migrant social capital and education in migrant-sending areas of bangladesh: complements or...

8/8/2019 Migrant Social Capital and Education in Migrant-Sending Areas of Bangladesh: Complements or Substitutes?

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Migrant Social Capital and Education in Migrant-Sending Areas of

Bangladesh: Complements or Substitutes?

(Manuscript)

Randall S. KuhnUniversity of ColoradoInstitute of Behavioral Science*

484 UCB

Boulder, CO 80309-0494

[email protected]

Jane A. Menken

University of ColoradoInstitute of Behavioral Science / Department of Sociology

Boulder, CO [email protected]


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Abstract

This paper studies the role of migrant social capital on children s education in Matlab, an

area of rural Bangladesh with high rates of rural-urban and international out-migration,

and high dependence on urban-rural and international financial transfers. A primary point

of focus is the role of social capital, or origin-area connections to current destination-area

residents, as complements or substitutes for investments in childrens education. Past

research shows how investments in childrens human capital act as a substitute for

retirement insurance in developing societies. In areas of high out-migration, however,

high social costs and risks associated with migration may reduce the parents perceived

marginal returns to educational investment. The current analysis combines household

survey data with a series of demographic surveillance data, predicting education among

current children in terms of past migration experience at the village level. We find that a

history of male migration in the village increases the likelihood of parental investment in

girls education, yet has no effect on investment in the education of boys, who are the

group most likely to actually migrate. These effects persist in the presence of controls for

household assets, which are likely to rise with the increased practice of migration by

members of the household or village. Girls in high out-migration villages achieve parity

with boys in terms of completing primary school, but remain significantly less likely to

complete secondary school. Although migrant social capital encourages parents to

allocate more of their household educational budget to girls schooling, household

budgets, as determined by assets, still play a larger role in determining daughters

schooling investments.


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I. IntroductionPast research has demonstrated the role of investments in childrens human capital as a

form of retirement insurance in societies that have achieved rapid economic growth and

high returns to human capital, yet have few formal social insurance mechanisms (Becker

1991; Lillard and Willis 1997; Willis 1982). In rapid-growth settings such as Taiwan in

the post-War era, increasing human capital investment encouraged further economic

productivity growth, leading to a cycle of simultaneous growth in investment and

productivity (Lee et al. 1994). In the case of Taiwan, rapid growth also facilitated a

complete quality-quantity tradeoff in investment in children, with fertility declining to

replacement-level in a single generation, and a complete transition from a predominantly

rural society to an overwhelmingly urban one.

For societies with less rapid economic growth such as Bangladesh, these tradeoffs

are clearly not complete, and individuals must make difficult economic choices that allow

to simultaneously pursue economic innovation while also minimizing risk. In

Bangladesh in the last generation, fertility has declined rapidly from 6.5 children per

woman to 3.5. Education levels have risen rapidly for both men and women, with

women beginning to close the gap with men (Figure 1). Following the male education

deficit associated with the 1971 Liberation War, the school cohorts of study in this paper

(20-25 years olds) will likely represent some of the last cohorts to display prominent

gender differences in education. Urbanization has also continued apace, with the

proportion of population living in cities expanding from 5% in 1960 to 24% today. None

of these transitions is complete, however, and extensive research suggests that all are

linked to one another. A body of evidence suggests that economic growth must add fuel

to this process.


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This paper addresses Bangladeshs incomplete educational transition, attempting

to measure the impact of migration experience, knowledge and exposure in a village on

parental efforts to educate their children. The research is conducted in the context of

educations complex role education as a potential outlet for investments in old age

support, as an engine of urban economic development, and as a precious resource that is

subject to sometimes extreme budget constraints. It focuses on two basic questions that

lie at the heart of national efforts at both rural and urban development. One is a simple

question that is not asked often enough: does migration affect educational investment?

The other is a difficult question that has no easy answers: does migrant social capital

enhance or supersede the incentive to educate children?

A first set of analyses will model the overall impact of migration experience on

childrens educational investments. These models will first address the impact of

migration on parental budget constraints. Does the economic impact of migration simply

make it possible to provide more education for children? Further refinement of this

model will account for the impact of wealth, and attempt to understand the more general

impact of information about and exposure to migration on educational investments.

A second set of models looks at differential educational attainment between girls

and boys, addressing questions about the relationship between migrant social capital and

human capital. Two major hypotheses prevail in this regard. First, that social capital, by

offering an entrepot to the city, enhances the value of parents educational investments.

If this were the case, then past migration experience at the village level should have a

positive effect on childrens education, particularly for those most likely to migrate, in

this case boys. Second, social capital may offer a reasonable substitute for human


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child. Educational budget constraints act to limit parental ability to incur educational

expenditure, as well as to raise opportunity costs of having children in school instead of

working in household economic activities.

Migration may raise constraints on household budgets in two primary ways. First,

those who have migrated themselves are likely to have accumulated more assets.

Research on transfers of wealth and skills from rural-urban and international migrants

show that a significant portion of all income in migrant-sending areas such as Matlab

derive from migrant transfers, which play an important role in both household expansion

of landholdings, and preventing entry into a vulnerable period of debt (Gardner 1995;

Kuhn 2001b). A significant body of research has documented the extensive use of

transfer income to pay for childrens education (Massey et al. 1998).

Migration may also loosen constraints on the household budgets of non-migrants

living in villages with high rates of out-migration. Migrant financial transfers are often

expended locally, creating income multipliers that can affect the entire village economy.

In the case of international migration in particular, successful migrants may endow a

local school, generating a permanent impact on educational attainment at the village

level.

Migrant social capital complements education

Controlling for migrations effect on educational budget constraints, past migration may

have an effect on parental incentives to educate children through the introduction of

migration-specific social capital. Information, contacts and specific connections to labor

and housing opportunities may increase the returns to migration by enhancing expected

income and reducing the likelihood of unemployment for potential migrants (Boyd 1989;

Taylor 1986). As a form of capital that can only be vested through migration, migration-


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specific social capital should only directly influence the fortunes of those most likely to

migrate, in this case boys.

Migration-specific social capital is also likely to condition a migrants likely

returns to education, but the direction of this effect is likely to depend on the structure of

urban labor markets and likely pathway through which social capital operates (ie.

increasing income expectation, or reducing unemployment risks). If social capital

generates a rising income expectation conditional on employment, this may in turn

enhance the value of education and other skills that increase expected earnings. If the

expected marginal return to an additional year of schooling is higher in the presence of

migration-specific social capital, parental incentives to invest in migrants education

would rise, and parents may focus their investments on the education of those most likely

to migrate. In some cases, investments in likely migrants may rise at the expense of

necessary investments in the education of other children.

