Insurance Motives for Migration: Evidence from Thailand

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Insurance Motives for Migration: Evidence from Thailand

Anna L. PaulsonKellogg Graduate School of Management

Northwestern University

March 2000

Abstract

Migrants often maintain important connections with their origin communities. In particular, many migrants provide support to non-migrants, usually relatives, throughremittances. Remittances are often an important source of protection against adverseevents for the receiving household. The prevalence and the importance of remittancesfrom migrants both as a source of income and as insurance, suggest that migrants mayconsider their role as future remitters in deciding where to locate. This paper examineswhether the location choices of migrants who remit are consistent with a desire tomitigate the risk faced by the remitting and the receiving households. Cross-sectionalhousehold data on the location and earnings of remitters and remittees is used togetherwith historical rainfall and gross domestic product data at the provincial level to examinethis question for Thailand. The results indicate that insurance motives play an importantrole in explaining migration patterns. Empirical estimates that include insurancevariables perform better than estimates that only consider income maximization. I findthat remitters are significantly less likely to move to Bangkok the more it covaries withthe province they remit to. This is particularly true for remitters who support rural

households who are likely to be poorer and to have less access to national levelinstitutions (like banks and insurance companies) that could be used to mitigate local risk.

Key words: Insurance, Migration, RemittancesJEL classification: D81, O15, R23


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1. Introduction

Migrants often maintain important connections to their origin communities. In particular, many migrants provide support to non-migrants, usually relatives, throughremittances. Migrants living in developed countries remitted $28 billion to households indeveloping countries through official channels in 1988. Total remittances are muchhigher since informal transfers (transfers in-kind or through other non-official channels)and remittances from internal migrants are not included in this figure (Russell 1992).Households in many developing countries rely on remittances from internal and

international migrants for a large fraction of their income. In El Salvador, for example,33% of the rural poor surveyed in 1976 received remittances which made up 39% of theirincome. Ninety-three percent of a sample of rural Indian households receivedremittances, and in Malaysia remittances accounted for nearly half of the income of the poorest fifth of households (Cox and Jimenez, 1990). Thirty-five percent of the Thaihouseholds studied in this paper either send or receive remittances and remittancesaccount for nearly one-third of the income of receiving households.

Remittances are often an important source of protection against adverse events forthe receiving household. For example, Lillard and Willis (1997) find that the probabilityand the amount of remittances from Malaysian children to their parents are sensitive tothe current and permanent income of the childs family. Income transfers to ruralhouseholds in India vary inversely with agricultural profits (Rosenzweig, 1988). In astudy which uses the same data analyzed in this paper, Miller and Paulson (1999) provideevidence that Thai remittances also behave in a way that is consistent with insurance.Thai remittances are higher when the receiving households income is lower. They also

respond to conditions in the receiving households community: remittances are higherwhen the receiving household lives in an area that has below average rainfall. Theprevalence and the importance of remittances from migrants both as a source of incomeand as insurance, suggest that migrants may consider their role as future remitters indeciding where to relocate.1

This paper examines whether the location choices of migrants who remit areconsistent with a desire to mitigate income risk faced by the remitting and the receiving

households. I consider a framework that is appropriate for analyzing migration decisionswhen the migrant and the rest of the family will continue to pool income even after themigrant moves. The model implies that families will diversify across locations, whichare not perfectly correlated, and send remittances in order to reduce the variance ofconsumption. Cross-sectional household data on the location and earnings of remitters


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and remittees is used together with historical rainfall and gross domestic product data atthe provincial level to test the implications of the model for Thailand.

The framework that is considered here is very similar to the one that Rosenzweigand Stark (1989) describe in their analysis of migration at marriage in rural India. Theytest the hypothesis that rural families diversify spatially covariant risk by linkingthemselves to other villages via marriage. Using longitudinal data from rural India, theyfind that households who experience more exogenous variability in agricultural incomesare more likely to be linked by marriage to more distant villages. These strategies to dealwith exogenous risk appear to be successful. In particular, for a given level of profit

variability, the variability of household food expenditures is decreasing in both thenumber of married women in the household and in the distance between the householdand the origin household of the married women. This paper considers whether theRosenzweig and Stark framework might help to explain migration patterns moregenerally, given how frequently migrants remain connected to their origin communitiesthrough remittances.

The degree to which the migrant and the rest of the family can smoothconsumption via remittances depends on the covariance of income shocks to the twolocations. Migrants who move to places where income shocks covary negatively with theplace they remit to are likely to be a good position to provide insurance in the form ofremittances when their families experience bad times. So from a migration/insurance perspective greater covariance is undesirable. However, covariance in income shocksmay also capture other aspects of similarity between two places that might translate intoutility or income gains for the migrant. Work skills may transfer more readily between

two locations that are highly covariant. Language, culture, food and so on may also bemore likely to be similar. The insurance benefits of moving to a location that isdissimilar in terms of covariance may be offset by the costs associated with theunfamiliar. Households who lack access to other means of insurance will be unable totake advantage of the gains associated with moving to a place that is similar. Householdswho do have access to other forms of insurance will be able to make migration decisionspurely on efficiency/utility grounds.

Insurance considerations may be particularly important for migrants who comefrom rural areas. Forty-three percent of rural Thai households grow rice and the riceharvest varies significantly with rainfall. Paxson (1992) finds that the income of ricefarmers would increase by 13% if rainfall were one standard deviation above its long-runmean from April to June.2 Much of the income risk faced by these households is likely tobe shared by their neighbors. Local efforts to cope with this risk are unlikely to succeed.3


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common risk that they are confronted with. The desire to be in a good position to insuretheir families should play a smaller role in determining the location of migrants who

remit to households in urban areas.

