Modern Economy, 2013, 4, 535-550
http://dx.doi.org/10.4236/me.2013.48057 Published Online August 2013 (http://www.scirp.org/journal/me)
Familial Relationship of Migrants and Remittances
Behavior: Theory and Evidence from Ecuador
Hilcías E. Morán
Department of EconomicsResearch, Banco de Guatemala, Ciudad de Guatemala, Guatemala
Received February 2, 2013; revised March 12, 2013; accepted April 12, 2013
Copyright © 2013 Hilcías E. Morán. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
This paper develops a simple analytical model with altruistically motivated remittances to analyze the determinants of
remittances using household data from Ecuador. The model predicts that migrant remittance behavior and household
migration size are non-monotonically correlated. The empirical work suggests that migrant remittances are a non-in-
creasing function of the number of migrants within the household. If there is a positive selection of migrants, then one
would expect that the forgone household income due to migration is higher than when there is a negative selection. Ac-
cording to the Ecuadorian data of households with at least 1 migrant, prior to migration the individuals who left had a
higher education level than those relatives left behind. The average years of schooling of the migrants are 3.5 years,
higher than the non-migrants. It seems that when migration size changes from 2 to 3 and from 3 to 4 migrants within the
same household, the forgone household income due to migration might have a positive effect on altruistically motivated
remittances, which compensates for the negative effect of the increased number of migrants on the individual amount of
remittances (Nash assumption). The results of allowing a non-linear relationship between migrant remittance behavior
and household migration are partially distinguished from those reached when there is a linear relationship and also con-
trast with the predictions of rent-seeking literature. Moreover, it shows that Ecuadorian migrants who moved to Spain
were less likely to remit and remit less than those migrants whose destination country was the United States.
Keywords: Remittances; Migration; Private Transfers; Altruism; Ecuador
1. Introduction
According to World Bank data, the share of remittances
as a percentage of gross domestic product has grown
steadily through the last three decades. By the end of the
1970s remittances for all developing countries repre-
sented only around 0.5 percent of the GDP while in 2006
it reached around 2.0 percent. Remittances have become
the second-largest source of international financial re-
sources for developing countries, after foreign direct
investment, and in many cases are the largest source of
external inflows. Remittances have been regarded as an
important source of external funding for stimulating eco-
nomic development [1]. Because of these facts, scholars,
policy makers and international financial agencies have
become worried about the potentially transitory versus
permanent nature of remittances. Understanding the
determinants of migrant remittance behavior can help to
predict the future pattern of remittance flows for deve-
loping.
Although Ecuador is a small Latin American country
of approximately 13.9 million people, Ecuadorians are
one of the largest immigrant groups in metro New York
and the second largest immigrant group in Spain1. A
massive emigration from Ecuador occurred between
1999 and 2004 as a response to the national economic
crisis of 1998 and 1999, caused by the closure of the
banks, devaluation (from 5,000 sucres to 25,000 sucres to
the dollar), company bankruptcies and financial insta-
bility. During these two years, Ecuador’s Gross Domestic
Product fell by 27 percent, while per capita household
consumption was lower in 1999 than 10 years earlier2.
According to the Central Bank of Ecuador, from 1996 to
2006 remittances grew at an average rate of 19 percent
annually, and since 1999 have become the second-largest
source of foreign income after oil exports, exceeding
official development aid and foreign direct investment.
In 2006 remittances totaled 2.9 billion dollars, which
represented 7.0 percent of GDP and 22.2 percent of total
1[2].
2[3].
C
opyright © 2013 SciRes. ME

H. E. MORÁN
536
exports of goods and services.
There is a growing body of literature which focuses on
the microeconomic motives behind remittances [4-8].
These surveys list three basic motives for remittances:
altruism, insurance (indemnifying the human and social
development of the family left behind against income
shocks), and investment (asset accumulation back home
as part of the migration life-cycle planning)3.
Similar to [5], this study proposes a behavioral model
of remittances based on altruism. It is assumed that an
individual migrant takes as given the amount of remit-
tances sent by all other migrants within the same house-
hold (Nash assumption). Existing studies have used the
Nash assumption as well. They predict that the individual
remittance behavior is negatively associated with the
number of other migrants within the same household
[5,6]. This paper contributes both theoretically and
empirically to this branch of the literature. In particular,
the analytical model emphasizes the relationship between
individual migrant remittance behavior and the number
of migrants within the same household.
The key difference between this model and previous
analytical frameworks based on altruism is that, when the
migrant opportunity cost of forgone household labor
income is taken into account, this model suggests that
migrant remittance behavior and household migration
size are non-monotonically correlated. In other words,
the relationship between individual remittance behavior
and the number of other migrants within the same house-
hold is not theoretically determined. Whereas remittances
decrease with the number of migrants within the same
household due to the Nash assumption (more remittances
sent by the others migrants, less remittances sent by one),
remittances increase with the number of migrants be-
cause the household’s labor income is negatively affected
by the reduction of the labor supply in the source country.
Whether remittances increase or decline will depend on
which effect dominates. Hence, this relationship may be
empirically addressed.
Using household data from Ecuador, the empirical part
of this paper presents an analysis to assess the deter-
minants of remittances. According to the 2004 “Demo-
graphic, Maternal and Infant Health Survey” (ENDEM
AIN), Ecuadorian migrants are spread across 30 coun-
tries, of which the most significant destination countries
are Spain, the US and Italy, respectively. Most of the
migrants are close relatives of the household head (80
percent are parents, children or spouses). The survey
includes migrants who left the country between 1960 and
2004. Most of those left between 1999 and 2004 (more
than 70 percent). While the preferred countries during the
last surge of migration were European such as Spain and
Italy, the U.S. was a secondary destination. For instance,
of the total number of Ecuadorian migrants living in
Spain, around 90 percent arrived between 1999 and 2004.
During the 1980’s, most of the Ecuadorian migrants paid
intermediaries, coyotes or a document forger, for clan-
destine passage to the United States, whereas the vast
majority of the migrants during the last massive mig-
ration chose Spain. The main reason for this was an exis-
ting agreement between Spain and Ecuador that allowed
Ecuadorians to enter the country as tourists without visas
(the law changed in 2003). The main motivations for
leaving were to search for work or to accept a job offer in
the destination country.
The empirical work provides evidence that migrant
remittances are a non-increasing function of the number
of migrants within the household. If there is a positive
selection of migrants in the sense that the more educated
individuals within the household are those who migrate,
then one would expect that the forgone household
income due to migration is higher than when there is a
negative selection. According to the Ecuadorian data of
households with at least 1 migrant, prior to migration the
individuals who left had a higher education level than
those relatives left behind. The average years of
schooling of the migrants were 3.5 years higher than the
non-migrants. It seems that when migration size changes
from 2 to 3 and from 3 to 4 migrants within the same
household, the forgone household income due to
migration might have a positive effect on altruistically
motivated remittances, which compensates for the nega-
tive effect of the increased number of migrants on the
individual amount of remittances. The results of allowing
a non-linear relationship between migrant remittance
behavior and household migration are partially distin-
guished from those reached when there is a linear
relationship and also contrast with the predictions of
rent-seeking literature4. Moreover, it shows robust evi-
dence both for altruistically motivated remittance be-
havior and for the fact that the size of remittances de-
creases over time since the migration. Finally, the em-
pirical section shows that Ecuadorian migrants who
moved to Spain were less likely to remit and remit less
than those migrants whose destination country was the
United States, which might reflect the lower unem-
ployment rate and the higher potential earnings in the
United States relative to Spain5.
The rest of the paper is organized as follows: Section 2
includes a basic theoretical framework and reduced form
equation for the migrants’ remittance behavior; Section 3
discusses the empirical strategy and results; and the last
section offers some concluding remarks.
4As pointed out by [9], if we allow for multiple migrants competing for
inheritance, then “we would expect remittances per migrant to first
increase and then decrease with the number of other migrants as the
effect of competition is offset by the decrease in one’s probability o
f
inheritance”.
5See [10].
3For a more comprehensive review of these arguments, see [4] and the
survey in [9].

