Remittances and the Brain Drain: Evidence
from Microdata for Sub-Saharan Africa∗
Julia Bredtmann1 , Fernanda Martinez Flores1,2 , and Sebastian Otten1,2,3
1
RWI, Rheinisch-Westfälisches Institut für Wirtschaftsforschung
2
Ruhr University Bochum
3
University College London
This version: January 2016
Abstract
Using comprehensive microdata from five Sub-Saharan African countries, we investigate the effect of education on migrants’ remittance behavior. By observing
multiple migrants per household, we are able to control for unobserved characteristics
of the household at the origin country and thus to identify an unbiased effect of
education on migrants’ remittances. Our results show that migrants’ education has
no significant impact on the likelihood of sending remittances. However, we find
a strong positive effect of tertiary education on the amount of remittances sent,
providing evidence that high-skilled migrants send more money to the households
left behind.
JEL Classifications: F22, F24, O15
Keywords: migration, remittances, skill level, brain drain, Sub-Saharan Africa
∗
Preliminary version: please do not cite. – All correspondence to: Fernanda Martinez Flores, RheinischWestfälisches Institut für Wirtschaftsforschung, Hohenzollerstr. 1-3, 45128 Essen, Germany, Email:
[email protected].
1
Introduction
In 2013, one in every nine tertiary-educated persons born in Africa resided in an OECD
country, representing the highest emigration rate among developing regions with the
exception of Latin America and the Caribbean OECD-UNDESA (2013). In Sub-Saharan
Africa, the emigration rate of high-skilled individuals increased from 19% to 25% between
1990 and 2010, surpassing the high-skilled emigration rate of East Asia and the Pacific (see
Figure 1). The so-called “brain-drain” or the migration of high-skilled individuals to other
regions where human capital is abundant is a major concern for developing countries with
a relatively small number of highly educated individuals, as it represents the loss of their
most talented workers. The most evident way through which the negative externalities of
the brain drain can be somewhat offset are remittances.
Theoretically, there are several reasons to expect differences in the remittance behavior
of high-skilled and less skilled migrants. On the one hand, high-skilled migrants may
remit less because they often come from better-off families with lower income constraints.
Besides, high-skilled migrants may have a higher propensity to migrate with their entire
household and they may have lower intentions to return to their origin countries, which
decreases their incentives to invest in their home community. On the other hand, highskilled migrants face lower transaction costs because they are less likely to be illegal, more
likely to earn higher wages and more likely to have access to bank accounts and other
financial services. These factors may result in a higher transfer of resources. In addition,
remittances may serve as a way of repayment if family members at the origin country
funded the education of the migrant (Docquier et al., 2012). It is therefore not clear if
highly educated migrants remit more or less than less skilled migrants.
Most of the existing empirical evidence investigating the effect of education on remittance patterns is based on macrodata. These studies usually conclude that the negative
impacts of the brain drain cannot be counterbalanced because high-skilled individuals send
less remittances (e.g. Faini, 2007; Adams, 2009; Niimi et al., 2010). The main disadvantage
of these cross-country studies is that they are only able to reveal evidence on whether
countries sending a higher share of skilled migrants receive more or less remittances than
countries sending fewer skilled migrants, instead of analyzing if more skilled migrants
remit more or not.
The few studies using microdata find, in general, inconclusive results: some studies
suggest that there is a negative relationship between education and remittances (see e.g.
Dustmann and Mestres, 2010; Duval and Wolff, 2010), while others suggest a positive one
(e.g. Bollard et al., 2011). A common issue that these studies share is that they are not able
to sufficiently control for characteristics of the household left behind. If these unobserved
household characteristics are correlated with both the education level of migrants and
1
their remittance behavior, then not controlling for these would lead to biased estimates.
We contribute to the literature by analyzing the effect of migrants’ education on their
remittance behavior controlling for unobserved characteristics of the household at the
origin country. Our empirical analysis is based on household survey data from five SubSaharan African countries collected in 2009, which contain information on the education
and remittance behavior of all migrants having left the household. Using a fixed-effects
approach that is based on variation in education and remittances of migrants within the
same household, we do not find evidence that the level of education is a determinant on the
decision to send remittances. It is, however, an important factor to determine the amount
of remittances sent. Conditional on sending remittances, highly educated migrants send a
significantly higher amount of remittances than migrants with lower levels of education.
The structure of the paper is as follows: in the next section, we discuss related literature.