Migrant social capital substitutes for education

In a context where the risks to returns on educational investments are higher, however,

evidence on the relationship between social capital and human capital is more

inconclusive. While social capital can enhance the value of education by converting

skills into better jobs, barriers to labor market entry may place a premium on social

connections at the expense of educational investment, particularly if the most readily

available jobs are not skilled professions. The risk of defection and a failure to remit

income, even by successful migrants, also places a premium on social contacts as a

means of ensuring migrant loyalty. Further, the risk of defection may create incentive to

provide locally-resident children with similar or greater years of schooling than migrant

children. In the context of migration from Western Mexico, human capital and social


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capital are both useful for migration to urban areas in Mexico, while social capital had far

greater value than human capital for migration the United States (Massey et al. 1987). As

immigration reform laws increased barriers to entry and the risk of defection, migrant

human capital attainment continued to decline (Phillips and Massey 1999).

Research in Bangladesh finds a strong positive relationship between financial

transfers to the elderly and childrens education, but this relationship is subject to a great

deal of uncertainty (Kuhn 2001). While no political barriers separate urban and rural

areas, social barriers limit access to employment. Slow economic growth also leads to

persistently uncertain employment tenure and earnings growth. For these reasons,

qualitative respondents suggest that parental investments in childrens education are both

risky and best accompanied by strong social connections to current urban residents.

While urban social connections lower the risks of childrens employment and permit

greater parental control over children, older couples in areas of high migrant social

capital also tend to have a greater number and greater proportion of children participating

in urban labor markets, exposing them to greater risk of defection.

Taken in this context, social capitals role in reducing the risk of unemployment

and defection represent a reasonable substitute for educational investments. If migrant

social capital reduces the marginal impact of boys education on transfers by mitigating

the risks of unemployment, parents might prefer to emphasize the education of a group

less likely to migrate, such as girls. In a setting in which migration rates for boys are

high and rising and incentives to educate boys are diminishing, girls education

represents a powerful alternative for two reasons: 1) girls may be more likely to remain


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near the parents household and remain in contact; and 2) the marginal value of girls

education may be higher as opportunities for womens migration emerge.

III. Data and Sample ConstructionStudies of childrens education in the developing world typically focus on attainment or

current school attendance for young cohorts that are likely to remain co-resident with

parents, thus avoiding any reporting bias for non-resident children. To study the impact

of migration intentions on education, however, it is important to capture achievement of

higher levels of education that are more likely to hold value in urban labor markets.

While qualitative research respondents in Bangladesh acknowledge that gaining a

primary education is important for migrants, secondary education is viewed as the most

significant threshold for gaining access to formal sector employment. Further, expansion

of educational infrastructure and incentives for daughters schooling have eliminated

male-female attendance differentials for current young school-going cohorts, with only

moderate attainment differentials remaining for the poorest households (Figure 2).

Gender differentials in current school-going cohorts are manifested primarily in the

tendency for girls to fall behind faster than boys, or to fail to move past secondary school.

The analysis thus focuses on the educational attainment of 20 to 25 year old children,

permitting an analysis of achievement of any, primary or secondary education for

members of this cohort.

The analysis includes data for all children, age 20 to 25, of adult respondents to

the individual adult questionnaire of the Matlab Health and Socioeconomic Survey

(MHSS). The MHSS, collected in 1996 by an interdisciplinary group from University of

Pennsylavnia, RAND and Harvard University, focuses on adult health and household

economic decision making. The primary sample of 4,632 includes two households from


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a randomly selected 15% sample of all baris in the area served by ICDDR,Bs

Demographic Surevillance System (DSS).1 Within each sample household, the

individual adult questionnaire was administered to the household head, the spouse of

head, all members over age 50 and their spouses, and two additional members over age

15.

The MHSS data are unique, among other reasons, for asking respondents to

identify all non-householder children, both living and dead, providing childrens gender,

exact age (on June 1, 1996), educational attainment, location and information about

contact and financial exchange with the child. These data permit an analysis of

educational attainment for the significant proportion (24 for males, 19 for females) of

children in the sample who have moved away from home. While the females in this

group largely constitute marital migration episodes, the males are largely labor migrants,

among the most educated of the children covered in the sample (Table 1). Given the

focus on migration experience as the primary dependent variable, it is essential to capture

these children, but theyre presence raises concern over the precision and possible bias in

the parental reports.

The creation ofan analytic file employs both mother and fathers reports of non-

householder childrens education, as well as correction from demographic surveillance

records for the entire sample population. The initial file is constructed by combining data

for all children, of any age, from all parental reports of non-householder children and

household roster data for all householder children. These data are merged to individual

1 The within-bari sample consists of one randomly chosen household, and one based on a purposive selection processwhich gives preference to close kin. The analysis is weighted according to the likelihood of inclusion in the sample,

where households from large baris are under-represented.


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survey data for each available parent, and parental reportds are matched to one another.

If parental reports of non-household children generate a clear match (based on childs age

and gender), they are included in the file in an unduplicated form. If there is any doubt

about the quality of a match, both children are included in the file. This creates child-

level records for every child born to parents answering the individual survey, with

references to the parents household roster number or, if absent, their location. These

records are matched toparents age and educational attainment data from the individual

survey, as well as to parents household asset from the household economic survey.

While MHSS data are unique for collecting data on non-householder kin, their

added utility lies in matching the data to ICDDR,Bs Demographic Surveillance System

(DSS), which has recorded every birth, death, marriage and migration within the Matlab

Surveillance Area from 1966 to the present. DSS data provide village-level migration

data which constitute the primary set of predictors, and they address two major data

concerns inherent to the MHSS file. First, census data can be used to identify counts and

ages of children; second, census data can be used to update age and education data for

absent or deceased spouses in cases where only one spouse was available to answer the

individual survey.

While the model is designed to focus on the parental side of parental investments

in children, it is crucial to get the best possible estimates of the three categories of child

variable: own age, own gender, and counts of other childrens age and gender. One of

the greastest drawbacks to secondary report data, particularly in the LDC context, is

imprecision and frequent bias in age reporting data. For people born after 1966, DSS

census ages are based on records ofa childs date of birth, recorded within a month of the


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event. While MHSS household heads were prompted with DSS roster data when

constructing the MHSS roster, non-householder children reports were not. As children of

respondents living the DSS area, however, a large majority of the non-household children

were born in the DSS area and have records that can be matched to their mothers

identification number. Using 1982 census data, supplemented with 1993 census data for

missing mothers or mothers with missing husbands, a roster of all living children is

generated for 1982, at which time the children in the study would range in age from 6 to

11.