H. E. MORÁN 537
2. Basic Theoretical Framework
2.1. The Model
Following to [5], this study proposes a behavioral model
of remittances based on altruism. It is assumed that an
individual migrant takes as given the amount of
remittances sent by all other migrants within the same
household (Nash assumption). Existing studies have used
the Nash assumption as well, but have not formally
stated and tested this assumption. They predict that the
individual remittance behavior is negatively associated
with the number of other migrants within the same
household [5,6]. Since here it is assumed that migrants
from the same household make a voluntary contribution
to finance consumption in that household, non-migrant
consumption might be thought of as a public good, where
the possibility of migrants that free ride exists in this
framework. This model contributes theoretically to this
branch of the literature. In particular, the model em-
phasizes the relationship between individual migrant
remittance behavior and the number of migrants within
the same household. The key difference between this
model and previous analytical frameworks based on
altruism is that, when the migrant opportunity cost of
forgone household labor income is taken into account,
this model suggests that migrant remittance behavior and
household migration size are non-monotonically corre-
lated.
Consider a two-agent model: the migrant and the non-
migrant. The individual migrant worker is represented by
from household who lives and works
in a foreign country
=1,2,,m ilj
, ,=1,2,
f
F
=1,2,jS

,
; the non-migrant
refers to household in the source country,
which can consist of one or more individuals. There are
several assumptions in this framework. First, it ignores
the reasons for the migration decision, which implies that
the migrants are exogenously located in different host
countries6. Second, each migrant is altruistic toward the
non-migrant members of her own family7, which means
that migrant i, in addition to choosing her own
consumption
f
i
c, has to decide how much money to
transfer to her relatives in the source country (remittance
size ij ). Next, an individual migrant takes the
amount of remittances sent by all other migrants within
the same household as given (Nash assumption),
which is denoted as ij
ai
j
a

. Prices are the same across the
host countries and the source country, and are normalized
to 18. Finally, all income in the source country is
consumed and all migrant income net of remittances is
consumed as well in the host country.
Thus, each migrant from household in host
country
ij
f
, who values both her own utility and the
utility of household in the source country, seeks to
maximize a log utility function as follows:
j
{,
f
ij
j
 
}
log log

f
h
j
c
ij
VMa c 
ac

i
x (1)
..
s
t
f
f
ij w ij
ca (2)


hh
j
ij ij
h
jj
a a
cn


lw
ij 
(3)
0,a (4)
for all , =1,,m il=1, ,
f
F, and ,
taking as given non-migrant consumption
=1, ,jS
h
j
c, the
amount of remittances sent by all other migrants within
the same household ij a

, the exogenous migrant’s labor
income
f
w
hh
j
lw
, and the exogenous household’s total labor
income , where h
j
l is the number of working
members within household in the source country,
denotes real wages in the source country, j is the
number of individuals in household (including
children), and
j
h
wn
j


0,1   represents a taste parameter
that characterizes heterogeneous preferences for each
migrant. This taste parameter, in particular, represents
the migrant’s degree of altruism toward her relatives in
the source country. Expressions (1)-(4) represent the
migrant utility function, migrant budget constraint, non-
migrant budget constraint and the non-negative of
remittances condition, respectively9.
The utility maximization problem is:



Max loglog
hh
j
ij ij
fij
aij j
lw a a
wa n




6Some of the leading papers dealing with migration decision theory are
[11-14], in which the migration decision is a function of two main
variables: wage differential and migration cost. Similar representations
also can be found in [15], where the authors construct a discrete time
model of equilibrium migration with endogenous moving costs. In this
setup the cost of moving also depends on the stock of migrants already
settled in the host country, which captures the “networks externality”
effect.
7For a general discussion about the different motives for remitting cov-
ered in the literature, see [4] and [9]. In addition to the altruistic behav-
ior of migrants, these authors include other motives for remitting such
as exchange, investment and inheritance-seeking. Under the exchange
motive, for instance, the migrants’ remittances may be viewed as re-
p
ayments of loans used to finance the moving costs or the migrants’
investment in human capital. Investment and inheritance-seeking mo-
tives are defined by [4] as self-interest motives.
..
s
t
0.
ij
a
8This assumption does not change any of the substantive predictions o
f
the model considered here. [16-18] consider international migration
models in which it is assumed that prices are higher in the host country
relative to prices in the source country. This issue is not considered here
mainly to maintain simplicity and partially because it would be more
relevant if we were modeling return migration as analyzed by [16-18].
9This approach looks similar to that discussed in the literature of private
p
rovision of public goods. See [19-22].

