what motivates gifts? intra-family transfers in rural malawi

ORIGINAL PAPER

What Motivates Gifts? Intra-Family Transfers in Rural Malawi

Simon Davies

Published online: 8 July 2010

� Springer Science+Business Media, LLC 2010

Abstract This paper uses Family Transfers Project data

collected in rural Malawi during 1999 to ascertain the

motivation for gift-giving using discriminating hypotheses.

The study models monetary and monetized gifts sent and

received between the survey respondents and their parents,

their children, and their siblings as a function of sender and

receiver characteristics. Individual analyses are compared

with household level models to reveal that both individual

and household characteristics can matter in different cases.

OLS, Probit and Tobit models are compared to conclude

that, as with other similar studies, a wide range of moti-

vations exist including altruism, (co-)insurance, and an

inheritance motive. Motivations differ slightly depending

upon the relationship between the sender and receiver,

however, no single motive can be attributed to any given

relationship.

Keywords Africa � Altruism � Family transfers �Family economics � Gift-exchange � Insurance

Introduction

The growing importance of transfer flows and the realiza-

tion that these can be a means to both alleviate poverty and

moderate the impact of negative income shocks has

encouraged work attempting to understand the motivation

for remitting and the impact of transfers in developing

countries. Models of transfer motivations can be divided

into several main branches: (1) those modeling transfers as

altruistic behavior in which the utility of the sender is

influenced by the utility of the receiver; and (2) (partly)

self-interested models. It should be noted that in (2), we

have both the investment/exchange motive and the ‘‘warm

glow’’ motive.1

Self-interested models can be further split into subcat-

egories: (2a) suggests that transfers are primarily used for

investment purposes and will therefore respond to the

macroeconomic climate—for example, investing in busi-

nesses; (2b) views transfers as part of a ‘‘joint optimiza-

tion’’ agreement in which both the remitter and receiver

gain from risk-sharing. Different income sources shared

permit the reduction of income risk, and allow such

transfers to be termed ‘‘insurance payment’’2; (2c) views

transfers as repayments of implicit or explicit loans made

to the remitter in the past to say, fund education or

migration. This group of models can be termed ‘‘family as

bank’’ models; (2d) views transfers as a means of safe-

guarding inheritance.3

S. Davies (&)

Department of Economics and International Development,

University of Bath, Bath BA2 7AY, UK

e-mail: [email protected]; [email protected]

1 When even pure altruists would not have an incentive to give, but

are shown to do so, this is termed the ‘‘warm glow’’ motive to giving

(see e.g. Crumpler and Grossman 2008). We would like to thank an

anonymous referee for suggesting this as an alternative explanation.

Interestingly Cowley et al. (2004) find that some families give gifts to

their church which have a negative impact on the family’s financial

situation. This suggests either some social pressure or else non-

financial compensation such as the ‘‘warm glow’’ motive.2 This is particularly important in an agricultural economy such as

Malawi. For example, households living in different areas might

implicitly agree to send each other transfers whenever one has

suffered from drought and the other not. Alternatively, urban and

rural income may follow different patterns making it possible to share

income through implicit transfer agreements in order that when one

household suffers a negative shock, it is supported by the other.3 An additional motivation can be found by distinguishing between

giving to one’s children and the ultimate aim of ‘‘dynastic altruism’’

or the ‘‘survival of the gene’’ (see e.g. Horioka 2002; Fan 2005).

123

J Fam Econ Iss (2011) 32:473–492

DOI 10.1007/s10834-010-9216-1

Why study motivations for remitting? Untangling

motivations for transfers is an important component in

understanding transfer flows in general. Most importantly,

different motivations to remit imply different impacts.

Altruistic motivations may act as a counter-cyclical force

helping to reduce risk of poverty during negative shocks

(such as droughts or macroeconomic shocks). Altruistic

motivations combined with asymmetric information may

have the effect of reducing labor-market participation

which could reduce welfare (Azam and Gubert 2004).

Altruistic and ‘‘family as bank’’ motivations to remit could

increase consumption at the micro level, but have negative

‘‘Dutch Disease’’ effects at the macro level if the transfers

are from abroad. ‘‘Dutch Disease’’ is an effect whereby a

large amount of money coming from abroad causes the

value of the currency to appreciate as demand for it rises.

This makes goods produced in the country for export to

appear more expensive for foreign buyers making it more

difficult for certain industries to survive in the face of

international competition. This can hurt production and

jobs. See, for example, Sachs and Warner (2001). Insur-

ance motivations serve to reduce risk for the family, per-

haps encouraging greater risk taking (perhaps investment)

and increasing welfare. Such risk pooling may also

increase crop yields as households switch to riskier, higher

yield crops (Dercon 1996), and prevent the selling of

productive assets (Fafchamps et al. 1998). Finally, invest-

ment motivations suggest that transfers can help to improve

output and productivity with the caveat that investment in

existing housing stock and land may encourage a Balassa–

Samuelson effect with negative macro consequences. That

is, increases in the prices of these may pass through to

other areas of the economy causing some industries to

become less competitive in export markets. Any transfers

from abroad may impact on the exchange rate (Amuedo-

Dorantes and Pozo 2004).

Various authors have made attempts to understand the

motivations for remitting using discriminating hypotheses

to test between different motivations whilst others have

noted correlations between remittances and household

characteristics (e.g. Gupta and Hegde 2009). Despite the

importance of understanding transfers, there has been little

progress, with authors largely concluding that they are

unable to rule out certain motivations. This study extends

existing papers in the following ways: firstly we look at

transfer flows going in both directions—that is it looks at

both ascending (children to parents) and descending (par-

ents to children) transfers.4 See Sheng and Killian (2009)

for a study of inter-generational transfers. With the

exception of VanWey (2004) no study has, to the author’s

knowledge, analyzed transfer flows in each direction of the

transfer relationship in a developing country (See Koh and

MacDonald 2006 for an example in a Western context).

This is surprising given the potential importance of co-

insurance in remitting behavior, and the importance of

mutual gift-giving in many developing countries.

As well as analyzing monetary transfer flows in both

directions, this paper makes one further extension. We do

not model transfer flows only between a household of

origin and migrants, but between a central household and

their children, parents and siblings. It is likely that different

family members have different motivations for transfers

potentially allowing us to disentangle the motivations

which other authors have, so far, been unable to achieve.

Thus, for example, we might expect to find an inheritance

motivation amongst children of respondents, but not for the

parents of respondents. In addition to the contributions

stated above, this paper takes advantage of a previously

unused data set.

The remainder of this paper is organized as follows:

‘‘Theoretical and Empirical Setting’’ section reviews the

relevant theoretical models before summarizing empirical

findings from previous related studies in developing

countries. ‘‘Data’’ section discusses the descriptive data

and ‘‘Empirical Analysis’’ presents the econometric mod-

eling and discusses the results. Conclusions are drawn in

the last section.

Theoretical and Empirical Setting

Studies analyzing motivations to remit use either Tobit

models to estimate the value of transfers, Probit models to

estimate the probability of sending or receiving transfers or

OLS to estimate net transfers received. The independent

variables focus on the receiver’s and sender’s characteris-

tics. Regressions thus take the form:

Transfers ¼ f�Receiver characteristics;

Sender characteristics; X�

ð1Þ

where Transfers is value of transfers sent or received

(Tobit), net transfers (OLS), or whether or not transfers

were received (Probit) and f(.) is the relevant function

(Tobit, OLS, Probit). The X represents any other study

specific variables included. This paper concentrates on

monetary transfers and monetized value of physical gifts

such as food. It does not therefore look at other transfers

such as time (see Hayhoe and Stevenson 2007 or Cao 2006,

for a discussion of the link between time and financial

transfers).

Rapoport and Docquier (2006) provide an excellent

review of the theoretical and empirical literature on moti-

vations for remitting. They begin by illustrating an4 We would like to thank an anonymous referee for pointing this out.

474 J Fam Econ Iss (2011) 32:473–492

123

altruistic theory in which both the sender (s) and the

receiver (r) exhibit altruism towards each other and in

which the utility of the sender, UsðCs;CrÞ is a weighted

average of his/her felicity derived from his/her own con-

sumption,5 VsðCsÞ and the utility of the receiver,

UrðCr;CsÞ.UsðCs;CrÞ ¼ ð1� bsÞVsðCsÞ þ bsUrðCr;CsÞ ð2ÞUrðCr;CsÞ ¼ ð1� brÞVrðCrÞ þ brUsðCs;CrÞ ð3Þ

where felicity exhibits diminishing marginal return in

consumption, V0[ 0 and V00\ 0 and the 0� bi� 1=2giving the degree of altruism. If bs ¼ 1=2 then the sender

values the receiver’s happiness resulting from consumption

as much as he values his own. Values above � in which the

sender values the receiver’s happiness above his/her own

are excluded, and a value of bs ¼ 0 results in a purely

selfish model in which the sender (usually a family

migrant) does not consider the utility of his/her family at

all. It should be noted that this form of analysis can be used

to study both ascending (from children to parents) and

descending (parents to children) transfers. This paper

extends previous literature by looking at both.

Rapoport and Docquier (2006) incorporate transfers by

re-writing consumption as equal to income, I, less transfers,

T. In addition, they rule out the possibility of negative

transfers from the sender to the receiver, and impose a

felicity function satisfying V0[ 0 and V00\ 0, V(.) = ln(.)

and solve for the optimal level of transfers from the sen-

der’s perspective. The resulting altruistic model has several

properties: (1) Transfers are increasing in the sender’s

income; (2) Transfers are falling in the receiver’s income;

(3) Transfers are increasing in the sender’s altruism; and

(4) Transfers are falling in the degree of altruism of the

receiving household.

This provides several testable hypotheses, but these

results could also be generated by other factors. Rapoport

and Docquier (2006, p. 12) note that the ‘‘main testable

implication of the altruistic model is that transfers cannot

increase with the recipient’s income’’.

Agarwal and Horowitz (2002) formally model a sug-

gestion by Funkhouser (1995) that under altruism, transfers

from any one remitter (a migrant in their model) should

decline in the number of remitters (migrants), but this

should not be the case under an insurance hypothesis. This

provides an additional testable implication.

