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Estimation of French Internal Migration
in the Period 1990-1999 and Comparison
with Earlier Periods
Daniel COURGEAU and Eva LELIEVRE
Beginning in 1962, each French Census has included a question that asks
for a person’s place of residence on 1 January of the year of the last Census1.
However, if we want to measure temporal trends in internal migration in France
the information provided by this question cannot be used directly and in fact
raises two problems that are hard to overcome.
First, even if the period over which migrant numbers are measured is
constant (for example an interval of five years, as in Australia, Canada, Great
Britain, Japan and the United States, around the 1970s (Long and Boertlein,
1976)), an estimate has to be given of the annual average migration rate. Yet
many studies have shown that because of repeat and return migration by the
same individual, the number of migrants such a question measures is not even
approximately equal to five times the annual number (Courgeau, 1973; Long,
1988; Res, 1977). Without additional information, therefore, this migrant count
measured over a period of five years cannot be used to estimate the
corresponding annual number. The simple solution to this problem is to ask the
same question for a period of one year, as has been done in Australia, Great
Britain, Japan and the United States, in the same Censuses (Long and Boertlein,
1976). It then becomes clear that the rate over five years is not five times higher
than the annual rate. In the United States, for instance, for an annual rate of
19.2% the corresponding five-year rate is 47.0% as compared with an expected
rate of 96.0%. Repeat and return migration is responsible for this two-fold
difference.
Second, unlike other countries where this question is asked over a period
of five years prior to the Census, allowing measurement over a constant period
between successive Censuses, estimation of annual rates is made even harder in
France by the large variation in the periodicity of the Census (ranging between
six and nine years).
These two problems must be addressed by analysing the successive moves made
by individuals, based on finely detailed survey data, and by modelling this
distribution over time using a small number of parameters.
1 One of the reasons advanced for this practice is the possibility, by comparing these data with the numbers
measured in a previous census, of inferring an estimate of the number of emigrants going abroad during the
inter-censal period. However, this measurement is subject to so large a measurement error that it has hardly ever
been possible to use it for this purpose (Baccaïni, 1999).
1. Short presentation of the model
and the evaluation of its parameters
We demonstrated (Courgeau, 1973) that this was possible and that the
model applied to migration in such diverse contexts as the United States
(Morrisson, 1970), Sweden (Wendel, 1953) and France (for which the
estimation was made using an INED demographic situation survey, presented in
Girard and Zucker, 1968). It should be noted, however, that although the model
for these three countries is the same, the parameter estimates differ greatly. For
any one country, these parameters will also vary over time. In what follows we
present a new estimation of their value based on the Labour Force Surveys from
1991 to 1999 and the Young People and Careers survey of 1997 (L’Hospital,
2001) and we apply these new parameters to data from the 1999 Census.
Longitudinal and period analysis of data for France and for the United
States and Sweden lead to the following conclusions:
1) The probability for a person who has migrated once of migrating again
in the future, K, is largely independent of the previous migration’s rank order
and of the birth cohort but is affected by the geographical subdivision on which
moves are measured.
2) For the population that will migrate again in the future, the annual
hazard rate for this migration is independent of the duration of residence
between each migration, the rank order of this migration and the geographical
subdivision used. The instantaneous hazard rate of a new migration, k, has of
course the same properties.
3) Return migration to an area of origin is proportional, in the ratio l, to
moves of a rank higher than one, made throughout the period under study. It
follows that the probability for a person who has migrated of making a return
migration is Kl.
If we assume also that the instantaneous hazard rate for migration of any
rank, p, is virtually constant over the inter-censal period under consideration and
that the total population, P, varies little (these conditions simplify the
formulation of the solution but can be removed without difficulty), then we show
that the number of migrants during a given time period t can be written:
[1]
We have merely to estimate using existing data a probability of migration
corrected for return migration2 K (1 + l) and the instantaneous hazard rate for a
2 For changes of address, the parameter corresponds closely to pure moving, since moves back
to a previous address are negligible.
new migration k, in order to get from a number of migrants M (t) to a number of
migrations Ppi made in the same period, and hence to an instantaneous hazard
rate of migration.
