JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999, pp. 103–123
MIGRATION AND CHANGING EMPLOYMENT STATUS:
A HAZARD FUNCTION ANALYSIS*
Cecile Detang-Dessendre
INRA-ESR, Dijon, France. E-mail: detang@enesad.inra.fr
Ian Molho
Department of Economics, University of Newcastle, U.K. E-mail: Ian.Molho@ncl.ac.uk
ABSTRACT. The effects of different employment-status transitions on migration choices
are considered from a search-theoretic perspective. A discrete-time hazard function for
migration decisions is estimated on data for young males of rural origin in France.
Employment-status transitions are handled as endogenous time-varying covariates. The
model is estimated by distance of move. The results show that the long-distance migration
hazard is significantly related to labor market variables, and, ceteris paribus, is highest
among job-gainers compared to the other transition groups. The probability of contracted
(long-distance) migration is found to be higher than that of speculative migration for
unemployed workers, especially those who are low-educated. Evidence consistent with
cumulative inertia is found for long-distance moves. Short-distance migration hazards are
found to be unrelated to labor market variables (including employment-status transitions)
and to display no systematic pattern of duration dependence.
1.
INTRODUCTION
In this paper we model the relationship between employment-status shifts
and migration. In particular, we are interested in estimating the impact on
migration probabilities of being unemployed as against employed. We also seek
to discover the extent to which the movement of (initially) unemployed workers
is triggered by finding a job in a distant location, as opposed to movement with
no immediate job contract lined-up at the point of destination.
Our theoretical approach is to model long-distance migration as part of a
job-search process. The process may end in finding work and may also result in
migration. Those who move with a job already lined-up at the destination are
termed contracted migrants; those who move without such a contract are termed
speculative migrants. Consider the case of an unemployed worker who takes a
job and possibly moves in the process. There are two-way interactions here: the
*Cécile Détang-Dessendre was visiting researcher at the Department of Economics, Newcastle, U.K. from January to July 1996. The authors would like to thank three anonymous referees
for constructive comments, and colleagues at Newcastle for helpful discussion. All errors remain our
own responsibility.
Received January 1997; revised August 1997; accepted November 1997.
© Blackwell Publishers 1999.
Blackwell Publishers, 350 Main Street, Malden, MA 02148, USA and 108 Cowley Road, Oxford, OX4 1JF, UK.
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JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999
change in employment status may cause migration, but at the same time
migration may be required for the individual to take advantage of the job
opportunity. We analyze the choices involved in a framework which captures this
endogeneity between migration and employment-status shifts.
Empirically, we analyze migration in a duration-model context where movement is seen as terminating an observed residence spell. Duration models allow
us to view residence spells in a historical context where individual migration
decisions may in part reflect past experience as well as the current situation.
Changes in employment status over the course of a residence spell are modeled
as endogenous time-varying covariates. We distinguish between short- and
long-distance moves in our empirical work because the former type of move may
be induced by housing rather than labor market processes. The duration model
treats short- and long-distance migration as competing risks. Finally, we look
for evidence of cumulative inertia in residence decisions.
The empirical analysis relates to a sample of young men from rural areas
in France. The data track the location history and labor market experience of
these individuals. We model their first migration decision after leaving school.
This decision represents an important part of the process of labor market
integration for these workers. The econometric results suggest that the probability of long-distance migration is significantly higher for the unemployed who
find work compared to those continuously unemployed or continuously employed. The evidence suggests that the probability of contracted movement is
generally higher than speculative movement for the unemployed, and especially
for those who are low-educated. Local migration is unaffected by employmentstatus shifts. We also find evidence consistent with cumulative inertia for
long-distance moves, but not for local moves.
In the following section we give an outline of our theoretical approach to
modeling migration. The data are described in Section 3. In Section 4 we set out
the empirical framework for duration analysis, and present the main results in
Section 5. In Section 6 we provide a summary and conclusion.
2.
THEORY AND HYPOTHESES
In this section we consider search-theoretic perspectives on migration as the
basis for our empirical model (see,for example,David,1974;Gordon and Vickerman,
1982; Greenwood, 1985; Molho, 1986). We make three important distinctions: first,
between housing-related and job-related moves (Gordon, 1982; Krumm, 1983);
second, between speculative and contracted migration (Silvers, 1977); and finally,
between “on-the-job” and unemployed search (Mortenson, 1986).
Housing moves tend to be localized in nature whereas job-related migration
may involve wider search fields. Thus, while the underlying motivation for
moving is typically unobserved, in practice it is important to distinguish between
long- and short-distance migration. The former is likely to consist purely of
job-related movers whereas the latter may contain a mixture of both types.
Virtually all housing-related moves are contracted (the exceptions are those who
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DETANG-DESSENDRE & MOLHO: MIGRATION AND EMPLOYMENT STATUS
105
move and “sleep rough”). Job-related moves may be either contracted or speculative, depending on whether or not the individual has work lined-up at the
destination at time of move. Speculative migration may be seen to arise from a
“move then search” strategy, where migrants move to a suitable base from which
to conduct search for an acceptable opportunity. Contracted migration reflects
a “search then move” strategy, where migration occurs as the outcome of a search
process once the individual has located an acceptable opportunity in a distant
region. The “contracted/ speculative” migration distinction relates to whether
people are unemployed or employed at point of destination after migration. Our
third and final distinction concerns whether people are employed or unemployed
before migration, at point of origin. Job-related moves may arise for people who
are originally employed as the outcome of an on-the-job search process, and for
people who are originally unemployed.
Putting the latter two distinctions together, we can conceive of the following
classes of job-related migration. First, there are movers who are continuously
unemployed over the move; second, there are those who are continuously
employed; third, there are those who shift from employment to unemployment
over the move; finally, there are those who shift from unemployment to employment over the move.