MHSS child rosters are matched to DSS rosters by mothers and fathers

ICDDR,B identification number, and matched to subsequent DSS death records to ensure

that they were alive in 1996 or when last recorded leaving DSS. All householder children

are easily matched to their DSS records, leaving all remaining MHSS non-householder

child reports to be reconciled to DSS child reports; mothers and fathers reports are used

when available, but DSS records are held up as the gold standard. An iterative matching

process attempts to find the best match between the two data sources based on age,

gender and migration data from the surveillance system. Exact age/gender matches are

marked as correct matches and eliminated from the process. If DSS and MHSS child

gender counts match, and specific child ages match within three years, then DSS ages are

applied and a match is recorded. Next, if DSS and MHSS child gender counts match but

ages are not within a three year range, MHSS ages are replaced by DSS ages by gender

and age rank. Next, DSS data are used to settle conflicts between fathers and mothers

child counts by gender, and the preceding three steps of reconciliation are repeated.

Extraneous MHSS children that cannot be attributed to births to deceased spouses or


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births outside the surveillance area are dropped, and the three age reassignment steps are

again repeated for these children. Finally, the few remaining unmatched DSS children

are retained with their DSS ages. Using these thoroughly updated child age files, we

produce counts for children over age 25 from the corrected data, counts for children

under 15 from MHSS rosters, and restrict the sample to children whose adjusted

DSS/MHSS age is between 20 and 25.

DSS data are also used, when possible, to correct missing parental age and

educational attainment reports. If one parent is missing from the MHSS data or if either

respondent parent provided no data for age or education, these data are garnered from the

1982 census or, if necessary, from the 1993 census. Since the youngest possible parents

in the sample would have been 30 years old (or 35 for men), these provide

comprehensive educational attainment data. If any age data are missing after the DSS

match, missing husbands ages are set to seven years greater than their wives, and vice

versa. Missing values for education are imputed to the median by gender, age and

village. Measures of fathers current or past migration status are also supplemented with

data from DSS out- and in-migration records.

Finally, we use DSS out-migration records to construct yearly measures of

migration at the village level. The measures focus on mens migration, because most

labor migration episodes during the period were mens moves. Census population counts

from 1982 and 1993 are updated to reflect changes due to death and migration, creating a

measure of men age 24 to 60 who lived in the village in each year from 1982 to 1996.

Out- and in-migration files are each coded to identify all moves to an urban center or

district headquarters (rural-urban migration), and all moves to another country. All


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moves in and out are recorded for men aged 24 to 60 in each year, disaggregating by

rural-urban and international destination/origin. Rates of in- and out-migration are

summed to produce measures of gross rural-urban and international migration for each

village-year.

Gross village-level migration rates (hence referred to as GVMRs) are

characterized by wide variation between villages, but extremely high yearly correlations

within villages. Figure 3 shows the yearly trend in out-migration rates for Matlab as a

whole (weighted by total males age 25-60 in the village in the year), and for three groups

assigned by the GVMR in 1982. Given the high correlation of GVMRs within village, it

is important to capture the impact of migration rates at multiple points in time without

over-specifying the model. We do this by generating period and cohort measures of

rural-urban and international migration.

We calculate average GVMRs for a three year period centered on the 13th

birthday of the average age respondent (1986-1988), as well as the GVMR for the year in

which the respondent turned 13 years old. The first measure captures the level of

migration information and exposure experienced during a general period in which all

sampled children were of school age, the second captures a level of migration

information particular to children of a specific age. Since GVMRs are correlated over

time, period measures capture the migration experience of a much broader period than

just three years, while the one-year snapshot of age-13 GVMRs captures the effects of

rising or falling rates throughout the observation period. Because the period and cohort

GVMRs are highly collinear, they are entered in terms of a mean or permanent effect (the

period GVMR), and a difference or transitory effect (the difference between the age-13


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GVMR and the period GVMR). In preliminary analysis, we tested broader definitions of

period and cohort GVMR and models in which only period or cohort measures were

included, all of which remained qualitatively similar to the results presented below.

IV. MethodsThe model focuses on the role of parental decision-making on childrens education. The

assumptions of the model, which are difficult to test under any circumstances, are that the

impact of child aptitude and desire on relative educational attainment do not interact with

the measures of migration exposure included in the model. The analysis would thus not

distinguish between the impact of migration exposure on parental educational

investments and the interaction b/w migration exposure and child characteristics.

The analysis centers on multinomial logistic regression models of child

educational attainment, focusing on three levels of attainment that hold societal

importance and are easily recalled by parents. Any schooling is a good measure of

parental intentions to educate, and is easy for parental respondents to distinguish from no

education. Completion of primary schooling (5 years or more) often involves a shift in

school location, carries some state recognition in low-level job applications, and is widely

recognized by qualitative research respondents as the level of education required for

gaining low-skill service jobs. Completion of a Secondary School Certificate (measured

here by 10 years of schooling or more) offers a credential and is widely recognized as the

minimum requirement for most clerical or skilled service jobs.

The analysis focuses on a sample of children aged 20-25 in order to maximize the

number of possible educational attainment categories. Students in rural Bangladeshi

schools often fall behind due to work obligations, the primary source of differentiation

for current schoolgoing cohorts, but many of them complete a level of school several


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years later than would be expected in MDC educational systems. Samples that included

children that were too young to have completed a level would have confounded cohort

effects towards higher attainment and age effects of merely being old enough to complete

a level. Very few MHSS respondents completed an SSC after their 20th birthday, so only

a multinomial model for the 20-25 year old group can include four categories of

attainment (0, 1-4, 5-9, 10+).

We test multinomial and ordered logistic regression models of educational

attainment under this four-category definition. The basic model includes parent

characteristics, sibling composition, child age/gender, and period and cohort GVMRs.

The tables present the effect of each variable on the log-odds of finishing school in a

particular range of educational attainment (those in the 10+ group may still be in school),

as well as standard error of these effects. Tables present likelihood chi-square tests

indicating whether each additional group of variables adds explanatory power to the

model (as well as a pseudo-R2 for each specification).

A second specification for each group adds five-category measure of the value of

parents household assets, including agricultural land, livestock, homestead plot, rental

property and any non-productive assets such as jewelry. The asset controls account for

the wealth effect associated with a history of out-migration, as well as the tendency for

parents to educate sons first, while daughters education is more asset-dependent. Table

1 presents the mean value of assets (in taka=1/46 US dollar), period GVMR (1990-1992)

and mean sons and daughters education for each of the five asset quintiles.