In their two period models, a transfer sender faces cer-

tain income in period 1, equal to Is. In period 2, s/he faces

uncertainty with high income, IsG, with a probability of

1� p and low income, IsB with a probabilityp ð0\p\1Þ.

S/he can choose to remit to a receiver an amount T in the

first period and receive an actuarially fair indemnity

(s ¼ T=p) in the case of a negative shock in the second

period. The receiver (insurer) is assumed to face no

uncertainty. Denoting, as before VsðCsÞ and VrðCrÞ the

sender’s, s, and receiver’s, r, felicity functions, the sender’s

expected utility (EU) is denoted:

EU ¼ VsðIs � TÞ þ ð1� pÞVsðIsGÞ þ pVsðIs

B þ sÞ ð4Þ

where felicity functions are kept constant across time and

state and the sender’s utility depends only on his/her own

consumption, and not that of the receiver, unlike in the

altruistic model.

Using log utility as before, which satisfies decreasing

marginal utility to consumption and risk aversion, the

optimal level of transfers or transfers, T* can be shown to

be:

pð1þ pÞ½I

s � IsB� ¼ T� ð5Þ

This insurance model has several properties:

(1) Transfers are increasing in the sender’s first period

income (as with previous altruistic model);

(2) Transfers are decreasing in the sender’s bad state

income;

(3) Transfers are increasing in the probability of a bad

state (potentially proxied empirically by education,

unemployment or legal status if abroad).

Agarwal and Horowitz (2002) go onto extend the altru-

istic model sketched above to include the fact that house-

holds receive transfers from several senders (migrants). The

model provides one further testable implication: ‘‘[u]nder

pure insurance (or other self-interest) motives, the number

of other migrants would not affect own-transfers. On the

other hand, under altruism where migrants are concerned

with the welfare of the nonmigrating household, the pres-

ence of multiple remitting migrants will affect the average

transfer level’’ (p. 2036). Rapoport and Docquier (2006)

point out that this assumes the exogeneity of the number of

remitters a household benefits from (those with more vol-

atile income or that are more risk averse may ensure they

have more transfer relationships). In addition, they note if

household income is affected by moral hazard then house-

hold income might not necessarily be assumed exogenous.

Moral hazard and transfers are modeled by Azam and

Gubert (2004). Rapoport and Docquier (2006) also sketch a

version of Cox (1987) in which transfers are viewed as

payment for services and Laferrere and Wolff (2006)

describe in detail gift exchange when viewed as intra-family

transfers.

5 ‘‘Felicity’’ is used here in order to distinguish between total utility

(derived from both one’s own consumption and the consumption of

the other) and the utility derived only from one’s own consumption,

which has been called ‘felicity’. For example, the Giver’s Util-

ity = f(Utility of Receiver; His/Her own Felicity) where his/her own

felicity is derived from his/her own consumption.

J Fam Econ Iss (2011) 32:473–492 475

123

On an empirical level finding suitable discriminating

hypotheses is challenging. Authors have used a wide range

of methods drawn from the theory described above to draw

conclusions. Principal results are summarized in Table 1.

Despite efforts to disentangle motivations behind

transfer flows, it is important to note that motivations are

not mutually exclusive. For example, it could be that a

threat to disinherit a child enforces an insurance payout to

be made. Given this, it is not surprising that most studies

have difficulties in concluding unambiguously in favor of a

single motivation. This study aims however to shed further

light on transfer motivations by comparing and contrasting

evidence of different transfer motivations according to

familial relationship between the sender and the receiver.

Data

Descriptive Statistics

This study uses the rural Malawian Family Transfers Pro-

ject (FTP) dataset collected by the University of Pennsyl-

vania Population Studies Center (Social Networks) for the

purpose of analyzing gifts and transfer flows from a

number of different perspectives. The survey was carried

out in three rural areas of Malawi (Balaka in the southern

region, Mchinji in the centre and Rumphi in the north)

between June and August, 1999. The three areas in which

the survey was conducted are both similar and broadly

representative of rural areas in Malawi in socioeconomic

terms and with regards to commercial activities (markets,

banks,…) and institutions (post offices, clinics,…)

(Weinreb 2001, 2002). Unfortunately the data remain

under-used from an econometric point of view. One

exception is Mtika and Doctor (2002) who study differ-

ences in transfer behaviour between matrilineal and patri-

lineal tribes in Malawi and conclude that wealth exchange

in matrilineal tribes are biased towards female relatives

whilst the inverse is true under patriliny.

After cleaning, there are 616 females and 501 males in

the main households surveyed. There are more females due

to lower response rates amongst males, polygamy and

absence. The sampling deliberately targeted working-age

households and made efforts to interview both the house-

hold head and his wife. Males and Females reported

transfers sent and received on an individual (not house-

hold) level. This has several advantages and disadvantages.

The major disadvantage is that transfers can be viewed as a

part of a household activity. That is, transfers might be

given to one member but used for the benefit of several

members, or, alternatively, that the household has decided

as a whole to send a migrant away to earn money and to

pool income. This would make the household level more

appropriate, and is supported by literature stemming from

the New Economics of Labor Migration (Bloom and Stark

1985). This however makes an assumption which is not

entirely appropriate for the current data. Firstly, it is not

clear that all households that receive transfers do so

because of an economic decision to migrate. Rather, other

(cultural) factors are likely to predominate—notably

Table 1 Summary of key findings from studies on motivations to remit

Study Key conclusions

Lucas and Stark (1985)—Botswana Positive association between remittance receipts from children and per capita household

income (altruism). Sons remit more the wealthier is the household (inheritance)

Ilahi and Jafarey (1999)—Pakistan Return Pakistani migrants remit less to their immediate family, the more they have borrowed

from extended family (repayment of past loans)

Agarwal and Horowitz

(2002)—Guyana

The more migrants in the household, the less a migrant will remit (altruism). Lower household

income is associated with higher remittance receipts (altruism)

Naufal (2008)—Nicaragua As the number of migrants increase, remittances from any one sender decline (altruism). As

income risk of the household increases, remittances increase (altruism)

Amuedo-Dorantes and Pozo

(2006)—Mexico/United States

Mexican migrants in the U.S. remit more home to Mexico as their income risk increases

(insurance). Larger home households increase remittances (altruism)

De la Briere et al.

(2002)—Dominican Sierra

Remittances are increasing in work day losses due to sickness for the home household

(altruism, insurance, reverse causality?). Remittances are increasing in inheritable land, but

decreasing in the number of heirs (inheritance)

Van Dalen et al. (2005)—Morocco,

Egypt and Turkey

Higher remittances as home households perceives its financial situation to be ‘insufficient’

(altruism)

VanWey (2004)—Thailand Male migrants more likely to remit to landless households (altruism) and both male and female

migrants remit less as the number of migrants in the household increases (altruism). Female

migrants remit less the more land the household owns (altruism)

Grigorian and Melkonyan

(2008)—Armenia

High unemployment discourages remittance flows to a region (undefined selfish motives)

476 J Fam Econ Iss (2011) 32:473–492

123

marriage or, in the case of children, individual career

aspirations. This is particularly the case for siblings who

have left the same household as the respondents (that of

their parents). Secondly, studies which analyze transfers on

a household level tend to use the head’s characteristics.

Here information on transfers and characteristics were

collected on an individual level allowing me to use the

husband’s or wife’s characteristics, as appropriate. One

possibility would be to include the characteristics of both

the husband and the wife, but this would result in all

observations for which data is absent on one of the partners

(single heads or no-response) being dropped. In any case,

as much information as is used in most studies is used in

estimating the empirical models. Given these data limita-

tions, we proceed with studying transfers from the indi-

vidual perspective.

There are 1145 potential transfer flows between the male

or female and their parents; 522 between the male or

female and their children, and 3945 with siblings. Transfer

flows are studied for all relationships described separately,

and only for adult relations who do not reside in the same

household as the respondent, explaining the small number

of children. Each potential transfer relationship between

two individuals is termed a ‘‘transfer dyad’’. Thus, one

dyad is between the respondent and her father, and another

dyad is the respondent and her mother. We therefore ana-

lyze transfer flows that are inter-household but intra-fam-

ily. Table 2 reports summary statistics for the respondents.

The average age of respondents was around 33 years with

males being on average around 6 years older than females.

Almost all respondents are married and respondents have

3.7 years of education. Interestingly, there is little differ-

ence between males and females in this respect. (Amongst

their parents however, only around 50% of mothers had

any education compared with over 80% of fathers, showing

key generational difference.) Self-reported health status

was, on average over 8/10 although 28% of respondents

reported having suffered from ill health during the previous

month. Average weekly wage income was aver MK300 for

men compared with around MK100 for females, and on

average, men had a far higher asset index score In addition,

many respondents reported looking after the children of the

relatives with whom they have transfer relationships.

Table 3 shows summary statistics for parents. On

average, parents are 60 years (fathers 64 years and mothers

57 years), and respondents rated their parents’ health at

around 6.2/10 on average (little difference between fathers

and mothers). Parents had an average of 7.9 heirs, and 66%

of them reported having some schooling (54% of mothers

and 82% of fathers). Over a third of parents live in the

same village as the interviewed child. Nearly 7% of

respondents reported looking after a sibling.

Since working age respondents were purposively target,

and this study analyses only intra-household transfers, very

few respondents had adult children living outside of the

family home. There are thus only 522 potential dyads, or

transfer relationships. Table 4 presents summary statistics

for children.

The average reported health level of the children was

around 8.4/10, and children were on average around

23 years of age. More were daughters, probably due to

daughters leaving the home to marry at a younger age than

sons, indeed on average daughters were younger than sons.

Around two-thirds of children were married, and 27% had

moved to a city or abroad. Respondents reported having an

average of 4.4 children, and receive transfers from an

average of 1.35 of these. Over 17% of respondents reported

having a grandchild in their household but the data to

not permit me to ascertain to which child the grandchild

belongs.

Table 2 Respondents’ characteristics

Obs Mean Std. Dev.

