the dreaded embankment paper

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Marriage Market Effects of a Wealth Shock in Bangladesh

A. Mushfiq Mobarak*Yale University

Randall Kuhn

University of Denver

Christina PetersMetro State College of Denver

AbstractConditions of marriage such as dowries and consanguinity influence women’s subsequent lifeoutcomes. However, research on the determinants of these conditions is largely descriptive. Thispaper uses a wealth shock from the construction of a flood protection embankment in ruralBangladesh coupled with data on the universe of all 52,000 marriage decisions between 1982 and1996 to examine changes in marital prospects for protected households after embankmentconstruction relative to unprotected households living on the other side of the river. First we usetwo-sided matching models with difference-in-difference specifications to describe the changes inthe marriage market, and show that protected households commanded larger dowries, marriedinto wealthier families, and became less likely to marry biological relatives. The marriage market

becomes more segregated by wealth, but the positive wealth shock does not allow women todelay marriage or reduce spousal age gaps. The same family is 40% less likely to marry ayounger child to a cousin after the wealth shock, compared to their older child who married priorto the embankment construction. Second, we try to understand the structural changes that led tothis drop in consanguinity, and find that liquidity-constrained households use within-familymarriage (where one can promise ex-post payments) as a form of credit to meet up-front dowrydemands, and the wealth shock relaxed this need for taking an adverse biological risk.

JEL Codes : O1, J12, O13Keywords : Marriage, Embankment, Flood Protection, Consanguinity

November, 2009



1. Introduction

Across the world, a woman’s marital prospects have important implications for

her subsequent life outcomes. Characteristics of the bride and her family at the time of

marriage in conjunction with the characteristics of her spouse and his family determine

the conditions of marriage such as dowries, marrying biological relatives, and age at

marriage. 1 These conditions in turn affect socio-economic outcomes for the woman and

her children, including the likelihood that she will have to endure domestic violence, her

social status in her husband’s home, her school attainment, health status, and her control

over reproductive choices. 2 Marrying a cousin or uncle, a surprisingly common practice

around the developing world, can decrease the amount of dowry required, but increases

the risk of genetic diseases among offspring. 3

Although the literature on the consequences of marriage is large, the evidence on

the determinants of the conditions of spousal matching is mostly qualitative or descriptive

(e.g. Fruzzetti, 1982; Huq and Amin, 2001). A few studies account for multiple co-

varying determinants of marital prospects, and use cross-sectional regressions on

relatively small samples of survey data from rural India to show that older, taller, more

educated grooms of high caste living in areas with a larger supply of potential brides

command larger dowries, and that spouses mate assortatively in age and education. 4 The

fact that families can offer compensating differentials along many unobservable

dimensions in order to secure a desirable match is a significant challenge to empirically



identifying the precise determinants of marriage outcomes in these studies. We exploit

the construction of a flood protection embankment in rural Bangladesh coupled with pre

and post embankment data on 33,000 marriages in treatment and control villages to

examine how a plausibly exogenous change in certain households’ wealth manifests itself

in marriage market outcomes such as dowries, socio-economic status of the spouse, age

at marriage, and consanguinity (i.e. marrying biological relatives).

The flood protection embankment in rural Bangladesh that we study induced a

discrete improvement in socio-economic conditions for families living on the

embankment side of the river relative to the opposite bank that remained unprotected.

The major effect of the embankment was to extend the crop growing season, thereby

increasing relative wealth for households on the protected side, though it also may have

reduced flood risk exposure. We investigate differential changes in the conditions of

marriage for protected households using panel data on the entire universe of marriages

across a fourteen-year pre and post-embankment period.

Our paper is constructed in two parts. The first part uses stylized two-sided

matching models of the marriage market along with difference-in-difference

specifications to describe changes in the marriage market following the wealth shock, and

documents changes in dowries, spousal socio-economic status, and a drop in marriages

between biological relatives. In the second part, we explore structural changes that led to

this drop in consanguinity. We find that liquidity-constrained households facing dowry



For the first part, the model predicts that the protected are likely to secure better

matches only along characteristics that are complementary. For example, if the wealth

that a man and woman bring in to a marriage are complementary inputs in generating

marital surplus, then the protected would in general choose to (and be able to) marry into

wealthier households. If age at marriage is not a complementary input, protection will

not necessarily change spousal age or age gaps, since the protected are not willing to pay

relatively more than the unprotected for this characteristic. 5 Furthermore, if the

embankment’s primary contribution is to lower flood risk exposure, then we should

observe negative assortative matching in protection post-embankment. The unprotected

have the largest marginal gain from bonding with a protected family, and are therefore

willing to pay the most to secure that match. A corollary is that the protected should

receive larger dowries.

Difference-in-difference specifications that explore changes in marriages post-

embankment show that individuals from protected households experienced a 3 percent

higher likelihood of marrying into wealthier households (as measured by land ownership)

post embankment relative to those that remained unprotected. The embankment had a

larger wealth effect on farmers, and triple difference (pre/post, un/protected, by

occupation) results confirm that agricultural households drive this change. Protected men

command larger dowries following embankment construction, but there are no significant

changes in age at marriage. We also do not find evidence of assortative matching in



becomes increasingly segregated (beneficiaries of the environmental shock command

larger dowries, marry wealthy families and crowd out others). Norms regarding the

proper age at marriage and spousal age gaps appear quite inelastic even for those who

receive the large positive shock to wealth. This first part of the paper documents general

equilibrium effects of an infrastructure commonly constructed in flood-exposed

developing countries.

The second part of the paper turns to changes in consanguinity – the practice of

marrying biological relatives – which is surprisingly common in many parts of

Bangladesh, in neighboring India and Pakistan, and more generally in the developing

countries of Asia and Africa. 6 Such marriages impose adverse biological risk on children

in the increased likelihood of receiving two copies of a deleterious gene from parents,

which manifests itself in larger child morbidity and mortality rates (Bittles and Makov,

1988; Bittles, 2001; Shah et al., 1998). 7 Social scientists have a limited understanding of

why so many couples accept these risks, and the wealth shock associated with the

embankment provides a unique opportunity to gain further insight into this practice.

In difference-in-difference estimates using household fixed effects, protected

households show a 3.3 percentage point larger drop (a 40 percent decrease at the mean) in

the likelihood of forming consanguineous unions following the construction of the

embankment relative to unprotected households living on the other side of the river.

Multiple mechanisms can link flood protection to consanguinity prevalence, but our



ancillary evidence indicates that marrying consanguineously reduces the need for dowry

payments which are often difficult for brides from poor families to make, and this dowry

constraint is relaxed for protected households following embankment construction. 8

Since a bride’s parents often do not have either cash on hand or access to credit to make

the up-front dowry payment, they use within-family marriage (where it becomes possible

to promise ex-post payments) as a form of credit. Triple difference results by gender

show that it is protected women who show the larger drop in consanguinity (rather than

protected men), which is consistent with this dowry-credit constraint story, since the

dowry payment is uni-directional from women to men. The contract theory model in Do,

Iyer and Joshi (2009) formalizes this story, and we bring credible evidence to bear on this

mechanism by taking advantage of an exogenous environmental shock.

