SEASONAL AND PERMANENT MIGRATION IN BURKINA FASO
Adama KONSEIGA
ABSTRACT
This study examines the determinants of households’ decision to migrate from Burkina Faso to
Cote d’Ivoire, a leading country in the Regional West African Union. The paper, unlike most of
the previous works, uses a dataset constituted from a two-sided survey conducted in 2002 at the
origin and the host country. Therefore, it enables to study two groups of migrants: the seasonal
and the permanent migrant households. As the migrants are not randomly selected from the
population, Heckman procedures are used to estimate consistent migration incomes. Then the
structural model of migration decision is analyzed after correction for complex sampling
schemes. The empirical results found significant effects of income differential on migration
decision only for the case of permanent migration whereas other motivations explain the
likelihood of being seasonal migrant. Among these factors are the need to diversify income
sources, education, ethnic network, population density, social capital, use of modern agricultural
inputs, natural resources endowments and their management. In the Sahel region of origin subject
to unstable climate, households who stay home diversify their incomes sources through off- farm
activities while others receive remittances, the later being a better risk pooling strategy.
JEL Classifications: C25, F15, F22, R23, J31
Keywords: International migration, Todaro model, New Economics of Migration, sample selection,
income functions.
Contact information:
akonseig@uni-bonn.de
Center for Development Research (ZEF)
1
1. Introduction
Restriction of the movement of persons is increasingly gaining recognition as a severe
impediment to trade, particularly in services. Removal of these restrictions could result in
important benefits to the world as a whole and in particular to the suppliers of this labor. The
migrant workers produce, earn wages, pay taxes and consume in the host country, as well as send
remittances back to their home countries. Even though these benefits are dampened with the
brain-drain phenomenon, Barro et al. (1999) view the later as extreme case that is likely to offset
the benefits in conditions of crumbling empires.
Based on a household survey conducted in Summer 2002, this study aims to shed light on the
migration phenomenon and to allow a better understanding of migration in Burkina Faso as it
plays a central role in its decision to participate profitably in the regional union of UEMOA1.
Burkina Faso is the largest supplier of migration labor to Cote d’Ivoire and it is worth assessing
the welfare and policy implications of theories of migration concerning its participation to the
common union.
The purpose is to develop a model that deals with the so far ignored question of the benefits of
further regional liberalization of the movement of labor and re-examines the uncertain economic
impact of the Union on landlocked countries (Decaluwé, B. et al., 2000).
The migration model introduced by Todaro (1969) and Harris and Todaro (1970) has been for
long time the dominant formal theory of migration in developing countries. As suggested by
Todaro, income gap (or expected income) constitutes the principal aspect of migration
motivation. The larger is this gap, the stronger is the migration propensity.
Burkina Faso has shown a long history of migration with Cote d’Ivoire that started even before
the formal constitution of the two countries, during French colonization. However, considered as
a labor pool for surrounding countries economic development, the erstwhile forced migration
became the outcome of free decision of Burkinabe households after independence.
This labor mobility has been also reinforced by the constitution of regional blocks. The
differences of natural resources endowments and the capacity of Cote d’Ivoire to attract foreign
direct investment brought several decades of rapid growth and consolidated large income gaps
between the two neighboring countries. The sole factor of income gaps gives enough incentives
1
West African Economic and Monetary Union of 8 francophone countries.
2
to farmers to leave their hard dry land agricultural conditions in Burkina Faso for available cocoa
and coffee farms in Cote d’Ivoire.
Using our recent survey data collected in both countries, we try to test the prediction of the
Todaro model, which suggests that migration decision depends on income gaps. However, with
the New Economics of Migration, migration is no more solely an individual decision but rather a
decision made at household level. Other than income gaps, factors such as individual and family
characteristics, labor and capital market conditions in the destination and home countries
influence migration decisions, too. The empirical work conducted in this paper firstly analyzes
the determinants of migrants' income at home and in the host country. In a second step, we study
the impact of income gaps on migration decision. There are two important types of migration to
Cote d’Ivoire that are considered: the seasonal migration and the permanent one.
The rest of the paper is organized as follows. Section 2 presents the econometric model used.
Then, the data and the estimation methods are described and related methodological problems
highlighted. The econometric results follow in the fourth section. We close the study by drawing
the main conclusions and subsequent research perspectives.
2. Econometric methodology
Analyzing migrant households behavior from a population leads to incidental truncation
problem because migrants are a restricted nonrandom part of an entire population2 . With such a
distortion, results from a standard OLS are simply biased.
The regression model that includes the above selection issue is the migration model à la
Nakosteen and Zimmer (1980). The simultaneous system writes:
Net benefit of moving:
Vi * = α ' Z i + γ ' X i + ε i
Income of migrant households:
(1)
log w fi = β ' f . X fi + µ fi
and income of nonmigrant households:
( 2)
log w hi = β ' h . X hi + µ hi
( 3)
2
34% of the seasonal migration sample is migrants households, whose migration project appears beneficial to them
according to the theory. Migrants are not randomly and uniformly distributed in the population so that there is a
selectivity phenomenon of migration. The same apply to the households that supply migrants labor, therefore these
households may possess unobserved characteristics that are generally positively related to the income resulting in a
sample selection bias.
3
To estimate the simultaneous migratio n income and decision equations, it is assumed that
log w f and V * have a bivariate normal distribution with correlation ρ .
A preliminary analysis of the last two equations is necessary in the process of
understanding the structural model of migration decision based of the net benefit of moving.
However, an analysis of income in either sub-sample must account first for the structural
differences of both markets and for the incidental truncation of the mover’s (stayer’s) income on
the sign of the net benefit.
To face estimation problems of model with sample selection, a double Heckman two-step
procedure is used. The Heckman regression model can be written for the selected sample as:
- Selection model
Pi* = α ' Z i + γ ' X i + ε ' i
(1)' where
P * is the probability of the variable indicator of the sign of the selection criteria, that is the net
benefit from migration.
Z i and X i represent the independent variables of the selection equation identification and those
of the income equation respectively.