Age 1010 33.29 (10.64)

Years education 1131 3.71 (3.32)

Married 1166 97.00%

Female respondent 1166 55.06%

Eldest child 1166 19.47%

Health rating (1 = lowest to

10 = highest)

1165 8.14 (1.94)

Health problem in last month 1166 28.22%

Paid income last week (MK) 1117 222.33 (1087.00)

Asset index (males)a 501 0.05 (1.73)

Asset index (females)a 616 0.01 (1.52)

Sibling in house 1166 6.35%

Nephew/Niece in house 1166 12.18%

Grandchild in house 1166 9.35%

Matrilineal ethnicity 1166 23.76%

Patrilineal ethnicity 1166 37.14%

Mixed (Chewa) ethnicity 1166 39.11%

a Created using Principle Components Analysis and includes own-

ership of bed, radio, bike, lamps, pit latrines, cattle, goats, pigs,

poultry, land and quality of housing material

Table 3 Parents’ characteristics

Obs Mean Std. Dev.

Age parents 1144 60.09 (11.88)

Health parents 1139 6.23 (2.35)

Heirs 1147 7.89 (2.74)

Sibling in respondent’s house 1147 6.80%

Schooling 1147 66.17%

Parent lives in same village as respondent 962 37.63%

J Fam Econ Iss (2011) 32:473–492 477

123

Respondents’ siblings are on average around 31.8 years

and respondents rated their health at 8.14 on average.

Around half of siblings are sisters and half brothers. Nearly

a quarter of all siblings live either abroad or in a city inside

Malawi. In nearly 13% of cases, the respondents reported

looking after a sibling’s child. Again, unfortunately the

data do not permit me to ascertain to which sibling the

child belongs. Table 5 presents summary statistics for

siblings.

Transfer Flows

Although both husbands and wives were interviewed in the

majority of cases, respondents reported transfers sent and

received on an individual bases. Thus, transfers sent from

the male to a son is not the same as that sent from the

female to the same son. Respondents reported transfers

sent and received since the end of the previous growing

season—a period of around 3 months. Detailed informa-

tion is given regarding the transfers, and estimated values

of goods (collected in the field) are used to value physical

(as opposed to cash) gifts. Thus, transfer flows analyzed in

this paper include cash and the monetized value of physical

gifts. During the course of the survey, the interview team

regularly verified the market value of the gifts given at the

place of receipt. It was possible to monetize quantities in

local market places or in the nearest market towns. This

was done regularly in order to ensure the current market

values were correct. Secondi (1997) reports very similar

determinants for cash and physical gifts.

There are 1145 potential ‘‘transfer dyads’’ between the

respondents and their parents (see Table 6). Of these,

respondents reported remitting to parents since the last

agricultural season in around 65% of cases, and received

transfers from parents in 50% of cases. Excluding zero

transfer flows, the average transfers sent to parents was

MK245 and the average value of transfers received from

parents was MK205. (MK is Malawi Kwacha, the local

currency unit. At the time of the survey US$1 & MK70.)

Between respondents and children, there are 522

potential transfer relationships with respondents giving to

children in just over half of all cases an average amount of

MK300, and received transfers from children in around

38% of cases with the average amount received being

around MK200.

Respondents reported remitting to siblings in 35% of the

3945 potential cases, and received from them in around a

quarter of cases. Average transfers sent and received are

similar at around MK185.

It is possible that there is some degree of reporting bias

since more respondents reported receiving than giving in

all cases. However, due to sampling, this seems realistic in

the case of parents and children. It is perhaps less likely

with regard to siblings, and it is interesting to note that

there is little difference in the value of transfers sent and

received in this case, and the gap between the number of

respondents reporting sending and receiving transfers is

smaller for siblings than for parents and children.

Empirical Analysis

Econometric Modeling

The main household surveyed is thus the household on

which we focus and transfer functions are estimated for

each of the three transfer relationships. Since transfers are

estimated in dyads, it is possible one person to appear in

the regression more than once, and members of the same

family will appear multiple times in a single regression.

This introduces a potential problem of clustering of errors

into the regressions. All regressions are therefore corrected

for potential clustering at the household level. In addition,

all standard errors are corrected for potential heteroske-

dasticity using White (1980).

We begin by estimating OLS regressions for net transfer

receipts by the respondents in the central household sur-

veyed for each of the three relationships, where net

Table 4 Children’s characteristics

Obs Mean Std.

Dev.

Health of son/daughter (1 = lowest,

10 = highest)

522 8.43 (1.78)

Number of children parents have 522 4.38 (1.89)

Number of sons/daughters remitting to

parents

522 1.35 (1.36)

Age of son/daughter 522 22.94 (5.76)

Eldest son/daughter 522 45.02%

Parents have one of children’s children in

household

522 17.43%

Daughter (not son) 522 59.77%

Son/Daughter lives in city or abroad 522 27.01%

Son/Daughter married 522 64.56%

Table 5 Siblings’ characteristics

Obs Mean Std. Dev.

Age of sibling 2916 31.78 (11.49)

Health of sibling 3945 8.14 (1.95)

Sister 3945 50.37%

Eldest sibling 3945 12.19%

Sibling lives abroad or in city 3945 24.41%%

Respondent household has nephew/niece 3945 12.75%%

478 J Fam Econ Iss (2011) 32:473–492

123

transfers are the amount received by the respondent minus

the amount the remitted to the same person. Thus, we

estimate:

Ri ¼ Xibþ ei ð6Þ

where Ri is net transfer receipts by the individual from the

sender, Xi the regressors indicating sender and receiver

characteristics, and b are the parameter estimates. The error

term, ei is assumed is corrected for potential heteroske-

dasticity and clustering at the household level as discussed.

We next go onto extend the analysis by estimating a

series of Probit regressions indicating both whether or not

transfers were sent and received from a potential transfer

partner.

PðRi ¼ 1jXi ¼ xiÞ ¼ UðXbÞ ð7Þ

where U represents the cumulative distribution function of

the normal distribution. Standard errors are again corrected

for heteroskedasticity and clustering.

We present other estimates in addition to net transfer

flows because, although net flows capture an important

component of transfer relationships, there is little distinc-

tion between those who do not engage in a potential

transfer relationship and those who give and receive a

similar amount. For this reason, probit estimates are pro-

vided alongside net transfers.

In addition, this study reports results from Tobit models

estimating the value of transfers sent/received by each

dyad, and signs and significances of variables are very

close to the Probit models presented.

Discriminating hypotheses for the respondent–parent

transfer perspective are shows in Table 7 and are based on

previous studies. This table summarizes the key focus

of the discussion in the ‘‘Econometric Results and

Discussion’’ section, and similar discriminating hypotheses

are used for other relationships.

Economic specifications used are based on theory which

indicates that motivations for remitting can be inferred

from the impact of certain sender and receiver character-

istics on remittance flows. Of particular interest are any

indicators of income, wealth, recent shocks and any ser-

vices provided by the receiver for the sender. Our data

provide information on recently earned income, household

assets (wealth), recent health shocks and general health as

well whether the respondent household looks after any

children of remittance partners (a service). The model is

augmented by variables known to be important at the

household level in Malawi—for example, we include

whether a household has matrilineal or patrilineal heritage

as this can be important for the inheritance motivation.

Table 8 shows the signs expected on different variables

under different motivations and the specification used for

each model and justification is given below:

Under altruism, remittance receipts from a parent would

be decreasing in the respondent’s wealth, as indicated by

the theoretical model presented above (Rapoport and

Docquier 2006). If remittance flows are altruistic, a parent

would also increase remittance flows (or the likelihood of

remitting) if the respondent suffers from a health shock and

decrease remittance flows as the respondent’s health

improves.

If a parent’s remittances are an insurance premium, they

are likely to increase as the respondent become a more

reliable insurer. Thus, they are increasing in the respon-

dent’s wealth and health. In addition, they can be expected

to increase as the likelihood that a parent suffers from a

negative shock increases, as shown in the theoretical

insurance model above (Agarwal and Horowitz, 2002).

Table 6 Incidence and values

of transfer flowsObs Mean Std. Dev. Min Max

Respondent $ Parents

Respondent ? Parent 1145 64.37%

Parent ? Respondent 1145 50.57%

Value of transfers to parent (excl. zeros) 737 244.57 435.6 1 5000

Value of transfers from parent (excl. zeros) 579 205.13 489.84 5 6350

Respondent $ Children

Respondent ? Children 522 51.72% 0 1

Children ? Household 522 37.74% 0 1

Value of transfers to children (excl. zeros) 275 300.24 455.16 4 4000

Value of transfers from children (excl. zeros) 198 200.82 420.65 4 5000

Respondent $ Siblings

Respondent ? Sibling 3945 35.18%

Sibling ? Respondent 3945 26.84%

Value of transfers to sibling (excl. zeros) 1389 180.03 418.73 1 7503

Value of transfers from sibling (excl. zeros) 1061 188.4 572.3 2 15000

J Fam Econ Iss (2011) 32:473–492 479

123

Ta

ble

7D

efin

itio

nan

dd

iscu

ssio

no

fv

aria

ble

s

Par

ents

Ch

ild

ren

Sib

lin

gs

Res

po

nd

ents

’ch

arac

teri

stic

s

Inco

me

Res

po

nd

ents

earn

edin

com

e(i

nM

alaw

ian

Kw

ach

a)in

wee

kp

rio

rto

the

surv

ey.

Am

easu

reo

fw

ealt

hb

ut

also

cap

ture

scu

rren

tin

com

eto

wh

ich

may

be

‘in

sure

d’

dif

fere

ntl

yfr

om

oth

eras

sets

Ass

etin

dex

(far

m)

Co

nst

ruct

edu

sin

gP

rin

cip

leC

om

po

nen

tsA

nal

ysi

s.C

aptu

res

farm

wea

lth

.R

emit

tan

ces

may

resp

on

dd

iffe

ren

tly

tofa

rmw

ealt

hco

mpar

ed

too

ther

wea

lth

soth

isis

ente

red

sep

arat

ely

Ass

etin

dex

(no

n-f

arm

)C

on

stru

cted

usi

ng

Pri

nci

ple

Co

mp

on

ents

An

aly

sis

Ed

uca

tio

nY

ears

of

edu

cati

on

of

the

resp

on

den

t.A

nin

dic

ato

ro

fq

ual

ity

of

the

resp

on

den

tas

anin

sure

r

Ag

eA

ge

iny

ears

of

the

resp

on

den

t

Mal

eE

qu

alto

1if

the

resp

on

den

tis

mal

e,0

oth

erw

ise

Mar

ried

Eq

ual

to1

ifth

ere

spo

nd

ent

ism

arri

ed,

0o

ther

wis

e

Ho

use

ho

ldsi

zeN

um

ber

of

ho

use

ho

ldm

emb

ers

Res

po

nd

ent

eld

est

chil

dA

nin

dic

ato

rfo

rw

het

her

or

no

tth

ere

spo

nd

ent

isan

eld

est

chil

d.

