Wozniak 955
variables that indicate whether i is a member of each of four education groups indexed by g.
Distance from birth state, the dummy for division of birth, and long run migration flows capture stable migration relationships between an individual’s birth state and
the potential choice states. Distance between the choice state and birth state is mea- sured as hundreds of miles between the two state capitals. The indicator for whether
a potential choice state is in an individual’s division of birth accounts for the pull of nearby states and varies across birth state-destination state pairs. The lagged flows
account for the pull forces generated by previous migration patterns. These include long-term trends such as migration to the South and to the coasts as well as numerous
other important but less obvious state-to-state migration trends.
19
Importantly, these flows also capture “spurious” movements between states that result from metropol-
itan areas that span more than one state. The flow measure between birth state a and residence state b equals the share of 31–35 year old natives of a residing in b
at the time of the Census, conditional on educational attainment. If b⳱a this is simply the share of natives continuing to reside in their birth state.
Finally, fixed effects for potential choice states capture stable differences across states in terms of their attractiveness for migrants. These state fixed effects also
account for states that have relatively diversified economies that contain more than one distinct labor market. State-wide conditions may be relatively poor measures of
the relevant local labor market conditions in these states. Choice state region times birth year fixed effects account for aggregate conditions common to all cohorts.
20
IV. The Effects of Labor Market Conditions on State of Residence Choices
Before going into estimates of the conditional logit model, I estimate a linear probability model of a binary location decision: the zero-one choice of
whether to reside outside one’s state of birth. The model is the following:
g g
move ⳱ Ⳮ LMC Ⳮeduc Ⳮeduc
LMC 7
isb 1
sb sb
i i
g s
g b
ⳭX Ⳮeduc ␦ Ⳮeduc ␦ Ⳮε
i i
i isb
i i
The dependent variable is a dummy equal to 1 if individual i born in state s in year b resides outside her state of birth when I observe her in the Census. LMC is
one of the measures of birth state labor market conditions at individual i’s time of labor market entry. As in the conditional logit model, educ
i g
represents a set of dummies for four education groups. The main effect is included, as are interactions
of the education group dummies with a full set of state of birth and year of birth fixed effects, ␦
i s
and ␦
i b
, respectively. The vector of personal characteristics X
i
includes
19. For more on these and their great variety, see U.S. Census Bureau 2003 and Foote and Kahn 2003. Davies et al. 2001 examines the role of some of these fixed state factors in state to state migration.
20. Birth year cannot be included directly since it does not vary across the potential choice states.
956 The Journal of Human Resources
Table 3 Estimates of the Linear Probability Migration Model
Panel A Bartik Measures of Birth State Labor Market Conditions LMC
Normalized LMC Measure
Bartik Bartik5yr
Ed_bartik Ed_bartik5yr
LMC main effect ⳮ
0.016 0.006
ⳮ 0.012
0.004 ⳮ
0.007 0.003
ⳮ 0.008
0.003 Dropout
ⳮ 0.056
0.009 ⳮ
0.061 0.010
ⳮ 0.055
0.009 ⳮ
0.055 0.009
Some college 0.073
0.011 0.075
0.012 0.073
0.011 0.079
0.011 College graduate
0.159 0.011
0.160 0.012
0.168 0.012
0.162 0.011
DO ⳯ LMC 0.000
0.007 ⳮ
0.005 0.005
0.002 0.004
0.002 0.003
SC ⳯ LMC 0.003
0.007 0.001
0.004 0.003
0.005 0.002
0.004 CG ⳯ LMC
0.013 0.009
ⳮ 0.006
0.005 ⳮ
0.005 0.005
ⳮ 0.008
0.004 R-squared
0.057 0.058
0.057 0.058
N 1.60EⳭ06
1.60EⳭ06 1.60EⳭ06
1.60EⳭ06 continued
controls for race, Hispanic ethnicity, sex, marital status, and presence of i’s children in the household.