- Income model
log wi = β ' . X i + β λ λi + ν i
( 2 − 3)'
where the following relationship exists between the coefficient of the inverse mills ratio λ and
the model statistics:
β λ = ρσ µ . The inverse mills ratio itself evaluates as the ratio of the
probability and cumulative density functions from the selection equation. Heckman (1979) argues
that this function is a monotone decreasing function of the probability that an observation is
selected into the analyzed sample.
The Heckman’s two-step estimation procedure applies to each of the selected group taking into
account the fact that migrants and nonmigrants face distinct labor market structure respectively in
Cote d’Ivoire and in Burkina Faso. For observations in each group, the probit equation (1)’ is
estimated to obtain estimates of α and γ and compute the inverse Mills’ ratio. At a second step,
the inverse Mills’ ratio is added to the earnings equation to produce the estimated coefficients of
β and β λ .
Finally the structural model of migration can be studied for the prediction of Todaro model and
those of the New Economics of migration using the expected income gap for each household.
4
P * = α ' Z i + η (log wˆ fi − log wˆ hi ) + ε ' i
( 4)
However the coefficients estimated measure how the log-odds in favor of migrating
change as the independent variables change by a unit. For interpretation, marginal effects should
then be computed together with several other approaches for interpreting nonlinear outcomes for
meaningful profiles of the independent variables (Long and Freese, 2001).
3. Estimation
There is a considerable body of empirical work on intra-country migration using crosssectional survey data that have been developed based on a discrete choice model. Lucas (1985)
and Zhu (2001) are some applications on Botswana and China, respectively. However, the
specificity of the current paper remains the dataset as well as the region of interest. The two-sided
survey data collected in 2002 at the origin and the host country (Burkina Faso and Cote d’Ivoire)
allows a detailed analysis of migration in West Africa.
At the core of the model is an earning equation expressing households’ income as a
function of individual and external characteristics (Ghatak, 1996). Firstly, we estimate
simultaneously income equations for the migrants and nonmigrants in Burkina Faso. Secondly we
study the impact of income gaps between these two groups on migration decision. The method is
a structural probit model using the two-step procedure developed by Heckman (1979) and applied
in previous studies such as Nakosteen et al. (1980); Perloff (1991); Agesa et al. (1999).
3.1. Data description
Two sources of data from surveys conducted in summer 2002 in Burkina Faso 3 and in
Cote d’Ivoire 4 are used for the empirical study. In Burkina Faso, the head of household of the
randomly constituted sample had to answer the household questionnaire whereas the village
leaders were chosen to answer two additional questionnaires: the village questionnaire and the
institutional questionnaire. The survey in Burkina is a second round of ILRI project (CAPRI 2)
started in 2000, however the migration aspects of interest were completely revised.
3
As a collaboration project of ILRI and ZEF, this survey concerned 48 villages of two northern provinces: Oudalan
and Seno.
4
This project is a collaboration between ZEF and two other research centers in Bu rkina Faso (UERD) and in Cote
d’Ivoire (ENSEA).
5
In Cote d’Ivoire, only the household level questionnaire was applied. The total sample
included 513 households among which 250 in Burkina Faso and 263 in Cote d’Ivoire.
3.2 Estimation samples
The analysis applies to two types of migration: the seasonal migrants that have been
interviewed in the Sahel of Northern Burkina and the permanent migrants interviewed in Cote
d’Ivoire at their residence place. The nonmigrants living in the Sahel constitute the reference
group for both categories of migrants.
As summarized in tables A and B (see Appendix), the survey completed in Burkina Faso
concerned 102 migrant households to Cote d’Ivoire, 135 nonmigrant households and 13
households that do not sent a member in Cote d’Ivoire but elsewhere (only 5% of the sample).
Among the 102 migrant households, while 14 cases have only a relative external to the household
composition in Cote d’Ivoire, 69 are defined as seasonal migrants as the migrant returned yearly
home for the agricultural activities of the rainy season that last on average 3 months of intensive
work.
The remaining 19 migrant households are permanent migrants who established durably in
Cote d’Ivoire (RCI). This later group of migrants is studied through the survey of Burkinabe
residing in Cote d’Ivoire where necessary information on their incomes and other characteristics
are collected.
Overall the potential estimation samples are respectively 204 and 398 households for the
seasonal and the permanent migration study.
It is assumed that the integrated regional market within the UEMO A5 facilitates the migration
decision. However, the permanent migrants live in Cote d’Ivoire and own cocoa farms that
constitute a very important source of income whereas the seasonal migrants are forced to
temporary positions in towns where they work in non-qualified positions (guards or butchers) for
less than 12 months. The second group generally can just get positions that do not interest
Ivorians whereas the first group asserted that they earn a much better living than the local people.
5
West African Economic and Monetary Union of 8 francophone countries
6
3.3 Variables
The objective of the following empirical work is to analyze the impact of income gaps on
migration behavior of these two groups of migrants (respectively the seasonal and permanent)
from Burkina to Cote d’Ivoire.
Following the Heckman procedure, the income regression equation and the selection equation are
simultaneously estimated before the structural migration economy can then be studied. The
migration income (households with observed remittances flows) regression model is estimated
using the Heckman procedure to take into account the fact that the assumption of randomparticipation- in-the-migration is unlikely to be true and thus, standard regression techniques
would yield biased results. Households who would have negative benefit of migrating may be
unlikely to choose to migrate (Ghatak, 1996), their personal reservation income (including the
off- farm income) being greater than the income offered by moving from home.
Two distinct migration models for the seasonal migrants and the permanent migrants are
successively studied. Variables and their theoretical expected sign for the migration decision
(wherever it is non-ambiguous) are summarized in the following tables.
The selection binary variable is dmover that includes 34% of seasonal migration and therefore
identifies the households for which the migration income is observed or not observed. In the
permanent migration model, dmove plays an identical role .
Table C in Appendix summarizes the statistics of the variables used in the seasonal migration
study whereas the statistical information on permanent migration are summarized in table 1bis.
