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Are International Remittances Altruism or Insurance? Evidence from Guyana Using Multiple-Migrant Households
World Development Vol. 30, No. 11, pp. 2033–2044, 2002 Ó 2002 Elsevier Science Ltd. All rights reserved Printed in Great Britain 0305-750X/02/$ - see front matter
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Are International Remittances Altruism or Insurance? Evidence from Guyana Using Multiple-Migrant Households REENA AGARWAL Vanderbilt University, Nashville, Tennessee, USA and ANDREW W. HOROWITZ * University of Arkansas, Fayetteville, USA Summary. — While international remittances provide significant disposable income for many households in less-developed countries, there is no consensus on migrantsÕ remittance motivation. Two principal competing explanations for remittances are altruism and risk sharing. This paper employs previously unanalyzed data to bring new evidence to the debate. We develop a simple theoretical model that yields distinct testable predictions for each motivation. Among the modelÕs testable predictions is differential remittance behavior by migrants from households with multiple versus single migrants under altruism and risk sharing. Our estimation finds significant differences in remittance behavior of multiple and single migrants and these differences support the altruistic incentive to remit. Ó 2002 Elsevier Science Ltd. All rights reserved. Key words — remittances, migration, South America, Guyana
1. INTRODUCTION For many households in developing countries, international remittances provide a significant share of disposable income. A burgeoning literature explores motivations to remit and their effect on household expenditure patterns. The two primary remittance incentives explored in the literature are altruism and risk sharing. Yet the verdict remains out as to the relative importance of these two incentives, and their effects on recipient households. This paper employs a previously unanalyzed data set to bring new evidence to bear on the debate. It is helpful to precede discussion of remittances with a brief survey of the migration literature, as migration is a pre-condition for remittance. Though much international migration is motivated by political (and other noneconomic) factors, the economics literature focuses nearly exclusively upon the greater
earning potential in the destination country. Typically, migration is modeled as an optimizing choice by a utility-maximizing individual; individuals migrate when doing so increases expected utility. Models of migration motivated solely by individual considerations include Hay (1980), Kalzuny (1975), Nakosteen and Zimmer (1980), Navratil and Doyle (1977), Yezer and Thurston (1976), and Vijverberg (1993). Todaro and Maruszko (1989) provide a survey of the international migration literature. Some migration models consider household characteristics as well as those of the migrant. Funkhouser (1992) finds migration propensity among Nicaraguans to be increasing in
* We thank J.S. Butler, Brad Barham, Kathy Anderson, Michael Foster, Diana Weinhold, and Gary Ferrier for helpful comments. Remaining errors are ours. Final revision accepted: 29 May 2002.
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household income. Contrarily, using Egyptian data Adams (1993) finds males from poor landless households have the highest propensity to migrate. Mincer (1978) explores the effect of family ties on migration, finding that stronger family ties (e.g., marriage) deter migration. Root and De Jong (1991) explore the effect of householdÕs social structural characteristics on migration probability. In moving from models where the migrant is motivated solely by individual incentives to models where household characteristics influence the migration decision, the issue of remittances arises naturally. The conventional approach assumes, in effect, that migrants remain linked (or retain membership) in their original household. Household therefore refers to the migrantÕs household of origin and migrant refers to a household member who is spatially separated from her household. We adhere to this terminology. 1 When households are spatially dispersed (as when one member migrates) interaction continues in the form of transfers. The spatial dimension, however, separates the migration/remittance literature from the extensive literature on intrahousehold transfers among co-residing households. As noted, altruism and/or risk sharing typically motivate the link between migrant and household in the literature. Models of altruism simply imbed the utility of other household members in the migrantÕs utility function. Models in which altruism plays a significant role in motivating remittances include Banerjee (1984), and Johnson and Whitelaw (1974). Models in which risk-sharing motivates remittances include Stark (1991) and Stark and Levhari (1982). In this literature, less-developed country rural-to-urban migration allows riskaverse households to diversify income. Lucas and Stark (1985) and Stark and Lucas (1988) view remittances as components of a selfenforcing, co-operative contract between the migrant and household. Their studies of Botswana suggest that remittances may be repayment for the cost of migrant education and transportation. The remittance flow allows the household to undertake riskier, higher expected yield ventures. Rosenzweig and Stark (1989) hypothesize that ‘‘dispersed yet kinship-related households’’ in rural India enter into implicit risk-sharing contracts through the marriage of daughters. In addition to the altruism or insurance motivations to remit, remittances may also constitute an investment in the home country.
Hoddinott (1994) is representative of models that focus on this incentive. In his model the migrant and household maximize a joint utility function and remittances from the migrant reflect the ability of the family (the parents in particular) to offer rewards in the form of land bequests. Though our discussion is couched in terms of the altruism versus insurance motivations it should be noted that this is a special case of the more general alternatives of altruism versus ‘‘self-interest’’ motivations to remit. As in the case of insurance, self-interest investment motives to remit should be more weakly related to the presence of other migrants than altruistic motivates for interior (rather than corner) solutions. 2 Our objective in this paper is to develop a model that yields testable predictions associated with the altruism and risk-sharing motivations to remit and to test our model using Guyanese data. The organization of the remainder of the paper is as follows: Section 2 develops a simple theoretical model that will guide the empirical exploration of motivations to remit. Section 3 describes the Guyanese setting and data. Section 4 presents the estimation methodology and results. Section 5 concludes. 2. CONCEPTUAL FRAMEWORK This section presents simple models of motivations to remit. The risk-sharing motive can be modeled as a remittance/insurance scheme where the remittances from migrant to household (or vice versa) are premium payments. 3 For simplicity suppose the migrant lives in a two-period world with first period income of Ym and remittances of r P 0 to the (home country) household. Second period migrant income is unknown when remittances are sent and may be high (Ym1 ) or low (Ym2 ¼ Ym1 L). The badstate (Ym2 ) occurs with probability p and the good-state (Ym1 ) with probability (1 p), where 0 < p < 1 and L > 0. 4 In the bad-state an indemnity, ‘‘s,’’ is made from household to migrant in period 2. With actuarially fair insurance, r ¼ ps, and the risk-averse migrant fully insures. Let Vm be the migrantÕs period 1 utility and Vmj the discounted period 2 utility in state j. Then the migrantÕs expected utility is EU ¼ Vm ðYm rÞ þ ð1 pÞVm1 ðYm1 Þ þ pVm2 ðYm2 þ sÞ:
ð1Þ
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Note that s ¼ r=p, os=or ¼ ð1=pÞ > 0, and o2 s=or2 ¼ 0. The first-order condition with respect to r is 0
Vm0 þ pVm2 s0 ¼ 0:
ð2Þ
Eqn. (2) defines an implicit remittance function for the insurance (self-interest) motive: rI ¼ rðYm ; Ym2 ; pÞ:
ð3Þ
Using the implicit function theorem the remittance function partials are or Vm00 ¼ 00 > 0; oYm Vm þ pVm200 s0 s0 00 or pVm2 s0 ¼ < 0; oYm2 Vm00 þ pVm200 s0 s0 0 or ½Vm2 s0 > 0: ¼ 00 op Vm þ pVm200 s0 s0
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household to migrant may occur (since altruism may be two-sided), but are no longer a choice variable of the migrant. Assuming the household is egalitarian, percapita household consumption in period 1 is given by ðYh þ bðr þ xmÞÞ=n. In period 2, household per-capita consumption is ðYh þ ð1 bÞ ðr þ xmÞÞ=n in the good-state and ðYh t T þ ð1 bÞðr þ xmÞÞ=n in the bad-state. The second period consumption level of the migrant is Ym1 in the good-state and Ym2 þ t in the bad-state. Given this notation the ex-ante expected utility of the altruistic migrant is EU ¼ Vm ðYm rÞ þ "
ð4Þ
Remittances are thus positively related to period 1 income and the probability of the badstate and negatively related to the bad-state income. Note that an indemnity contract between a migrant and household is not, in general, affected by the presence of other migrants from the same household. Each risk-averse migrant would independently fully insure. Now consider the case where remittances are motivated solely by altruism toward the (nonmigrating) household. Let Yh be total household income, Vhij be the discounted utility of the ith household member in the jth state, and again let Vmj be discounted migrant utility in state j (j ¼ 1; 2). Denote the number of (nonmigrating) household members by n and let ai be the migrantÕs ‘‘altruism weight’’ toward the ith household member. We again denote remittances from the migrant to the household as r. To distinguish payments from household to migrant in the insurance and altruism models we now denote any payment from household to the migrant as t (transfer). Suppose there are m additional migrants who remit x dollars, on average, to the household in period 1. 5 The altruistic migrant takes xm as given and that if the bad-state is realized the household will transfer a total of T to the other m migrants in the second period. 6 The household consumes a fraction b of all remittances in period 1 and consumes the remainder in period 2. Note that when remittances are insurance payments, r and s (premium and indemnity) are functionally related. The migrant, in effect, chooses the indemnity (s) when contracting for insurance. Under altruism, payments from
n X
ai Vhi
i¼1
Yh þ bðr þ xmÞ n
þ ð1 pÞ Vm1 ðYm1 Þ þ
n X
ai Vhi1
i¼1
Yh þ ð1 bÞðr þ xmÞ n
#
"
þ p Vm2 ðYm2 þ tÞ þ
n X i¼1
ai Vhi2
Yh t T þ ð1 bÞðr þ xmÞ n
# :
ð5Þ Note that if ai ¼ 0 for all i, then Eqn. (5) collapses to Eqn. (1). Again recalling that in the altruism model t is exogenous to the migrant, the first order condition with respect to r yields: n X b ai Vhi0 Vm0 ¼ n i¼1 n X 1b ðð1 pÞVhi1 þ pVhi2 Þ: ð6Þ ai þ n i¼1 Eqn. (6) indicates that altruistic migrant remits so as to equate the marginal utility from own consumption to an average of the weighted marginal utility of the other household members, adjusted for the remittances of other migrants. In the extreme case when each ai ¼ 1, migrants equate their marginal utility to that of nonmigrating household members. Eqn. (6) defines an implicit remittance function for the case of pure altruism: rA ¼ ðYm ; Yh ; ai ; n; m; x; pÞ:
ð7Þ
Using the implicit function theorem we obtain the following partial signs: or=oYm > 0, or= oYh < 0, or=oai > 0, or=om < 0, or=ox < 0, or= on and or=op ambiguous. Appendix A contains
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the explicit partials. With regard to the ambiguous partial or=on, it can be shown that if absolute risk-aversion (Arrow-Pratt) is sufficiently large, or=on < 0. A key testable implication of the model is the affect of multiple migrants upon remittance levels. Under pure insurance (or other selfinterest) motives, the number of other migrants would not affect own-remittance. On the other hand, under altruism where migrants are concerned with the welfare of the nonmigrating household, the presence of multiple remitting migrants will affect the average remittance level. To our knowledge, ours is the first paper that links the remittance behavior of sole versus multiple-migrants to remittance motivation. We now employ the Guyana data in light of our model.