In the next two sections, we describe our data and present evidence that the

embankment created plausibly exogenous variation in wealth. Section 4 presents two-

sided matching models of the marriage market to explore how this is expected to affect

marriage market behavior. Section 5 empirically describes the changes in the marriage

market, and finally section 6 tries to understand the structural changes that led to the large

drop in consanguinity.

2. Data and Setting

Since 1963, the International Centre for Diarrhoeal Disease Research, Bangladesh



of residents in 148 villages in Matlab district. Residents of this rural area are mostly poor

and landless, subsisting off fishing, agricultural labor, and sharecropping. The Meghna-

Dhonagoda River runs through the middle of the study area, and the Water and Power

Development Authority in Bangladesh used external donor funds in 1987 to construct a

65 km embankment along the northwest bank that prevents water overflow and provides

systems for pumped drainage and irrigation along the waterway (Strong and Minkin,

1992). The embankment was breached during abnormally high floods in 1987 and 1988,

after which it was strengthened and resealed in 1989. Consequently, in the empirical

specifications, the pre-embankment period becomes 1982-1986, and the post-

embankment period covers 1989-1996.

Using ICDDR,B’s data, we observe all 33,000 marriages (or 52,000 marriage

decisions) of Matlab residents between 1982 and 1996, and we can merge census data

from 1982 to these marriage files. We know the age at marriage of each individual and

their spouse, any biological relationship between them (i.e. consanguinity status), their

wealth, occupation, and location (and therefore embankment protection status). The vital

events database also allows us to examine fertility, mortality and migration patterns at

monthly intervals for the full sample of households over the entire sample period.

We supplement these data with retrospective dowry information, cropping

practices, and land value information reported in the 1996 Matlab Health and

Socioeconomic Survey. The MHSS cross-sectional dataset covers a random sub-sample



by women who are interviewed separately from their husbands. Appendix table A1 lists

the variables used in this study and their definitions.

3. What the Embankment Construction Helps Identify

The key identifying assumption in our statistical analysis is that the construction

of the Meghna-Dhonogoda embankment induced measurable changes in socio-economic

status for people resident on the northwest side of the Dhonagoda river in Matlab district,

relative to people resident on the other side of that river (see figure 1). Before turning to

any marriage outcomes, in this section we explore: (a) whether the embankment

construction can be treated as exogenous (i.e. uncorrelated with other coincident changes

in events and conditions), and (b) what exactly were the socio-economic changes the

embankment effectuated – changes in wealth or risk exposure – that ultimately resulted in

changes in the marriage market.

The embankment was not designed as a scientifically random experiment. We

thus explore in what ways the event falls short of the ideal of a “natural experiment” or

strictly “exogenous” change. One potential concern is that people resident on the south-

east bank of the river are not an appropriate control group for the “treated” households,

since the placement of the embankment on the northwest bank may itself signal some

pre-existing differences between the two groups.

During fieldwork interviews we conducted in 2006, Matlab residents indicated



politicians in conjunction with the largest local landowners who actually live in Dhaka

(Briscoe, 1998; Kabir 2004). Neither of those groups are part of our sample since they do

not live in Matlab district. However, this may indicate possibility of other forms of

preferential treatment for the protected side of the river, and we therefore delve into the

program evaluation and anthropological literatures to examine these issues.

It is possible that agriculture practices are systematically different across the two

banks due to drainage differences, although program evaluations of the embankment

project report that the geographic areas experienced similar weather patterns, households

grew similar crops and reported similar incomes, and demographic distributions were

nearly identical. We find no mention of any other significant differences in technology

or other construction such as bridges that may have happened to coincide with

embankment construction (Strong and Minkin, 1992; Thompson and Sultana, 1996;

Briscoe, 1998). 9 Descriptive statistics from our own data show that the protected and

unprotected groups are similar prior to construction along most observable dimensions

(see Table 1). Due to the large sample size, the marriage outcomes of interest are often

statistically different across the two groups prior to completion of the embankment, but

the magnitude of those differences remains very small.

One exception is the baseline consanguinity rate, which is appreciably larger prior

to embankment construction on the protected side. However, since our difference-in-

difference estimates rely on relative changes in marriage outcomes, the most important



We show that protected and unprotected trends in the marriage outcomes of interest prior

to embankment construction were not statistically different, and the magnitudes of the

differences are very small when compared with the actual embankment effect estimates

we present later (see table 2). In fact, the (statistically insignificant) point estimate shows

that if anything, consanguinity on the embankment side was increasing relative to the

unprotected side (which also means that the relative decrease in consanguinity on the

protected side post embankment construction that we will demonstrate will be

statistically different from both the zero effect and from its pre-existing trend). Any

relative changes in trends post-embankment that our difference-in-difference estimates

uncover are therefore not merely a continuation of pre-existing differences in trends.

We also conduct falsification exercises on our DID estimates (modeled after

Aghion et al. (2008)), which replace the indicator for the actual year of embankment

construction with every possible false embankment year of the sample. In other tests, we

replace the variable for protection status (indicating which side of the embankment a

household is on) with false embankment locations of northern vs. southern villages and

the treatment and control groups of the Matlab Maternal and Child Health and Family

Planning Program, an experimental program present in the area. These tests for false

times and locations all show that statistical impacts are absent in cases where we should

not observe them (e.g. there are no statistical differences in behavior across two sub-

periods other than that of embankment construction), and that the actual embankment



Thus, though the embankment may not be a perfectly clean experiment, we have

searched for possible threats to the embankment as a source of identification. The results

support its use as a plausibly exogenous source of variation in a difference-in-difference

setting across the protected and unprotected banks of the river. It is a useful source of

variation to examine the effects of changes in wealth and risk exposure on marriage

outcomes. Furthermore, the embankment is an infrastructure projects whose effects on

the social and economic lives of the village residents are inherently interesting to

examine. We next explore the relative changes in wealth and risk exposure it induced.

3.1 Impacts of the Embankment on Income and Risk

Frequent flooding in Matlab destroys crops and induces volatility in household

income, and the embankment provides security by extending growing seasons and

increasing overall farm incomes for protected agricultural households. During fieldwork

we conducted in December 2006, Matlab residents often reported that the primary effect

of the embankment was to increase the number of crop cycles from only one per calendar

year to two or three. 11 Consistent with our informal interviews, Matlab data from 1996

indicates that protected rice farmers enjoy almost one extra growing season per calendar

year compared to farmers on the other side of the river (t-test significant at the 1 percent

level). Thompson and Sultana (1996) mention that the largest effects of flood protection

projects should be on monsoon crops, and accordingly, our data shows that protected

farmers grow 2-3 times more Aman and Aus paddy (the two varieties grown during the



significant at the 5 percent level). Meanwhile, the embankment had no discernible effect

on yields for the dry season Boro paddy.