The next section of the paper discusses the framework and develops implicationsfor the location decisions of migrants who are concerned with diversifying risk. Section3 describes the cross-sectional and the time series data that are used in this exercise. Insection 4 an econometric analysis of the location decisions of remitters is performed, tosee whether they are consistent with a desire to diversify risk. Section 5 concludes. Theresults indicate that insurance motives play an important role in explaining the migration

pattern of remitters. Empirical estimates that include insurance variables perform betterthan estimates that consider only the desire to maximize income as a motive formigration. Remitters are less likely to move to Bangkok the more shocks to Bangkokcovary with the province that they remit to. This is particularly true for rural householdswho are likely to be poorer and to have less access to national level institutions that theycould use to mitigate local risk. In contrast, there is some evidence that migrants fromurban areas are more likely to move to Bangkok the more it covaries with the place theysend remittances to. They have less reason to worry about migrating to deal with local

risk, so they are free to enjoy the efficiency gains associated with higher covariance.

2. Framework

This paper is concerned with understanding patterns of migration in a settingwhere migrants maintain ties to their origin communities by sending remittances that aresensitive to the conditions of the sending and the receiving households. In this settingmigrants will take their future role as remitters into account in making decisions aboutwhere to live. In order to make the discussion more concrete and to motivate the specificform of the estimates that are presented below, consider the expected utility of a familythat is made up of two individuals, the migrant (m) and the non-migrant (n). The migrantand the non-migrant will pool resources regardless of where they live.4

Suppose that the migrant moves to province p and that the non-migrant remains inprovince o (origin). Family resources are equal to the sum of the migrants and the non-

migrants earnings. Total resources will be a function of the characteristics of theindividuals who make up the family and the provinces they live in. There is uncertaintyassociated with both the individual and the provincial components of income. Provincelevel characteristics are common to all individuals who live in the province and the onlyway to mitigate the risk associated with a given province is to be linked to a remitter wholives in another province.5 To summarize, total family resources, Y, can be written:


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,nmopnm wwY +++++=

where wm denotes the expected earnings of the migrant in province p and wn is the

expected earnings of the non-migrant in province o. The variable p represents thecommunity level shock to earnings that is common to everyone who lives in province p.

Similarly, o is the common shock to earnings in province o. Uncertainty in the

individual earnings of the migrant and the non-migrant are captured by m and n,respectively. I assume that community and individual shocks are mean zero, thatindividual shocks are independent of other individual shocks and that they areuncorrelated with community shocks. The variances of the individual shocks are given

by 2

m and 2

n. The variance of the shock to province p is represented by 2

p and 2

o isthe variance of shocks to province o. In contrast to individual shocks, the shock to province p may be correlated with the shock to province o. The covariance between

shocks to provinces p and o is denoted by p,o.

Earnings are a function of an individual's characteristics, which are inelasticallysupplied to the labor market in the region that they live in. Each individual has a j x 1vector of characteristics, X. Some characteristics may be common to both the migrant

and the non-migrant; others may be specific to the individual: age, sex, education andability.6 Production functions, F, are assumed to be constant returns to scale in individualcharacteristics and are allowed to vary by region (North, Northeast, Central, South andBangkok).7 Specifically, expected earnings in region r are equal to:

).(XFw r=

The familys utility is equal to the sum of the expected utilities of the migrant and

the non-migrant: [1]])()(E[ nm cvcv +

where cm and cn represent the consumption of the migrant and the non-migrant. I assumethat it is optimal for the family to divide total resources equally among the migrant andthe non-migrant, so that the consumption of both the migrant and the non-migrant isgiven by:

)(2

1pnmopnmnm Dwwccc +++++===

The variableDp denotes the cost of sending the migrant to province p, as well as the costsassociated with splitting the family up. In general,Dpshould be an increasing function ofdistance, since the cost of moving and the cost of maintaining ties to family memberswho have moved is increasing in distance.


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The empirical work focuses on estimating a version equation [1] that assumes thatutility functions are quadratic. This assumption means that families only care about the

mean and the variance of consumption. If shocks to earnings were normally distributed,then utility would depend only on first and second moments.8 While mean-varianceutility provides only a rough approximation of utility functions that depend on highermoments, it is a much richer approximation than the typical assumption in the migrationliterature of risk neutrality. Assume that utility functions for both the migrant and thenon-migrant are given by:

0,2

)( 2


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with their effect on migrants who remit to urban household.9 Banks and insurancecompanies are less active in rural areas and rural households may face more common risk

than urban households, so covariance should be more costly for rural households.

Insurance considerations should play no role in the location decision of migrantswho are the sole source of support for the households they remit to. If the receivinghousehold does not face shocks to the local area because all of their income comes fromremittances, then there is no local risk to insure. Inactive households come close tomimicking this situation. The survey defines inactive households as those who receiveonly property and remittance income. Remitters who support inactive households should

not be affected by insurance concerns in choosing where to live. Only income shouldmatter to them.

It is not clear what effect the variance of shocks to the receiving households province should have on the remitters location choice. On the one hand, householdswho live in areas that have more variable local conditions will have a higher demand forinsurance, so remitters who come from more variable places may be more concerned withinsurance in choosing destinations. On the other hand, the variability of income in the

receiving province will reduce the expected utility from any particular location chosen bythe remitter. This is the effect that is captured in equation [2]. In addition, the effect ofmay differ for rural and urban households. If urban households have greater access toinstitutions to diversify local risk, then variance will be less of a factor in their choice oflocation, compared to rural households who may not have access to these institutions.

The migration literature emphasizes network and information reasons inexplaining why people from the same area often migrate to the same place (see Masseyet. al., 1993, for example). The framework outlined above provides an alternativeexplanation. If Bangkok is a good insurance match for your household (from the point ofview of diversifying common local risk), then it will also be a good insurance match foryour neighbors.