H. E. MORÁN
538
This maximization problem yields a continuous
function called the migrant best response function:
th
i



max,0 .
1
fhh
jij
ij
wlwa
a















(5)
As expected, the individual migrant ’s best response
function for remittances is a decreasing function of the
amount of remittances sent home by all other migrants
within the same household, ij . Now, let
i
am
j
l denote
the number of migrants within household
j
such that
the total household labor supply is =mh
j
jj
. Then,
under the assumption of a symmetric equilibrium, let
lll
=m
j
jij
ala
denote the total amount received by house-
hold
j
in equilibrium. As follows from (5), the indivi-
dual migrant’s optimal amount of remittances sent to her
relatives left behind is

 
max,0,,, .
fmh
jj fm
ij m
j
wllw
a
l











a wll
(6)
From (6), remittances are an increasing function of the
migrant’s labor income,
f
w, and of the migrant’s degree
of altruism toward non-migrants,

, and a decreasing
function of the household labor income, . The
relationship between the migrant worker remittances and
the number of migrants within the same household,
however, is ambiguously determined. When household
migration increases exogenously, the amount of
remittances sent by all other migrants within the house-
hold increases, while migrant ’s best response function
would predict that migrant i’s remittances would
decline. The latter prediction might be counter-balanced,
however, by a decrease in the household labor income
due to the lower household labor supply in the source
country (i.e. forgone migrant wages in the source
country), which would imply that remittances increase
when household migration increases10. As follows from
(6), the derivative of remittances with respect to
migration size is given by
h
w
i



2.
hf
mm
www
a
ll





h

(7)
Since wage differential is positive,

>
h
f
h
w
ww

,
which implies that migrant remittances are negatively
associated with migration size. When

<
h
f
h
w
ww

,
migrant remittances are positively associated with the
number of migrants within the household. Hence, the
relationship between remittance behavior and migration
size depends on the wage gap and the unobservable
migrant’s degree of altruism. If the migrant’s degree of
altruism is sufficiently large (small) and there is a large
(small) wage gap between host country
f
and the mig-
rant source country, it is more likely that remittance be-
havior and migration size are negatively (positively)
correlated. Since the relationship between migration size
and individual migrant remittance behavior is not de-
termined, the empirical work of this paper examines this
relationship.
To close the model, let

f
w


i
be a critical level of
migrant labor income such that the migrant equilibrium
remittances for each individual from household is
given by
j




 
if >
0,
fmh mh
jj jj
ff
m
ij j
wllw llw
ww
al
Otherwise





 






(8)
where the inequality condition on the right side of (8)
states that if the actual migrant labor income is greater
than her critical level


mh
jj
llw

, then migrant i
sends a positive amount of remittances to her relatives in
the source country. There are several implications from
(8). First, individual remittance behavior is ambiguously
associated with the number of other migrants within the
same household, which is a direct consequence of the
Nash assumption and the altruistically motivated migrant
remittance behavior (see Equation (7)). Hence, this
relationship may be empirically addressed. Second,
migrants with different tastes have different critical
levels of income. Conditional on the household labor
income and the number of migrants within the same
household, the decision to remit or not to remit (free rider)
depends on whether the actual migrant labor income is
greater than (does remit) or less than (does not remit) her
critical level of labor income. Migrants who do not remit
are those with relatively low labor income, a low degree
of altruism toward their relatives left behind, or both. On
the other hand, migrants who remit are those with
relatively high labor income, a high degree of altruism,
or both. Third, since individual migrants stay in different
host countries, migrants who migrate to higher earnings
countries are more likely to remit and, they remit more
than those who migrate to lower earnings countries. Fi-
10Here, the exogenous change of household migration might be a strong
assumption, but it can be thought of as a remarkable change in the
immigration policies of a host country that would allow migrant work-
ers with particular qualifications or from a specific source country to
move to that host country without any significant moving cost. For
example, Mexican workers who live next to the Mexican-US border
might easily migrate to the US if border enforcement policy in the US
was markedl
y
chan
g
ed.

H. E. MORÁN 539
nally, this model predicts that individual migrant remit-
tances are a decreasing function of household labor
income.
2.2. Time Profile of Remittances
Moving beyond the predictions of the one period model
described above, this section discusses the time profile of
migrant remittance behavior. A survey conducted by
Multilateral Investment Fund and Pew Hispanic Center
in 2003 found that 42 percent of migrant workers from
Latin American countries (about six million people) send
remittances home on a regular basis. However, the
observed probability of remitting is not constant across
that population but is instead higher among more
recently arrived migrant workers. While half of all Latin
American migrant workers who have been in the United
States for 10 years or less are regular remittance senders,
the observed probability for those who have been there
between 10 and 20 years is about 40 percent and for
those between 20 and 30 years it is about 20 percent,
suggesting that the likelihood of remitting declines over
time. However, from a theoretical view the relationship
between remittance behavior and the duration of the
migration is ambiguously determined11.
In order to address the time profile of remittances, we
construct a multi-period model, similar to that described
above, in which all income is spent in each period by
both the migrants and the non-migrants (i.e. there is no
saving and no intertemporal discount factor). The
optimal solution for remittances is similar to that shown
by expression (8), except that it would have a script
denoting time, ,ij t 12. Without any loss of generality for
simplicity, we can assume that migrant labor income and
household labor income can vary over time while
household migration is maintained constant over time.
Migrant labor income can increase over time with labor
market experience in the host country while household
labor income can increase over time as household
members improve their educational attainment over time,
gain labor market experience or increase household labor
supply over time (children become adults). Moreover, we
assume that no moral hazard is involved in the sense of
household members reducing effort over time. Then, the
time profile of remittance behavior would depend on the
earnings profiles of the migrants and the households.
Whether remittances increase or decline over time will
depend on the relative changes in the time profile of the
migrant’s wages and the household’s income. A greater
number of years since the migration implies more labor
market experience, which in turn implies higher wages
and higher remittances. On the other hand, higher
household income over time implies lower remittances.
Thus, the relationship between migrant worker remit-
tances and the length of stay in the host country might be
non-monotonic over time.
t
a
3. Empirical Approach
3.1. Reduced form of Remittances Equation
The reduced form expression for the binary choice
variable determining the fraction of migrants who do
remit and the size of remittances is given by:




max,,,, 0,
m
aaWRZlX
ijiijjj




(9)
for and. The set of observable
variables included in (9) is used to approximate equation
(8) as follows. First, i denotes a vector that includes
all characteristics of the individual migrant that de-
termine migrant wages in the host country, including
years of experience in the host country, destination
country (wages vary across developed countries), moti-
vation for leaving the source country and the migrant’s
education level prior to migration. Next, ij is a vector
that represents migrant ’s status within household
(i.e. the migrant is the household head’s spouse, parent,
child, etc.) and is used to approximate migrant ’s
degree of altruism toward household .13 j
=1, ,iM=1
, ,j 
W
i
S
i
Rj
i
j
Z
is a vec-
tor that includes all characteristics of household that
determine its labor income (education level of house-
hold’s head, ratio of children to adults within the house-
hold and gender of household’s head). Finally,
j
m
j
lj re-
presents the number of migrants within household .
According to the discussion above, there are five
testable hypotheses associated with the migrant’s de-
cision to remit and the amount to be transferred to her
relatives in the source country. First, migrants with
higher labor income are more likely to remit and tend to
remit more. Second, households with lower income tend
to receive more remittances. Third, both the likelihood of
remitting and remittance size are positively related to the
degree of proximity between the migrants and the
remaining household members in the source country.
Fourth, the relationship between migrant worker remit-
tances and the length of stay in the host country might be
non-monotonic over time. Fifth, remittances per migrant
are ambiguously associated with the number of migrants
within the same household.
11[5] examines this relationship in a multi-period model. He shows that
migrant remittance behavior and time since migration are ambiguously
determined.
12That is,


,,
>=
=
,
0Otherwise.
fmh mh
wllw llw
tjtjt jt jt
ff
if ww
tt
am
l ij tj




 









13Usually the unobservable migrant’s degree of altruism is approxi-
mated by a vector of observable variables that measure the degree o
f
p
roximity between individual migrants and their families in the source
country, which is the case in [4,5,8], among others.