7
Table 1:
Variables considered in model 1 of seasonal migration study
Variable names
Dgor
SocialK
Qtotbov_02
Paginput
Pbovinp
Ceekol
Seeno
inov_d1
Lmyield
Mage
Mecol_d3
Mans
elev_ind
typac150
Hdethag
Monogam
T150med
T150high
Loghhinc
Dmover
Popdens
Distdogo
Inno
Children
Sqmage
risk3
risk4
Apparent_02
Dstayer
Qtotbov_02
Dhilo
Labels and expected sign of variables
Residential region Gorom-Gorom indicator (+)
never confide cattle to other villager due to mistrust
total number of bovines per village (+)
input spending per ha (-)
input spending per bovine (-)
frequency of the best soil quality (Ceekol) at household
level (-)
frequency of the second best soil quality (Seeno) at
household level
Technological innovation indicator in agricultural plots
management (+)
Logarithmic mean of millet yield (-)
mean household age (+)
number of members who attended kuranic school (+)
number of schooling years per households (+)
main source=livestock
small dikes in village (-)
head of household is a cropper
monogamist household (+)
dikes*rainmed
dikes*rainhigh
Logarithmic household total income (in CFA francs)
seasonal and nonmigrant household indicator
density household (+)
distance to regional capital (+)
household uses agricultural innovation (zai, diguette or
both)
number of kids under 12 (+)
mean household age squared
household risk coping strategy is gold panning (+)
Household risk coping strategy is migration (+)
Household receives aid from extended family in case of
shock
choice of not to migrate
total number of bovine per village (+)
income gap between seasonal and nonmigration choices
(+)
Similarly the variables used in the permanent migration study are summarized as follows:
8
Table 1bis: Mean value of explanatory variables in model 2 of permanent migration study
Model 2
Variables names
Variables labels
Permanent
Nonmigrants
Migrants
hhsize
Household total size
4.84
5.70
Age
Head of household’s age
45.17
52.3
Radio
Number of radios in the Household
0.59
0.37
Dumorg
dummy, Household participates to a 0.30
0.64
local organization (social capital)
Meannsch
average
education
years
per 1.66
0.63
household
popdens
provincial population density
64.96
24.95
Dhhcoran
dummy, Head of Household 0.14
0.38
attended coranic school
Loginc
Annual income of the household 1648
347
(thousand of CFA francs)
Number of observations
263
135
*Note: only variables effectively used in model 2 are summarized in the table. The design of the two surveys is not
fully identical and some information in the CAPRI survey is missing in the one conducted in Cote d’Ivoire.
4. Empirical results
This section implements the econometric analysis and interprets successively the income
model and the migration participation structural model. The later evaluates the impact of the
income gap corrected for selection bias. As the methods applied identically to the two groups of
interest, the seasonal migrat ion is studied first and the results of the permanent migration follow
with.
The income model
Unlike the permanent migrants, the seasonal migrants and the nonmigrants have similar
income sources as they try to cope with agricultural risks related to the dry lands of Burkina Faso.
For all households in rural Sahel, only 55% have farm activities as main source of their earnings
while off- farm and migration activities represent 45%.
Remittances alone represent the main source of income in more than 21% of households
while other local off- farm activities stand for more than 22%.
The truncated migration income distribution follows a nonlinear function (Greene, 2000,
p. 901) and incomes in the population are supposed lognormally distributed. This assumption is
supported by the graphical test (see graph A in Appendix) and justifies the use of the natural
9
logarithm of household annual income (measured in the UEMOA common currency, francs
CFA) as dependent variable.
The independent variables for the “seasonal” income equation as described above are
mans, risk3, socialK, paginput, inov_d1, t150med, t150high, and typac150 whereas those of the
selection equation identification are tothh, hdethag, monogam, children, mage and sqmage. The
later that strongly affect the chances for migration (the cost of migrating, the reservation income
and therefore the net benefit) in the model may not influence the offer earnings.
Table2:
Heckman selection model for seasonal migration
(1)
loghhinc
mans
0.089
(2.08)**
-1.732
(-4.55)***
0.549
(2.03)**
0.000
(2.63)***
0.226
(2.58)***
-0.779
(-3.49)***
-0.650
(-2.05)**
0.005
(0.02)
risk3
socialK
paginput
inov_d1
t150med
(2)
dmover
-0.069
(-1.07)
-0.821
(-1.93)*
-0.707
(-1.92)*
0.000
(0.48)
-1.269
(-2.69)***
t150high
-1.598
(-3.11)***
typac150
0.357
(0.99)
inno
0.550
(1.45)
hdethag
0.669
(2.18)**
monogam
0.704
(1.57)
popdens
11.793
(2.96)***
qtotbov_02
0.000
(1.47)
distdogo
0.003
(0.37)
Constant
12.662
-1.560
(45.78)*** (-2.56)**
Observations
126
126
Absolute value of z statistics in parentheses
* significant at 10%; ** significant at 5%; *** significant at 1%
Wald chi2(15)
= 69.97
Prob > chi2
=
0.0000
10
The results support that the seasonal migrant households earnings is a function of education, risk
coping strategy, social capital, use of agricultural inputs (fertilizer etc.), innovation adoption and
climatic variables and their interaction to natural resources management (adoption of dikes
against erosion). The likelihood of migrating is significantly dependent on income factors as well
as village population density and household's ethnic network.
However, the parameters estimated under the earnings regression are the marginal effects of
the regressors in the income equation representing the entire population. It should therefore be
noted that the coefficients ß can be used for inference only when analyzing the whole population.
The marginal effects in the income regression for the subgroup of migrants are different
from the estimated coefficients and can be obtained from equation (2)’:
[
]
[
]
E log w i V > 0 = E log w fi = β f . X
'
fi
+ ρ σ w λ fi
( 5)
It
follows that the marginal change in income as one independent variable changes, holding all
other variables constant is:
∂E (log w fi )
∂X i
δ
i
= β − γ (β λ )δ i 6 where
= λ 2 i − ψλ
i
(
, ψ = − α ´Z i + γ ' X i
)
It is necessary while studying migration to evaluate these quantities because it is quite
possible that the magnitude, sign, and statistical significance of the real marginal effects might all
be different from those of the Heckman estimate of β (Greene, 2000). The outcome depends on
the level of all variables in the model and is evaluated in the next table by computing the
marginal effect for each observation in the sample and then averaging across all values.
