Content uploaded by Gabriel Montes-Rojas
Author content
All content in this area was uploaded by Gabriel Montes-Rojas on Jul 21, 2015
Content may be subject to copyright.
Migration, Remittances and Capital Accumulation:
Evidence from Rural Mexico
Vera Chiodi
Esteban Jaimovich
Gabriel Montes-Rojas
No. 140
February 2010
www.carloalberto.org/working_papers
© 2010 by Vera Chiodi, Esteban Jaimovich and Gabriel Montes-Rojas. Any opinions expressed here
are those of the authors and not those of the Collegio Carlo Alberto.
Migration, Remittances and Capital Accumulation:
Evidence from Rural Mexico
Vera Chiodi∗Esteban Jaimovich†Gabriel Montes-Rojas‡
This version: February 2010
Abstract
This paper studies the link between migration, remittances and productive assets
accumulation for a panel of poor rural households in Mexico over the period 1997-
2006. In a context of financial markets imperfections, migration may act as a
substitute for imperfect credit and insurance provision (through remittances from
migrants) and, thus, exert a positive effect on investment. However, it may well be
the case that remittances are channelled towards increasing consumption and leisure
goods. Exploiting within family variation and an instrumental variable strategy, we
show that migration indeed accelerates productive assets accumulation. Moreover,
when we look at the effect of migration on consumption of non-productive assets
(durable goods), we find instead a negative effect. Our results then suggest that
poor rural families resort to migration as a way to mitigate constraints that prevent
them from investing in productive assets.
JEL Classification: O15, D31, J24, R23, F22.
Key Words: Migration; Remittances; Capital Accumulation; Rural Poverty.
∗Paris School of Economics (PSE) and Poverty Action Lab (J-PAL Europe), 48 Boulevard Jourdan,
75014 Paris, France. chiodi@pse.ens.fr
†Collegio Carlo Alberto, Via Real Collegio 30, Moncalieri (TO) 10024, Italy. este-
ban.jaimovich@carloalberto.org
‡Corresponding Author: City University London, Department of Economics, D306 Social Sci-
ences Building, Northampton Square, London EC1V 0HB, United Kingdom. Email: Gabriel.Montes-
Rojas.1@city.ac.uk Tel: +44 (0)20 7040 8919
1
1 Introduction
The migration of labor out of agriculture has represented a fundamental issue in the early
models of development economics (Lewis, 1954; Sen 1966; Harris and Todaro, 1970; see
Ghatak, Levine and Wheatly Price, 1996, for an excellent survey). In these models, the
agricultural sector is typically characterized by stagnation and under-productive use of
labor, while the urban industrial sector is viewed as the one that contributes most to
economic development and modernization. The above literature has then seen migration
from the rural to the urban sector basically as a road out of backwardness and poverty,
which are intrinsically linked to agricultural production.1
However, recent work has argued that rural migration may also exert a positive effect
on the rural sector itself. The general argument is that migration and remittances may
contribute to alleviate financial and productive constraints in the rural sector.2More
specifically, Stark (1991) sustains that migrants may play the role of financial interme-
diaries, enabling rural households to overcome credit constraints and missing insurance
markets. Furthermore, migration may mitigate the impact of agricultural income shocks
by allowing families to relocate labor to the cities when that is needed (Lucas and Stark,
1985). In fact, it is often the case that individuals in a household commonly pool resources
to finance migration of one of their members who later on repays by remitting a part of
his/her income back to the family. Thus, households tend to optimally spread their labor
force over different geographic markets in order to better pool risks. Adams (1998) studies
the effect of remittances in rural Pakistan and found that they help to increase investment
in rural assets by raising the marginal propensity to invest for migrant households.
Our paper contributes to this latter stand of literature by assessing the effect of mi-
gration on the process of asset accumulation using household data from poor rural areas
1For some more recent models in this vein see Banerjee and Newman (1998) and Lloyd-Ellis and
Bernhardt (2000).
2See, for example, Stark and Levhari (1982), and Rozelle, Taylor and DeBrauw (1999, 2003).
2
in Mexico. We propose a differentiation by the types of assets that are accumulated by
households, i.e. productive vs. non-productive assets, and we especially focus on the role
played by remittances in helping the accumulation of productive assets.
In that regard, rural Mexico represents a promising setup where to study the link be-
tween migration, remittances and assets accumulation, owing to the very high incidence
of deprivation in terms of access to formal financial markets and perceived well-being. In
such a context, migration may exert a positive effect on asset accumulation and, thus,
help lift families permanently out of poverty. Additionally, asset accumulation may also
represent an important component for consumption smoothing as shown by Rosenzweig
and Wolpin (1993). Using a unique panel database for Mexican rural households, the
econometric results presented in this paper show that migration and remittances indeed
open up a possibility for poor households to accelerate assets accumulation, particularly
in productive assets. The fact that rural households use remittances to increase the accu-
mulation of productive assets represents an important and, at the same time, not obvious
result. More precisely, it may well be the case that remittances are channelled instead to-
wards increasing consumption and leisure goods, which may increase households’ current
well-being, but will not help to improve their dynamic prospects. These conclusions are
similar to those of Adams (1998) for Pakistan.
Several endogeneity issues need to be addressed in order to avoid potential biases.
First, households may respond to adverse or positive shocks by changing the number of
migrants or the nature of migration (temporal vs. permanent). Second, selection bias may
occur if migrant households are intrinsically different from non-migrant ones. Following
previous work on this subject (see Acosta, 2006, and McKenzie and Sasin, 2007) we
deploy an instrumental variable strategy in order to cope with endogeneity issues based on
migration networks. Because we have a panel data, we can include this variable together
with the fixed effects at the household level. Therefore, our identification strategy relies
3
on variation in aggregate migration across time and space.
We frame the empirical results within a two-period model of investment and migration
decisions of credit constrained rural households. The model shows that migration affects
investment only for moderately poor households, while it leads to increasing consumption
for the very poor and relatively rich households. Moreover, the model makes it explicit
that household characteristics need to be properly controlled for in the empirical setting
in order to obtain unbiased results of the effect of migration on investment. More pre-
cisely, the model shows that migration decisions correlate with certain household-specific
characteristics that also influence migration and investment choices. Controlling for these
household-specific characteristics is then crucial for insulating the effect of sending out a
migrant on the investment behavior of those who remain at the rural village. Interest-
ingly, the predicted bias in the theoretical model goes in the same direction as that in our
empirical results.
Migration and remittances have been largely studied in the microeconometric liter-
ature with respect to the accumulation of human capital. As argued in Hanson and
Woodruff (2003) the additional income from remittances may allow children to delay en-
tering the work force. Yang (2008) also finds a positive effect of remittances on child
schooling and educational expenditure in Philippines using exchange rate shocks as a
source of exogenous variation for remittances. However, it has been argued as well that
migration may alter the family structure, raising child-rearing responsibilities and, there-
fore, having negative consequences on household welfare. Moreover, Acosta (2006) sus-
tains that it may well be expected that recipient families will expand their consumption
of leisure (and reduce labor supply) and increase their dependence on external transfers
accordingly.