3. THE GUYANESE SETTING AND DATA Roughly the size of Wisconsin, Guyana is bordered by Venezuela to the north, Suriname to the south, and the Atlantic to the east. Though located in the northeast of South America, Guyana is more integrated, both culturally and economically, with the Caribbean than with Latin America. GuyanaÕs population is approximately 800,000, and emigration has been significant in recent decades, motivated by a weak economy and unstable political situation. The cumulative stock of Guyanese residing abroad has been estimated near 500,000 and the US Immigration and Naturalization Service reports over 107,000 Guyanese immigrants in the United States during 1981–91. 7 Given this large migrant population, Guyana provides a rich setting in which to explore remittance issues. In 1992 and 1993 the Guyana Bureau of Statistics (GBS) undertook two ambitious data collection projects. The first was a household income and expenditure survey (HIES) funded by the United Nations Development Programme (UNDP). Independently, GBS collaborated with the World Bank in the construction and administration of a living standard measurement study (LSMS). LSMS contains a migration module with detailed migrant characteristics. HIES provides detailed characteristics of the nonmigrating households and indicates whether they received remittances, but provides no information on the migrants themselves.
During one of the four subrounds of the HIES, the LSMS was administered by GBS to the same national sample of 1,819 households. This circumstance provides a rare opportunity to connect household income and expenditure characteristics (contained in HIES) with a detailed profile of their migrants (contained in LSMS). Connecting these two independently designed micro data sets, however, is problematic in some respects. 8 Specifically, the complete migration module of LSMS can be matched with the HIES data for 270 households containing 524 migrants, though there is evidence from HIES that more households have migrants who remitted. This precludes construction of a model of selection into our migrant sample from the general population–– which under ideal circumstances (data) would precede the remittance model. Instead, we must assume our LSMS sample is representative of all migrants. In any case, as our objective is to determine whether the data are more supportive of altruism or risk-sharing motives among those who remit, it is not essential to model the migration decision. A final important issue concerns the bidirectional resource flow implied by our theoretical model. That is, the model suggests flows from the migrant(s) to household when conditions are favorable in the host country and from household to migrant when conditions are relatively bad in the host country. In fact, we observe virtually no resource flow from Guyana to migrants. 9 This is reflected in the estimation that follows. The explanation for the unidirectional resource flow becomes obvious when one examines economic conditions in Guyana over the sample period––that is, conditions were so poor in Guyana relative to those in destination countries over the survey interval that unidirectional flow is consistent with either remittance model.
4. EMPIRICAL METHODOLOGY The LSMS migration module provides personal characteristics of 524 Guyanese migrants. 10 These individuals come from 270 distinct households. Of these 270 households, 100 received zero remittances from their migrants. We account for this by estimating two equations: the first equation models the decision to provide positive remittances, and the second models the amount of remittances. The
INTERNATIONAL REMITTANCES
first equation is based on the entire LSMS migration module, and the second equation is based on only those individuals that sent positive remittances. The decision to remit positively is given by the following equation: ð8Þ Ii ¼ Zi c þ ui ; where c is a vector of parameter estimates, Z is a vector of regressors (discussed below), u is the error term, and Ii is the dichotomous variable denoting the decision to send remittances: Ii ¼ 1 if a migrant sends positive remittances and Ii ¼ 0 if the migrant remits zero. Eqn. (8) is estimated using a probit model. The remittance equation is R i ¼ X i B þ mi ;
ð9Þ
where Ri is a vector containing log of remittances received by the ith household, Xi the regressors (discussed below), B the parameter estimates and m the disturbance. Ordinary least squares (OLS) is inappropriate for estimating (9) since it does not take into account the selection involved in the decision to send positive remittances. We employ the Heckman (1976) procedure to correct for selection bias and employ maximum likelihood estimation to obtain consistent and efficient estimates. 11 (a) Variables used in estimation In our model, remittances depend on income levels of the household and migrant, number of migrants, number of household members, and a proxy for ‘‘bad-state’’ probability. We have data on household income, but not migrant income. But, we have primitive migrant characteristics, which, according to the standard human capital model, determine income. In particular, income depends on education and experience (which we proxy with years abroad). Moreover, in this context migrant income is also affected by destination, with the United States having a significantly higher wage profile than neighboring Caribbean countries. Finally, gender typically affects income through a variety of channels. Thus we implicitly assume a function Ym (education, experience, gender, destination), and include these arguments directly in the regressor vector Xi . Beyond the migrant income proxies, we also have variables that indicate the migrantÕs reason for leaving, and whether the migrantÕs household of origin was in a rural area. The migrant and household characteristic variables used in the analysis are defined and
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summarized as a table in Appendix B.2. Detailed description of LSMS data and modules can be found on the World Bank LSMS web page. Each observation is a migrant who at the time of the survey had lived away from the household for more than six months and is reported by the household in the MIGRATN module of the LSMS data set. Following Hoddinott (1992) the decision to remit positively and the remittance level models are identified by whether a migrant had been settled in the host country for more than one year. The rationale is that there is likely a lag in the remittance flow initiation associated with settling in the host country. Indeed, in our sample approximately 12% of the migrants had arrived in the destination country within a year of the survey, and these account for a large share of the nonremitting migrants. There are, of course, unobserved idiosyncratic reasons some migrants are not remitters during our survey year, but recent arrival appears a primary cause. Once the remittance flow is initiated, however, its level should not be affected by an initiation lag. The dependent variable r, is the log of the monthly remittances sent by a migrant. The HIES data reports which migrants are remitting positive sums, whether these remittances were in cash or kind, and total cash remittances received by a household. It does not however, indicate the specific amount remitted by each individual remitter. Therefore, if a household has m remitting migrants, we only know the sum of their remittances. For purposes of estimation, we assume that each remitting migrant contributes r ¼ R=m, where R is the total amount of remittance received by a household and m is the number of migrants who send positive cash remittances. 12 The number of household members, n, is denoted by an equivalent household size figure that takes into account the age groups of the various members. For the sample, the average equivalent household size is 3.03. The amount of land owned by a household is also included as a regressor since it represents the earning potential for the agricultural households. The other household characteristics used in this analysis that would affect the level of remittances and are: location, ethnicity, gender, and age of the head of the household. Of the 270 households, 117 are of East Indian origin. Around 57% of the heads of household are male and 62% are less than 60 years of age. Among the migrants, approximately 63% have secondary education or higher, and there are
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more female than male migrants. On average, the migrants have lived away from home for more than seven years and a significant proportion (65%) of them have migrated to the United States or Canada. Individuals who migrate face several kinds of risk to their incomes, the most important being unemployment. The MIGRATN module reports the destination of the migrant under the following categories: the United States, Canada, Europe, Asia, Africa, Caribbean, South America and Other. The unemployment rate for each continent (excluding the United States) is a weighted average of the unemployment figures of the commonwealth countries in that continent. The weights used are the population levels of the countries. Since Guyana is a commonwealth country, it is assumed that the destination of a Guyanese migrant would also be an English speaking country. Appendix A shows the unemployment rate used for each area and the countries included in each area. The positive remittance decision equation that is estimated is specified as follows: I ¼ a0 þ c1 ðYR AB > 1Þ þ c2 ðRURALÞ þ c3 ðE INDÞ þ c4 ðLND OWNÞ þ c5 ðHOH AGEÞ þ c6 ðHH SIZEÞ þ c7 ðHH PCIÞ þ c8 ðOTHER MÞ þ c9 ðMIG MALEÞ þ c10 ðM EDU SECÞ þ c11 ðM EDU UNIVÞ þ c12 ðUNEMPLÞ þ c13 ðWHY LEFTÞ þ c14 ðDESTNÞ þ c15 ðEDU HOHÞ þ u:
ð10Þ
The remittance equation is specified as follows: R ¼ a1 þ b1 ðRURALÞ þ b2 ðE INDÞ þ b3 ðLND OWNÞ þ b4 ðHOH AGEÞ þ b5 ðHH SIZEÞ þ b6 ðHH PCIÞ þ b7 ðOTHER MÞ þ b8 ðMIG MALEÞ þ b9 ðM EDU SECÞ þ b10 ðM EDU UNIVÞ þ b11 ðUNEMPLÞ þ b12 ðWHY LEFTÞ þ b13 ðDESTNÞ þ b14 ðEDU HOHÞ þ m: ð11Þ
Descriptions and moments of these variables are found in table of Appendix B.2. Our theoretical model predicts that in the case of pure altruism the average remittance level will fall with the number of migrants while under insurance it would be unaffected by the