Presentation of education models for all children will be followed by gender-

specific models that look at the differential impact of mens migration on boys and girls.


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Much of the difference between boys and girls will be captured in the intercept of these

models, and a positive effect on girls education at one of the intermediate levels could

indicate a number of explanations: that they are gaining more from migration than boys

and closing the education differential, or merely that their gains are occurring at a less

advanced transition (ie. from any to primary) than boys (who could be moving from

primary to SSC). The ordered logistic regression models will offer some direct

comparison between the overall effect of GVMR on educational attainment, but the rise

in levels cannot necessarily be interpreted as linear between the four levels of attainment.

The analysis concludes with a presentation of predicted education levels for boys and

girls under different village migration scenarios, employing a Multiple Classification

Analysis to account for the state-dependence between multinomial effects and their

predicted probabilities.

Regression models also attempt to account for the effects of endogenous selection

out of the sample ifboth parents were absent or deceased at the time of survey, the only

scenario in which non-householder children should be missed. We match 1982 census

data for all couples having a child between 6 and 11 years of age at the time (20-25 in

1996) to migration and mortality surveillance records for the intervening years to track

out-migration (and no subsequent return migration) or death for both parents. Two

logistic regression models predict the likelihood that both migrate or both die (ignoring

the rare cases in which one migrates and one dies) in the subsequent period. The

likelihood of both parents dying is predicted from a logistic regression model in terms of

parents age and education, as well as total household land holdings. The likelihood of

both having migrated and not returned is predicted from a logistic regression model in


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terms of those variables, as well as a set of village-level controls. Predicted probabilities

for each event are matched to MHSS parent/child files, and entered as selection controls

in the model of childrens educational attainment. Since the selection criteria expects that

children cannot be in the sample if their parents are not available for survey, MHSS

respondents or roster entries age 20 to 25 are not included if neither parent was included

in the MHSS individual survey.

V. ResultsCombined Models

Table 3 begins with a four-category education model with no asset controls. Parental

education controls account for the strong relationship between parents and childrens

education. The relationship is significant and grows progressively larger for both

parents education, but the effect is stronger for mothers education, perhaps owing to the

greater differentiation in womens education for this generation. The effects of sibling

competition are limited when males and females are pooled, although having brothers

over age 25 reduces the likelihood of finishing 10

th

grade.

The gender effect for the ordered model shows that males are more likely to

achieve higher levels of education, while the multinomial model suggests a more

complex pattern. Males are more likely than females to have 1-4 or 10+ years of

education, but they are no more likely to have 5-9 years. This suggests that the pace of

educational differentiation among the 20-25 year old group differs for males and females,

as females move into the 5-9 group while boys move into 10+. The negative relationship

between age and 5-9 years of schooling also suggests that female advancement into this

group may be a cohort effect in progress; we test this directly in the gender-specific

models.


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GVMRs have strong positive effects on education, particularly at higher levels.

Children educated in villages with high period rural-urban and international migration

rates were significantly more likely to have completed 10th

grade. Cohort-specific rural-

urban migration rates have a further association with finishing 10th grade, suggesting that

incentives for educating children are greater still villages in which the migration process

is still growing. The likelihood of completing 5-9 years of schooling is also enhanced by

changes in the cohort GVMR.

These GVMR effects control for fathers own current and past migration

experience, both of which has no significant association with any level of educational

attainment. Fathers migration effects might be expected to capture a wealth effect, but

the measure of past migration may not be refined enough to indicate success. But the

result also suggests that the values and connections acquired while being a migrant do not

increase the incentive to invest in childrens education. It appears that it is not the

practice of migration itself that encourages educational investments in children, but the

possibility of migration. One explanation for this result is that the immediate connections

derived from a fathers own migrant experience may be sufficient to acquire employment

for a son, eliminating any need for enhanced educational investment. The children who

require education to get ahead in the city may be the ones who only hold the more

general association with past migrants capture by the GVMR. It is also likely that those

fathers who were successful in skilled professions requiring education may have found

permanent homes for their families in the city, and are thus not captured in the sample.

The second model specification introduces the asset measures to account for the

impact of migration on individual wealth and on the village economy in raising


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educational budget constraints or promoting school development. The asset effects are

highly significant, but they also demonstrate the declining impact of assets when a level

of educational attainment becomes universal. Only one group has a higher likelihood of

achieving 1-4 years of schooling (4th quartile), and the asset variables have no joint effect

on achieving this educational category. For the top two categories, the lowest two

quintiles are statistically similar to one another, but the other three quintiles show a

progressively higher likelihood of completing either category, with a much stronger

relationship between wealth and achieving 10+ years of schooling. The ordered logistic

results bear out these findings, with progressively higher odds of moving up a category

for each of the top three quintiles.

The introduction of asset variables explains some though not all of the GVMR

effect. Their inclusion deflates the log-odds of international GVMR on the likelihood of

10+ years of schooling and on moving up a category in the ordered model, and all effects

for international GVMR are no longer statistically different from zero (at the p


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male and female education. Educational opportunities appear to be constrained by the

presence of older same-sex siblings, with older sisters negatively affecting the likelihood

of girls achieving 1-4 and 5-9 years of education, and older brothers negatively affecting

the likelihood of all three levels of schooling for boys . Childs age does have a negative

and significant effect only on female education, supporting the claim that cohort changes

in female educational attainment were occurring during the period of analysis.

Asset effects differ for males and females, reflecting parents desire to give

educational priority to boys even in an environment of diminishing gender differentials.

Females in the second lowest asset quartile had an advantage over those in the lowest

quartile in achieving each higher level of education, and were the most likely to finish

with 1-4 years of education. The top three asset quartiles make up this deficit by showing

higher propensities for moving on to 5-9 or 10+ years of schooling, with large differences

between the 2nd

and 3rd

quartiles in particular. The top two quartiles appear to show little

difference between one another in female schooling.

Asset differentiation is far less for boys, with all quartiles equally likely to

achieve 1-4 years, and no difference between the bottom two quartiles in achieving any

level of education. The top three quartiles have incrementally higher likelihoods of

achieiving 5-9 or 10+ years of schooling. The 10+ effects are significantly smaller than

for girls, but 5-9 effects are of a similar scale, while the effects for the male ordered

model are significantly lower than for the female one. This again suggests that females

of this cohort in Matlab were making a universal transition towards completing primary

school, but were not yet catching up to males in terms of completing secondary school.