In section 3, we describe the data and sample used. Section 4 outlines the empirical
methodology. In Section 5, we show the estimation results and section 6 concludes.
2
Related Literature
The vast majority of studies that investigate whether the brain drain is associated with a
larger flow of remittances is based on macro data, estimating the relationship between highskilled emigration rates and remittances inflows at the country level. Faini (2007) proposes
a simple theoretical model of the relationship between the share of high-skilled migrants
leaving the household and the amount of remittances sent home. The model predicts
that with an increase in the share of high-skilled migrants per-capita remittances increase
due to migrants’ higher wages (“wage effect”), but decrease because high-skilled migrants
are more likely to reunite with close family members in the host country (“reunification
effect”), leaving the overall effect ambiguous. In his empirical analysis based on data for
OECD destination countries, he finds some support for a negative relationship between
migrants’ education and remittances, though the respective coefficient is not statistically
significant. Similar results are found by Niimi et al. (2010), who take into account the
endogeneity of migration and migrant’s education using instrumental variables, such as
the distance between home and host countries and the labor market participation rate
in the home country. By restricting his analysis to 76 low- and middle-income sending
countries for which this information is available, Adams (2009) further controls for the
level of poverty in the home country. His results, however, still suggest that countries
that export a larger share of high skilled migrants receive less per capita remittances
than those exporting a larger share of unskilled migrants. An exception to this is the
study by Docquier et al. (2012), who use bilateral remittances data and find an ambiguous
relationship between the level of remittances and education.
2
The main disadvantage of using macrodata to estimate the effect of high-skilled
emigration on remittances is that these studies are only able to identify whether countries
that send a larger share of educated migrants receive larger or smaller remittance flows,
and not whether more educated individuals send a higher amount of remittances (Bollard
et al. 2011). Microdata allow associating the education level of the migrant to their
remitting behavior in terms of the likelihood of sending remittances and amount sent.
Empirical evidence using microdata is still inconclusive. Some studies find that the
effect is negative. For example, Dustmann and Mestres (2010) use the German SocioEconomic panel and find a negative relationship between years of schooling and remittances
after controlling for return intentions and family members who are living in the country of
origin (spouse and children). Similar to the previous study, Duval and Wolff (2010) find
that highly educated children in Albania are less likely to send money to their parents and
Bouoiyour and Miftah (2015) argue that a high level of education has no significant effect
on the remitting behavior of the migrants. A positive relationship between education and
remittances is found by Bollard et al. (2011) using household survey data from 11 OECD
countries. The study finds mixed evidence at the extensive margin (the decision to remit),
but a strong positive relationship at the intensive margin (the decision on how much to
remit). For the overall effect, the study finds that when compared to migrants without a
degree, migrants with a university degree send $300 more per year .
3
The Data
The data used for this article come from the Migration and Remittances Households
Surveys conducted by the World Bank in six Sub-Saharan African countries: Burkina
Faso, Kenya, Nigeria, Senegal, Uganda, and South Africa. The surveys, which are part of
the Africa Migration Project in 2009, are standardized across countries 1 . South Africa is
excluded from this analysis because it is regarded as a migrant-receiving country, thus,
the questionnaire differs from the other countries.
The survey collects comprehensive information on the household at the origin country,
as well as on the characteristics of all household members –of those still living in the
household and of former household members who migrated. With respect to the latter,
the database includes information on demographic characteristics, migration motives, and
remittance patterns of each individual. In each country, about 2,000 households were
interviewed.
In contrast to other data sources, we are able to observe remittances at the individual
1
The surveys are nationally representative for Nigeria, Senegal, and Uganda. In the case of Burkina
Faso and Kenya, they are representative for the 10 largest provinces and the top 17 districts with the
highest concentration of migrants, respectively.
3
level sent through formal or informal channels. The households report if the migrants sent
remittances back home in the last 12 months before the interview was conducted, as well
as the amount sent in local currency. For comparison purposes, we convert all financial
values to U.S. dollars using the average exchange rate for 2009 for each country. For the
purpose of our identification strategy, we restrict the sample to households that report
having two or more individuals who were former household members and migrated before
the interview was conducted. Our final database consists of 6,648 migrants.
With respect to our variable of interest, the surveys report the migrant’s education level
in different categories across countries, we re-classify them into four groups. i) Migrants
without formal education: where we include individuals with no education, or who did
not complete primary schooling. ii) Primary: which refers to individuals who completed
primary education. iii) Secondary: which includes individuals who have completed general
middle education or vocational trainings. iv) Tertiary: which includes individuals who
have a university degree or postgraduate studies.