3. DataThe implications discussed above are evaluated using cross-sectional data from

the 1988 Thai Socio Economic Survey (SES) combined with time series information onrainfall and gross domestic product for each of Thailands 73 provinces over the period1978 1987. The Thai SES records data for approximately 11,000 households in 1988.The survey includes detailed consumption and income information for each of the


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If a surveyed household sends a remittance, the value of the transfer, how it was

delivered and whether it was for educational purposes are recorded. In addition, thesurvey reports the receiver's province, occupation, industry, community type (rural,urban, foreign), as well as the relationship of the sender and the receiver. Unfortunately,the income of the receiving household (other than the amount of the remittance) is notrecorded. There are similar data about the remitter if someone in the household receivesa transfer. If the surveyed household receives a transfer, the total income of the sendinghousehold is not known, although the province, occupation, industry, community typeand relationship of the sender and the receiver are recorded.

Table 1 provides a summary of the data depending on whether or not the surveyhousehold sent a remittance, received a remittance, did both, or did neither in the yearprior to the survey. The income of households who send remittances is nearly twice thatof households who receive remittances. In addition, the transfers recorded in the SESflow from households who are headed by people who have three years more schoolingand are ten years younger than the heads of recipient households. Table 1 also describesthe regional and occupational distribution of the sample by remittance status. Receivers

are over-represented in the very poor northeastern region of Thailand, while remitters aremore likely to live in Bangkok. Remitting households are more likely to live in urbanareas (55%), compared to receiving households (32%). Remitters are also more likely tobe entrepreneurs and professionals than are households who receive transfers. Receivinghouseholds, on the other hand, are likely to farm or be economically inactive.10

Table 2 documents the importance of remittances in supplementing the income ofreceiving households. Remittances account for 33% of the total income (that is,including remittances) of receiving households, and remittances account for about 16% ofsending households' total income. Most remittances flow from children to their parents.58% of households who receive remittances report that the sender was their son ordaughter. Fifty-four percent of households who send remittance send them to their parents. Almost 60% of remittances were delivered in person. This supports using thedistance between the giving and the receiving province as a proxy for the cost ofmigrating.

While remitters report doing so to help pay for educational expenses more than30% of the time, only 9% of receiving households report that the remittance was intendedfor this purpose. This is likely to be a feature of who was included in the sample, ratherthan evidence of moral hazard. The number of people who actually receive remittancesfor educational purposes is likely to be much higher than the percentage reported in the


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purposes is consistent with the fraction received from parents (in the case of householdswho received a remittance) and with the fraction of households giving to sons or

daughters (in the case of households who gave a remittance).11

The picture that emerges from these two summary tables is that remittances are primarily old-age support. They flow from young, relatively well-educated, urbanindividuals with high incomes to their older, less-educated, low-income parents who livein rural areas. Miller and Paulson (1999) document that this support of the elderlythrough remittances is coupled with insurance: remittances are larger when the receiving province or the receiving household experience bad times. The fact that remittances

appear to provide old-age support is important for this paper because it means that we donot need to be overly concerned with sample selection issues.

The sample selection problem of concern is that the survey only providesinformation about the province that a household is linked to when the surveyedhousehold sent a remittance at some point in the 12 months prior to the survey. Ifhouseholds only send remittances when they experience a good shock and the family thatthey support receives a bad shock, then the sample of remitters will consist

disproportionately of people who live in places that covary very little (or negatively) withthe places they remit to.12 Because most observed remittances appear to provide old-agesupport and are therefore likely to flow consistently from remitting households toreceiving households, the potential for sample selection problems is mitigated. Ratherthan determining whether a household sends a remittance, shocks to the income of thesending and the receiving household will determine the size of the transfer. In any case,to the extent that the sample of remitting households is affected by selection, this effect islikely to make it harder to find evidence that location decisions are systematicallyinfluenced by the insurance characteristics of potential destinations. The robustness ofthe results to sample selection effects is also investigated by analyzing location choices ofa particularly selected sample households that sent remittances in the month prior to thesurvey.

In addition to providing information on remitters and remittees, the SES identifieshouseholds who have changed "amphoes" (a geographic unit similar to a county in the

United States) in the last ten years. I call these households "migrants". The earnings ofmigrants who live in Bangkok areas are used to estimate expected earnings in Bangkokfor remitters. The percentage of urban migrants in Bangkok who remit is substantial,more than 60%, suggesting that they are a good comparison group. Like remitters, the

11The empirical work treats all remittance the same regardless of whether they were sent for educational


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heads of urban migrant households tend to be young and highly educated (relative to theirnon-migrating counterparts), although urban migrants are somewhat younger than

remitters.

Time series data on rainfall and gross domestic product are used to estimate thecovariance pattern of shocks to provincial income. Annual provincial per capita grossdomestic product data for 1978 to 1987 from the Office of the National Economic andSocial Development Board, Office of the Prime Minister and annual observations of provincial rainfall over the same period are used to create estimates of the variance-covariance matrix of provincial income shocks. The rainfall data come from the

Meteorological Department of the Ministry of Communications, which measures rainfallat 61 meteorological stations.13 Rainfall is an important predictor of income, especiallyfor agricultural households. In 1988, 66% of the Thai labor force was employed inagriculture according to the Thai Labor Force Survey, and agriculture made up 17% ofGDP.

One concern is that rainfall shocks may not be a good measure of incomeuncertainty in Bangkok and other urban areas. But it turns out that GDP in Bangkok is

correlated with the previous years rainfall. It appears that additional rainfall boosts theharvest and shows up in Bangkok incomes in the following calendar year. One check onthe reliability of the provincial GDP data is to look at how it is related to provincialrainfall. When provincial GDP is regressed on rainfall, controlling for year and regioneffects, the results indicate that mean per capita provincial GDP would increase by 17%if rainfall were one standard deviation above its mean. This regression has an adjusted R2

of 57%. Using the same rainfall data and observations on household income from theThai SES, Paxson (1992) finds roughly the same relationship between rainfall and theincome of rice farmers: their mean income would increase by 13% if rainfall were onestandard deviation above the mean from April to June.