Po

ten

tial

lyim

po

rtan

tw

ith

reg

ard

sto

rem

itta

nce

sw

ith

par

ents

Hea

lth

(1–

10

)S

ub

ject

ive

var

iab

lein

dic

atin

gcu

rren

th

ealt

h(1

bei

ng

low

and

10

bei

ng

hig

h).

Rel

evan

tb

oth

asan

ind

icat

or

of

qu

alit

yo

fin

sure

r(r

elat

ed

toin

sura

nce

pre

miu

ms)

and

po

ten

tial

of

rece

ivin

gin

sura

nce

ind

emn

itie

so

ral

tru

isti

cre

mit

tan

ces

Hea

lth

pro

ble

min

last

mo

nth

Ind

icat

or

var

iab

leeq

ual

to1

ifre

spo

nd

ent

rep

ort

edb

ein

gsi

ckin

the

pre

vio

us

mo

nth

,0

oth

erw

ise.

Var

iab

lein

dic

ates

are

cen

th

ealt

h

sho

ck

Rem

itta

nce

par

tner

’sch

ild

inh

ou

seIn

form

atio

no

nw

het

her

resp

on

den

t’s

ho

use

ho

ldlo

ok

saf

ter

po

ten

tial

rem

itta

nce

par

tner

’sch

ild

.A

nex

amp

leo

fa

serv

ice

pro

vid

edb

yth

e

resp

on

den

th

ou

seh

old

Giv

entr

ansf

ers

tore

mit

tan

cep

artn

erIn

dic

ates

wh

eth

ero

rn

ot

giv

enre

mit

tan

ces

top

ote

nti

altr

ansf

erp

artn

er.

Cap

ture

sre

cip

roci

ty

Rec

eiv

edtr

ansf

ers

fro

mre

mit

tan

cep

artn

erIn

dic

ates

wh

eth

ero

rn

ot

rece

ived

rem

itta

nce

sfr

om

po

ten

tial

tran

sfer

par

tner

.C

aptu

res

reci

pro

city

Rem

itta

nce

par

tner

char

acte

rist

ics

Ag

eo

fre

mit

tan

cep

artn

erA

ge

iny

ears

of

rem

itta

nce

sp

artn

er

Nu

mb

ero

fch

ild

ren

rem

itti

ng

Ind

icat

esh

ow

man

ych

ild

ren

are

rem

itti

ng

toth

ere

spo

nd

ent.

Incl

ud

ed

bas

edo

nth

eory

sep

arat

ing

altr

uis

tic

fro

mo

ther

mo

tiv

atio

ns

for

rem

itti

ng

(see

Ag

arw

alan

dH

oro

wit

z2

00

2)

Hea

lth

of

rem

itta

nce

par

tner

Th

esu

bje

ctiv

ew

ealt

ho

fth

ere

mit

tan

cep

artn

er.R

elev

ant

bo

thfo

rth

eq

ual

ity

of

insu

rer

the

par

tner

mig

ht

mak

ean

dan

ind

icat

or

of

rece

nt

hea

lth

sho

cks

Eld

est

chil

d/s

ibli

ng

An

ind

icat

or

equ

alto

1if

the

chil

dis

anel

des

tch

ild

,0

oth

erw

ise.

Eld

est

chil

dre

nm

ayh

ave

mo

re

resp

on

sib

ilit

ies

vis

-a-v

isth

eir

par

ents

than

oth

ers

An

ind

icat

or

var

iab

leeq

ual

to1

ifth

e

sib

lin

gis

the

eld

est

sib

lin

g.

Po

ten

tial

lyre

lev

ant

for

tran

sfer

rela

tio

nsh

ips

bet

wee

nth

ere

spo

nd

ent

and

the

sib

lin

g

Par

ent

has

som

esc

ho

oli

ng

Du

mm

yv

aria

ble

equ

alto

1if

the

par

ent

has

som

esc

ho

oli

ng

,0

oth

erw

ise

Sib

lin

g/c

hil

dli

ves

abro

ado

rin

city

Eq

ual

to1

ifre

mit

tan

cep

artn

erli

ves

abro

ado

rin

aci

ty,

0o

ther

wis

e.R

elev

ant

for

insu

ran

cem

od

els

inan

agri

cult

ura

lec

on

om

yin

wh

ich

tho

sew

ho

liv

ein

citi

esan

d

abro

adh

ave

inco

me

less

corr

elat

edw

ith

resp

on

den

tsth

anth

ose

wh

oli

ve

nea

rby

480 J Fam Econ Iss (2011) 32:473–492

123

Thus, as a parent ages, s/he is likely to increase remit-

tances, and parents in better health will remit less. Under

the co-insurance/income pooling hypothesis, we are likely

to observe remittance flows in each direction. Therefore,

there will be a positive association between the likelihood

that a parent remits to the respondent, and the likelihood

that a respondent remits to his/her parent. This variable

might also capture traditional gift-sharing motivations

(Mauss 1990).

If remittances from a parent are insurance payouts, a

respondent is more likely to receive or to receive more

remittances as wealth decreases. Having suffered from a

health shock will increase remittance flows under this

motivation.

The data permit the analysis of one service provided by

the respondent for their parents: that of looking after a

sibling. If remittances are payment for this service then

remittances or likelihood of receiving remittances will be

higher for respondents who reported looking after a sibling.

If respondents remit to parents for altruistic motivations,

the number of heirs (potential remitters) will have a neg-

ative impact on remittances as other people are likely to

ensure that the parents have a good quality of life (Agarwal

and Horowitz 2002). Respondents will be less likely to

remit or remit less to their parents if their parents are in

better health.

Econometric Results and Discussion

Respondent–Parent Transfer Flows

Regression results for respondent–parent transfers are

reported in Table 9. Net transfers from parents are

decreasing in the asset index, that is, those with lower

wealth receive more net transfers from their parents than

wealthier individuals. This is consistent with parental

altruism. The Tobit model in column 5 reveals that this is

largely driven by the fact that wealthier individuals are

more likely to give to their parents than their poorer

counterparts, with non-farm assets being highly significant

at the 1% level. Van Dalen et al. (2005) and De la Briere

et al. (2002) show that this is consistent with both altruism

and inheritance motivations.

Net transfers from parents are higher for individuals

who reported suffering from a health problem during the

previous month. This is consistent with both altruistic

motivations and insurance payouts from parents to chil-

dren. However, this is significant only at the 10% meaning

it is difficult to draw strong conclusions from this result.

Although there are no data on whether or not a parent

recently suffered from a health problem, a similar result

can be seen with respect to parents’ general health withTa

ble

7co

nti

nu

ed

Par

ents

Ch

ild

ren

Sib

lin

gs

Par

ent

liv

esin

sam

ev

illa

ge

Du

mm

yv

aria

ble

equ

alto

1if

par

ent

liv

esin

sam

ev

illa

ge

asre

spo

nd

ent

Nu

mb

ero

fh

eirs

par

ent

has

Rel

evan

tfo

rin

her

itan

cem

oti

vat

ion

s

Rem

itta

nce

par

tner

fem

ale

An

ind

icat

or

var

iab

leeq

ual

to1

ifth

ep

ote

nti

alre

mit

tan

cep

artn

eris

fem

ale,

0o

ther

wis

e

Ch

ild

mar

ried

Eq

ual

to1

ifth

ech

ild

ism

arri

ed,

0

oth

erw

ise.

Mar

riag

ere

du

ces

lin

ks

wit

hth

eh

ou

seh

old

of

ori

gin

and

is

lik

ely

tocr

eate

oth

erre

spo

nsi

bil

itie

s

mak

ing

this

anu

sefu

lco

ntr

ol

var

iab

le

Join

tch

arac

teri

stic

s

Mat

rili

nea

l/p

arti

lin

eal

her

itag

eA

nim

po

rtan

tin

dic

ato

rv

aria

ble

ina

cou

ntr

yin

wh

ich

wea

lth

flo

ws

and

lik

elih

oo

do

fin

her

itan

ced

iffe

rb

ytr

ibe

Vil

lag

ein

dic

ato

rsD

um

my

vil

lag

ev

aria

ble

sar

ein

clu

ded

toca

ptu

resy

stem

atic

beh

avio

ral

dif

fere

nce

sb

etw

een

vil

lag

es

J Fam Econ Iss (2011) 32:473–492 481

123

respondents being less likely to remit to parents who are in

better health.

The results show that individuals who receive transfers

from their parents are more likely to remit in turn, and vice

versa. A large proportion of transfer relationships are

therefore bidirectional. This result is consistent with trans-

fers serving as insurance or income pooling as part of a

survival strategy. This result is a strong one with highly

significant coefficients in all cases, and is in line with

‘‘balanced reciprocity’’ as mutual insurance discussed by

Platteau (1997).

Net transfers from parents who live in the same village

as the respondent tend to be lower than for those who live

in another village. The Probit and Tobit models in columns

4 and 5 reveal that this is due to the fact that respondents

are more likely to remit to parents who live in the same

village. In addition, the coefficients on these models are

highly significant. This could be as a result of transaction

costs of remitting to parents who live further afield.

Equally, social pressure to support parents can be stronger

when both live in the same village.

The Probit models in columns 2 and 4 reveal that the

more heirs a parent has, the more likely respondents are to

remit to them and the more likely they are to receive from

them. It is interesting that this appears to influence only

likelihood of remitting or receiving and not the amount, as

the Tobit model coefficients are insignificant. However, the

Probit model coefficients are significant only at the 10%

level for sending and the 5% level for receiving. Although

there is no information on parental wealth, this is tentative

evidence of an inheritance motivation for remitting.