Protected farmers should have experienced a substantial wealth effect from these

large increases in rice yields. We measure this effect directly by using principal

components analysis to construct asset indices for household wealth in 1982 and 1996

(see table 3).12

Compared to unprotected households, protected households experience a

greater increase (significant at the 1 percent level) in asset ownership between 1982 (pre-

embankment) and 1996 (post-embankment). Moreover, this change is almost entirely

driven by farmers. 13 A hedonic regression of the value of land using cross-sectional 1996

data reveals that post-embankment, the unit price of land is over 3000 taka higher per

decimal on the protected side. 14 Table 4 shows that the variance of assets across

households within a village (which would be linked to changes in risk exposure) does not

differentially change across the protected or unprotected banks. Consistent with the

fieldwork findings, the mean wealth effect of the embankment thus appears to dominate

changes in variance.

In addition to this wealth effect, the embankment also may have reduced the

health risk posed by stagnant floodwaters. Using DID specifications to test the

12 The index measures household ownership of any combination of the following assets: a radio, a watch orclock, a bicycle, cows, and a hurricane lamp. Data is taken from Matlab DSS (Demographic SurveillanceSystem) 1982 and 1996 household censuses. We compare asset indices for only those households withinformation in both years. Occupation of household head is kept as reported in 1982.13 Both landowners and tenant farmer benefited in significant ways which indicates that the extended



embankment’s effect across different sex-age categories, we find a differentially greater

decline for protected households than unprotected households in mortality rates from

diarrheal diseases and other water-borne infectious diseases. 15 Specifically, adults

protected by the embankment are less likely to die from diarrheal diseases (including all

adults between 30-35, females between 45-50, and males between 55-60), and protected

adult males between 30-35 are less likely to die from infectious diseases.16

In summary, while the embankment may have affected both the mean and

variance of outcomes (wealth and risk exposure), both our fieldwork and the data analysis

are strongly suggestive the wealth effect is likely the most salient change and the

dominant embankment effect for protected Matlab residents. As figure 2a indicates, the

embankment is not a soaring barrier that can protect residents from the gushing

floodwaters that are an enduring risk to life and property periodically faced by rural

Bangladeshis. It is a more modest barrier designed to protect agricultural fields from

seasonal variation in water levels that render those fields in-arable during the monsoons.

The data on cropping cycles, agricultural yields, land values, wealth by occupation all

indicate that the embankment performs this limited function well, and bestows a positive

wealth shock on protected households.

4. Effects of the Embankment in Stylized Models of the Marriage Market

The wealth and risk effects outlined above may each have very different



help us understand what effects we expect to observe on a variety of marriage outcomes.

Most existing models of the marriage market describe matching functions under the

assumption that each person brings a single trait to marriage (e.g. Browning, Chiappori

and Weiss 2003, Anderson 2007, Weiss 1997), which makes generating predictions on

assortative matching relatively simple (e.g. Bergstrom 1997, Siow 1998, Anderson 2000).

We take the following approach to describe matches in a situation where multiple

attributes of protection can affect outcomes:

1. We derive analytical predictions on matching in a simplified transferable-utility

marriage market where each person has only two discrete attributes –

embankment protection status and wealth status. This model helps clarify the

basic economic intuition behind the dynamics of a more complicated market.

2. We then relax the assumptions on transferable utility, endow each person with

multiple continuous characteristics, and simulate stable matches in a larger

marriage market characterized by search frictions.

4.1 A Transferable Utility Model of the Marriage Market

Since marriages in Matlab are typically arranged by the families of the groom and

bride, we assume that preferences of the bride and her family are grouped together, as are

the preferences of the groom and his family. Males and females on the marriage market

are indexed by m and f . Each potential spouse has two relevant characteristics: the level

of wealth ( w f or wm), which can be either high ( H ) or low ( L), and the embankment status



assumption (e.g. Weiss 1997, Siow 1998, Anderson 2007) simplifies the matching

problem by allowing each person to use z fm in comparing the gains across different types

of matches and against the payoff from remaining single. d fm regulates the division

between spouses, so that each person’s decision is conveniently split into: (1) choose the

match that maximizes the surplus generated from marriage, and (2) choose a value of d fm

to split that surplus.

The groom’s payoff from the marriage is d fm, while the bride’s payoff is z fm -

d fm.17 We assume the following general form for z fm: ),(),max( m f m f fm wwee z Π⋅= .

This formulation reflects the fact that embankment protection increases the productivity

of land by extending the crop season, which is the principal component of wealth in rural

Bangladesh. The embankment also protects from flood risk, and it is most important to

have at least one side of the newly joined families be protected, an idea embodied in the

max( e f , em) function. 18 A woman may gain from starting to live under embankment

protection after marriage, and conversely, an unprotected groom’s family may gain from

forming a marital bond with a protected family where they can take refuge during a flood.

Variables e f , em (which can take on values P and U ) and w f , wm (with values H or

L) are all assumed to be strictly positive so that greater wealth can be valuable even in the

absence of protection. P>U , H > L, and f

f w∂

Π∂=Π and

mm w∂

Π∂=Π are positive, so that

protection and greater wealth are both positive characteristics in the marriage market.



Further, we will focus on the case where there are gains to marriage: 0 >Π fm . All

couples with any wealth gain from being married relative to remaining single

when 0>Π fm , and for the unprotected there are additional gains from marrying into a

family protected by the embankment.

Our task is to uncover a stable set of matches for the four types of men and

women in this marriage market, such that no married person would rather be single and

that no two people, married or single, would prefer to form a new union. Stability implies

a participation constraint for each woman which specifies that her payoff from marriage

must be as large as her payoff from remaining single:

)0,(),(),max( f f fmm f m f wed wwee Π⋅≥−Π⋅ . Similarly, the participation constraint for

each man requires ),0( mm fm wed Π⋅≥ . For stable matches, a set of incentive

compatability constraints must also be satisfied for each person that specify that the

payoff from the chosen match is larger than under alternate matches:

mnd wweed wwee fnn f n f fmm f m f ≠∀−Π⋅≥−Π⋅ ,),(),max(),(),max( , and

f gd d gm fm≠∀≥ .

Since there are only four types of each gender, the above represents three

incentive compatibility constraints for women and a further three for men. A final

market clearing condition stipulates that for a match of type f and type m to be feasible in

the aggregate, the supply of these types must be equal.



possible assignments. 19 It is easy to verify that under complementarity ( 0 >Π fm ), the

only stable set of matches is where type ( P , H ) get matched to type ( U , H ) of the opposite

gender, while ( P , L) and ( U , L) also form bonds. In other words, we observe positive

assortative matching in wealth, but negative assortative matching in protection status.