Moreover, our database allows us to include different characteristics of the migrant that
influence the probability of sending remittances and the amount transferred. We control
for demographic characteristics such as age, marital status, gender and years spent at the
destination country. In order to capture non-linearities between the control variables and
the outcome, we include the squared term of the migrant’s age and of the years spent at
the destination country. As we do not have information on the income level of the migrant,
we include a vector of dummies that indicate the labor force status of the migrant.
Another important determinant for the remitting behavior is the reason to migrate. For
instance, we would expect that individuals who migrated to pursue a degree are less likely
to send remittances. We include dummy variables indicating if the migration decision was
made to search for a job, to pursue education, to reunite with family members, or other
reasons (e.g. related to conflict or weather conditions).
As we restrict the sample to households with at least two migrants, the relationship
from the migrant to the current household head in the origin country may be an important
determinant for the remitting behavior. We, therefore, include dummy variables indicating
if the migrant is the partner, child, sibling, or other relative of the household head. Finally,
the data includes information on the destination country of the migrant, we use this
information to condition on destination fixed effects and alternatively, we build dummy
variables indicating if the individual migrated locally, or to another destination in Africa,
America, Europe, and Asia or Oceania.
Table 1 presents descriptive statistics for the full sample and compares the characteristics
of migrants who remitted in the last 12 months before the interview with those who did not
remit. The data highlights some of the characteristics of Sub-Saharan African migrants:
66% of the migrants in the sample are men and 46% send remittances. There is a large
4
number of local migrants who moved from rural to urban areas (63%), and the average
time spent in the migration location is 6.8 years. The main reason to migrate is work
related (42%) and most of the migrants are either full-time employed or self-employed.
Furthermore, most of the migrants are either low-skilled or medium skilled, as only about
8% has completed tertiary education.
About half of the migrants in the sample remit, sending on average $803 annually.
The data show that there are significant differences between migrants who send or not
remittances. For example, those individuals who send remittances are more likely to be
married, male, and older compared to non-remitters. On average, 46% of the remitters are
full-time employees, compared with 20% of non-remitters. Remitters are less likely to be
inactive and more likely to be close relatives to the household head in the origin country.
In terms of destination, remitters are more likely to be located in America or Europe.
To gain some first insight into the relationship between migrants’ education and the
amount of remittances sent to the household left behind, Figure 2 shows the underlying
distribution of the logarithm of remittances (conditional on sending remittances) by skill
group 2 . In contrast to low-skilled individuals, the distribution of remittances sent by
medium-skilled migrants is shifted to the right, and even more for high-skilled migrants.
This provides some first descriptive evidence of a positive relationship between migrants’
education and the amount of remittances sent to the households left behind.
4
Methodology
Our main objective is to provide evidence that the education level of the migrant has
an impact on the amount of remittances sent back home. We estimate the following
regression:
Rijd = α0 + µ0 Ei + θ0 Xi + δj + δd + ijd
(1)
where Rijd measures remittances sent by migrant i, currently living in destination
d, to household j. Ei is a vector of education dummies, Xi a vector of demographic
characteristics of the migrants as described above, and δj and δd are household and
destination fixed effects, respectively. α0 , β, θ are parameter estimates and ijd the error
term.
We use three alternative measures of remittances in equation 1. First, in order to
capture the overall effect of migrants’ education we use the logarithm of the amount
remitted unconditional of remitting. We add a factor of one to the total amount of
2
We define skill groups in the following way: low-skilled include individuals with no formal and primary
education, medium-skilled refer to individuals who completed secondary education, and high-skilled those
who completed tertiary education.
5
remittances and then take the log, in order to allow for non-positive remittances. Second,
we create a binary variable that indicates whether the migrant remits or not (extensive
margin). Third, we use the logarithm of the total amount of remittances conditional on
remitting (intensive margin).
By including household fixed effects, we are able to control for unobserved characteristics
of the family at the origin country that may be correlated with the education level of the
migrant and the decision to send remittances. For example, migrants that come from
families with a higher socioeconomic status may have a higher education level and a
lower need of sending remittances. Therefore, not controlling for the characteristics of the
household in the origin country may lead to biased estimates. It is worth mentioning that
we take into account the correlation of migrants that come from the same households by
clustering the standard errors at the origin household level.