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The stratified models reveal distinct differences in the effects of GVMR measures

on male and female educational attainment. While GVMR effects for completion of 1-4

or 5-9 years are largely insignificant for both groups, we see that the positive effects of

village-level rural-urban and international migration experience on education, seen

above, are primarily focused on girls schooling. Period rural-urban and international

GVMRs have a strong positive association with girls reaching 10+ years of schooling,

and with girls moving up one level in the ordered models. No such effects are significant

for boys, and they are greatly reduced in size from the pooled models. Similarly, the

cohort-specific measure of rural-urban GVMR, while insignificant for boys, also has a

strong effect on girls reaching 10+ and on their moving up a level in the ordered model.

The results of ordered logistic regression models suggest that high and rising

GVMRs are likely to have a far greater impact on girls educational investment than on

boys. This supports the hypothesis that migration-specific social capital is a substitute

for investment in boys education, and does not appear to offer parents any increased

incentive to focus on boys education. It furthers suggests that security afforded by

migration-specific social capital allows parents to focus on educating girls, who may be

more likely to remain near the home.

Summary Predictions

The gender-stratified models suggest that the impact of village-level migration on the

education of a recent educational cohort unequivocally favors females, the group that has

been historically unlikely to migrate. Yet given the level of educational inequality that

had persisted in the cohorts preceding this one, most of these gains merely allow

daughters to gain ground on sons. Predicted probabilities of achieving a given level of

education are shown in Table 5, in terms of rural-urban VGMR, international VGMR and


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21

asset values (for comparison). The composite indicator of rural-urban combines period

and cohort VGMR as if both were held at the selected quantile (eg. 25 th percentile

indicates a household in the 25th

percentile of period VGMR, and in 25th

percentile of the

cohort VGMR deviation; so migration rates in the village were lower than in most; and

that villages migration rates were lower in that year than in most).

These results show substantial gains for females, particularly in villages with a

high and growing practice of rural-urban migration. Yet most of these gains merely close

some of the gap with males. The proportion having 10+ years of schooling rises from

7.3% in low rural-urban migration villages to 12.3% in high migration ones, whereas

males, who show little change in the proportion achieving this level, complete 10+ years

27.7% of the time even if they live in low migration villages. Taken together, however,

female gains in the two top groups combined bring rates of primary school completion to

near equality with males. The proportion of girls completing primary school rises from

60.3 to 68.1% between low- and high-migration villages, putting them in range of the

proportion of males completing primary school (ranging from 67.6% to 70.5%). In

general, females in high out-migration villages are able to achieve parity in terms of

completing primary school, but can make only limited gains in terms of completing

secondary school. Similar but smaller effects hold for the impact of past international

migration on male and female education, for which there are even fewer incentives to

invest in male education, but also less relationship to female education.

Incentives such as migration-specific social capital play a significant role in

governing a childrearing couples child investment decisions, yet resources still play a

more immediate role. Table 5 predicts the powerful impact of parental assets on


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22

educational attainment, particularly for girls. For boys, moving from the 2nd

to 4th

quartile of household assets increases the predicted probability of completing secondary

school from 33.9% to 43.4%; girls jump from 19.5% completing secondary to 36.2%.

Again with assets, girls never quite catch boys in terms of completing secondary school,

but their gains in completing primary school make them close to parity in this respect.

VI. ConclusionsThe preceding analysis has focused on two primary question regard ing migrations role in

parental decisions to invest in childrens education. First, does migration increase

parental incentives to invest in child schooling? The answer to the first question is a

definite yes. While basic controls for own past migration experience appear to have little

effect on educational investments, even net of wealth effects, measures of migration

experience during childrearing years have a significant appreciable effect on the level of

childrens educational attainment. Some of this can be explained by a wealth effect,

suggesting that migration, particularly of the international sort, does increase wealth and

loosen budget constraints. Much of the migration effect cannot be explained by wealth

alone, however. Changes appear to emerge in the structure of educational investment as

villages achieves high and growing migration rates.

The analysis of differences in boys and girls education attempts to pick up

where the first question leaves of, addressing the role of migrant social capital, or at least

migration history, as a complement to specific educational investments in migrant, or an

opportunity to focus investment elsewhere. In spite of a long and continuing history of

male temporary out-migration in the area of study, migration primarily impacted the

education of females, with positive effects for the level and growth of rural-urban

migration, as well as the level of international migration.


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23

In interpreting the meaning of these effects, it is useful to think separately about

the role of rural-urban and international migration on education. The effect of rural-

urban migration on girls education could suggest that parents were focusing on a shift

towards educating girls for future labor migrationwhile male migrant social capital

might be somewhat transferable to girls migration, there might be a premium placed on

enhancing girls human capital as the flow developed. This conclusion would be

tenuous, however, given that the 20-25 year cohort turned 13 between 1984 and 1989.

The ready-made garments industry, the first and primary source of urban employment for

young women, only emerged in 1990 and reached critical mass in the mid-1990s.

This suggests other possible explanations. It is quite possible that there is little to

be gained from further investment in sons education given the likely educational returns

to urban and overseas income. This is particularly true for international migration, which

as in many guest worker situations, appears to offer limited income returns to human

capital above a minimal level of education (Kuhn 2001). The strength of the

international migration effect for female education supports this. Another related

hypothesis suggests that daughters education may gain value as theirpost-marital role in

caring for parents expands. In an environment rapidly shifting towards two-child

families, evidence strongly suggests a move towards mobilizing all possible familial

resources in securing care in old age. With sons and increasingly daughters-in-law

leaving for the city or abroad, daughters are next in line. While parents may not find

education to be essential for this role, they may value it just the same, and exercise that

preference after at least one son has advanced in school. Finally, some of this result is

likely to stem from the mere emergence of a notion of daughters value and of womens


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24

rights. While the government has made extensive efforts to finance the girls schooling,

it is clear that parents must always bear a burden of expenditure and opportunity cost. If

the average boy has achieved sufficient education to gain a job in an environment of

strong destination-area social connections, then the benefits of migration may be for this

increased focus on girls education.

The results of this analysis are encouraging from a policy standpoint, in that they

again assert that girls are catching up in Bangladeshi schools. They also address the oft-

held suspicion that most of migrations impact on the local economy comes in

consumption, asset expenditure or investing in future migrants -- not in supposedly

productive investments. The results suggest that not only does past migration have a

strong positive relationship with subsequent educational investment, but that this

relationship largely only applies for girls, the children who traditionally dont migrate.