In order to illustrate why these matches are optimal, it is useful to derive the result

assuming a market structure where the women can bid for the men and are the residual

claimant of the marital surplus generated (the results are analogous when men bid). The

maximum willingness to pay for a ( P , H ) man by each type of woman is as follows:

By a ( P , H ) woman, )0,(),( H P H H PWTP PH PH

Π⋅−Π⋅=

By a ( P , L) woman, )0,(),( LP H LPWTP PH PL

Π⋅−Π⋅=

By a ( U , H ) woman, )0,(),( H U H H PWTP PH UH

Π⋅−Π⋅=

By a ( U , L) woman, )0,(),( LU H LPWTP PH UL

Π⋅−Π⋅=

Since P>U , PH PH

PH UH WTPWTP > and PH

PLPH

UL WTPWTP > . This is because a protected

man offers greater value added to an unprotected woman than he does to a protected

woman, and the unprotected woman will therefore be willing to outbid the protected

woman. Also, PH UL

PH UH WTPWTP > when 0>Π

mf . Under complementarity in the

husband’s and wife’s wealth, a wealthy woman gains greater surplus from a wealthy man

than does a low wealth woman, and will therefore be willing to outbid her. Thus the ( U ,

H) woman can outbid all other types of women in order to match with a ( P H ) man



the ( P , H ) man – that a marriage to this man generates more surplus for her than a

marriage to any other man. If the ( P , H ) - ( U , H ) match is surplus maximizing, then we

can find a transfer d fm such that the ( U , H ) woman and ( P , H ) man are better off under

this match than under any other pairing. This is easily established, as we can use the

assumptions P>U , 0>Πm , 0>Π

f , and 0>Π fm to show that PH

UH WTP exceeds PLUH WTP ,

UH UH WTP , and UL

UH WTP . In other words, a protected, high-wealth woman’s desire for an

unprotected high-wealth man exceeds her desire for any other type of man.

Analogous arguments establish that ( P , H ) type women have the highest

willingness to pay for ( U , H ) type men, and achieve the largest surplus from those

matches. So for both men and women, all matches are of the form ( P , H ) - ( U , H ). Once

all these ( P , H ) - ( U , H ) men and women are paired up, the remaining ( U , L) women in

the market place the highest bid for ( P , L) men (their surplus maximizing choice). So the

remaining matches for both men and women are of the form ( P , L) - ( U , L).20

The general result highlighted by this model is that we should observe positive

assortative matching in men’s and women’s characteristics that are complements (such as

wealth) and negative assortative matching in characteristics that are substitutes (such as

protection status). Although the transfer payments from wives to husbands are not

precisely pinned down in the general model (the participation and incentive compatibility

constraints only place upper and lower bounds on the feasible values of d fm), we can also

d h d f ll b k d f k



setting. If there are multiple ( U , H ) women bidding for the same ( P , H ) man, the women

would compete away the entire surplus generated by this man, and dowry payments

would increase with protection status after embankment construction, since the man’s

contribution to the total marital surplus increases with his protection status.

4.3 Embankment Effects in a Simulated Gale and Shapley (1962) Matching Model

We now relax a number of the restrictive assumptions made in the model outlined

above and simulate the dynamics of matching in a more general model. Potential spouses

can offer compensating differentials along multiple dimensions in order to secure a

desirable match. For example, a family could make up any deficiency in its relative

wealth position by offering their candidate at the age most desirable by the opposite sex,

or accepting a candidate of a less desirable age. Thus, we now endow each candidate

with a continuous characteristic that is complementary to embankment protection (such

as the amount of land or wealth), another continuous characteristic relevant to spousal

choice which is neither a complement nor a substitute to protection (e.g. age at marriage),

a discrete protection status, and an idiosyncratic attractiveness parameter.

A male m’s payoff from marrying a female f is postulated to be:

f m f f m f m f

f m aawwees ε β α +−−+Π⋅= 2* )(),(),max( (1)

e is embankment protection status, w is wealth, a is age, * f a (a constant) is the most

desired female age at marriage from a man’s perspective, f mε is the idiosyncratic pair-



husband’s and wife’s protection status substitutes. Candidates are penalized if their age

at marriage differs from some optimal age at marriage. Female f has an analogousscoring function over each male m that she uses to evaluate which proposal to accept:

m f mmm f m f

m f aawwees ε β α +−−+Π⋅= 2* )(),(),max( (2)

With a total of M men and F women on the market, we can use (1) and (2) to

define an M x F matrix of scores over all men and women. Since we cannot describe

analytical solutions to the matches that occur, we simulate the matches by endowing 2500

men and 2500 women with a distribution of wealth, age, protection and attractiveness

characteristics. We assume that initially each individual gets an independent draw on

wealth from a truncated normal distribution over positive support, a draw on age from a

uniform distribution (on support 16-22 for women with an optimal age at marriage, * f a of

19, and on support 21-27 for men, with *ma =24), and a draw on preferences for each

individual of the opposite gender from a normal (0,1) distribution. Half of all men and all

women are randomly assigned to each bank of a river with an embankment on only one

side. We add search frictions to this model by assuming that individuals are more likely

to see (and propose to) others on the market who are physically closer to them. The Gale

and Shapley (1962) algorithm identifies the stable set of matches in this market.21

Results of the matching simulation show that for both protected men and women,

the wealth (land) distribution of spouses they match with shifts to the right following



unprotected) following embankment construction. Thus, the protected are able to secure

wealthier spouses at the expense of the unprotected. Individuals residing on the two sidesof the river are in direct competition in the marriage market, and this result comes about

because (a) the protected have an extra desirable characteristic to offer, so their offers are

more likely to be accepted and (b) due to complementarity in protection status and land,

they are more likely to extend offers of marriage to higher-wealth individuals.

For age at marriage, where no such complementarity exists, figure 4 shows that

there no clear trend to indicate that the protected are better able to secure partners at the

“optimal” age, or reduce spousal age gaps. Complementarity in inputs is therefore key to

understanding the potential effects of the embankment on the variety of possible marriage

outcomes. The model also exhibits negative assortative matching in the substitute

characteristic – protection status. Within-bank marriages are less likely to occur after

embankment construction, even with cross-bank search frictions (figure 5).

Although dowries are not well defined in this non-transferable utility model,

figure 6 plots the surplus accruing to matched men and women if the marital surplus

(over the payoff from remaining single) is divided between spouses according to Nash

bargaining. The distribution of surpluses shifts to the right for both protected men and

women. Since the dowry payment would be a positive function of the difference between

the man’s surplus and woman’s surplus, when protected men (women) marry unprotected

women (men), dowry payments increase (decrease). This result would also be predicted



Thus, our simulated matching model generates predictions consistent with the

results of our analytical model:1. If the embankment mitigates risk, then we should observe more cross-river

marriages after embankment construction. Search frictions across the river may

dampen this effect.

2. Positive wealth or health benefits conferred by the embankment that are

complementary across spouses should lead to positive assortative pull by

protection status. Characteristics independent of the embankment (such as age)

should remain unaffected.

3. In general, the level of dowry received by protected men should increase

following embankment construction.

5. Empirical Results

5.1 Basic Estimation Strategy

Our difference-in-difference set-up compares the marriage market outcomes for protected

households following embankment construction to their pre-embankment outcomes, after

differencing out the corresponding change in unprotected household outcomes. 22 We

include household fixed effects where possible, which controls for household-specific

unobservable preferences such as heterogeneous attitudes toward risk. In this case, each

observation becomes a household experiencing at least one marriage before and at least



important to note that the empirical results presented next do not constitute a direct test of

the models developed in the previous section, but jointly the theory and the empiricalresults help us sensibly describe the changes in the marriage market following

embankment construction.