H. E. MORÁN
540
3.2. Data
The data used in this paper come from a national
household survey entitled “Demographic, Maternal and
Infant Health Survey” (ENDEMAIN) undertaken by the
Center of Population Studies and Social Development in
Ecuador in 2004. The empirical work focuses on
households with at least one migrant, which comprise
around 10 percent of the households sample covered in
the Ecuadorian household survey. The sample includes
migrants age 15 or older. The Ecuadorian households in
this survey have from one to five migrants.
The survey provides information about each of the
household members in Ecuador and about each of the
migrants within the household. The migrants’ data
includes information about the length of migration, the
host country, the status within the household, the
motivation for migration, the years of schooling prior
migration and the individual amount of remittances sent
by each of them. Table 1 presents the migrant remittance
behavior by the number of migrants within the
households, with the remittances expressed in US dollars.
The first column shows the full sample migrant
remittance behavior, whereas Columns (1) to (5) show
the statistics for individuals who come from households
with 1, 2, 3, 4, and 5 migrants, in that order. Panel (A) of
Table 1 shows the amount of remittances, including
those who remit (remitter) and those who do not remit
(non-remitter), to have averaged 1164 and 353 dollars
sent per migrant and received per household member,
respectively14. Panel (B) shows the amount of remit-
tances of only those who remit to have averaged 1870
and 567 dollars sent per migrant and received per house-
hold member, correspondingly. The percent of migrants
who remit by number of migrants within the household
range from 55 to 66 percent, with the average being 62
percent.
Table 2 shows the descriptive statistics of migrant
characteristics and household characteristics. Ecuadorian
migrants are spread across 30 countries, of which the
most significant destination countries are Spain, the US
and Italy, respectively. Most of the migrants are close
relatives of the household head (80 percent are parents,
children or spouses). The survey includes migrants who
left the country between 1960 and 2004. Most of those
left between 1999 and 2004 (more than 70 percent),
which might be associated with the volatile macroe-
conomic situation of the late 1990’s and the early 2000’s.
While the preferred countries during the last surge of
migration were European such as Spain and Italy, the US
was a secondary destination. For instance, of the total
number of Ecuadorian migrants living in Spain, around
90 percent arrived between 1999 and 2004. The main
motivations for leaving were to search for work or to
accept a job offer in the destination country. Since there
are some differences of remittance behavior of Ecua-
dorian migrants according to the host country, the
number of migrants within the same household and the
years since migration, in the empirical work we take into
account those factors that affect the likelihood of
remitting and the size of remittances.
3.3. Methodology
Using household data from Ecuador, this study attempts
to answer questions such as “Who remits? “Why?” and
“How much?”. In particular, the empirical work em-
phasizes the relationship between individual migrant
remittance behavior and the number of migrants within
the same household. This section explores two different
ways to introduce household migration into the econo-
metric model. First, similarly to [5,6], household mig-
ration enters linearly into the regression model by using
the number of migrants within the household. Second, in
order to explore the potentially non-linear relationship
between remittances and household migration, it uses
indicators for the number of migrants within the house-
hold (i.e. 1 if household has 1 migrant, 1 if household has
2 migrants, etc.). The non-linear approach allows one to
investigate whether remittance behavior changes between
migrants who come from households with 1 migrant and
those from households with 2, between migrants who
come from households with 2 migrants and those from
households with 3, and so on. Both sets of regressions
are reported in the empirical results section.
Since migrant remittance behavior implies a two-step
decision (see Equation (8)), the decision to remit or not
and, conditional upon remitting, the amount decision,
let’s consider a censoring from below (zero) or from the
left mechanism in which is observed
>0
=00
aifa
aif a




.

(10)
Censoring can be fully parametrically specified. We
consider maximum likelihood estimation (MLE) given
censoring from zero15. For the density of is
the same as that for , so
>0aa

a
 
|= |
f
axfyx
, where
x
represents the set of exogenous variables defined in
expression (10). For , the density is equal to the
probability of observing , or equal to
=0
a
a
0


0|
F
x
.
Hence, the censoring mechanism can be written
 

|if>
|= 0|if =0.
fax a
fax Fx a







0
(11)
15According to the theoretical model, the altruistically motivated remit-
tances are allowed only in one direction, namely from migrants to
non-migrants, but not from non-migrants to migrants.
14Remittances received per household member were computed as the
ratio of the individual migrant amount of remittance to household size
in Ecuador.

H. E. MORÁN
541
Table 1. Ecuadorian migrant remittance be havior by number of migrants wi thin the household.
Number of Migrants within the Household
Variables Full
Sample One Two Three Four Five
A. Remitter and Non-Remitter
Remittances per migrant 1,164 1,539 879 843 808 749
Remittances per household member 353 490 257 261 201 146
Number of observations 1529 705 389 202 149 84
B. Only Remitter
Remittances per migrant 1870 2369 1500 1281 1469 1234
Remittances per household member 567 754 438 397 365 241
Number of observations 952 458 228 133 82 51
C. Percentage of migrants who remit 0.62 0.65 0.59 0.66 0.55 0.61
Data Source: 2004 Demographic, Maternal, and Infant Health Survey, Center of Population Studies and Social Development, Ecuador. Remittances are ex-
pressed in U.S. dollars.
Table 2. Ecuador: Migrant and Non-migrant characteristics.
Number of Migrants within the Household
Variables Full Sample One Two Three Four Five
A. Household characteristics
Size of migration 2.030 1 2 3 4 5
Migration rate 0.337 0.235 0.360 0.452 0.470 0.584
Household’s head is female 0.332 0.381 0.254 0.356 0.281 0.309
Years of schooling 6.5 7.4 6.2 5.9 5.2 5.0
Ratio children to adults 19.25 21.92 17.05 15.1 21.1 13.4
B. Migrant characteristics
B1. Length of migration
From 0 to 1 year 0.147 0.173 0.118 0.173 0.114 0.071
From 2 to 5 years 0.599 0.639 0.614 0.475 0.510 0.654
Years since migration 4.9 4.4 5.3 5.3 5.8 5
B2. Host Country
Host country is Spain 0.454 0.496 0.437 0.381 0.463 0.345
Host country is Italy 0.057 0.069 0.061 0.039 0.040 0.011
Host country is other (27 others) 0.068 0.093 0.061 0.034 0.053 0
B3. Status within the household
Migrant is not a close relative 0.207 0.194 0.205 0.287 0.154 0.226
B4. motive for migration
Left for studying/unifying family 0.170 0.180 0.179 0.138 0.100 0.238
B5. Education
Years of schooling 9.8 10.1 9.8 8.9 10.0 9.3
Data Source: 2004 Demographic, Maternal, and Infant Health Survey, Center of Population Studies and Social Development, Ecuador. Total annual and aver-
age annual remittances are expressed in US dollars. Household size was computed by adding up the number of migrants within the household and the number
of individuals age 15 or older within the household who stay in Ecuador.