Despite its limitations on the choice variable, this model has features that com- plement the subsequent analysis. First, the estimating equation is the same as the
estimated wage equation, except of course for the dependent variable. This makes comparisons between wage and migration effects more transparent. Second, this
model allows direct inclusion of cohort fixed effects, which are precluded by the form of the conditional logit since they do not vary across choice states.
Table 3 presents estimates of the Equation 7 model. Panel A shows estimates using the Bartik measures of early labor market conditions. A standard deviation
increase in birth state Bartik reduces the probability that a high school graduate the omitted group resides outside his birth state by 1.6 percentage points. The effect of
Bartik5yr is similar. There are no significant differences across education groups in
the migration response to either measure. Effects are different when the education- specific Bartik measures are used. The main effect is smaller, but there are significant
differences between college graduates and everyone else in the response to Ed_Bartik5yr
. A standard deviation increase in Ed_bartik5yr decreases a college
Wozniak 957
Table 3 continued
Panel B Unemployment Rate Measures of Birth State Labor Market Conditions LMC
Normalized LMC Measure
UR UR5yr
Ed_UR Ed_UR5yr
LMC main effect 0.007
0.002 0.005
0.002 0.014
0.002 0.018
0.002 Dropout
ⳮ 0.053
0.009 ⳮ
0.052 0.009
ⳮ 0.078
0.014 ⳮ
0.085 0.015
Some college 0.077
0.011 0.077
0.011 0.095
0.012 0.107
0.010 College graduate
0.171 0.010
0.174 0.010
0.231 0.013
0.258 0.015
DO ⳯ LMC 0.003
0.003 0.004
0.003 0.011
0.004 0.014
0.004 SC ⳯ LMC
0.007 0.003
0.006 0.003
0.029 0.005
0.051 0.006
CG ⳯ LMC 0.016
0.003 0.017
0.003 0.034
0.005 0.064
0.006 R-squared
0.058 0.058
0.062 0.063
N 1.60EⳭ06
1.60EⳭ06 1.00EⳭ06
1.00EⳭ06
Notes: Data are taken from IPUMS versions of U.S. Census 5 percent samples for 1980, 1990, and 2000. Sample is noninstitutionalized U.S. natives with 5–8 years of potential labor market experience and positive
wages in the previous year. All specifications include dummy variables for sex, race BlackWhite, His- panic ethnicity, marital status married or not, and children present or not. Education group specific birth
state and birth year fixed effects are also included.
graduate’s probability of residing in her birth state by about 1.6 percentage points, but the effect for other workers is only half this, about 0.75 percentage points. These
results are somewhat surprising in that they imply that less-educated workers are more responsive to conditions that reflect average labor market changes than to those
that reflect changes in their specific labor market opportunities. I return to this puzzle in the next section.
To assess the magnitude of these effects, recall that the probability of residing out of one’s birth state varies considerably by education. For college graduates it is
roughly 0.45, for those with some college 0.35, 0.25 for high school graduates and 0.26 for dropouts. These probabilities vary slightly across Census years. Thus, a
percentage point increase in the probability of staying in one’s birth state corresponds to a 1.8 percent increase in the probability of staying for college graduates, 1.5
percent for those with some college, and 1.3 percent for high school graduates and dropouts. In other words, a change in conditions that increases the probability of
staying by one percentage point for all workers will increase the overall probability
958 The Journal of Human Resources
of staying by 38 percent more for college graduates than high school graduates. Results in which the probability changes significantly more than the average for
college graduates will magnify this difference dramatically. Panel B of Table 3 uses the unemployment rate measures to estimate Equation 7.