Our analysis is principally concerned with the relationship between migration and these differing employment-status transitions. We take the employment status of the individual at start of period as exogenous to migration
decisions over the period. However, the employment status at end of period is
clearly endogenous in that the decision to accept a distant job offer, for example,
will result in migration and also affect employment status. Our empirical
framework attempts to allow for this endogeneity in estimating the impact of
employment-status shifts on migration.
Hypotheses
Employment-status effects. Employment-status shifts are relevant only to
job-related migration and not to housing moves. Therefore one would expect
short-distance moves to be less strongly affected than long-distance ones by
employment status shifts, in so far as they are more likely to contain housingrelated migrants. Turning to job-related migration, it is commonly perceived
that in developed economies people rarely move speculatively in search of work
because of the risks involved in such a strategy. Hence, ceteris paribus we would
expect an individual who ended an observation period in unemployment to have
relatively low long-distance migration probabilities. However for individuals
employed at the end of an observation period, we expect those who gained work
over the period to be more likely to move long distances than the continuously
employed, ceteris paribus, because the latter must have received and accepted
a new job offer, whereas the former may have stayed at their original job.
Duration-dependence effects. There are good reasons for expecting duration
dependence in migration behavior. The marginal costs and benefits of search
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JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999
may change over the residence spell, especially in relation to move costs.
Individuals may form attachments to their home or area of residence which may
grow over time, for example as their local knowledge and social and economic
ties develop. This may lead to a process of cumulative inertia. In particular, Shaw
(1991) points to the growth of local human capital over time. Processes of this
kind would lead one to expect falling migration probabilities over the course of
a residence spell. Alternatively, the stochastic evolution of individuals’ preferences or local area attributes may cause individuals to increasingly outgrow
their original habitat, potentially leading to higher migration probabilities over
time (Gordon and Molho, 1995).
3.
THE DATA
The model was estimated using individual-level panel data from a 1993
survey including 551 young, French, male workers of rural origin (Dessendre,
1994). Seven rural districts were chosen on economic, demographic, and geographical criteria, to cover a broad spectrum of French rural areas. Within these
districts specific villages were then selected for analysis. All these villages were
far away from the nearest major town.1 The sampling frame included all people
aged 21–22 or 26–27 in 1993 who had lived in the district at any time up to age
10, and it was constructed using local population records. Extensive efforts were
made to track down all individuals who had since moved, as reflected in the high
response rates of 83 percent overall, and 75 percent for migrants. Information
was collected using an interview questionnaire. This included recall questions
that allowed us to build a monthly record of employment status and residence
for each individual. In constructing this data set the intention was to investigate
the labor-market-integration process of young workers from rural backgrounds.
The period under analysis (up to 1993) is of interest because it covered a period
of apparent slowdown in the decline of rural populations in France (Cavailhes
et al., 1994).
Long-distance migration is defined as moves over 60 kilometers (37 miles).
This definition allows a reasonable sample of long-distance movers, without too
serious a risk that someone would move that far and continue to commute to
their original district. Two versions of short-distance migration were tried,
moves less than 60 kilometers and moves less than 25 kilometers (excluding
moves within the same village). The latter are likely to contain the highest
proportion of purely housing-related moves.
We analyzed the residence duration among males up to the first migration
away from the district of origin after leaving full-time education. Individuals left
full-time education at different ages, so the maximum potential residence
1
The areas chosen were villages around the following areas (distance to the nearest town with
more than 100,000 inhabitants given in brackets): Dole (50 km) and Louhans (70 km), Burgundy;
Morlaix (50 km) and Redow (75 km), Brittany; Saint-Dié-des-Vosges (75 km) in the East; Le Luc
(100 km) in the South; and Saint-Jean-de-Maurienne (70 km) in the Alpes.
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DETANG-DESSENDRE & MOLHO: MIGRATION AND EMPLOYMENT STATUS
107
duration up to first migration varied accordingly. Table 1 gives an indication of
the distribution of complete residence durations in our data, using various
definitions of migration according to distance of move. We see from this table
that migration of some description is observed for nearly half the sample, and
nearly a quarter moved long-distance. The time pattern shows that the rate of
movement decreases with duration (some explanations for this are discussed
later). Three quarters of long-distance moves and a third of short-distance moves
(less than 25 kilometers) take place in the first year.
Turning to the issue of employment-status effects, people tended to record
related job and residence changes as happening at the same time, rather than
distinguishing the exact timing of the moves. This is probably an advantage from
our point of view as it makes clear the relation between the decisions. We do not
have to hunt out employment-status changes that may have happened soon after
the residence move, but which in reality may have been a “cause” of the move
in the search process; the fact that the two are so closely linked in peoples’ minds
that they date them together tells us that they are determined simultaneously.
Likewise, differing definitions of the time over which employment-status
changes took place tended not to affect the results substantially. However, it does
highlight the importance of treating employment-status effects as endogenous
in the econometric modeling.
Table 2 indicates the number of migrants by distance of move and changes
in employment status over the three-month period prior to the move. This table
shows only eight cases where a continuously unemployed individual moved, even
though there were 589 spells of unemployment for 551 men in our data. These
raw data seemingly points to low levels of speculative migration, though at this
stage we have not controlled for other influences on migration or allowed for
endogeneity between employment transitions and mobility. Most of the moves
in Table 2 are concentrated among the continuously employed or those gaining
TABLE 1: Residence Durations by Distance of Move
[1 month–6 months]
[7 months–12 months]
[13 months–18 months]
[19 months–24 months]
[25 months–30 months]
[31 months–36 months]
year 4
year 5
year 6
year 7
year 8
more than 8 years
Total
All Migration
Migration
≥ 60 km
Migration
< 60 km
Migration
< 25 km
108
47
25
15
7
11
14
13
13
2
2
4
261
69
22
9
8
3
3
4
2
2
0
0
2
124
39
25
16
7
4
9
10
11
11
2
2
2
137
13
13
11
5
2
4
9
9
9
2
2
2
81
Notes: The Full Sample consisted of 551 men; censored (Incomplete) cases not shown.