Annual rainfall shocks are constructed by subtracting the long run average foreach province from each annual observation. I use a variety of methods to calculate GDPshocks for each province. The GDP covariances used in the analysis are based on theresiduals from an ar(1) regression of GDP for each province. With only ten years of GDP

data available for each province, the potential for over fitting the data is very real. Thear(1) regressions seem like a reasonable compromise which allows for meaningful trendsin GDP as well as for sufficient flexibility. In any case, the results are robust toalternative methods of constructing GDP shocks. Covariances calculated from theresiduals of province by province regressions of GDP on a constant term and a lineartime trend deliver the same results. As do covariances that are calculated from the


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Distance data are also collected at the provincial level. The road distance inkilometers between the capital of the remittee's province and Bangkok is used to proxy

for the cost of moving to Bangkok. The source of this data is a kilometer chart on a mapof Thailand.

4. Empirical AnalysisIn order to evaluate whether the location choices of remitters are consistent with

the implications of insurance motivated migration, I consider how the decision to move toBangkok is related to the variables that are suggested by equation [2]. The idea is that the

remitter will be more likely to move to Bangkok the higher the expected utility theextended family enjoys when the remitter is in Bangkok, where utility depends on thevariance and covariance of income as well as on its magnitude. Bangkok is a majormigrant destination. Thirty-five percent of remitting households live in Bangkok,compared to only 18% of households who neither send nor receive remittances. Moststudies report that migration to Bangkok is motivated primarily by the desire to improveincome (see Adulavidhaya and Onchan, 1985, for example). Bangkok accounts forapproximately 30% of Thai GDP and is the only major city in Thailand, with close to 6

million people (or 10% of the Thai population) living in the city and surrounding suburbsin 1988. The next largest city, Chiang Mai, had approximately 164,000 residents in1988. Since Bangkok is the focus of rural to urban migration in Thailand and the desireto raise incomes clearly plays a role in why people move there, it is particularlyinteresting to see whether the decision to move to Bangkok is influenced by insuranceconsiderations.

The expected utility of the extended family if the remitter lives in Bangkok is notobservable. Instead, the remitters decision to live in Bangkok is observed. Let B beequal to one if moving to Bangkok maximizes the familys expected utility, otherwise Bwill equal 0. Then the probability of observing B equal to one for household i can berepresented by:

)(1

)()1(

i

ii

ZF

ZPBP

=

>==

where I assume that the error term, i, is distributed log Weibull, and is due to householdheterogeneity. The function F is the cumulative logistic function, Z is the vector ofindependent variables, and is the vector of parameters to be estimated. Given theseassumptions, the maximum likelihood logit model can be used to estimate the probabilityof moving to Bangkok 14


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The independent variables are chosen to mimic the utility of having the remitter inBangkok under the assumption of quadratic utility (equation 2). They include variables

that capture the potential income gains and the costs of having the remitter live inBangkok: an estimate of expected earnings in Bangkok for the remitter and the distancein kilometers between the receiving households provincial capital and Bangkok. Thequadratic utility assumption also dictates including quadratic terms in these variables andtheir interactions with one another. Higher expected income in Bangkok will increase thelikelihood that the remitter moves to the capital, although at a decreasing rate. Similarly,the greater the distance between Bangkok and the receiving households province, theless likely it will be that the remitter moves to Bangkok. This effect should diminish as

distance increases.

The expected income of the receiving household also plays a role in determiningthe utility of having the remitter in Bangkok. The greater the income of the receivinghousehold, the greater the utility of having the remitter live in Bangkok. This utility isdecreased by the interaction between the remitters expected income and the receivinghouseholds expected income. The expected utility of having the remitter in Bangkok isincreasing in the interaction of the distance between Bangkok and the receiving

households province and the receiving households expected income. For a givenfamily, the expected income of the receiving household will be fixed regardless of wherethe remitter lives. However, this variable will vary in the cross-section. The estimatedcoefficients for these variables will indicate to what extent people who remit to higherincome households are more likely to move to Bangkok, and the degree to which thislikelihood increases when Bangkok is further from the place they remit to and decreaseswhen their expected earnings in Bangkok are high.

The estimates of who moves to Bangkok also include insurance variables thatcapture the effect of the variance and the covariance of province level income shocks tothe remitting and the receiving households. Depending on the specification, the estimatesinclude either the estimated variance of rainfall or GDP in the receiving households province and the covariance of either rainfall or GDP shocks to Bangkok and thereceiving households province. The more Bangkok covaries with the receivinghouseholds province the more difficult it will be to diversify provincial level risk by

living in Bangkok, so greater covariance should be associated with a lower probability ofmoving to Bangkok. If the assumption of quadratic utility is correct, we expect that themore variable income in the receiving households province is the lower the utility ofhaving the remitter living in Bangkok. Equation [2] suggests that the variance of shocksto Bangkok will also be important and that the more variable income in Bangkok is theless likely people will be to move there. Clearly this variable will be the same for all


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particular location, it should be possible to smooth them locally. If this is the case, theirvariance will not play a role in determining who moves to Bangkok.

In addition to the variables suggested by equation [2], the estimates also includecontrols for the receiving households province. These control variables are added toensure that the covariance and variance terms really capture the potential for providinginsurance by having the remitter live in Bangkok, rather than some other characteristic ofthe receiving province. The addition of the province controls means that the estimatedcoefficients measure the effect of the independent variable on the likelihood of moving toBangkok, relative to the average likelihood that someone who remits to a particular

province will move to Bangkok.

A number of the independent variables are estimated: the expected income of thereceiving household, the remitters expected income in Bangkok, the variance of shocksto the receiving province and the covariance between Bangkok and the receiving province. The procedure for estimating provincial variances and covariances fromrainfall and GDP data is described in the previous section. Estimates of the expectedincome of the receiving household are based on regressions presented in Table 3. For

each region, the per capita monthly income (net of remittances) of receiving householdsis regressed on characteristics of the household head: age, age squared, years of schoolingand years of schooling squared. The estimates also include controls for whether or notthe household farms and whether they live in an urban area. The regression results areused to create measures of expected income for each receiving household. Next theseexpected income measures are averaged by province and community type (urban orrural). This procedure delivers a measure of the expected income of the receivinghousehold for each province and community type that respects the distribution ofcharacteristics among households who receive remittances across provinces andcommunities. For each household who sends a remittance, the information about theprovince and the community type of the receiving household determines the measure ofthe expected income of the receiving household that is used in the analysis.