Although the database we use provides a rich set of covariates and allows us to include
different control variables for the migrant and the household at the origin country, there are
clear limitations. First, we do not have information on the migrant’s income level, which
is a key determinant on decision of sending remittances and the amount of remittances
sent. Instead, we control for the labor force status of the migrant, as both measures are
highly correlated. We expect that highly educated migrants have a higher probability of
being employed at the destination country. Second, we limit our sample to households that
have more than one migrant. This is a concern if individuals that come from one-migrant
households differ systematically from those coming from multiple-migrant households.
5
Results
We report the results for regressions of three remittance measures on education. In Table
2 we report the overall effect using the log of the total amount of remittance for the
unconditioned sample. Table 3 reports the results at the extensive margin using a binary
variable indicating if the migrant sent remittances in the past year. Table 4 reports the
results for the intensive margin, where we use the log of total remittances conditional
on remitting. The regressions include different sets of fixed effects: column I reports the
regression results including country of origin fixed effects. Column II, III, and V include
household fixed effects, column IV includes both, household and destination fixed effects.
In Table 2 we can observe that the effect of education on remittances is not statistically
significant for the overall sample. This effect is driven by the decision to send remittances
and the amount transferred, so it is relevant to look at both effects separately. Concerning
the likelihood of sending remittances, Table 3 shows that the level of education of the
migrant is not a relevant determinant. High-skilled migrants seem to have a lower
probability of sending remittances but this effect is not statistically significant. If we do
6
not control for the employment status of the migrant the coefficient is positive. With
respect to the amount of remittances sent, Table 4 shows that in contrast with migrants
with no formal education, highly-educated migrants send more remittances. The coefficient
is positive and highly significant across specifications, but when conditioning on household
and destination fixed effects the tertiary educated coefficient is reduced considerably. This
could be due to unobserved factors that determine remittance patterns and the education
level of the migrant. In general, our results suggest that the level of education of the
migrant has no impact at the extensive level, but a large positive impact at the intensive
level.
In addition, the results show that some of the migrant characteristics have a significant
effect on both, the likelihood of remitting and the amount sent. Gender is an important
determinant at the extensive and intensive margins. Male migrants are more likely to
send remittances and to send higher amounts. As well, the variable age is positive in
both cases and the quadratic term negative, which implies that remittances increase at a
decreasing rate. The martial status of the migrant only affects the probability of sending
remittances but has no significant impact on the amount sent. The migrant’s destination is
also relevant, in contrast to local migrants, international migrants have a higher probability
of transferring money and their transfers are larger.
Variables related to the migration reason are only relevant for the decision to send
remittance. However, once we control for the current employment status of the migrant,
the effect is not significant anymore. The employment status of the migrant in the
host country is an essential determinant in the decision to send or not remittances. In
contrast to full time employees, immigrants who work part-time, are self-employed, or
inactive, have a lower probability to send remittances. Correspondingly, at the intensive
margin, inactive migrants send significantly less remittances when compared with full-time
employed migrants.
Results show that there are no significant differences between close relatives in terms of
the likelihood of sending remittances. When compared to adult children of the household
head (at the origin country), the partner, and siblings do not differ significantly in the
probability of sending remittances. Yet, at the intensive margin when conditioning on those
who remit, we can observe that the partner of the household head sends more remittances
than the children. As expected, more distant relatives have a lower probability of sending
remittances and they transfer significantly less than the children of the household.
6
Conclusion
This paper investigates the relationship between remittances and migrants’ education
using microdata from five countries in Sub-Saharan Africa. To the best of our knowledge,
7
this is the first econometric study that estimates the impact of education on remittances
using microdata from the migrant and their origin households for this region. We focus
on households with two or more migrants, which allow us to include household of origin
fixed effects in the estimations. By doing this, we control for unobserved characteristics of
the household left behind that may influence the education level of the migrant and their
remittance behavior.
Our results suggest that when combining the extensive and intensive margins there is
no significant effect of education on remittances. However, once we separate these effects,
we observe that education has no impact on the propensity of sending remittances, but
that highly educated migrants send significantly higher amounts of money to their origin
household. This could be explained by the fact that high-skilled migrants have access to
better jobs and earn more money at the destination country.
The “brain drain” is associated with negative externalities on the source country. One
of these concerns is that high-skilled migrants send fewer remittances to their household of
origin, which is not supported by the empirical evidence. If remittances increase with the
level of education, some of the negative externalities of the “brain drain” on the source
country may be counterbalanced.
8
References
Adams, R. H. (2009). The determinants of international remittances in developing
countries. World Development, 37 (1), 93–103.
