Since Schultz' important paper on human capital (Schultz
1961), human migration has been an important topic of economic research.
Much of the human capital literature on internal migration has
emphasized expected net earnings benefits as the major factor driving
human migration decisions, e.g., see Schwartz (1976), Schlottmann and
Herzog (1981), Herzog and Schlottmann (1984), Sandefur (1985),
Pissarides and Wadsworth (1989), and Detang-Dessendre and Molho (1999).
A number of recent studies have also used aggregate data on migration,
but aggregate data contain considerable migration against the economic
gradient and are hard to interpret, for example, see articles by Deller
et al. (2001), Huang, Orazem, and Wohlgemuth (2002), and Hunter, White,
and Little (2003). Models underlying these studies assume that an
individual moves in the first period in which he is made better off at a
new destination than at the origin, but this approach ignores the timing
decision, i.e., an individual should move when the payoff from migration
is at a maximum rather than when it is first positive. Also, these
studies ignore individual heterogeneity that can bias parameter
estimates. Moreover, fixed costs and location-specific amenities are
important to migration decisions, and they have received less attention
(Greenwood 1997). Exceptions, however, are Mueser and Graves (1995),
Deller et al. (2001), and Hunter, White, and Little (2003).
This article presents econometric estimates of the adult
working-age male hazard function of interstate migration. The hazard
function is the conceptually correct statement of the migration
decision, i.e., it gives the probability that an individual moves at a
given point in time, given that he has not yet moved. As such, it easily
accommodates the migration timing decision. Furthermore, we include
individual heterogeneity in the model to reduce omitted variables bias.
The empirical hazard rate model is derived for an individual who has a
finite-life and consumes leisure, purchased goods and local amenities,
and incurs significant fixed costs of moving. The econometric hazard
model of migration uses data on the length of an individual's
resident spells, and the spells for this study are obtained from
following a sample of adult males for a twenty-year period. These males
were first interviewed in 1968, when they were nineteen to forty-five
years of age, and hence, after twenty years of migration experience, the
oldest males are sixty-five years of age. This is a relatively large
amount of migration experience, given that we are primarily interested
in the migration behavior of economically active males rather than males
contemplating immediate retirement. The econometric results show a
strong negative effect of men's real wage difference between origin
and destination and of fixed costs associated with a move, and a
positive effect of the local crime rate, a disamenity, on the hazard of
interstate migration. Farmers and other self-employed men who own
above-average location-specific assets have an unusually low hazard rate
of interstate migration compared to wage earners.
The story unfolds in the following sections. First, a very brief
summary of the economic problem is presented. Second, we present the
econometric model and data, and third, we present the empirical results.
The final section contains conclusions.
The Conceptual Model of Internal Migration
Males are born into a particular region, move with their parents
until they are eighteen years of age, and then are assumed to make
independent decisions about their residence for the remainder of their
life. We assume an adult male receives utility from consuming his
leisure time, purchased goods, and local amenities. Local amenities,
representing location-specific culture, climate, topography (e.g.,
parks, access to the sea, mountains, plains), and environmental
conditions (crime, pollution, congestion) are a type of local public
good to an individual. An adult male chooses between staying at his
current residence, the origin (o), or migrating to a new area or
destination (d), and he is uncertain about future real wage and amenity
outcomes at these locations. Let his expected indirect utility function
for each year be [V.sub.j]([w.sub.jt], [x.sub.jt]), where [w.sub.j] is
the expected real wage and [x.sub.j] is an indicator of the expected
local amenities in location j,j = o, d (Greenwood 1997, pp. 668, 677).
Let all expected relocation costs associated with moving from o to d in
t, except for the foregone earnings, be represented by [c.sub.dt], and
to simplify, assume that [c.sub.dt] is fixed and invariant with the
distance moved. Also for simplicity, assume that local amenities and
relocation costs can be measured in real wage units.
An adult male is assumed to choose a residence that gives him
maximum utility. To further simplify, assume that he is risk neutral,
that migration does not affect his length of remaining life, and ignore
discounting. He then migrates when the summation of net real benefits is
a maximum, provided that the summation is positive. (1) Let this maximum
be:
(1) -[n.summation over (t=1)]([w.sub.ot] + [x.sub.ot] - [c.sub.d1]
+ [n.summation over (t=1])([w.sub.t] + [x.sub.dt]) [much greater than] 0
where n is his number of remaining years of life.