H. E. MORÁN
542
Now, let an indicator variable be introduced
1if >0
=0if =0,
a
da


 (12)
and therefore the conditional density given censoring
from zero is given by
 

1
|= |0|
dd
faxf axFx



. (13)
For a sample of independent observations, the
censored MLE for the migrant remittance behavior
maximizes
N
   
=1
=,10
N
Niiiii
i
lnLd lnfaxdlnFx ,,







(14)
where

are the parameters of the distribution of .
The censored MLE is consistent and asymptotically
normal, provided that the density of the uncensored
variable is correctly specified

a


,
ii
fax

16. A few
econometric issues arise in the estimation of expression
(14), however. First, since there is a considerable number
of zeros on the left side of (14), 38 percent of migrants
do not remit, one has to take into account the zero-
inflated issue. Second, since the Tobit estimation makes
a strong assumption that the same probability mechanism
generates both the zeros and the positive value of
remittances, the Tobit estimates are biased if there is
heteroscedasticity in the residuals of the participation
regression and/or outcome regression. To account for
these econometric issues, the zero-inflated nature of the
dependent variable and the biased estimates from the
standard Tobit model, a censored or two-part model is
used, which is more flexible to allow for the possibility
that the zero and positive values are generated by
different mechanisms.17 Hence, the two-part estimation
employs a logit regression for the censoring mechanism
(decision to remit or not) and, conditional on the out-
come (amount decision) being observed, it uses a log-
normal model for remittance size18. The two parts are
assumed to be independent and estimated separately as
shown in the next section19. Moreover, since correlation
among error terms of all migrants experiencing the same
shocks within a given host country may bias the sample
errors downward, all standard errors

ijf

are clustered
by the migrant’s host country.
The econometric work estimates the following model
to examine the determinants of remittance behavior:

=max,0 ,
m
j
ijj ijf
ijf
ijf
lR Z
aa
i
W

 

 




 
j
(15)
where ijf is a binary variable which takes the value of
one or zero for the migrant decision to remit or not to
remit and, conditional on the sending of remittances, it
measures the annual amount of remittances sent by
individual migrant to household from host
country
a
i
f
, is the migration size or the indicator of
the number of migrants from household , ij is a
dummy indicating migrant ’s relationship with the
household head in the source country, i is a vector of
dummy variables that includes all characteristics of
migrant including her host country, length of stay,
education level prior to migration and motives for
leaving the source country, and j
m
j
ljR
iW
i
Z
denotes a vector of
household characteristics, which includes the gender of
the household’s head and the ratio of children to adults
within the household.
4. Results
The main results of the estimates of the determinants of
remittances in Ecuador are shown in Tables 3-6, in
which we report the estimates of the two-part model of
Equation (15). The dependent variable for the logit
regression model is equal to 1 if the migrant remits and
equal to 0 if the migrant does not remit. For the OLS
regression model, the dependent variable in Tables 3 and
4 is the log of the annual amount of remittances, in U.S.
dollars, sent per each migrant, whereas in Tables 5 and 6
the dependent variable is the annual amount of remit-
tances, in US dollars, received per household member in
Ecuador. Columns (1) and (2) show the average marginal
probability computed from the logit regression model for
the decision to remit or not to remit and Columns (3) and
(4) report the estimates of OLS regression to examine the
determinants of the amount of remittances20. The only
difference between Column) and (2) and between
16For a further discussion see [23,24].
17The distribution that applies to is a mixture of discrete and continu-
ous distributions. Under such circumstances, there are a variety of mo-
dels that could be estimated to account for the combined nature of the
distribution of . See [7] for a discussion on those alternative models.
i
a
i
a
18Here, to ensure a positive value for the dependent variable, the density
should be that for a positive-valued random variable, such as the
log-normal, or an appropriate density such as the normal truncated
distribution from below at zero. Also, in a random utility model (RUM)
which is compatible with the theoretical model discussed in this paper,
assuming that the random component of both utilities are extreme value
Type I distributed, it can be shown that the resultant distribution is a
logistic distribution for the censoring mechanism. Hence, the logistic
distribution assumption for the censoring mechanism is a proper as-
sumption here, but also one can assume a probit model and the esti-
mates would be unchanged.
s (1
19Allowing for the errors to be correlated as assumed in the sample
selection model (MLE and the two-step Heckman sample selection
model) does not affect the main findings of the empirical work shown
in the next section. This is because both the inverse Mill’s ratio and the
correlation between errors of the decision to remit or not and the
amount decision regression



are statistically insignificant (results
can be provided upon request to the author).
20Notice that the estimates reported in Columns (1) and (2) of tables 3
and 5 are the same because the dependent variable for the logit regres-
sions is the same in both tables, namely equal to 1 if the migrant remits
and equal to 0 if the migrant does not remit. For the same reason, the
estimates in Columns (1) and (2) of tables 4 and 6 are the same.

H. E. MORÁN 543
Table 3. Equal to 1 if migrant remits for the logit model and the log of individual migrant remittances in US dollars for the
OLS model.
Two Part Model
Variable
Logit Model (AME) OLS Model
(1) (2) (3) (4)
A. Household (HH) Characteristics
Size of migration −0.020** −0.020*** −0.137*** −0.137***
(0.0098) (0.0076) (0.0374) (0.0409)
HH head is female 0.119*** 0.119*** 0.347*** 0.347***
(0.0248) (0.0156) (0.0923) (0.1179)
Ratio of children to adults 0.0022*** 0.002*** 0.012*** 0.012***
(0.0006) (0.0006) (0.0026) (0.0012)
B. Migrant Characteristics
Length since migration 0.017** 0.017*** −0.062*** −0.062***
(0.0088) (0.0051) (0.0228) (0.0206)
Length since migration squared −0.001* −0.001*** 0.002** 0.002***
(0.0004) (0.0001) (0.0008) (0.0004)
Host country is Spain −0.023 −0.023*** −0.201* −0.201***
(0.0269) (0.0075) (0.1031) (0.0328)
Host country is Italy −0.009 −0.009 −0.152 −0.152**
(0.0527) (0.0102) (0.1920) (0.0560)
Host country is other (27 others) −0.208*** −0.208*** −0.151 −0.151
(0.0512) (0.0730) (0.2471) (0.2107)
Migrant is not a close relative −0.238*** −0.238*** −0.224* −0.224*
(0.0301) (0.0444) (0.1295) (0.1169)
Left for studying or unifying family −0.149*** −0.1493*** −0.459*** −0.459*
(0.0336) (0.0551) (0.1423) (0.2392)
Years of schooling −0.004 −0.0042 0.035*** 0.035**
(0.0029) (0.0032) (0.0115) (0.0123)
Observations 1529 1529 952 952
Data Source: 2004 Demographic, Maternal and Infant Health Survey, Center of Population Studies and Social Development, Ecuador. While Columns (1) and
(3) report robust standard errors, Columns (2) and (4) report cluster—robust standard errors at the migrant host country level. Coefficients of the logit model are
the average marginal effects (AME), which are computed using the added command margeff. * significant at 10%, ** significant at 5%, and *** significant at 1%.