number of migrants. The model also predicts that under altruism, the higher are the home householdsÕ earnings, the lower are the remittances they receive. On the other hand, if remittances are premiums, household earnings should not affect remittance levels. The model predicts the sign of the household size (HH SIZE) variable to be indeterminate in the case of pure altruism, and zero if the motive is insurance. As noted above, migrantsÕ earnings are not observed but are proxied by their gender, education, destination and experience. If an individual migrated the United States, Canada, or a European country, then we expect them to have higher incomes than their counterparts in other countries. In addition, with more education, a migrant is likely to find a better job and hence earn a higher income. A higher level of education can also have a countereffect. Since more education likely reduces unemployment risk, it may reduce the migrantÕs insurance motivation to remit. The sign of the coefficient of the risk variable, UNEMPL, is positive if the motive is insurance and indeterminate if the motive is altruism. (b) Results Table 1 provides the estimation results for the two models (i) insurance, and (ii) altruism. Columns 1 and 2 give results for the insurance model, columns 3 and 4 for the altruism model. For each of the two models two equations are estimated: one for the decision to remit and the second for the amount of remittances. Columns 1 and 3 provide results for the probit model estimating the decision to remit. The dependent variable is ‘‘I,’’ where I is equal to 1 if the migrant remits and equal to 0 if the migrant does not remit. Columns 2 and 4 show remittance equation results. We first examine the insurance model results. Recall that if insurance (or other self-interest) were the sole motive for remittances, the remittance function (Eqn. (3)) depends only on migrant characteristics. Variables relating to other migrants or the nonmigrating household are therefore not included in the insurance model estimation. Inspection of column 2 of Table 1 reveals that the insurance model has weak explanatory power with only the migrant gender variable significant at the 5% level. In the selection probit in column 1 only the identifier (YR AB > 1), Rural, and destination
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Table 1. Estimation results Insurance motive
Variable Constant YR AB > 1 RURAL E IND
Altruism motive
Column 1
Column 2
Column 3
Column 4
Decision to remit (I) )0.27422 ()0.666) 0.47115
(2.539) )0.33477
()2.386) 0.13382 (0.99)
Remittance level (R) 6.702 (7.539)
Remittance level (R) 8.8788
(7.711)
)0.09412 ()0.799) 0.17554 (1.291) 0.01294 (0.079) )0.02417 ()1.188) 0.21149 (1.607) 0.32151
(2.070) )0.01745 ()0.271)
0.44514
(2.318) 0.396 (1.552) )0.37861 ()1.553) 0.02027 (0.517) )0.10999 ()0.493) 0.32421 (1.016) 0.08776 (0.973) 1.4098 (8.508) 0.25168 (0.38) 268
Decision to remit (I) 3.0286
(5.182) 0.67531
(3.369) )0.51859
()3.348) 0.39035
(2.495) )0.00646
()2.833) )0.02242 ()0.155) )0.08766
)2.225 )0.43495
()7.354) )0.10526
()2.893) )0.11431 ()0.891) 0.27538
(1.834) )0.0646 ()0.354) )0.01277 ()0.571) 0.14718 (1.002) 0.33117
(2.015) 0.10521 (1.398)
)0.01602 ()0.054) )0.10278 ()0.495)
LND OWN HOH AGE HH SIZE HH PCI OTHER M MIG MALE M EDU SEC M EDU UNIV UNEMPL WHY LEFT DESTN EDU HOH SIGMA RHO Observations
492
492
)0.04993 ()0.187) 0.31397 (1.399) )0.00312 ()0.474) )0.44818
()2.639) 0.17294
(2.752) )0.21816 ()1.31) )0.26109
()4.583) 0.38086
(2.330) 0.30569 (1.354) )0.30425 ()1.433) 0.01226 (0.353) )0.34467
()1.882) )0.12928 ()0.489) 0.16733
(2.007) 1.1727 (19.339) )0.03665 ()0.064) 268
t-statistics in parenthesis. Significant at 0.05. ** Significant at 0.10. *
(DESTN) are significant at the 5% level. Moreover, the likelihood ratio test on the altruism model of the restrictions implied by the insurance-only model reject the insurance-only model at the 1% level of significance with log likelihood values for the altruism model and insurance models of )703.26 and )790.32, respectively. The likelihood ratio test statistic of 174.11 is sufficient to reject the insurance only model. 13
Turning to the results for the altruism-only model (column 4 of the table in Appendix B.1) recall that this estimation is motivated by implicit remittance function of Eqn. (7). Since remittances in this model depend on characteristics of both migrants and the nonmigrating household it is not surprising that it has greater explanatory power. But, unlike the migrant characteristic variables, many of the nonmigrating household variables are significant in
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explaining remittances and are of the predicted sign. Of particular interest is the other-migrants (OTHER M) variable. As discussed previously, if nonmigrating household welfare motivates remittances, the presence of other remitting migrants will reduce per-migrant remittance levels. Alternatively, if insurance (or other selfinterest) motivates remittances, it is more difficult to construct a scenario where the presence of other migrants would affect per-migrant remittance levels. In the altruism model of column 4, OTHER M is of the predicted sign and significant at the 1% level. In addition, household size (HH SIZE) and age of the household head (HOH AGE) are significant at the 5% level. 5. CONCLUSION The motivation for international remittances has been the subject of much debate in the development literature. Two of the competing explanations for remittance flows are altruism and risk sharing. Risk sharing may be modeled as an ‘‘insurance’’ contract (perhaps implicit) between household and migrant. In this paper we develop simple theoretical models of altruism and insurance, and use these models to guide estimation. Our model suggests that the remittance behavior of migrants from multiple-migrant households and single-migranthouseholds may signal remittance motivation. In particular, we expect per-migrant remittances to decline in the number of migrants if altruism motivates remittance. Alternatively, if remittances are insurance premiums the presence (or absence) of other migrants should not significantly affect per-migrant remittance levels. As noted in the paper, our models focus on the interior solution of a migrantÕs optimal remittance problem. The multiple-migrant signal is generated because the interior solution to the migrantÕs remittance problem is independent of the presence of other migrants under an insurance motive, but not under altruism. If the home country household faces binding constraints in contracting with migrants, corner
solutions may arise that obfuscate the multiplemigrant signal. Still, the interior solution seems the natural starting point in broaching the difficult issue of remittance motivation––and in this case the diagnostic power of the signal is alluring. We employ a little analyzed data set from Guyana to test the multiple-migrant signal and other predictions of the model. We find that per-migrant remittances are significantly and negatively related to the number of migrants. This and our other estimation results lend support to the altruism remittance motive and weigh against the insurance motive. One implication of the finding that per-migrant remittances decrease with the number of migrants is the possibility of an extreme point (maximum) in the relationship between total household remittances and the number of migrants (see also Poirine, 1997). From a policy perspective, this raises the possibility of ‘‘remittance maximizing’’ emigration policy. As noted frequently in this paper, risk-sharing (insurance) is only one item on a large menu of potential self-interest contracts between migrant and household. Though the theoretical model and estimation of this paper focus on this self-interest motivation, the signal of remittance flows from single versus multiple-migrant households applies to self-interest contracts more generally. That is, if a migrant-to-household remittance is a payment associated with some self-interest contract, each migrant should contract independently of other migrants under an interior solution. Evidence of differential remittance behavior of sole and multiplemigrants, while consistent with altruism, is inconsistent with interior solutions to a broad class of self-interest contracts. Finally, it is clear that more evidence from other data sets is needed before the robustness of the multiple-migrant signal can be asserted. In addition, exploration of the constraint set faced by the household in their interactions with their migrants, and the empirical implications of corner solutions in these dimensions is needed. These constitute the next step in this line of research.
NOTES 1. See, for example, Rosenzweig and Stark (1989). In practice, migrants establish distinct households, and a
more accurate terminology might reflect that remittances are in fact an interaction between households.
INTERNATIONAL REMITTANCES 2. If a migrant remitting for a self-interest motive faces a household that is resource constrained in its ability to provide the good for which remittance are payment (be it insurance or investment) a negative correlation between the number of migrants and remittances might also arise. Corner solutions of this nature therefore weaken the multiple-migrant signal. 3. Lucas and Stark (1985) and Stark and Lucas (1988) address the enforcement mechanisms in such contractual arrangements. We focus instead in this section on developing a simple model to guide empirical analysis of the insurance versus altruism hypotheses. 4. We assume no uncertainty in the home country. As discussed in Section 3, the remittance flow is in fact almost completely unidirectional––from migrant to household. Conditions in the home country (Guyana) were sufficiently dismal relative to destination countries that abstracting from home country state-variability represents only a modest simplification in this context. The loss, L, incurred by migrants in the host country ‘‘bad-state’’ might be associated with sickness, accident, or unemployment. 5. For simplicity, we abstract from coordination issues among remitting migrants and focus on the subsequent decision of an altruistic remitter who takes the selfexclusive remittance level of (xm) as given. 6. This implicitly assumes all migrants face the same ‘‘bad-state’’ probability. 7. Inter-American Development Bank 1993 estimates (see the INS statistics homepage at: http://www.ins.gov/ graphics/aboutins/statistics/index.html).
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8. The precise algorithm for connecting these two disparate data sets is available from the authors upon request. 9. The HIES survey contains a household expenditure item for ‘‘remittances, gifts, and other transfers.’’ Of the 270 households in the migration module, only 18 have a positive entry for this variable. Of these 18, only four households receive remittances from abroad. Given that the expenditure item includes gifts and domestic transfers there is an upper bound of four households out of 270 with bidirectional remittance flow and an upper bound of 14 households with only household to migrant remittances. 10. Though the MIGRATN sample contains 524 migrants, some data are missing for 32 of the migrants. Estimation therefore utilizes 492 migrants. 11. In the first stage the decision equation is estimated using the probit procedure. The fitted value of c is used to estimate Zi c^ and k^i (the inverse MillÕs ratio). In stage two, k^i is inserted into the remittance equation and it is then estimated using the OLS procedure. 12. Clearly, it would be preferable to have the precise amount contributed by each remitter. Such data are rare, however, and our procedure utilizes a valuable piece of information––which migrants are remitters and which are not. This information would be lost were we to just model total household remittance. 13. The ‘‘insurance-only’’ model embodies five constraints on the altruism model. The critical value of a chi-squared distribution with five degrees of freedom at 0.01 level of significance is 15.086.