6
δ i is strictly comprised between 0 and 1, playing then an attenuation role.
11
Table 2: Marginal effect of seasonal
income
MIGRANTS
mans_ma
0.083
risk3_ma
-1.616
SocialK_ma
0.512
Paginput_ma
0.00002
inov_d1_ma
0 .211
t150med_ma
-0.727
t150high_ma
-0.607
typac150_ma
0.0046
migration factors on
TOTAL POPULATION
0.089
-1.732
0.549
0.00003
0.226
-0.779
-0.65
0.0049
Now we can proceed with the interpretation of results for the seasonal migration.
As expected the results indicate that the migration income depends positively on
household human capital (mans), household’s investment in agricultural inputs 7 (paginput) and
innovation in agricultural plots management mainly technologies used against erosion. However,
the level of mistrust regarding the main activity of livestock farming in the region appears to have
a positive impact on migration income. The level of dead or stolen bovines in case of supervision
contract could partly explain this unexpected result. On the other hands, households who do not
confide their assets (cattle) to other villagers by lack of trust are most often the nonmigrants (39%
versus 12%), therefore the seasonal migrants may not suffer for this problem of mistrust level in
the village. The later have better income when they avoid leaving their cattle to other villagers
during the absence of some members while the same variable appears non significant in the
nonmigrants regression, giving support to the argument (table 2c in Appendix).
The expected sign from theory is that a household who implements innovation to contain
erosion would have better income in the event of high or medium rainfalls compare to low
rainfall8 . This is not apparent in some aspects of our results and to understand the variation of the
effect of innovation on income and migration along with climatic conditions, the interaction of
innovation in dikes and rainfall is added through the three variables t150med t150high typac150.
The reference group of low rainfalls is captured by the remaining main effect of innovation in
7
Investments in modern agricultural inputs are mainly fertilizer, pesticides, animal work force and tractors.
8
As there are three variables that reflect the impact of innovation (t150med t150high typac150), a test of innovation
in dikes significance involve a joint hypothesis test, which reveals significant effect of the interaction between
innovation in natural resource management and rainfall.
12
small dikes that appears in typac150. The resulting interaction effects are significantly negative
but do not imply negative effects of these multiplicative variables. Rainfall (high and medium)
relatively to low rainfall would decrease the chances of migrating for a household who innovated
in dikes, meaning that households would stay home if the climatic situation were favorable. But
simultaneously this would decrease the household average income probably because agriculture
contributes less in households income compare to migration. Therefore the marginal impact on
income of investing in dikes are obviously lower when the rainy season is average or above
compare to a situation where there are low rainfalls (Graph B in Appendix). This outcome should
be related to the unstable climatic conditions in the Sahel and the migration as a coping strategy
(Stark, 1991). To cope with the difficult climatic conditions that correspond to low rainfalls on
average in the region, it seems that households who are selected for migration have the highest
impact on income from these climatic conditions, as they are able to diversify their income
sources. The interpretation that implies high and medium rainfall coupled with innovation
represent households who have more propensity to stay home is supported by the same graph
where the implicit positive effect of innovation make fighting against erosion more favorable in
case of high rainfall compare to medium ones.
The direct effect of innovation can also be deducted from the regression outputs of table 2
because households who innovate have greater income regarding the positive effect of variable
inov_d1.
A second Heckman selection model is applied to the non-selected group under assumption of
households choosing not to participate into migration and the results presented in table 2c (see
Appendix) allow estimating the income gap for each household conditional to his participation or
not to migration.
The structural migration decision model
The income differential between seasonal and nonmigration choices dhilo is now used to
study the structural model of migration where additional control variables are mage, sqmage,
tothh, hdethag, monogam, children.
In a simple OLS regression, the estimators and its standard errors are derived under the
assumption of simple random sampling with replacement. However, due to practical fieldwork
difficulties, the sampling probabilities might differ between observations and estimations should
be corrected for complex sampling schemes (equations 2 and 3 of table 3).
13
One aspect of the survey bias arises as households differ by their sizes and members may have
different probabilities of selection. Therefore, one needs a weighting variable, which is equal (or
proportional) to the inverse of the probability being sampled to avoid bias of estimates and
standard errors. Here we use household size (hhtotsiz) as sampling weight and the results are
stronger and presented in table 3 (column 2).
On the other hands, Drabo et al. (2001) stressed the bias introduced by the enumerators during
the first round of the CAPRI survey. Some responses are over-represented depending on the
enumerator’s specific representation of questions even though they all follow the same training
and pre-test. In fact every enumerator may have introduced a bias of dependency among the cases
he or she interviewed. Column 3 of table 3 takes this additional bias into account by using the
variable psu that is the enumerator identifier number.
Finally the complex sampling schemes results are presented together with the marginal effects of
the independent variables (column 3b).
The village level density of households has a significant positive effect on the probability of
migrating. Similarly richer villages (cattle size) produce positive externalities on the household
decision to migrate and this supports the argument that financing the initial cost of moving may
constitute a barrier for a household that wants to send a member abroad. Households usually
receive financing from relative in addition to their own savings.
Households from Gorom-Gorom region have relative higher propensity to migrate as well 9 .
Ceekol, the best quality of soil in the region prevents households from migrating, which supports
the idea that uncertain climatic conditions, drought and famine may constitute a push-factor that
motivates them to leave their home areas. Migration is then viewed as a survival strategy for the
sample households and a mean to cope with income risk. This is strengthened by the effect of
elev_ind, which indicates that in the Sahel region where livestock farming is the most successful
activity, a household with livestock as main income source may have relatively less incentive to
migrate.
Households with more education (kuranic education, mecol_d3) show also higher propensity to
migrate as expected.
9
The survey was conducted in 2 provinces of northern Burkina (Seno and Oudalan in respectively three localities
(Dori, Bani and Gorgadji) and one locality (Gorom-Gorom).