H. E. MORÁN
544
Table 4. Equal to 1 if migrant remits for the logit model and the log of individual migrant remittances in US dollars for the
OLS model.
Two Part Model
Logit Model (AME) OLS Model Variable
(1) (2) (3) (4)
A. Household (HH) Characteristics
HH has 2 migrants −0.051* −0.051 −0.321*** −0.321***
(0.0298) (0.0451) (0.1106) (0.0548)
HH has 3 migrants 0.016 0.016 −0.352** −0.352***
(0.0390) (0.0612) (0.1461) (0.1033)
HH has 4 migrants −0.137*** −0.137*** −0.239 −0.239**
(0.0428) (0.0441) (0.1480) (0.1131)
HH has 5 migrants −0.037 −0.037 −0.686*** −0.686*
(0.0535) (0.0446) (0.1986) (0.3503)
HH head is female 0.114*** 0.114*** 0.344*** 0.344***
(0.0250) (0.0136) (0.0924) (0.1123)
Ratio of children to adults 0.002*** 0.002*** 0.012*** 0.012***
(0.0006) (0.0007) (0.0026) (0.0014)
B. Migrant Characteristics
Length since migration 0.0191** 0.019*** −0.066*** −0.066***
(0.0089) (0.0047) (0.0229) (0.0195)
Length since migration squared −0.001* −0.001*** 0.002** 0.002***
(0.0004) (0.0001) (0.0008) (0.0004)
Host country is Spain −0.017 −0.017** −0.227** −0.227***
(0.0268) (0.0078) (0.1043) (0.0383)
Host country is Italy −0.004 −0.0042 −0.161 −0.161**
(0.0521) (0.0128) (0.1928) (0.0588)
Host country is other (27 others) −0.200*** −0.200*** −0.178 −0.178
(0.0521) (0.0748) (0.2514) (0.2138)
Close relative −0.246*** −0.246*** −0.209 −0.209
(0.0300) (0.0408) (0.1317) (0.1302)
Left for studying or unifying Family −0.152*** −0.152*** −0.440*** −0.440*
(0.0335) (0.0484) (0.1419) (0.2132)
Years of schooling −0.003 −0.003 0.034*** 0.034**
(0.0028) (0.0037) (0.0116) (0.0143)
Data Source: 2004 Demographic, Maternal and Infant Health Survey, Center of Population Studies and Social Development, Ecuador. While Columns (1) and
(3) report robust standard errors, Columns (2) and (4) report cluster—robust standard errors at the migrant host country level. Coefficients of the logit model are
the average marginal effects (AME), which are computed using the added command margeff. * significant at 10%, ** significant at 5%, and *** significant at 1%.

H. E. MORÁN
545
Table 5. Equal to 1 if migrant remits for the logit model and the log of individual migrant remittances per household member
(receivers) in US Dollars for the OLS model.
Two Part Model
Logit Model (AME) OLS Model
Variable
(1) (2) (3) (4)
A. Household (HH) Characteristics
Size of migration −0.020** −0.020*** −0.185*** −0.185***
(0.0098) (0.0076) (0.0370) (0.0307)
HH head is female 0.119*** 0.119*** 0.675*** 0.675***
(0.0248) (0.0156) (0.0923) (0.1421)
Ratio of children to adults 0.002*** 0.002*** −0.007*** −0.007***
(0.0006) (0.0006) (0.0026) (0.0015)
B. Migrant Characteristics
Length since migration 0.017** 0.017*** −0.057** −0.057**
(0.0088) (0.0051) (0.0224) (0.0247)
Length since migration squared −0.001* −0.001*** 0.002*** 0.002***
(0.0004) (0.0001) (0.0007) (0.0006)
Host country is Spain −0.023 −0.023*** −0.121 −0.121***
(0.0269) (0.0075) (0.1031) (0.0406)
Host country is Italy −0.0098 −0.009 −0.036 −0.036
(0.0527) (0.0102) (0.2057) (0.0443)
Host country is other (27 more) −0.208*** −0.208*** −0.059 −0.059
(0.0512) (0.0730) (0.2745) (0.2178)
Migrant is not a close relative −0.238*** −0.238*** −0.273** −0.273**
(0.0301) (0.0444) (0.1320) (0.1088)
Left for studying/unifying family −0.149*** −0.149*** −0.440*** −0.440*
(0.0336) (0.0551) (0.1454) (0.2203)
Years of schooling −0.004 −0.004 0.044*** 0.044***
(0.0029) (0.0032) (0.0115) (0.0085)
Data Source: 2004 Demographic, Maternal and Infant Health Survey, Center of Population Studies and Social Development, Ecuador. While Columns (1) and
(3) report robust standard errors, Columns (2) and (4) report cluster—robust standard errors at the migrant host country level. Coefficients of the logit model are
the average marginal effect (AME), which are computed using the added stata command margeff* significant at 10%, ** significant at 5%, and *** significant at
1%.
Columns (3) and (4) of those tables is that Columns (1)
and (3) report the standard-robust errors and Columns (2)
and (4) report the cluster-robust standard errors at the
migrant host country level. Tables 3 and 5 provide the
estimation results for the model with linear household
migration (size of migration), whereas Tables 4 and 6
give the results for the model with non-linear household
migration (dummies for the number of migrants within
the household).
The estimates shown by Table 3 are qualitatively
similar to those findings reported by Table 5, while the
estimates of Table 4 are also similar to those results