In equation (1), clearly [[summation].sup.n.sub.t=1]([w.sub.ot] +
[x.sub.ot]) < [[summation].sup.n.sub.t=1]([w.sub.dt] + [x.sub.dt])
and [[summation].sup.n.sub.t=1]([x.sub.ot] - [x.sub.dt]) <
[[summation].sup.n.sub.t=1]([w.sub.dt] - [w.sub.ot]). Hence, over n the
summation of the difference in expected value of amenities between the
origin and destination is less than the sum, over n, of the difference
in expected real wage at the origin and destination. However, only if
[[summation].sup.n.sub.t=1]([x.sub.ot] - (x.sub.dt]) = 0 can we say for
certain that the expected real wage difference between the origin and
destination will be positive. For example, if a destination gives an
individual higher expected amenity value relative to the origin, it may
be lifetime-utility-maximizing for him to migrate from o to d even, when
the accumulated wage difference after migration is negative. (2)
Define D as an indicator variable taking a value of 1 if [NG.sub.t]
= -[[summation].sup.n.sub.t'=t+1]([w.sub.ot] + [x.sub.ot]) -
[c.sub.d1] + [[summation].sup.n.sub.t'=t+1]([w.sub.dt] +
[x.sub.dt]) is a maximum (and positive) and 0 otherwise. Then the
probability of an adult male migrating from o to d can be represented by
the following probability statement:
(2) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].
The following comparative static results hold:
(3) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
(4) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
(5) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].
Hence, the probability of an adult male migrating from o to d is
increasing in his destination wage [w.sub.d] and amenity [x.sub.d], and
decreasing in his wage and amenity at the origin. It is also decreasing
in the fixed cost of migration.
In a static environment, a strong economic incentive exists for a
finite-life individual to migrate from o to d early, perhaps in the
first period, or to stay at his current location. However, given that a
move from o to d is completed, it is possible for additional moves to be
optimal.
The Econometric Model and Data
At any point in time, each adult male is located at some residence
location (o). Furthermore, as time in a particular place increases, he
accumulates information on local conditions (knowledge of social
networks, local culture, local businesses, local personal friends, and
local amenities) that can be expected to strengthen his ties to a place
and reduce mobility. However, if a change occurs in family composition
such as a child leaves home, the local real wage declines, or if distant
economic conditions improve, he may choose to move to a new location
(d). Therefore, we expect the migration hazard, i.e., the probability
that the resident spell ends at this time, to be a function of
covariates (Greenwood 1997) and to be time dependent. Also, we can
incorporate individual heterogeneity into the model. Because the
migration hazard can accommodate these features, the hazard analysis of
migration reveals a rich picture of interstate migration relative to a
discrete choice model. (3)
The Hazard Model of Migration
Unlike discrete choice models of migration, the hazard rate model
of migration treats the length of the resident spell as the dependent
variable. For a given individual, define T as the duration or length of
time that he has resided at a particular location, and t as a particular
realization of T. T has an associated cumulative distribution function
F(t) and probability density function f(t). An adult male's hazard
of migration is represented as the limiting probability that a resident
spell is completed at t + [DELTA], given that it has lasted until time
t, or:
(6) H(t) = [lim.sub.h[right arrow]o][P.sub.r](t < T [less than
or equal to] t + hIT > t)/h
= f(t)/[1 - F(t)] = f(t)/S(t)
where S(t) = [P.sub.r](T > t) is the individual's survival
function for his current location. S(t) expresses the probability that a
resident spell is of a length of at least t (Kiefer 1988; Lancaster
1990; Greene 2003, pp. 790-94).
We expect an adult male's hazard of migration to be time
dependent and depends on a set of covariates, X. This is a so-called
accelerated failure-time model (Greene 2003, p. 769). In addition,
individual heterogeneity may exist because of (i) individual-specific
unmeasured effects, e.g., intensity of psychological costs of moving,
(ii) measurement error in X, or (iii) measurement error in the duration
of a resident spell. Following others, for example, Heckman and Singer
(1985), we impose the Weibull distribution on the density function for
residency at a particular location (t), which permits constant,
decreasing, or increasing time dependence of the hazard function for
migration determined by the sign of [sigma]. If [sigma] is one, then the
hazard of migration is not time dependent. We define [upsilon] to be
individual-specific unmeasured heterogeneity, and assume [upsilon] is
distributed gamma with unit mean and variance [theta]. It is
incorporated as in Heckman and Singer (1985) or Greene (2003, pp.
797-798). Hence, a male's mixed resident-survivor function is
(7) S(t, X, [beta], [sigma], [theta]) = [{1 + [theta][[t
exp(X[beta])].sup.1/[sigma]}.sup.-1/[theta].