The topic addressed here is also related to the effect of credit constraints in the urban
informal sector. Woodruff and Zenteno (2007) found a positive impact of remittances in
4
Mexico (they are shown to be responsible for almost 20% of the capital invested). In
the same vein, Mesnard and Ravaillon (2002) and Mesnard (2004) studied the temporary
migration decision of workers who are credit constrained in Tunisia and evaluates the
extent to which liquidity constraints affect self-employment decisions of returned migrants.
There is also some evidence on this issue for the case of internal migration in India
(Banerjee and Bucci, 1994). Our paper extends these results to rural poor households.
Finally, the effects of remittances on capital accumulation has also been studied at
the macroeconomic level by Glytsos (1993) and Giuliano and Ruiz-Arranz (2009) who
provide evidence that remittances tend to particularly foster growth in countries with less
developed financial systems by helping them overcome liquidity constraints. Their results
are thus consistent with ours based on household-level data.
The rest of paper is organized as follows. Section 2 presents a model that accounts
for migration and investment decisions. Section 3 describes the unique dataset used to
construct the panel of rural households. Section 4 presents the methodology used for
constructing the asset indexes. Section 5 presents some descriptive statistics. Section 6
carries the econometric analysis showing the effect of migration on asset accumulation.
Section 7 concludes.
5
2 Migration and investment decisions in a two-period
maximization problem
This section proposes a very simple model to illustrate how relatively poor families may
resort to migration as a response to credit constraints that prevent them from investing in
productive assets. In particular, the model aims at showing that poor families may, under
certain conditions, choose to send migrants so as to use their remittances to overcome
credit constraints.
We will first start with a two-period model in which the possibility of sending migrants
is excluded. This will set a benchmark upon which we can then compare the optimal
behavior of families when they do have the opportunity to send a migrant to a richer
region or city, and receive positive remittances from the migrant.
2.1 No-migration regime
There is a continuum of rural families (or households) i∈ I who live for two periods,
t={1,2}.At the beginning of each period teach family ireceives an amount of income
equal to yt,i, where yt,i is the realization of a random variable uniformly and indepen-
dently distributed across families along the interval [1, y], where y > 1. We assume that
y1,i =y2,i =yi; that is, income realizations are persistent within families. More broadly
speaking, we could also interpret the variable yias capturing the effect of family specific
productive assets (for example, different families may own plots of land that differ in
terms of their level of fertility); in the econometric terminology used below, the variable
yicaptures family-specific fixed-effects.
Families derive log-utility from consumption at the end of each period tand we assume
no discount factor is applied on future consumption.3All families are credit-constrained,
3No future discounting is just a simplifying assumption, useful for the algebraic derivations but without
6
and then, they cannot increase current consumption by borrowing against future income.
Families, however, have access to a storing technology (with no depreciation), hence they
may transfer present income to the future in case they wish so.
All families have access also to an indivisible investment project (an investment in
productive assets that increases productivity in the future, for example, investing in irri-
gation or buying a new tractor). In particular, in period 1 families can choose whether or
not to invest in a project that requires 1 unit of capital as investment, and yields R > 1
units of income at the end of period 2.
The families’ optimization problem may be approached by noting that it involves two
different issues: first, choosing whether or not to invest in the project at the beginning
of t= 1; second, choosing the optimal consumption flow, conditional on the former
investment decision. We can then solve the problem for family isimply by comparing
the maximum utility achieved in each of the two possible scenarios: (a) investing in the
project; (b) not investing in it. We denote by ct,i consumption in period tand by s1,i the
amount of income stored from period 1 until period 2.
Case (a): Invest in the project. Family isolves:
max : Ui,I = ln(c1,i) + ln(c2,i ) (1)
subject to: c1,i =yi−s1,i −1,
c2,i =yi+s1,i +R,
s1,i ≥0.
It is straightforward to observe that in problem (1) the constraint s1,i ≥0 will bind
in the optimum (i.e., families would like to borrow against future income so as to smooth
any important implication. The log-utility is also assumed mainly for algebraic simplicity (in particular,
it allows us to obtain a closed-form solution for the model), and could be replaced by a general CRRA
utility function without changing the main insights of the model (as we will see below, it is important
though that utility displays decreasing absolute risk aversion).
7
consumption, but they are not able to do so). Hence, families will set optimally s∗
1,i = 0,
implying that: c∗
1,i,I =yi−1 and c∗
2,i,I =yi+R. As a result, the maximum utility achieved
by a family with income yithat invests in the project is given by:
U∗
i,I = ln (yi−1) + ln (yi+R).(2)
Case (b): No investment. Family isolves:
max : Ui,NI = ln(c1,i) + ln(c2,i) (3)
subject to: c1,i =yi−s1,i,
c2,i =yi+s1,i,
s1,i ≥0.
Since the income flow is identical in both periods and future is not discounted, fam-
ilies will optimally consume the yiin each of the two periods, so as to achieve perfect
consumption smoothing. That is, c∗
1,i,NI =c∗
2,i,NI =yi, which in turn implies s∗
1,i,NI = 0.
Hence, the utility achieved by a family with income yithat decides not to invest is given
by:
U∗
i,NI = ln y2
i.(4)
Finally, families will choose to invest if and only if that allows them to obtain higher
intertemporal utility than not investing. Henceforth, we let I= 1 (I= 0) denote the
choice to invest in the productive asset (not to invest in it) in t= 1. Then, comparing
(2) and (4) implies:
I= 1 ⇔yi>R
R−1.(5)
The expression (5) stipulates that only families with (permanent) income larger than
R/(R−1) will invest in the project. The reason for this is that, in the presence of credit
8
constraints, given that utility displays decreasing absolute risk aversion, only sufficiently
rich families are willing to give away one unit of consumption in t= 1 in order to be
able to invest and increase consumption t= 2 by Runits.4Henceforth, we assume that
y > R/(R−1), so that there exist some families who are willing to invest.
2.2 Migration allowed
Assume now that after observing the income realization yiat the beginning of t= 1, family
icould choose whether or not to send one of their members to a richer city or region in
the first period. Sending a migrant imposes an “emotional” cost M > 0, measured in
terms of utility.5Migration is treated as a risky asset when compared with the risk-free
income in the village. The migrant may get a good job in the region he migrated to, which
yields net income υ, where 1 ≤υ < 1 + R. Instead, if migrant fails to find a good job, he
receives net income equal to 0.6
We assume that local networks in the city where migrants move to make it easier for
them to obtain a good job.7In particular, we postulate that the migrant from family
iwill manage to find good job with probability p(ni) = ni, where ni∈[0,1] represents
the ’network density’ that family ihas got in the recipient city. We assume that niis
uniformly distributed along the interval [0,1] in the population, and that the correlation
between niand yiin the population equals zero.
4Strictly speaking, there is no risk. Hence, the DARA property should be simply understood as an
assumption on the degree of concavity of the utility function, which in turn governs the intertemporal
elasticity of substitution, and therefore how willing agents are to transfer resources across the two periods.
5In the literature this is known as “psychological costs”, and there exists some evidence for intra-
European migration (Molle and van Mourik, 1988). We could also add to the model some pecuniary cost
attached to sending a migrant (i.e. transportation costs), although it is important for our argument that
the expected pecuniary return from sending a migrant is positive.
6The lower bound, υ≥1, essentially says that the good jobs are sufficiently productive, making
migration (possibly) an attractive option. The upper bound, υ < 1 + R, is just posed to focus only on
those cases in which the credit constraint, si≥0, binds in the optimum (as we will see later on, υ < 1 + R
implies that total family income in t= 1 never exceeds that of t= 2).