The regression presented in Table 4 is used to estimate remitters expectedearnings in Bangkok. The per capita income of economically active, male-headed

households who have migrated to Bangkok in the past 10 years is regressed on the age,age squared and years of schooling of the head of the household. Expected earnings inBangkok are calculated using the coefficients from this regression together with therelevant characteristics of the head of the remitting household.

Because the logit estimates of whether remitters will move to Bangkok include


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and then estimating who will move to Bangkok for the bootstrap sample of remitters.15

The standard errors are not corrected to account for the estimation of the insurance

variables. This is equivalent to assuming that the agents in the model have access to thesame data about rainfall and GDP that is used in the analysis and that they use the sameprocedure that I do to come up with estimates of the variance and covariance terms.

Four estimates of the probability of moving to Bangkok are presented in Table 5.The first two estimates (columns 1 and 2) use GDP shocks to calculate variances andcovariances. The third and fourth estimates (columns 3 and 4) use covariance andvariance measures that are based on rainfall shocks. The first estimate in Table 5

indicates that remitters are significantly more likely to move to Bangkok the higher theirexpected income in the capital and that this effect diminishes as expected income getshigher. Remitters are also more likely to move to Bangkok the higher the expectedincome of the receiving household. This effect also diminishes as the expected income ofthe receiving households goes up. Distance does not play a significant role indetermining who moves to Bangkok, nor do the varia bles that interact sending andreceiving household expected income and distance.16 In this specification, whichmeasures variance and covariance based on an the residuals of an ar(1) regression of

GDP for each province, the insurance variables do not have a statistically significanteffect on the decision to move to Bangkok.

The second estimates for GDP and rainfall (columns 2 and 4) allow thecoefficients on the insurance variables to vary depending on whether the remittersupports a household who lives in an urban or a rural community. The idea is thatinsurance concerns may be more important for remitters who support households in ruralareas for at least three reasons. First, rural households in the same community may facemore covariant risk than urban households because rural agricultural income is especiallysensitive to variations in weather that influence all local crops. Second, rural householdsare likely to have less access to institutional/market mechanisms that could be used todiversify provincial risk than their urban counterparts. Finally, rural households arelikely to be poorer than urban households and therefore rural households will be lessequipped to self-insure through savings or by buying and selling assets. The findings thatare reported in column 2 support this story. Remitters who give to rural areas are

significantly less likely to move to Bangkok the more it covaries with the province theyremit to (the coefficient is less than zero at an 8.5% significance level, based on a one-sided test). Although the coefficient is not significant, remitters who support urbanhouseholds appear to be more likely to move to Bangkok the more it covaries with theprovince they remit to. This suggests that the covariance of GDP between the sendingand the receiving province may measure positive aspects of moving to a place that is


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households. The likelihood of moving to Bangkok is not significantly affected by thevariance of GDP in the receiving households province, regardless of whether the

receiving household is rural or urban. The magnitude and the significance of the othervariables do not change when the effect of the insurance variables can differ dependingon rural/urban status of the receiving household.

When we use rainfall shocks to calculate the variance and the covariance terms,the insurance effect is important and significant even when there are not separatecoefficients for rural and urban receivers (column 3 of Table 5). In this specification,remitters are significantly less likely to move to Bangkok the more it covaries with the

province they remit to, as we would expect if insurance motives play a role in themigration decisions of remitters. The effect of the receiving households rainfall varianceis not significantly different from zero. Using rainfall data to calculate variances andcovariances does not change the impact of most of the other explanatory variablescompared to the estimate that uses GDP data to calculate the insurance terms. Oneexception is the effect of distance. While the coefficients on distance and distancesquared remain insignificant, their signs conform to the predictions of the model in thisestimate: remitters are less likely to move to Bangkok the further it is from the place they

remit to and this effect diminishes with distance. In general, rainfall has more stable timeseries properties than GDP does, and rainfall measures are also less likely to be affectedby measurement error. These attributes may make covariances based on rainfall shocksmore desirable. On the other hand, the covariance terms are meant to capture thecovariance of income shocks between provinces. Rainfall has only an indirect effect onincome and the importance of rainfall for income will vary depending on the location andthe occupation of a particular household.

The last column of Table 5 presents estimates of the likelihood of moving toBangkok that use rainfall shocks to calculate variances and covariances and that allow theeffect of the insurance variables to depend on whether the receiving household lives in arural or an urban area. This estimate indicates that both remitters who support ruralhouseholds and remitters who support urban households are less likely to move toBangkok the more it covaries with the province that they remit to. One possibility is thatthe desirable similarity of provinces that seems to be picked up by covariances that areestimated from GDP shocks is not reflected in rainfall covariances.

The results discussed so far suggest that the desire to diversify provincial levelrisk plays an important role in determining who moves to Bangkok, particularly forremitters who support rural households. The next issue to consider is the importance ofinsurance considerations relative to the desire to improve income by migrating. The


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(or 1,500 baht), the likelihood of moving to Bangkok increases by 22%, this is a 73%increase over the average predicted probability of moving to Bangkok of 30%. This

increase is mitigated by the effect of increases in expected income operating throughexpected income squared (-18%) and the interaction of expected income in Bangkok withdistance (-5.5%). However, these changes are not measured very precisely.17 The impactof a one standard deviation increase in the expected income of the receiving householdsincome has about one-half the effect of the same increase in the expected income of theremitting household. If the receiving households income increases by one standarddeviation (or 2,849 baht), the remitter will be 9.5% more likely to move to Bangkok. Themagnitude of these effects does not depend on whether GDP or rainfall shocks are used

to measure variances and covariances.