His associated hazard function of migration is
(8) H(t, X, [beta], [sigma], [theta])
= [[S(t, X, [beta], [sigma], [theta])].sup.[theta]]
(1/[sigma])[t.sup.(1/[sigma])-l][[exp(X[beta])].sup.1/[sigma]]. (4)
If [sigma] is not significantly different from zero, the hazard of
migration is monotone in duration. An important feature of this
specification is that the effect of heterogeneity is increasing in
[theta], but as [theta] goes to zero, heterogeneity vanishes. (5)
Some variables in X for the ith individual, say [X.sub.ij], change
over time and are jointly determined with duration. For example, an
individual who has children and chooses a place to reside may make a
joint decision. When this is the case, [X.sub.itj] is typically assigned
its value at the beginning of the resident spell, say [X.sub.ij0],
(Lancaster 1990; Greene 2003, pp. 790-800). Other variables are time
varying but not endogenous to duration, e.g., marital status, and actual
value during the spell can be included as a regressor.
The Data
Individuals in this study are working-age adult males of the Panel
Study of Income Dynamics (PSID). We use the Survey Research Center (SRC)
Sample, which was a probability sample of all U.S. households in the
contiguous forty-eight states in 1968 and not the Survey of Economic
Opportunity sample, which drew mainly from low-income households.
Critical to this study, the PSID identifies the state of residence.
These males were first surveyed for the PSID in 1968 when they were
nineteen to forty-five years of age. Males were surveyed annually
starting in the year after they completed school and were reinterviewed
annually until they retired, died, or disappeared. We have data on
twenty years of migration experience for 915 men where 193 of them had
at least one move in the twenty-year period; 10.6% moved once, and the
remaining 10.5% moved more than once (table 1).
From the full number of males, we derived a total of 865 resident
spells having known starting dates, and 1,268 residence spells that have
adjusted starting dates. The 1,268 open and "closed" spells
are the total number of observations in our econometric hazard rate of
interstate migration analysis. The adjustment closes the migration
interval when it is open on the left, i.e., when we do not have data on
the starting date. This provides a relatively large amount of
information on resident-location decisions of working-age adult males
and variation in length of resident spells. However, the oldest-aged
males are sixty-five years, and some of them have retired or are
contemplating retirement. (6) The PSID has major advantages over
cross-sectional micro-data sets on migration, because we have twenty
years of information on migration decisions, and for the most part we
know the individual's attributes at the start of each resident
spell. Data for these adult males are supplemented with data from other
sources for their resident area.
For working-age males, internal migration over a long distance is
generally associated with a change in employment, whereas short-distance
migration is frequently associated with a change only in residence or
housing. Since the latter is not of interest to us, we choose to define
migration decisions for adult working-age males as interstate moves,
which is the most frequently used area designation for migration studies
in the U.S. States have fixed geographical boundaries over time and are
exhaustive in their geographical coverage. Occupational licensing
practices and union membership policies are set at the state level, and
the state government is a major fiscal and jurisdictional authority. (7)
Some important amenity benefits of states include the quality of local
public goods, such as public schools and park areas, air and water
quality, crime rate and "mildness" of the climate. Much of the
important variation in topography is also captured at the state level.
Other migration studies that have analyzed interstate migration include
Sandefur (1985), Brinig and Buckley (1996), Clark, Knapp, and White
(1996), and Pashigan (1979). (8)
For our study, an adult male is defined as a long-distance migrant
if he moves across a state boundary. Although states differ in area,
population, employment, and number of cities, we ignore these
differences. Adult males and families may be affected marginally by the
distance of a move to a new destination, but a major part of the cost is
fixed. Costs of moving for an adult male with a family include: time
spent weighing the decision to move, finding new places to shop, finding
schools for kids, finding a church to attend, and finding and making new
friends. They also include the cost of selling a house or ending a lease
at the origin, finding a new house or apartment at a destination, and
the cost of loading one's possessions at the origin, and unloading
and putting them in order at the new destination.
Since destinations and their location-specific amenities are a
choice, all potential working-age males face a similar common fixed
amenity-effect of potential destinations. Hence, when they live in
different places, the local amenity attributes that differ among them,
and that is relevant to their decision to migrate is the amenity
attributes of their current location (Huang, Orazem, and Wohlgemuth
2002).
The PSID identifies the state of residence of each adult male in
the survey We focus on a twenty-year period, starting in 1968.