5.2 Household Responses to Changes in Risk Exposure

We first examine whether marriage outcomes respond to the embankment in ways

that are consistent with the construction reducing exposure to flood risk. For risk averse

individuals the embankment may lower their demand for mitigating risk through other

channels (e.g. marrying daughters into geographically distant households not subject to

the same weather patterns or planting different crops, a la Rosenzweig and Stark, 1989).

We would then expect to see changes in female migration patterns for marriage following

embankment construction. Difference-in-difference results from table 5 find no evidence

of such behavior, in that female marriage migration rates into households outside their

own villages or outside the Matlab area do not differentially change following

embankment construction for women from protected households.

Further, the theoretical model showed that if risk mitigation benefits of the

embankment were an important consideration, then we would expect to see negative

assortative matching in protection status. The last two columns of table 5 (and the last

column of table 10) show no evidence of assortative matching by embankment protection

in either direction. 24 In general, households are much more likely to marry others who



are located closer to them (see the coefficients on “protected” in table 5), an indication of

search frictions in the marriage market, but this propensity to marry close does not

differentially change after embankment construction. This highlights the possibility that

empirical results on assortative matching based on cross-sectional data may be

uninformative, since it is difficult to separate out search frictions from true assortative

matching in cross-sectional data. Our panel data, which allows us to control for both

“protected” and “post*protected” (labeled “embankment”) helps resolve the issue.

In table 6, we do find that protected households become 4-5 percentage points less

likely to marry into different occupations following embankment construction, an

indication that some households use the marriage market to diversify income risk. This

result may merely indicate assortative matching in wealth rather than response to risk if

the correlation is driven by rich protected farmers marrying other rich protected farmers

following embankment construction. However, the last two columns indicate otherwise;

the correlation is entirely driven by households engaged in non-farming occupations.

In summary, the embankment does not differentially change households’

propensity to diversify and hedge against risk through marriage along most dimensions,

which is further evidence that changes in risk exposure is not the dominant effect of the

embankment. Non-farmers on the protected side (who did not experience the agricultural

wealth shock) are less likely to occupationally diversify through marriage. This could be

a response to lowered risk exposure, or because non-farmers find it more difficult to



5.3 Household Responses to the Wealth Shock

If protected households become wealthier following embankment construction

and can offer “protection” as a desirable marriage market characteristic, then the

matching model predicts that such households would seek out better spousal

characteristics that are complementary to their own in producing marital surplus (e.g.

socio-economic status), but not necessarily characteristics which are not complementary.

Since land is the primary asset for Matlab households, we measure a spouse’s socio-

economic status according to the amount of land owned by the head of the household in

1982. 25 We also look for effects on changes on age at marriage and spousal age gaps.

Protected households become 3 percentage points more likely to marry into

households with above average amounts of land after embankment construction relative

to the unprotected (a 10 percent increase from their pre-embankment likelihood of 32

percent, see table 7). The direction of this change is robust to including household fixed

effects, although the effect becomes statistically weaker when the identification comes

from multiple marriages before and after embankment construction in the same

household. 26 Furthermore, a triple difference in table 7 confirms that the effects are

entirely driven by protected farmers, who experienced the largest gain in assets between

1982 and 1996 and are the primary beneficiaries of embankment construction. Protected

farmers (who form 58 percent of our sample) exhibit a 5.3 percentage point increase in



the propensity to marry into wealthy households (a 16 percent increase at the mean)

following embankment construction relative to unprotected farmers, whereas the effect

among non-farmers is essentially zero.

In order to establish that our results are not crucially dependent on data from the

years farthest after embankment construction, we include additional specifications that

eliminate the last three years of the sample (the post-embankment years then become

1989-1993). This limited sample does not alter the results (in fact, makes them stronger),

supporting embankment construction as the cause of a discrete one-time change in socio-

economic conditions for the protected.

Consistent with theory, we find no effect of the embankment on either age at

marriage or spousal age gaps, which we interpret as independent (i.e. not complementary

or substitute) characteristics in the marriage market. The coefficient signs are indicative

that both protected men and women are able to differentially delay marriage, but these

age effects are statistically indistinguishable from zero (see table 8).

Tobit models in table 9 regress each woman’s report of dowry payment from the

1996 MHSS data as a function of husband’s embankment protection status. Comparing

the “Protected Husband” and “Year*Protected” coefficients, we see that protected men

start receiving larger dowries than unprotected men in 1989 or 1990 (the beginning of the

post-embankment period) for all specifications. 27 The “premium” that protected men

command in the years after embankment construction is quite larger - roughly 40% of the



We check that our results are not generated by the endogenous sorting of

households around the embankment after construction. 5 percent of our sample migrates

to another area in Matlab during the post-embankment period for a reason other than

marriage, and re-estimating the models without these migrants does not qualitatively

change the results (see Appendix table A3). 28 In addition, our results are robust to

exclusion of marriage observations that end in divorce and to the exclusion of non-

Muslims, who follow different marriage customs and face a narrower market.

6. Effects on Consanguinity

Although the biological and genetic risks for the offspring of the union of

biologically close relatives are well understood in the scientific community,

consanguineous marriages remain common practice in much of the developing world

(Grant and Bittles, 1997; New York Times, 2003). While the rates of consanguinity are

falling over time in both protected and unprotected households in Matlab, table 10 shows

the drop is much larger among protected households after the embankment is built. In the

difference-in-difference, protected households show a 2.5 percentage point greater

decrease in the likelihood of marrying a biological relative (i.e. second cousin or closer)

after the embankment over and above the change among the unprotected. In the

household fixed effects specification controlling for gender and birth order effects, we

find that the same family is 40% (about 3 percentage points) less likely to marry a



younger child to a biological relative after they are protected by embankment, than their

older child who married prior to the embankment construction. 29

There are several possible conceptual links between the embankment and rates of

consanguinity. First, if consanguinity is a desirable marriage outcome based on cultural

or religious preferences, then protected households experiencing a positive wealth shock

from the embankment may become more able to attract (or pay for) such marriages. This

theory is rejected by our difference-in-difference results showing differential decreases in

consanguinity following embankment construction rather than increases. Other possible

motivations for consanguinity are consistent with these results. For instance, if

consanguinity is an inferior marriage outcome, protected households, with the additional

attractive characteristic they offer on the marriage market, may be more likely to avoid

this outcome. Consanguinity may also be a response to risk exposure if households

prefer to marry cousins in order to form robust inter-generational bonds with the

extended family. 30

Finally, it has been postulated that households marry within the family in South

Asia in order to avoid large dowry payments at the time of marriage (Caldwell et al.,

1983; Bittles, 1994; Do, Iyer and Joshi 2009). This last link must be a little more

complicated, since one must explain why a rational male would forego larger dowry

payments in the outside market in order to marry his female cousin. Do, Iyer, and Joshi

(2009) develop a model predicting that dowry and consanguinity act as substitutes to



instead choose to free-ride on investments by the groom’s family. The authors outline an

optimal tradeoff between dowries (pre-marital transfers) and consanguinity (close family

ties) that can help overcome this problem. One implication of their model is that in the

presence of tight credit markets, dowries will become more costly relative to

consanguinity. In this case, we should expect to see higher levels of consanguinity

among poorer families, since credit constrained households who cannot borrow to pay

dowries at the time of marriage may use consanguinity as a way to delay payments. The

promise to pay over a longer period is more credible when made within the family.