The results do raise questions, however, about the effectiveness of parental educational

investments as a form of private transfer from the rural to the urban sector. If the limits

to economic growth have capped parents incentives to invest heavily in the education of

likely migrants, then urban productivity is unlikely to advance quickly, and the

development of a skilled service sector may be slowed. Stagnation of investment in

international migration further suggests a stationary role in manual jobs in overseas

destinations, generating only limited investment capital and insurance for rural areas like

Matlab that provide most of Bangladeshs guest workers. While this analysis does not

address change in education beyond the 10th grade level, it is suggestive of a leveling off

in the human capital pool. This again represents the difficulty in achieving complete

demographic and economic tradeoffs in the absence of economic growth.


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25

Finally, the results address some methodological and contextual concerns

frequently issues about research conducted in Matlab and other settings of high research

activity. Critics point to high overall levels of educational attainment and low gender

differentials in attainment as evidence of change induced by ICDDR,B, yet the results of

this paper suggest that while these changes occur more rapidly in Matlab than in some

regions of Bangladesh, they are certainly not merely the result of ICDDR,Bs presence.

Matlab, like most out-migrant sending regions of southeastern Bangladesh, benefits from

the transfer of capital and values associated with migration. Migration introduced the

capital necessary to invest equally in girls education. It also introduced new reasons to

invest in girls education, whether they remain at home or go to migrant destinations as

well. While ICDDR,B may have had a similar influence on values and labor markets as

migration opportunities, few parts of any country that have no such opportunities. Trends

such as gender equality and fertility decline are often manifest in Matlab before other

parts of Bangladesh, but other areas quickly follow suit because they are good ideas.

The methodology employed in this research would only have been possible in an

area like Matlab, where detailed origin-area survey data are supplemented by 36 years of

demographic surveillance data. These data provide precise ages not just for the current

resident population, but for anyone who can be linked to prior residence in the area.

Similarly, surveillance data allow the research to identify the extent of sample attrition

into a survey such as the MHSS, and model its causes accordingly. These and other uses

offer invaluable tools for social research when the quality of cross-sectional data is

limited. The opportunities afforded by 35 years of data collection justify any of the

changes that have occurred during the same period.


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26

References

Becker, Gary S. (1991). A Treatise on The Family, Enlarged Edition. Cambridge,MA:

Harvard Univerity Press.

Kuhn, Randall (2001). Never Far From Home: Parental Assets and Migrant Transfers in

Matlab, Bangladesh. Presented at Population Association of America Meetings,March 2001, Washington, DC.

Lee, Yean Ju, William Parish and Robert Willis (1994). Sons, Daughters, and Inter-

Generational Support in Taiwan. American Journal of Sociology 99(4):1010-1041.

Lillard, Lee A. and Robert J. Willis (1994). Intergenerational Educational Mobility:

Effects ofFamily and State in Malaysia. Journal of Human Resources 29:1126-1167.

Massey, Douglas S., Rafael Alarcon, Jorge Durand, and Humberto Gonzales (1987).Return to Aztlan: The Social Process of International Migration from Western

Mexico. Berkeley: University of California Press.

Phillips, Julie A. and Douglas S. Massey (1999). The New Labor Market: Immigrants

and Wages after IRCA. Demography 36(2):233-246.

Phillips, Julie A. and Douglas S. Massey (2000). "Engines of Immigration: Stocks ofHuman and Social Capital in Mexico" Social Science Quarterly 81(1): 33-48.

Willis, Robert (1982). The Direction of Intergenerational Transfers and DemographicTransition. Population and Development Review 8(3): 207-234.


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27

Child's Location 0 Years 1-4 Years 5-9 Years 10+ Years Total

551 501 1154 455 2661

20.7% 18.8% 43.4% 17.1%

65 9 27 17 11855.1% 7.6% 22.9% 14.4%

108 78 149 98 433

24.9% 18.0% 34.4% 22.6%

14 23 51 44 132

10.6% 17.4% 38.6% 33.3%

(Code) 0 Years 1-4 Years 5-9 Years 10+ Years Total

227 259 753 217 1456

15.6% 17.8% 51.7% 14.9%

345 169 371 60 945

36.5% 17.9% 39.3% 6.3%121 65 164 52 402

30.1% 16.2% 40.8% 12.9%

3 4 10 4 21

14.3% 19.0% 47.6% 19.0%

In Household

Elsewhere in Rural

AreaUrban Area

Abroad

Table 1: Child's Level of Educational Attainment by Location

Males:

Females:

Elsewhere in RuralArea

In Household

Urban Area

Abroad

Asset Quintile N

Asset Value

(in taka)

Period Rural-

Urban GVMR

Period Int'l

GVMR

Girls' Mean

Education

Boys' Mean

EducationBottom Quintile 541 5861 2.21% 0.92% 2.22 3.64

2nd Quintile 558 31024 2.27% 0.73% 3.55 3.89

3rd Quintile 631 55217 2.24% 0.85% 4.52 5.37

4th Quintile 653 91173 2.28% 0.91% 5.74 6.81

Top Quintile 683 428507 2.22% 0.85% 7.82 8.63

Table 2: Selected Variable Means by Asset Quintile


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28

Ordered Ordered

1-4 Years 5-9 Years 10+ Years Logit 1-4 Years 5-9 Years 10+ Years Logit

-0.172 -0.35 -0.856* -0.487* -0.164 -0.209 -0.678 -0.343-0.335 -0.287 -0.388 -0.223 -0.33 -0.29 -0.392 -0.22

-0.415 -0.446 0.322 -0.003 -0.34 -0.396 0.344 -0.031

-0.628 -0.496 -0.565 -0.442 -0.619 -0.477 -0.575 -0.398

-0.109 0.218 0.075 0.092 -0.103 0.259 0.169 0.156-0.223 -0.199 -0.231 -0.128 -0.229 -0.214 -0.25 -0.135

1.591* 1.253* 1.064 0.122 1.518* 1.002 0.713 -0.062-0.715 -0.611 -0.69 -0.303 -0.721 -0.677 -0.773 -0.3810.03 0.009 0.034 0.015 0.028 0.004 0.025 0.012

-0.018 -0.015 -0.019 -0.011 -0.018 -0.016 -0.021 -0.012

-0.016 -0.001 0.007 0 -0.018 -0.011 -0.003 -0.004

-0.023 -0.019 -0.024 -0.015 -0.022 -0.019 -0.025 -0.015

0.172** 0.242** 0.351** 0.180** 0.160** 0.200** 0.293** 0.144**-0.049 -0.046 -0.049 -0.022 -0.05 -0.047 -0.049 -0.022