To allow for possible variations over time, different surveys have been
used:
- for the period 1954-1975, evaluation of the parameters is based on use of
a retrospective survey conducted by INED in 1968 on a total of 2,464 persons
(Courgeau, 1973);
- for the period 1975-1990, the values are estimated from annual Labour
Force Surveys for the period 1976-1988. The numbers involved this time are
large but the level of non-response is high in some regions (Courgeau, 1986;
INED, 1989; Baccaïni et al., 1993);
- last, a new estimation of their value, based on Labour Force Surveys
between 1991 and 1999, in which the non-response rate is greatly improved, and
on the Young People and Careers survey of 1997 (L’Hospital, 2001), has been
applied to the 1999 Census data.
L’Hospital demonstrates that the assumptions of this model are always
well verified, but that the parameter values have varied in France between the
1970s and the 1990s. Let us look first at the situation for moving corrected for
returns (Table 1).
Table 1: Estimates of the probability of moving corrected for return migration, France over
the last forty years
Changes of: Parameter K(1+l)
1954-75 1975-90 1990-99
Region
Department
Commune
Residence
0,77
0,80
0,76
0,78
0,59
0,70
0,79
-
0,61
0,68
0,78
0,91
Sources: Courgeau (1973) for the 1970s, Courgeau (1995) for the 1980s and L’Hospital (2001) for the 1990s
detailed in the text. The Labour Force Survey data on changes of place of residence were not available for the
1980s.
Table 1 makes clear that in the 1970s this parameter was largely
independent of the spatial subdivision used for making the analysis3. For the two
subsequent periods, it increases quite sharply from the largest geographical
subdivision, the region, to the lowest level of subdivision, the individual place of
3 The estimates for the 1970s are not significantly different, the variations are random and caused by the small
numbers used for the estimation.
residence, though for any given subdivision it remains constant between
consecutive periods. The small numbers observed for the 1970s (2,464 persons
having reported their moves) may explain part of these changes. This pattern of
increase tells us that the longer the distance of the migration, the lower the
probability of making a new migration: in other words, if you have already
moved a long way you will move less.
The instantaneous hazard rate for a new migration, k, was estimated at
0.18 for the periods before the 1990s. It takes a much higher value, estimated at
0.26, for the 1990s. A more linear variation in this parameter over time appears
likely, but would involve a more complex model, which we will not develop
here.
2. Estimation and comparison of total and age-specific rates of mobility
Using these parameter values and formula [1], it is possible to get from
the numbers of migrants measured over different inter-censal periods, to
instantaneous migration hazard rates that are assumed to be constant over each
inter-censal period but variable from one period to the next.
Table 2 gives the results for the various inter-censal periods since 1954
(Courgeau, 1978, 1990; Baccaïni et al., 1993).
Table 2: Estimates of the instantaneous hazard rates of migration
(per 1,000) for the various inter-censal periods
Period
Changes of:
Region Department Commune Residence
1954-1962 13,3 20,0* 48,7 **
1962-1968 15,1 25,1 53,4 **
1968-1975 17,9 29,0 60,5 97,7
1975-1982 16,5 26,5 58,8 94,7
1982-1990 16,2 25,8 55,6 85,6
1990-1999 16,8 28,7 67,8 122,0
* In 1954-1962 the division of France into departments differed from that in the subsequent periods
** The 1962 and 1968 Censuses did not include a change of place of residence question
This table shows us that the decline in mobility observed since the period
1975-1982, stopped and even went strongly into reverse in the last inter-censal
period. This increase, quite small for inter-regional moves, was largest for
changes of residence. This change is confirmed by other measurements of
residential mobility. Courgeau et al. (1999) used data from the French electricity
utility company (EDF) to show that this measurement of residential mobility
registered a strong recovery from the end of 1986, a trend that continued
throughout the 1990s. The data from the Labour Force Survey also show a
recovery in mobility from the end of 1985 (INED, 1989; Courgeau, 1995;
L’Hospital, 2001).