Bollard, A., McKenzie, D., Morten, M. and Rapoport, H. (2011). Remittances
and the brain drain revisited: The microdata show that more educated migrants remit
more. The World Bank Economic Review, 25 (1), 132–156.
Bouoiyour, J. and Miftah, A. (2015). Why do migrants remit? testing hypotheses for
the case of morocco. IZA Journal of Migration, 4 (1), 1–20.
Brücker, H., Capuano, S. and Marfouk, A. (2013). Education, gender and international migration: insights from a panel-dataset 1980-2010. http://www.iab.de/en/
daten/iab-brain-drain-data.aspx, accessed: 2015-11-16.
Docquier, F., Rapoport, H. and Salomone, S. (2012). Remittances, migrants’
education and immigration policy: Theory and evidence from bilateral data. Regional
Science and Urban Economics, 42 (5), 817–828.
Dustmann, C. and Mestres, J. (2010). Remittances and temporary migration. Journal
of Development Economics, 92 (1), 62–70.
Duval, L. and Wolff, F.-C. (2010). Remittances matter: Longitudinal evidence from
albania. Post-Communist Economies, 22 (1), 73–97.
Faini, R. (2007). Remittances and the brain drain: Do more skilled migrants remit more?
The World Bank Economic Review, 21 (2), 177–191.
Niimi, Y., Ozden, C. and Schiff, M. (2010). Remittances and the brain drain: skilled
migrants do remit less. Annals of Economics and Statistics/Annales d’Économie et de
Statistique, pp. 123–141.
OECD-UNDESA (2013). World migration in figures. http://www.oecd.org/els/mig/
World-Migration-in-Figures.pdf, accessed: 2016-01-04.
9
Figures
Source: Authors’ analysis based on data from Brücker et al. (2013).
Figure 1: Evolution of High-Skilled Migration by Region
Source: Authors’ analysis based on data described in the text.
Figure 2: Kernel Density Distribution by Skill Group
10
Tables
Table 1: Descriptive Statistics of the Sample
All migrants
Probability of remitting
Total remittances
Male
Age
Married
Years since emigration
Education
No formal education
Primary
Secondary
Tertiary
Migration reason
Education
Job
Family
Other
Labor Force Status
Full time employed
Part time employed
Self employed
Not in labor force
Relationship to head
Son/daughter
Partner/head
Sibling
Other relative
Destination
Africa
America
Asia or Oceania
Europe
Within country
Observations
Significance levels:
∗
5%,
∗∗
Non-remitters
Remitters
mean
0.464
372.931
0.664
31.166
0.556
6.868
s.d.
0.499
1434.202
0.472
9.694
0.497
6.692
mean
–
–
0.597
28.715
0.452
6.208
s.d.
–
–
0.491
9.330
0.498
6.603
mean
–
803.125
0.741
33.994
0.676
7.629
s.d.
–
2021.105
0.438
9.330
0.468
6.713
t-test.
–
–
12.53∗∗∗
23.01∗∗∗
18.83∗∗∗
8.68∗∗∗
0.276
0.170
0.479
0.075
0.447
0.376
0.500
0.263
0.266
0.192
0.474
0.067
0.442
0.394
0.499
0.250
0.287
0.144
0.485
0.084
0.452
0.351
0.500
0.277
1.89
−5.23∗∗∗
0.89
2.55∗
0.192
0.417
0.365
0.026
0.394
0.493
0.481
0.160
0.274
0.343
0.348
0.035
0.446
0.475
0.476
0.183
0.097
0.502
0.384
0.016
0.296
0.500
0.486
0.126
−18.71∗∗∗
13.28∗∗∗
3.06∗∗
−4.75∗∗∗
0.319
0.059
0.351
0.272
0.466
0.235
0.477
0.445
0.200
0.053
0.301
0.445
0.400
0.224
0.459
0.497
0.457
0.065
0.408
0.071
0.498
0.246
0.491
0.257
23.26∗∗∗
2.03∗
9.10∗∗∗
−37.72∗∗∗
0.627
0.027
0.196
0.097
0.484
0.163
0.397
0.296
0.608
0.017
0.204
0.104
0.488
0.128
0.403
0.306
0.649
0.040
0.188
0.089
0.477
0.196
0.391
0.284
3.46∗∗∗
5.81∗∗∗
−1.64
−2.16∗
0.190
0.062
0.009
0.108
0.631
6648
0.392
0.241
0.095
0.310
0.483
0.184
0.049
0.008
0.067
0.691
3561
0.388
0.216
0.090
0.250
0.462
0.197
0.076
0.010
0.155
0.562
3087
0.398
0.265
0.100
0.362
0.496
1.26
4.57∗∗∗
0.82
11.65∗∗∗
−10.97∗∗∗
1%,
∗∗∗
.1%.
t-test: remitters vs non-remitters.