H. E. MORÁN
546
Table 6. Equal to 1 if migrant remits for the logit model and the log of individual migrant remittances per household member
(receivers) in US dollars for the OLS model.
Two Part Model
Variable Logit Model (AME) OLS Model
(1) (2) (3) (4)
A. Household (HH) Characteristics
HH has 2 migrants −0.051* −0.051 −0.436*** −0.436***
(0.0298) (0.0451) (0.1122) (0.0431)
HH has 3 migrants 0.016 0.016 −0.461*** −0.461***
(0.0390) (0.0612) (0.1509) (0.0909)
HH has 4 migrants −0.137*** −0.137*** −0.367** −0.367***
(0.0428) (0.0441) (0.1458) (0.1127)
HH has 5 migrants −0.037 −0.037 −0.894*** −0.894***
(0.0535) (0.0446) (0.1943) (0.3011)
HH head is female 0.114*** 0.114*** 0.669*** 0.669***
(0.0250) (0.0136) (0.0923) (0.1373)
Ratio children to adults 0.002*** 0.002*** −0.008*** −0.008***
(0.0006) (0.0007) (0.0026) (0.0015)
B. Migrant Characteristics
Length of migration 0.019** 0.019*** −0.061*** −0.061**
(0.0089) (0.0047) (0.0226) (0.0234)
Length since migration squared −0.001* −0.001*** 0.002*** 0.002***
(0.0004) (0.0001) (0.0007) (0.0006)
Host country Spain −0.0170 −0.017** −0.152 −0.152***
(0.0268) (0.0078) (0.1045) (0.0406)
Host country is Italy −0.0042 −0.0042 −0.047 −0.047
(0.0521) (0.0128) (0.2058) (0.0487)
Host country other (27 others) −0.200*** −0.200*** −0.090 −0.090
(0.0521) (0.0748) (0.2805) (0.2233)
Close relative −0.2469*** −0.246*** −0.256* −0.256**
(0.0300) (0.0408) (0.1342) (0.1121)
Left for studying −0.152*** −0.152*** −0.419*** −0.419**
(0.0335) (0.0484) (0.1449) (0.1894)
Years of schooling −0.003 −0.003 0.044*** 0.044***
(0.0028) (0.0037) (0.0116) (0.0102)
Data Source: 2004 Demographic, Maternal and Infant Health Survey, Center of Population Studies and Social Development, Ecuador. While Columns (1) and
(3) report robust standard errors, Columns (2) and (4) report cluster—robust standard errors at the migrant host country level. Coefficients of the logit model are
the average marginal effects (AME), which are computed using the added stata command margeff. * significant at 10%, ** significant at 5%, and *** significant
at 1%.

H. E. MORÁN 547
shown by Table 621. Therefore, the estimates shown in
this paper seem to be robust to an alternative measure of
remittance size.
The results for Ecuadorian migrants are generally sup-
portive of the predictions of the model. Tables 3 and 5
show that there is a negative relationship between mig-
rant remittance behavior and the migration size within
the same household. Both the decision to remit and the
size of remittance are negatively associated with mig-
ration size and are statistically significant22. If the mig-
ration size increases by 1 migrant, the likelihood of sen-
ding or receiving remittances decline, on average, 2 per-
cent and the amount sent per migrant and received per
household member decline, on average, 13.7 and 18.5
percent, respectively. Even though this result may be
consistent with the prediction of the theoretical model, it
might require a deeper inspection23. It could be induced
by the large difference between the amount sent by
individual migrants who come from households with one
migrant and the amount sent by those who come from
households with more than one migrant. In fact, the
average amount of remittances sent by those individuals
who are the sole migrants within their households is
almost twice as large as that sent by those who come
from households with 2, 3 and 4 migrants (see Table 1).
In order to show a more complete picture of the
relationship between migrant remittance behavior and
household migration, Tables 4 and 6 show the estimates
of indicators for those coming from households with 2, 3,
4 or 5 migrants, where the omitted group is migrants who
are the sole migrants within their households. As
expected, the amount of remittance sent by individual
migrants who come from households with 2 to 5 mig-
rants is significantly lower than that sent by individuals
who are the sole migrants within their household. As
follows from Table 4, the migrants who come from
households with 2, 3, 4 and 5 migrants remit 32.1, 35.2,
23.9 and 34.4 percent less, respectively, than the mig-
rants who come from households with one migrant. Si-
milarly from Table 6, the amount received per house-
hold member with 2, 3, 4, and 5 migrants is 43.6, 46.1,
36.7 and 89.4 percent lower, in that order, than the
amount received by those individuals from households
with only one migrant, in that order. However, only those
individuals who come from households with 4 migrants
have a statistically significant lower likelihood of remit-
ting than those from households with only 1 migrant
(−13.7 percent). A closer inspection of the estimates of
the remittance size regression reveals that coefficients for
migrants who come from households with 2, 3 and 4
migrants are not statistically different24. However,
migrants who come from households with 5 migrants
tend to remit a significantly lower amount of remittances
than those migrants who come from households with 2, 3
or 4 migrants.
These findings suggest that remittance size and
household migration are no increasing associated. Hence,
it seems that when migration size changes from 2 to 3
and from 3 to 4 migrants within the same household, the
forgone household income due to migration might have a
positive effect on altruistically motivated remittances,
which compensates for the negative effect of the
increased number of migrants on the individual amount
of remittances. If there is a positive selection of migrants
in the sense that the more educated individuals within the
household are those who migrate, then one would expect
that the forgone household income due to migration is
higher than when there is a negative selection. According
to the Ecuadorian data of households with at least 1
migrant, prior to migration the individuals who left had a
higher education level than those relatives left behind.
The average years of schooling of the migrants was 3.5
years higher than the non-migrants. Moreover, the higher
the migration rate within a household, the more the labor
supply of that household is reduced25. The results of
allowing a non-linear relationship between migrant remi-
ttance behavior and household migration are partially
distinguished from those reached when there is a linear
relationship (Tables 3 and 5) and also contrast with the
predictions of rent-seeking literature. Summarizing, the
empirical evidence discussed above suggests that migrant
worker remittance behavior is a non-increasing function
of the number of migrants within the household.
21Notice that the same regressors that are statistically significant in Ta-
ble 3 are significant in Table 5 as well. In general, the same applies for
the estimates reported by Tables 4 and 6, except for the coefficients o
f
the regressors of both “migrant’s host country is Italy” and “migrant’s
status within the family is not a close relative”. The migrant’s host
country coefficient is statistically significant in Table 4, but it is not in
Table 6 and the migrant’s status within the household is not statistically
significant in Table 4, but it is in Table 6.
22Henceforth the magnitude results shown here are taken from Tables 4
and 6. The estimates of the logit regressions are the same in both tables,
but the estimates of the OLS regression are different, namely, the esti-
mates of Table 4 refer to amount sent per migrant and the estimates o
f
Table 6 refer to the amount received per household member in Ecua-
dor.
23The theoretical model described above predicts that more migrants
imply lower remittances by the Nash assumption. If this effect over-
comes the implied reduction of household income due to the forgone
earnings, then migrant remittance behavior and migration size would be
ne
g
ativel
y
associated.
This paper also finds robust evidence in favor of
altruistically motivated remittance behavior. There are
several signs that remittances might be altruistically mo-
tivated. First, households headed by females are more
likely both to receive and to receive more remittances
than households whose heads are males. If the house-
holds’ heads are females, they are on average, 11.4 per-
24One cannot reject the null hypothesis that coefficients are equal.
25Here, migration rate is defined as the ratio of the number of migrants
within the household to the total number of individuals age 15 or olde
r
within the household (including migrants).