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JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999
employment. Of those gaining employment 53 percent of moves were longdistance compared to 44 percent for the continuously employed, suggesting that
moves associated with finding employment tend to be job-related as one would
expect. These would appear to be contracted moves, in the sense that the
individual moved their place of residence over the period and ended up gaining
a job in the process.
Information was also available on the temporary or permanent nature of
employment contracts. These data showed 90 cases of people who changed from
temporary to permanent work (in continuous employment); nine percent of these
changes were associated with migration, but these were primarily local moves,
with only two percent of the 90 combined with long-distance migration. Of the
transitions from unemployment to permanent work, eight percent were associated with a long distance move. These figures are consistent with the view that
temporary workers search locally for permanent jobs rather than more widely,
perhaps reflecting the development of links and contacts in the area.
4.
THE EMPIRICAL FRAMEWORK
In this paper we analyze the data using a duration model that may be seen to
emerge as the outcome of a search process (see Kiefer, 1988) for a discussion of the
derivation of duration models from search theory). It is convenient to model
durations in terms of the “hazard,” that is the probability of leaving a state (in this
case a residence) in some period t, conditional on not having left in the period up
until t. We model this hazard using a discrete-time logit specification
(1)
λkt = [ 1 + exp(–θt – Xkβ – Yktγ )]–1
where λkt is the hazard for individual k at time t; θt is the “baseline hazard” that
captures changes in the average hazard for all individuals over the course of the
residence spell; X is a vector of explanatory variables or “covariates” that are
fixed over time but vary over individuals, such as gender or qualifications at
time of leaving school; and Y is a vector of ‘time-varying covariates’, i.e.,
explanatory variables that vary across time and individuals, such as changing
employment status over the spell. β and γ are conformable vectors of coefficients.
This model involves organizing the data so that each observed time period
becomes a separate observation. Thus for example, say that a ten-period residence
TABLE 2: Completed Residence Duration by Employment-Status Change
Stay Unemployed
All migration
Migration ≥ 60 km
Migration < 60 km
Migration < 25 km
8
6
2
1
Find Job
85
45
40
18
Lose Job
7
2
5
1
Stay Job
Total
161
71
92
63
261
124
137
83
Notes: Number of unemployment spells in data equals 589; number of employment spells in
data equals 768.
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DETANG-DESSENDRE & MOLHO: MIGRATION AND EMPLOYMENT STATUS
109
spell ending in migration is observed for some individual. Such a case would
involve nine separate observations of zero for the dependent variable, followed
by an observation of a one. Some residence spells are likely to be incomplete in
the sense that the spell was still ongoing and migration had not yet occured by
the time of interview. Such spells are termed “censored” and enter the data as a
series of zeroes with no one at the end. Standard binary logit models may be
used to estimate the model (see for example, Allison, 1982; Jenkins, 1995).
The model is in discrete-time in the sense that it specifically allows for only
positive integer values of durations. For example, a residence spell may last one
period, or two periods, or three periods, etc. This contrasts with continuous-time
models such as the Cox Proportional Hazards Model, where the spell may end
at any positive time t (see Cox and Oakes, 1984).2 The discrete-time formulation
is appropriate to our problem because this is the format in which our duration
data are observed (this is typically the case in most duration studies). It has the
further advantage that time-varying covariates are easily handled, and ‘ties’ in
the data (cases where multiple individuals have exactly the same observed
residence duration) present no special estimation problems.
An interesting aspect of duration analysis concerns the issue of duration
dependence, where migration probabilities may causally depend on the length
of the spell to date. Such effects show up in the baseline hazard. A decreasing
baseline over time would, for example, be consistent with cumulative inertia. In
order to model empirically these effects, it is important that the form of the
baseline hazard should be as flexible as possible. One attraction of the logit
model is that it does not require prior restrictions on the shape of the baseline
hazard. Free estimation is allowed via coefficients on a sequence of appropriately
defined time-dummies. The Cox Proportional Hazards Model shares this great
flexibility in that it too does not pre-specify the shape of the baseline hazard.
The advantage of the logit model over the Cox model is that it allows us to
estimate the baseline explicitly, whereas in the Cox model it is treated as a
nuisance term and is not explicitly estimated.
We compared the performance of the logit model against various alternatives. The Cox Proportional Hazards Model specification proved problematic. A
variety of tests suggested that the proportionality assumption embedded in this
model was inappropriate to our data.3 First, we looked at Kaplan–Meier plots
of the survivor function. In principle we would expect these plots to be parallel
if the proportionality assumption held (see Cox and Oakes, 1984). Inspection of
the plots in Figure 1 indicates that proportionality does not hold for our data.
Second, we applied tests of the form suggested by Blossfeld, Hamerle, and Mayer
(1989). This involved construction of variables such as z=xln(t), where t is the
The Cox Proportional Hazard Model is specified as λkt = θtexp(Xkβ + Yktγ).
The Cox model (specified in note 2) implies that the ratio of the hazards for two individuals
with different X covariates is independent of the baseline hazard. The logit model involves no such
assumption.
2
3
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JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999
FIGURE 1: Kaplan–Meier Survival Estimates by Strata.
observed duration to date and x is a covariate of the original specification (in
particular we tried educational qualification). If the proportionality assumption
held we would expect variables such as z to attract insignificant coefficients in
the estimated Cox model. In fact, they turned out to be highly significant.4 In
view of these empirical findings and given the advantages of the logit specification outlined above, we decided to reject the Cox model.