For a remitter who supports a rural household, a one standard deviation increasein the covariance between GDP shocks to the receiving province and Bangkok decreasesthe likelihood of moving to Bangkok by 3%, a 10% decrease in the average predictedprobability of migrating to the capital. This is equivalent to decreasing expected incomein Bangkok by 200 baht per month, or 6% of average expected income. The covariance between Bangkok and the receiving households province has no significant effect on

households who remit to urban households, when GDP shocks are used. The varianceterms are also insignificant in this specification. When the variance and covariance termsare derived from rainfall shocks, a one standard deviation increase in the covariance between rainfall shocks in Bangkok and the receiving province substantially decreasesthe likelihood of moving to Bangkok for all remitters, regardless of whether the remit torural or urban households. For households who remit to rural households, the probabilityof moving to Bangkok decreases by 7.3%, for remitters who support urban householdsthe probability declines by 7.8%. A 500 baht reduction (15%) in average expected

income in Bangkok would decrease the likelihood of moving to the capital by roughly thesame amount. In the rainfall specification, the variance of the receiving households province plays a significant role in determining whether remitters who support ruralhouseholds will move to Bangkok. A one standard deviation increase in rainfall varianceincreases the likelihood that these remitters will migrate to Bangkok by 4%. A 300 bahtincrease in expected income in the capital would have the same effect.

Having established that insurance concerns play an important role in themigration decisions of remitting households, from both a statistical and an economic perspective, I now consider whether adding the insurance variables significantlyimproves the predictive power of the estimates. Table 7 presents two estimates of thelikelihood of moving to Bangkok, neither of which includes insurance variables. Theindependent variables in the first estimate correspond to an assumption of risk neutrality.


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Bangkok is about 275% bigger in the estimate for inactive receivers compared to theestimate for active receiving households.

The third column in Table 8 presents estimates of the likelihood of moving toBangkok for a sample of remitters that excludes households who send remittances topeople who live in Bangkok. The concern is that urban means something different inBangkok compared to outside the capital. Given the difference in population betweenBangkok and the next largest city, I want to make sure that households who receiveremittances and live in Bangkok are not somehow driving the results. This does not seemto be the case. In this specification households who remit to rural households are also

less likely to move to Bangkok the more it covaries with the province they remit to. Infact, the estimate that excludes receiving households who live in Bangkok, produces alarger and more significant coefficient compared to Table 5, specification [2].

The last estimate in Table 8 considers the impact of sample selection. Thisestimate includes only households who sent a remittance in the month prior to the survey.The criteria for being included in all of the other estimates that have been discussed so faris to have sent a remittance at some point in the 12 months prior to the survey.

Approximately 68% of the households who sent a remittance in the 12 months prior tothe survey are included in the more selected sample of households who sent a remittancein the month prior to the survey. It is not clear that sample selection will significantly bias the results (see the discussion in Section 3 for more details). However, if sampleselection is playing an important role, then the findings should change significantly whenonly the more selected sample is considered. The estimates indicate that the results arenot sensitive to sample selection. Among the more selected sample, remitters whosupport rural households are significantly less likely to move to Bangkok the more it

covaries with the province they remit to. If anything, this finding seems to be larger anda bit more significant for the more selected sample or remitters.

5. Conclusions

This paper examines whether insurance motives play an important role in

explaining the location choices of migrants who support other households throughremittances. The results indicate that insurance motives do influence migration patterns inimportant ways. Empirical estimates that include insurance variables perform better thanestimates that only consider income maximization. Remitters are significantly less likelyto move to Bangkok the more it covaries with the province they remit to. The effect iseconomically substantial a one standard deviation increase in the covariance of income


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These findings are robust to alternative methods of measuring the covariance of

income shocks and to sample selection. Perhaps even more convincingly, the insurancevariables do not significantly effect the migration decision of a group of remitters forwhom insurance considerations should not be important. There is some tentativeevidence that migrants who remit to urban households may find locations that covarymore with the province they remit to more attractive. This suggests that measures of thecovariance of income shocks may also capture other aspects of similarity between two places that might translate into utility or income gains for the migrant. This findingseems to depend on how income shocks are measured. When income shocks are

measured using rainfall instead of GDP, remitters who support urban households are alsoless likely to move to Bangkok the more it covaries with the province they remit to.

The evidence suggests that migration flows from rural areas will be sensitive toincreases in local opportunities for insurance. Migration flows to Bangkok, for example,might be substantially higher if insurance concerns did not affect the decision to move toBangkok. According to the estimates described above, the average predicted probabilityof moving to the capital would increase from 30% to 50% if the covariance of rainfall

with the Bangkok were zero for all provinces. Given the number of internal andinternational migrants who support family members in their origin communities wherethere are only limited sources of insurance, the findings presented in this paper indicatethat insurance motives may be an important factor in explaining migration patterns moregenerally.


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References

Adulavidhaya, Kamphol and Tongroj Onchan. Migration and Agricultural

Development of Thailand: Past and Future. in Hauser et. al. eds., Urbanizationand Migration in ASEAN Development, Tokyo: National Institute for ResearchAdvancement, 1985.

Becker, Gary.A Treatise on the Family. Cambridge, Massachusetts: Harvard UniversityPress, 1981.

____________ and Bronars, S. G. "Immigration and the Family." Journal of Labor

Economics 9(April 1991):123-148.

Cochrane, John H. "A Simple Test of Consumption Insurance." Journal of PoliticalEconomy 99(1991):957-976.

Cox, Donald and Jimenez, Emmanuel. "Achieving Social Objectives Through PrivateTransfers, A Review." The World Bank Research Observer. Volume 5, no. 2 (July1990), pp. 205-18.

Da Vanzo, Julie. "Why Families Move." RAND Report R-1972-DOL. Santa Monica,CA. 1976.

Deaton, Angus. "On Risk, Insurance and Intra-Village Consumption Smoothing."Manuscript, Princeton University, 1990.

Feder, Gershon. et. al. Land Policies and Farm Productivity in Thailand. Baltimore:Johns Hopkins University Press, 1988.