7The role of networks on migration has been extensively studied in the literature (see for instance
Munshi, 2003, and the references therein).
9
We denote by e
U∗
ithe utility achieved by family iif they choose to send a migrant
(whereas, as before, U∗
idenotes the utility of family if they do not send a migrant).
Relatively rich families: Consider family iwith network density ni∈[0,1] and income
yi≥R/ (R−1). From the previous analysis, it follows that this family will always invest
in the project. That is, it will invest regardless of whether it chooses to send a migrant
or not, and, in the case they do send a migrant, regardless of whether the migrant finds
a good job or not. As a result, if they do not send a migrant, their utility equals that
written before in (2). On the other hand, if they do send a migrant, their utility is given
by:
e
U∗,rich
i,I =ni[ln (yi+υ−1) + ln(yi+R)] + (1 −ni) [ln (yi−1) + ln(yi+R)] −M. (6)
A family with yi≥R/ (R−1) will thus send a migrant if and only if e
U∗,rich
i,I > U∗
i,I , which
in turn leads to:
If yi≥R/ (R−1) , send migrant iff: ni[ln (yi+υ−1) −ln (yi−1)] ≥M. (7)
Relatively poor families: Consider now the case of family iwith ni∈[0,1] and
yi< R/ (R−1). From the previous analysis, it follows that such a family will not invest
in the project if, after sending a migrant, this migrant fails to obtain a good job. Nor will
they invest in the project when they do not send a migrant, as this situation is isomorphic
to the no-migration regime.
The first question to address is then the following: should a family that sent a migrant
invest in the project when the migrant obtains a good job? Consider such a family: the
two expressions below show the utility achieved by the family, first, in the case it invests
10
in the project and, second, in the case it does not.
e
U∗,poor
i,I =ni[ln (y+υ−1) + ln(y+R)] + (1 −ni)ln y2
i−M, (8)
e
U∗,poor
i,NI =nihln yi+υ
22i+ (1 −ni)ln y2
i−M. (9)
Hence, comparing (8) and (9), it follows that families with yi< R/ (R−1) who send a
migrant will invest in the project, if and only if the migrant finds a good job and the
following condition holds:
yi>R
R−1−υR−υ
4
R−1≡by. (10)
Notice that by < R
R−1. In fact, it may well be that by < 1.8
The second question to deal with is, bearing in mind equations (8) and (9), should a
family with ni∈[0,1] and yi< R/ (R−1) send a migrant or not? Answering this question
demands comparing U∗
i,NI to e
U∗,poor
i,I for those families with yi∈by, R
R−1, whereas for those
families whose yi≤bywe must compare U∗
i,NI to e
U∗,poor
i,NI . We can thus obtain the following
two conditions:
If yi∈by, R
R−1, send migrant iff: ni[ln (yi+υ−1) + ln(yi+R)−ln (y2
i)] ≥M;
(11)
If yi<by, send migrant iff: nihln yi+υ
22−ln (y2
i)i≥M. (12)
Since a larger network, ni, increases the chances the migrant finds a good job (or,
in other words, the expected return from sending a migrant increases with ni), families
with a larger niwill naturally tend to be more prone to send a migrant. The following
8More precisely, by < 1 whenever R≥υ−1+υ
4.Notice, too, that both a larger Rand larger υmake
this last inequality more likely to hold. This is quite intuitive, since the (expected) return from migration
is increasing in Rand υ; in the former case indirectly through investment returns, in the latter directly
through earnings.
11
proposition states this result more formally.
Proposition 1 There exists a continuous and strictly increasing function en(y) : R++ →
R++, such that for all ni≥en(yi) :
(i) If yi∈R
R−1, y, then condition (7) holds.
(ii) If yi≥1and yi∈by, R
R−1, then condition (11) holds.
(iii) If yi≥1and yi≤by, then condition (12) holds.
Furthermore, if M≤ln(R), then for y=R
R−1, we have that 0<enR
R−1<1.
Proof. In Appendix.
Proposition 1 states that, for each family iwith income yi∈[1, y], there exists a
threshold in the network density, en(yi), such that if ni≥en(yi) this family chooses to send
a migrant. The network threshold en(y) is strictly increasing in y, implying that a larger
mass of migrants will originate from relatively poor families than from relatively rich
ones. The intuition for this is that the marginal utility of consumption is decreasing in
the level of consumption, while the disutility from migration, M, is constant for any level
of consumption. As a result, poorer families will be more eager to endure the emotional
cost M, because their marginal return of migration in terms of (expected) utility of
additional consumption is larger. Notice, finally, that Proposition 1 does not explicitly
restrict en(y)≤1. In fact, it may well be the case that en(y)>1 for some y > 1, implying
that no migrants will originate from families with incomes above that level.
From now onwards we let M≤ln(R) hold. This assumption can be read as saying
that the emotional cost of migration, M, is not too large relative to the returns from
investing in risky assets, R. Notice from the last sentence in Proposition 1 that, since
M≤ln(R) implies enR
R−1<1, then there will exist some families whose incomes are
below the threshold level R/(R−1) who will choose to send migrants.
12
The next step is to study how migration decisions interact with investment decisions.
In particular, we are interested in studying whether families send migrants with the aim
to increase their capacity to invest in the projects. By merging the migration results in
Proposition 1 with the preceding discussion in this section, we can summarize households’
optimal decisions concerning migration and investment in the following corollary.
Corollary 1
(i) If R≥υ−1+υ
4. Then by≤1, and:
a) For any y∈R
R−1, y: If ni≥en(y)and yi=y, family isends a migrant. If ni<en(y)
and yi=y, family idoes not send a migrant. Family ialways invests in the project.
b) For any y∈1,R
R−1: If ni≥en(y)and yi=y, family isends a migrant and invests in
the project if and only if the migrant finds a good job. If ni<en(y)and yi=y, family i
does not send a migrant and does not invest in the project.
(ii) If R < υ−1+υ
4. Then by > 1, and:
a) For any y∈R
R−1, y: If ni≥en(y)and yi=y, family isends a migrant. If ni<en(y)
and yi=y, family idoes not send a migrant. Family ialways invests in the project.
b) For any y∈by, R
R−1: If ni≥en(y)and yi=y, family isends a migrant and invests in
the project if and only if the migrant finds a good job. If ni<en(y)and yi=y, family i
does not send a migrant and does not invest in the project.
c) For any y∈[1,by]: If ni≥en(y)and yi=y, family isends a migrant. If ni<en(y)and
yi=y, family idoes not send a migrant. Family inever invests in the project.
The results from Corollary 1 can be visually summarized in Figure 1. The key insight
of the corollary can be gleaned from point b), both for cases (i) and (ii) therein. The
result in b) says there exist some families who use migration as a mechanism to mitigate
credit constraints that prevent them from investing in projects that would raise their
intertemporal income. Essentially, those families send a migrant, betting on the chance
13
that this migrant finds a good job, which would increase their total income in t= 1 and,
thus, place them in better position to undertake the unit investment that yields R > 1
units of income in t= 2.