0.290** 0.441** 0.622** 0.287** 0.276** 0.413** 0.588** 0.261**

-0.084 -0.079 -0.083 -0.031 -0.085 -0.08 -0.084 -0.033

0.209 -0.182 -0.237 -0.195 0.154 -0.304 -0.363 -0.231

-0.307 -0.254 -0.315 -0.201 -0.312 -0.265 -0.329 -0.201-0.06 0.035 -0.238 -0.117 -0.03 0.061 -0.251 -0.136-0.216 -0.191 -0.232 -0.138 -0.216 -0.196 -0.24 -0.139

-0.275 -0.397 -0.915** -0.421* -0.306 -0.379 -0.848* -0.364

-0.303 -0.291 -0.338 -0.194 -0.303 -0.3 -0.345 -0.196

0.064 0.16 0.347 0.149 -0.009 -0.005 0.175 0.031

-0.257 -0.241 -0.282 -0.165 -0.258 -0.252 -0.286 -0.165

0.418* 0.196 1.166** 0.522** 0.448* 0.211 1.170** 0.499**

-0.183 -0.146 -0.185 -0.104 -0.184 -0.151 -0.189 -0.105

-0.017 -0.100* -0.043 -0.035 -0.014 -0.100* -0.045 -0.041-0.052 -0.045 -0.054 -0.031 -0.052 -0.047 -0.055 -0.031

10.261 12.153 20.572** 12.834** 10.155 9.88 16.407* 9.341*

-6.596 -6.551 -7.541 -4.607 -6.717 -6.972 -8.17 -4.7322.045 19.667 30.423* 17.836* 24.945 18.774 25.302 14.176

-12.99 -10.783 -12.965 -7.748 -13.18 -11.375 -13.831 -8.405

11.563 11.606* 17.546** 10.027** 11.158 10.069* 15.165* 8.264*

-6.129 -4.779 -5.734 -3.226 -6.193 -4.924 -5.93 -3.25

11.756 -8.667 -17.115 -8.323 14.297 -6.11 -17.234 -8.08

-8.701 -8.616 -9.822 -5.608 -8.802 -8.566 -10.723 -5.823

0.237 0.338 0.599 0.34

-0.247 -0.251 -0.35 -0.18

0.262 0.885** 1.536** 0.881**

-0.261 -0.241 -0.306 -0.173

0.569* 1.508** 2.241** 1.345**-0.287 -0.269 -0.342 -0.1860.496 1.875** 2.760** 1.582**

-0.363 -0.314 -0.362 -0.186

-2.527 -2.588* -3.578* -1.908* -2.103 -1.03 -1.415 -0.626-1.42 -1.234 -1.44 -0.796 -1.52 -1.309 -1.625 -0.906

-6.164 1.896 5.792 4.205 -4.451 2.568 5.475 2.772-10.57 -8.638 -10.095 -6.364 -10.525 -8.604 -10.304 -6.147

-1.876 1.258 -3.447 -1.924 1.411 -3.538

-1.656 -1.429 -1.836 -1.648 -1.489 -1.928

G1 301.6 (10) / 0.114**

G1+G2 341.8 (14) / 0.118**G1+G2+G3 381.4 (16) / 0.125**

G1+G2+G3+G4 392.3 (20) / 0.130**

G1+G2+G3+G4+G5 476.7 (24) / 0.159**Observations 3025

Table 3: Four-Category Models of Educational Attainment, Males and Females Combined

3026

221.0 (30) / 0.125**

272.7 (42) / 0.132**311.5 (48) / 0.144**

347.2 (60) / 0.151**

464.2 (72) / 0.181*

Father Lives Away from

Home

Father Migrated in Past

Mother Dead/Lives Away

Father's Age

Parental Individual Characteristics (G1):