Consistent with this hypothesis, while the MHSS data shows that the dowry transfer at

the time of marriage is much smaller in consanguineous unions (see table 9),

conversations with Matlab residents during our fieldwork indicated the total amount of

effective dowry transfer over the course of the marriage may not be any different.

In order to identify these effects further, we look for additional ancillary evidence

in favor of the credit constraint story above as a motivation for consanguinity that

explains its link to the embankment. The embankment, by increasing wealth on the

protected side, relaxed the liquidity constraint (in the sense that these now wealthier

households had more dowry to offer at the time of marriage), taking away this important

motivation for marrying within the family. We first show that the dowry transfer at the

time of marriage is almost 50% lower at the mean in consanguineous unions (see table 9).

Second, we show that the drop in consanguinity following embankment construction is



drop among protected females relatively more than males if the dowry payment and

credit constraint story is correct. The same protected family is up to 4.7 percentage

points less likely to marry their younger daughter to her cousin after embankment

construction relative to her older sibling, while for males, this drop is only about half as

large and not significantly different from zero in the fixed effects specifications.

7. Conclusion

Although the placement of the embankment may not be entirely random, it

provides plausibly exogenous variation to examine the determinants of conditions of

marriage through more rigorous empirical analysis than had been possible previously in

the large literatures on marriage in sociology and in economics. The first part of the

paper documents the following changes in marriage markets following wealth gains that

accrue to a subset of Matlab residents:

1. There is increasing segregation in the marriage market in terms of spousal wealth.

Members of farming households who benefit from the wealth shock are differentially

more likely to marry into wealthy households, and non-farmers living on the

protected side of the river find it increasingly difficult to marry into the now-

wealthier farming households.

2. Men from these protected (wealthier) households start commanding larger dowries.

3. Neither women nor men from protected households are able to delay marriage. Nor



The second part of the paper establishes that the practice of marrying biological

relatives appears to be closely linked to the institution of dowry. Rural Bangladeshi

households engage in consanguinity in response to the absence of credit for paying

dowries at the time of marriage. With the high prevalence of consanguinity in South

Asia, Middle East and North Africa it is important to understand the underlying socio-

economic drivers of this practice. The triple difference created by the embankmentconstruction (by gender, protected side of river, pre/post) allowed us to bring credible

empirical evidence to bear on this important question for the first time. The high child

morbidity and mortality effects of consanguinity reported in the literature imply that

liquidity constraints and lack of access to credit impose yet another costly burden on poor

households in developing countries through their marriage market choices.

Finally, our paper documents the general equilibrium changes associated with an

infrastructure project in disaster mitigation. Evaluation of such projects typically focus

on direct impacts on ecosystem equilibrium, agricultural practices and incomes, and

health (e.g. Haque and Zaman, 1993; Myaux et al., 1997; Paul, 1995; Thompson and

Sultana, 1996), and we show that indirect general equilibrium changes can be quite

substantial, and need to be taken into account in program evaluation.



31

Figure 1: Matlab Surveillance Area,

the River and the Embankment

The light colored (green) polygons arevillages, the blue (double) line is the river,and the red line is the embankment

Figure 2a: The Embankment: Not Very High

and Reinforced with Sandbags

Figure 2b: Protected Bank from the Top of the

Embankment: Agricultural Fields Very Close tothe Embankment



Figure 3: Land Distribution of Spouses of Protected and Unprotected Men in theSimulated Gale-Shapley Marriage Market



Figure 4: Age Distribution of Spouses for Protected Men and Women in theSimulated Gale-Shapley Marriage Market

Panel A: Spousal Age Gap

Panel B: Age at Marriage



Figure 5: Assortative Matching in Protection Status in the Simulated Model

Low Search Friction in Marriage Market:qb = 0.90 (probability of seeing a partner on the same bank)qs = 0.50 (probability of seeing a partner on the opposite bank)

Before Embankment After EmbankmentSame Side of River

Matches64% 30%

Across the River Matches 36% 70%

With Greater Search Friction in Marriages Across the River: (qb = 0.95, qs = 0.30)

Before Embankment After EmbankmentSame Side of River

Matches78% 42%

Across the River Matches 22% 56%

Figure 6: Nash Bargaining Surplus for Men and Women Pre and Post Embankment



Table 1: Descriptive Statistics

Full Sample (ICDDR Matlab Demographic Surveillance Area)

Before Embankment After EmbankmentMean: protected unprotected protected unprotected Percentage of Consanguineous Marriages .081** .067** .044*** .059***

(.01) (.00) (.00) (.00)Female Age at Marriage 19.02 19.04 19.90 19.84

(.06) (.05) (.06) (.04)

Male Age at Marriage 26.93 25.71 27.33*** 25.96***(.15) (.10) (.12) (.08)Household Land Owned (1982) 11.12*** 10.28*** 11.44*** 10.64***

(.23) (.15) (.19) (.13)Land Owned by Spouse's Household (1982) 10.82** 9.89** 11.13*** 10.22***

(.31) (.31) (.27) (.20)Percentage of Marriages to Spouse from outside Matlab .478*** .576*** .512*** .597***

(.01) (.00) (.01) (.00)Percentage of Marriages to Spouse from outside Village .864*** .880*** .882*** .895***

(.00) (.00) (.00) (.00)Total Marriage Observations 5225 11050 8438 17896Standard errors in parentheses. *** indicates t-test significant at 1%, **indicates significant at 5% level. Pre indicates 1985-1986

for consanguinity and 1982-1986 for all other variables. Post indicates 1989-1996.

MHSS 1996 Sample

Pre PostMean: protected unprotected protected unprotected Percentage of Men Receiving Dowry .11 .12 .42 .43

(.01) (.01) (.04) (.02)Value of Dowry Received by Men 576.08 575.27 4077.90 4193.57

(83.88) (46.82) (487.01) (378.62)Value of Dowry Received by Men in 389.47 524.73 5708.33* 3658.67*

Consanguineous Marriages (113.34) (109.60) (1310.88) (561.12)Total Marriage Observations 1119 2543 182 422

Standard errors in parentheses. * indicates t-test significant at 10% level. Pre indicates 1982-1986; Post indicates1989-1996. Data taken from 1996 MHSS.



36

Table 2: No Pre-existing Differences in Trends in Marriage Outcomes by Protection Status

Standard errors in parentheses. *** indicates t-test significant at 1% level. All models estimated using probit. All specifications include pre-embankment years 1982-1986 only. Marginal effects calculated at means of explanatory variables.