11
Table 2: Pooled OLS: Determinants of Remittances (Full-Sample)
Education (Ref: No Education)
Primary
Secondary
Tertiary
Reason (Ref: Work Related)
Education
Family
Other
Status (Ref: Full time employed)
Part time employed
Self employed
Not in labor force
Relationship to head (Ref: Child)
Partner
I
II
III
IV
V
−0.166
(0.119)
0.110
(0.119)
0.391∗
(0.220)
−0.196
(0.175)
−0.078
(0.169)
0.312
(0.282)
−0.096
(0.167)
−0.065
(0.162)
0.143
(0.266)
−0.201
(0.165)
−0.166
(0.163)
0.026
(0.269)
−0.076
(0.168)
0.143
(0.168)
0.583∗∗
(0.276)
−0.076
−0.068
−0.182
−0.238
−1.048∗∗∗
(0.140)
(0.174)
(0.159)
(0.158)
(0.157)
−0.005
−0.319∗∗ −0.099
−0.134
−0.325∗∗
(0.105)
(0.145)
(0.139)
(0.137)
(0.145)
−0.954∗∗∗ −0.973∗∗∗ −0.766∗∗∗ −0.754∗∗∗ −1.035∗∗∗
(0.182)
(0.284)
(0.268)
(0.284)
(0.298)
−0.618∗∗∗ −0.958∗∗∗ −1.075∗∗∗ −1.022∗∗∗
(0.230)
(0.258)
(0.244)
(0.246)
−0.961∗∗∗ −0.857∗∗∗ −0.694∗∗∗ −0.679∗∗∗
(0.121)
(0.149)
(0.140)
(0.141)
−2.623∗∗∗ −2.338∗∗∗ −2.159∗∗∗ −2.170∗∗∗
(0.126)
(0.160)
(0.152)
(0.149)
0.573∗∗
(0.287)
−0.569∗∗∗
(0.114)
−0.305∗∗
(0.133)
0.335∗∗∗
(0.083)
0.091∗∗∗
(0.020)
−0.001∗∗∗
(0.000)
0.446∗∗∗
(0.092)
0.048∗∗∗
(0.016)
−0.002∗∗∗
(0.001)
0.533
(0.349)
−0.306
(0.192)
−0.696∗∗∗
(0.192)
0.181∗
(0.096)
0.115∗∗∗
(0.027)
−0.001∗∗
(0.000)
0.400∗∗∗
(0.115)
0.029
(0.019)
−0.001
(0.001)
–
–
America
–
–
Asia or Oceania
–
–
Europe
–
–
0.393
(0.375)
no
no
yes
6648
0.306
−0.304
(0.517)
yes
no
no
6648
0.508
Sibling
Other relative
Male
Age
Age2
Married
Years since emigration
Years since emigration2
Destination (Ref: Local migrant)
Africa
Constant
Household FE
Destination FE
Origin FE
Observations
Adj-R2
0.632∗∗
(0.319)
−0.362∗∗
(0.174)
−0.745∗∗∗
(0.183)
0.261∗∗∗
(0.092)
0.091∗∗∗
(0.026)
−0.001∗∗
(0.000)
0.329∗∗∗
(0.111)
0.032∗
(0.018)
−0.001
(0.001)
0.628∗∗∗
–
(0.175)
1.777∗∗∗
–
(0.278)
0.378
–
(0.654)
2.195∗∗∗
–
(0.212)
−0.318
−0.022
(0.487)
(0.475)
yes
yes
no
yes
no
no
6648
6648
0.543
0.555
Note: Standard errors in parentheses (clustered at the household level).
Significance levels: ∗ 10%, ∗∗ 5%, ∗∗∗ 1%.