Finally, we compared the logit model with two alternative discrete-time
specifications of the hazard function: the probit and the complementary log-log
(or extreme-value-distribution) models.5 The latter results are presented in
4
For example, the variable ‘(educational level of baccalaureat or above) × [log(duration)]’ had
a t-statistic of 7.3 in our analysis. We also tried variables of this kind in the logit model and found
them to be completely insignificant, providing further support for the logit model.
5
The extreme-value distribution model may be derived as a discrete-time analogue to the Cox
Proportional Hazards Model (see Narendranathan and Stewart, 1993). Note however, that this model
does not itself imply proportionality in the hazards; indeed, proportionality is inconsistent with
discrete-time analysis where the hazard is bounded above by unity. The term ‘extreme value’ is
applied to this distribution because it specifies the hazard as negative double exponential:
λkt = 1 – exp{–exp[θ(t) + Xkβ + Yktγ]}.
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DETANG-DESSENDRE & MOLHO: MIGRATION AND EMPLOYMENT STATUS
111
Appendix 1; the findings proved very similar to those obtained with the logit
model. We chose to concentrate on the logit specification because it is perhaps
the most familiar and easiest to interpret. Differentiating Equation (1) we find
∂λ/∂x = βλ(1 – λ)
(2)
where x is an explanatory variable in Equation (1) and β is the associated
coefficient. Note in particular that a positive β means that an increase in the
value of x increases the hazard, thus implying a greater risk of migration and a
shorter likely residence duration (and vice versa for a negative coefficient).
Employment status is included in the Y covariates in our model. Changes
in employment status enter as time-varying covariates because by definition
they capture changes in individual circumstances over the residence spell. We
argued in the previous section that changes in employment status are likely to
be endogenous to the migration decision. Therefore, we instrumented this
variable in our main estimations using lagged values of employment status,
information on educational field, father’s dwelling, and siblings as our instruments (see Appendix 2 for details).
We argued above that housing moves are likely to differ from job moves. The
model is one of competing risks in the sense that there are two alternative exits
from a residence spell: local- or long-distance migration. Therefore we estimated
different models according to the distance of move. We estimated hazard functions for long moves (over 60 kilometers), treating short-distance movers (less
than 60 kilometers) and nonmovers as censored; and we estimated hazard
functions for short moves, treating long-distance movers and nonmovers as censored.6 This procedure amounts to conditional maximum-likelihood estimation of
a discrete-time competing-risks model, as discussed in Allison (1982).7 For
comparison purposes we also estimated models for all moves, and for shortdistance moves of less than 25 kilometers.
5.
RESULTS
Estimated logit equations for the hazard functions for migration are presented in Table 3. The sample consists of 22,223 cases because in this framework
each month of observation counts as a separate case for each of the 551
individuals. We interpret the results in terms of the migration hazard (the
probability that a move will occur in the current period, given that no move
occured up to that time).
6
Recall that moves within the same village are not counted.
The econometrics of competing-risk models in a discrete-time framework are not well
developed. Allison (1982) argues that the procedure adopted here is consistent but not fully efficient.
7
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112
© Blackwell Publishers 1999.
TABLE 3: Migration Hazards, The Logit Model
All Migration
†
Educational level :
0.81
Reference
–0.66***
0.58
1.04***
0.40
Reference
–0.43***
0.04
–0.98***
–0.29
0.53
1.22***
1.10***
Reference
–0.61**
–0.83***
Reference
0.17
–0.07
–0.06
–0.68**
–1.12***
–1.86***
–1.31***
–1.48***
–1.61***
(0.14)
Migration > 60 km
***
(0.26)
(0.27)
1.28
Reference
–0.57
1.35***
1.13***
–0.42
Reference
–0.21
–0.01
–0.99***
–0.34
1.80***
2.42***
1.41***
Reference
–1.67***
–1.88***
(0.29)
(0.30)
(0.30)
(0.30)
(0.36)
(0.44)
(0.38)
(0.42)
(0.47)
Reference
0.48
–0.13
0.20
–1.06**
–1.17**
–2.07***
–1.94***
–1.69***
–2.58***
(0.24)
(0.37)
(0.15)
(0.32)
(0.16)
(0.19)
(0.23)
(0.22)
(0.56)
(0.30)
(0.31)
(0.19)
Migration < 60 km
(0.50)
(0.43)
0.29
Reference
–0.69**
–0.43
0.90***
0.66*
Reference
–0.61***
0.02
–0.93***
–0.27
–0.59
0.37
0.70
Reference
–0.06
–0.04
(0.40)
(0.44)
(0.42)
(0.48)
(0.50)
(0.67)
(0.68)
(0.67)
(1.06)
Reference
–0.04
0.12
–0.07
–0.26
–0.98*
–1.52**
–0.76
–1.14**
–0.99*
(0.40)
(0.44)
(0.21)
(0.74)
(0.23)
(0.28)
(0.37)
(0.32)
(0.69)
(0.47)
(0.47)
(0.21)
Migration < 25 km
(0.29)
(0.33)
0.29
Reference
–0.25
–0.70
0.92***
0.53*
Reference
–0.68**
0.25
–0.93**
–0.20
–0.67
0.22
0.72
Reference
0.26
0.19
(0.42)
(0.41)
(0.43)
(0.40)
(0.52)
(0.60)
(0.48)
(0.55)
(0.56)
Reference
0.31
0.59
0.48
0.57
0.05
–1.01
–0.27
0.02
–0.04
(0.31)
(0.72)
(0.22)
(0.35)
(0.22)
(0.26)
(0.30)
(0.28)
(0.98)
(0.42)
(0.48)
(0.30)
(0.34)
(1.01)
(0.31)
(0.33)
(0.31)
(0.33)
(0.39)
(0.36)
(1.21)
(0.59)
(0.50)
(0.32)
(0.41)
(0.67)
(0.64)
(0.66)
(0.61)
(0.69)
(0.88)
(0.73)
(0.70)
(0.74)
JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999
Baccalaureat and above
Technical college
Low level
Distance from school more than 100 km
National Service done before t
Not single at t
Original Region :
Burgundy
Brittany
Alps
South
East
P(lost a job between t-3 and t) ††
P(found a job between t-3 and t) ††
P(stayed unemployed between t-3 and t ) ††