REFERENCES Adams, R. H. (1993). The economic and demographic determinants of international migration in rural Egypt. The Journal of Development Studies, 30(1), 146–167. Banerjee, B. (1984). The probability, size, and uses of remittances from urban to rural areas in India. Journal of Development Economics, 16, 293–311. Funkhouser, E. (1992). Migration from Nicaragua: some recent evidence. World Development, 20(8), 1209–1218. Hay, M. J. (1980). A structural equations model of migration in Tunisia. Economic Development and Cultural Change, 28, 345–358.
Heckman, J. J. (1976). The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. Annals of Economic and Social Measurement, 5(4), 475–492. Hoddinott, J. (1992). Modeling remittance flows in Kenya. Journal of African Economies, 1(2), 206– 232. Hoddinott, J. (1994). A model of migration and remittances applied to western Kenya. Oxford Economic Papers, 46, 459–476. Johnson, G. E., & Whitelaw, W. E. (1974). Urban-rural income transfers in Kenya: an estimated-remittances
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function. Economic Development and Cultural Change, 22, 473–479. Kalzuny, R. L. (1975). Determinants of household migration: a comparative study by race and poverty level. Review of Economics and Statistics, 57, 269– 274. Lucas, R. E. B., & Stark, O. (1985). Motivations to remit: evidence from Botswana. Journal of Political Economy, 93, 901–918. Mincer, J. (1978). Family migration decisions. Journal of Political Economy, 86(5), 749–773. Nakosteen, R. A., & Zimmer, M. (1980). Migration and income: the question of self selection. Southern Economic Journal, 46, 84–85. Navratil, F. J., & Doyle, J. J. (1977). The socio economic determinants of migration and the level of aggregation. Southern Economic Journal, 43(4), 1547–1559. Poirine, B. (1997). A theory of remittances as an implicit family loan arrangement. World Development, 25(4), 589–611. Root, B. D., & De Jong, G. F. (1991). Family migration in a developing country. Population Studies, 45, 221– 233.
Rosenzweig, M. R., & Stark, O. (1989). Consumption smoothing, migration, and marriage: evidence from rural India. Journal of Political Economy, 97, 905– 926. Stark, O. (1991). Migration in LDCs: risk, remittances, and the family. Finance and Development, 28(4), 39–41. Stark, O., & Levhari, D. (1982). On migration and risk in LDCs. Economic Development and Cultural Change, 31, 191–196. Stark, O., & Lucas, R. E. B. (1988). Migration, remittances and the family. Economic Development and Cultural Change, 36(3), 465–481. Todaro, M. P., & Maruszko, L. (1989). International migration. In J. Eatwell, M. Milgate, & P. Newman (Eds.), The new Palgrave. New York: Macmillian Press Limited. Vijverberg, W. P. M. (1993). Labour market performance as a determinant of migration. Economica, 60, 143–160. Yezer, A. M. J., & Thurston, L. (1976). Migration patterns and income change: implications for the human capital approach to migration. Southern Economic Journal, 42(4), 693–702.
APPENDIX A REMITTANCE FUNCTIOIN PARTIALS FOR THE CASE OF PURE ALTURISM or Vm00 >0 ¼
2 2 P Pn P 200 ð1bÞ oYm V 00 þ n ai V 00 b22 þ ð1 pÞ n ai V 100 ð1bÞ þ ðpÞ a V i 2 2 hi n hi hi m i¼1 i¼1 i¼1 n n
ðA:1Þ
hP
i Pn P 00 n 00 b 100 1b þ p ni¼1 ai Vhi2 1b i¼1 ai Vhi n2 þ ð1 pÞ i¼1 ai Vhi or n2 n2 <0 ¼ oYh V 00 þ Pn ai V 00 b2 þ ð1 pÞ Pn ai V 100 ð1bÞ2 þ ðpÞ Pn ai V 200 ð1bÞ2 2 2 2 hi n hi hi i¼1 i¼1 i¼1 m n n
ðA:2Þ
hP i
Pn P 0 n 0 b 10 ð1bÞ þ ðpÞ ni¼1 ai Vhi2 ð1bÞ i¼1 ai Vhi n þ ð1 pÞ i¼1 ai Vhi n n or >0 ¼ oai V 00 þ Pn ai V 00 b2 þ ð1 pÞ Pn ai V 100 ð1bÞ2 þ ðpÞ Pn ai V 200 ð1bÞ2 hi n2 hi hi m i¼1 i¼1 i¼1 n2 n2
ðA:3Þ
hP i 2 2 P P 00 00 n x x 00 bðbxÞ þ ð1 pÞ ni¼1 ai Vhi1 ð1bÞ þ p ni¼1 ai Vhi2 ð1bÞ i¼1 ai Vhi or n2 n2 n2 <0 ¼ 2
2 2 P P P 00 00 om þ ðpÞ n ai V 2 ð1bÞ V 00 þ n ai V 00 b2 þ ð1 pÞ n ai V 1 ð1bÞ 2 2 m
i¼1
hi
n
i¼1
hi
n
i¼1
hi
ðA:4Þ
n
hP i 2 2 Pn Pn n 00 bðbmÞ 100 ð1bÞ m 200 ð1bÞ m þ ð1 pÞ þ p a V a V a V i i i 2 2 2 i¼1 i¼1 i¼1 hi hi hi n n n or <0 ¼
2 2 Pn Pn Pn 00 b2 100 ð1bÞ 200 ð1bÞ ox 00 Vm þ i¼1 ai Vhi n2 þ ð1 pÞ i¼1 ai Vhi þ ðpÞ i¼1 ai Vhi n2 n2
ðA:5Þ
INTERNATIONAL REMITTANCES
P i P 00 00 1b 1b þð1 pÞ ni¼1 ai Vhi1 Yh þbðrþxmÞ þp ni¼1 ai Vhi2 Yh þbðrþxmÞ n n n2 n2 2 2 Pn Pn Pn 00 b2 100 ð1bÞ 200 ð1bÞ 00 Vm þ i¼1 ai Vhi n2 þ ð1 pÞ i¼1 ai Vhi þ ðpÞ i¼1 ai Vhi n2 n2
Pn P P 0 n n 0 b 10 1b þ p i¼1 ai Vhi2 1b i¼1 ai Vhi n2 þ ð1 pÞ i¼1 ai Vhi n2 n2 ðA:6Þ þ
2 2 Pn Pn Pn 00 b2 100 ð1bÞ 200 ð1bÞ 00 þ ðpÞ a V Vm þ i¼1 ai Vhi n2 þ ð1 pÞ i¼1 ai Vhi i 2 2 hi i¼1 n n h P P i 0 0 ni¼1 ai Vhi1 1b þ ni¼1 ai Vhi2 1b n n or ¼ ðA:7Þ 2 2 P P P 2 00 00 n n ð1bÞ b op V 00 þ 00 þ ð1 pÞ i¼1 ai Vhi1 þ ðpÞ ni¼1 ai Vhi2 ð1bÞ m i¼1 ai Vhi n2 n2 n2
or ¼ on
hP
n 00 i¼1 ai Vhi
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Yh þbðrþxmÞ n2
b n
APPENDIX B VARIABLE DESCRIPTION B.1. Unemployment by region Area
Unemployment rate (%)
Countries
Caribbean S. America USA Canada Europe Asia Africa
16.63 6.03 6.6 10.3 8.1 18.43 33.33
Other
10.53
Bahamas, Barbados, Belize, Jamaica, Trinidad and Tobago Argentina, Brazil, Suriname United States Canada United Kingdom India, Malaysia, Pakistan, Singapore, Sri Lanka S. Africa, Ghana, Botswana, Cameroon, Lesotho, Mauritius, Mozambique, Namibia, Seychelles Australia, New Zealand
B.2. LSMS/HEIS variable summary statistics Variable r E IND HH PCI HOH AGE HOH MAL LND OWN HH SIZE Rural DESTN M EDU SEC M EDU UNI WHY LEFT MIG MAL OTHER M UNEMPL YR AB YR AB SQ
Mean
Std. dev.
Log of monthly remittances sent by a migrant 4.37 4.13 Ethnicity variable, equal to 1 if the head of the household was an 0.49 0.50 East Indian Log of household per-capita income from all sources except 8.21 1.55 foreign remittances Age of the head of household, equal to 1 if 60 years or older 0.43 0.49 Gender of the head of household, equal to 1 if male 0.65 0.47 Land owned by the household in acres 4.52 28.07 Equivalent household size: age 0–6 ¼ 0:2, 7–12 ¼ 0:3, 3.03 1.65 13–17 ¼ 0:5, >17 ¼ 1 Location of the source household, equal to 1 if rural 0.53 0.50 Destination of the migrant, equal to 1 if USA, Canada or Europe 0.69 0.45 Education of the migrant prior to departure, equal to 1 if the 0.63 0.48 individual had a secondary education Equals 1 if the individual had a university education prior to 0.19 0.39 departure, 0 otherwise Reason for migration. Equals 1 if reason was work, 0 otherwise 0.3394 0.47 Gender of the migrant, equal to 1 if male 0.47 0.50 Number of other migrants abroad from the same household 2.06 2.07 Unemployment rate of the area to which the individual migrated 8.66 3.58 Number of years the migrant has been abroad 7.68 7.06 Square of the number of years abroad 108.94 205.84 (Appendix B––continued overleaf )
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(continued) Variable YR AB > 1 EDU_HOH
Dummy variable equal to 1 if the migrant has been abroad for more than a year Education level of head of household: ¼ 1 if illiterate; ¼ 2 if literate but no schooling; ¼ 3 if below primary; ¼ 4 if primary; ¼ 5 if secondary; ¼ 6 if graduate; ¼ 7 if post graduate
Mean
Std. dev.