2.3 Effect of migration on investment decisions
We now study the effect of migration on families’ investment decisions. The migration
effect results from calculating the difference in investment decisions between migrant and
non-migrant families. First consider E[I|m= 1, y]−E[I|m= 0, y], where Iand m
are indicator functions regarding investment and migration decisions, respectively. In
relation to the empirical results in this paper, we refer to this model as fixed-effects
(FE) model, because by conditioning on ywe are controlling for the family-specific FE.
Note from Corollary 1 that, for any y≥R
R−1, families choose I= 1 irrespective of their
migration choice; while (in case (ii) of the corollary), for y < by, families always set I= 0,
regardless of their migration choices. It follows then that migration has only an effect on
the investment behavior of families with by≤y < R
R−1; in particular:
EI|m= 1,by≤y < R
R−1
| {z }
>0
−EI|m= 0,by≤y < R
R−1
| {z }
= 0
>0 (13)
Equation (13) makes it explicit that migration exerts a positive effect on investment
decisions.9However, notice that a key feature of the problem is the fact that intrinsic
family characteristics need to be taken into account when evaluating the effect of migration
on investment. In fact, if those characteristics are not controlled for, the measured effect
9The analytical expression for EhI|m= 1,by≤y < R
R−1iis given by:
R/(R−1)
Zby
[1 −en(yi)] dyi
−1R/(R−1)
Zby
1 + en(yi)
2[1 −en(yi)] dyi.
14
of migration on investment may turn out to be incorrect, because by simply comparing
the average behavior of families with and without migrants, we may also be capturing the
influence of other variables that somehow correlate with migration decisions.
To make this last argument more precise, consider now the overall association between
migration and investment in the population; this results from calculating the difference,
E[I|m= 1] −E[I|m= 0]. In parallel with the empirical results, we refer to this model
as ordinary least-squares (OLS) effect. After some algebra we obtain
E[I|m= 1] −E[I|m= 0] = Pr hby < y < R
R−1m= 1i·EhIm= 1,by < y < R
R−1i
| {z }
positive
+
nPr hy≥R
R−1m= 1i−Pr hy≥R
R−1m= 0io
| {z }
,
negative
(14)
where Pr y≥R
R−1m= 1<Pr y≥R
R−1m= 0follows from the fact that the threshold-
function en(y) is monotonically increasing in y.
The first thing that can be observed from (14) is that it is no longer true that families
with migrants tend to invest more than families without migrants; that is, E[I|m= 1] −
E[I|m= 0] ≶0. Furthermore, we can also show that OLS effect is always smaller that
the FE effect. We refer to this difference as the OLS bias.
Proposition 2 The OLS bias is negative, that is:
[E(I|m= 1) −E(I|m= 0)]−EI|m= 1,by≤y < R
R−1−EI|m= 0,by≤y < R
R−1<0
(15)
Proof. Note: The following proof is conducted for the case in which by≤1. The proof
for the case in which by > 1 is almost identical to this one, and it is available from the
authors upon request.
15
The expression (15) can be re-ordered as follows:
OLS bias = EI|m= 0,by≤y < R
R−1−E(I|m= 0)
| {z }
A
−EI|m= 1,by≤y < R
R−1−E(I|m= 1)
| {z }
B
.
(16)
Recalling (13), we can observe that the first member of (16) simplifies to:
A= 0 −Pr y≥R
R−1m= 0=−Pr y≥R
R−1m= 0.
In the case of the second member of (16), we have:
B=EI|m= 1,by≤y < R
R−1−Pr by≤y < R
R−1m= 1EIm= 1,by≤y < R
R−1
−Pr y≥R
R−1m= 1
=EI|m= 1,by≤y < R
R−11−Pr by≤y < R
R−1m= 1
| {z }
Pry≥R
R−1m=1
−Pr y≥R
R−1m= 1
=−Pr y≥R
R−1m= 11−EI|m= 1,by≤y < R
R−1
Therefore, we can in the end obtain:
A−B=−Pr y≥R
R−1m= 0+ Pr y≥R
R−1m= 11−EI|m= 1,by≤y < R
R−1
which is always strictly negative for the combined effect of the following two properties:
1) The monotonicity of en(y) implies that: Pr y≥R
R−1m= 0>Pr y≥R
R−1m= 1.
2) The fact that EI|m= 1,by≤y < R
R−1<1. This is because, among the families with
by≤y < R/(R−1) and send migrants, only in those cases in which the migrant manages
to find a good job (which occurs with probability ni) do families invest in the project.
16
The OLS bias arises because the OLS regression underestimate the effect of migration
on investment. This occurs because the family-specific level of income (yi) and the mi-
gration decision cannot be separated. In consequence, it is important to control for the
level of income yior other family-specific characteristics to get an unambiguous effect.
17
3 Data
We make use of a unique new dataset available for poor rural households in Mexico.
The data was collected for administrative purposes by the Oportunidades (ex Progresa)
program.10 Thanks to retrospective information, we managed to construct a panel of
households based on three surveys. In December 2006, the Instituto Nacional de Salud
P´ublica conducted a survey11 of recipient households in the rural localities where the Opor-
tunidades program started in 1997 with a 10% random sample, stratified by state. This
database is then matched to another survey, the ENCASEH (Encuesta de Caracteristicas
Socioeconomicas de los Hogares), carried out in 1997 and 1998, and to the ENCRECEH
(Encuesta de Recertificaci´on de los Hogares) carried out in 2001. This allows us to build
a balanced panel database composed of three time observations (1997, 2001 and 2006) for
4,365 households from 130 rural localities.
This constructed database includes detailed information on each beneficiary household,
including household demographics, income level and sources, education and several types
of assets. It also includes locality-level data, mainly regarding infrastructure. Although
it was not designed to evaluate migration patterns the database contains a few questions
about household members that migrated. Moreover, from the income data we obtain
information about remittances. Given the risk of attrition bias in our estimation, we
compared the distributions between the balanced panel of 4,365 and the unbalanced panel.
The distributions of the kernel density estimates appear to be very close to each other
and this is confirmed by the results of Kolmogorov-Smirnov tests that we run on the
hypothesis that the distributions of the balanced and unbalanced panels are the same for
10Launched in Mexico in 1997, it is a program whose main aim is to improve the process of human
capital accumulation in the poorest communities by providing conditional cash transfers on specific types
of behavior in three key areas: nutrition, health and education. Nevertheless, these households are also
targeted by other social programs.
11Encuesta de “Re-evaluaci´on de localidades incorporadas en las primeras fases del Programa (1997-
1998).” INSP, 2006.
18
some key variables. The null hypothesis cannot be rejected across all tests12.
In sum the dataset seems well suited for the purposes of the paper, because it allows
us to capture diverse information on households along with the time dimension that is
useful to control for the household fixed-effects. However, it should be noted that this
database may not be representative of rural Mexico because it was designed to cover a
particular subset of the population (i.e. those receiving Oportunidades). Therefore the
conclusions from the empirical results may only apply to this group.
12Not shown but available from the authors upon request.