P(stayed employed between t-3 and t) ††
Found a permanent job in the last year †††
Found a temporary job in the last 6 months †††
Baseline Hazard
[1–3 months]
[4–6 months]
[7–9 months]
[10–12 months]
[13–18 months]
[19–24 months]
[25–30 months]
[31–36 months]
[37–42 months]
[43–48 months]
***
[49–60 months] (5th year)
[61–72 months] (6th year)
more than 72 months (after 6 years)
intercept
N
–2logL
***
–1.46
–1.21***
–2.51***
–4.48***
22223
2437.93
(0.37)
(0.37)
(0.44)
(0.28)
Migration > 60 km
***
–2.45
–2.18***
–2.98***
–5.71***
22223
1173.21
(0.79)
(0.79)
(0.79)
(0.43)
Migration < 60 km
*
–0.84
–0.61
–2.02**
–4.98***
22223
1550.91
(0.46)
(0.46)
(0.54)
(0.33)
Migration < 25 km
0.15
0.39
–0.82
–6.23***
22223
1026.74
(0.64)
(0.65)
(0.69)
(0.58)
Notes: Standard errors in brackets.
*** indicates significant at 1% level; ** indicates significant at 5% level; * indicates significant at 10 percent level –2logL : –2 times the value of
the maximized log likelihood function.
† Baccalaureat and above; technical college: diploma from technical college and drop out from general college without baccalaureat; low level:
drop out from technical college without diploma and before general college.
†† These variables have been instrumented.
††† Defined as 1 if the individual entered into a permanent (temporary) contract in the year (6 months) up to t-3.
DETANG-DESSENDRE & MOLHO: NEED SHORTER RUNNING HEAD
© Blackwell Publishers 1999.
All Migration
113
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JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999
The Control Variables
Ceteris paribus the estimated equations show that the hazard for longdistance migration is significantly higher for highly educated workers (baccalaureat or higher) than for the less-educated. This result may reflect a greater
willingness to move among these workers; it also fits in with the notion that
young workers from rural backgrounds must move if their aim is a high-level
career. The results show that the hazard increases significantly for individuals
who were educated in a location over 100 kilometers away from their district of
origin, all else being equal. This variable is intended to capture an artefact of
the data whereby people who studied away from home may choose to stay at
their place of study and hence count as migrants on entry to the labor market.
It may also reflect “broader horizons” for such workers. Interestingly, these
education variables are not significant for local moves of less than 25 kilometers
and are only marginally so for moves of less than 60 kilometers.
At the time of the survey, young men in France were required to do National
Service (unless they had some form of exemption—true for 21 percent of our
sample). This episode of a young man’s career (and any associated migration) is
clearly a special circumstance, and hence such periods have been dropped in our
data. However, it is still arguable that the periods before and after National
Service may be different with respect to migration. The results show that ceteris
paribus the migration hazard after National Service is significantly higher than
before. This is consistent with the idea that young men delay important decisions
of this nature until after National Service is complete.
Some regional effects are also in evidence, with low migration hazards most
evident in the south.8 Being single as opposed to married or living together
appears to exert little effect on migration, even over short distances. This result
appears surprising at first because one would expect housing moves to be closely
related to family structure. However, it is consistent with a common perception
in these rural areas that, upon marrying it is usually the woman who moves to
join the man.
The Baseline Hazard
No obvious time pattern for local moves is detectable in the baseline hazard
(at least for moves less than 25 kilometers), indicating an absence of durationdependence effects. However, for long-distance moves there is a clear and
pronounced tendency for the hazard to fall over time (after controlling for other
observed influences on migration), consistent with the presence of cumulative
inertia. These results imply that attachments to the immediate residence do not
depend on time, but ties to the general area do.9 It appears that once people
8
The two districts in Burgundy were amalgamated into one, as were those in Brittany, leaving
five regions from the original seven districts.
9
The declining baseline long-distance migration hazard may also be interpreted in other ways,
e.g., in terms of aging of the worker, or falling job offer rates over time.
© Blackwell Publishers 1999.
DETANG-DESSENDRE & MOLHO: MIGRATION AND EMPLOYMENT STATUS
115
organize their career and private life in a geographical space, they may change
their specific location within that space but find it increasingly difficult to move
away altogether. A trend-fitting exercise yielded the following results for the
baseline hazard for long-distance moves
θ$ = 0.85− 0.419 t
(2.70)
b –7.73g
with
R2 = 0.844; F = 59.7; n = 13
t-statistics in brackets (time measured at mid-points). The predicted trend falls
at a decreasing rate.
Labor Market and Employment-Status Effects
The first and most obvious point to note is that labor market employmentstatus effects are only significant for long-distance moves, in keeping with the
view that long-distance migration is more likely to be job-related. Lagged
experience of finding either permanent or temporary employment significantly
reduces the hazard for long-distance migration; an individual who finds a job
may move at the time, but afterwards is unlikely to move.10 Note that in this
case finding employment includes people who (for example) switched from
temporary to permanent work.