The respondent’s education is positive and significant at

the 1% level in both the Probit and Tobit for receiving

transfers from their parents. Better educated respondents

are both more likely to receive transfers from their parents,

and receive more from them. This is in line with the theory

that parents ‘‘insure’’ themselves with children who make

the best insurers such as those with better education.

Interestingly, parents who have some schooling are also

both more likely to send transfers to the respondents—their

children (as revealed by the Probit model in column 2) and

remit more (see Tobit model in column 3). Combined with

the fact that parents remit to better educated respondents,

this might be a further indication of a balanced reciprocity

amongst those who are more likely to be able to help each

other. This has the interesting and important economic

consequences that those who are able to ‘‘insure’’ each

other, do so, whilst those who are not able to insure others

might lack insurance themselves in the event of a shock,

causing them to remain in a poverty trap.

Motivations for respondent–parent transfer flows are

mixed. Nonetheless, there is strong support for altruistic

giving in both directions. In addition, the results are also

consistent with co-insurance motivations.

The low pseudo r-squared are disappointing but not too

much cause for concern. Such results are normal in the

literature (e.g. Cao 2006), and often for Probit and Tobit

models in other fields.

Table 8 Discriminating hypotheses from respondent-parent transfer perspective

Altruism Co-insurance

(premium)

Co-insurance

(indemnity)

Inheritance Implicit payment

for services

Parent’s motivations for remitting to Respondent

Respondent’s wealth Negative Positive Negative – No direct impact

Respondent’s general health Negative Positive No direct impact – No direct impact

Respondent suffered health shock Positive No direct impact Positive – No direct impact

Respondent looks after sibling No direct impact No direct impact No direct impact – No direct impact

Respondent sends transfers No direct impact Positive No direct impact – Positive

Parent’s age No direct impact Positive No direct impact – No direct impact

Parent’s health No direct impact Negative No direct impact – No direct impact

Parent lives in same village No direct impact Negative No direct impact – No direct impact

Respondent’s motivation for remitting to parent

Parent’s age No direct impact Negative No direct impact Positive –

Parent’s heirs Negative No direct impact No direct impact Positive/Negative –

Parent’s health Negative Positive Negative/No direct

impact

Negative –

Parent’s schooling No direct impact Positive No direct impact Positive –

Parent sends transfers No direct impact Positive No direct impact No direct impact –

Parent lives in same village No direct impact Negative No direct impact ? –

482 J Fam Econ Iss (2011) 32:473–492

123

Table 9 Respondent–Parent transfer flows

Net transfers received

from parent

Received transfers

from parent

Value of transfers

from parent

Sent parent

transfers

Value of transfers

sent to parent

(1) Robust OLS (2) Probit (3) Tobit (4) Probit (5) Tobit

Income 0.007 -0.000 -0.022 -0.000 -0.029***

(1.347) (-0.986) (-1.257) (-1.556) (-3.025)

Asset index (farm) -22.725 -0.069 -16.322 0.063 29.257**

(-1.557) (-1.644) (-0.936) (1.427) (2.342)

Asset index (non-farm) -37.262** 0.030 -2.441 0.061 47.103***

(-2.561) (0.920) (-0.150) (1.637) (3.025)

Education 1.738 0.062*** 30.682*** -0.036* 9.231

(0.188) (3.605) (3.364) (-1.858) (1.062)

Age -1.980 -0.010 4.766 0.000 5.830

(-0.220) (-0.304) (0.386) (0.012) (0.572)

Age square 0.044 -0.000 -0.137 -0.000 -0.108

(0.324) (-0.220) (-0.729) (-0.046) (-0.677)

Maleb -7.428 -0.480*** 29.235 0.682*** 247.020***

(-0.154) (-3.626) (0.467) (4.949) (5.339)

Married -478.170 0.348 -502.684 0.659* 123.774

(-1.618) (1.124) (-1.376) (1.926) (1.202)

Household size -16.636* -0.049* -8.628 -0.004 19.163*

(-1.830) (-1.768) (-0.709) (-0.146) (1.778)

Respondent eldest child 8.789 0.074 25.688 0.051 20.249

(0.176) (0.639) (0.446) (0.430) (0.413)

Health (1–10) 6.748 0.033 11.142 0.040 -0.430

(0.728) (1.100) (0.969) (1.229) (-0.039)

Health problem in last month 53.859* 0.195 60.140 -0.046 -52.851

(1.679) (1.610) (1.436) (-0.345) (-1.373)

Sibling in house 64.391 0.063 -18.262 0.114 -83.139*

(1.435) (0.477) (-0.323) (0.702) (-1.719)

Given transfers to parent 0.661*** 245.675***

(6.618) (3.857)

Received transfers from parent 0.653*** 98.823***

(6.651) (2.776)

Parent’s age 3.585 0.022** 9.391** -0.010 -1.997

(1.312) (2.438) (1.984) (-1.101) (-0.594)

Parent’s age square -0.041 -0.000** -0.123** 0.000 0.020

(-1.296) (-2.544) (-2.232) (1.445) (0.547)

Parent’s health -0.879 0.017 4.762 -0.069*** -9.482

(-0.136) (0.857) (0.546) (-3.213) (-1.309)

Parent lives in same village -84.438** 0.144 -10.092 0.554*** 119.718***

(-2.324) (1.483) (-0.225) (4.795) (3.238)

Parent has some schooling 39.246 0.223** 111.516*** -0.006 5.757

(1.054) (2.124) (2.646) (-0.062) (0.129)

Number of heirs parent has 0.873 0.032** 7.120 0.030* 4.828

(0.212) (1.967) (1.146) (1.662) (0.901)

Motherb -151.606** 0.158 -74.225 0.126 95.845*

(-2.472) (1.248) (-0.954) (1.020) (1.948)

Matrilineal heritagea -26.418 0.122 22.384 -0.790*** -123.757

(-0.392) (0.642) (0.308) (-4.047) (-1.549)

J Fam Econ Iss (2011) 32:473–492 483

123

Respondent–Children Transfer Flows

We report regressions for respondent–child transfers in

Table 10. Net transfer receipts from children are decreas-

ing in income and this is significant at the 1% level. The

Probit and Tobit models in columns 2 and 3 reveal that

respondents do not receive more money from children as

their income increases. Instead, the Tobit model in column

5 shows that this result appears to be driven by the fact that

respondents with higher income send their children more

money. The theoretical models discussed above indicate

that this result is consistent with altruism towards their

children on the part of the respondent. Interestingly, the

coefficient on income in the Probit in column 4 is insig-

nificant suggesting that income does not influence whether

or not the respondent sends transfers to their children, but

only the amount sent.

Although income is significant in some models, wealth

is insignificant with only one exception: farming wealth is

positive and significant at the 5% level in column 5. This

suggests that those with greater farm assets send more to

their children than others. However, farm wealth appears to

influence only the amount of transfers sent, and not the

decision to send or not, since the coefficients on wealth in

the Probit model in column 4 remain insignificant.

Although children do not appear to ‘‘insure’’ health

shocks, net transfer receipts and probability of receiving

transfers from children is increasing in respondents’ health.

In addition, the better the child’s health and this is signif-

icant at the 5% level. That is, the lower net transfers

respondents receive. This is consistent with the hypothesis

that children choose to insure themselves with their par-

ents; parents in better health make better insurers, and

children in better health are less likely to require their

parents’ insurance.

Respondents receive less net transfers from their chil-

dren if they look after their grandchildren. Although the

data do not permit me to know to which son/daughter the

grandchild belongs, the evidence indicates transfers are not

a payment for this service. Indeed, respondents who look

after their grandchildren receive less than others. Although

potentially initially surprising, this should not be unex-

pected within the Malawian context where 14% of the

population is HIV positive (Morah 2007). HIV dispropor-

tionally affects those of working and child-bearing age and

grandparents are often left to care for the children (Conroy

et al. 2006). This result could therefore be interpreted as

evidence of altruism of grandparents towards their grand-

children and is consistent with theoretical models which

posit dynastic altruism

Table 9 continued


from parent

Received transfers

from parent

Value of transfers

from parent

Sent parent

transfers

Value of transfers

sent to parent


Patrilineal heritagea -6.595 0.199 -102.043 0.008 -105.696

(-0.097) (1.075) (-1.011) (0.042) (-1.472)

Northern village (Mwankhunikira)c -58.019 -0.485* -34.805 -0.511* 26.398

(-0.568) (-1.803) (-0.331) (-1.828) (0.214)

Northern village (Mwahenga)c -16.800 -0.501** 12.386 -0.134 68.606

(-0.180) (-2.222) (0.114) (-0.580) (0.727)

Central village (Mkanda)c 25.401 -0.150 -1.067 -0.415** -81.220

(0.509) (-0.900) (-0.017) (-2.406) (-1.397)

Mother—Female transfer flowb 164.198** -0.211 80.066 0.370** 10.353

(2.486) (-1.403) (1.044) (2.414) (0.195)

Constant 385.078* -1.373** -255.275 -0.703 -365.624

(1.658) (-1.973) (-0.848) (-0.900) (-1.454)

Standard error of regression 536.491*** 450.169***

(6.297) (8.582)

N 1083 1083 1083 1083 1083

(Pseudo) r2 0.083 0.114 0.013 0.161 0.016

Clusters 483 483 483 483 483

Goodness of fit F: 2.889 Chi 2: 148.590 F: 1.863 Chi 2: 203.302 F: 3.724

Notes: t-stats in parentheses below coefficients. *, ** and *** indicate significance at 10%, 5% and 1%, respectivelya Excluded dummy is Mixed Heritage (Chewa tribe); b Indicated dummy variables capture all transfer flow relationships between them

(excluded Father-Male transfer flow); c Excluded village dummy is Southern Village (Kalembo)