19
4 The construction of an asset index
The first step in the empirical analysis is to reduce the household assets into unidimen-
sional measures. This requires either complete knowledge of the market value of each
asset owned or the construction of an asset index. Given that the prices of many assets
owned by households are often unknown or difficult to determine, we construct the as-
set index using the methodology used by Adato et al. (2006): the household income13
is regressed on the household’s stock of assets. The household asset index is then the
household income predicted from the estimated coefficients in the first year (1997), which
are used to extrapolate to every year. The equation we estimate is of the form:
yi,1997 =β0+β1x1i,1997 +β2x2i,1997 +ST AT Ei+ei,1997 ,(17)
where yi,t is the per-capita income by household, x1i,t is a vector of household assets we are
interested in, x2i,t is a vector of other household characteristics and STATE correspond
to state dummy variables. The asset index is then constructed as
Ai,t =ˆ
β1x1i,t.(18)
The asset index is standardized by its standard deviation. This simplifies the interpre-
tation of the regression analysis results (i.e. a regression coefficient of one means one
standard deviation of the index).
We consider three asset indexes and four categories of assets:
-AP: Productive assets: owner of a truck, agricultural land, irrigated land, working
animals;
13Income aggregates were created and broken down into five categories: agricultural wage employ-
ment, non-farm wage employment, self employment, transfers and other (including income from rent and
interests).
20
-ANP : Non-Productive (leisure) assets: ownership of radios, TV, refrigerator, gas
stove, washing machine and vehicles;
-AT: Total assets: APand ANP ;
- Other dwelling and household characteristics such as: electricity, earth floor, roof
weak, domestic animals, own house, years of education of the household head.
We compute the asset indexes for the different periods in the panel in Table 1. The
table shows that there is a marked increase in asset accumulation for all households (HH)
during the ten-year period. In Figure 2 we present density plots for migrant and non-
migrant households for each type of asset. Overall, the figures show that there are no
considerable differences across migrant and non-migrant HHs.
21
5 Descriptive statistics
According to the Bank of Mexico, Mexican migrants have remitted in 1998 an amount
of income that equals approximately 1.5% of Mexican GDP. Household level surveys also
show that remittances tend to play a key role on the survival and livelihood strategies for
many (typically rural) poor households (Rapoport and Docquier, 2005). We take advan-
tage of our detailed panel database to describe the economic role played by remittances
in the rural poor households. Tables 1, 2 and 3 present summary statistics of the vari-
ables of interest for the balanced panel of Mexican rural households. This information is
presented for the pooled database and disaggregated for the three different periods of the
panel: 1997, 2001 and 2006.
We construct a dummy variable at the household level that indicates whether the
household has at least one member who is a migrant (i.e., working in another locality,
state or abroad). As can be observed in Table 2, in 1997, 5% of the households had a
migrant member, while 3% had a member in the US. These percentages are somewhat
reduced in 2001 (3% and 2%, respectively), but increase considerably in 2006 (10% and
7%, respectively). These results show that even when we follow the same households over
a long period of time (10 years), there is considerable variation in migration statistics at
the household level.
Other summary statistics appear in Table 3. The table shows that remittances rep-
resent less than 10% of the total income in the household (0.6/7.7). Surprisingly, this
ratio is very similar for households with current member/migrants and for those without
(the reason for this is that remittances may come from past migrants). The (pooled)
average household has a household head with 3.3 years of schooling and has 1.4 male
adults in the labor force. Both schooling and labor participation increase in 2006. The
table also reports community level variables that will be used as an instrumental variable
in the next section. HH w/mig / #HH (at com.) is the proportion of households at the
22
community level with at least one household member being a migrant. HH w/USmig /
#HH (at com.) represents a similar ratio but for the case when the migrant lives in the
US. As explained in the next section, the instrumental variable will work well if there is
enough variation both across levels and across type of households. A visual inspection of
the table reveals that this is indeed the case.
23
6 Econometric analysis
Let Ait be an asset index for family iand year t. We are mostly interested in household-
specific asset dynamics, that is in Gi,t ≡Ai,t −Ai,t−1. Let Mi,t be a variable that cap-
tures the migration-related nature of the household; Xit be household characteristics; and
(µi+it) be an error component with household fixed-effects and idiosyncratic temporary
shocks. We consider the following asset dynamics equation:
Gi,t =αAi,t−1+βMi,t +δXi,t +µi+i,t (19)
We are mostly concerned with β≡∂E[Gi,t |Ai,t−1,Mi,t,Xi,t−1,µi,η t]
∂M , which denotes the con-
ditional effect of migration on asset accumulation. We extend this analysis to a multi-
dimensional measure of assets A={AP, ANP }, where APdenotes productive assets and
ANP non-productive assets. As argued above, the question we want to address here is the
effect of migration on the type of assets that families accumulate.
We study the effect of migration on asset accumulation using three different measures
of migration. First, we consider a dummy variable for households that declare having
at least one migrant member, Migrant HH (see Table 4). Second, we use the number
of migrants in the household, Number of Migrants by HH (see Table 5). Third, we use
remittances per capita (see Table 6). In each case, we separately study the effect migration
on: (i) total assets, (ii) productive assets, and (iii) non-productive assets.
6.1 Endogeneity
Several endogeneity issues need to be addressed in order to avoid potential biases in
this estimator. First, households may respond to adverse or positive shocks () changing
the number of migrants or the nature of migration (temporal vs. permanent). Second,
selection bias may occur if migrant households are intrinsically different from non-migrant
24
ones.14 Acosta (2006) uses migration networks and history (at the village or household
level) as instruments for migration (or remittances) postulating that these variables have
a positive impact on the opportunity to migrate but no additional impact on income,
schooling, or nutrition at home. McKenzie and Sasin (2007) argue that these instruments
are suitable to study the migration impact at the originary location as in our case.
Following previous work on this subject, the IV strategy we follow uses the percentage
of migrants (to all destinations and to the US, separately) at the community level as
an instrument for the household level decision. Because we have a panel data, we can
include this variable together with the fixed effects at the household level. Therefore, our
identification strategy relies on variation in aggregate migration across time and space.
The Sargan test for overidentification in the following tables has an average p-value of 0.1
for total and productive assets, and 0.4 for non-productive assets. As a result they do not
reject the null hypothesis of exogeneity of the instrumental variables.15 Moreover, both
instruments are significant on the first stage of the regression with high F-values.
6.2 Second stage
In all cases the OLS effect of migration on assets accumulation is negative and statistically
significant. However, when we include the household-level FE this effect becomes non-
significant, except for non-productive assets where it continues to display a negative sign
and significant. The FE results also show that total and productive assets may have a
positive correlation with migration. The differences between OLS and FE are in line with
those outlined before in Section 2. That is, when comparing (13) with ( 14), the effect of
14Regarding the relationship between migration and self-selection, Borjas (1987, 1991) has formalized
the endogeneity of the migration decision, showing that the welfare impact of immigrants is crucially
dependent on the degree of transferability of their unobservable and observable variables, and that affects
the labour market.
15However, when we use migration to the US as our endogenous variable, Sargan tests reject the null
hypothesis of exogeneity of the IV. Therefore, we only evaluate the effect of total migration.
25
FE should be bigger than that of OLS.
Next, we follow the IV strategy described above. Both total assets and productive
assets become positive and statistically significant while non-productive assets is, in gen-
eral, negative and statistically significant. A striking feature is actually the magnitude
of the effect. The coefficient of the migrant dummy variable can be interpreted as the
change in standard deviation units of the corresponding asset. Therefore this shows that
having a migrant household increases total asset accumulation by 0.8 standard deviation
units. Moreover, one additional household migrant contributes to 0.2 total assets stan-
dard deviation units. Finally, doubling the amount of remittances per capita increases
assets by 1.2/10 of a standard deviation.