Grimard, Franque. "Household Consumption Through Ethnic Ties: Evidence fromCOTE D'IVOIRE." Manuscript, Princeton University, 1992.

Heckman, James and Schienkman, Jose. "The Importance of Bundling in a Gorman-Lancaster Model of Earnings." The Review of Economic Studies (April 1987):243-

256.

Katz, E. and Stark, Oded. "Labor Migration and Risk Aversion in Less DevelopedCountries."Journal of Labor Economics 4(1986):134-149.

Lucas Robert E B and Stark Oded "Motivations to Remit: Evidence from Botswana "


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McFadden, Daniel. "Econometric Analysis of Qualitative Response Models," in Z.

Griliches and M. Intrilligator, eds., Handbook of Econometrics, Vol. 2,Amsterdam: North Holland, 1984.

Mincer, Jacob. "Family Migration Decisions." Journal of Political Economy 86(October1978):749-773.

Miller, Douglas and Anna Paulson. Informal Insurance and Moral Hazard: Gamblingand Remittances in Thailand. Manuscript. Northwestern University, 1999.

Morduch, Jonathan. "Consumption Smoothing Across Space: Tests for Village-LevelResponses to Risk." Manuscript. Harvard University, 1990.

Newey, Whitney. "A Method of Moments Interpretation of Sequential Estimators."Economic Letters 14 (1984): 201-206.

Paxson, Christina H. "Using Weather Variability to Estimate the Response of Savings to

Transitory Income in Thailand."American Economic Review 82 (March 1992)15-33.

____________. "Consumption and Income Seasonality in Thailand."Journal of PoliticalEconomy (1993).

Rashid, Mansoora. "Rural Consumption: The Evidence from Pakistan." Dissertation,University of Chicago, 1990.

Rosen, Sherwin. "Linear Skill Aggregation."Journal of Human Resources (1982).

Rosenzweig, Mark R. and Stark, Oded. "Consumption Smoothing, Migration andMarriage: Evidence from Rural India." Journal of Political Economy97(1989):905-926.

Russell, Sharon S. "Migrant Remittances and Development." International Migration(1992) 30(3):267-287.

Sandell, Steven H. "The Economics of Family Migration." NLS Report on Dual Careers,Volume 4. Columbus, Ohio: Center for Human Resources Research. December,1975.


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Table 1: Characteristics of Sample Households, by Remittance Status

Give = 0,Get = 0

Give = 0,Get = 1

Give = 1,Get = 0

Give = 1,Get = 1

# Household% of Sample

7149(64.73)

2174(19.68)

1503(13.61)

219(1.98)

Characteristics of Household

Age of head 44.01(14.31)

51.05(16.77)

39.42(12.66)

46.00(15.31)

Education of head(years)

5.23(4.02)

4.84(4.22)

7.80(5.01)

7.21(5.25)

Income 1543.36(1986.06)

1561.49(1679.84)

3023.64(2617.24)

2593.08(1847.82)

Size 4.11(1.76)

3.60(1.82)

3.31(1.73)

3.59(1.83)

% Male head 82.67 58.37 83.97 63.01% Urban 32.49 31.92 54.96 42.92% Migrant 20.32 17.34 40.59 31.05Regional Distribution (Percent)North 22.24 22.08 14.57 15.98

Northeast 21.99 29.25 16.63 29.22Central 21.11 20.29 18.16 14.16South 16.97 10.63 15.97 12.33Bangkok 17.69 17.76 34.66 28.31Occupational Distribution (Percent)Farmers 34.33 23.18 12.77 17.35Entrepreneurs 19.61 11.73 22.69 14.61Professionals 7.02 4.65 17.10 20.09Laborers 11.06 6.72 2.86 1.37

Other Employees 25.07 16.47 41.92 24.66Inactive 2.91 37.26 2.66 21.92

Notes: "Give" means someone in the household reported giving a cash or in-kind remittance during the 12 months preceding the survey. "Get" means someonein the household reported receiving a remittance during the 12 months preceding the survey. Income is in 1988 per capita (standardized using adult maleequivalents) Thai baht per month (25 baht = $1). "Migrant" means the household has changed amphoes (county) in the last ten years. Standard deviations are inparentheses.


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Table 3: Income Estimates for Receiving Households

North Northeast Central South Bangkok

s.e. s.e. s.e. s.e. s.e.

Age of Head 26.080 20.801 22.546 37.206 21.555 15.975 81.884 29.352 115.728 30.568

Age of Head2 -0.187 0.197 -0.071 0.361 -0.216 0.146 -0.534* 0.278 -0.859 0.310

Years of Schooling, Head 43.901 44.026 118.232 91.212 -41.241 33.950 41.822 66.865 139.687 65.817

Years of Schooling, Head2 3.642 2.582 3.276 5.377 8.925 2.188 5.254 3.822 0.721 3.491

Farm (=1 if farm HH) -52.769 148.754 221.574 199.898 285.454 113.367 -146.676 232.558 536.371 613.387

Urban (=1 if urban HH) 534.140 151.043 -300.100 284.547 319.879 105.627 330.078 231.858 410.780 312.447

Constant -142.043 554.050 -1065.619 984.415 344.926 432.864 -2270.412

855.014 -2238.531

807.418Adjusted R2 9.6% 2.5% 10.9% 6.3% 9.7%

Number of Observations 660 850 626 353 563

The symbols *, , and indicate that the coefficient is significantly different from zero at at least the 10 percent, 5 percent and 1 percent level, respectively.

Table 4: Estimates of Income in Bangkok

Active, Male-headed Households who have Migrated to Bangkok in Past 10 years

Bangkok

s.e.

Age of Head 115.033 46.729

Age of Head2 -1.128* 0.599

Years of Schooling, Head 296.102 19.339

Constant -1532.503* 858.576

Adjusted R2 28.3%

Number of Observations 641

The symbols *, , and indicate that the coefficient is significantly different from zero at at least the 10percent, 5 percent and 1 percent level, respectively.