14
Table 3: Structural model of decision to migrate
(1)
(2)
(3)
(3)b
dmover
dmover
dmover
dy/dx
hdethag
0.328
0.308
0.308
0.116
(0.98)
(0.92)
(0.99)
(1.00)
monogam
0.583
0.389
0.389
0.137
(1.10)
(0.84)
(0.88)
(0.95)
popdens
13.665
14.542
14.542
5.49
(3.07)***
(3.75)***
(3.62)***
(3.56)***
qtotbov_02 0.0001
0.0001
0.0001
0.0001
(1.67)*
(2.13)**
(2.80)**
(2.82)**
distdogo
0.009
0.010
0.010
0.004
(0.95)
(1.12)
(1.22)
(1.24)
children
0.018
0.028
0.028
0.011
(0.37)
(0.62)
(0.57)
(0.57)
dgor
1.362
1.449
1.449
0.510
(3.73)***
(3.72)***
(5.50)***
(7.04)***
Ceekol
-0.574
-0.653
-0.653
-0.247
(1.68)*
(2.20)**
(1.83)
(-1.80)*
lmyield
-0.289
-0.362
-0.362
-0.137
(1.02)
(1.43)
(1.12)
(-1.11)
mecol_d3
0.229
0.262
0.262
0.099
(2.10)**
(2.95)***
(3.05)***
(2.95)***
elev_ind
-1.019
-0.964
-0.964
-0.360
(-3.27)*** (-2.84)*** (-7.63)*** (-7.84)
dhilo
0.038
0.070
0.070
0.026
(0.17)
(0.27)
(0.33)
(0.33)
Constant
-0.618
-0.183
-0.183
(0.32)
(0.11)
(0.10)
Observations 115
115
115
Absolute value of z statistics in parentheses
*** significant at 1%
** significant at 5%;
* significant at 10%
(3)b represent the marginal effects after the survey probit (3).
(1) scalar measures of fit:
Prob > chi2
=
0.0000
Pseudo R2
=
0.3312
Log likelihood = -51.76175
Count R2:
0.79110 Adj Count R2:
McKelvey and Zavoina's R2:
0.575
0.478
Additionally we also constructed and tested the direct effect of some risk management indicator
variables on migration decision in the Sahel. According to the New Economics of Migration,
families indeed spread their labor assets over geographically dispersed and structurally different
markets to reduce risks. Even though a positive effect of migration as a diversification strategy is
confirmed when households’ risk coping strategy is migration (risk4 in table 2b, Appendix), gold
panning strategy (risk3) shows the opposite sign in the selection equation of table 2 above.
However, using a one-tailed test, the hypothesis that risk3 does significantly affect a household's
10
Contructed using observed and predicted values of the model. As suggested by Long et al. (2002) it is corrected for
the largest row marginal.
15
probability to migrate is rejected 11 . Even though risk4 needs to be checked for possible
endogeneity, the results seem give support to migration as a better strategy regarding risk
management than off-farm income sources such as gold panning, small commerce in the village
as the later source of income is positively correlated to agricultural outcomes in the region.
Finally, the most important result is the unexpected non-significant effect of income gap. This
indicates that for seasonal migration, other factors (income diversification, population density,
natural resources, ethnic network) are stronger than the traditional income motivations. In the
Sahel subject to unstable climate, households who stay home diversify their incomes sources
through off-farm activities 12 while others receive remittances, the later being a better risk pooling
strategy.
Summary statistics of the predicted probabilities of migration shows a median probability
of about 0.37 while the average predicted chance of migrating in the population is around 0.4
with a standard error of 0.3. This low average propensity to migrate suggests examining the
predictions at specific values and for some profiles of interest. Table E to G in Appendix are
computed for this purpose.
The table E shows the strong impact of household’s endowments on his decision to send a
member in migration. A household who relies on livestock farming with high number of educated
members would probability leave a place of origin densely populated.
Table F is used to interpret the combined potential effect of number of children and kuranic
schooling attendance. The table confirms the potential positive effect on migration decision of
having children at home, effect that get stronger with the human capital level of the household.
The last table (G) shows that households choose to stay home when their endowment of good
quality soil increase. However, the effect of soil in keeping households home is stronger of
course with cropper groups. This difference between croppers and others narrows as expected
with the frequency of good natural resources in the region of origin.
Finally Graph C in Appendix illustrates the effect of village wealth (total number of cattle) on
migration decision for quartiles of population density. The probabilities increase both with wealth
of a village and with population density. The graph reveals that under extreme assumption of a
11 Since the coefficient is in the wrong direction regarding the theory, a one-tailed test must then be conducted,
which shows variable risk3 does not significantly affect a household's probability to migrate.
12 22% of all households obtained their income mainly from off-farm activities while 21% from migration.
16
rich village with very high population density, all households would have strong incentive to
move abroad.
Determinants of the permanent migration decision
Several stylized facts exp lain a particular attention to permanent migrants groups. The
observed income gaps between permanent migrant and nonmigrant is very high (5 times) whereas
the income of the seasonal migrants is only 1.38 times the income of the same group of
nonmigrants. As expected, permanent migrants are also younger than nonmigrants. These
migrants who reside now in Cote d’Ivoire have smaller family size than households at home.
Formal education is nearly inexistent in rural Sahel and households attend quranic school where a
good deal of learning is about religion whereas migrants in Cote d’Ivoire have access to much
more public school education.
The Heckman two-step results are presented in this section in two parts. The estimated
reduced form probit equation of selection, which includes the independent variables of the
migration decision function and those of the income function, is presented in Appendix (table
4b). Then, we estimate both migrants income equation and the stayers’ income equation
respectively, introducin g the inverse Mills ratios that come from the reduced form probit equation
to correct for the sample selection bias (table 4).
Models 2 results show a significant threshold effect of age regarding permanent migrants income
residing in Cote d’Ivoire (Regression 1). As age increases, migrants’ human capital (experience
and personnel connections in the host country) increases as well causing an increase of their
income. However, age reaches an optimal level above which, a further increase of age affects
negatively the efficiency and income of these rural and physical workers in their farms.
Social capital in a form of participation to local organization seems to negatively affect migrants
income in Cote d’Ivoire, which means that time allocated to a community association
(associations reserved to Burkinabe living in Cote d’Ivoire) does not improve private income.