The magnitude and sign of the effect on productive assets follow closely that of total
assets. Having a migrant household increases productive asset accumulation by 0.8 stan-
dard deviation units. Moreover, one additional household migrant contributes to 0.2 total
assets standard deviation units. Finally, doubling the amount of remittances per capita
increases assets by 1/10 of a standard deviation. However, there is a negative and sta-
tistically significant effect on non-productive asset accumulation of a similar magnitude.
We consider that the negative coefficient in non-productive assets is also an interesting
result in itself. It suggests that some families with migrants reduce their spending in
non-productive asset so as to leave additional funds available for the accumulation of pro-
ductive assets.16 This result can in fact be related to our model in Section 2. There, we
have shown the existence of a minimum initial level of wealth that is necessary to hold in
order to invest. Families at the margin of y=R/(R−1), who now choose to invest as a
consequence of migration, may reinforce the magnitude of their project by concomitantly
reducing consumption.17
16Unfortunately, the surveys do not have current consumption.
17Strictly speaking, this does not occur in our (highly) stylized model because we assume that υ≥1 (see
equation (8)) together with a fixed level of investment. However, letting υ > 0 would straightforwardly
lead to the result that households at the margin of y=R/(R−1) will reduce consumption to help raising
26
Overall the results show that migration can be seen as a long-term investment for the
household. Therefore, the income sent back home by the migrant is used to accumulate
productive assets, rather than non-productive assets. These findings appear across all the
different specifications presented in the tables.
funds for investment, when the migrant finds a good job and 0 < υ < 1.
27
7 Conclusion
This paper aims at explaining the link between migration and asset dynamics for a panel
of poor rural households in Mexico over the period 1997-2006. Our results suggest that
migration may be used by households as a mechanism to accelerate asset accumulation
in productive assets. The general idea is that remittances may help alleviate credit con-
straints for poor households, thus allowing them to invest in productive assets that would
be optimal under complete markets. Furthermore, our estimations also suggest that fam-
ilies who send migrants with the intention to channel remittances towards investment
in productive assets, concomitantly reduce their accumulation of non-productive assets,
possibly to further contribute to raising funds for physical investment.
An important caveat concerning our analysis is that it has abstracted from general
equilibrium interactions, so as to focus exclusively on the direct effect of migration on
capital accumulation via remittances. One specific general equilibrium effect that may
be particularly relevant in our context is the fact that migration decisions will necessarily
affect the aggregate labor supply at the home village. On the one hand, migration lowers
aggregate labor supply at the village level, which in turn would raise equilibrium wages
and household incomes (see Jaimovich, 2010, for a growth model where this mechanism
is at play). However, looking at the household level, sending out a migrant also means
losing one of their workers (and, possibly, the most productive worker). Furthermore, it
may well be the case that the wealth effect brought about by the migrant leads household
members who remain at the village to increase their leisure consumption. In that regard,
two remarks apply here. First, although we acknowledge that these effects imply that
migration may influence accumulation also by other channels other than remittances, we
are agnostic concerning the overall sign of these additional effects. Second, the above
general equilibrium effect on the wage, which could be expected to induce an upwards
bias on the effect of remittances, will be of significant magnitude only if the total number
28
of migrants from the rural village varies substantially across our years of observations. In
that respect, the results in Table 2 show that the percentage of families with at least one
migrant ranges within 3% to 10% of the sampled households.
In a similar vein the effect of migration and remittances are both confounded. We
should expect that remittances increase the probability of capital accumulation as it
relaxes credit constraints. On the other hand, migration would decrease that probability
because of the loss of household members and/or less incentives to work. Both effects
could be further exploited, as for example studying whether results change or not when
we analyze the impact of remittances for the sub-sample of migrant households vis-a-vis
non-migrant households.
29
Appendix
Proof of Proposition 1.
Step 1: Let yi∈R
R−1, yand define:
en1(yi)≡M
ln (yi+υ−1) −ln (yi−1).(20)
Notice first that en1(yi)>0 and finite, since both the numerator and denominator in (20)
are strictly positive and finite. Secondly, differentiating (20) with respect to yiyields:
den1
dyi
=M
[ln (yi+υ−1) −ln (yi−1)]21
yi−1−1
yi+υ−1>0,
where the result en0
1(yi)>0 follows from the fact that yi−1< yi+υ−1. Finally, since
the left-hand side in (7) is strictly increasing in ni, it immediately follows that for any
ni>en1(yi) condition (7) holds.
Step 2: Let yi≥1 and yi∈by, R
R−1and define:
en2(yi)≡M
ln (yi+υ−1) + ln(yi+R)−ln (y2
i).(21)
Firstly, en2(yi)>0 and finite, because both the numerator and denominator in (21) are
strictly positive and finite. Secondly, differentiating (21) with respect to yiyields:
den2
dyi
=M
[ln (yi+υ−1) + ln(yi+R)−ln (y2
i)]22
yi
−2yi+R+ 2(υ−1)
y2
i+R(yi−1) + yi(υ−1) + υR >0,
(22)
where en0
2(yi)>0 obtains after some algebra on the second term in right-hand side of (22),
which leads to the condition that en0
2(yi)>0 iff yi(R−1) + yiυ+ 2R(υ−1) >0.Lastly,
since the left-hand side in (11) is strictly increasing in ni, it immediately follows that for
30
any ni>en2(yi) condition (11) prevails.
Step 3: Let yi≥1 and yi≤byand define:
en3(yi)≡M
ln yi+υ
22−ln (y2
i).(23)
As in the previous two cases, en3(yi)>0 and finite, as both the numerator and denominator
in (23) are strictly positive and finite. Next, differentiating (23) with respect to yiyields:
den3
dyi
=M
hln yi+υ
22−ln (y2
i)i2 2
yi
−2yi+υ
y2
i+υ2
4+yiυ!>0,(24)
where en0
3(yi)>0 obtains after some algebra on the second term in right-hand side of (24),
which leads to the condition that en0
3(yi)>0 iff υ2
2+yiυ > 0.Finally, since the left-hand
side in (12) is strictly increasing in ni, it trivially follows that for any ni>en3(yi) condition
(12) holds.
Step 4: Let now,
en(yi) =
en1(yi) if R
R−1≤yi≤y,
en2(yi) if yi≥1 and by < yi<R
R−1,
en3(yi) if yi≥1 and yi≤by.
Replacing yi=R
R−1into (20) and (21), we can observe after some simple algebra that
en1R
R−1=en2R
R−1. Similarly, from the definition of byin (10), replacing yi=byinto (21)
and (23), it follows that en2(by) = en3(by). As a consequence, it follows that en(yi) portrays
a continuous and strictly increasing function and en(yi) : R++ →R++.
Step 5: Finally, to prove that enR
R−1<1, notice that plugging yi=R
R−1into (20) leads
31
to:
en1R
R−1=M
ln 1
R−1+υ−ln 1
R−1=M
ln 1+υ(R−1)
R−1
1
R−1=M
ln [1 + υ(R−1)].
Therefore, en1R
R−1<1 iff M < ln [1 + υ(R−1)], which is guaranteed by M≤ln(R)
together with υ≥1 and R > 1.
32
References
Acosta P. (2006) “Labor Supply, School Attendance, and Remittances from International Mi-
gration: The Case of El Salvador,” World Bank Policy Research Working Paper N.3903.