Coeff. ME Coeff. ME Coeff. ME Coeff. ME

Protected -2.553 -2.579 -2.893 -1.525(8.164) (2.223) (1.784) (1.414)

Year -.280*** .001 .012 .002(.056) (.016) (.012) (.010)

Protected * Year .031 .031 .034 .015(.096) (.026) (.021) (.017)

Land Owned -.002 .003*** .002***(.002) (.001) (.001)

Above Avg. Amount of Land .144***(.035)

Age at Marriage

R-squaredTotal obs.Sample

.000

Pre-Embankment Years (1982-1986) Only

.011

.003

-.000

.052

6199.0117130 14938 14946

-.254

-.037

.004

ConsanguineousMarriage?

Spouse from OutsideMatlab?

-.772 -.549

Spouse Owns AboveAverage Amount of

Land?

-.656

.003

.001

.001

Spouse from DifferentVillage?

.006

.001

.006

.001

.002



37

Table 3: Effect of the Embankment on Wealth

Asset Indices

Pre-embankment Post-embankment Difference

Protected Unprotected Protected Unprotected Protected Unprotected

Farmers(Landowners)

-.05*** .05*** .38 .38 .43*** .34***

(.02) (.01) (.02) (.02) (.02) (.02)

Obs. 3377 7270 3377 7270 3377 7270

Farmers(Tenant)

-.48 -.44 .03*** -.21*** .50*** .23***

(.03) (.02) (.04) (.03) (.05) (.03)

Obs. 552 1179 552 1179 552 1179

Non-FarmOccupations

.07*** .16*** .33*** .42*** .26 .26

(.03) (.02) (.02) (.02) (.03) (.02)

Obs. 1530 3207 1530 3207 1530 3207

AllOccupations

-.06*** .03*** .33 .33 .39*** .30***(.01) (.01) (.01) (.01) (.02) (.01)

Obs. 5459 11656 5459 11656 5459 11656

Standard errors in parentheses. *** indicates t-test significant at 1% level; ** indicates significance at 5% level. Asset index constructed using principal components factor analysis, and measureshousehold ownership of any combination of the following assets: radio, watch or clock, bicycle, cows, and hurricane lamp. Data taken from Matlab DSS 1982 and 1996 household censuses.



Table 4: Changes in the Variance in Assets across Households within VillageFollowing Embankment ConstructionWithin-Village Variance in Assets

Pre-embankment Post-embankmentProtected .87 .91

(.04) (.04)Obs. 32

Unprotected 1.02 .96(.04) (.02)

Obs. 93 Each observation is a village in Matlab, and the table reports the variance in assets across householdswithin a village. Only villages with >80% of land on one side of the embankment or the other are included.

Table 5: Location of Spouse does not Change Differentially after Embankment

Spouse fromOutside Matlab

Spouse fromDifferentVillage

Marrying Across the River

Northern Villages Southern Villages

Coeff. ME Coeff. ME Coeff. ME Coeff. MEProtected -.247*** -.098 -.075*** -.015 1.06*** .381 1.28*** .470

(.021) (.027) (.051) (.046)Post .053*** .021 .082*** .016 .009 .003 -.040 -.015

(.015) (.020) (.041) (.040)Embankment .031

.012.002

.000.012

.004.028

.011(.027) (.034) (.066) (.060)

R-squared .006 .002 .116 .175No. of obs. 42609 42596 7662 8596

Table 6: …but Unprotected Households More Likely to Marry into Different Occupations Marrying into Different Occupation

- Full SampleMarrying into Different Occupation

- Farmers/Non-Farmers

Diff-in-Diff Household

FE Farmers Non-FarmersCoeff. ME Coeff. Coeff. ME Coeff. ME

Protected .150*** .051 .131 .006 .187*** .074(.041) (.080) (.052)

Post -.019 -.001 .014 .067 .003 .078 .031( 031) ( 026) ( 064) ( 040)



39

Table 7: Protected Households Differentially More Likely to Marry Into Wealthier Households after EmbankmentConstruction

Dependent Variable:Spouse LandOwnership Spouse Owns Above Avg. Land

Sample: FullEx. Top 5%Landowners

Full(1982 - 1996)

Ex. Last 3 years(1982 - 1993) Farmers Non-Farmers

Coeff. ME Coeff. ME Coeff. ME Coeff. ME

Protected.871** .851** .062*

0.022.064*

0.0230.001

0.139**

0.048(0.41) (0.41) (0.035) (0.035) (0.044) (0.055)

Post 0.273 0.208 -0.025 -0.009 -0.023 -0.008 -0.034 -0.012 -0.022 -0.008(0.317) (0.317) (0.027) (0.029) (0.035) (0.042)

Embankment(Protected*Post)

0.082 0.035 .080* 0.029 .108** 0.039 .143** 0.053 0.007 0.003(0.528) (0.528) (0.044) (0.048) (0.058) (0.07)

Land Owned .073*** .103***(0.008) (0.013)

Above Avg. Amountof Land .202*** 0.073 .115*** 0.041 .159*** 0.058 .237*** 0.084(0.022) (0.025) (0.028) (0.041)

R-squared 0.006 0.005 0.006 0.003 0.004 0.007Total obs. 15649 15047 15649 12659 9075 6425

Standard errors in parentheses. *** indicates significance at 1% level; ** indicates significance at 5% level; * indicates significance at 10% level. DID indicates difference-in-difference; ME indicatesmarginal effects (calculated at means of explanatory variables); FE indicates fixed effects. DID Above Avg. Land estimated using probit; all other models estimated using OLS.



Table 8: The Embankment has Weaker Effects on Age at Marriage

Dependent Variable: Age at Marriage Spouse Age at Marriage Age GapFemales Males Female Spouses Male Spouses Male Spouses

DID FE DID FE DID FE DID FE DID ME FEProtected -.028 .215 -.067 -.051 -.056* -.019

(.091) (.182) (.095) (.127) (.033)Post .801*** 1.423*** 1.248*** 1.267*** .675*** .846*** .159* .632*** .009 .003 .024*

(.066) (.096) (.131) (.201) (.068) (.120) (.089) (.159) (.023) (.013)Embankment .086 .116 .158 .391 .012 -.095 -.013 -.257 .046 .015 .003

(.116) (.169) (.231) (.363) (.119) (.212) (.156) (.274) (.040) (.022)Age at Marriage .321*** .343*** .676*** .659*** -.051*** -.017 -.008***

(.004) (.009) (.008) (.021) (.002) (.002)R-squared .010 .023 .008 .010 .310 .316 .224 .204 .018 .012Total obs. 24349 9914 18255 6781 17362 6145 22479 8680 22749 8680Number of groups 4037 2804 2575 3602 3602

Standard errors in parentheses. *** indicates significance at 1% level; * indicates significance at 10% level. DID Age Gap estimated using probit; all other models estimated using OLS. Fixed Effects models include household fixed effects.