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Table 5: Estimates which Include Insurance Variables

Logit Estimates of the Probability of Locating in Bangkok, Remitting Households

GDP shocks Rainfall Shocks

[1] [2] [3] [4]

s.e. s.e. s.e. s.e.

E[Income], Remitter in Bangkok 706.700 356.600 709.700 356.300 706.700 356.600 705.900 356.500

E[Income], Receiver 168.200 96.200 167.900 96.200 168.200 96.200 165.100 96.800

Distance to Bangkok 391.000 22849.600 1321.100 22874.800 -5439.200 19103.000 -4716.100 18889.900

E[Income]2, Remitter in Bangkok -0.106 0.046 -0.106 0.046 -0.106 0.046 -0.107 0.046

E[Income]2, Receiver -0.005 0.008 -0.005 0.008 -0.005 0.008 -0.005 0.008

Distance to Bangkok2 -2.550 20.800 -2.960 20.900 0.511 16.000 0.043 15.800

E[Income], RemitterE[Income], Receiver 0.010 0.024 0.010 0.024 0.010 0.024 0.012 0.024

E[Income], RemitterDistance to Bangkok -0.250 0.215 -0.256 0.216 -0.250 0.215 -0.227 0.213

E[Income], ReceiverDistance to Bangkok 0.028 0.116 0.031 0.117 0.028 0.116 0.027 0.118

Covariance of Receivers province and Bangkok -1.460 9.840 . . -29.000* 41.700 . .

Covariance Urban Receiver . . 0.537 10.100 . . -24.100* 41.900

Covariance Rural Receiver . . -12.700 14.000 . . -26.300* 41.700

Variance of Receivers province -1.700 2.340 . . 8.500 21.000 . .

Variance Urban Receiver . . -1.270 2.620 . . 6.390 20.400Variance Rural Receiver . . -1.750 2.670 . . 10.700 20.700

Constant -1609149 2172208 -2045874 2231063 1414703 3441931 966430 3330124

2-test for joint significance of province controls 105.53 102.47 109.57 102.50

Prob > 2 0.00 0.00 0.00 0.00

Adjusted R2 10.1% 10.2% 10.1% 10.2%

Number of Observations 1670 1670 1670 1670

Notes: reported coefficients and standard errors are the estimated ones multiplied by 1,000,000. The reported standard errors are bootstrapped, based on 1,000repetitions. The symbols *, , and indicate that the coefficient is significantly different from zero at at least the 10 percent, 5 percent and 1 percent level,respectively. In addition to the independent variables reported in the table, the estimates also include controls for the province of the receiving household.


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Table 6: Average Change in Predicted Probability of Locating in Bangkok from 1 Standard Deviation Increase in Dependent Variable

GDP shocks

Separate Rural and Urban Effects

Rainfall Shocks

Separate Rural and Urban Effects

From estimate [2], Table 5 From estimate [4], Table 5

change in prob s.e. change in prob s.e.

E[Income], Remitter in Bangkok 0.220 0.100 0.219 0.100

E[Income], Receiver 0.095 0.053 0.093 0.053

Distance to Bangkok 0.088 0.298 -0.210 0.275

E[Income]2, Remitter in Bangkok -0.182 0.048 -0.182 0.048

E[Income]2, Receiver -0.048 0.036 -0.049 0.037

Distance to Bangkok2 -0.152 0.271 0.003 0.253

E[Income], RemitterE[Income], Receiver 0.030 0.069 0.035 0.070

E[Income], RemitterDistance to Bangkok -0.055 0.041 -0.049 0.041

E[Income], ReceiverDistance to Bangkok 0.009 0.033 0.008 0.033

Covariance Urban Receiver 0.005 0.058 -0.078* 0.089

Covariance Rural Receiver -0.027 0.025 -0.073* 0.073

Variance Urban Receiver -0.018 0.033 0.022 0.058

Variance Rural Receiver -0.029 0.033 0.039* 0.064

Average Predicted Probability of Locating in Bangkok 0.303 0.013 0.303 0.013

The reported standard errors are bootstrapped, based on 1,000 repetitions. The symbols *, , and indicate that the change in probability is significantlydifferent from zero at at least the 10 percent, 5 percent and 1 percent level, respectively.


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Table 7: Estimates which Exclude Insurance Variables

Logit Estimates of the Probability of Locating in Bangkok, Remitting Households

Linear Model Quadratic Model

[1] [2]

s.e. s.e.

E[Income], Remitter in Bangkok -115.000 61.200 706.300 357.200

E[Income], Receiver 129.400 28.400 167.900 96.200

Distance to Bangkok -2261.500 7598.200 -3335.900 18413.000

E[Income]2, Remitter in Bangkok . . -0.106 0.046

E[Income]2, Receiver . . -0.005 0.008

Distance to Bangkok2 . . 0.076 17.200

E[Income], RemitterE[Income], Receiver . . 0.011 0.024

E[Income], RemitterDistance to Bangkok . . -0.258 0.215

E[Income], ReceiverDistance to Bangkok . . 0.031 0.116

Constant 247687.900 6153185.000 -594031.100 1513631.000

2-test for joint significance of province controls 113.91 109.73

Prob > 2 0.00 0.00

Adjusted R2 9.0% 10.1%

Likelihood Ratio Test v. Estimate [2], Table 5

2-statistic 28.19 5.59

Prob > 2 0.0001 0.0181

Likelihood Ratio Test v. Estimate [4], Table 5

2-statistic 28.35 5.75

Prob > 2 0.0001 0.0165

Notes: reported coefficients and standard errors are the estimated ones multiplied by 1,000,000. The reported standard errors are bootstrapped, based on 1,000repetitions. The symbols *, , and indicate that the coefficient is significantly different from zero at at least the 10 percent, 5 percent and 1 percent level,

respectively. In addition to the independent variables reported in the table, the estimates also include controls for the province of the receiving household.


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Insurance Motives for Migration: Evidence from Thailand

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