12
0.626∗∗
(0.314)
−0.310∗
(0.171)
−0.666∗∗∗
(0.184)
0.250∗∗∗
(0.092)
0.081∗∗∗
(0.025)
−0.001∗
(0.000)
0.310∗∗∗
(0.109)
0.038∗∗
(0.019)
−0.001
(0.001)
–
–
–
0.863∗∗∗
(0.320)
−0.212
(0.181)
−0.751∗∗∗
(0.194)
0.571∗∗∗
(0.097)
0.153∗∗∗
(0.027)
−0.001∗∗∗
(0.000)
0.334∗∗∗
(0.117)
0.056∗∗∗
(0.019)
−0.001∗∗
(0.001)
–
–
–
–
−2.612∗∗∗
(0.472)
yes
yes
no
6648
0.514
Table 3: Pooled OLS: Determinants of Remittances (Extensive Margin)
Education (Ref: No Education)
Primary
Secondary
Tertiary
Reason (Ref: Work Related)
Education
Family
Other
Status (Ref: Full time employed)
Part time employed
Self employed
Not in labor force
Relationship to head (Ref: Child)
Partner
I
II
III
IV
V
−0.006
(0.023)
0.002
(0.022)
−0.004
(0.035)
−0.017
(0.032)
−0.019
(0.029)
0.001
(0.046)
−0.005
(0.031)
−0.014
(0.029)
−0.012
(0.045)
−0.014
(0.032)
−0.023
(0.030)
−0.024
(0.046)
0.007
(0.032)
0.029
(0.031)
0.072
(0.047)
−0.071∗∗∗ −0.041
−0.051∗
−0.060∗∗ −0.212∗∗∗
(0.023)
(0.028)
(0.027)
(0.027)
(0.028)
−0.021
−0.065∗∗∗ −0.042∗
−0.046∗
−0.083∗∗∗
(0.019)
(0.025)
(0.024)
(0.024)
(0.026)
−0.160∗∗∗ −0.173∗∗∗ −0.151∗∗∗ −0.153∗∗∗ −0.205∗∗∗
(0.039)
(0.055)
(0.054)
(0.055)
(0.059)
−0.090∗∗ −0.140∗∗∗ −0.151∗∗∗ −0.147∗∗∗
(0.037)
(0.046)
(0.046)
(0.045)
−0.131∗∗∗ −0.122∗∗∗ −0.109∗∗∗ −0.106∗∗∗
(0.021)
(0.025)
(0.025)
(0.025)
−0.434∗∗∗ −0.406∗∗∗ −0.390∗∗∗ −0.390∗∗∗
(0.022)
(0.027)
(0.027)
(0.027)
–
–
–
0.001
(0.045)
−0.115∗∗∗
(0.020)
−0.065∗∗∗
(0.024)
0.045∗∗∗
(0.015)
0.015∗∗∗
(0.004)
−0.000∗∗∗
(0.000)
0.083∗∗∗
(0.017)
0.005∗
(0.003)
−0.000∗
(0.000)
0.014
(0.055)
−0.050
(0.031)
−0.098∗∗∗
(0.034)
0.029∗
(0.016)
0.018∗∗∗
(0.005)
−0.000∗∗
(0.000)
0.071∗∗∗
(0.020)
0.003
(0.003)
−0.000
(0.000)
0.023
(0.053)
−0.057∗
(0.030)
−0.104∗∗∗
(0.033)
0.036∗∗
(0.016)
0.016∗∗∗
(0.005)
−0.000∗∗
(0.000)
0.065∗∗∗
(0.020)
0.004
(0.003)
−0.000
(0.000)
0.021
(0.053)
−0.055∗
(0.030)
−0.097∗∗∗
(0.034)
0.035∗∗
(0.016)
0.015∗∗∗
(0.005)
−0.000∗∗
(0.000)
0.063∗∗∗
(0.020)
0.005
(0.004)
−0.000
(0.000)
0.065
(0.055)
−0.037
(0.032)
−0.112∗∗∗
(0.035)
0.095∗∗∗
(0.017)
0.028∗∗∗
(0.005)
−0.000∗∗∗
(0.000)
0.066∗∗∗
(0.021)
0.008∗∗
(0.004)
−0.000∗
(0.000)
–
–
–
–
America
–
–
–
–
Asia or Oceania
–
–
–
–
Europe
–
–
0.080∗∗
(0.031)
0.171∗∗∗
(0.042)
0.048
(0.106)
0.204∗∗∗
(0.033)
−0.026
(0.094)
yes
no
no
6648
0.499
–
–
0.000
(0.093)
yes
yes
no
6648
0.504
−0.456∗∗∗
(0.091)
yes
yes
no
6648
0.459
Sibling
Other relative
Male
Age
Age2
Married
Years since emigration
Years since emigration2
Destination (Ref: Local migrant)
Africa
Constant
Household FE
Destination FE
Origin FE
Observations
Adj-R2
0.248∗∗∗ −0.022
(0.072)
(0.096)
no
yes
no
no
yes
no
6648
6648
0.248
0.488
Note: Standard errors in parentheses (clustered at the household level).
