wisconsin
New Mexico
Minnesota
Pennsylvania
North Carolina
Ohio
Georgia
Maine
Indiana
Texas
Idaho
South Carolina
Montana
Missouri
Tennessee
Wyoming
Louisiana
Iowa
Kansas
Nebraska
Alabama
Kentucky
Arkansas
Oklahoma
South Dakota
West Virginia
Mississippi
North Dakota
1,211,537
33,871,648
8,414,350
3,405,565
6,349,097
18,976,457
5,894,121
4,301,261
1,235,786
572,059
626,932
5,296,486
3,421,399
1,048,319
12,419,293
1,998,257
5,130,632
783,600
2,233,169
15,982,378
608,827
9,938,444
7,078,515
5,363,675
1,819,046
4,919,479
12,281,054
8,049,313
11,353,140
8,186,453
1,274,923
6,080,485
20,851,820
1,293,953
4,012,012
902,195
5,595,211
5,689,283
493,782
4,468,976
2,926,324
2,688,418
1,711,263
4,447,100
4,041,769
2,673,400
3,450,654
754,844
1,808,344
2,844,658
642,200
-0.015
0.126
0.189
0.165
0.094
0.120
0.026
-0.016
0.033
0.126
0.050
0.110
-0.045
0.021
0.065
0.054
-0.027
0.043
-0.055
-0.060
-0.172
0.051
-0.035
-0.035
-0.148
-0.009
-0.011
-0.084
-0.024
-0.021
-0.160
-0.031
-0.041
-0.148
-0.100
-0.256
-0.106
-0.101
-0.193
-0.104
-0.140
-0.132
-0.174
-0.114
-0.121
-0.185
-0.178
-0.252
-0.161
-0.167
-0.234
0.530
0.458
0.336
0.278
0.251
0.199
0.181
0.172
0.164
0.154
0.130
0.126
0.106
0.082
0.063
0.054
0.019
-0.010
-0.023
-0.036
-0.056
-0.061
-0.085
-0.099
-0.136
-0.134
-0.135
-0.141
-0.143
-0.145
-0.171
-0.168
-0.203
-0.212
-0.214
-0.237
-0.247
-0.249
-0.264
-0.264
-0.293
-0.312
-0.319
-0.318
-0.326
-0.364
-0.369
-0.389
-0.392
-0.427
-0.495
2.311
1.615
0.919
0.737
0.816
0.524
0.706
0.781
0.613
0.314
0.418
0.239
0.579
0.294
0.091
0.082
0.158
-0.159
0.053
0.013
0.232
-0.402
-0.268
-0.328
-0.176
-0.548
-0.549
-0.373
-0.548
-0.562
-0.294
-0.633
-0.754
-0.502
-0.640
-0.313
-0.766
-0.787
-0.599
-0.844
-0.870
-0.975
-0.886
-1.051
-1.066
-1.050
-1.091
-0.974
-1.239
-1.370
-1.478
1.852
1.360
0.832
0.678
0.749
0.416
0.631
0.705
0.566
0.307
0.399
0.229
0.534
0.283
0.025
0.069
0.150
-0.167
0.046
-0.009
0.213
-0.444
-0.313
-0.371
-0.229
-0.629
-0.623
-0.403
-0.614
-0.637
-0.318
-0.730
-0.891
-0.538
-0.713
-0.330
-0.859
-0.899
-0.639
-0.990
-0.986
-1.131
-1.007
-1.264
-1.286
-1.223
-1.269
-1.088
-1.511
-1.738
-1.831
0.182
0.085
0.012
0.006
0.034
0.003
0.046
0.065
0.037
-0.015
0.016
-0.016
0.058
0.016
-0.013
-0.010
0.021
-0.025
0.021
0.020
0.071
-0.047
-0.010
-0.014
0.032
-0.039
-0.039
-0.003
-0.035
-0.037
0.027
-0.039
-0.045
0.007
-0.018
0.055
-0.026
-0.029
0.014
-0.032
-0.024
-0.034
-0.014
-0.046
-0.045
-0.023
-0.029
0.003
-0.045
-0.054
-0.041
1
2
18
20
9
22
7
4
8
.
15
29
5
16
26
25
13
33
12
14
3
49
24
28
10
43
41
23
39
40
11
42
46
19
30
6
34
36
17
37
32
38
27
48
45
31
35
21
47
50
44
0.045
0.148
0.186
0.160
0.101
0.116
0.040
0.006
0.044
0.116
0.054
0.101
-0.024
0.026
0.058
0.048
-0.019
0.033
-0.046
-0.051
-0.142
0.034
-0.036
-0.038
-0.132
-0.021
-0.023
-0.081
-0.034
-0.032
-0.145
-0.043
-0.054
-0.139
-0.102
-0.227
-0.111
-0.107
-0.181
-0.111
-0.142
-0.137
-0.172
-0.124
-0.130
-0.185
-0.181
-0.240
-0.169
-0.178
-0.238
10
3
1
2
5
4
12
16
11
.
8
6
20
15
7
9
17
14
26
27
39
13
23
24
36
18
19
29
22
21
41
25
28
38
30
48
32
31
46
33
40
37
43
34
35
47
45
50
42
44
49
-0.02
0.019
0.039
0.034
0.017
0.025
0.001
-0.01
0.004
0.028
0.009
0.025
-0.015
0.003
0.015
0.012
-0.008
0.011
-0.014
-0.015
-0.043
0.015
-0.006
-0.006
-0.034
0.002
0.001
-0.017
-0.002
-0.001
-0.036
-0.003
-0.005
-0.032
-0.019
-0.059
-0.02
-0.019
-0.042
-0.019
-0.027
-0.025
-0.035
-0.02
-0.021
-0.037
-0.035
-0.053
-0.03
-0.03
-0.046
Total Amenity
Values
Value
Rank
0.211
0.18
0.131
0.108
0.098
0.077
0.072
0.069
0.065
0.059
0.051
0.049
0.043
0.032
0.024
0.021
0.008
-0.005
-0.008
-0.013
-0.02
-0.025
-0.033
-0.039
-0.052
-0.053
-0.054
-0.055
-0.057
-0.057
-0.066
-0.066
-0.08
-0.082
-0.083
-0.09
-0.097
-0.097
-0.102
-0.103
-0.114
-0.122
-0.124
-0.125
-0.128
-0.142
-0.144
-0.151
-0.154
-0.167
-0.193
1
2
3
4
5
6
7
8
9
.
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50