0.88
0.32
4.16
0.99
www.elsevier.com/locate/worlddev
PII: S0305-750X(02)00118-3
Are International Remittances Altruism or Insurance? Evidence from Guyana Using Multiple-Migrant Households REENA AGARWAL Vanderbilt University, Nashville, Tennessee, USA and ANDREW W. HOROWITZ * University of Arkansas, Fayetteville, USA Summary. — While international remittances provide significant disposable income for many households in less-developed countries, there is no consensus on migrantsÕ remittance motivation. Two principal competing explanations for remittances are altruism and risk sharing. This paper employs previously unanalyzed data to bring new evidence to the debate. We develop a simple theoretical model that yields distinct testable predictions for each motivation. Among the modelÕs testable predictions is differential remittance behavior by migrants from households with multiple versus single migrants under altruism and risk sharing. Our estimation finds significant differences in remittance behavior of multiple and single migrants and these differences support the altruistic incentive to remit. Ó 2002 Elsevier Science Ltd. All rights reserved. Key words — remittances, migration, South America, Guyana
1. INTRODUCTION For many households in developing countries, international remittances provide a significant share of disposable income. A burgeoning literature explores motivations to remit and their effect on household expenditure patterns. The two primary remittance incentives explored in the literature are altruism and risk sharing. Yet the verdict remains out as to the relative importance of these two incentives, and their effects on recipient households. This paper employs a previously unanalyzed data set to bring new evidence to bear on the debate. It is helpful to precede discussion of remittances with a brief survey of the migration literature, as migration is a pre-condition for remittance. Though much international migration is motivated by political (and other noneconomic) factors, the economics literature focuses nearly exclusively upon the greater
earning potential in the destination country. Typically, migration is modeled as an optimizing choice by a utility-maximizing individual; individuals migrate when doing so increases expected utility. Models of migration motivated solely by individual considerations include Hay (1980), Kalzuny (1975), Nakosteen and Zimmer (1980), Navratil and Doyle (1977), Yezer and Thurston (1976), and Vijverberg (1993). Todaro and Maruszko (1989) provide a survey of the international migration literature. Some migration models consider household characteristics as well as those of the migrant. Funkhouser (1992) finds migration propensity among Nicaraguans to be increasing in
* We thank J.S. Butler, Brad Barham, Kathy Anderson, Michael Foster, Diana Weinhold, and Gary Ferrier for helpful comments. Remaining errors are ours. Final revision accepted: 29 May 2002.
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household income. Contrarily, using Egyptian data Adams (1993) finds males from poor landless households have the highest propensity to migrate. Mincer (1978) explores the effect of family ties on migration, finding that stronger family ties (e.g., marriage) deter migration. Root and De Jong (1991) explore the effect of householdÕs social structural characteristics on migration probability. In moving from models where the migrant is motivated solely by individual incentives to models where household characteristics influence the migration decision, the issue of remittances arises naturally. The conventional approach assumes, in effect, that migrants remain linked (or retain membership) in their original household. Household therefore refers to the migrantÕs household of origin and migrant refers to a household member who is spatially separated from her household. We adhere to this terminology. 1 When households are spatially dispersed (as when one member migrates) interaction continues in the form of transfers. The spatial dimension, however, separates the migration/remittance literature from the extensive literature on intrahousehold transfers among co-residing households. As noted, altruism and/or risk sharing typically motivate the link between migrant and household in the literature. Models of altruism simply imbed the utility of other household members in the migrantÕs utility function. Models in which altruism plays a significant role in motivating remittances include Banerjee (1984), and Johnson and Whitelaw (1974). Models in which risk-sharing motivates remittances include Stark (1991) and Stark and Levhari (1982). In this literature, less-developed country rural-to-urban migration allows riskaverse households to diversify income. Lucas and Stark (1985) and Stark and Lucas (1988) view remittances as components of a selfenforcing, co-operative contract between the migrant and household. Their studies of Botswana suggest that remittances may be repayment for the cost of migrant education and transportation. The remittance flow allows the household to undertake riskier, higher expected yield ventures. Rosenzweig and Stark (1989) hypothesize that ‘‘dispersed yet kinship-related households’’ in rural India enter into implicit risk-sharing contracts through the marriage of daughters. In addition to the altruism or insurance motivations to remit, remittances may also constitute an investment in the home country.
Hoddinott (1994) is representative of models that focus on this incentive. In his model the migrant and household maximize a joint utility function and remittances from the migrant reflect the ability of the family (the parents in particular) to offer rewards in the form of land bequests. Though our discussion is couched in terms of the altruism versus insurance motivations it should be noted that this is a special case of the more general alternatives of altruism versus ‘‘self-interest’’ motivations to remit. As in the case of insurance, self-interest investment motives to remit should be more weakly related to the presence of other migrants than altruistic motivates for interior (rather than corner) solutions. 2 Our objective in this paper is to develop a model that yields testable predictions associated with the altruism and risk-sharing motivations to remit and to test our model using Guyanese data. The organization of the remainder of the paper is as follows: Section 2 develops a simple theoretical model that will guide the empirical exploration of motivations to remit. Section 3 describes the Guyanese setting and data. Section 4 presents the estimation methodology and results. Section 5 concludes. 2. CONCEPTUAL FRAMEWORK This section presents simple models of motivations to remit. The risk-sharing motive can be modeled as a remittance/insurance scheme where the remittances from migrant to household (or vice versa) are premium payments. 3 For simplicity suppose the migrant lives in a two-period world with first period income of Ym and remittances of r P 0 to the (home country) household. Second period migrant income is unknown when remittances are sent and may be high (Ym1 ) or low (Ym2 ¼ Ym1 L). The badstate (Ym2 ) occurs with probability p and the good-state (Ym1 ) with probability (1 p), where 0 < p < 1 and L > 0. 4 In the bad-state an indemnity, ‘‘s,’’ is made from household to migrant in period 2. With actuarially fair insurance, r ¼ ps, and the risk-averse migrant fully insures. Let Vm be the migrantÕs period 1 utility and Vmj the discounted period 2 utility in state j. Then the migrantÕs expected utility is EU ¼ Vm ðYm rÞ þ ð1 pÞVm1 ðYm1 Þ þ pVm2 ðYm2 þ sÞ:
ð1Þ
INTERNATIONAL REMITTANCES
Note that s ¼ r=p, os=or ¼ ð1=pÞ > 0, and o2 s=or2 ¼ 0. The first-order condition with respect to r is 0
Vm0 þ pVm2 s0 ¼ 0:
ð2Þ
Eqn. (2) defines an implicit remittance function for the insurance (self-interest) motive: rI ¼ rðYm ; Ym2 ; pÞ:
ð3Þ
Using the implicit function theorem the remittance function partials are or Vm00 ¼ 00 > 0; oYm Vm þ pVm200 s0 s0 00 or pVm2 s0 ¼ < 0; oYm2 Vm00 þ pVm200 s0 s0 0 or ½Vm2 s0 > 0: ¼ 00 op Vm þ pVm200 s0 s0
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household to migrant may occur (since altruism may be two-sided), but are no longer a choice variable of the migrant. Assuming the household is egalitarian, percapita household consumption in period 1 is given by ðYh þ bðr þ xmÞÞ=n. In period 2, household per-capita consumption is ðYh þ ð1 bÞ ðr þ xmÞÞ=n in the good-state and ðYh t T þ ð1 bÞðr þ xmÞÞ=n in the bad-state. The second period consumption level of the migrant is Ym1 in the good-state and Ym2 þ t in the bad-state. Given this notation the ex-ante expected utility of the altruistic migrant is EU ¼ Vm ðYm rÞ þ "
ð4Þ
Remittances are thus positively related to period 1 income and the probability of the badstate and negatively related to the bad-state income. Note that an indemnity contract between a migrant and household is not, in general, affected by the presence of other migrants from the same household. Each risk-averse migrant would independently fully insure. Now consider the case where remittances are motivated solely by altruism toward the (nonmigrating) household. Let Yh be total household income, Vhij be the discounted utility of the ith household member in the jth state, and again let Vmj be discounted migrant utility in state j (j ¼ 1; 2). Denote the number of (nonmigrating) household members by n and let ai be the migrantÕs ‘‘altruism weight’’ toward the ith household member. We again denote remittances from the migrant to the household as r. To distinguish payments from household to migrant in the insurance and altruism models we now denote any payment from household to the migrant as t (transfer). Suppose there are m additional migrants who remit x dollars, on average, to the household in period 1. 5 The altruistic migrant takes xm as given and that if the bad-state is realized the household will transfer a total of T to the other m migrants in the second period. 6 The household consumes a fraction b of all remittances in period 1 and consumes the remainder in period 2. Note that when remittances are insurance payments, r and s (premium and indemnity) are functionally related. The migrant, in effect, chooses the indemnity (s) when contracting for insurance. Under altruism, payments from
n X
ai Vhi
i¼1
Yh þ bðr þ xmÞ n
þ ð1 pÞ Vm1 ðYm1 Þ þ
n X
ai Vhi1
i¼1
Yh þ ð1 bÞðr þ xmÞ n
#
"
þ p Vm2 ðYm2 þ tÞ þ
n X i¼1
ai Vhi2
Yh t T þ ð1 bÞðr þ xmÞ n
# :
ð5Þ Note that if ai ¼ 0 for all i, then Eqn. (5) collapses to Eqn. (1). Again recalling that in the altruism model t is exogenous to the migrant, the first order condition with respect to r yields: n X b ai Vhi0 Vm0 ¼ n i¼1 n X 1b ðð1 pÞVhi1 þ pVhi2 Þ: ð6Þ ai þ n i¼1 Eqn. (6) indicates that altruistic migrant remits so as to equate the marginal utility from own consumption to an average of the weighted marginal utility of the other household members, adjusted for the remittances of other migrants. In the extreme case when each ai ¼ 1, migrants equate their marginal utility to that of nonmigrating household members. Eqn. (6) defines an implicit remittance function for the case of pure altruism: rA ¼ ðYm ; Yh ; ai ; n; m; x; pÞ:
ð7Þ
Using the implicit function theorem we obtain the following partial signs: or=oYm > 0, or= oYh < 0, or=oai > 0, or=om < 0, or=ox < 0, or= on and or=op ambiguous. Appendix A contains
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WORLD DEVELOPMENT
the explicit partials. With regard to the ambiguous partial or=on, it can be shown that if absolute risk-aversion (Arrow-Pratt) is sufficiently large, or=on < 0. A key testable implication of the model is the affect of multiple migrants upon remittance levels. Under pure insurance (or other selfinterest) motives, the number of other migrants would not affect own-remittance. On the other hand, under altruism where migrants are concerned with the welfare of the nonmigrating household, the presence of multiple remitting migrants will affect the average remittance level. To our knowledge, ours is the first paper that links the remittance behavior of sole versus multiple-migrants to remittance motivation. We now employ the Guyana data in light of our model.