Significance levels: ∗ 10%, ∗∗ 5%, ∗∗∗ 1%.
13
Table 4: Pooled OLS: Determinants of Remittances (Intensive Margin)
I
Education (Ref: No Education)
Primary
Secondary
Tertiary
Reason (Ref: Work Related)
Education
Family
Other
Status (Ref: Full time employed)
Part time employed
Self employed
Not in labor force
Relationship to head (Ref: Child)
Partner
II
−0.300∗∗∗ −0.010
(0.103)
(0.201)
0.318∗∗∗
0.242
(0.107)
(0.178)
1.025∗∗∗
0.611∗∗
(0.166)
(0.250)
0.622∗∗∗
0.175
(0.137)
(0.220)
0.172∗∗ −0.039
(0.086)
(0.145)
−0.537∗∗∗ −0.564
(0.203)
(0.460)
III
IV
0.040
−0.092
(0.187)
(0.183)
0.286∗
0.152
(0.164)
(0.163)
0.579∗∗∗
0.404∗
(0.222)
(0.229)
−0.053
(0.191)
0.122
(0.128)
−0.418
(0.389)
−0.045
(0.190)
0.093
(0.127)
−0.484
(0.400)
−0.125
−0.225
−0.271
−0.204
(0.155)
(0.212)
(0.197)
(0.195)
−0.371∗∗∗ −0.218
−0.078
−0.078
(0.084)
(0.134)
(0.123)
(0.122)
−1.015∗∗∗ −0.588∗∗∗ −0.579∗∗∗ −0.556∗∗∗
(0.145)
(0.217)
(0.197)
(0.195)
V
−0.079
(0.183)
0.217
(0.169)
0.476∗∗
(0.234)
−0.098
(0.189)
0.057
(0.127)
−0.483
(0.405)
–
–
–
0.831∗∗∗
(0.153)
0.054
(0.086)
0.099
(0.105)
0.122∗
(0.070)
0.107∗∗∗
(0.019)
−0.001∗∗∗
(0.000)
−0.026
(0.073)
0.030∗∗∗
(0.011)
−0.001∗∗∗
(0.000)
0.529∗
(0.307)
−0.028
(0.211)
−0.393∗∗
(0.198)
0.117
(0.091)
0.102∗∗∗
(0.034)
−0.001∗∗
(0.000)
0.045
(0.110)
−0.001
(0.016)
−0.000
(0.000)
0.574∗∗
(0.270)
−0.143
(0.180)
−0.434∗∗∗
(0.164)
0.182∗∗
(0.084)
0.087∗∗∗
(0.031)
−0.001∗∗
(0.000)
0.024
(0.098)
−0.006
(0.015)
0.000
(0.000)
0.567∗∗
(0.260)
−0.085
(0.174)
−0.353∗∗
(0.153)
0.181∗∗
(0.084)
0.074∗∗
(0.031)
−0.001∗
(0.000)
0.024
(0.097)
0.001
(0.014)
−0.000
(0.000)
0.597∗∗
(0.259)
−0.065
(0.171)
−0.366∗∗
(0.154)
0.238∗∗∗
(0.086)
0.083∗∗∗
(0.032)
−0.001∗∗
(0.000)
0.015
(0.098)
0.004
(0.014)
−0.000
(0.000)
–
–
–
–
America
–
–
–
–
Asia or Oceania
–
–
–
–
Europe
–
–
–
–
1.743∗∗∗
(0.362)
no
no
yes
3087
0.359
1.524∗∗
(0.673)
yes
no
no
3087
0.609
0.315∗∗
(0.160)
1.226∗∗∗
(0.190)
0.200
(0.504)
1.644∗∗∗
(0.157)
2.112∗∗∗
(0.622)
yes
no
no
3087
0.669
2.505∗∗∗
(0.620)
yes
yes
no
3087
0.684
2.309∗∗∗
(0.641)
yes
yes
no
3087
0.681
Sibling
Other relative
Male
Age
Age2
Married
Years since emigration
Years since emigration2
Destination (Ref: Local migrant)
Africa
Constant
Household FE
Destination FE
Origin FE
Observations
Adj-R2
Note: Standard errors in parentheses (clustered at the household level).
Significance levels: ∗ 10%, ∗∗ 5%, ∗∗∗ 1%.
14