H. E. MORÁN
548
cent more likely to receive remittances than households
headed by males and when the former do receive remit-
tances, the amount sent per migrant and received per
household member is 34.4 and 66.9 percent higher, res-
pectively, than the amount sent to and received by the
latter households. The facts that the female labor parti-
cipation rate is likely lower than the male labor parti-
cipation rate in developing countries and that female
wages are likely lower than those earned by their male
counterparts support the altruistically motivated remit-
tance hypothesis. Second, households with higher ratios
of children to adults are more likely to receive remit-
tances and in greater amounts. When the percentage of
children within the households increases 1 percent, the
probability of sending or receiving remittances increases
0.2 percent and the amount sent per migrant and received
per household member increases 1.2 and 0.8 percent,
respectively. A higher child ratio means lower labor
income per individual within the household and this
result implies that such households are more likely to
receive remittances and to receive more than households
with a lower ratio of children to adults. According to the
Ecuadorian data, of the sample of households with at
least one migrant, less than 7 percent of the children were
involved in household labor activities or in remunerated
labor.
The migrant’s status within the household is also
relevant in determining remittance behavior. Migrants
who are not spouses, parents or children of the remaining
household head are less likely to send money and send
less than those who are. When the migrant is not a close
relative of the household’s head, the likelihood of sen-
ding remittances is 24.6 percent lower than when mig-
rants are close relative. The amount received per house-
hold member from migrants who are not close relatives is
25.6 percent lower than the amount received from mig-
rants who are close relatives. Migrants whose motivation
for migration was studying or unifying the family are less
likely to remit (−15.2 percent) and remit less than
migrants whose motivation for migration was the search
for work or accepting a labor offer in the host country.
The elasticity of the amount sent per migrant and re-
ceived per household member with respect to the
migrant’s left for studying or unifying family is equal to
44.0 and 41.9 percent, respectively. Finally, the mig-
rant’s years of schooling are not significantly correlated
with the probability to remit, but of those migrants who
remit, the more educated persons tend to send a higher
amount of remittances26. The elasticity of the amount
sent per migrant and received per household member
with respect to the migrant’s years of schooling is equal
to 3.4 and 4.4 percent, respectively.
Also of note, while the relationship between the
likelihood of remitting and the length of time since
migration seems to show a kind of U-inverse-shaped
curve (increasing at the beginning of the stay in the host
country and declining later), the relationship between the
amount of remittances and the length of stay appears to
show a U-shaped curve27. The migrant’s host country
also seems to be important in explaining migrant worker
remittance behavior. Ecuadorian migrants who moved to
Spain are 1.7 percent less likely to remit and remit 22.7
percent less than those migrants whose destination
country was the United States, which might reflect the
lower unemployment rate and the higher potential
earnings in the United States relative to Spain. According
to World Bank data, while the per capita income in the
US was 36,451 dollars in 2004 (constant 2000 U.S.
dollars), the per capita incomes in Spain was 15,356
dollars in 2004. Likewise, the unemployment rates in the
US and Spain in 2004 were 6 percent and 11 percent,
respectively. The fact that European countries such as
Spain and Italy were choices for Ecuadorian migrants,
despite the lower potential earnings there relative to the
US, could reveal the depth of the decline of per capita
income in Ecuador during the last migration surge (1999
to 2004). Thus, it might suggest that the extent of the
income differential became sufficiently high that
migration to Spain and Italy was then profitable, which
perhaps would not happen given a predominance of long
term economic conditions. As a matter of fact, before
1999 the preferred destination country for Ecuadorian
migrants was the US.
5. Conclusions and Final Comments
The analytical model developed in this paper analyzes
the determinants of individual migrant remittance beha-
vior and extends the altruism-based frameworks propo-
sed by [4,5]. This model predicts that migrants with
higher labor income are more likely to remit and tend to
remit more, households with lower income tend to re-
ceive more remittances, both the likelihood of remitting
and remittance size are positively related to the degree of
proximity between the migrants and the remaining hou-
27Additional estimations using dummies instead of years of stay show
that migrants who left the source country within 5 years tend to remit
more than those migrants who stayed in the host country for more than
5 years. However, the probability to remit is not significantly affected
b
y the migrant’s years of stay in the host country. [4] links duration o
f
migration with remittances as follows: “If out of sight, out of mind
were the rule, one should expect remittances to fade with duration o
f
absence. If repayment of school costs were the target, again remittances
should ultimately cease”. Therefore, another competing hypothesis that
may justify the remittance behavior reported here would be “repayment
motives”, which may include repayments of incurred moving costs or
repayments of education costs. A further discussion can be seen in [9],
in which the authors contrast predictions of competing hypotheses.
26This finding is similar to that found by [8], where remittances are
more likely motivated by investment motives or saving motives in the
source country. It is also consistent with the predictions of repay-
ment-motivated remittances.

H. E. MORÁN 549
sehold members in the source country and the rela-
tionship between migrant worker remittances and the
length of stay in the host country might be non-mono-
tonic over time. It also demonstrates that when forgone
household labor income is taken into account the indivi-
dual migrant remittance is a non-increasing function of
household migration size. The main findings in the empi-
rical part of this paper are generally supportive of the
predictions of the model.
Future research related to remittances might be fo-
cused on the consequences of remittances for developing
countries. Remittances may prove poverty-alleviating
and reduce inequality, either directly through flows to the
poor, if not the poorest, or indirectly through a stimulant
effect on the local economy. Moreover, remittances may
have long-term effects by overcoming liquidity con-
straints and allowing investment in the education and
health care of receiving families. Similarly, remittances
create a stable source of income which has a positive
effect on exchange reserves and the balance of payments
and might enhance financial development in small cities
or towns of the source country. As foreign exchange
inflow, remittances enter the economy in a different way
than private capital inflows, foreign investment or
financial aid, and, until now, there is no systematic study
for a better understanding of those differences. In fact,
macroeconomic effects remain poorly modeled and
poorly understood. What particularly lacking are models
that may facilitate the evaluation of both migration and
remittance effects. However, many nations, like Ecuador,
presume major benefits from remittance inflow and some
actively promote additional flow, both through efforts to
lower transfer fees and through offers of alternatives for
investment with government and inter- national agency
support.
6. Acknowledgements
I would like to thank Gerhard Glomm, Michael Alexeev,
Rubiana Chamarbagwala and Ricardo López for insight-
ful comments and suggestions. Any errors are the respon-
sibility of the author. The views expressed here are of the
author only and do not necessarily reflect the views of
the Banco de Guatemala.
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