3. THE GUYANESE SETTING AND DATA Roughly the size of Wisconsin, Guyana is bordered by Venezuela to the north, Suriname to the south, and the Atlantic to the east. Though located in the northeast of South America, Guyana is more integrated, both culturally and economically, with the Caribbean than with Latin America. GuyanaÕs population is approximately 800,000, and emigration has been significant in recent decades, motivated by a weak economy and unstable political situation. The cumulative stock of Guyanese residing abroad has been estimated near 500,000 and the US Immigration and Naturalization Service reports over 107,000 Guyanese immigrants in the United States during 1981–91. 7 Given this large migrant population, Guyana provides a rich setting in which to explore remittance issues. In 1992 and 1993 the Guyana Bureau of Statistics (GBS) undertook two ambitious data collection projects. The first was a household income and expenditure survey (HIES) funded by the United Nations Development Programme (UNDP). Independently, GBS collaborated with the World Bank in the construction and administration of a living standard measurement study (LSMS). LSMS contains a migration module with detailed migrant characteristics. HIES provides detailed characteristics of the nonmigrating households and indicates whether they received remittances, but provides no information on the migrants themselves.
During one of the four subrounds of the HIES, the LSMS was administered by GBS to the same national sample of 1,819 households. This circumstance provides a rare opportunity to connect household income and expenditure characteristics (contained in HIES) with a detailed profile of their migrants (contained in LSMS). Connecting these two independently designed micro data sets, however, is problematic in some respects. 8 Specifically, the complete migration module of LSMS can be matched with the HIES data for 270 households containing 524 migrants, though there is evidence from HIES that more households have migrants who remitted. This precludes construction of a model of selection into our migrant sample from the general population–– which under ideal circumstances (data) would precede the remittance model. Instead, we must assume our LSMS sample is representative of all migrants. In any case, as our objective is to determine whether the data are more supportive of altruism or risk-sharing motives among those who remit, it is not essential to model the migration decision. A final important issue concerns the bidirectional resource flow implied by our theoretical model. That is, the model suggests flows from the migrant(s) to household when conditions are favorable in the host country and from household to migrant when conditions are relatively bad in the host country. In fact, we observe virtually no resource flow from Guyana to migrants. 9 This is reflected in the estimation that follows. The explanation for the unidirectional resource flow becomes obvious when one examines economic conditions in Guyana over the sample period––that is, conditions were so poor in Guyana relative to those in destination countries over the survey interval that unidirectional flow is consistent with either remittance model.
4. EMPIRICAL METHODOLOGY The LSMS migration module provides personal characteristics of 524 Guyanese migrants. 10 These individuals come from 270 distinct households. Of these 270 households, 100 received zero remittances from their migrants. We account for this by estimating two equations: the first equation models the decision to provide positive remittances, and the second models the amount of remittances. The
INTERNATIONAL REMITTANCES
first equation is based on the entire LSMS migration module, and the second equation is based on only those individuals that sent positive remittances. The decision to remit positively is given by the following equation: ð8Þ Ii ¼ Zi c þ ui ; where c is a vector of parameter estimates, Z is a vector of regressors (discussed below), u is the error term, and Ii is the dichotomous variable denoting the decision to send remittances: Ii ¼ 1 if a migrant sends positive remittances and Ii ¼ 0 if the migrant remits zero. Eqn. (8) is estimated using a probit model. The remittance equation is R i ¼ X i B þ mi ;
ð9Þ
where Ri is a vector containing log of remittances received by the ith household, Xi the regressors (discussed below), B the parameter estimates and m the disturbance. Ordinary least squares (OLS) is inappropriate for estimating (9) since it does not take into account the selection involved in the decision to send positive remittances. We employ the Heckman (1976) procedure to correct for selection bias and employ maximum likelihood estimation to obtain consistent and efficient estimates. 11 (a) Variables used in estimation In our model, remittances depend on income levels of the household and migrant, number of migrants, number of household members, and a proxy for ‘‘bad-state’’ probability. We have data on household income, but not migrant income. But, we have primitive migrant characteristics, which, according to the standard human capital model, determine income. In particular, income depends on education and experience (which we proxy with years abroad). Moreover, in this context migrant income is also affected by destination, with the United States having a significantly higher wage profile than neighboring Caribbean countries. Finally, gender typically affects income through a variety of channels. Thus we implicitly assume a function Ym (education, experience, gender, destination), and include these arguments directly in the regressor vector Xi . Beyond the migrant income proxies, we also have variables that indicate the migrantÕs reason for leaving, and whether the migrantÕs household of origin was in a rural area. The migrant and household characteristic variables used in the analysis are defined and
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summarized as a table in Appendix B.2. Detailed description of LSMS data and modules can be found on the World Bank LSMS web page. Each observation is a migrant who at the time of the survey had lived away from the household for more than six months and is reported by the household in the MIGRATN module of the LSMS data set. Following Hoddinott (1992) the decision to remit positively and the remittance level models are identified by whether a migrant had been settled in the host country for more than one year. The rationale is that there is likely a lag in the remittance flow initiation associated with settling in the host country. Indeed, in our sample approximately 12% of the migrants had arrived in the destination country within a year of the survey, and these account for a large share of the nonremitting migrants. There are, of course, unobserved idiosyncratic reasons some migrants are not remitters during our survey year, but recent arrival appears a primary cause. Once the remittance flow is initiated, however, its level should not be affected by an initiation lag. The dependent variable r, is the log of the monthly remittances sent by a migrant. The HIES data reports which migrants are remitting positive sums, whether these remittances were in cash or kind, and total cash remittances received by a household. It does not however, indicate the specific amount remitted by each individual remitter. Therefore, if a household has m remitting migrants, we only know the sum of their remittances. For purposes of estimation, we assume that each remitting migrant contributes r ¼ R=m, where R is the total amount of remittance received by a household and m is the number of migrants who send positive cash remittances. 12 The number of household members, n, is denoted by an equivalent household size figure that takes into account the age groups of the various members. For the sample, the average equivalent household size is 3.03. The amount of land owned by a household is also included as a regressor since it represents the earning potential for the agricultural households. The other household characteristics used in this analysis that would affect the level of remittances and are: location, ethnicity, gender, and age of the head of the household. Of the 270 households, 117 are of East Indian origin. Around 57% of the heads of household are male and 62% are less than 60 years of age. Among the migrants, approximately 63% have secondary education or higher, and there are
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more female than male migrants. On average, the migrants have lived away from home for more than seven years and a significant proportion (65%) of them have migrated to the United States or Canada. Individuals who migrate face several kinds of risk to their incomes, the most important being unemployment. The MIGRATN module reports the destination of the migrant under the following categories: the United States, Canada, Europe, Asia, Africa, Caribbean, South America and Other. The unemployment rate for each continent (excluding the United States) is a weighted average of the unemployment figures of the commonwealth countries in that continent. The weights used are the population levels of the countries. Since Guyana is a commonwealth country, it is assumed that the destination of a Guyanese migrant would also be an English speaking country. Appendix A shows the unemployment rate used for each area and the countries included in each area. The positive remittance decision equation that is estimated is specified as follows: I ¼ a0 þ c1 ðYR AB > 1Þ þ c2 ðRURALÞ þ c3 ðE INDÞ þ c4 ðLND OWNÞ þ c5 ðHOH AGEÞ þ c6 ðHH SIZEÞ þ c7 ðHH PCIÞ þ c8 ðOTHER MÞ þ c9 ðMIG MALEÞ þ c10 ðM EDU SECÞ þ c11 ðM EDU UNIVÞ þ c12 ðUNEMPLÞ þ c13 ðWHY LEFTÞ þ c14 ðDESTNÞ þ c15 ðEDU HOHÞ þ u:
ð10Þ
The remittance equation is specified as follows: R ¼ a1 þ b1 ðRURALÞ þ b2 ðE INDÞ þ b3 ðLND OWNÞ þ b4 ðHOH AGEÞ þ b5 ðHH SIZEÞ þ b6 ðHH PCIÞ þ b7 ðOTHER MÞ þ b8 ðMIG MALEÞ þ b9 ðM EDU SECÞ þ b10 ðM EDU UNIVÞ þ b11 ðUNEMPLÞ þ b12 ðWHY LEFTÞ þ b13 ðDESTNÞ þ b14 ðEDU HOHÞ þ m: ð11Þ
Descriptions and moments of these variables are found in table of Appendix B.2. Our theoretical model predicts that in the case of pure altruism the average remittance level will fall with the number of migrants while under insurance it would be unaffected by the