Adams R. (1998) “Remittances, Investment, and Rural Asset Accumulation in Pakistan,”
Economic Development and Cultural Change 47:1, 155-173.
Adato, M., Carter, M.R. and May, J. (2006) “Exploring poverty traps and social exclusion
in South Africa using qualitative and quantitative data,” Journal of Development Studies 42:2,
226 - 247.
Banerjee, B. and Bucci, G. A. (1994) “On-The-Job Search After Entering Urban Employ-
ment: An Analysis Based On Indian Migrants,” Oxford Bulletin Of Economics And Statistics
56, 1, 33-47.
Banerjee, A. and Newman, A., (1998) ”Information, the Dual Economy, and Development,”
Review of Economic Studies, 65, 631-653.
Borjas, G. J. (1987) “Self-Selection And The Earnings Of Immigrants,” American Economic
Review 77, 531-53.
Borjas, G. J. (1991) “Immigration And Self-Selection,” in Abowd and Freeman 27-96.
Carter, M. R. and Barrett C. (2006) “The Economics of Poverty Traps and Persistent
Poverty: An Asset-Based Approach,” Journal of Development Studies 42, 178-199.
Das Gupta, M. (1987) “Informal Security Mechanisms and Population Retention in Rural
India,” Economic Development and Cultural Change 36, 101-120.
Durand, J. and Massey D. S. (2001) “Mexican Migration to the United States: a critical
review,” Latin American Research Review 27, 3-42.
Ghatak, S., Levine, P. and Wheatly Price, S. (1996) “Migration Theories and Evidence: An
Assesment,” Journal of Economic Surveys 10, 159-198.
Giuliano, P. and Ruiz-Arranz, M. (2009) “Remittances, financial development, and growth,”
Journal of Development Economics 90, 144–152.
33
Glytsos, N. P. (1993) “Measuring The Income Effects of Migrant remittances: A Method-
ological Approach Applied to Greece,” Economic Development and Cultural Change 42, 131-168.
Hanson, G.H. and Woodruff, C. (2003) “Emigration and Educational Attainment in Mexico,”
mimeo.
Harris, J.R. and Todaro, M.P. (1970) “Migration, Unemployment And Development: A
Two-Sector Analysis,” American Economic Review 60, 126-142.
Jaimovich, E. (2010) “Adverse Selection and Entrepreneurship in a Model of Development,”
Scandinavian Journal of Economics 112, March 2010.
Lewis, W.A. (1954) “Economics Development With Unlmimited Supplies Of Labor,” Manch-
ester School of Economic and Social Studies 22, 139-191.
Lloyd-Ellis, H. and Bernhardt, D. (2000) ”Enterprise, Inequality and Economic Develop-
ment,” Review of Economic Studies 67, 147-168.
Lucas, R.E.B. and Stark, O. (1985) “Motivations to Remit: Evidence from Botswana,’
Journal of Political Economy 93, 901-918.
McKenzie, D. and Sasin, M.J. (2007) “Migration, Remittances, Poverty, and Human Capital:
Conceptual and empirical challenges,” World Bank Policy Research Working Paper N.4272.
McKenzie, D. and Woodruff, C. (2006) “Do entry costs provide an empirical basis for poverty
traps? Evidence from Mexican microenterprises,” Economic Development and Cultural Change
55, 3-42.
Mesnard, A. (2004) ”Temporary migration and capital market imperfections,” Oxford Eco-
nomic Papers 56, 242–262.
Mesnard, A. and Ravallion, M. (2006) “The wealth effect on new business startups in a
developing economy,” Economica 73, 367–392.
Molle, W. and Van Mourik, A. (1988) “International movements of labour under conditions
of economic integration: the case of Western Europe,” Journal of Common Market Studies 26,
317-42.
34
Munshi K. (2003) “Networks in the modern economy: Mexican Migrants in the U.S. Labor
Market,” Quarterly Journal of Economics 118, 549-599.
Montgomery, J. (1991) “Social Networks and Labor-Market Outcomes: Toward an Economic
Analysis,” American Economic Review 81, 1408–1418.
Rapoport, H. and Docquier, F. (2005) “The Economics of Migrants’ Remittances”, IZA
Discussion Paper No. 1531, chapter prepared for the Handbook on the Economics of Reciprocity,
Giving and Altruism, Elsevier- North Holland, forthcoming (Editors: L.-A. Gerard-Varet, S.-C.
Kolm and J. Mercier-Ythier).
Rapoport, H. (2002) “Migration, credit constraints and self-employment: A simple model of
occupational choice, inequality and growth.” Economics Bulletin 15, 1-5
Rosenzweig, M.R. and Wolpin, K.I. (1993) “Credit market constraints, consumption smooth-
ing, and the accumulation of durable production assets in low-income countries: invesment in
bullocks in India,” Journal of Political Economy 101, 223–244.
Rozelle, S., Taylor, J.E. and DeBrauw, A. (1999) “Migration, Remittances, and Agricultural
Productivity in China,” American Economic Review 89, 287-291.
Rozelle, S., Taylor, J.E. and DeBrauw, A. (2003) ”Migration and Incomes in Source Com-
munities: A New Economics of Migration Perspective from China,” Economic Development and
Cultural Change 52, 75-101.
Sen, A.K. (1966) ”Peasants and Dualism with or without Surplus Labour,” Journal of Po-
litical Economy 74, 425-450.
Stark, O. (1991) “The Migration Of Labor,” Cambridge, Mass.: Basil Blackwell.
Stark, O. and Levhari, D. (1982) “On Migration and Risk in LDCs,” Economic Development
and Cultural Change 31, 191-196.
Woodruff, C. and Zenteno, R. (2007) “Migration networks and microenterprises in Mexico,”
Journal of Development Economics 82, 509-528.
Yang, D.. (2008) “International Migration, Remittances, and Household Investment: Evi-
35
dence from Philippine Migrants’ Exchange Rate Shocks,” Economic Journal 118, 591-630.