Finally we turn to the main focus of our study: the current employmentstatus transition variables. These variables capture contemporaneous effects of
employment transition on migration and have been instrumented in the manner
described in Appendix 1. Staying in employment is treated as the reference
category. The continuously unemployed are found to be significantly more likely
to move long distance, all else held constant.11 We discuss this result more fully
below. Loss of employment also attracts significant positive coefficients, perhaps
reflecting forced migration. However, the sample of movers in this category is
small so this result should be interpreted tentatively.
More interestingly, the results suggest that finding employment significantly raises the migration hazard compared to that for the continuously
employed and the continuously unemployed. The former effect is reflected in the
t-statistic on this coefficient which is larger than 5; evidence of the latter effect may
be seen in the fact that the estimated coefficient of 2.42 for job-finders is more than
2 standard errors above that of 1.41 for the unemployed. This result suggests a
powerful long-distance migration response results from finding employment.
10
The variable for permanent work is defined for the year up to t-3; for temporary work it is
for the 6 months up to t-3. We found that finding temporary work prior to this had no significant
effect, while finding permanent work had the same effect in the first six months as in the last.
11
Lagged unemployment-experience variables had insignificant coefficients and have been
deleted.
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JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999
The data in Table 2 shows 45 long-distance migrants who found work, and
71 who stayed in work. However, the model predicts a higher migration hazard
among the former group than the latter. These findings partly reflect the larger
sample of persons who are at risk of migration among the latter group, the fact
that the model controls for other effects to give the impact of employment-status
shifts for an otherwise identical individual, and the fact that current employment status is treated as endogenous.
Table 4 evaluates the predicted hazard for the explanatory variables at the
sample means. An individual who was continuously employed but conformed to
the sample average in all other respects is predicted to have a very low
long-distance migration hazard of 0.08 percent; alternatively if the individual
were continuously unemployed this hazard would increase to 0.3 percent; finally,
if he were unemployed then found work the hazard would rise further to 0.9
percent. Table 4 also gives some illustrative calculations for other case-types.
These show important interactions in the effects of the covariates. For example,
gaining employment raises the long-distance migration hazard for a highly
educated individual in the first year of his residence spell from 3.9 to 12.3
percent, whereas for an average individual the effect is much smaller.
Speculative and Contracted Migration
Our model allows us to estimate the probability of an unemployed worker
undertaking long-distance contracted migration as compared to long-distance
speculative migration. The former refers to changes of address where the
individual has work lined up at the destination, and so is observed in employment immediately on arrival at their new location at time t; the latter refers to
the opposite case. Consider an unemployed worker with characteristics (education, location, length of residence, etc.) given by the vector Z. The probability of
the worker finding employment found using our intrumentation of employment
TABLE 4: Predicted Migration Hazards (Percentage)
All migration
Average individual (AI)
Average individual (AI) continuously employed
AI continuously employed and in first year of spell
AI, gains employment
AI, in first year of spell
AI, highly educated
AI, gains employment and in first year of spell
AI, gains employment, in first year of spell and highly educated
AI continuously unemployed
0.6
0.4
1.4
1.4
1.79
1.31
4.5
9.9
1.3
Migration > 60 km
0.12
0.08
0.36
0.86
0.56
0.42
3.87
12.28
0.32
Notes: Figures based on the logit models in Tables 3. Predictions calculated by setting all
explanatory variables to their sample mean values (including the baseline hazard). The hazard for
those continuously employed was then calculated by setting P (stay in employment) = 1, P (stay
unemployed) = 0, etc. Similar calculations were done for those finding employment, etc.
© Blackwell Publishers 1999.
DETANG-DESSENDRE & MOLHO: MIGRATION AND EMPLOYMENT STATUS
117
status (described in Appendix 1) is given as p(Z). We evaluate the long-distance
migration hazard given that he finds work as λ(Zp(Z)=1), and calculate the
probability of contracted migration as λ(Zp(Z)=1) × p(Z). Similarly, the probability of the same unemployed worker undertaking speculative migration is
calculated as λ(Zp(Z)=0) × (1–p(Z)).
Table 5 presents the results of such an analysis for various types of workers,
fixing the values of Z to their sample average for the unemployed unless
otherwise stated. The probability of contracted migration is higher than that for
speculative migration for all groups, despite the low probability of finding work.
This qualitative result accords with prior expectations. The only cases where
the difference is small tend to be those where the migration probability is very
low; these cases are likely to be heavily influenced by individual idiosyncracies
in the data. The ratio of contracted to speculative migration tends to be higher
for low—as opposed to high—educated workers, perhaps reflecting the greater
chance of the latter group to find work after moving. Overall, the prevalence of
speculative migration appears somewhat higher in Table 5 than we had originally anticipated in view of the small number of continuously unemployed
movers in the raw data (see Table 2). However, this result should be treated as
tentative because the numbers of continuously unemployed movers are small.
Employment Status: Exogenous or Endogenous?
In Table 6 we report the results of fitting the long-distance migration hazard
function using actual values for employment-status transitions, as opposed to
the instrumented values.12 The “continuously unemployed effect” is negative in
this model, and the difference in coefficients between the unemployed who gain
work and those who do not is around 3.9. This difference falls to around 1 after
instrumenting for the employment-transition variables. This finding is consistent with the view that some unemployed individuals move because they find
work whereas others find work because they are prepared to move. The estimated impact of finding work (compared to continuous unemployment) on
migration is therefore weaker once we have purged the data of this latter effect.