number of migrants. The model also predicts that under altruism, the higher are the home householdsÕ earnings, the lower are the remittances they receive. On the other hand, if remittances are premiums, household earnings should not affect remittance levels. The model predicts the sign of the household size (HH SIZE) variable to be indeterminate in the case of pure altruism, and zero if the motive is insurance. As noted above, migrantsÕ earnings are not observed but are proxied by their gender, education, destination and experience. If an individual migrated the United States, Canada, or a European country, then we expect them to have higher incomes than their counterparts in other countries. In addition, with more education, a migrant is likely to find a better job and hence earn a higher income. A higher level of education can also have a countereffect. Since more education likely reduces unemployment risk, it may reduce the migrantÕs insurance motivation to remit. The sign of the coefficient of the risk variable, UNEMPL, is positive if the motive is insurance and indeterminate if the motive is altruism. (b) Results Table 1 provides the estimation results for the two models (i) insurance, and (ii) altruism. Columns 1 and 2 give results for the insurance model, columns 3 and 4 for the altruism model. For each of the two models two equations are estimated: one for the decision to remit and the second for the amount of remittances. Columns 1 and 3 provide results for the probit model estimating the decision to remit. The dependent variable is ‘‘I,’’ where I is equal to 1 if the migrant remits and equal to 0 if the migrant does not remit. Columns 2 and 4 show remittance equation results. We first examine the insurance model results. Recall that if insurance (or other self-interest) were the sole motive for remittances, the remittance function (Eqn. (3)) depends only on migrant characteristics. Variables relating to other migrants or the nonmigrating household are therefore not included in the insurance model estimation. Inspection of column 2 of Table 1 reveals that the insurance model has weak explanatory power with only the migrant gender variable significant at the 5% level. In the selection probit in column 1 only the identifier (YR AB > 1), Rural, and destination
INTERNATIONAL REMITTANCES
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Table 1. Estimation results Insurance motive
Variable Constant YR AB > 1 RURAL E IND
Altruism motive
Column 1
Column 2
Column 3
Column 4
Decision to remit (I) )0.27422 ()0.666) 0.47115
(2.539) )0.33477
()2.386) 0.13382 (0.99)
Remittance level (R) 6.702 (7.539)
Remittance level (R) 8.8788
(7.711)
)0.09412 ()0.799) 0.17554 (1.291) 0.01294 (0.079) )0.02417 ()1.188) 0.21149 (1.607) 0.32151
(2.070) )0.01745 ()0.271)
0.44514
(2.318) 0.396 (1.552) )0.37861 ()1.553) 0.02027 (0.517) )0.10999 ()0.493) 0.32421 (1.016) 0.08776 (0.973) 1.4098 (8.508) 0.25168 (0.38) 268
Decision to remit (I) 3.0286
(5.182) 0.67531
(3.369) )0.51859
()3.348) 0.39035
(2.495) )0.00646
()2.833) )0.02242 ()0.155) )0.08766
)2.225 )0.43495
()7.354) )0.10526
()2.893) )0.11431 ()0.891) 0.27538
(1.834) )0.0646 ()0.354) )0.01277 ()0.571) 0.14718 (1.002) 0.33117
(2.015) 0.10521 (1.398)
)0.01602 ()0.054) )0.10278 ()0.495)
LND OWN HOH AGE HH SIZE HH PCI OTHER M MIG MALE M EDU SEC M EDU UNIV UNEMPL WHY LEFT DESTN EDU HOH SIGMA RHO Observations
492
492
)0.04993 ()0.187) 0.31397 (1.399) )0.00312 ()0.474) )0.44818
()2.639) 0.17294
(2.752) )0.21816 ()1.31) )0.26109
()4.583) 0.38086
(2.330) 0.30569 (1.354) )0.30425 ()1.433) 0.01226 (0.353) )0.34467
()1.882) )0.12928 ()0.489) 0.16733
(2.007) 1.1727 (19.339) )0.03665 ()0.064) 268
t-statistics in parenthesis. Significant at 0.05. ** Significant at 0.10. *
(DESTN) are significant at the 5% level. Moreover, the likelihood ratio test on the altruism model of the restrictions implied by the insurance-only model reject the insurance-only model at the 1% level of significance with log likelihood values for the altruism model and insurance models of )703.26 and )790.32, respectively. The likelihood ratio test statistic of 174.11 is sufficient to reject the insurance only model. 13
Turning to the results for the altruism-only model (column 4 of the table in Appendix B.1) recall that this estimation is motivated by implicit remittance function of Eqn. (7). Since remittances in this model depend on characteristics of both migrants and the nonmigrating household it is not surprising that it has greater explanatory power. But, unlike the migrant characteristic variables, many of the nonmigrating household variables are significant in
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explaining remittances and are of the predicted sign. Of particular interest is the other-migrants (OTHER M) variable. As discussed previously, if nonmigrating household welfare motivates remittances, the presence of other remitting migrants will reduce per-migrant remittance levels. Alternatively, if insurance (or other selfinterest) motivates remittances, it is more difficult to construct a scenario where the presence of other migrants would affect per-migrant remittance levels. In the altruism model of column 4, OTHER M is of the predicted sign and significant at the 1% level. In addition, household size (HH SIZE) and age of the household head (HOH AGE) are significant at the 5% level. 5. CONCLUSION The motivation for international remittances has been the subject of much debate in the development literature. Two of the competing explanations for remittance flows are altruism and risk sharing. Risk sharing may be modeled as an ‘‘insurance’’ contract (perhaps implicit) between household and migrant. In this paper we develop simple theoretical models of altruism and insurance, and use these models to guide estimation. Our model suggests that the remittance behavior of migrants from multiple-migrant households and single-migranthouseholds may signal remittance motivation. In particular, we expect per-migrant remittances to decline in the number of migrants if altruism motivates remittance. Alternatively, if remittances are insurance premiums the presence (or absence) of other migrants should not significantly affect per-migrant remittance levels. As noted in the paper, our models focus on the interior solution of a migrantÕs optimal remittance problem. The multiple-migrant signal is generated because the interior solution to the migrantÕs remittance problem is independent of the presence of other migrants under an insurance motive, but not under altruism. If the home country household faces binding constraints in contracting with migrants, corner
solutions may arise that obfuscate the multiplemigrant signal. Still, the interior solution seems the natural starting point in broaching the difficult issue of remittance motivation––and in this case the diagnostic power of the signal is alluring. We employ a little analyzed data set from Guyana to test the multiple-migrant signal and other predictions of the model. We find that per-migrant remittances are significantly and negatively related to the number of migrants. This and our other estimation results lend support to the altruism remittance motive and weigh against the insurance motive. One implication of the finding that per-migrant remittances decrease with the number of migrants is the possibility of an extreme point (maximum) in the relationship between total household remittances and the number of migrants (see also Poirine, 1997). From a policy perspective, this raises the possibility of ‘‘remittance maximizing’’ emigration policy. As noted frequently in this paper, risk-sharing (insurance) is only one item on a large menu of potential self-interest contracts between migrant and household. Though the theoretical model and estimation of this paper focus on this self-interest motivation, the signal of remittance flows from single versus multiple-migrant households applies to self-interest contracts more generally. That is, if a migrant-to-household remittance is a payment associated with some self-interest contract, each migrant should contract independently of other migrants under an interior solution. Evidence of differential remittance behavior of sole and multiplemigrants, while consistent with altruism, is inconsistent with interior solutions to a broad class of self-interest contracts. Finally, it is clear that more evidence from other data sets is needed before the robustness of the multiple-migrant signal can be asserted. In addition, exploration of the constraint set faced by the household in their interactions with their migrants, and the empirical implications of corner solutions in these dimensions is needed. These constitute the next step in this line of research.
NOTES 1. See, for example, Rosenzweig and Stark (1989). In practice, migrants establish distinct households, and a
more accurate terminology might reflect that remittances are in fact an interaction between households.
INTERNATIONAL REMITTANCES 2. If a migrant remitting for a self-interest motive faces a household that is resource constrained in its ability to provide the good for which remittance are payment (be it insurance or investment) a negative correlation between the number of migrants and remittances might also arise. Corner solutions of this nature therefore weaken the multiple-migrant signal. 3. Lucas and Stark (1985) and Stark and Lucas (1988) address the enforcement mechanisms in such contractual arrangements. We focus instead in this section on developing a simple model to guide empirical analysis of the insurance versus altruism hypotheses. 4. We assume no uncertainty in the home country. As discussed in Section 3, the remittance flow is in fact almost completely unidirectional––from migrant to household. Conditions in the home country (Guyana) were sufficiently dismal relative to destination countries that abstracting from home country state-variability represents only a modest simplification in this context. The loss, L, incurred by migrants in the host country ‘‘bad-state’’ might be associated with sickness, accident, or unemployment. 5. For simplicity, we abstract from coordination issues among remitting migrants and focus on the subsequent decision of an altruistic remitter who takes the selfexclusive remittance level of (xm) as given. 6. This implicitly assumes all migrants face the same ‘‘bad-state’’ probability. 7. Inter-American Development Bank 1993 estimates (see the INS statistics homepage at: http://www.ins.gov/ graphics/aboutins/statistics/index.html).
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8. The precise algorithm for connecting these two disparate data sets is available from the authors upon request. 9. The HIES survey contains a household expenditure item for ‘‘remittances, gifts, and other transfers.’’ Of the 270 households in the migration module, only 18 have a positive entry for this variable. Of these 18, only four households receive remittances from abroad. Given that the expenditure item includes gifts and domestic transfers there is an upper bound of four households out of 270 with bidirectional remittance flow and an upper bound of 14 households with only household to migrant remittances. 10. Though the MIGRATN sample contains 524 migrants, some data are missing for 32 of the migrants. Estimation therefore utilizes 492 migrants. 11. In the first stage the decision equation is estimated using the probit procedure. The fitted value of c is used to estimate Zi c^ and k^i (the inverse MillÕs ratio). In stage two, k^i is inserted into the remittance equation and it is then estimated using the OLS procedure. 12. Clearly, it would be preferable to have the precise amount contributed by each remitter. Such data are rare, however, and our procedure utilizes a valuable piece of information––which migrants are remitters and which are not. This information would be lost were we to just model total household remittance. 13. The ‘‘insurance-only’’ model embodies five constraints on the altruism model. The critical value of a chi-squared distribution with five degrees of freedom at 0.01 level of significance is 15.086.