484 J Fam Econ Iss (2011) 32:473–492

123

Table 10 Respondent-Children transfer flows


from child

Received transfers

from child

Value of transfers

from child

Gave transfer

to child

Value of transfer

sent to child


Income -0.063*** -0.000 -0.011 0.000 0.054***

(-5.450) (-0.350) (-0.369) (0.578) (3.217)

Asset indexd -31.560 -0.007 -31.707

(-0.967) (-0.083) (-0.967)

Asset index (farm)d 0.116 63.068**

(1.436) (2.093)

Asset index (non-farm)d -0.005 7.730

(-0.076) (0.282)

Education 3.815 0.064* 29.003** -0.006 9.985

(0.374) (1.930) (2.154) (-0.197) (0.795)

Age 0.769 -0.002 -8.666 -0.047 -10.963

(0.035) (-0.022) (-0.355) (-0.560) (-0.299)

Age square 0.023 -0.000 -0.009 0.000 -0.015

(0.098) (-0.182) (-0.037) (0.205) (-0.039)

Maleb -99.572 0.159 116.401 0.411 199.189*

(-1.007) (0.660) (0.881) (1.394) (1.722)

Married 54.570 -0.715** -63.622 1.133*** 404.216**

(0.636) (-2.170) (-0.566) (2.785) (2.299)

Household size 15.529 0.078 38.944 -0.033 -3.903

(0.809) (1.541) (1.572) (-0.718) (-0.163)

Health (1-10) 35.574** 0.088** 34.675** -0.016 -28.947

(2.470) (2.308) (2.081) (-0.304) (-1.394)

Health problem in last month 66.623 0.199 167.573 -0.199 -74.805

(0.829) (1.066) (1.457) (-1.073) (-0.847)

Grandchild in house -186.738** -0.196 -101.821 0.112 143.362

(-2.185) (-0.722) (-0.995) (0.427) (1.290)

Given transfer to child -0.087 -73.483

(-0.531) (-1.121)

Received transfers from child 0.327** 1.096

(2.149) (0.018)

Number of children -7.365 -0.215*** -35.756** 0.078 10.380

(-0.351) (-3.437) (-2.210) (1.511) (0.372)

Number of children remitting 57.762** 0.841*** 224.886***

(2.251) (9.961) (3.471)

Number children remitting*Asset index -6.818 -0.016 9.228

(-0.469) (-0.283) (0.574)

Child’s age -2.707 0.073* 24.633 -0.033 8.052

(-0.198) (1.736) (1.474) (-0.926) (0.515)

Child’s age square 0.046 -0.001* -0.209 0.000 -0.078

(0.381) (-1.712) (-1.408) (1.046) (-0.559)

Eldest child -12.499 0.412* 115.390 0.152 67.992

(-0.201) (1.895) (1.517) (0.927) (0.961)

Child’s health -24.444** 0.024 -20.433 -0.003 27.701*

(-2.315) (0.441) (-1.083) (-0.075) (1.756)

Child lives city/abroad -11.353 -0.236 -28.035 -0.223 -9.205

(-0.147) (-1.255) (-0.316) (-1.214) (-0.123)

J Fam Econ Iss (2011) 32:473–492 485

123

The coefficients on number of children and number of

children remitting in the Probit model in column 2 are of

particular interest. The more children a respondent has, the

less likely s/he is to receive transfers from any one of the

children with this variable being significant at the 1% level.

Agarwal and Horowitz (2002) show that this can be

interpreted as evidence of altruism as the more children

there are to look after the parents the less responsibility

there is on any one of them to do so. In contrast, the more

children there are actually remitting, the more likely the

respondent is to receive transfers from any one of them.

This coefficient is also highly significant at the 1% level

and suggests that there is competition amongst children to

be seen to assist their parents.

This is consistent with inheritance motivations to remit

since, in Malawi, inheritance does not automatically go to

the eldest child, but is decided by a committee of surviving

senior relatives on the basis of who is seen to have fulfilled

their duty towards their parents (Takane 2007). To assess

this hypothesis further we interact the number of children

remitting with the asset index. We expect a positive coef-

ficient on this since the children will be prepared to com-

pete more strongly the more they stand to benefit. This

coefficient is however, insignificant for the pooled sample.

It does however turn significant for male transfer receipts

when male and female are analyzed separately (results

available on request). On average, the male asset index is

significantly higher than that for females.

Respondent–children transfers show evidence of chil-

dren insuring their parents against income (but not health)

shocks, parents showing altruism towards grandchildren,

children exhibiting both altruism and inheritance motiva-

tions. No one motivation appears to dominate in this case

however.

Table 10 continued


from child

Received transfers

from child

Value of transfers

from child

Gave transfer

to child

Value of transfer

sent to child


Child married 173.701** 0.813*** 332.894** -0.342* -213.014**

(2.315) (3.127) (2.022) (-1.917) (-2.360)

Child daughterb -48.032 -0.620** -199.928** 0.012 34.566

(-0.709) (-2.455) (-2.383) (0.062) (0.383)

Matrilineal heritagea -4.333 0.543** 147.043 -0.307 -26.153

(-0.049) (2.098) (1.529) (-0.948) (-0.204)

Patrilineal heritagea 150.050* 0.288 282.936* 0.339 42.837

(1.709) (1.060) (1.917) (1.156) (0.477)

Northern village (Mwankhunikira)c -543.996*** -0.873 -448.529** -0.099 359.716*

(-2.979) (-1.571) (-1.980) (-0.232) (1.660)

Northern village (Mwahenga)c -138.781 -0.228 -126.777 -0.469 10.891

(-1.248) (-0.841) (-0.902) (-1.297) (0.111)

Central village (Mkanda)c -84.811 -0.154 -64.259 -0.083 74.216

(-1.170) (-0.591) (-0.577) (-0.296) (0.846)

Daughter–Mother transfer flowb -49.813 0.372 59.683 0.333 99.883

(-0.484) (1.218) (0.568) (1.164) (0.737)

Constant -327.070 -2.936 -1140.232* 1.280 -223.096

(-0.694) (-1.540) (-1.786) (0.625) (-0.290)

Standard error of the regression 510.692*** 508.374***

(3.323) (6.952)

N 430 430 430 430 430

(Pseudo) r2 0.1976 0.394 0.051 0.101 0.023

Clusters 144 144 144 144 144



(excluded Son-Male transfer flow); c Excluded village dummy is Southern Village (Kalembo); d A pooled asset index is included in some

regressions due to the Asset * Remitters interaction term. Results do not differ substantially if separate farm and non-farm asset indexes are

entered

486 J Fam Econ Iss (2011) 32:473–492

123

Respondent-Sibling Transfer Flows

Results from Respondent-Sibling regressions are presented

in Table 11. Net transfers from siblings are decreasing in

asset wealth of the respondent but increasing in income.

The probit regressions show that this is driven by transfers

sent by respondents to their siblings. Respondents appear to

share their (long term asset) wealth with siblings, but not

their short term income.

Net transfers from siblings increase as the respondents’

health declines suggesting siblings remit for altruistic

motives. In addition, siblings are more likely to remit to

respondents who have recently suffered from a health

problem. This is consistent with insurance payouts or

altruistic motivations. Interestingly, respondents who

reported suffering from a recent health problem were also

more likely to send transfers to their siblings. This could be

an indication that siblings provided help to sick respondents,

but that respondents ‘‘repaid’’ the help. The distinction

between gifts and loans may not be clear. Udry (1990) for

example finds that repayment of loans in rural Northern

Nigeria is conditional upon the borrower’s and lender’s

economic situation. A similar situation may exist in rural

Malawi. Both respondents and their siblings are more likely

to remit the better their own health is, and both are more

likely to send to each other the worse the other’s health is.

As with parents, there appears to be a great deal of

reciprocity with those giving to siblings more likely to

receive from them, and vice versa. This is consistent with

co-insurance/income pooling motivations for remitting.

Net transfers from siblings residing in a city or abroad are

higher than from those residing in rural Malawi. The Tobit

model indicates that total transfers received are higher from

siblings living in cities or abroad, but transfers sent to them

(and the likelihood of remitting to them) are lower. Since

such siblings are likely to earn more, this could be evidence

of altruism (they give because they can afford to), or of a

family survival strategy (sending some people to work in the

city), or repayment of implicit loans to fund the migration.

Alternatively the transaction costs involved in such transfers

are prohibitive for rural Malawians but not for those residing

in cities or abroad. Without additional information, it is not

possible to untangle this further.

Respondent-Sibling transfer flows reveal a strong

altruistic component. In addition, there is evidence of co-

insurance. Additional information is required to understand

the rural–urban linkage discussed.

Transfers From a Household Perspective

It is useful for comparison to re-run the basic regressions at

a household level in order to permit comparison with the

previous results. This is shown in Table 12 for net remit-

tances received from each of the three sets of remittance

partners. Several choices have to be made regarding

inclusion of variables for such an analysis.

Firstly, for the respondent household we are able to

include either male or female characteristics for the

household as a whole or else include both. In the latter

case, any single households or those for which data was not

collected on one of the partners will be lost from the

sample, however, it will make it possible to study whether

it is male or female characteristics which drive transfer

behavior. In the former case, it is not obvious which set of

characteristics should be included and excluding relevant

variables risks leading to missing variable bias. We have

chosen to include both male and female characteristics

leading to a reduction in the number of observations, but

enabling additional information to be extracted from the

study.

Secondly, regarding the transfer partner households, we

are able to include all remittance transfers with relevant

characteristics attached to the partner. Alternatively, it is

possible to sum all remittance flows and assume the

characteristics of, for example, the eldest sibling or child.

We have opted for the former option in the results

reported.

Children are a special case as they may remit to either

parent whereas, (at least in our data), parents and siblings

tend to remit to their own children/siblings. At the house-

hold level therefore, total remittances have been summed

and a transfer to the mother is no longer considered as

different to that of a remittance to the father.

Male and Female Characteristics

Before comparing household level results, it is instructive

to note the different impacts of male and female charac-

teristics. These results are particularly interesting for chil-

dren and siblings. Net remittances from children fall their

mother’s income rises, but do not appear to be influenced

by father’s income. This is some evidence that children

behave altruistically towards their mother.