Migrants seem to have understood that because organization life is significantly less active in
Cote d’Ivoire than in Burkina Faso (30% against 65% of participation level respectively). It is a
better strategy to integrate the local community and expand personnel connections.
17
Table 4 – Logarithmic income equation corrected for sample selection bias (Model 2 of
permanent migration)
Dependent variable:
Logarithmic income
Head of household’s age
Regression 1
Permanent Migrants
0.075
(2.30)**
Squared age
-.001
(-2.39)***
Household participation to a -0.586
local organization
(-5.32)***
Average education years per 0.006
household
(0.14)
Squared education years per -0.001
household
(-0.38)
Head of Household attended -0.289
coranic school
(-2.00)**
Household total size
0.107
(5.40)***
Inverse Mills Ratio
0.078
(0.53)
Constant
11.924
(15.49)***
Adjusted R2
0.191
Number of observations used 251
Regression2
Nonmigrants
0.072
(2.33)**
-.001
(-2.49)***
-0.485
(-2.82)***
-0.002
(-0.02)
0.016
(0.88)
-0.236
(-1.22)
0.142
(5.39)***
1.208
(3.61)***
10.463
(12.23)***
0.252
132
Note: The t-students are presented in parentheses. *** indicates coefficient significant at 1%; ** indicates coefficient
significant at 5% level; * indicates coefficient significant at 10% level.
In the context of Cote d’Ivoire, the head of household attendance to quranic school has a negative
impact on income as well. In general people who learned Quran have a very influential social
status and allocate a lot of their time in teaching religion and to other religious activities, which
may conflict with farms activities. Additionally the result indicates that the human capital in form
of Quranic education is not rewarded in the ivorian labor market where formal schooling in rural
areas is much more developed. On the opposite, hous eholds with high formal education are more
prone to migration as indicated in the selection probit (table 4b).
Finally household size (number of members above 12 years old) has a very important positive
role in income determination because these farmers need manual labor for their cacao farming.
Using predicted income gaps between permanent migrants and nonmigrant families, the
structural probit of migration decision can then be studied as presented in table 5.
18
Table 5 – Decision to migrate: structural model
Dependent variable: Migrant=1, NonModel 2
Marginal effects
migrant=0
Permanent
Permanent Migrants
Migrants
Head of household’s age
0.357
0.059
(1.88)*
(1.88)*
Squared age
-0.004
-0.001
(-1.82)*
(-1.82)*
Ethnic group
0.156
0.026
(3.53)***
(3.53)***
Income gap
2.297
0.379
(6.02)***
(6.02)***
Constant
-15.953
(-3.20)***
Log likelihood
-29.024
-29.024
Correctly classified
95.60%
95.60%
Number of observations used
250
250
Note: The t-statistics are presented in parentheses.
***, **, * indicate significance at 1%, 5%, and 10% level, respectively.
Network represented by ethnic conclaves acts as strongly in favor of migrants choice to establish
durably in Cote d’Ivoire while age indicates a U- form effect to that migration decis ion.
The most appealing result from the above structural migration equation is the confirmation of
Todaro’s prediction. When the migration project leads the household to reside permanently in
Cote d’Ivoire and integrate to the host society, the income gain relative to the counterfactual of
staying home has a strong positive effect (Graph D).
As indicated in the previous section, the Sahel is a region that is subject to strong climatic risk
and nonmigrant households’ strategy to cope with such a risk in gene ral is to diversify their
activity into non- farm sectors. However such strategy is available for households who have
investment funds on their own, credit market being inexistent. On the contrary migrant
households send their members in Cote d’Ivoire where they find funds for the domestic
investment and consumption plans. Big size households are more likely to send their extra- labor
force abroad so that the opportunity cost of migration in term of the shortage of labor force at
home may not offset the benefit of owning a farm abroad. This result is strengthened if
remittances sent home are invested in modern agricultural inputs, a substitution effect of labor
factor by capital.
19
5. Conclusion
To our knowledge, this paper is the first applied work to migration decision inside
UEMOA. The results confirm the prediction of the Todaro model only in one case: permanent
migrants stay in Cote d’Ivoire because they earn incomes that are more than 5 times of what they
could expect if they would stay home.
However, other factors explain seasonal and permanent migration decision as well.
Among these factors are the need to diversify income sources, education, ethnic network,
population density, natural resources and endowments and their management.
An interesting questio n raised by the current study remains the household’s selection of
different migration types. Why do some households become seasonal and others permanent
migrants?
The next steps of this analysis include also an analysis of income gaps of the two groups
of migrants. A natural approach following the migrants and nonmigrants incomes analyses in this
paper is the Oaxaca approach while it may be interesting to consider a counterfactual approach
comparing the incomes prospects of migrant households with and without remittances, the later
considered as substitute for home earnings (Barham et al., 1998). Finally further investigations of
migration and its economy-wide and regional impact in West Africa request extension of the
analyses in a general equilibrium framework (Decaluwé et al., 1998 and 2000; Robinson et al.,
1995).
20
References
Agesa, J., & Agesa, R.U., (1999). Gender Differences in the Incidence of Rural to Urban
Migration: Evidence from Kenya”, The Journal of Development Studies, 35(6), 36-58.
Barham B., & Boucher S. (1998). Migration, remittances, and inequality: estimating the net
effects of migration on income distribution, Journal of Development Economics, 55, 307-331.
Barro, R.J. & Sala- i-Martin X. (1999). Economic growt. Cambridge, Massachusetts, London,
England: The MIT Press. 544p.
Decaluwé, B., Dissou, Y. & Patry, A. (1998). Union douanière au sein de l’UEMOA: une analyse
quantitative, http://www.crefa.ecn.ulaval.ca/cahier/liste98.html.
Decaluwé, B. Dumont, J-C, Mesplé-Somps, S. & Robichaud, Y. (2000). Union économique et
mobilité des facteurs: le cas de l’UEMOA”, http://www.crefa.ecn.ulaval.ca/cahier/liste00.html
Drabo B., Dutilly- Diané C., Grell H. & McCarthy N. (2001). Institutions, action collective et
utilisation des ressources pastorales dans le Sahel Burkinabé: Rapport Final, CAPRI-IFPRI-ILRIPSB/GTZ.