36
Table 1: Asset Indexes, by HH migrant status
All HH HH with HH without
migrants migrants
All years
Asset Index 0.5 0.503 0.499
[0.45] [0.471] [ 0.447 ]
Non-productive Asset Index 0.418 0.422 0.417
[0.357] [0.363] [ 0.356]
Productive Asset Index 0.1 0.085 0.102
[0.198] [0.214] [0.196]
N 13,095 1,443 11,652
1997
Asset Index 0.388 0.387 0.388
[0.44] [ 0.452] [0.438]
Non-productive Asset Index 0.344 0.342 0.345
[ 0.336] [0.345] [0.335 ]
Productive Asset Index 0.037 0.027 0.038
[0.203] [0.2] [0.203]
2001
Asset Index 0.478 0.474 0.478
[0.445] [0.466] [0.442]
Non-productive Asset Index 0.391 0.387 0.392
[0.363] [0.359] [0.363]
Productive Asset Index 0.123 0.103 0.126
[0.189] [0.214] [0.185]
2006
Asset Index 0.634 0.649 0.632
[0.43] [0.457] [0.427]
Non-productive Asset Index 0.517 0.536 0.515
[0.348] [0.357] [0.346]
Productive Asset Index 0.142 0.126 0.143
[0.186] [0.214] [0.182]
37
Table 2: Summary statistics: migration
1997 2001 2006
Migration HH 0.05 0.03 0.10
Migration HH to the US 0.03 0.02 0.07
Number of HH 4365 4365 4365
38
Table 3: Summary Statistics
HH All HH HH w/mig HH wo/mig
Variable Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
All years
Per capita inc 7.7 1.9 7.847 1.70 7.667 1.95
Remittances 0.6 3.3 1.694 8.17 0.462 1.96
Yrs educ (head) 3.362 2.079 3.977 2.23 3.271 2.45
HH male adults 1.436 1.18 1.784 1.31 1.393 1.15
#HH w/mig / #HH (at com.) 0.012 0.027 0.021 0.05 0.011 0.02
#HH w/USmig / #HH (at com.) 0.01 0.025 0.018 0.05 0.009 0.01
1997
Per Capita inc 7.289 2.536 7.424 2.275 7.272 2.566
Remittances 0.4 2.262 0.4 2.235 0.4 2.265
Yrs educ (head) 3.273 2.296 3.662 2.089 3.216 2.32
HH male adults 1.256 1.03 1.426 1.099 1.235 1.02
#HH w/mig / #HH (at com.) 0.011 0.031 0.018 0.05 0.011 0.028
#HH w/USmig / #HH (at com.) 0.008 0.026 0.015 0.05 0.008 0.021
2001
Per capita inc 7.776 1.502 7.925 1.198 7.757 1.535
Remittances 0.503 1.836 0.305 1.398 0.528 1.882
Yrs educ (head) 3.245 2.376 3.85 2.155 3.156 2.394
HH male adults 1.29 1.046 1.674 1.204 1.243 1.015
#HH w/mig / #HH (at com.) 0.008 0.021 0.015 0.049 0.007 0.013
#HH w/USmig / #HH (at com.) 0.006 0.019 0.012 0.048 0.005 0.011
2006
Per capita inc 7.997 1.503 8.193 1.334 7.972 1.521
Remittances 0.883 4.914 4.315 13.436 0.454 1.725
Yrs educ (head) 3.567 2.609 4.42 2.381 3.44 2.617
HH male adults 1.763 1.364 2.254 1.477 1.702 1.337
#HH w/mig / #HH (at com.) 0.017 0.029 0.029 0.068 0.015 0.019
#HH w/USmig / #HH (at com.) 0.014 0.027 0.026 0.067 0.013 0.016
39
Table 4: Growth of the Asset Index - Migrant Household
(1) (2) (3)
OLS FE IV-FE
ALL ASSETS
Asset Indext−1-0.569*** -1.334*** -1.357***
(0.00986) (0.0126) (0.0154)
Migrant HH -0.140*** 0.0601 0.827***
(0.0414) (0.0455) (0.279)
HH male adults -0.0715*** 0.0836*** 0.0607***
(0.00783) (0.0128) (0.0156)
R20.282 0.729 0.712
Sargan Test 0.0942
First Stage F-Test 63.81
PRODUCTIVE ASSETS
Asset Indext−1-0.553*** -1.349*** -1.367***
(0.00981) (0.0125) (0.0148)
Migrant HH -0.136*** 0.0596 0.689***
(0.0413) (0.0444) (0.267)
HH male adults -0.0799*** 0.0538*** 0.0347**
(0.00781) (0.0125) (0.0151)
R20.274 0.738 0.726
Sargan Test 0.0965
First Stage F-Test 64.87
NON PRODUCTIVE ASSETS
Asset Indext−1-0.657*** -1.491*** -1.485***
(0.0120) (0.0161) (0.0165)
Migrant HH -0.135*** -0.162*** -0.678**
(0.0467) (0.0543) (0.300)
HH male adults -0.0516*** -0.0304** -0.0113
(0.00885) (0.0150) (0.0187)
R20.258 0.664 0.657
Sargan Test 0.434
First Stage F-Test 75.36
Observations 8.730 8.730 8.730
Households 4.365 4.365
Notes: Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. See text for
variable definitions.
40
Table 5: Growth of the Asset Index - Number of Migrants by Household
(1) (2) (3)
OLS FE IV-FE
ALL ASSETS
Asset Indext−1-0.569*** -1.333*** -1.357***
(0.00986) (0.0125) (0.0158)
Number of Migrants by HH -0.0290*** 0.00847 0.220***
(0.00911) (0.0101) (0.0768)
HH male adults -0.0703*** 0.0841*** 0.0510***
(0.00790) (0.0129) (0.0180)
R20.282 0.729 0.702
Sargan Test 0.0812
First Stage F-Test 41.93
PRODUCTIVE ASSETS
Asset Indext−1-0.554*** -1.348*** -1.367***
(0.00981) (0.0124) (0.0150)
Number of Migrants by HH -0.0299*** 0.00826 0.182**
(0.00909) (0.00982) (0.0732)
HH male adults -0.0784*** 0.0543*** 0.0266
(0.00788) (0.0126) (0.0174)
R20.274 0.738 0.719
Sargan Test 0.0844
First Stage F-Test 42.80
NON PRODUCTIVE ASSETS
Asset Indext−1-0.657*** -1.491*** -1.483***
(0.0120) (0.0161) (0.0169)
Number of Migrants by HH -0.0211** -0.0281** -0.186**
(0.0103) (0.0120) (0.0820)
HH male adults -0.0516*** -0.0312** -0.00241
(0.00893) (0.0151) (0.0213)
R20.258 0.663 0.650
Sargan Test 0.480
First Stage F-Test 49.78
Observations 8.730 8.730 8.730
Households 4.365 4.365
Notes: Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. See text for
variable definitions.
41
Table 6: Growth of the Asset Index - Remittances per capita
(1) (2) (3)
OLS FE IV
ALL ASSETS
Asset Indext−1-0.570*** -1.334*** -1.352***
(0.00986) (0.0125) (0.0155)
Remittances per capita 0.000863 0.00849** 0.124***
(0.00341) (0.00378) (0.0447)
HH male adults -0.0750*** 0.0841*** 0.0660***
(0.00776) (0.0128) (0.0157)
R20.281 0.729 0.671
Sargan Test 0.117
First Stage F-Test 19.05
PRODUCTIVE ASSETS
Asset Indext−1-0.554*** -1.348*** -1.362***
(0.00982) (0.0124) (0.0147)
Remittances per capita 0.000579 0.00608* 0.104**
(0.00340) (0.00369) (0.0423)
HH male adults -0.0833*** 0.0546*** 0.0390***
(0.00774) (0.0125) (0.0150)
R20.274 0.738 0.696
Sargan Test 0.115
First Stage F-Test 19.42
NON-PRODUCTIVE ASSETS
Asset Indext−1-0.658*** -1.492*** -1.481***
(0.0120) (0.0161) (0.0177)
Remittances per capita -0.0122*** -0.00499 -0.104**
(0.00384) (0.00453) (0.0480)
HH male adults -0.0553*** -0.0354** -0.0163
(0.00877) (0.0149) (0.0182)
R20.258 0.663 0.626
Sargan Test 0.454
First Stage F-Test 21.75
Observations 8.730 8.730 8.730
Households 4.365 4.365
Notes: Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. See text for
variable definitions.
42
Figure 1: Migration and investment decisions
43
Figure 2: Kernel density estimates for asset indexes
44