Consistent with Panel A, the coefficients on the main effect show that workers who faced worse entry labor market conditions are more likely to leave their birth states
across the four unemployment rate measures. Note that the expected signs are re- versed across the Bartik and UR measures. The impact of a standard deviation
increase in entry unemployment rates is to raise the probability of leaving by 0.5 to 1.8 percentage points. There are also significant differences in the response to these
conditions across education groups. College graduates and those with some college are significantly more likely to leave in all specifications, with larger effects on
college graduates. A standard deviation increase in UR and UR5yr increases the probability that an individual with some college leaves his birth state by a little more
than 1 percentage point, and that a college graduate leaves by more than 2 percentage points as compared to 0.5 percentage points for high school graduates.
Responses of those with some college or a college degree to conditions as mea- sured by education-specific unemployment rates are even larger. A standard devia-
tion increase in these measures increases the probability that a skilled individual leaves his birth state by roughly 4.4 to 7.3 percentage points in total.
21
To convert these into changes in the probability of staying in one’s birth state, consider the
Ed_UR5yr specification. A standard deviation increase in this measure decreases the
probability that a high school graduate stays from 0.75 to 0.732, a decrease of 2.4 percent. The same shock decreases a college graduate’s probability of staying from
0.55 to 0.486, a change of about 12 percent, or six times the change for a high school graduate.
Results from the linear probability model show that entry labor market conditions in the birth state affect the location choices of young workers. College graduates
also appear to be more sensitive to these conditions than other education groups. But do entry labor market conditions in other states have a similar impact on an
individual’s location choice? Or are individuals uniquely attuned to conditions in their birth states?
The conditional logit specification allows me to answer these questions. Results from its estimation are presented in Tables 4a and 4b. The top numbers in each cell
are odds ratios implied by the estimated coefficients unreported.
22
Z-statistics based on standard errors clustered on labor market entry year are in parentheses.
23
Due to the significant computational requirements of estimating the conditional logit, I use
21. The significant interaction of dropout and LMC in the education-specific unemployment rate specifi- cations is not robust. It reverses sign in the conditional logit specification.
22. Note that an odds ratio equal to one indicates that the dependent variable has no effect on an indi- vidual’s odds of choosing a state.
23. Standard errors are not adjusted for the fact that some of the covariates are themselves estimated for example, shares of education group employed in an industry is part of the education-specific Bartik mea-
sure, long term flows between states, etc.. Bootstrapping standard errors to account for this is prohibitive given the already considerable computational requirements of the conditional logit estimation.
Wozniak 959
Table 4a Estimates from the McFadden Conditional Logit State Choice Model
Normalized LMC Measure
Bartik Bartik5yr
Ed_bartik Ed_bartik5
LMC main effect 0.91
ⳮ1.52 0.97
ⳮ1.84 1.00
ⳮ0.06 1.02
1.32 DO ⳯ LMC
0.86 ⳮ3.75
0.93 ⳮ3.52
1.00 0.24
0.99 ⳮ1.00
SC ⳯ LMC 1.14
3.4 1.08
8.69 1.01
0.36 1.01
0.45 CG ⳯ LMC
1.26 2.94
1.14 6.87
1.12 2.1
1.08 3.93
Black ⳯ LMC 0.91
ⳮ0.63 0.93
ⳮ1.88 0.89
ⳮ1.03 0.86
ⳮ9.74 Hispanic ⳯ LMC
2.07 4.74
1.41 9.46
1.35 5.74
1.18 12.76
Female ⳯ LMC 0.98
ⳮ0.99 0.99
ⳮ1.04 1.01
1.14 1.01
1.82 Distance from birth state
0.96 ⳮ46.98
0.96 ⳮ48.89
0.96 ⳮ50.52
0.96 ⳮ49.51
Education-specific income 2.97
10.11 2.79
9.35 2.91
9.8 2.78
9.07 In division of birth
2.74 62.63
2.73 61.84
2.74 63.67
2.73 64.3
Education-specific flows 253.16
169.17 252.52
166.76 253.62
168.83 253.50
171.5 Choice State FEs
Yes Yes
Yes Yes
Choice Region ⳯ birth year FE Yes
Yes Yes
Yes
Notes: Top number in cell is implied odds ratio. Z-statistics computed using clustered standard errors on labor market entry year are in parentheses. indicates significance at the 5 percent level, at the 1
percent level. Data are taken from IPUMS versions of U.S. Census 5 percent samples for 1980, 1990, and 2000. Sample is noninstitutionalized U.S. natives with 5–8 years of potential labor market experience and
positive wages in the previous year. Observations used in estimation were a random 50 percent sample of the sample used in Table 3.