In contrast, net transfers from siblings do not respond to

the female’s income, but rather respond positively to the

male’s income. As the male respondent earns more, the

household is likely to receive more remittances from sib-

lings. This could be the result of a co-insurance premium.

In addition, siblings’ transfers decrease as the male’s

education increases and decrease as his health improves.

Increased remittances when sick may be the result of a

co-insurance pay-out. It is not clear why siblings appear to

exhibit insurance behavior with regards to income with the

male but not the female.

J Fam Econ Iss (2011) 32:473–492 487

123

Table 11 Respondent-Sibling transfer flows


from sibling

Received transfers

from sibling

Value of transfers

from sibling

Sent transfers

to sibling

Value of transfers

sent to sibling


Income 0.011*** 0.000 0.010 -0.000*** -0.033***

(2.816) (0.888) (0.974) (-3.451) (-3.068)

Asset index (farm) -6.962 0.035 13.948 -0.011 10.841

(-0.896) (1.505) (1.423) (-0.455) (0.736)

Asset index (non-farm) -10.916* -0.043* -14.806 0.075*** 33.674***

(-1.784) (-1.833) (-1.479) (3.382) (2.795)

Education -2.171 -0.009 3.455 0.031** 16.687***

(-0.634) (-0.778) (0.561) (2.506) (2.824)

Age 0.447 -0.030 -16.588 -0.004 -7.566

(0.094) (-1.353) (-1.576) (-0.167) (-0.892)

Age square -0.052 0.000 0.138 0.000 0.171

(-0.879) (0.855) (1.060) (0.771) (1.500)

Maleb -32.353 0.019 26.672 0.249*** 128.396***

(-1.195) (0.199) (0.584) (2.739) (3.296)

Married -43.964 -0.085 -65.774 0.127 57.637

(-0.819) (-0.387) (-0.610) (0.504) (0.580)

Household size -1.218 -0.017 4.423 -0.003 9.626

(-0.257) (-0.909) (0.535) (-0.155) (1.366)

Respondent eldest child -10.254 -0.116 -0.427 0.038 43.107

(-0.348) (-1.234) (-0.010) (0.428) (0.950)

Health (1–10) -9.204** 0.006 -3.023 0.058*** 25.751***

(-2.203) (0.272) (-0.325) (2.685) (2.925)

Health problem in last month -8.812 0.194** 47.270 0.154* 47.453

(-0.481) (2.152) (1.308) (1.771) (1.480)

Nephew/Niece in house 36.670 0.008 31.465 0.010 -16.615

(1.278) (0.072) (0.550) (0.108) (-0.460)

Given transfers to sibling 0.563*** 199.164***

(9.175) (5.304)

Received transfers from sibling 0.585*** 187.620***

(9.338) (6.033)

Sibling’s age 8.211*** 0.100*** 44.126*** -0.050*** -14.441***

(3.627) (7.235) (4.960) (-3.977) (-2.638)

Sibling’s age square -0.065** -0.001*** -0.472*** 0.000*** 0.115*

(-2.349) (-5.890) (-4.514) (2.990) (1.775)

Eldest sibling -24.590 0.055 -19.817 -0.046 -13.680

(-1.294) (0.633) (-0.584) (-0.513) (-0.364)

Sibling’s health 6.461* 0.045*** 18.981** -0.048*** -15.836**

(1.835) (2.767) (2.530) (-3.011) (-2.163)

Sibling lives abroad or in city 82.477*** 0.071 109.691*** -0.610*** -200.600***

(4.601) (0.972) (3.075) (-8.474) (-4.263)

Sisterb 3.352 -0.109 -75.184** -0.146* -85.267**

(0.177) (-1.337) (-2.113) (-1.796) (-2.450)

Matrilineal heritagea 59.033* -0.007 18.968 -0.123 -98.337

(1.839) (-0.046) (0.285) (-0.822) (-1.602)

Patrilineal heritagea 34.014* 0.213* 102.160** -0.040 -40.518

(1.804) (1.768) (2.132) (-0.317) (-0.839)

488 J Fam Econ Iss (2011) 32:473–492

123

Comparing Household with Individual Transfers

The major changes can be seen with respect to transfers

between respondents and their children with little differ-

ences being observed with respect to other transfer

relationships.

Table 10 shows that children remit more net transfers

to respondents and respondents’ income declines.

Table 12 shows that this is particularly driven by the

mother’s income. In contrast, the household asset index is

insignificant at the individual level. This turns signifi-

cantly negative when remittances are viewed from the

household perspective. This suggests that children con-

sider more the household level of wealth and that it

matters less to whom they remit. The results suggest that

children transfer more to their parents the poorer the

parents are. Other results do not differ significantly from

the previous analysis.

The household level analysis does not show significant

changes regarding sibling characteristics. However, the

results reveal that it is male income that is the key (posi-

tive) driver of net transfers and siblings’ transfers respond

more to male sickness than female sickness. The household

non-farm asset index remains negative and significant

suggesting that siblings transfer more to respondents with

fewer non-farm assets.

Conclusion

This paper has aimed to disentangle motivations for

remitting by analyzing transfers separately for transfer

relationships between the respondents and their parents,

siblings and children. However, the results confirm the

conclusions of all papers to date, that is, that transfer

motivations are difficult to disentangle. One motivation to

remit does not preclude another, even at an individual

level.

A word of warning is in order when comparing the

results from the different transfer relationships due to the

different number of observations in each category. None-

theless, the results are robust to numerous alterations to

model specification, and the large number of significant

variables in each specification can be taken as a sign that,

despite the difference in observations, the results are

reliable.

All transfer relationships showed evidence of altruism.

For example, net transfers from parents and siblings

increase as respondents’ asset indexes decline. However,

there is also strong evidence of co-insurance, particularly

between respondents and their parents, and respondents

and their siblings with both of these groups more likely to

give if they receive and vice versa. In addition, children

appear to insure respondents’ short term income and

Table 11 continued


from sibling

Received transfers

from sibling

Value of transfers

from sibling

Sent transfers

to sibling

Value of transfers

sent to sibling


Northern village (Mwankhunikira)c 31.070 -0.091 -24.076 0.014 -15.710

(0.775) (-0.538) (-0.331) (0.088) (-0.224)

Northern village (Mwahenga)c -27.945 -0.154 -82.149 -0.071 -4.788

(-0.869) (-1.055) (-1.315) (-0.484) (-0.075)

Central village (Mkanda)c 17.883 -0.196 -78.806 -0.101 -71.220

(0.751) (-1.560) (-1.571) (-0.776) (-1.248)

Sister-Female transfer flowb -11.143 0.077 47.816 0.256** 103.045**

(-0.385) (0.670) (0.943) (2.224) (2.147)

Constant -102.571 -2.261*** -1025.598*** 0.159 -134.272

(-0.813) (-4.402) (-3.978) (0.306) (-0.649)

Standard error of regression 474.482*** 468.252***

(5.721) (6.335)

N 2876 2876 2876 2876 2876

(Pseudo) r2 0.041 0.076 0.016 0.087 0.015

Clusters 540 540 540 540 540



(excluded Brother-Male transfer flow); c Excluded village dummy is Southern Village (Kalembo)

J Fam Econ Iss (2011) 32:473–492 489

123

parents and siblings increase their net transfers to respon-

dents who have suffered from a recent health shock. There

is also evidence of inheritance motivations in transfers

from children to respondents.

These mixed results could be the result of a large degree

of heterogeneity in the population with regards to familial

transfers, with different individuals and families having

different motivations. Alternatively, or in addition, it is

possible for one person to have more than one single

motivation when choosing to give. These facts mean it is

unlikely to be able to conclude in favor of one particular

motive.

One interesting observation is that individual and

household level analyses reveal similar results with respect

to remitter characteristics. However remitters may respond

more to male or female respondents suggesting that, at

least in part, the individual (and not just the household)

Table 12 Household level transfer flows

Children Siblings Parents

Household characteristics—female

Income -0.048*** 0.002 0.002

(-2.870) (0.818) (0.236)

Education -9.831 -2.625 3.311

(-0.766) (-1.252) (0.337)

Age -74.230 2.320 18.363

(-1.130) (0.459) (1.074)

Age square 0.819 -0.045 -0.205

(1.135) (-0.609) (-0.849)

Health (1–10) -20.766 -4.060 12.563

(-0.854) (-0.785) (1.277)

Health problem in last month 46.066 -3.234 25.609

(0.469) (-0.149) (0.572)

Household characteristics—male

Income 0.014 0.015*** 0.007

(0.328) (3.410) (0.774)

Education -8.767 -3.611* -5.689

(-0.779) (-1.823) (-0.928)

Age -0.201 -0.917 -27.428**

(-0.010) (-0.317) (-2.157)

Age square -0.082 -0.006 0.331**

(-0.356) (-0.160) (2.204)

Health (1–10) 6.463 -6.852** 15.358

(0.342) (-1.984) (1.308)

Health problem in last month 124.370 -3.742 60.962

(0.937) (-0.261) (1.352)

Other household characteristics

Respondent maleb -67.822 -48.514** 20.050

(-0.820) (-2.385) (0.369)

Respondent eldest child 5.806 -19.528

(0.265) (-0.357)

Household size 8.282 -3.669 -26.572**

(0.353) (-0.848) (-2.418)

Asset index (Farm) -99.918*** -8.733 -14.977

(-2.646) (-0.948) (-0.705)

Asset index (non-farm) 43.471 -9.226* -36.024**

(1.428) (-1.934) (-2.382)

Household looks after remittance

partner’s child

-241.557** 28.742 101.770**

(-2.111) (1.576) (2.087)

Number of children -8.325

(-0.303)

Number of children remitting 61.866***

(2.651)

Number of children remitting*asset index -5.951

(-0.310)

Remittance partner characteristics

Age 19.320 6.895*** 3.686

(1.173) (3.531) (1.102)

Age square -0.133 -0.062*** -0.050

(-0.886) (-2.719) (-1.250)

Eldest child -129.704 -9.695

(-1.274) (-0.497)

Health (1–10) -31.796* 3.518 -7.308

(-1.885) (1.101) (-0.919)

Table 12 continued

Children Siblings Parents

Lives in city/abroad 93.958 64.933***

(1.144) (4.375)