Mother's Age

Father's Education

Mother's Education

Father Dead

Child's Age

Rural Urban, 1986-88

With Asset Controls

Any sisters >=25 Years

Any siblings


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29

Ordered Ordered

1-4 Years 5-9 Years 10+ Years Logit 1-4 Years 5-9 Years 10+ Years Logit

-0.094 -0.058 -1.304* -0.407 -0.202 -0.368 -0.552 -0.343

-0.436 -0.392 -0.624 -0.292 -0.431 -0.365 -0.457 -0.269

1.094 -0.029 1.222 0.469 -1.581* -0.494 -0.124 -0.33

-0.791 -0.627 -0.795 -0.485 -0.703 -0.62 -0.726 -0.519

-0.291 0.201 0.097 0.151 -0.003 0.309 0.193 0.152

-0.37 -0.315 -0.399 -0.206 -0.293 -0.264 -0.284 -0.161

1.541 -0.11 1.622 0.488 2.413 2.804* 1.48 -0.22

-1.019 -0.998 -0.947 -0.488 -1.244 -1.126 -1.252 -0.491

0.03 0.006 0.018 0.009 0.024 -0.004 0.02 0.009

-0.024 -0.022 -0.029 -0.015 -0.025 -0.021 -0.026 -0.016

-0.025 -0.011 0.029 0.01 0 -0.009 -0.017 -0.012

-0.035 -0.026 -0.039 -0.019 -0.028 -0.025 -0.029 -0.019

0.222** 0.284** 0.340** 0.161** 0.104 0.113* 0.250** 0.142**

-0.059 -0.051 -0.061 -0.03 -0.059 -0.057 -0.055 -0.028

0.188 0.358** 0.570** 0.256** 0.330** 0.458** 0.605** 0.274**

-0.096 -0.085 -0.098 -0.045 -0.127 -0.125 -0.123 -0.043

0.388 -0.286 -0.2 -0.266 -0.03 -0.405 -0.344 -0.251

-0.449 -0.38 -0.572 -0.317 -0.41 -0.338 -0.372 -0.239

0.035 0.117 -0.285 -0.051 -0.027 0.051 -0.207 -0.126

-0.329 -0.287 -0.392 -0.209 -0.272 -0.246 -0.277 -0.17

0.422 0.263 -0.156 0.054 -0.939* -0.890* -1.369** -0.727**

-0.404 -0.373 -0.482 -0.239 -0.403 -0.389 -0.436 -0.276

-0.836* -0.700* -0.574 -0.403 0.577 0.517 0.669 0.391

-0.345 -0.32 -0.406 -0.209 -0.337 -0.314 -0.343 -0.223

0.009 -0.118 -0.123 -0.094 0.017 -0.047 0.038 0.006

-0.077 -0.069 -0.093 -0.048 -0.073 -0.068 -0.075 -0.042

13.324 17.545 31.430* 15.796* 12.168 9.199 11.701 6.211

-9.569 -10.026 -12.853 -6.655 -8.335 -7.758 -9.856 -6.042

12.992 22.268 43.750* 22.426* 35.824* 19.72 24.391 9.736

-17.685 -15.16 -19.08 -10.54 -18.192 -15.773 -17.576 -11.131

17.505 12.983 28.285** 12.011* 7.744 9.93 8.155 4.422

-9.017 -7.345 -9.385 -4.802 -7.924 -6.745 -7.805 -4.452

8.727 -3.794 -15.207 -4.426 19.689 -7.395 -16.539 -15.233

-9.62 -10.508 -14.797 -6.575 -15.985 -13.8 -14.428 -9.329

1.018** 0.991** 1.762** 0.861** -0.383 -0.227 0.084 -0.069

-0.361 -0.336 -0.62 -0.238 -0.311 -0.321 -0.387 -0.228

0.716 1.036** 2.373** 0.951** -0.196 0.725* 1.186** 0.748**

-0.376 -0.334 -0.538 -0.238 -0.357 -0.322 -0.349 -0.217

0.753 1.723** 3.430** 1.690** 0.387 1.358** 1.805** 1.050**

-0.404 -0.349 -0.555 -0.263 -0.376 -0.364 -0.42 -0.2270.438 1.764** 3.610** 1.792** 0.589 2.068** 2.686** 1.414**

-0.487 -0.427 -0.6 -0.279 -0.479 -0.414 -0.418 -0.217

-0.741 -1.33 -3.592 -1.47 -3.129 -0.766 -0.481 -0.028

-2.136 -1.822 -2.509 -1.186 -1.81 -1.727 -1.916 -1.153

0.415 -2.813 -12.531 -7.187 -12.636 7.33 17.301 13.075

-12.119 -9.966 -13.778 -6.782 -14.608 -12.225 -12.752 -8.437

-3.062 1.180 -4.328 -2.062 1.247 -2.871

-2.283 -2.006 -2.912 -2.281 -2.079 -2.467

G1 177.4 (10) / 0.129** 186.3 (10) / 0.116**

G1+G2 190.7 (14) / 0.131** 212.7 (14) / 0.123

G1+G2+G3 198.8 (15) / 0.135 212.8 (15) / 0.123

G1+G2+G3+G4 209.7 (19) / 0.143** 220.7 (19) / 0.127*

G1+G2+G3+G4+G5 270.3 / (23) / 0.177** 268.3 / (23) / 0.155**

Observations 1394 16321394

141.5 (30) / 0.151**

168.9 (42) / 0.157**

170.9 (45) / 0.161**

198.3 (57) / 0.173**

Father's Age

Multinomial Logit

Father Dead

271.5 (69) / 0.210**

Any siblings =25 Years

Rural-Urban, Age 13

International, Age 13

Child is Male

Child's Age

Asset 2nd Quintile

Asset 3rd Quintile

Asset 4th Quintile

Parental Assets (G5):

Asset Top Quintile

Predicted Probability, both

Parents out-Migrated

Predicted Probability, both

Parents are Dead

Selection Parameters:

Child Characteristics (G3):

Gross Village Migration Rates for Males (G4):

Rural Urban, 1986-88

International, 1986-88

Table 4: Gender-Stratified Models of Educational Attainment

Multinomial Logit

Parental Individual Characteristics (G1):

Sibling Characteristics (G2):

Mother's Age

Father's Education

Mother's Education

Father Lives Away from

Home

Father Migrated in Past

Mother Dead/Lives Away

212.3 (57) / 0.143

261.5 (69) / 0.177**

1632

Female Male

Log Likelihood Chi-Square / Pseudo-R^2 adding Variable Groups

163.5 (30) / 0.127**

196.9 (42) / 0.136

200.9 (45) / 0.137

Constant


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30

Gender Level 0 Years 1-4 Years 5-9 Years 10+ Years 5+ Years

Female 25th 25.2% 14.5% 53.0% 7.3% 60.3%

50th 19.9% 15.1% 55.1% 9.8% 65.0%75th 16.3% 15.5% 55.9% 12.3% 68.1%

Male 25th 15.5% 16.9% 39.9% 27.7% 67.6%

50th 13.3% 17.4% 40.8% 28.5% 69.3%

75th 11.8% 17.7% 41.5% 29.0% 70.5%

Gender Level 0 Years 1-4 Years 5-9 Years 10+ Years

Female 25th 21.9% 15.7% 53.9% 8.5% 62.4%

50th 20.0% 15.3% 55.0% 9.7% 64.7%

75th 18.6% 14.9% 55.8% 10.7% 66.5%

Male 25th 15.1% 15.8% 41.2% 27.9% 69.1%50th 13.6% 17.0% 40.9% 28.4% 69.4%

75th 12.5% 18.0% 40.7% 28.8% 69.5%

Gender Level 0 Years 1-4 Years 5-9 Years 10+ Years

Female Bottom 19.3% 15.1% 55.4% 10.2% 65.6%

2nd 8.6% 15.7% 56.2% 19.5% 75.7%

3rd 8.0% 11.1% 52.7% 28.1% 80.8%

4th 4.8% 6.7% 52.3% 36.2% 88.5%

Top 4.5% 4.9% 50.9% 39.7% 90.6%

Male Bottom 13.1% 17.5% 40.8% 28.6% 69.4%

2nd 14.4% 14.2% 37.5% 33.9% 71.4%3rd 7.5% 8.6% 41.7% 42.1% 83.9%

4th 4.7% 8.6% 43.3% 43.4% 86.7%

Top 2.8% 5.9% 42.9% 48.5% 91.4%

Change in Period and Cohort Rural-Urban VGMR:

Change in Period International VGMR:

Change in Asset Ranking:

Table 5: Predicted Effect of Differences in VGMR and Assets on Education


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Figure 1: Mean Years of Schooling: by age and sex, 1996/7

0

1

2

3

4

5

6

7

15 20 25 30 35 40 45 50 55 60 65 70

Age

Years

ofSchooling

Women

Men

Figure 2: Years of Schooling by Age and Sex, Matlab 1996/7

0

1

2

3

4

5

6 7 8 9 10 11 12 13 14

Age

Years

ofSchooling

Girls

Boys

migrant social capital and education in migrant-sending areas of bangladesh: complements or...

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