a 50 percent random sub-sample of the Table 3 sample to estimate the equations in 4a and 4b.
Odds ratios in the top row of Table 4a show that the Bartik measures have no detectable effects on the likelihood that a high school graduate chooses to reside in
any given state.
24
College-educated individuals, however, show significant, right-
24. Odds ratios are constant across values of the other covariates, so they do not have the usual “all else equal” interpretation. But it is still the case that the main effect reflects the effect of LMCs on the location
decisions of high school graduates. See Gould 2000 on this. The accumulated effect of odds ratios is
960 The Journal of Human Resources
signed responses to state conditions. The odds ratios on the interactions of LMCs with dummies for some college and college graduate are greater than one in both
the Bartik and Bartik5yr specifications. Since odds ratios are multiplicative rather than additive, these results show that individuals with some college education are
more sensitive to entry labor market conditions in a state than are high school graduates. For an individual with some college, a standard deviation increase in
these measures boosts the odds implied by the main effect by 8–14 percent. For college graduates, the boost is 14–26 percent. In total, a standard deviation increase
in Bartik-measured LMCs leads to an 11–14 percent increase in the odds of a college graduate choosing a state, as compared with no effect for high school graduates.
Using the education-specific Bartik measures, the only significant education group interaction is with college graduate. The total effects here are similar to those in the
Bartik
specifications, with a standard deviation increase in LMCs leading to about a 12 percent increase in the overall odds that a college graduate chooses a state.
Coefficients on other included controls largely have the expected signs. Distance from birth state decreases the odds that an individual chooses a particular state,
while education group long term income, location within one’s division of birth, and the historical migration flow of one’s education group to a state all increase the odds
of choosing it. The gender difference in response to these conditions is insignificant in all specifications. Blacks have a weakly wrong-signed response to Bartik-mea-
sured LMCs while Hispanics are more responsive, roughly on the same order of magnitude as college graduates, although some of these raceethnicity interactions
change signs and significance in the unemployment rate specifications.
Table 4b presents estimates of the conditional logit model using the unemploy- ment rate measures of entry labor market conditions. Since increases in a state’s
unemployment rate should make it less attractive, we expect odds ratios of less than one on the main effect. This is what I find in the first row of Table 4b. A standard
deviation increase in the unemployment rate lowers the odds of an individual choos- ing a state by about 6 percent. Dropouts are less responsive than this, while college-
educated individuals are more responsive although the college graduate interaction with LMC is only significant using the education-specific unemployment rates. A
standard deviation increase in unemployment conditions reduces the odds of a col- lege-educated individual choosing a state by about 10–20 percent overall.
To test whether the results in Tables 4a and 4b are driven by birth state conditions alone, I reestimated the specifications in 4a and 4b adding interactions of the LMC
main effect and its education interactions with an indicator for whether the choice state was an individual’s birth state. I also included the main effect of birth state.
Coefficient estimates and significance patterns on the LMC main effect and educa- tion group interactions were essentially unchanged in this exercise. In fact, the wrong
signed coefficients on the main effect in the Bartik and Bartik5yr specifications are driven by wrong signed responses to birth state conditions in these models. If the
response to birth state conditions alone were driving the estimates in the first four
their product, so the total effect of LMCs on college graduates in the Bartik specification is 0.93 ⳯ 1.338 ⳱
1.24. In other words, a 34 percent increase over the main effect odds means that a one unit change in LMCs increases the overall odds that a college graduate chooses a state by 24 percent, as compared to no
effect or a small decrease for high school graduates.