Table 9: Husbands from Families Protected by the Embankment Command LargerDowries after 1989:

Dependent Variable: Dowry paid by WomenProtected Husband -255708.2***-244390.2***-242281.9***

(83419.0) (83769.6) (83753.4)Year 733.9*** 738.9*** 740.6***

(25.8) (26.0) (26.0)Year * Protected Husband 128.6*** 122.9*** 121.8***

(42.2) (42.3) (42.3)Age at Marriage -544.9*** -547.6*** -560.1***

(55.9) (56.2) (56.3)Consanguineous Marriage -2346.8***

(596.1)Consan. (Maternal Relative) -2521.7**

(1128.2)Consan. (Paternal Relative) -1698.9*

(949.9)R-squared .045 .046 .046Total obs. 5699 5674 5674

Standard errors in parentheses. *** indicates significance at 1% level; ** indicates significancet 5% l l * i di t i ifi t 10% l l All d l t bit d 1996 MHSS d t



Table 10: Rates of Consanguinity Drop in Protected Households

Consanguinity Protected SpouseDID DID FE FE DID

Coeff. ME Coeff. ME Coeff. MEProtected .089* .011 .090* .011 1.194*** .437(.047) (.047) (.034)Post -.070** -.008 -.069** -.008 -.010* -.014** -.015** -.016 -.006(.032) (.034) (.006) (.006) (.007) (.029)Embankment -.236*** -.025 -.228*** -.024 -.028***-.033*** -.030** .011 .004(.056) (.059) (.010) (.011) (.012) (.044)Land Owned -.001 -.000 -.001 -.000 .001** .000

(.001) (.001) (.001)Male .039 .038(.031) (.034)

Oldest Child -.005 -.015(.009) (.012)

Youngest Child .007 -.002(.009) (.011)

Male * Oldest Child -.007 .010(.016) (.021)

Male * Youngest Child -.033*** -.020(.012) (.015)

Further Controls:Number of Brothers No Yes YesNumber of Sisters No Yes YesSample Years 82-96 82-93 82-96 82-96 82-93 82-96R-squared .004 .004 .001 .001 .001 .150Total obs. 31189 23463 14707 12230 9258 16302

Number of groups 5436 4675 3721Standard errors in parentheses. *** indicates significance at 1% level; ** indicates significance at 5% level; * indicates significanceat 10% level. DID indicates difference-in-difference; ME indicates marginal effects (calculated at means of explanatory variables);FE indicates fixed effects. DID estimated using probit; FE estimated using OLS. Fixed Effects models include household fixed effects.



Table 11: The Change in Consanguinity Rates by Gender

MalesConsanguineous Marriage

DID DID FE FE FECoeff. ME Coeff. ME

Protected .067 .008 .067 .008(.072) (.072)Post -.079* -.009 -.085* -.010 -.005 -.028* -.027

(.048) (.051) (.011) (.015) (.017)

Embankment -.204** -.022 -.173* -.019 -.032 -.021 -.025(.085) (.090) (.020) (.024) (.026)Land Owned -.002 -.000 -.002 -.000(.001) (.001)Oldest Child -.022 -.025

(.019) (.025)Youngest Child -.043*** -.037**

(.014) (.016)Sample Years 82-96 82-93 82-96 82-96 82-93R-squared .004 .004 .001 .005 .006Total obs. 13446 10071 4259 3161 2321Number of groups 1837 1383 1037

FemalesConsanguineous Marriage

DID DID FE FE FECoeff. ME Coeff. ME

Protected .109* .013 .109* .013(.063) (.063)Post -.062 -.007 -.056 -.007 -.005 -.006 -.006(.043) (.045) (.009) (.010) (.011)

Embankment -.262*** -.027 -.272*** -.028-.043*** -.047*** -.042**

(.074) (.079) (.015) (.016) (.018)Land Owned -.000 -.000 -.000 -.000(.001) (.001)Oldest Child -.004 -.012

(.012) (.016)Youngest Child .003 .003

(.011) (.013)Sample Years 82-96 82-93 82-96 82-96 82-93R-squared .005 .005 .001 .001 .001Total obs. 17743 13392 6186 5403 4034



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Appendix

Table A1: List of Variables

Marriage Outcome DefinitionValue of Dowry Total estimated cash value of dowry paid to husband in TakaConsanguinity Individual married 1 st, 2nd, or other cousinSpouse Land Owned Land owned by head of spouse’s household in 1982

(measured in decimals)Spouse Above Average Land Spouse’s household owns more than 10.347 decimals of land

(avg. land owned by households of spouses chosen within thesample period)

Age at Marriage Age of individual at time of marriageSpouse Age at Marriage Age of spouse at time of marriageSpouse Age Gap Indicator for whether or not male spouse is more than 10 years

older than the female marriage observation Protected Spouse Group status of spouse (time-invariant indicator for whether or

not spouse’s household is protected by embankment)

Explanatory Variables Definition Protected Group status of individual Post Indicator equal to 1 if marriage year between 1989-1996 and 0 if

marriage year between 1982-1987 Embankment Embankment effect (interaction of Protected and Post ) Land Owned Land owned by head of individual’s household in 1982 Above Average Land Owned Household owns more than 10.744 decimals of land (avg. land

owned by households of individuals getting married within thesample period)

Farmer Indicator equal to 1 if household head is a farmer and 0otherwise



Table A2: Descriptive Statistics for Fixed Effects Sample

Pre-embankment Post-embankmentMean: Protected Unprotected Protected Unprotected Percentage of Consanguineous Marriages .09*** .06*** .05 .05

(.01) (.00) (.00) (.00)Female Age at Marriage 18.86 18.97 19.98 19.92

(.11) (.10) (.10) (.07)Male Age at Marriage 24.21 24.20 25.76 25.64

(.17) (.13) (.13) (.09)Household Land Owned (1982) 11.03 10.25 11.13** 10.35**

(.43) (.29) (.33) (.20)

Land Owned by Spouse's Household (1982) 10.69 10.14 11.24* 10.28*(.66) (.47) (.48) (.32)

Percentage of Marriages to Spouse from outside Matlab .47*** .59*** .51*** .59***(.01) (.01) (.01) (.01)

Percentage of Marriages to Spouse from outside Village .87** .89** .89 .90(.01) (.01) (.01) (.00)

Total Marriage Observations 1233 2699 2626 5672Standard errors in parentheses. *** indicates t-test significant at 1%, **indicates significant at 5% level, * indicates significant at 10% level. Pre indicates 1985-1986; Post indicates 1989-1996. Sample taken from fixed effects estimation for consanguinity.

Family Size in 1982 Pre-embankment Post-embankment

Mean: Protected Unprotected Protected Unprotected DID Sample 6.69 6.61 6.85 6.82

(.07) (.04) (.03) (.02)Obs. 2288 4842 7741 16318

Fixed Effects Sample 6.76 6.71 6.90 6.90(.09) (.06) (.06) (.04)

Obs. 1152 2498 2458 5353Standard errors in parentheses. Pre indicates 1985-1986; Post indicates 1989-1996. Samples

taken from difference-in-difference and fixed effects estimations for consanguinity.

the dreaded embankment paper

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