These results may be compared to the findings of Van Dijk et al. (1988). They
used a binary logit framework applied to pure cross-section data on one-year
migration probabilities in the Netherlands, and five-year migration probabilities
in the northeastern States of the U.S. They treated employment-status shifts as
exogenous. For the Netherlands they found that the unemployed who found jobs
were significantly more likely to migrate than the employed, but that ceteris
paribus, the continuously unemployed were not. In contrast, for the U.S. they
found that all (initially) unemployed individuals were more likely to move than
the employed, regardless of whether they found work. They attributed the
12
We found the estimated hazard coefficients of the model using instrumented employmentstatus variables to be more stable with respect to inclusion and exclusion of variables than the results
using the raw variables. This finding seems to support our use of an instrumented variables approach.
© Blackwell Publishers 1999.
118
© Blackwell Publishers 1999.
TABLE 5: Predicted Contracted and Speculative Long-Distance Migration (Percentage)
Category (# in sample)
prob
(finding job)
hazard
(migfind job)
hazard
(migstay unemp)
prob
(contracted migration)
prob
(speculative migration)
36.5
32.9
6.9
1.2
2.6
0.4
2.5
0.4
1.6
0.2
Well-educated + first year (745)
Well-educated + not in first year (287)
Low-educated + first year (2197)
Low-educated + not in first year (2079)
44.6
19.3
50.1
18.8
9.4
3.0
2.4
0.6
3.6
1.0
0.9
0.2
4.2
0.6
1.2
0.1
2.0
0.8
0.4
0.1
Well-educated before NS (628)
Well-educated after NS (404)
Low-educated before NS (1815)
Low-educated after NS (2461)
30.4
46.9
35.1
31.4
6.1
8.2
1.2
1.2
2.3
3.1
0.4
0.4
1.8
3.8
0.4
0.4
1.6
1.6
0.2
0.3
Notes: Prob (find job) and migration hazard respectively constructed from the model described in Appendix 1 and from Table 3 (long-migration
hazard), setting all variables to their sample average for the unemployed unless otherwise stated. Prob (contracted migration) is given as column 1
multiplied by column 2; prob(speculative migration) given as (1-column 1) multiplied by column 3.
JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999
Well-educated people (1032)
Low-educated people (4276)
DETANG-DESSENDRE & MOLHO: MIGRATION AND EMPLOYMENT STATUS
119
TABLE 6: Employment Status Effects in the Long Migration
Hazard (Percentage)
Observed variables
Gain employment
Stay unemployed
Lose employment
2.68
–1.21
–3.47
(0.28)
(0.46)
(0.72)
Instrumented variables
2.42
1.41
1.80
(0.47)
(0.47)
(0.69)
Notes: The table reports coefficients (standard errors in brackets) on the employment transition
variables in the long migration hazard function, based on observed employment transitions compared with the instrumented variables. Results for control variables not reported.
difference between the two countries to higher levels of speculative migration
in the U.S. Our results suggest that their method may underestimate the
prevalence of speculative migration because the continuously unemployed appear more likely to migrate once we have accounted for the simultaneity between
migration and employment-status shifts.
Alternative Distribution Functions
Finally, we compared estimates of the long-distance migration hazard
function based on the logit model with those derived using the probit and the
extreme value (complementary log-log) models. Full results are reported in
Appendix 1. In terms of signs and significance of estimated coefficients the
models are very similar. The logit and extreme value models appear to fit the
data somewhat better than the probit, judging by the maximized log likelihood
values. Table 7 reports predictions of the long-distance migration hazard evaluated at the sample means, using the different models. The predicted effects of
the various employment-status transitions are very similar in the extreme value
and logit models. This finding reflects the fact that the migration hazards are
low, and hence lie in the tail of the distribution where the logit and extreme value
models are quite similar. The probit model tends to produce rather larger effects
for employment gainers compared to other transition groups, though not dramatically so.
TABLE 7: Predicted Migration Hazards with Alternative
Distribution Functions
Average Individual (AI)
AI continuously employed
AI gains employment
AI continuously unemployed
Notes: See Table 4.
© Blackwell Publishers 1999.
Logit
Long-migration
Probit
Extreme Value
0.12
0.08
0.86
0.32
0.18
0.1
1.19
0.57
0.12
0.08
0.86
0.32
120
6.
JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999
CONCLUSIONS
In this paper we analyze the effects of employment-status transitions on
migration. The issue was considered from a search-theoretic perspective. A
discrete-time hazard function model was fitted to duration data on a sample of
young men completing school in rural locations in France. This econometric
approach was used partly because it handles durations as discrete, which is the
form in which such data are invariably collected, and partly because we found
that the more traditional proportional hazard specification was inappropriate
for our data. The discrete-time model allows a flexible baseline hazard to be
explicitly estimated. Employment-status transitions were treated as endogenous
in the estimated model. We also distinguished between short- and long-distance
moves, in a competing-risks framework.
Our main results may be summarized as follows:
(a) Labor market variables were found to significantly affect long-distance
migration but not short-distance moves. This result is consistent with the view
that long-distance moves are primarily job-related whereas many local moves
are primarily housing-related.
(b) We found that unemployed individuals who gain employment are significantly more likely to move long-distances than those who are continuously
employed or unemployed, all else held constant.
(c) Unemployed individuals are more likely to undertake contracted as opposed
to speculative long-distance migration, this is especially so among the lesseducated.
(d) The baseline hazard for local moves was found to display no apparent
duration-dependence effects, but for long-distance moves there was significant
evidence of a decreasing hazard over time, consistent with cumulative inertia.
Rural populations in France are historically in decline, although recent
evidence suggests this trend may be faltering (Cavailhes et al., 1994). Our results
suggest that the out-migration of young men from such areas is at least in part
motivated by labor market considerations. In future research it would be
interesting to investigate the permanence of such outward moves and, in
particular, whether there is evidence of return migration in later life.
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© Blackwell Publishers 1999.