Wozniak 961
Table 4b Estimates from the McFadden Conditional Logit State Choice Model
Normalized LMC Measure
UR UR5yr
Ed_UR Ed_UR5yr
LMC main effect 0.93
ⳮ4.16 0.93
ⳮ3.87 0.96
ⳮ2.23 0.94
ⳮ4.64 DO ⳯ LMC
1.03 2.98
1.03 4.03
1.05 3.76
1.05 3.89
SC ⳯ LMC 0.95
ⳮ3.2 0.96
ⳮ2.95 0.91
ⳮ4.54 0.85
ⳮ7.58 CG ⳯ LMC
0.96 ⳮ1.43
0.96 ⳮ1.61
0.90 ⳮ2.17
0.82 ⳮ3.58
Black ⳯ LMC 1.15
5.37 1.16
4.89 1.20
8.96 1.22
7.76 Hispanic ⳯ LMC
1.23 2.54
1.23 2.61
1.16 1.58
1.18 1.75
Female ⳯ LMC 1.01
1.36 1.01
1.46 1.01
1.31 1.02
1.54 Distance from birth state
0.96 ⳮ53.3
0.96 ⳮ66.45
0.96 ⳮ39.66
0.96 ⳮ39.33
Education-specific income 2.80
7.34 2.74
6.91 4.74
9.09 4.67
7.94 In division of birth
2.74 63.06
2.73 65.61
2.65 46.87
2.65 46.68
Education-specific flows 253.72
160.62 256.49
150.96 237.76
245.13 237.58
251.42 Choice state FEs
Yes Yes
Yes Yes
Choice region ⳯ birth year FE Yes
Yes Yes
Yes
Notes: Top number in cell is implied odds ratio. Z-statistics computed using clustered standard errors on labor market entry year are in parentheses. indicates significance at the 5 percent level, at the 1
percent level. Data are taken from IPUMS versions of U.S. Census 5 percent samples for 1980, 1990, and 2000. Sample is noninstitutionalized U.S. natives with 5–8 years of potential labor market experience and
positive wages in the previous year. Observations used in estimation were a random 50 percent sample of the sample used in Table 3.
rows of 4a and 4b, then interactions of these covariates with birth state would be of similar magnitude and significance as the reported estimates. Instead, the interactions
with birth state are rarely significant, although birth state itself is.
25
These results imply that individuals give equal weight to states’ entry labor market conditions in
choosing their residence state.
25. The only significant triple interactions are dropoutLMCbirthstate in the Ed_Bartik5yr, Ed_UR and Ed_UR5yr
specifications, and some collegeLMCbirthstate in Ed_UR and Ed_UR5yr. In several specifi- cations, birthstateLMC is significant but of the wrong sign. This at first seems at odds with the main
effect estimates in Table 3, but it simply means that individuals are less responsive to conditions in their birth state than they are to conditions in other states when choosing between the two.
962 The Journal of Human Resources
Are the responses estimated in Tables 4a and 4b large or small? On the face of it, a roughly 15 percent change in the odds as implied by both the education-specific
Bartik and UR measures that a college graduate chooses a state seems large, but assessing the magnitude of these estimates requires that odds ratios be converted
into probabilities. If a state initially has a 0.55 probability of retaining a college graduate which is what the sample means suggest, then that probability improves
to 0.58 following an improvement in entry labor market conditions that improves relative odds by 15 percent.
26
This represents a 5.4 percent increase in the probability of retaining a college graduate. On the other hand, if a state has a 2 percent chance
of attracting a random college graduate, say from another state, then the same change increases this probability to 0.023, an increase of about 15 percent.
27
V. Does the Response to Local Conditions Affect Labor Market Outcomes?