The Empirical Hazard Function of Migration
The systematic part of the hazard function of interstate migration
is specified as:
(9) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
where i = 1, 2, ..., n, denotes sample males, s = 1, 2, ...,
[C.sub.i], denotes the resident spells, W([ot.sub.is]) denotes the real
wage for ith male at his current location or the origin o in time t
during spell s, W([dt.sub.is]) denotes the real wage for the ith male at
a new destination in spell s, AGE is the age of the ith male at the
beginning of spell s, EDU([t.sub.is]) denotes the years of schooling
completed by the ith male at the beginning of spell s,
DSLFEMP([t.sub.is]) denotes an indicator for the ith male being
self-employed or being a farmer at the beginning of spell s,
UNION([t.sub.is]) denotes an indicator for the ith male being a labor
union member at the beginning of spell s, UNEM([t.sub.is]) denotes the
ith male's unemployed status in resident spell s, MARR([t.sub.is])
denotes the ith male's marital status in spell s, CHILD([t.sub.is])
denotes the ith male's number of children at the start of spell s,
CRIME([t.sub.is]) denotes the crime rate at the origin for the ith male
relative to the U.S. average in spell s, PARKS([t.sub.is]) denotes for
the ith male the share of origin or of the resident state that is in
state and federal parks relative to the U.S. average at the start of
spell s, JAN([t.sub.is]) denotes for the ith male the thirty-year
average January temperature at the origin or resident state relative to
the U.S. average at the start of spell s, JULY([t.sub.is]) denotes for
the ith male the thirty-year average July temperature at the origin or
resident state relative to the U.S. average at the start of spell s, and
DWHITE([t.sub.is]) denotes the ith male's race.
The set of covariates is largely defined as the beginning of a
resident spell, especially for those variables that seem most likely to
be jointly determined with the probability that a residence spell
ends--EDU, DSLFEMP, UNION, and CHILD. (9) For other variables that are
time varying, but that are not jointly determined with the probability
of a spell ending, we use a summary of the variable over the resident
spell, for example, MARR. (10) Also, we choose to incorporate the effect
of unemployment on the probability of a resident spell ending as the
average amount of time in the spell that the individual was unemployed.
(11)
Expectations about the signs of the [beta]'s are as follows.
If a working-age male's real wage increases at his current location
[W([ot.sub.is])], this will reduce the attractiveness of an interstate
move, other things equal, and reduce his hazard of interstate migration.
An increase in his real wage at a new destination [W([dt.sub.is])],
other things equal, will increase his hazard of interstate migration.
Although there is a stronger incentive for young adult males to migrate
than older preretirement males, the hazard of interstate migration may
not peak at a young age for working males (Greenwood 1997, pp. 655-656).
Because an adult male's life is finite and the life cycle plays an
important role in the timing of his human capital investment and family
decisions, the marginal effect of his age [AGE([t.sub.is])] on the
hazard of interstate migration may be nonlinear. Thus,
[AGE.sup.2]([t.sub.is]) is also included as a regressor. We expect
[[beta].sub.3] > 0 and [[beta].sub.4] < 0.
The next seven variables are associated with a working-age adult
male's cost of migration. A male who has more education is expected
to have a higher hazard of interstate migration, other things equal
(i.e., [[beta].sub.5] > 0). Other studies have shown that an
individual's education is associated with the ability to acquire
and process information, and to make efficient decisions (Schultz 1975;
Schwartz 1976; Huffman 1977), and this seems likely to extend to
interstate migration. Moving to a new location carries some uncertainty,
and additional education, which aids the acquisition and interpretation
of information, can greatly reduce this uncertainty. Moreover,
Detang-Dessendre and Molho (1999, 2000) have shown that an
individual's schooling increases his/her hazard of migration.
If a working-age male is a farmer, he has control over farmland now
and has knowledge of the local land market, but it may be difficult for
him to acquire farmland in a new location. If he is a professional or
trade association member, his income is linked to a local clientele base
that also ties him to his current location (Pashigan 1979; Goss and Paul
1990). If he is a union member, he frequently has nontransferable
seniority and pension rights. These attributes of an individual's
local occupation are expected to increase his utility at his current
residence relative to a new location and to reduce his hazard of
interstate migration (i.e., [[beta].sub.6], [[beta].sub.7] < 0). When
a working-age male experiences temporary unemployment, this reduces his
opportunity cost of searching for a new location (Herzog and Schlottmann
1984; Pissarides and Wadsworth 1989; Goss and Paul 1990), and we expect
his hazard of interstate migration to increase (i.e., [[beta].sub.8]
> 0).