Ghatak, S., Levine, P. & Price, S.W. (1996). Migration Theories and Evidence: An assessment,
Journal of Economic Surveys, 10(2), 159-197.
Greene, W.H. (2000). Econometric Analysis, New Jersey, Prentice-Hall International, Inc.
Todaro, M. P. (1969). A Model of Labour Migration and Urban Unemployment in Less
Developed Countries, American Economic Review , 59(1), 138-148.
Harris, J. & Todaro M.P. (1970). Migration, Unemployment and Development: a Two-Sectors
Analysis, American Economic Review, 60, 126-142.
Heckman J. (1979). Sample selection bias as a specification error, Econometrica, 47, 153-161.
Long, J.S. & Freese J. (2001). Regression models for categorical dependent variables using
Stata. Stata press, 344 p.
Nakosteen, R.A. & Zimmer, M.A. (1980). Migration and Income : The Question of SelfSelection. Southern Economic Journal, 46, 840-851.
Oaxaca, R. (1973). Male- female wage differentials in urban labor markets, International
Economic Review, 14(3), 693-709.
Oaxaca, R. & Ransom, M. R. (1994). On discrimination and the decomposition of wage
differentials, Journal of Econometrics, 61, 5-21.
Perloff, J. M. (1991). The Impact of Wage Differentials on Choosing to Work in Agriculture,
American Journal of Agricultural Economics, 73(3), 671-80.
Robinson, S, Burfisher, M, & Thierfelder, K (1995). The impact of the Mexican crisis on trade,
agriculture and migration. TMD Discussion Paper No . 8.
Stark, O. (1991), The Migration of Labor, Oxford: Basil Blackwell Inc., 406 p.
Todaro, M. P. (1969). A Model of Labor Migration and Urban Unemployment in Less Developed
Countries. American Economic Review , 59(1), 138-148.
Todaro, P.M. (2000). Urbanization and rural-urban migration: theory and policy, in: Economic
Development, ed: P. Michael Todaro, pp.291-325.
Zhu N. (2002), “The impacts of income gaps on migration decisions in China”, China Economic
Review, vol. 113, p.1-18.
21
Appendix
Table A:
GROUPS
FINAL SAMPLE
INCLUSION
(seasonal migration)
Definition of groups involved in CAPRI 2002
NONMIGRANT
HOUSEHOLDS (nonrecipients of remittances)
A
(Not observed in migration)
RECIPIENT HOUSEHOLDS (a member or non-member in
migration)
B: member + non-member
in RCI
C: only
member in
RCI
D: only
nonmember in
RCI
E*: No
contact in
RCI but in
other
destination.
+
+
Seasonal if
length of stay
<=12 months
+
seas
onal
-
-
Table B: CAPRI 2 Sample structure
Flow direction
Seasonal
Migrants
Burkina Faso to Cote
69
d’Ivoire
Burkina Faso to other
direction
Non-migrants
135
(Reference group)
Sample for seasonal
204
migration analysis
*Non-membership to the household.
Permanent
Migrants
19
permanent if
l.o.s>12
Other migrants
14*
13
22
-
Table C: mean value of explanatory variables
--- Quantiles -------------Variable
Mean
S.D.
Min
.25
Mdn
.75
Max
n
Dgor
0.41
0.49
0.00
0.00
0.00
1.00
1.00
250
SocialK
0.20
0.40
0.00
0.00
0.00
0.00
1.00
250
Qtotbov_02
943.20
1480.41
54.00
310.00
597.00
850.00
8960.00
250
Paginput
2001.37
5651.74
0.00
0.00
120.00
1600.00
57242.06
155
Pbovinp
3409.56
3543.38
77.50
1243.75
2445.45
4333.33
27075.00
174
Ceekol
0.36
0.66
0.00
0.00
0.00
1.00
3.00
250
Seeno
1.14
1.12
0.00
0.00
1.00
2.00
7.00
250
Inov_d1
0.56
1.03
0.00
0.00
0.00
1.00
5.00
250
Lmyield
5.78
0.58
3.61
5.40
5.87
6.18
6.91
227
Mage
36.13
21.71
18.67
29.57
33.10
38.64
353.50
250
mecol_d3
0.96
1.44
0.00
0.00
0.00
1.00
8.00
250
Mans
1.79
2.68
0.00
0.00
0.82
3.00
20.00
250
Elev_ind
0.55
0.50
0.00
0.00
1.00
1.00
1.00
250
Typac150
0.69
0.46
0.00
0.00
1.00
1.00
1.00
248
Hdethag
0.49
0.50
0.00
0.00
0.00
1.00
1.00
248
Monogam
0.88
0.33
0.00
1.00
1.00
1.00
1.00
248
t150med
0.22
0.41
0.00
0.00
0.00
0.00
1.00
248
t150high
0.23
0.42
0.00
0.00
0.00
0.00
1.00
248
Loghhinc
12.58
0.87
9.80
12.04
12.68
13.16
14.49
250
Dmover
0.34
0.47
0.00
0.00
0.00
1.00
1.00
204
Popdens
0.04
0.04
0.01
0.03
0.03
0.05
0.20
248
Distdogo
34.61
19.71
3.00
18.00
35.00
47.50
73.00
248
Inno
0.22
0.42
0.00
0.00
0.00
0.00
1.00
250
Children
3.62
3.10
0.00
2.00
3.00
4.00
22.00
250
Sqmage
1775.26
7865.98
348.44
874.47
1095.89
1492.77
124962.25
250
risk3
0.10
0.31
0.00
0.00
0.00
0.00
1.00
250
risk4
0.34
0.48
0.00
0.00
0.00
1.00
1.00
250
aparent_02
0.31
0.46
0.00
0.00
0.00
1
1
250
dstayer
0.66
0.47
0.00
0.00
1.00
1.00
1.00
204
lamda
1.15
0.60
0.07
0.64
1.11
1.55
3.03
154
dhilo
0.29
0.65
-1.64
0.32
0.32
0.61
2.95
154
23
Table D: Sources of incomes
Main source of income
Rainfall agriculture
Livestock farming
Migration activities
Craft industry
Truck farming
Retail trade
Paid activities including gold panning
Other
% of Sample households
(CAPRI2)
0.40
56.40
21.60
2.40
0.80
3.20
11.20
1.20
Table E: Specific groups profiles
Specific groups
Predicted probability of seasonal
migration
Average household
Non-livestock
0.35
farmer
household
from
low
households density area, without education
and quality soil endowments
0.32
Households with major source of income in
livestock
farming
and
highly
endowed
in
0.99
education and from high-density area.