This result contradicts those obtained using the parameter estimates for
the period of the 1980s with no updating, which showed a continuous decline in
mobility (Baccaïni, 2001). This estimation gave values for the rates (per 1,000)
of 15.9 for changing region, 25.2 for changing department, 53.2 for changing
commune, and 80.7 for changing dwelling. As already noted, however, the
estimated parameter k of the model varied strongly at the end of the 1980s.
Thus it is important to update the estimation of the model’s parameters for
each period in order to ensure the validity of the migration rates, since these can
vary widely from one period to the next, as happened for parameter k between
the 1980s and the 1990s.
With this method we can also calculate migration rates by age and by sex
similar to those provided by Baccaïni (2001), under the assumption that the
parameters change little with age. Although this assumption is questionable, it
provides us here with an approximate estimate of these rates, presented in Figure
1. It should be noted that the ages shown in this diagram are those reached in
1999 but that the migration takes place on average 4 to 5 years earlier. The
discussion that follows considers ages at migration.
Figure 1: Annual rates of migration by age and sex, for changes of residence, commune,
department and region (period 1990-1999)
The shape of these curves changes little compared with the earlier
periods4: up to age 14, the migration rates are falling and parallel those for adults
aged 25-40, corresponding to childhood moves induced by those of their
parents; between ages 15 and 25, they rise sharply as a result of the mobility
associated with labour market entry and with union formation, which occurs
earlier for women; between ages 25 and 56, stabilization in the labour market
and the education of children are responsible for a sharp decrease in mobility;
between 56 and 65, it recovers slightly due to retirement, followed by a decrease
up to around 76; lastly, renewed mobility that increases with age appears after
age 77 and corresponds to moving into institutional accommodation or to
returning to live with children for elderly parents. As this diagram shows, the
variations by age are large, and are linked to the main stages of the individual
life course. This simplified schema is of course much more complex in reality
and can only be analysed using data on event histories (Courgeau and Lelièvre,
1989).
4 For the comparison with the period 1975-1982, see Courgeau (1990).
3. Discussion and conclusions
Under certain conditions, the modelling approach proposed here allows
data on migrants to be used to obtain results for migrations, which are
comparable across successive inter-censal periods of possibly varying length,
and to observe the evolution of mobility in France over a period of forty-five
years. It can also be used to derive estimates for regional in-migration and out-
migration flows in France (Courgeau, 1986, 1988). However, these results are
still only approximate and the estimate obtained is not as precise as would be
obtained if the Census included a question on address one year ago. For as well
as the precision of the model used, we also need to consider the precision of the
estimators of the model’s parameters, which depends on the sources employed.
This precision has certainly improved markedly over time, first through use of
the Labour Force Surveys that cover some 60,000 households and whose non-
response rate has fallen considerably in recent years (Courgeau, 1995;
L’Hospital, 2001), particularly for the Ile-de-France region. Next, the Young
People and Careers survey conducted by INSEE in 1997 on over 20,000
individuals aged 19-45, provides longitudinal information on the migration
history of a very large population. The INED surveys used initially for
estimating the parameters covered at most 3,000 individuals.
This issue will take a different form in future because of the introduction
of the redesigned continuous French Census, in which a sample of the
population is interviewed each year and asked a question on address five years
ago. This time the period is the same from one year to the next and will thus
allow simple comparisons, by calculation of the five-year rates. On the other
hand, however, the first problem, which arose over estimating annual rates, is
not solved by this new mode of data collection: measurement of a five-year rate
still does not allow us to estimate an annual rate. The “migrants-migrations”
model will thus still have to be used for this estimation.
Acknowledgments. We would like to thank Laurent Toulemon and an
anonymous reviewer for their valuable comments on an earlier draft of this
article.
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