Spouses can either reduce or increase interstate mobility,
depending on their current job and amenity matches. Having children is
an irreversible decision, and children only know what they have
experienced. Hence, for an adult male having school-aged children is
expected to increase the psychological and monetary costs of an
interstate move (Mincer 1978; Greenwood 1997, pp. 701-705) and to reduce
the likelihood of a resident spell ending, e.g., [[beta].sub.10] < 0.
Local amenities at the origin, relative to those at a new
destination, are expected to affect a working-age male's hazard of
interstate migration (Greenwood 1997, pp. 676-677). We focus on the
crime rate, land in state and national parks, and long-term normal July
and January temperatures. A higher local crime rate against persons and
against real property is a negative local public good for non-criminals,
imposing psychic and self-protection costs and lowering the
residents' utility, other things equal. Hence, we expect a larger
value of CRIME to increase a working-age male's hazard of
interstate migration, i.e., [[beta].sub.11] > 0, or to reduce
resident duration. In contrast, local area parks provide a positive
local public good, and can be expected to reduce a male's hazard
for interstate migration (i.e., [[beta].sub.12] < 0). Long-term
average January and July temperatures play an important role in home
utility bills and affect the types of winter- and summer-season outdoor
recreational opportunities that are available in an area. Some effects
of weather, which have both cost of living and amenity dimensions,
should be incorporated into local wage rates and create compensating
differentials across states, but other effects may not (Roback 1988). If
there are other effects, then [[beta].sub.13] and [[beta].sub.14] will
be statistically different from zero.
White working-age males are expected to have a higher hazard of
interstate migration, other things equal, than nonwhites (i.e.,
[[beta].sub.15] > 0), because discrimination against nonwhites limits
the number of destinations where they can expect to be made better off
by a move relative to their current location. In particular, Filler
(1992) has shown that whites in the United States have greater
opportunities for superior location moves than nonwhites. His finding
suggests white males, on average, will experience a shorter duration at
any location than nonwhite males, other things equal.
The Wage at Potential Destinations
An adult working male's expected real wage at a new location
is an important variable for the hazard of interstate migration. It
reflects a location choice that is endogenous, however, and is not
generally available. Hence, we include a proxy variable for this
opportunity wage (Wooldridge 2002, pp. 63-67, 83-86; Greene 2003, pp.
86-88). This proxy variable should have the property that it is
correlated with the "true" but unobserved wage, but
uncorrelated with the error term in the decision to migrate. Otherwise
it should not be part of the structural model. If we assume that the
U.S. interstate labor markets for males are approximately in
equilibrium, then a hedonic wage equation fitted to data on male wages
pooled across states provides a method for valuing individual and
location-specific attributes (Hoch and Drake 1974; Kenny and Denslow
1980; Roback 1982; Rosen 1986; Topel 1986; Tokle and Huffman 1991). In
particular, state labor market units provide valuable information about
the parameters of the male-wage offer equation.
Following Kenny and Denslow (1980), Adams (1985), Topel (1986), and
Tokle and Huffman (1991), we adopt a real hourly wage equation where
differences in the cost of living are being adjusted for over time using
the implicit price deflator for personal consumption expenditures (U.S.
Dept. of Commerce) and across geography using state average land prices,
normal January and July temperatures, extent of urbanization, and the
crime rate:
(10)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
where [EDU.sub.iy] denotes years of schooling completed by ith male
in yth year, [EXP.sub.iy] denotes labor market experience computed as
the ith male's AGE - EDU - 6 in years, [RACE.sub.iy] denotes an
indicator for the ith male being white, [PLAND.sub.ky]/[P.sub.y] denotes
the real price of land in state k where the ith male resides in year y,
[URBAN.sub.ky] denotes the proportion of the population in the ith
male's resident state k that is urban in year y, [CRIME.sub.ky]
denotes the crime rate in the ith male's resident state k in year
y, [JAN.sub.k] denotes the thirty-year average January temperature in
the ith male's resident state k, [JULY.sub.k] denotes the
thirty-year average July temperature in the ith male's resident
state k, [PJOBGR.sub.ky] denotes predicted job growth in the ith
male's resident state s in year y, [PURATE.sub.ky] denotes the
predicted unemployment rate in the ith male's resident state s in
year y, [RSHOCK.sub.ky] denotes the relative employment shock in the ith
male's resident state s in year y, [RURATE.sub.ky] denotes the
residual unemployment rate in the ith male's resident state s in
year y, and [DS.sub.ky], [DW.sub.ky], and [DNC.sub.ky] denote that the
ith male resides in census region South, West, or North Central,
respectively. TIME and TIME-squared are included to allow for a possible
long-term negative trend in male real wage rates (Blundell and MaCurdy
1999). (12) The [[zeta].sub.j]s are unknown parameters, and
[[epsilon].sub.iky]' is a zero mean random disturbance term.