24
Table F
Number of
children
under 12
0
1
2
3
4
5
6
7
8
9
10
Table G
Predicted Probability according to Kuranic school attendance
Number of members who attended koranic
school
0
3
6
8
0.2319
0.2376
0.2433
0.2491
0.2550
0.2610
0.2670
0.2731
0.2792
0.2855
0.2918
0.7400
0.7460
0.7518
0.7576
0.7633
0.7690
0.7746
0.7801
0.7855
0.7908
0.7961
0.8648
0.8687
0.8726
0.8764
0.8802
0.8838
0.8874
0.8908
0.8943
0.8976
0.9008
Predicted Probability according to activity
Number of plots with
the best soil quality
0
1
2
3
0.4822
0.4896
0.4969
0.5043
0.5116
0.5190
0.5263
0.5337
0.5410
0.5483
0.5556
Not
cropper
0.3486
0.1678
0.0622
0.0174
Cropper
Ethnic group
0.4758
0.2629
0.1135
0.0374
25
Table2b:
Heckman selection model for seasonal migration: migration as a risk coping
strategy13
(1)
loghhinc
mans
risk3
risk4
aparent_02
socialK
paginput
inov_d1
t150med
0.087
(2.08)**
-2.171
(-6.35)***
0.421
(2.09)**
-0.111
(-0.65)
0.518
(2.09)**
0.000
(3.08)***
0.231
(2.53)**
-0.991
(-4.06)***
-1.101
(-4.46)***
0.252
(1.10)
(2)
dmover
-0.054
(-0.79)
-0.488
(-1.05)
1.663
(5.09)***
0.667
(2.08)**
-0.332
(-0.84)
0.000
(1.51)
-0.637
(-1.43)
t150high
-0.947
(-2.27)**
typac150
0.178
(0.49)
mage
0.035
(3.22)***
sqmage
-0.000
(-2.63)***
inno
0.629
(2.02)**
tothh
0.002
(1.56)
hdethag
0.525
(1.51)
monogam
0.981
(1.84)*
children
-0.130
(-2.48)**
Constant
12.153
-3.122
(42.89)*** (-3.82)***
Observations 126
126
Absolute value of z statistics in parentheses
* significant at 10%;** significant at 5%; *** significant at 1%
Log likelihood = -67.28029
Wald chi2(10)
= 94.96
Prob > chi2
= 0.0000
13
One has to be cautious in interpreting the impact of risk4 because even though the correlation between dmover and
risk4 is not perfect (0.62), the unpaired test of mean comparison do not reject the null hypothesis of mean equality.
26
Table 2c: Heckman nonselection model for nonmigrants households
mans
mage
risk3
socialK
inov_d1
t150med
(1)
loghhinc
-0.061
(-2.17)**
-0.003
(-1.51)
-0.186
(-0.53)
0.265
(1.22)
0.166
(2.07)**
-0.784
(-3.10)***
-0.434*
(-1.68)
0.349
(1.61)*
0.000
(1.20)
0.160
(2.07)**
(2)
dstayer
0.016
(0.19)
-0.012
(0.49)
0.135
(0.19)
0.985
(1.87)*
1.022
(1.78)*
t150high
1.836
(2.47)**
typac150
0.048
(0.11)
pbovinp
0.000
(1.07)
Seeno
-0.113
(0.73)
sqmage
0.000
(0.70)
paginput
-0.000
(0.52)
inno
-1.541
(-2.51)**
hdethag
-0.876
(-2.25)**
monogam
-0.648
(-1.23)
children
0.054
(0.90)
popdens
-11.451
(-2.33)**
qtotbov_02
-0.000
(-1.47)
distdogo
-0.023
(-1.99)**
Constant
12.750
2.380
(49.21)***
(2.17)**
Observations 92
92
Absolute value of z statistics in parentheses
*** significant at 1%, ** significant at 5%;
Wald chi2(19)
= 40.40
Prob > chi2
= 0.0029
27
* significant at 10%
Table 4b: Probability of migration (Selection equation of Model 2 in Heckman procedure)
Dependent variable: Migrant=1, Nonmigrant=0
Log likelihood
Correctly classified
Selection model
Permanent Migrants
-0.035
(-2.50)***
-1.431
(-4.39)***
0.383
(3.16)***
0.172
(6.50)***
-1.442
(-3.48)***
-3.442
(-3.59)***
-44.502
96.61%
Number of observations used
383
Head of household’s age
Household participation to a local organization
Average education years per household
Provincial population density
Head of Household attended Quranic school
Constant
Note: The t-statistics are presented in parentheses.
*** and ** indicate significance at 1% and 5% level, respectively.
28
Graph A: Skewness and Kurtosis of income variable
Quantiles of totrev in 1000 CFA
1.7e-06
Density
Quantiles of totrev in 1000 CFA
1958
1.4e-08
18
-58952.3
0
Graph B:
.25
.5
Fraction of the data
.75
2.0e+06
hh'S grand total income
1
Kernel Density Estimate
Conditional effects of rainfall and dikes interactions on income variable
cond eff of dikes on inc
high rainfalls with inno on inc
c eff of med rainfalls with inn
13.1371
12.5109
0
1
innovation
29
Graph C: Effects of village wealth in livestock on migration decision for quartiles
of population density
popdens=minimum
popdens quantile .75
popdens=median
popdens=max
1
Pr(migrating)
.75
.5
.25
0
100
1000
2500
5000
village livestock
30
7500
9000
Graph D: Todaro’s Prediction in the model of permanent migration
Predicted probability to migrate
1
.5
0
0
5
income gap
Relation between probability of migration and income gap
31
10