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JOURNAL OF REGIONAL SCIENCE, VOL. 39, NO. 1, 1999
APPENDIX 1: Results for Long-Distance Migration Based on Alternative
Discrete-Time Hazard Models
Logit
Educational level †
baccalaureat and above
technical college
low level
Distance from school more than
100 km
National Service done before t
Not single at t
Original region
Burgundy
Brittany
Alps
South
East
P(lost a job between t-3 and t) ††
P(found a job between t-3 and t) ††
P(stayed unemployed between
t-3 and t ) ††
P(stayed employed between
t-3 and t) ††
Found a permanent job in the
last year †††
Found a temporary job in the
last 6 months †††
Baseline Hazard
[1–3 months]
[4–6 months]
[7–9 months]
[10–12 months]
[13–18 months]
[19–24 months]
[25–30 months]
[31–36 months]
[37–42 months]
[43–48 months]
[49–60 months] (5th year)
[61–72 months] (6th year)
more than 72 months (after 6 years)
intercept
N
–2logL
Notes: see Table 3.
© Blackwell Publishers 1999.
Probit
Extreme Value
1.28*** (0.19)
Reference
–0.57
(0.40)
0.49*** (0.08)
Reference
–0.18
(0.14)
1.26*** (0.19)
Reference
–0.57
(0.40)
1.35*** (0.44)
1.13*** (0.21)
–0.42
(0.74)
0.48***
0.46***
–0.19
(0.19)
(0.09)
(0.28)
1.35***
1.10***
–0.43
(0.42)
(0.21)
(0.73)
Reference
–0.21
(0.23)
–0.01
(0.28)
–0.99*** (0.37)
–0.34
(0.32)
1.80*** (0.69)
2.42*** (0.47)
Reference
–0.05
0.04
–0.33***
–0.11
0.62***
0.82***
(0.09)
(0.11)
(0.13)
(0.13)
(0.25)
(0.18)
Reference
–0.22
–0.04
–0.98***
–0.34
1.80***
2.43***
(0.23)
(0.27)
(0.37)
(0.31)
(0.69)
(0.46)
1.41*** (0.47)
0.56***
(0.18)
1.38***
(0.47)
Reference
Reference
Reference
–1.67*** (0.50)
–0.54***
(0.17)
–1.69***
(0.49)
–1.88*** (0.43)
–0.63***
(0.16)
–1.89***
(0.43)
Reference
0.48
(0.40)
–0.13
(0.44)
0.20
(0.42)
–1.06** (0.48)
–1.17** (0.50)
–2.07*** (0.67)
–1.94*** (0.68)
–1.69*** (0.67)
–2.58*** (1.06)
–2.45*** (0.79)
–2.18*** (0.79)
–2.98*** (0.79)
–5.71*** (0.43)
22223
1173.21
Reference
0.16
–0.09
0.03
–0.43**
–0.46**
–0.76***
–0.78***
–0.72***
–0.95***
–0.90***
–0.80***
–1.05***
–2.70***
22223
1185.18
(0.16)
(0.17)
(0.17)
(0.18)
(0.20)
(0.23)
(0.24)
(0.25)
(0.34)
(0.26)
(0.26)
(0.25)
(0.16)
Reference
0.49
–0.13
0.21
–1.03**
–1.14***
–2.05***
–1.91***
–1.65***
–2.54***
–2.43***
–2.15***
–2.95***
–5.71***
22223
1171.89
(0.40)
(0.44)
(0.41)
(0.47)
(0.49)
(0.66)
(0.67)
(0.67)
(1.06)
(0.78)
(0.79)
(0.79)
(0.42)
DETANG-DESSENDRE & MOLHO: MIGRATION AND EMPLOYMENT STATUS
123
APPENDIX 2: Instruments for Employment Status
Our aim here was to estimate a reduced-form model with which to predict
the current probability of employment. We modeled the probability of employment in the current spell pt using logit equations
pt = prob(jobt = 1) = logit(Zt ; jobt-m)
where jobt takes the value 0 if the individual is currently unemployed, and 1 if
he is employed; Z includes independent variables from the main analysis of
migration, plus other variables such as educational field (industrial, service,
agricultural, scientific or general), the employment of the father, parental
housing type, the number of siblings, and year dummies. We treat the lagged
employment status variables, jobt-m, as pre-determined. We estimated equations
of this form separately by employment status in t-1, and by sex and educational
qualification. We also treated individuals who had just left school separately
because lagged employment values were unavailable in these cases. One may
think of these as discrete-time duration models, where 1-pt is the hazard of
leaving employment for an employed individual (jobt-3 = 1), and pt is the hazard
for someone originally unemployed (jobt-3 = 0). Accordingly, we also included a
baseline hazard in Z to capture duration dependence effects in employment and
unemployment. The correlation coefficient between the actual jobt and predicted
probabilities pt* generated by this exercise had a value of 0.878.
We predicted variables for changes in employment status over a 3 month
period using pt* as
|RS0 if employed in t − 3
|Tb1 − p *g if unemployed in t − 3
0 if employed in t − 3
p(gain employment) = RS
T p * if unemployed in t − 3
R|0 if unemployed in t − 3
p(lose employment) = S
|Tb1 − p *g if employed in t − 3
p(stay unemployed)t =
t
t
t
t
t
again treating employment status in t-3 as predetermined. These predicted
transition variables correlated with the actual transitions as follows: stay
unemployed r = 0.84; find job r = 0.60; lose job r = 0.5. The predicted transition
variables were used in the explanation of residence durations, treating “stay in
employment” as the reference category. For individuals who had just left school
we initially included variables for the transitions “school to employment” and
“school to unemployment” in the migration equations; likelihood-ratio tests
suggested that we could treat the school period as unemployment, and hence for
the remainder of the analysis we adopted that treatment in the construction of
the above variables.
© Blackwell Publishers 1999.