In equation (10), the real price of land ([PLAND.sub.ky]/[P.sub.y])
is a key proxy for local housing prices, and [URBAN.sub.ky],
[CRIME.sub.ky], [JAN.sub.k], and [JULY.sub.k] are proxies for various
local amenity attributes. By including the regional fixed effects and a
time trend, we control for other omitted variables, and by doing so, our
parameter estimates in equation (10) should have better statistical
properties.
Sample mean values of the variables entering the hazard of
interstate migration and the real wage are reported in tables 2 and 3,
respectively.
The Empirical Results
This section reports the empirical results for the wage equation
and for the hazard function of interstate migration.
A Male's Hedonic Wage Equation
The wage equation (equation (10)) is fitted to all of the
observations for the 915 adult males in the PSID, pooled over twenty
years, starting in 1968. Results are reported in table 3. The
performance of the fitted male wage equation is generally in agreement
with results reported in other studies. A one-year increase in a
male's schooling increases his real wage by about 7.5%. An increase
in his experience has a positive effect on his real wage, but at a
diminishing marginal rate. The maximum effect of EXP on a male's
wage profile occurs at twenty-six years of experience (approximately
forty-five years of age). All other measured variables held equal, white
males earn 11% more than nonwhites. These results are consistent with
those reported by Neal and Johnson (1996), but lower than those by Topel
(1986).
Male real wage rates are shown to differ because of local
cost-of-living and amenity differences. Both the real price of land
(PLAND/[P.sub.y]) and congestion, as reflected in URBAN, have positive
effects on the real male wage rate. The land-price effect on the male
wage compares favorably to the findings of Kenny and Denslow (1980) and
Tokle and Huffman (1991), but the effects of URBAN are greater in this
study. Roback (1982) does not include a variable that is a good proxy
for the cost of housing in her earnings equations, and this may color
the interpretation and comparison of those results. However, Roback
(1988) includes the local cost-of-living index in some of her wage
equations.
Local amenities have significant effects on the male real wage. A
one percentage point increase in a state's crime rate increases the
male real wage by about 1.5% (significant at the 1% level), consistent
with findings reported by Roback (1982). Roback (1988), however, did not
find statistically significant effects of the crime rate in her wage
equations, with one exception. The impacts of climatic amenities on the
male wage are conditioned by the inclusion of regional dummy variables.
However, a higher average January (or July) temperature for an area,
holding the region constant, reduces the male real wage by 4 (17)
percent per 10 degree increase, suggesting positive amenity value for
higher values, other things equal. Roback (1982) includes weather
variables in her earnings equations and finds that heating-degree days,
snowfall, clear days, and cloudy days have statistically significant
effects. She, however, largely excludes weather variables in her 1988
article (Roback 1988). (13)
State labor-market characteristics have a significant effect on
male wage rates. A one percentage point increase in the anticipated job
growth rate for the resident state (PJOBGR) increases the male real wage
by 6.0%, and a one percentage point increase in the anticipated
unemployment rate for the resident state (PURATE) increases the male
real wage by 4.3%. This reflects the fact that over the long run, he
must be compensated for taking the higher risk of being unemployed. The
latter result is greater by a factor of three than those obtained by
Topel (1986) and Tokle and Huffman (1991). The signs of the coefficients
for unanticipated job growth (RSHOCK) and unemployment (RURATE), which
cannot be factored into long-run decision making, are consistent with
the results in Topel (1986) and Tokle and Huffman (1991).
Historically, the U.S. has had broad regional differences in real
wage rates, and our results show that they persist over the twenty-year
period of our data, even after controlling for land prices,
urbanization, crime rates, and climate. Compared to the Northeastern
region, the male real wage in the South is 8.9% lower and 13.7% lower in
the West. However, the male real wage rate in the North Central region
is not significantly different from that in the Northeast region.
Consistent with other evidence, the results show a statistically
significant negative trend in the male real wage rate of slightly less
than 1% per year.
The Hazard of Interstate Migration
The empirical hazard function of adult male interstate migration is
fitted to the data on resident spells over a twenty-year period starting
in 1968, which is a large amount of information on migrat