REFERENCES Adams, R. H. (1993). The economic and demographic determinants of international migration in rural Egypt. The Journal of Development Studies, 30(1), 146–167. Banerjee, B. (1984). The probability, size, and uses of remittances from urban to rural areas in India. Journal of Development Economics, 16, 293–311. Funkhouser, E. (1992). Migration from Nicaragua: some recent evidence. World Development, 20(8), 1209–1218. Hay, M. J. (1980). A structural equations model of migration in Tunisia. Economic Development and Cultural Change, 28, 345–358.
Heckman, J. J. (1976). The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. Annals of Economic and Social Measurement, 5(4), 475–492. Hoddinott, J. (1992). Modeling remittance flows in Kenya. Journal of African Economies, 1(2), 206– 232. Hoddinott, J. (1994). A model of migration and remittances applied to western Kenya. Oxford Economic Papers, 46, 459–476. Johnson, G. E., & Whitelaw, W. E. (1974). Urban-rural income transfers in Kenya: an estimated-remittances
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function. Economic Development and Cultural Change, 22, 473–479. Kalzuny, R. L. (1975). Determinants of household migration: a comparative study by race and poverty level. Review of Economics and Statistics, 57, 269– 274. Lucas, R. E. B., & Stark, O. (1985). Motivations to remit: evidence from Botswana. Journal of Political Economy, 93, 901–918. Mincer, J. (1978). Family migration decisions. Journal of Political Economy, 86(5), 749–773. Nakosteen, R. A., & Zimmer, M. (1980). Migration and income: the question of self selection. Southern Economic Journal, 46, 84–85. Navratil, F. J., & Doyle, J. J. (1977). The socio economic determinants of migration and the level of aggregation. Southern Economic Journal, 43(4), 1547–1559. Poirine, B. (1997). A theory of remittances as an implicit family loan arrangement. World Development, 25(4), 589–611. Root, B. D., & De Jong, G. F. (1991). Family migration in a developing country. Population Studies, 45, 221– 233.
Rosenzweig, M. R., & Stark, O. (1989). Consumption smoothing, migration, and marriage: evidence from rural India. Journal of Political Economy, 97, 905– 926. Stark, O. (1991). Migration in LDCs: risk, remittances, and the family. Finance and Development, 28(4), 39–41. Stark, O., & Levhari, D. (1982). On migration and risk in LDCs. Economic Development and Cultural Change, 31, 191–196. Stark, O., & Lucas, R. E. B. (1988). Migration, remittances and the family. Economic Development and Cultural Change, 36(3), 465–481. Todaro, M. P., & Maruszko, L. (1989). International migration. In J. Eatwell, M. Milgate, & P. Newman (Eds.), The new Palgrave. New York: Macmillian Press Limited. Vijverberg, W. P. M. (1993). Labour market performance as a determinant of migration. Economica, 60, 143–160. Yezer, A. M. J., & Thurston, L. (1976). Migration patterns and income change: implications for the human capital approach to migration. Southern Economic Journal, 42(4), 693–702.
APPENDIX A REMITTANCE FUNCTIOIN PARTIALS FOR THE CASE OF PURE ALTURISM or Vm00 >0 ¼
2 2 P Pn P 200 ð1bÞ oYm V 00 þ n ai V 00 b22 þ ð1 pÞ n ai V 100 ð1bÞ þ ðpÞ a V i 2 2 hi n hi hi m i¼1 i¼1 i¼1 n n
ðA:1Þ
hP
i Pn P 00 n 00 b 100 1b þ p ni¼1 ai Vhi2 1b i¼1 ai Vhi n2 þ ð1 pÞ i¼1 ai Vhi or n2 n2 <0 ¼ oYh V 00 þ Pn ai V 00 b2 þ ð1 pÞ Pn ai V 100 ð1bÞ2 þ ðpÞ Pn ai V 200 ð1bÞ2 2 2 2 hi n hi hi i¼1 i¼1 i¼1 m n n
ðA:2Þ
hP i
Pn P 0 n 0 b 10 ð1bÞ þ ðpÞ ni¼1 ai Vhi2 ð1bÞ i¼1 ai Vhi n þ ð1 pÞ i¼1 ai Vhi n n or >0 ¼ oai V 00 þ Pn ai V 00 b2 þ ð1 pÞ Pn ai V 100 ð1bÞ2 þ ðpÞ Pn ai V 200 ð1bÞ2 hi n2 hi hi m i¼1 i¼1 i¼1 n2 n2
ðA:3Þ
hP i 2 2 P P 00 00 n x x 00 bðbxÞ þ ð1 pÞ ni¼1 ai Vhi1 ð1bÞ þ p ni¼1 ai Vhi2 ð1bÞ i¼1 ai Vhi or n2 n2 n2 <0 ¼ 2
2 2 P P P 00 00 om þ ðpÞ n ai V 2 ð1bÞ V 00 þ n ai V 00 b2 þ ð1 pÞ n ai V 1 ð1bÞ 2 2 m
i¼1
hi
n
i¼1
hi
n
i¼1
hi
ðA:4Þ
n
hP i 2 2 Pn Pn n 00 bðbmÞ 100 ð1bÞ m 200 ð1bÞ m þ ð1 pÞ þ p a V a V a V i i i 2 2 2 i¼1 i¼1 i¼1 hi hi hi n n n or <0 ¼
2 2 Pn Pn Pn 00 b2 100 ð1bÞ 200 ð1bÞ ox 00 Vm þ i¼1 ai Vhi n2 þ ð1 pÞ i¼1 ai Vhi þ ðpÞ i¼1 ai Vhi n2 n2
ðA:5Þ
INTERNATIONAL REMITTANCES
P i P 00 00 1b 1b þð1 pÞ ni¼1 ai Vhi1 Yh þbðrþxmÞ þp ni¼1 ai Vhi2 Yh þbðrþxmÞ n n n2 n2 2 2 Pn Pn Pn 00 b2 100 ð1bÞ 200 ð1bÞ 00 Vm þ i¼1 ai Vhi n2 þ ð1 pÞ i¼1 ai Vhi þ ðpÞ i¼1 ai Vhi n2 n2
Pn P P 0 n n 0 b 10 1b þ p i¼1 ai Vhi2 1b i¼1 ai Vhi n2 þ ð1 pÞ i¼1 ai Vhi n2 n2 ðA:6Þ þ
2 2 Pn Pn Pn 00 b2 100 ð1bÞ 200 ð1bÞ 00 þ ðpÞ a V Vm þ i¼1 ai Vhi n2 þ ð1 pÞ i¼1 ai Vhi i 2 2 hi i¼1 n n h P P i 0 0 ni¼1 ai Vhi1 1b þ ni¼1 ai Vhi2 1b n n or ¼ ðA:7Þ 2 2 P P P 2 00 00 n n ð1bÞ b op V 00 þ 00 þ ð1 pÞ i¼1 ai Vhi1 þ ðpÞ ni¼1 ai Vhi2 ð1bÞ m i¼1 ai Vhi n2 n2 n2
or ¼ on
hP
n 00 i¼1 ai Vhi
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Yh þbðrþxmÞ n2
b n
APPENDIX B VARIABLE DESCRIPTION B.1. Unemployment by region Area
Unemployment rate (%)
Countries
Caribbean S. America USA Canada Europe Asia Africa
16.63 6.03 6.6 10.3 8.1 18.43 33.33
Other
10.53
Bahamas, Barbados, Belize, Jamaica, Trinidad and Tobago Argentina, Brazil, Suriname United States Canada United Kingdom India, Malaysia, Pakistan, Singapore, Sri Lanka S. Africa, Ghana, Botswana, Cameroon, Lesotho, Mauritius, Mozambique, Namibia, Seychelles Australia, New Zealand
B.2. LSMS/HEIS variable summary statistics Variable r E IND HH PCI HOH AGE HOH MAL LND OWN HH SIZE Rural DESTN M EDU SEC M EDU UNI WHY LEFT MIG MAL OTHER M UNEMPL YR AB YR AB SQ
Mean
Std. dev.
Log of monthly remittances sent by a migrant 4.37 4.13 Ethnicity variable, equal to 1 if the head of the household was an 0.49 0.50 East Indian Log of household per-capita income from all sources except 8.21 1.55 foreign remittances Age of the head of household, equal to 1 if 60 years or older 0.43 0.49 Gender of the head of household, equal to 1 if male 0.65 0.47 Land owned by the household in acres 4.52 28.07 Equivalent household size: age 0–6 ¼ 0:2, 7–12 ¼ 0:3, 3.03 1.65 13–17 ¼ 0:5, >17 ¼ 1 Location of the source household, equal to 1 if rural 0.53 0.50 Destination of the migrant, equal to 1 if USA, Canada or Europe 0.69 0.45 Education of the migrant prior to departure, equal to 1 if the 0.63 0.48 individual had a secondary education Equals 1 if the individual had a university education prior to 0.19 0.39 departure, 0 otherwise Reason for migration. Equals 1 if reason was work, 0 otherwise 0.3394 0.47 Gender of the migrant, equal to 1 if male 0.47 0.50 Number of other migrants abroad from the same household 2.06 2.07 Unemployment rate of the area to which the individual migrated 8.66 3.58 Number of years the migrant has been abroad 7.68 7.06 Square of the number of years abroad 108.94 205.84 (Appendix B––continued overleaf )
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(continued) Variable YR AB > 1 EDU_HOH
Dummy variable equal to 1 if the migrant has been abroad for more than a year Education level of head of household: ¼ 1 if illiterate; ¼ 2 if literate but no schooling; ¼ 3 if below primary; ¼ 4 if primary; ¼ 5 if secondary; ¼ 6 if graduate; ¼ 7 if post graduate
Mean
Std. dev.
0.88
0.32
4.16
0.99