Lives in same village as household -84.375**

(-2.145)

Married 241.307**

(2.494)

Has some schooling 62.736

(1.378)

Number of heirs -1.689

(-0.368)

Remittance Partner Femaleb -136.496 2.713 -

185.314***

(-1.551) (0.139) (-2.589)

Joint Characteristics

Matrilineal heritagea -61.175 53.144* -38.063

(-0.515) (1.939) (-0.414)

Patrilineal heritagea 165.993 52.508** 10.654

(1.305) (2.436) (0.124)

Northern village (Mwankhunikira)c -301.322** 53.047 -81.755

(-2.102) (1.264) (-0.698)

Northern village (Mwahenga)c -169.410 0.342 -93.388

(-1.560) (0.009) (-0.814)

Central village (Mkanda)c -39.633 44.185 4.793

(-0.484) (1.552) (0.074)

Female to Female Transferb -26.801 -17.662 203.670***

(-0.236) (-0.746) (2.699)

Constant 1805.235 -89.656 36.664

(1.261) (-0.844) (0.158)

N 286 2283 846

r2 0.251 0.039 0.095

F 3.570 3.172 2.364

Notes: t-stats in parentheses below coefficients. *, ** and *** indicate significance at

10%, 5% and 1%, respectivelya Excluded dummy is Mixed Heritage (Chewa tribe); b Indicated dummy variables

capture all transfer flow relationships between them; c Excluded village dummy is

Southern Village (Kalembo)

490 J Fam Econ Iss (2011) 32:473–492

123

they are remitting to matters. One highly notable exception

to this is that children appear to remit to the household

rather than the individual when considering household

wealth. That is, if children support parents with low levels

of wealth they may give to either parent and it is necessary

to conduct the analysis at a household and not individual

levels to observe this result.

Although efforts were made to ensure the reliability of

survey responses, this cannot always be guaranteed. In

particular, it can be difficult to obtain accurate information

on income and wealth, with this likely to be somewhat

underestimated in Malawi for cultural reasons.

This study has contributed to the literature analyzing

motivations for remitting by extending the analysis to

different transfer relationships within the family unit with

the understanding that different people have different

motivations for remitting. In addition, we have been able to

study transfer flows in both directions of the relationship,

something the data used in such research rarely allows

users to do. Finally we have used a previously unused data

set to examine the issue in a previously unstudied country.

The complexity of gift exchange in a developing context

is clearly shown in this study where, although differences

in motivations for remitting can be discerned between

different groups, no single motivation can be ascribed to

each group. This study has found evidence of altruism, co-

insurance and, for transfers sent by their children to

respondents, inheritance motivations.

References

Agarwal, R., & Horowitz, A. W. (2002). Are international transfers

altruism or insurance? Evidence from Guyana using multiple-

migrant households. World Development, 30(11), 2033–2044.

Amuedo-Dorantes, C., & Pozo, S. (2004). Workers’ transfers and the

real exchange rate: A paradox of gifts. World Development,32(8), 1407–1417.

Amuedo-Dorantes, C., & Pozo, S. (2006). Transfers as insurance:

Evidence from Mexican immigrants. Journal of PopulationEconomics, 19, 227–254.

Azam, J. P., & Gubert, F. (2004). Those in Kayes: The impact of

transfers on their recipients in Africa. Institute d’Economie

Industrielle (IDEI) Working Paper Series, 308.

Bloom, D. E., & Stark, O. (1985). The new economics of labor

migration. American Economic Review, 75(2), 173–178.

Cao, H. (2006). Time and financial transfers within and beyond the

family. Journal of Family and Economic Issues, 27(2), 375–400.

Conroy, A. C., Blackie, M. J., Whiteside, A., Malewezi, J. C., &

Sachs, J. D. (2006). Poverty, AIDS and hunger: Breaking thepoverty trap in Malawi. New York: Palgrave MacMillan.

Cowley, E. T., Paterson, J., & Williams, M. (2004). Traditional gift

giving among pacific families in New Zealand. Journal ofFamily and Economic Issues, 25(3), 431–444.

Cox, D. (1987). Motives for private income transfers. Journal ofPolitical Economy, 95(3), 508–546.

Crumpler, H., & Grossman, P. J. (2008). An experimental test of

warm glow giving. Journal of Public Economics, 92(5–6), 1011–

1021.

De la Briere, B., Sadoulet, E., de Janvry, A., & Lambert, S. (2002).

The roles of destination, gender, and household composition in

explaining transfers: An analysis for the Dominican Sierra.

Journal of Development Economics, 68, 309–328.

Dercon, S. (1996). Risk, crop choice and savings: Evidence from

Tanzania. Economic Development and Cultural Change, 44(3),

485–514.

Fafchamps, M., Udry, C., & Czukas, K. (1998). Drought and saving in

West Africa: Are livestock a buffer stock? Journal of Develop-ment Economics, 55(2), 273–305.

Fan, C. S. (2005). Survival of the gene, intergenerational transfers and

precautionary saving. Journal of Development Economics, 77,

451–479.

Funkhouser, E. (1995). Remittances from international migration: A

comparison of El Salvador and Nicaragua. Review of Economicsand Statistics, 77(1), 137–146.

Grigorian, D. A., & Melkonyan, T. A. (2008). Microeconomic

implications of transfers in an overlapping generations model

with altruism and self-interest. IMF Working Paper WP/08/19.

Gupta, R., & Hegde, S. A. (2009). An exploratory study of financial

remittances among non-resident Indians in the United States.

Journal of the Family and Economic Issues, 30(2), 184–192.

Hayhoe, C. R., & Stevenson, M. L. (2007). Financial attitudes and

inter vivos resource transfers from older parents to adult

children. Journal of the Family and Economic Issues, 28(1),

123–135.

Horioka, C. Y. (2002). Are the Japanese selfish, altruistic, or

dynastic? Japanese Economic Review, 53, 26–54.

Ilahi, N., & Jafarey, A. (1999). Guestworker migration, transfers and

the extended family: Evidence from Pakistan. Journal ofDevelopment Economics, 58, 458–512.

Koh, S.-K., & MacDonald, M. (2006). Financial reciprocity and elder

care: Interdependent resource transfers. Journal of Family andEconomic Issues, 27(3), 420–436.

Laferrere, A., & Wolff, F. C. (2006). Microeconomic models of

family transfers. In S. Kolm & J. M. Ythier (Eds.), Handbook ofeconomics of giving, altruism and reciprocity 2 (pp. 889–969).

Amsterdam: Elsevier.

Lucas, R. E. B., & Stark, O. (1985). Motivations to remit: Evidence

from Botswana. Journal of Political Economy, 93(5), 901–918.

Mauss, M. (1990). The gift: The form and reason for exchange inarchaic societies, Original title: Essai sur le don, Presses

Universitaires de France, 1950, Trans. Hall, W.D., London:

Routledge.

Morah, E. U. (2007). Are people aware of their HIV-positive status

responsible for driving the epidemic in sub-saharan Africa?

The case of Malawi. Development Policy Review, 25(2), 215–

242.

Mtika, M. M., & Doctor, H. V. (2002). Matriliny, patriliny, and

wealth flows in rural Malawi. African Sociological Review, 6(2),

71–97.

Naufal, G. S. (2008). Why remit? The case of Nicaragua. Institute for

the Study of Labor Discussion Paper, IZA DP 3276.

Platteau, J. P. (1997). Mutual insurance as an elusive concept in

traditional rural communities. Journal of Development Studies,33(6), 764–796.

Rapoport, H., & Docquier, F. (2006). The economics of migrants’

transfers, on the economics of giving, reciprocity and altruism. In

S. Kolm & J. M. Ythier (Eds.), Handbook of economics of giving,altruism and reciprocity 2 (pp. 1135–1198). Amsterdam: Elsevier.

Sachs, J. D., & Warner, A. M. (2001). Natural resources and

economic development: The curse of natural resources. Euro-pean Economic Review, 45, 827–838.

J Fam Econ Iss (2011) 32:473–492 491

123

Secondi, G. (1997). Private monetary transfers in rural China:

Are families altruistic? Journal of Development Studies, 33(4),

487–511.

Sheng, X., & Killian, T. S. (2009). Over time dynamics of monetary

intergenerational exchanges. Journal of Family and EconomicIssues, 30(3), 268–281.

Takane, T. (2007). Customary land tenure, inheritance rules, and

smallholder farmers in Malawi. Institute of Developing Econ-

omies Discussion Paper No.104.

Udry, C. (1990). Credit markets in northern Nigeria: Credit as

insurance in a rural economy. The World Bank EconomicReview, 4(3), 251–269.

Van Dalen, H. P, Groenewold, G., & Fokkema, T. (2005). Transfers

and their effect on emigration intentions in Egypt, Morocco and

Turkey. Tinbergen Institute Discussion paper, TI 2005-030/1.

VanWey, L. K. (2004). Altruistic and contractual transfers between

male and female migrants and households in rural Thailand.

Demography, 41(4), 739–756.

Weinreb, A. A. (2001). Substitution and substitutability: The effects

of kin availability on intergenerational transfers in Malawi.

In A. H. Gauthier, C. Chu, & S. Tuljapurkar (Eds.), The

distribution of private and public resources across generations.

Oxford: Oxford University Press/IUSSP.

Weinreb, A. A. (2002). Lateral and vertical intergenerational

exchange in rural Malawi. Journal of Cross-Cultural Gerontol-ogy, 1(38), 101–137.

White, H. (1980). Heteroscedasticity consistent covariance matrix and

a direct test for heterscedasticity. Econometrica, 48, 817–838.

Author Biography

Simon Davies is currently working as an advisor on macroeconomic

policy in the Ministry of Finance and Development Planning in

Lesotho. His research looks at household, individual and firm

behavior focusing on insurance, psychological choices, happiness,

intra-household dynamics, sexual attitudes related to HIV/AIDS and

investment decisions. He obtained his PhD from the University of

Bath in the UK in 2009 and is due to begin a position with the World

Bank in September 2010.

492 J Fam Econ Iss (2011) 32:473–492

123

what motivates gifts? intra-family transfers in rural malawi

Documents